Genetic insights of H9N2 avian inuenza viruses
circulating in Mali and phylogeographic patterns in
Northern and Western Africa
Idrissa Nonmon Sanogo,
1,2,†,
*
Claire Guinat,
1
Simon Dellicour,
3,4,‡
Mohamed Adama Diakité,
5
Mamadou Niang,
6
Ousmane A. Koita,
7
Christelle Camus,
1
and Mariette Ducatez
1,§,
*
1
Interactions Hôtes-Agents Pathogènes (IHAP), UMR 1225, Université de Toulouse, INRAE, ENVT, Toulouse 31076, France,
2
Faculté d’Agronomie et de Médecine
Animale (FAMA), Université de Ségou, Ségou BP 24, Mali,
3
Spatial Epidemiology Lab (SpELL), Université Libre de Bruxelles, Brussels B-1050, Belgium,
4
Department
of Microbiology, Immunology and Transplantation, Rega Institute, Laboratory for Clinical and Epidemiological Virology, KU Leuven, Leuven BE-3000, Belgium,
5
Service diagnostic et rechercheLaboratoire Central Vétérinaire, Bamako BP 2295, Mali,
6
Food and Agriculture Organization of the United Nations (FAO-UN),
Emergency Centre for Transboundary Animal Diseases (ECTAD), Regional Ofce for Africa (RAF), Accra BP 1628, Ghana and
7
Laboratoire de Biologie Moléculaire
Appliquée, Faculté des Sciences et Techniques (FAST), University of Sciences, Techniques and Technologies of Bamako (USTTB), Mali Université de Bamako,
Bamako E 3206, Mali
†
https://orcid.org/0000-0003-1372-7818
‡
https://orcid.org/0000-0001-9558-1052
§
https://orcid.org/0000-0001-9632-5499
*Corresponding authors: E-mail: idrissa.sanogo@envt.fr; mariette.ducatez@envt.fr
Abstract
Avian inuenza viruses (AIVs) of the H9N2 subtype have become widespread in Western Africa since their rst detection in 2017 in
Burkina Faso. However, the genetic characteristics and diffusion patterns of the H9N2 virus remain poorly understood in Western Africa,
mainly due to limited surveillance activities. In addition, Mali, a country considered to play an important role in the epidemiology
of AIVs in the region, lacks more comprehensive data on the genetic characteristics of these viruses, especially the H9N2 subtype.
To better understand the genetic characteristics and spatio-temporal dynamics of H9N2 virus within this region, we carried out a
comprehensive genetic characterization of H9N2 viruses collected through active surveillance in live bird markets in Mali between
2021 and 2022. We also performed a continuous phylogeographic analysis to unravel the dispersal history of H9N2 lineages between
Northern and Western Africa. The identied Malian H9N2 virus belonged to the G1 lineage, similar to viruses circulating in both Western
and Northern Africa, and possessed multiple molecular markers associated with an increased potential for zoonotic transmission and
virulence. Notably, some Malian strains carried the R-S-N-R motif at their cleavage site, mainly observed in H9N2 strains in Asia.
Our continuous phylogeographic analysis revealed a single and signicant long-distance lineage dispersal event of the H9N2 virus to
Western Africa, likely to have originated from Morocco in 2015, shaping the westward diffusion of the H9N2 virus. Our study highlights
the need for long-term surveillance of H9N2 viruses in poultry populations in Western Africa, which is crucial for a better understanding
of virus evolution and effective management against potential zoonotic AIV strain emergence.
Keywords: inuenza A virus; H9N2; molecular epidemiology; viral phylogeography; mali; western Africa.
© The Author(s) 2024. Published by Oxford University Press.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which
permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
Introduction
Low pathogenic AIVs (LPAIVs), particularly the H9N2 subtype,
pose a signicant global threat to both poultry and human health
(Pusch and Suarez 2018; Peacock et al. 2019). H9N2 viruses
have the potential to cross species barriers and infect various
mammalian species, including humans (Peiris et al. 1999; Qian
et al. 2021). While most H9N2 viruses circulate asymptomati-
cally in poultry and wild birds (Peacock et al. 2019), certain H9N2
strains have been associated with outbreaks causing high mortal-
ity among domestic poultry (El Houad et al. 2016; Perez and Wit
de 2016; Jeevan et al. 2019).
In Africa, H9N2 virus was rst reported in Egypt and Tunisia
in 2011, marking the beginning of its detection in Northern Africa
(Tombari et al. 2011; El-Zoghby et al. 2012; Monne et al. 2013).
Subsequently, since its emergence in Morocco in 2016, the virus
has rapidly disseminated across the country and to other regions
in Africa (El Houad et al. 2016; Nagy, Mettenleiter, and Abdel-
whab 2017). In Western Africa, the virus was initially detected in
2017 in Burkina Faso (Zecchin et al. 2017), followed by subsequent
identication in Senegal (Jallow et al. 2020), Ghana (Awuni et al.
2019; Kotey et al. 2022), Nigeria (Sulaiman et al. 2021), Benin, and
Togo (Fusade-Boyer et al. 2021). Notably, H9N2 viruses isolated in
Western Africa so far belong to the G1 lineage and share genetic
relatedness with those circulating in Northern Africa and the Mid-
dle East (El Houad et al. 2016; Barberis et al. 2020; El Mellouli
et al. 2022b). However, the extent and mechanisms of geographical
2 Virus Evolution
diffusion between Northern and Western Africa remain poorly
understood, primarily due to limited surveillance activities in
Western Africa, leading to under-reporting and underdetection of
H9N2 viruses (Pavade et al. 2011).
Phylogeographic analyses of avian inuenza virus (AIV)
genome sequences have proven to be powerful tools for unveiling
the complex spatio-temporal dynamics of these viruses. Speci-
cally, phylogeography contributed to unraveling the origin, migra-
tion patterns, and dispersal history of H9N2 viruses in different
regions such as Asia, the Middle East, and North Africa (Fusaro
et al. 2011; Jin et al. 2014; Li et al. 2020; El Mellouli et al. 2022b).
However, the application of these methodologies to understand
the transmission dynamics and introduction drivers of the H9N2
viruses in Western Africa remains largely unexplored. Therefore,
harnessing these analytical tools and data becomes essential to
shed light on the dissemination dynamics of H9N2 viruses within
the unique context of Western Africa (Yang et al. 2019).
Despite experiencing the circulation of LPAIVs in domestic
poultry since 2007 (Molia et al. 2010), Mali lacks comprehensive
studies on the epidemiology and genetic characteristics of these
viruses, particularly H9N2. Indeed, previous studies related to
AIVs in domestic and wild birds have mainly focused on the epi-
demiology of highly pathogenic avian inuenza viruses (HPAIVs)
and rarely addressed low pathogenic avian inuenza viruses
(LPAIVs). In addition, the country faces a heightened risk of new
AIV strain emergence due to the predominant practice of rearing
domestic birds in traditional farming systems without adequate
biosecurity measures (Traoré 2013; Molia et al. 2015). This is cou-
pled with the presence of the Inner Delta of the Niger River, which
serves as a signicant gathering site for millions of wild birds that
may carry AIVs (Gaidet et al. 2007; Cappelle et al. 2012).
Live bird markets (LBMs) in Mali play a substantial role in the
emergence and spread of new AIV strains, as various species of
domestic birds from different regions are brought together (Molia
et al. 2016). Risk factors associated with the presence of AIVs,
such as poor sanitary conditions and improper disposal of dead
birds, have been reported in the majority of Malian LBMs (Molia
et al. 2016). Therefore, active surveillance in LBMs is crucial for
understanding the genetic characteristics, geographic origins, and
dispersal patterns of the circulating H9N2 viruses in Mali.
In this study, we conducted a comprehensive genomic charac-
terization of H9N2 viruses collected from LBMs in Mali in 2022.
Additionally, we performed a continuous phylogeographic anal-
ysis to unravel the dispersal history of H9N2 lineages between
Northern and Western Africa. Our results indicate that the H9N2
virus detected in Mali belongs to the G1 lineage and shows a poten-
tial for zoonotic transmission. Additionally, we demonstrated that
a single signicant long-distance lineage dispersal event of the
H9N2 virus to Western Africa originated from Morocco, shaping
the westward diffusion of the H9N2 virus.
Materials and methods
H9N2 genome sequences from Mali
From June 2021 to April 2022, a total of 519 cloacal swabs were
collected from apparently healthy chickens in eleven LBMs in
Bamako, Ségou, and Mopti in Mali. Samples were usually collected
on the same day each month from the same stall. The swabs were
placed into sterile tubes containing 500 μl of phosphate-buffered
saline supplemented with antibiotics (100 IU/ml penicillin: and
streptomycin: 100 μg/ml). Samples were transported within 24 h
to the Central Veterinary Laboratory (LCV) of Bamako and stored
at −80
∘
C until further analysis.
Viral RNA was extracted from the swabs using the NucleoSpin
RNA extraction kit (Macherey-Nagel, Germany) according to the
manufacturer’s instructions. The extracted RNA samples were
tested in pools of ve using real-time reverse-transcription poly-
merase chain reaction (RT-PCR) targeting the inuenza A matrix
(M) gene at the LCV (Fouchier et al. 2000). A total of 100 μl of
positive samples were applied to Flinders Technology Associates
(FTA) cards to inactivate viral infectivity and preserve nucleic acid
integrity. The FTA cards were shipped to the VIRéMIE laboratory of
UMR IHAP in Toulouse for further analysis. RNA was eluted from
FTA cards by placing 6-mm-diameter disk fragments, cut from
the FTA card spots, in 200 μl of Tris ethylenediamine tetraacetic
acid (EDTA) buffer (10 mM Tris-HCl, pH 8.0 and 0.1 mM EDTA) fol-
lowing previously described protocols (Abdelwhab et al. 2011).
Viral RNA was extracted from the FTA cards using the Nucleo-
Mag Pathogen kit (Macherey-Nagel, Germany) with an automatic
KingFisher Flex Purication System (Thermo Fisher, Waltham, MA;
catalog number: 5400630). Samples were tested for AIV-specic
RNA using a SYBER Green real-time RT-PCR assay targeting the
AIV matrix gene (Fouchier et al. 2000). AIV samples with quan-
tication cycle (Cq) below thirty-four were considered positive
and were subsequently tested for the H9 subtype by RT-qPCR
(Monne et al. 2008).
The partial hemmaglutinin (HA) and neuraminidase (NA)
gene segments of positive samples were sequenced using Sanger
sequencing techniques to select representative strains for full-
genome sequencing (data not shown). Selected samples were
then subjected to full-genome sequencing using the Illumina
MiSeq System (Illumina, San Diego, CA, USA) following previ-
ously described protocols (Barman et al. 2019). Seven sequences
were generated and manually curated using BioEdit v7.2 (Hall,
Biosciences, and Carlsbad 2011).
Molecular characterization of H9N2 viruses
in Mali
To gain insights into the potential determinants of AIV transmis-
sion to mammalian species and identify key molecular markers
associated with increased virulence, an analysis of the deduced
amino acid sequences of the Malian H9N2 viruses was con-
ducted. Specically, the HA receptor–binding site (RBS) was exam-
ined using the H3 numbering system (Burke, Smith, and Digard
2014) to assess its binding afnity to mammalian-type recep-
tors (Wan and Perez 2007; Li et al. 2014). Furthermore, the
numbers and locations of N-linked glycosylation sites (Asn-Xaa-
Ser/Thr) were determined in the HA segment using the online pro-
gram NetNGlyc 1.0 (https://services.healthtech.dtu.dk/services/
NetNGlyc-1.0/). These glycosylation sites have been associated
with AIV antigenic escape from the host’s immune responses
(Schulze 1997). We also examined the other gene segments of
the H9N2 viruses from Mali to identify amino acid mutations
described in experimental studies as associated with increased
polymerase activity, enhanced replication in mammalian cells,
and increased virulence (PB2-D253N, PB2-I292V, PB2- A588V, PB2-
E627K, PB2-D710N, PB1-M185I, PB1-K577E, PA-N291S, PA-K356R,
NP-E434K) and antiviral resistance (M2-S31N).
Continuous phylogeographic analysis of H9N2 in
Northern and Western Africa
To conrm whether H9N2 sequences from Western and Northern
Africa form a unique clade, we rst inferred a maximum likeli-
hood tree (Supplementary Figure S1) from all H9N2 sequences of
the HA gene segment available on the Global Initiative on Sharing
I. N. Sanogo et al. 3
All Inuenza Data database with FastTree v2.1 using the gen-
eral time-reversible + CAT substitution model (Price, Dehal, and
Arkin 2009). H9N2 sequences of the HA gene segment for Northern
and Western Africa (Supplementary Table S2), covering the period
from 1 January 2011 to 7 February 2022 (n = 148) were then merged
with the newly generated Malian sequences (n = 7). The align-
ment of the sequences was performed using MAFFT v7.49 (Katoh
and Standley 2013) and checked using AliView v1.28 (Larsson
2014). Based on this alignment, a maximum likelihood phyloge-
netic analysis was rst performed using the program RAXML-NG
(Kozlov et al. 2019) with 1,000 bootstrap replicates to assess branch
support (Supplementary Figure S1). The temporal signal was eval-
uated using Tempest v1.5.3 (Rambaut et al. 2016). The linear
regression of the root-to-tip genetic distance against sampling
time showed a strong temporal signal (R
2
= 0.78) (Supplemen-
tary Figure S2). This preliminary phylogenetic inference aimed
to identify phylogenetic clusters of identical sequences sharing
the same geographic coordinates and sampling date. Consider-
ing that retaining more than one sequence per cluster would not
contribute signicant information to subsequent phylogeographic
analyses, we subsampled the original alignment to randomly
select only one sequence per phylogenetic cluster. As a result, the
nal dataset of seventy-eight sequences was derived from nine
countries in Northern and Western Africa (Algeria, Benin, Ghana,
Mali, Morocco, Nigeria, Senegal, Togo, and Tunisia), with sampling
dates ranging from 2 April 2012 to 7 February 2022 (Supplemen-
tary Table S1). To avoid duplication of sampling coordinates in this
nal dataset, we randomly sampled data within a circular buffer
zone of 0.25, excluding the sea areas if they fell within the buffer
(Dellicour et al. 2020).
The continuous phylogeographic analysis was performed using
Bayesian evolutionary analysis by sampling trees v1.10.4 (Drum-
mond and Rambaut 2007) along with the BEAGLE 3 library to
improve computational performance (Ayres et al. 2019). The sub-
stitution process was modeled using the HKY + Γ4 parameteriza-
tion (Shapiro, Rambaut, and Drummond 2006), the branch-specic
evolutionary rates were modeled using a relaxed molecular clock
with a lognormal distribution (Drummond et al. 2006), and a sky-
grid coalescent model was specied as tree topology prior (Hill
and Baele 2019). We used the relaxed random walk (RRW) dif-
fusion model (Lemey et al. 2010; Pybus et al. 2012) to perform
the continuous phylogeographic reconstruction, with the among-
branch heterogeneity in diffusion velocity modeled with a gamma
distribution. The RRW model does not allow different sequences
to be associated with identical geographical coordinates. There-
fore, a spatial buffer zone of 25 km was applied to the locations
of the sequences to prevent improper posteriors under the RRW
model, excluding sea areas falling within the buffer (Dellicour
et al. 2020). We ran and combined three independent analyses
for one billion generations, sampling every 100,000 generations.
Convergence and mixing properties were again assessed using
Tracer v1.7.1 (Rambaut et al. 2018), ensuring that all continuous
parameters were associated with an effective sample size value
of >200. After having discarded 10 per cent of sampled poste-
rior trees as burn-in, we obtained and annotated the maximum
clade credibility (MCC) tree using TreeAnnotator 1.10.4 (Suchard
et al. 2018). We identied phylogenetic trees that exhibit multi-
ple long-distance dispersal events by examining the latitudes of
branches in the trees. We used the latitude of 21.3 as a cut-off,
which was in a wide swath of geography between our Northern
and Western Africa samples. Trees with more than one branch
starting above the cut-off and ending below it are considered to
have multiple long-distance dispersal events. We used functions
Table 1. H9N2 viruses characterized in this study.
Sample ID Collection date Location
Accession
number
A/chicken/Mali/22-
A-01-113/2022
8 January 2022 Ségou OR133289–96
A/chicken/Mali/22-
A-02-123/2022
7 February 2022 Bamako OR133241–48
A/chicken/Mali/22-
A-02-128/2022
7 February 2022 Bamako OR133249–56
A/chicken/Mali/22-
A-02-139/2022
7 February 2022 Bamako OR133257–64
A/chicken/Mali/22-
A-02-143/2022
7 February 2022 Bamako OR133265–72
A/chicken/Mali/22-
A-02-152/2022
7 February 2022 Bamako OR133273–80
A/chicken/Mali/22-
A-02-158/2022
7 February 2022 Bamako OR133281–88
available in the R package ‘seraphim’ (Dellicour et al. 2016, 2017) to
extract spatio-temporal information embedded within posterior
trees and visualize the continuous phylogeographic reconstruc-
tions. We also used the ‘spreadStatistics’ function of the R package
‘seraphim’ to estimate the spatial wavefront distance from the
epidemic origin, the weighted lineage dispersal velocity over time,
and the weighted diffusion coefcient (Trovão et al. 2015).
Given the sensitivity of phylogeographic analyses to heteroge-
neous sampling efforts (Kalkauskas et al. 2021), we investigated
the potential impact of different sampling strategies on the esti-
mation of clock rate and time to the most common recent ancestor
parameters, as well as on the topology of the MCC tree. Two sam-
pling schemes were employed: (1) random sampling for equivalent
representation: sequences were randomly selected to ensure an
equivalent number of sequences from both Northern and Western
Africa to achieve balanced representation from these two regions
(n = 32 sequences in each region to allow an equal number of
sequences per region while using most of the available sequences)
and (2) random sampling for reduced biased sampling: sequences
were randomly sampled to reduce the number of sequences from
Morocco where high sampling was observed compared to other
countries to mitigate any potential bias arising from uneven sam-
pling effort across different countries (n = 13 sequences in Morocco
to ensure that this country did not have the highest number of
sequences). We ran three independent analyses for each sampling
scheme to explore variability.
Results
Identication and whole-genome sequencing of
Malian H9N2 viruses
We screened 519 oropharyngeal samples collected from LBMs
by RT-qPCR. Eighty-four samples (16 per cent) tested positive for
AIV specically for the H9 subtype with Cq values ranging from
20.7 to 35.4. Seven positive samples (Table 1) were selected for
whole-genome sequencing and fully sequenced. The nucleotide
sequence identity among the Malian H9N2 viruses ranged from
95.8 to 100 per cent in the eight gene segments. Sequences gener-
ated in this study were submitted to GenBank (accession numbers:
OR133241–OR133296).
Molecular characterization of Malian H9N2
viruses
A detailed analysis of the deduced amino acid sequences of
Malian H9N2 viruses identied certain molecular markers asso-
ciated with mammalian adaptation, enhanced replication, and
4 Virus Evolution
Table 2. Amino acid substitutions associated with host-shift and virulence in the Malian H9N2 viruses.
Gene Mutation A-01-113 A-02-123 A-02-128 A-02-139 A-02-143 A-02-152 A-02-158 Functions
HA I155T (145)
a
T T T T T T T Binding of AIV to mammalian receptors
(Li et al. 2014)
T190V (180) A A A A A A A Enhanced binding afnity to mam-
malian cells and replication in
mammalian cells (Teng et al. 2016)
Q226L (216) L L L L L L L Increased virus binding to α2–6,
enhanced replication in mammalian
cells and ferrets, enhanced transmis-
sion in ferrets (Wan and Perez 2007;
Wan et al. 2008; Peacock et al. 2021)
HA1/HA2
cleavage site
R-S-N-R R-S-R-R R-S-N-R R-S-R-R R-S-R-R R-S-R-R R-S-R-R
PB2 M185I I I I I I I I Enhanced pathogenicity in mice (Zhang
et al. 2011)
D253N D D D D D D D Increased polymerase activity in
mammalian cell lines (Zhang et al.
2018)
I292V I I I I I I I Increased polymerase activity in mam-
malian cell lines and increased
virulence in mice (Gao et al. 2019)
A588V A A A A A A A Increased polymerase activity and
replication in mammalian, increased
virulence in mice (Xiao et al. 2016)
E627K E E E E E E E Increased virulence and transmis-
sibility in mammals, increased
polymerase activity (Sang et al. 2015a;
Sediri et al. 2016)
D701N D D D D D D D Increased transmissibility in mammals
(Sediri et al. 2016)
PB1 K577E K K K K K K K Increased polymerase activity and
virulence in mice (Kamiki et al. 2018)
PA N291S S S S S S S S Enhanced pathogenicity in mice (Zhang
et al. 2011)
K356R K K K K K K K Increased polymerase activity and
enhanced replication in mammalian
cell lines, increased virulence in mice
(Xu et al. 2016)
NP E434K E E E E E E E Increased polymerase activity in
mammalian cell lines (Sang et al.
2015b)
M2 S31N N N N N N N N Amantadine resistance (Ilyushina,
Govorkova, and Webster 2005)
Mutations associated with mammalian adaptation or virulence are highlighted in bold.
a
Mature H3 HA numbering (mature H9 HA numbering).
antiviral resistance in the HA and M proteins (Table 2). In particu-
lar, all Malian H9N2 viruses carry the Q226L and I155T mutations
(H3 numbering) in the RBS of the HA protein. These mutations
are involved in the binding of AIVs to mammalian receptors and
enhanced replication in mammalian cells and ferrets (Table 2). On
the contrary, T190V substitution, associated with enhanced bind-
ing afnity for the human-type sialic acid receptor and replication,
was absent in the H9N2 virus of this study. The HA protein of ve
Malian H9N2 viruses had the RSSR/GLF amino acid motif at the
cleavage site, which is characteristic of LPAIV of H9 G1 lineage
(Banks et al. 2000). Conversely, two Malian H9N2 viruses harbor
the RSNR/GLF motif due to the substitution of S329N (H3 number-
ing). Although the RSNR motif had previously been reported in var-
ious studies on avian H9N2 viruses in Asia (Perk et al. 2006; Tosh
et al. 2008; Butt et al. 2010), to our knowledge, this is the rst report
of this motif in H9N2 viruses from Africa. Regarding the other
proteins of H9N2 viruses, the well-known mammalian adaptation
markers, PB2-E627K and D701N, were not found in any of the H9N2
viruses from Mali (Table 2). In addition, PB2-D253N, PB2-I292V, PB2-
A588V, PB1-K577E, PA-K356R, and NP-E434K mutations associated
with either increased polymerase activity in mammalian cells or
increased virulence were not present in H9N2 virus from Mali. In
contrast, S31N mutation in the M2 protein associated with aman-
tadine resistance and M185I in PB2 and N291S in PA associated
with increased pathogenicity in mice were observed in the seven
H9N2 viruses from Mali (Table 2). Based on the results predicted
using NetNGlyc 1.0 software, all the Malian H9N2 viruses had
seven N-linked glycosylation sites in the HA protein at positions
(29, 141, 218, 298, 305, 492, and 551).
Phylogenetic and phylogeographic analyses
The phylogenetic analysis of the HA gene segment (Fig. 1) showed
that the Malian H9N2 viruses belong to the H9N2 G1 lineage and
were closely related to viruses isolated in Western Africa between
2017 and 2019. The time to tMRCA of the HA gene segment for
Malian H9N2 viruses was estimated around 2019 with 95 per cent
I. N. Sanogo et al. 5
Figure 1. MCC tree estimated from H9N2 genome sequences collected from nine countries in Northern and Western Africa (Algeria, Benin, Ghana,
Mali, Morocco, Nigeria, Senegal, Togo, and Tunisia) from 2 April 2012 to 7 February 2022. Tip node colors indicate the country of sampling. Bars at
internal nodes represent the 95 per cent highest posterior density intervals of the node date. Numbers at internal branches show the clade posterior
probabilities above 0.75. The two tip nodes highlighted show the Malian sequences.
6 Virus Evolution
Figure 2. Continuous phylogeographic reconstruction of the dispersal history of H9N2 lineages in Northern and Western Africa. We here map the MCC
tree and 80 per cent highest posterior density regions (A) reecting the uncertainty related to the Bayesian phylogeographic inference. Nodes shaped
as circles and squares indicate internal and tip nodes, respectively, and are colored according to their time of occurrence. This gure also reports
dispersal statistics at the bottom: the date of the dispersal event to Western Africa (B), the evolution of the spatial wavefront distance from the origin
of the epidemic (C), and the evolution of the weighted lineage dispersal velocity through time (D).
Highest Posterior Distribution (HPD) between 2018 and 2021. The
tree topology indicated that the Malian strains had evolved into
two genetically distinct clusters within the country, comprising
ve and two sequences, respectively, out of the seven newly gen-
erated sequences (Supplementary Figure S1). It also shows that
sequences from Morocco were those most closely related to the
West African sequences. The tMRCA of the H9N2 virus in Northern
and Western Africa was estimated to have occurred in December
2003 (95 per cent HPD: May 1989 to April 2012), and the median
evolutionary rate was estimated to be 3.8 × 10
−3
substitutions per
site per year (95 per cent highest posterior density HPD: 2.7 × 10
−3
to 35.2 × 10
−3
).
Phylogeographic reconstructions revealed a single signicant
long-distance lineage dispersal event that consistently appeared
in all posterior trees (Fig. 2). The long-distance dispersal event was
followed by the spread of H9N2 lineages across various countries
in Western Africa (Fig. 2). The estimations of weighted lineage dis-
persal velocity revealed temporal variations in the rate of spread.
From 2003 to 2013, the median weighted lineage dispersal velocity
was approximately 92 km/year (95 per cent HPD 30–208). However,
from 2014 onwards, the dispersal velocity exhibited an increased
trend over time, with a notable increase up to 685 km/year (95 per
cent HPD 7–1,789) observed around 2015 (Fig. 2). This increase
corresponds to the expansion phase of the epidemic from North-
ern to Western Africa. Subsequently, between 2017 and 2020,
the dispersal velocity decreased to approximately 150 km/year
(95 per cent HPD 107–229), followed by a second notable increase
observed in 2021, reaching up to 607 km/year (95 per cent HPD
200–905). The observed temporal changes in weighted lineage
dispersal velocity were further supported by our analysis of the
spatial wavefront distance from the epidemic origin. This analysis
showed an approximate 1,000 km increase in the distance cov-
ered by lineages around 2015 (Fig. 2). Additionally, the median
weighted diffusion coefcient was 35,084 km
2
/year (95 per cent
HPD 23,962–49,492).
The sensitivity analysis revealed that the choice of sampling
schemes had a minimal impact on our conclusions related to
the spatio-temporal origin of the H9N2 lineages spread. The sin-
gle signicant long-distance lineage dispersal event originating
from Morocco was observed in all posterior trees in the reduced
sampling scheme (Supplementary Figure S3) and 98.5 per cent
(985 out of 1,000) of posterior trees in the equivalent sampling
scheme (Supplementary Figure S4). The median rate of evolu-
tionary change ranged from 3.8 × 10
−3
to 4.2 × 10
−3
substitutions
per site and per year, with overlapping 95 per cent HPD inter-
vals across all sampling schemes (Supplementary Figure S4). Only
I. N. Sanogo et al. 7
the estimated median tMRCA varied slightly across the different
sampling schemes due to the reduced number of sequences, rang-
ing from December 2003 to December 2005, which represents a
relatively short time scale related to the studied period.
Discussion
The G1 lineage has been known to be circulating in Africa since
2016 and has spread to different countries within the continent (El
Houad et al. 2016; Zecchin et al. 2017; Awuni et al. 2019; Fusade-
Boyer et al. 2021; Sulaiman et al. 2021). Previous studies reported
the circulation of AIVs among domestic and wild birds in Mali, but
they have mainly focused on HPAIV strains and did not provide
sufcient information on the genetic and molecular characteris-
tics of the circulating viruses (Molia et al. 2010, 2017; Cappelle
et al. 2012).
The positivity rate of AIVs among poultry in LBMs (16 per cent)
was higher than the AIV prevalence previously reported in back-
yard poultry (3.6 per cent) in Mali (Molia et al. 2010). However, our
result should be interpreted cautiously since the number of sam-
ples collected was very limited and was not representative of the
whole country. In addition, in our study, genetic characterization
was performed on samples from FTA cards, which could reduce
the viral load and affect the number of positive samples. The use of
FTA cards also made it impossible to culture and isolate the virus,
as FTA cards inactivate the virus while preserving the nucleic acids
(Abdelwhab et al. 2011). Despite these limitations, FTA cards were
essential to overcome the technical difculties of maintaining the
cold chain and storage.
LBMs are considered a potential reservoir for AIVs that can
play a major role in their amplication and dissemination among
poultry (Molia et al. 2016; Sulaiman et al. 2021). Additionally,
Malian LBMs were characterized by the main risk factors asso-
ciated with the presence of AIVs such as poor biosecurity and
hygienic practices and large catchment areas of backyard poultry
(Molia et al. 2016). Thus, it is possible that after being introduced
in the LBMs, AIVs were amplied and more easily spread among
birds.
Analysis of the amino acid sequences of the HA protein of the
ve out of seven Malian H9N2 viruses indicated the presence of
the RSSR/GLF motif at the HA cleavage site, which is identical to
those found in the LPAI H9N2 viruses circulating in Western Africa
and elsewhere. However, two Malian H9N2 viruses had a different
pattern (RSNR/GLF) at their cleavage site. To the best of our knowl-
edge, this pattern was described only in H9N2 viruses isolated in
humans and poultry in Israel and India (Perk et al. 2006; Tosh
et al. 2008). The signicance of this mutation on viral tness and
pathogenicity is not fully understood, and further study is needed.
Nevertheless, as the cleavage site is an indicator of pathogenicity,
these ndings may indicate a change in the degree of virulence of
these two viruses (Zhang et al. 2021).
All the Malian H9N2 viruses possess amino acid residues in
the HA RBS that were associated with an increased binding afn-
ity of AIVs to human-type (α-2,6-linked sialic acids) receptors
such as L-226 and T155 (Wan and Perez 2007; Li et al. 2014;
Peacock et al. 2021), indicating the potential for these viruses
to infect humans as reported in previous studies (Saito et al.
2001; Jallow et al. 2020). These mutations were observed in recent
H9N2 viruses belonging to the G1-lineage isolated in many coun-
tries in Africa (Awuni et al. 2019; Kariithi et al. 2020; Sulaiman
et al. 2021; Atim et al. 2022) conrming their spread within the
region. Of note, most of the H9N2 viruses of the G1 lineage
detected after 2,000 and almost all the H9N2 viruses identied
in humans and other mammalian species such as horse, dog,
and mink, harbor leucine at position 226 conrming preferential
binding to mammalian receptors (Chrzastek et al. 2018; Pusch and
Suarez 2018). Mutations associated with increased pathogenicity
in mice were observed in PB2 (M185I) and PA (N291S), conrm-
ing the ability of these H9N2 viruses to infect mammalian species
(Zhang et al. 2011).
All the H9N2 viruses from Mali have seven N-linked glycosyla-
tion sites (positions 21, 97, 133, 290, 297, 484, and 543, H3 number-
ing), which were identical to those found in H9N2 viruses isolated
in Northern Africa (Barberis et al. 2020; Larbi et al. 2022). The addi-
tional glycosylation sites reported in some H9N2 strains isolated
in Western Africa (Zecchin et al. 2017; Awuni et al. 2019; Fusade-
Boyer et al. 2021) were not observed in the Malian H9N2 viruses.
Compared to G1-like prototypes (A/quail/Hong Kong/G1/97), the
Malian H9N2 viruses lost two glycosylation sites at positions 198
and 210. Glycosylation plays an important role in viral biology,
regulating the virulence and receptor-binding specicity of AIVs
(Schulze 1997; Peng et al. 2019). Variations in the number or posi-
tion of glycosylation sites may affect the biology of AIVs and
could allow these viruses to escape host antibody recognition
(Peng et al. 2019).
This study also explores the spatio-temporal spread of H9N2
lineages in Northern and Western Africa. We performed con-
tinuous phylogeographic reconstructions based on the analysis
of H9N2 sequences of the HA gene segment, including recent
sequences generated from Mali. The tMRCA of the H9N2 virus
in Northern and Western Africa was estimated around Decem-
ber 2003 (95 per cent HPD: May 1989 to April 2012), while the rst
detection in these regions occurred in 2015/2016. This time differ-
ence may be explained by the low pathogenicity of the H9N2 virus
in poultry and wild birds and its mostly asymptomatic circulation,
making it difcult to detect through routine surveillance (Peacock
et al. 2019; Fusade-Boyer et al. 2021).
In addition, active surveillance for H9N2 viruses is limited
and the virus is usually detected only when it is associated with
mortality in poultry (El Houad et al. 2016; Jeevan et al. 2019).
The Malian H9N2 viruses closely clustered with H9N2 viruses
circulating in Western Africa (Zecchin et al. 2017; Awuni et al.
2019; Fusade-Boyer et al. 2021) and Northern Africa (El Houad
et al. 2016; El Mellouli et al. 2022b). Most of the borders in Western
Africa are known to be porous with illegal movements of animals
without any border controls (Apolloni et al. 2019). Therefore, it
can be assumed that these viruses most likely originated from the
region and might have been introduced in Mali from the neighbor-
ing countries by cross-border poultry movement and trade. Our
ndings also highlight the occurrence of a single long-dispersal
event around 2015, during which the virus spread from Morocco
to Western Africa at a velocity of approximately 685 km/year. This
dispersal event likely originated from Morocco, serving as the pri-
mary source for the subsequent expansion phase of H9N2 lineages
from Northern to Western Africa. While the involvement of long-
distance migratory wild birds during autumn migration could be
a potential contributing factor to the dissemination of H9N2 to
Western Africa, the likelihood of such virus dispersal over very
long distances by wildfowl is low and estimates of wild bird migra-
tion velocities are higher (Gaidet et al. 2010; Lemke et al. 2013;
Trierweiler et al. 2014). In addition, during active surveillance in
wild birds in Morocco between 2016 and 2019, AIVs were found to
be circulating at a very low prevalence (1.83 per cent) and no H9N2
virus was detected (El Mellouli et al. 2022a).
Given that H9N2 viruses become well adapted to poultry,
it is more likely that the dispersal event was inuenced by
8 Virus Evolution
anthropogenic factors, specically the movement of live poultry,
facilitating the transmission of H9N2 to Western Africa (Peacock
et al. 2019). Notably, several countries in Western Africa import
hatching eggs and day-old chicks from Morocco, and there has
been a signicant increase in import quantities between 2013 and
2015 (Supplementary Figure S6) (Hassan et al. 2017; FAO 2023).
It is therefore likely that such trade activities have contributed
to the introduction of H9N2 viruses into the region. This result
conrms previous studies suggesting the primary role played by
Morocco in the spread of H9N2 viruses in Africa (Zecchin et al.
2017; El Mellouli et al. 2022b). However, the lack of comprehensive
poultry trade data limits our ability to conduct a robust investiga-
tion into the inuence of live poultry trade on lineage dispersal.
Future studies incorporating more detailed and comprehensive
trade data would provide valuable insights into the dynamics of
H9N2 virus dissemination in the region.
Our results should be interpreted in light of some limitations. In
particular, the overall size of the genome data was limited, result-
ing in omitting transmission events and increased uncertainty in
our analyses. Notably, some countries in the affected region were
unsampled, potentially missing important transmission dynam-
ics. For example, Ivory Coast, which is closely connected to the
sampled countries according to poultry trade data (Supplemen-
tary Figure S5), lacks representation in our samples. Addition-
ally, we lack sampling from Niger and Mauritania, which shared
borders with affected countries in both Western and Northern
Africa. This implies the possibility of unobserved dispersal events
between intermediate countries. This limitation also applies to
the identied long-distance dispersal event from Morocco to West-
ern Africa, although the crossing region is a deserted area with a
limited poultry population along the way (Robinson et al. 2014).
The estimation of velocity during this period including the long-
distance dispersal event is also subject to heightened uncer-
tainty, primarily attributed to the limited number of sequences
around this event, adding complexity to the modeling process.
Furthermore, the lack of sequences before 2012 introduced addi-
tional uncertainties before that date. To address these limitations,
we advocate for an intensied genomic surveillance of H9N2 in
the region. This would facilitate more thorough phylogeographic
reconstructions and contribute to a more comprehensive under-
standing of the virus’s spread both in poultry and potentially in
wild bird populations.
The H9N2 virus has become endemic in several countries
around the world and occasionally causes outbreaks in poultry,
resulting in huge economic losses to the poultry industry. In West-
ern Africa, the surveillance of H9N2 viruses is very limited, making
it difcult to obtain reliable data on the genetic characteristics
and transmission patterns of this virus in the region. In addi-
tion, the current circulation of HPAI H5Nx of clade 2.3.4.4b could
contribute to the emergence of reassortant strains, as recently
demonstrated in Burkina Faso (Ouoba et al. 2022), posing a threat
to human and animal health. Therefore, long-term surveillance of
the H9N2 virus in poultry populations in Western Africa, particu-
larly in LBMs, is crucial for an improved understanding of viral
evolution and effective management against potential zoonotic
AIV strain emergence.
Data availability
All Malian H9N2 genome sequences are available on GenBank
under accession numbers: OR133241–OR133296 (https://www.
ncbi.nlm.nih.gov/nuccore/). H9N2 genome sequences used in this
study are available on the GISAID database (http://www.gisaid.
org). The BEAST 1 XML le used to perform the phylogeographic
analysis and the R scripts are available from https://github.com/
ClaireGuinat/h9n2_continous_phylo.git.
Supplementary data
Supplementary data is available at Virus Evolution online.
Acknowledgements
This study was funded by Institut national de recherche pour
l’agriculture, l’alimentation et l’environnement through Initia-
tive Transformer les Systèmes Alimentaires et l’Agriculture par
la Recherche en partenariat avec l’Afrique (TSARA) and Ecole
nationale des services vétérinaires-France - Vétérinaire interna-
tional (ENSV-FVI) Grant. I.N.S was supported by a PhD scholarship
from the French Embassy in Mali. We gratefully acknowledge all
data contributors, i.e. the authors and their originating laborato-
ries responsible for obtaining the specimens and their Submitting
laboratories for generating the genetic sequence and sharing via
the GISAID Initiative, on which this research is based.
Conict of interest: None declared.
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DOI: https://doi.org/10.1093/ve/veae011
Advance Access Publication 19 February 2024
Research Article
© The Author(s) 2024. Published by Oxford University Press.
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