COVID-19 Release Part 4 2016 DNI Gabbard

\Wuyan Final
Tahenm Repo et

A. COVER PAGE

FINAL

Project Title: Understanding the Risk of Bat Coronavirus Emergence

Grant Number: 5R01AI110964-05

Project/Grant Period: 06/01/2014 (spiro

Reporting Period: 06/01/2018 - 05/31/2019

Requested Budget Period: 06/01/2018 - 05/31/2019

Report Term Frequency: Annual

Date Submitted: 08/03/2021

PETER DASZAK , PHD BS

Program Director/Principal Investigator Information:

Recipient Organization:

ECOHEALTH ALLIANCE, INC.

ECOHEALTH ALLIANCE, INC. 520 EIGHTH AVENUE
NEW YORK, NY 100181620

ALcKSEI CHMURA
460 W 34th St., 17th Floor
New York, NY 10001

Phone Number: (b) (6) DUNS: 077090066
Email: (b) (6) EIN: 1311726494A1
RECIPIENT ID:
Change of Contact PD/PI: NA
~ inistrative Official: Signing Official:

ALEKSEI CHMURA
460 W 34th St., 17th Floor
New York, NY 10001

Exemption Number:
Phase III Clinical Trial: NA

Phone number: (b) (6) Phone number: (b) (6)
Email: (b) (6) Email: (b) (6)
Human Subjects: Yes

nie EXenps: NA Vertebrate Animals: NA

hESC: No

Inventions/Patents: No

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_~ B. ACCOMPLISHMENTS

B.1 WHAT ARE THE MAJOR GOALS OF THE PROJECT?

Zoonotic coronaviruses are a significant threat to global health, as demonstrated with the emergence of severe acute
respiratory syndrome coronavirus (SARS-CoV) in 2002, and the recent emergence Middle East Respiratory Syndrome (MERS-
CoV). The wildlife reservoirs of SARS-CoV were identified by our group as bat species, and since then hundreds of novel bat-
CoVs have been discovered (including >260 by our group). These, and other wildlife species, are hunted, traded, butchered
and consumed across Asia, creating a largescale human-wildlife interface, and high risk of future emergence of novel CoVs.
To understand the risk of zoonotic CoV emergence, we propose to examine 1) the transmission dynamics of bat-CoVs across
the human-wildlife interface, and 2) how this process is affected by CoV evolutionary potential, and how it might force CoV
evolution. We will assess the nature and frequency of contact among animals and people in two critical human-animal
interfaces: live animal markets in China and people who are highly: exposed to bats in rural China. In the markets we
hypothesize that viral emergence may be accelerated by heightened mixing of host species leading to viral evolution, and high
potential for contact with humans. In this study, we propose three specific aims and will screen free ranging and captive bats
in China for known and novel coronaviruses; screen people who have high occupational exposure to bats and other wildlife;
and examine the genetics and receptor binding properties of novel bat-CoVs we have already identified and those we will
discover. We will then use ecological and evolutionary analyses and predictive mathematical models to examine the risk of
future bat-CoV spillover to humans. This work will follow 3 specific aims:

Specific Aim 1: Assessment of CoV spillover potential at high risk human-wildlife interfaces. We will examine if: 1) wildlife

markets in China provide enhanced capacity for bat-CoVs to infect other hosts, either via evolutionary adaptation or

recombination; 2) the import of animals from throughout Southeast Asia introduces a higher genetic diversity of mammalian

CoVs in market systems compared to within intact ecosystems of China and Southeast Asia; We will interview people about

the nature and frequency of contact with bats and other wildlife; collect blood samples from people highly exposed to wildlife;
d collect. a full range of clinical samples from bats and other mammals in the wild and in wetmarkets; and screen these for
‘Vs using serological and molecular assays.

Specific Aim 2: Receptor evolution, host range and predictive modeling of bat-CoV emergence risk. We propose two
competing hypotheses: 1) CoV host-range in bats and other mammals is limited by the

phylogenetic relatedness of bats and evolutionary conservation of CoV receptors; 2) CoV host-range is limited by geographic
and ecological opportunity for contact between species so that the wildlife trade disrupts the ‘natural’ co-phylogeny, facilitates
spillover and promotes viral evolution. We will develop CoV phylogenies from sequence data collected previously by our group,
and in the proposed study, as well as from Genbank. We will examine co-evolutionary congruence of bat-CoVs and their hosts
using both functional (receptor) and neutral genes. We will predict host-range in unsampled species using a generalizable
model of host.and viral ecological and phylogenetic traits to explain patterns of viral sharing between species. We will test for
positive selection.in market vs. wild-sampled viruses, and use data to parameterize mathematical models that predict CoV
evolutionary and transmission dynamics. We will then examine scenarios of how CoVs with different transmissibility would
likely emerge in wildlife markets.

Specific Aim 3: Testing predictions of CoV inter-species transmission. We will test our models of host range (i.e. emergence
potential) experimentally using reverse genetics, pseudovirus and receptor binding assays, and virus infection experiments in
cell culture and humanized mice. With bat-CoVs that we've isolated or sequenced, and using live virus or pseudovirus infection
in cells of different origin or expressing different receptor molecules, we will assess potential for each isolated virus and those
with receptor binding site sequence, to spill over. We will do this by sequencing the spike (or other receptor binding/fusion)
protein genes from all our bat-CoVs, creating mutants to identify how significantly each would need to evolve to use ACE2,
CD26/DPP4 (MERS-CoV receptor) or other potential CoV receptors. We will then use receptor-mutant pseudovirus binding
assayS, in vitro studies in bat, primate, human and other species’ cell lines, and with humanized mice where particularly
interesting viruses are identified phylogenetically, or isolated. These tests will provide public health-relevant data, and also
iteratively improve our predictive model to better target bat species and CoVs during our field studies to obtain bat-CoV strains
of the greatest interest for understanding the mechanisms of cross-species transmission.

as

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F’*™ Have the major goals changed since the initial competing award or previous report?

No

B.2 WHAT WAS ACCOMPLISHED UNDER THESE GOALS?

File Uploaded : Year 5 NIAID CoV Report Accomplishments Final.pdf

B.3 COMPETITIVE REVISIONS/ADMINISTRATIVE SUPPLEMENTS

For this reporting period, is there one or more Revision/Supplement associated with this award for which reporting is
required?

No

B.4 WHAT OPPORTUNITIES FOR TRAINING AND PROFESSIONAL DEVELOPMENT HAS THE PROJECT PROVIDED?

File Uploaded : B4 Training.pdf

B.5 HOW HAVE THE RESULTS BEEN DISSEMINATED TO COMMUNITIES OF INTEREST?

1. Conference and University Lectures: PI Daszak and Co-investigators Shi, Epstein, Olival, and Zhang gave invited conference
and university lectures at The US-China Dialogue on the Challenges of Emerging Infections, Laboratory Safety and Global
Health Security in Galveston, US; the US-China Workshop on Frontiers in Ecology and Evolution of Infectious Diseases in
/™keley, US and Shenzhen, China; the Sino-Germany symposium “Globalization-Challenge and Response for Infectious

. eases” in Hamburg, Germany; the 8th International Symposium on Emerging Viral Diseases in Wuhan, China; the Global
Virome Project meeting, Bangkok, Thailand; the Western Asia Bat Research Network (WAB-Net) workshop, Tbilisi, Georgia;
the International Conference on Emerging Infectious Diseases (ICEID), Atlanta, US; the North American Society for Bat
Research (NASBR) Conference, Puerta Vallerta, Mexico; and the 3rd Symposium of Biodiversity and Health in Southeast Asia,
Chiayi, Taiwan

2. Agency and other briefing: PI Daszak and Co-investigators Shi, Olival presented this project at the Cary Institute for
Ecosystem Studies, New York, US; the National Institute for Viral Disease Control and Prevention, China CDC; the Chinese
Academy of Sciences; and the Chinese Academy of Medical Sciences

3. Public outreach: PI Daszak and Co-investigator Shi, Epstein, Olival, have presented this work to the general public in a
series of meetings over Year 5 including at a Cosmos Club briefing that EcoHealth Alliances hosts in Washington DC, multiple
meetings of the China National Virome Project and the Global Virome Project in China, Europe, Australia, Southeast Asia and
Latin America. As in Year 4, Co-Investigator Zhu introduced this work to the conservation and ecological research community
in China through field training workshops.

B.6 WHAT DO YOU PLAN TO DO DURING THE NEXT REPORTING PERIOD TO ACCOMPLISH THE GOALS?

Not Applicable

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B.2 (Year 5 NIAID Cov Heport Accomplisnments Final.pat)

~ The results of the 5" year of our RO1 work are detailed below. They include:

Specific Aim 1: Assessment of CoV spillover potential at high-risk human-wildlife

interfaces

During Year 5, we finalized the analysis of both quantitative and qualitative data from human
surveillance in three provinces in Southern China: Yunnan, Guangxi, and Guangdong
provinces.

1.1 High-risk human-animal interaction increase bat coronavirus spillover potential
among rural residents in southern China

We conducted a cross-sectional biological behavioral surveillance in Yunnan, Guangxi, and
Guangdong provinces from 2015 to 2017. From 8 study sites, a total of 1,596 residents were
enrolled, of these, 1,585 participants completed the questionnaires and 11 participants withdrew
from the questionnaire interview due to personal schedule reasons. After the interviews, 1,497
participants provided biological samples for lab analysis (Fig. 1).

N Fig. 1: Eight field
A surveillance sites for
' human
Ree questionnaire &
sero-surveillance
Q - with concurrent bat
0° 6 ® a? sampling in Yunnan,
Guangdong (n=423) Guangxi,
g#® Guangdong
* 5 S@ provinces in
Southern China. Bat

coronavirus sero-

positivity were
@ Human questionnaire & sero-surveillance sites @ Bat roosts } aye detected in human
® Human bat CoVs seropositive n=no. of enrolled participants per province population in four

‘ Yunnan (n=761) Guangxi {n=412)

sites in this study

1.1.1. Demographics

There were more female (62%) than male (38%) from the communities participated in this
Study. Most participants were adults over 45 years old (69%) and had been living in the
community for more than 5 years (97%) with their family members (95%). A majority relied on a
comparatively low family annual per capita income less than 10,000 RMB (86%), which is below
the national level of per capita disposable income of rural households from 2015 to 2017. Most
participants (98%) had not received a higher education from college and were making a living
on crop production (76%). 9% of the participants frequently traveled outside the county as
migrant laborers.Some participants were working in sectors where frequent human-animal
contacts occur, such as the animal production business (1.7%), wild animal trade (0.5%),
slaughterhouses or abattoirs (0.5%), protected nature reserve rangers (0.4%) or in wildlife
restaurants (0.3%). It was common for participants to have multiple part-time jobs as income
Ns, sources (Table 1).

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ca . Total
: Variable N Valid %
Gender (n= 1,574)
Femae 968 61.5
Mae 605 38.4
Other 1 0.1
Age (n=1,582)
Under 18 years 71 45
18 to 44 years 420 26.5
45 to 64 years 780 49.3
Age 65 or o der 311 19.7
Province (n=1,585)
Guang Dong , 420 26.5
Guang X 412 26.0
Yun Nan 753 47.5
Time of residence (n=1,568)
< 1 month 4 0.3
1 month — 7 year 42 0.8
1 year — 5 years 26 1.7
>.5 years 1,526 97.3
Family annual per capita income (RMB) (n=1,565)
<1000 271 17.3
1001-10000 1067 68.2
>10000 227 14.5
Activities to earn livelihood since last year
Extract on of mneras, gas, o , tmber (n=1,566) 5 0.3
Crop product on (n=1,569) 1,196 76.2
W d fe restaurant bus ness (n=1,564) 5 0.3
-_, W d/exotc an ma trade/market bus ness (n=1,566) 8 0.5
Rancher/farmer an ma _ producton bus ness (n=1,566) 27 1.7
Meat process ng, s aughterhouse, abatto r (n=1,567) 8 0.5
Zoo/sanctuary an ma heath care (n=1,565) 1 0.1
Protected area worker (n=1,567) 7 0.4
Hunter/trapper/f sher (n=1,565) 3 0.2
Forager/gatherer/non-t mber forest product co ector (n=1,566) 4 0.3
Mgrant aborer (n=1,567) 144 9.2
Nurse, doctor, hea er, commun ty hea th worker (n=1567) 7 0.4
Construct on (n=1,564) 41 2.6
Other (n=1,568) 293 18.7
Highest level of education you completed (n=1,570)
None 428 27.3
Pr mary Schoo 632 40.3
Secondary schoo /Po ytechn c schoo 479 30.5
Co ege/un vers ty/profess ona 31 2.0
Live with family (n=1,564)
No 73 4,7
Yes 1491 95.3

Table 1: Demographics of study participants. Total counts differ due to missing responses.

1.1.2 Animal contact and exposure to bat coronaviruses

Serological testing of serum samples from 1,497 local residents revealed 9 individuals (0.6%)

were positive for bat coronavirus, indicating exposure at any point in their life to bat-born SARS-

related Coronavirus (n=7, Yunnan) and HKU10 Coronavirus (n=2, Guangxi), or other

coronaviruses that are phylogenetically closely related to these two coronaviruses (Table 2). All
a individuals who tested positive (male=6, female=3) were over 45 years old, and most (n=8)

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B.2 (Year 5 NIAID CoV Report Accomplishments Final.pat)

were making a living from crop production.None of those participants reported any symptoms in
the preceding 12 months in the interview.

Site # Bat CoV+(%) SARSr-CoV HKU10 + HKU9 + MERS-CoV+
tested Rp3 + (%) (%) (%) (%)
J inning, Yunnan 209 6 (2.87) 6 (2.87) - - -
Meng a, Yunnan 168 1 (0.6) 1 (0.6) - - -
J nghong, Yunnan 212 - - - - -
Lufeng, Yunnan 144 - - ~ - -
Guangdong 420 - - - - -
Guangx 412 2 (0.48) - 2 (0.48) - -

Table 2: ELISA testing of human sera for 4 bat CoVs

Due to the low rate of sero-positivity, we did not conduct statistical comparisons. of animal-
contact behavior by coronavirus outcome. Figure 2 shows animal contact rates among the
survey population (n= 1,585) and among sero-positive individuals (n=9).Participants reported
common contact with poultry and rodents/shrews, and most animal contact occurred in
domestic settings through raising animal or food preparation activities.

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Fig. 2: Animal contact by taxa and activities. Values and shading represent survey population; red
numbers in upper-right corners of cells indicate the number of sero-positive individuals with the given
contact.

1.1.3 Self-report SARIJLI symptoms and animal contact

Among the 1,565 participants who responded, 17% (n=265) had experienced fever with cough
and shortness of breath or difficulty breathing (38, 14%), indicative of severe acute respiratory
infection (SARI), or fever with muscle aches: cough, or sore throat (192, 72%), indicative of
influenza like illness (ILI), or both symptoms (35, 13%) in the past 12 months.

LASSO analyses of the associations between animal contact and self-report SARI or ILI
symptoms showed that eating raw or undercooked carnivores (OR = 1.6; bootstrap support =
0.67) was the most salient predictor of experiencing SARI or ILI symptoms, followed by
slaughtering poultry as a resident of Guangxi province (OR = 1.4; support = 0.68); having an
-_ income below 10,000 as a resident of Guangxi province (OR = 1.3; support = 0.84); domestic

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b.2 (Year 5 NIAID Gov Heport Accomplishments Final.pdt)

contact with bats (OR = 1.3 ; support = 0.63) and domestic contact with rodents or shrews as a
resident of Guangdong province (OR = 1.2: support = 0.63) (Fig. 3).
. Fig. 3: Most salient

predictors of self-reported ILI

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last year (s = bootstrap
support; n = count positive

® out of 1585 respondents).

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0.6 are demonstrated here

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This study provides serological evidence of subclinical or asymptomatic bat-born SARS-related
Coronavirus and HKU10 Coronavirus spillover event(s) in rural communities in Southern China,
highlights the associations between human-animal interaction and zoonotic spillover risk. The
rate of seropositivity observed in this study is clearly lower than would be seen for established
human infections. However it has important implications for predicting and preventing
pandemics:

1. It indicates that spillover of novel bat-CoVs is detectable if populations that live within
areas inhabited by likely bats hosts are targeted. This provides a pathway to identify
Spillover events rapidly, perhaps even before a SARS-like disease can become
established in people;

2. It allows us to calculate the likely number of people infected by novel bat SARSr-CoVs
annually in this region. Our preliminary analyses suggest that if similar seroprevalence
occurs in human populations across the region bat SARSr-CoV hosts inhabit, there may
be as many as the low hundreds of thousands to over a million people infected
each year in South China and Southeast Asia. We aim to conduct a detailed analysis
of this in the future.

3. It highlights ways to refine surveillance that could help prevent pandemics, by targeting
populations where seroprevalence suggests that they are at higher risk due to
behavioral preferences (e.g. wildlife hunting, farming, or trading) or where early-
stage SARS-like illnesses could be identified using syndromic surveillance of
clinics.

Contact with poultry and rodents/shrews were commonly reported among participants and
associated with self-reported IL! and/or SARI symptoms, which suggests that domestic animals
in addition to wildlife, are an important link in understanding the coronavirus transmission from
bat to human populations, indirect exposure might occur through contact with live domestic
animals in house or market when the animals had prior exposure to bat coronavirus.

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B.2 (Year 5 NIAID Cov Report Accomplishments Final.pat)

When clinical evidence is limited. undiagnosed or subclinical symptoms similar to SARI and ILI
in a population should be brought to our attention as indicators in monitoring zoonotic pathogen
spillover events, and considered for prevention strategies. This is particularly important in rural
community settings, where people have a higher level of exposure to both domestic and wild
animals, but may not seek diagnosis or treatment in a timely fashion, thus slowing the
processes of early detection and response.

1.2 Qualitative Approach to Developing Zoonotic Risk Mitigation Strategies in Southern
China

To explore the potential drivers of zoonotic exposure and the opportunities for intervention, we

conducted field observation and semi-structured ethnographic interviews among 88 community
members who have frequent exposure to wildlife and domestic animals and/or have extensive

local knowledge in 9 sites in Yunnan, Guangdong, and Guangxi provinces.

The majority of participants in this study were adults between 31 to 50 years of age, residing in
rural or suburban areas. Most earned their livelihoods from multiple sources, primarily in crop
production, subsistence animal farming, small business, and other temporary jobs as migrant
workers. Risk and protective factors were identified at the individual, community, and policy
levels regarding potential zoonosis exposures, recommending risk-mitigation strategies with the
strengthened policy enforcement and multi-sectoral collaboration among human, animal, and
environment health programs (Fig. 4).

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B.2 (Year 5 NIAID CoV Heport Accomplishments Final.pat)

Fig. 1: Community Zoonosis Exposure Risk Mitigation Strategy Development Process. Leveraging
ethnographic interview and observational research data to identify risk and protective factors and develop
risk-mitigation recommendations

This demonstrated a qualitative approach to understand the zoonotic risks in community, and
provided guidance for future research and interventions with focused potential zoonotic risks for
disease control and prevention in southern China and a broader area with similar ecological,
culture, and demographic contexts.

Specific Aim 2: Receptor evolution, host range and predictive modeling of bat-CoV

‘emergence risk

2.1 Bat CoV PCR detection and sequencing from live-sampled bat populations

From May to October 2018, we collected 1,697 rectal swabs, oral swabs, and feces specimens
from 26 bat species in Hubei, Shandong, Yunnan and Guangdong Provinces across southern,
central and northern China in Year 5, all specimen were tested for CoV RNA and 109 (6.4%)
were positive. SARS-related coronaviruses were discovered in Rhinolophus sinicus samples
from Yunnan and Hubei provinces while HKU2-related coronaviruses were detected in R.sinicus
from Hubei. HKU5-related and HKU10-related coronaviruses were identified in Pipistrellus
abramus from Shandong and Hipposideros /arvatus from Guangdong, respetively. Scotophilus
coronavirus 512 was detected in
Guangdong. Addtionally, two
novel Pipistrellus

—, alphacoronaviruses were found
in Shandong province in
northern China (Fig. 5).

Fig. 2: Phylogenetic analysis of
partial RdRp gene of CoV (440-nt
partial sequence)

2.2 Bat coronavirus host-virus
phylogeography in China

Our dataset includes all CoV
RdRp sequences isolated from
bat specimens collected by our
team from 2008-2015 (Alpha-
CoVs: n = 491 — Beta-CoVs: n=
326), including those collected
under prior NIAID funding (1
RO1 Al079231), and funding
from Chinese Federal Agencies.
All Chinese bat CoV RdRp
sequences available in
GenBank were also added to

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B.2 (Year 5 NIAID Cov Report Accomplishments Final.pat)

our dataset (Alpha-CoVs: n = 226 — Beta-CoVs: n = 206). Phylogenetic trees were
reconstructed for Alpha- and Beta-CoVs separately using Bayesian inference (BEAST 1.8).

2.2.1 Ancestral hosts and cross-species transmission

We used ancestral character state reconstruction and a Bayesian stochastic search variable
selection (BSSVS) to identify host switches between bat families (Fig. 6) and genera (Fig. 7)
that occurred along the branches of the phylogenetic tree and calculated BF to estimate the
significance of these non-zero transition rates. We identified nine and three highly supported
(BF > 10) inter-family host transition rates for alpha- and beta-CoVs, respectively (Figs. 6A
and 6B). To quantify the intensity of these host switches, we estimated the number of state
changes (Markov jumps) along the significant inter-family transition rates (Figs. 6C and 6D).
The total estimated number of inter-family host jump events was more than eight times higher in
the evolutionary history of alpha- (n = 90) than beta-CoVs (n = 11) in China. Host transition
events from Rhinolophidae and Miniopteridae were greater than from other families for alpha-
CoVs while Rhinolophidae were the highest donor family for beta-CoVs. Rhinolophidae and
Hipposideridae were the families receiving the highest numbers of transition events for alpha-
and beta-CoVs, respectively (Figs. 6C and 6D).

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Figure 3: Non-zero transition rates between bat families for alpha- (A) and beta-CoVs (B) and their
significance level (Bayes factor, BF), BF < 10 are considered as non-significant. Arrows indicate the
direction of the transition; arrow thickness is proportional to the transition significance level. Histograms
show total number of state changes (Markov jumps) from/to each bat family along the significant inter-
family transition rates for alpha- (C) and beta-CoVs (D).

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Figure 4: Non-zero transition rates between bat genera for alpha- (A) and beta-CoVs (B) and their
significance level (Bayes factor, BF), BF < 10 are considered as non-significant. Lines with a rightward
curvature depict transitions from that bat genus, while lines with leftward curvature depict transition to that
bat genus. Inter-family transitions are highlighted in red.

At the genus level, we identified 20 highly supported inter-genus host transition rates for alpha-
CoVs (Fig. 7A). Rhinolophus and Myotis were the donor genera in four of these transitions
while Miniopterus and Rhinolophus were each the recipients of four of these transitions (Fig.
1A). Sixteen highly supported inter-genus transition rates were identified for beta-CoVs
(Fig.7B). Four of these 16 host switches originated in Cynopterus while three of them ended in
Myotis (Fig. 7B). Fifteen out of the 20 significant pairwise host transitions (75%) for alpha-CoVs
involved two genera belonging to different bat families, while this proportion is only 6/16 (37.5%)
for beta-CoVs. This confirmed the highest number of inter-family host transitions for alpha-
CoVs. The estimated total number of inter-genus host switches was almost two times higher for
alpha- (n = 123) than beta-CoVs (n = 70).

These findings indicate that alpha-~CoVs were able to switch hosts more frequently and between
more distantly related taxa during their evolution and suggest that phylogenetic distance among
hosts represents higher constraint on host switches for beta- than alpha-CoVs.

2.2.2 CoV spatiotemporal dispersal in China

We also used our Bayesian discrete phylogeographic model using zoogeographic regions as
character states to reconstruct the spatiotemporal dynamics of CoV dispersal in China. Eleven
‘and seven highly significant (BF > 10) dispersal routes within China were identified for alpha-
and beta-CoVs, respectively (Fig. 8A and 8B). The Rhinacovirus lineage that includes HKU2
and SADS-CoV likely originated in SO region while all other alpha-CoV lineages likely arose in
SW China and spread to other regions before several dispersal events occurred from SO and

NO in all directions (Fig. 8A).
Fig. 8: Significant dispersal routes

Aipha-CoVs among China zoogeographic regions

A, for alpha- (A) and beta-CoVs (B).
Arrows indicate the direction of the
transition; arrow thickness is

c proportional to the transition

; significance level. Darker arrow

colors indicate older dispersal
events. Fig. 8 (C & D) Histograms of

ws total number of state changes
4 (Markov jumps) from/to each region

State changa counts

Sigmficant dispursal routes
ween 100 BF

me BOE ER © TO}
mm 1G GF «40

Beta-CoVs
B.

NO

CN

Sw

ae

Interim RPPR

Stut

ee chanqe counts

along the significant dispersal routes
for alpha- (C) and beta-CoVs (D).
NO, Northern region; CN, Central
northern region; SW, South western
region; CE, Central region; SO,
Southern region; HI, Hainan island.

The oldest inferred dispersal
movements among beta-CoVs
occurred among SO and SW
regions (Fig. 8B). SO region is
the likely origin of Merbecovirus
(Lineage C, including HKU4 and

Page 11

B.2 (Year 9 NIAID VoV Keport Accomplisnments Final.pdt)

HKUS5) and Sarbecovirus subgenera (Lineage B, including HKU 3 and SARS-related CoVs)
while Nobecovirus (lineage D) and Hibecovirus (lineage E) subgenera originated in SW China.
Then several dispersal movements likely originated from SO and CE (Fig. 8B). More recent
southward dispersal from NO was observed.

The estimated total number of migration events along these significant dispersal routes is four
times higher for alpha- (n = 227) than beta-CoVs (n = 57). SO has the highest number of
outbound and inbound migration events for alpha-CoVs (Fig. 8C). For beta-CoVs, the highest
numbers of outbound migration events have been estimated from NO and SO while SO and SW
have the highest numbers of inbound migration events (Fig. 8D).

Our Bayesian ancestral reconstructions revealed the high importance of South western and
Southern China as centers of diversification for both alpha- and beta-CoVs. These two regions
are clearly hotspots of CoV phylo-diversity, harboring evolutionary old and phylogenetically
diverse lineages of alpha- and beta- CoVs.

2.2.3 Phylogenetic diversity

In order to quantitatively evaluate the diversity and the clustering process in our phylogenies,
the Mean Phylogenetic Distance (MPD) and the Mean Nearest Taxon Distance (MNTD)
statistics and their standardized effect size (SES) were calculated for each zoogeographic
region, bat family and genus. The SES corresponds to the difference between the phylogenetic
distances in the observed communities versus null communities built by randomly reshuffling tip
labels 1000 times along the entire phylogeny. Low and negative SES values denote
phylogenetic clustering, high and positive values indicate phylogenetic over-dispersion while
values close to 0 show random dispersion.

Significant negative SES MPD values (p < 0.05), indicating basal phylogenetic clustering, were
observed within all bat families and genera for both alpha- and beta-CoVs, except within
Aselliscus and Tylonycteris for alpha-CoVs (Figs. 9A & B). Negative and mostly significant SES
MNTD values, reflecting phylogenetic structure closer to the tips, were also observed within
most bat families and genera for alpha- and beta-CoVs but we found non-significant positive
SES MNTD value for Vespertilionidae and Pipistrellus for beta-CoVs (Fig. 4A and 4B). In

general, we observed lower phylogenetic diversity for beta- than alpha-CoVs within all bat
families and most genera when looking at SES MPD, while similar level of diversity are

observed when looking at SES MNTD (Figs. 9A & B). These results suggest stronger basal
clustering (at the deeper nodes) for beta-CoVs than alpha-CoVs.

Chinese zoogeographic regions don’t harbor a random set of CoVs as alpha- and beta-CoV
strains within most regions are more closely related than expected by chance as denoted by
negative and mostly significant values of MPD and MNTD (Fig. 9C). However, positive SES
MPD value for alpha-CoVs in SW indicate wider evolutionary diversity in that region (Fig. 9C).

Interim RPPR Page 12


B.2 (Year 5 NIAID CoV Report Accomplisnments Final.pat)

A. s £ ¢
wv x & &
& g = £
0 05 rs wv SS g
0 7
> 10 => 05
5 $5 4
= E25 *
-40 3
-60 35
BAlpha-CoVs SBeta-CoVs
: = Fe 1 ee &é oe od gS vé FE ee
EP -20 % x 3 * 28 oe wh . A
2% 50 x By -15 b l 4 ie 2 +1
ge * Ba 2 a d*
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2 "CE CN NO so. sw Ht o cE ON
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“8 a 3
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Fig. 9: CoV phylogenetic diversity bat families (A), genera (B), and zoogeographic regions (C): SES
MPD, standardized effect size of Mean Phylogenetic Distance (Left); and SES MNTD, standardized effect
size of Mean Nearest Taxon Distance (Right). Values departing significantly from null model (p-value <
0.05) indicated with an asterisk. NO, Northern region; CN, Central northern region; SW, South western
region; CE, Central region; SO, Southern region; HI, Hainan island.
2.3 Characterization of SADSr-CoV coronaviruses diversity and distributions
In previous project years, our team identified and characterized Swine Acute Diarrheal
Syndrome coronavirus (SADS-CoV), a novel swine virus causing outbreaks in farms in multiple
Chinese provinces.In this year, we were able to identify SADS-related CoVs in bats from our
wild bat sampling.In >17,000 bat and other mammals at 47 sites across southern China, we
found 78 new SADSr-CoVs", all in 9 bat species, with mean prevalence of 0.1 to 37.5%.
’ Our phylogenetic analysis suggests that pig SADS-CoV recently spilled over from R. sinicus or

Interim RPPR

R. affinis bats (Fig. 10 Left) However, analysis of full pig viral genomes from 4 initially infected

Page 13


B.2 (Year dD NIAIU LOV Keport Accomplisnments Final. par)

farms suggests that either the virus evolved as it circulated or that multiple spillover events
occurred (Fig. 10 Right).

es

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@) Roinofophus macros © Hipposideros cineraceus 2017-04-18
@ Rhinolophus ferrumequinum ©) Hipposideros prarti
@ Rhinolophus pusitius @ Hipposideros pomona
& Rhinotophus sp. @ Hipposideros armiger
Miniopterus fuliginosus © Myotis pilosus

Fig. 10: Left: Median joining network of conserved RdRp gene fragment of 198 unique SADSr-CoV

—, sequences discovered in China under our previous funding. Size of circle proportional to the number
specimens with identical viral sequences. Right: Median joining network of SADS-CoV full genome
sequence data from 4 infected pigs farms in S. China.

We built species distribution models of the major bat species hosts of SADSr-CoVs across
southeast Asia to determine the areas where their ranges intersect with large swine operations
similar to those of the original outbreak.We found that these are Southern China (including
Taiwan), throughout Vietnam, the Philippines, and Thailand. Compared to other countries,

: China had the largest area of bat-pig overlap with

329,847 km2 (3.4% of total country area) and
2,127,006 pigs located within predicted bat

distributions. By Chinese province, the largest. area
; of overlap was found in Jiangsu (35,226 km2
amounting to 34.3% of the province's area and
242,299 pigs within this area). Sichuan had the
largest pig population at risk (the pig population
within an area that intersects with predicted bat
occurrence), at 274,353 heads over 26,015 km2
(5.4% of the total area of the province) (Figs. 11 &

12).
Revonsiee tenamers Fig. 5: Areas of bat-pig overlap where probability of
Tyas SADS-CoV Rhinolophus spp. reservoir occurrence is
BE tteemare | high (>75%) and pig densities are indicative of intensive
on pig farming (>100 heads per km2).

Interim RPPR Page 14

K.2 (Year 5 NIAID Cov Heport Accomplisnments Final.pdt)

Cina
Yreinam.
Taivar
Pidippnes
Thasand,
Singapore
Malayca
Myanmar
Laos

5
fo
3
3
Oo

Indonesia ~~
Cambodia

6 509.000 1OMg.00 = 1 So0.nG0
Pig Population at Risk

nN
ot

0.609

AP

Fig. 6: Top: Country-level, and Bottom: province-level estimate of swine populations at-risk based on
overlap between modeled populations of bat species known to be SADSr-CoV hosts and large swine
operations.

Specific Aim 3: Testing Predictions of CoV Inter-Species Transmission

3.1 In vivo infection of Human ACE2 (hACE2) expressing mice with SARSr-CoV S protein
variants

In Year 5, we continued with in vivo infection experiments of diverse bat SARSr-CoVs on
transgenic mice expressing human ACE2. Mice were infected with 4 strains of SARSr-CoVs
with different S protein, including the full-length recombinant virus of SARSr-CoV WIV1 and

-_ three chimeric viruses with the backbone of WIV1 and S proteins of SHC014, WIV16 and
Rs4231, respectively. Pathogenicity of the 4 SARSr-CoVs was evaluated by recording the
survival rate of challenged mice in a 2-week course. All of the 4 SARSr-CoVs caused lethal
infection in hACE2 transgenic mice, but the mortality rate vary among 4 groups of infected mice
(Fig. 13a). 14 days post infection, 5 out of 7 mice infected with WIV1 remained alive (71.4%),
while only 2 of 8 mice infected with rWIV1-SHC014 S survived (25%). The survival rate of mice
infected with rWIV1-WIV16S and rWIV1-4231S were 50%. Viral replication was confirmed by
quantitative PCR in spleen, lung, intestine and brain of infected mice. In brain, rWIV1, rWIV1-
WIV16S and rWIV1-4231S cannot be detected 2 days or 4 days post infection. However,
rWIV1-SHC014 was detected at all time points and showed an increasing viral titer after
infection. The viral load reached more than 10° genome copies/g at the dead point (Fig. 13b).
We also conducted histopathological section examination in infected mice. Tissue lesion and
lymphocytes infiltration can be observed in lung, which is more significant in mice infected with
rWIV1-SHC014 S (Fig. 13d) than those infected with rWIV1 (Fig. 13c). These results suggest
that the pathogenicity of SHC014 is higher than other tested bat SARSr-CoVs in transgenic
mice that express hACE2.

Interim RPPR Page 15


B.2Z (YGar 0 NIAID Cov Kepor AccOomplsnments rinal.par)

A B
700+ ’ Brain
“| wi a wa WV.
zm — $ cod @ 104 We sHco
2 C a WV 16 8 EA wv.
3 604 |. 4231 g em 4234
€ g 5
8 3
& 24 8
Oo
Q 5 10 15
DPI

Fig. 13: In vivo infection of SARSr-CoV in hACE2-expressing mice. (A) Survival rate of hACE2_mice after
infection (B) Viral load in brains of infected hACE2-expressing mice. (C} Histopathological section of lung
tissue of mice infected with rWIV1. (D) Histopathological section of lung tissue of mice infected with
rWiV1-SHCO014 S.

3.2 Assessment of interspecies transmission risk of bat HKU4-related coronaviruses

Taking a similar reverse genetics strategy that we used in SARSr-CoV studies, we constructed
the full-length infectious clone of MERS-CoV, and replaced the RBD of MERS-CoV with the
RBDs of various strains of HKU4-related coronaviruses previously identified in bats from
different provinces in southern China. The full-length MERS-CoV and chimeric viruses with
RBDs of HKU4r-CoVs were then rescued. Immunofluorescence assay showed that these
chimeric MERS-HKU4rRBD coronaviruses were able to infect human cells from different tissues
including lung, liver, intestine and kidney (Fig. 14 Left). Moreover, efficient replication of the
chimeric HKU4r-CoVs were detected by real-time PCR in HeLa cells that expressed human
DPP4 receptor (Fig. 14 Right}. The results suggest potential risk of the bat HKU4r-CoVs for
cross-species infection in humans.

interim RPPR Page 16


b.zZ (Year 0 NIAID UOV Report Accompiisnments rinal.pat)

~
HeLa-human DPP4
os
10% a MERS-CoV
$a = E43 MFL-Bt/Macao
sim =} MFL-BUGD
g2 MFL-BUYN
e 8 & MFL-BUGX
82
Eo
§-
z4h 48h 72h
Hours post infection (h)
Fig. 7: Left: immunofluorescence assay confirms Infection of 4 chimeric viruses with the backbone of
MERS-CoV and RBD of bat HKU4r-CoVs in different cell lines derived from human tissues. Right:
Replication of MERS-HKU4rRBD CoVs in HeLa cells expressing human DPP4 was determined by real-
time PCR. ,
Interim RPPR

Page 17

B.4 (B4 training. pat)

—, 1. Conference and University lectures: We continued to provide human subject research
. trainings to chief physicians and nurses at local clinics, staff from Yunnan Institute of
Endemic Diseases Control and Prevention, students from Dali College and Wuhan
University for both qualitative and quantitative research.

2. Agency and other briefing: Dr. Guangjian Zhu provided training to 18 field team
members from the Dali College and 4 Wuhan Insitute of Virology laboratory team
members regarding biosafety and PPE use, bats and rodents sampling.

3. Public outreach: Pl Daszak, and Co-investigators Shi, Epstein, and Olival presented the
Year 5 results of this project to the public via interviews with national central and local
television, social media, newspaper and journals in China and the US.

Interim RPPR

Page 18


Interim RPPR FINAL

> C. PRODUCTS

C.1 PUBLICATIONS

Are there publications or manuscripts accepted for publication in a journal or other publication (e.g., book, one-time
publication, monograph) during the reporting period resulting directly from this award?

No
C.2 WEBSITE(S) OR OTHER INTERNET SITE(S)

NOTHING TO REPORT

C.3 TECHNOLOGIES OR TECHNIQUES

NOTHING TO REPORT
C.4 INVENTIONS, PATENT APPLICATIONS, AND/OR LICENSES

Have inventions, patent applications and/or licenses resulted from the award during the reporting period? No

If yes, has this information been previously provided to the PHS or to the official responsible for patent matters at the grantee
organization? No

C.5 OTHER PRODUCTS AND RESOURCE SHARING

*™\THING TO REPORT

Interim RPPR Page 19


Interim RPPR FINAL
~ D. PARTICIPANTS
D.1 WHAT INDIVIDUALS HAVE WORKED ON THE PROJECT?
DASZAK, PETER
a ~ Center for
; Disease
Co- BOO es eee See 3). Control and
KE, CHANGWEN PHD Investigator + £2293? prevention of CHINA NA
_- Guangdon g
Province
Yunnan
_ Provincial
Co- » Institute of
ZHANG, YUNZHI | PHD , - Endemic CHINA NA
Investigator == Diseases
~~ Control &
“Prevention
Co- East China
ZHU, GUANGJIAN | PHD . Normal CHINA NA
Investigator University
Non-Student
Chmura, Aleksei BS,PHD Research NA
Assistant
Ross, Noam Martin | PhD Co- NA
’ ; Investigator
. . Co-
Olival, Kevin J. PHD Investigator NA
Co- East China
Zhang, Shu-yi PHD . Normal CHINA NA
Investigator University
Co- _ Wuhan
SHI, ZHENGLI PhD Investigator ___ Institute of CHINA NA
gato} Virology
Co- Wuhan
GE, XINGYI PHD . -.© Institute of CHINA NA
Investigator Virology
EPSTEIN, Co- /
JONATHAN H MPH;DVM,BA/PHD | trvestigator NA
Glossary of acronyms: Foreign Org - Foreign Organization Affiliation
S/K - Senior/Key SS - Supplement Support
DOB - Date of Birth RE - Reentry Supplement
Cal - Person Months (Calendar) DI - Diversity Supplement
Aca - Person Months (Academic) OT ~ Other
Sum - Person Months (Summer) NA - Not Applicable
D.2 PERSONNEL UPDATES
D,2.a Level of Effort
Interim. RPPR Page 20

Interim RPPR FINAL

~s Applicable

D.2.b New Senior/Key Personnel

Not Applicable

D.2.c Changes in Other Support

Not Applicable

D.2.d New Other Significant Contributors

Not Applicable

D,2.e Multi-PI (MPI) Leadership Plan

Not Applicable

Interim RPPR Page 21

Interim RPPR FINAL

~ E, IMPACT

E.1 WHAT IS THE IMPACT ON THE DEVELOPMENT OF HUMAN RESOURCES?

Not Applicable

E.2 WHAT IS THE IMPACT ON PHYSICAL, INSTITUTIONAL, OR INFORMATION RESOURCES THAT FORM INFRASTRUCTURE?

NOTHING TO REPORT

E.3 WHAT IS THE IMPACT ON TECHNOLOGY TRANSFER?

Not Applicable

E.4 WHAT DOLLAR AMOUNT OF THE AWARD'S.BUDGET IS BEING SPENT IN FOREIGN COUNTRY(IES)?

Interim RPPR Page 22


Interim RPPR FINAL

G, SPECIAL REPORTING REQUIREMENTS SPECIAL REPORTING REQUIREMENTS

G.1 SPECIAL NOTICE OF AWARD TERMS AND FUNDING OPPORTUNITIES ANNOUNCEMENT REPORTING REQUIREMENTS

NOTHING TO REPORT

G.2 RESPONSIBLE CONDUCT OF RESEARCH

Not Applicable

G.3 MENTOR'S REPORT OR SPONSOR COMMENTS

Not Applicable

G.4 HUMAN SUBJECTS

Understanding the
Risk of Bat
Coronavirus
Emergence-
PROTOCOL-001

u.5 HUMAN SUBJECTS EDUCATION REQUIREMENT

NOT APPLICABLE

G.6 HUMAN EMBRYONIC STEM CELLS (HESCS)

Does this project involve human embryonic stem cells (only hESC lines listed as approved in the NIH Registry may be used in
NIH funded research)?

No

G.7 VERTEBRATE ANIMALS

Not Applicable

G.8 PROJECT/PERFORMANCE SITES

Not Applicable

G.9 FOREIGN COMPONENT

Organization Name: Wuhan Institute of Virology
“untry: CHINA

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Interim RPPR FINAL

D*“iption of Foreign Component:
Pi,..upal Laboratory for all Research in China and detailed in our Specific Aims

G.10 ESTIMATED UNOBLIGATED BALANCE

Not Applicable

G.11 PROGRAM INCOME

Not Applicable

G.12 F&A COSTS

Not Applicable

Interim RPPR Page 24

em,

Human Subject Heport (Understanding the Hisk of Bat Loronavirus Eiiergenuesr ny twee

Section 1 - Basic Information (Study 58010)

*, 41.4. Study Title *

Understanding the Risk of Bat Coronavirus Emergence-PROTOCOL-001

1.2. Is this study exempt from Federal

Regulations * O Yes @ No
1.3. Exemption Number 1 2 O3 4 m5
1.4, Clinical Trial Questionnaire *
1.4.a. Does the study involve human participants? @ Yes
1.4.b. Are the participants prospectively assigned to an intervention? O Yes

1.4.c. Is the study designed to evaluate the effect of the intervention on the
participants?

1.4.d. Is the effect that will be evaluated a health-related biomedical or a Y
behavioral outcome? O Yes

1.5. Provide the ClinicalTrials.gov Identifier (e.g.
NCT87654321) for this trial, if applicable

O Yes

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46 7

© No
@ No

@ No

o8

OMB Number: 0925-0001

Expiration Date: 02/28/2023

Page 2&