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Frontiers in Virology ·

Volume 6 - 2026 | https://doi.org/10.3389/fviro.2026.1815667

Tracking the phylogenetic profile of the MF-non-coding region of measles virus

  • Matteo Fracella1
  • Arnold Knijn2
  • Enrico Palermo3
  • Daniele Di Carlo3
  • Gloria Colonnelli1
  • Massimo Gentile1
  • Luca Maddaloni4
  • Alessandra Pierangeli5
  • Carolina Scagnolari1,3*corresponding author
  • Alessandra Lo Presti6*corresponding author
  1. 1Department of Molecular Medicine, Laboratory of Virology, Sapienza University of Rome, Rome, Italy
  2. 2Department of Food Safety, Nutrition and Veterinary Public Health, Istituto Superiore di Sanità, Rome, Italy
  3. 3Pasteur Laboratories, Istituto Pasteur Italia - Fondazione Cenci Bolognetti, Rome, Italy
  4. 4Department of Public Health and Infectious Diseases, Sapienza University of Rome, Rome, Italy
  5. 5Department of Well-being, Health and Environmental Sustainability (BeSSA), Sapienza University of Rome, Rome, Italy
  6. 6Department of Infectious Diseases, Istituto Superiore di Sanità, Rome, Italy
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Abstract

The highly variable non-coding region (NCR) between the matrix (M) and fusion (F) protein coding sequences (MF-NCR) has been proposed as a novel target for measles virus (MeV) molecular characterization and has been linked with cytopathogenicity and modulation of M and F protein expression. This study aimed to further characterize the MF-NCR of the four MeV genotypes (B3, D4, D8, and H1) that were most widely distributed before the COVID-19 pandemic, across different locations and collection periods, using phylogenetic analysis. Maximum likelihood phylogenetic analysis was performed using Phyml and average evolutionary divergence was estimated using MEGA X. Maximum likelihood analysis combined with genetic distance calculations provided information for understanding the evolutionary dynamics and genetic diversity of the MF-NCR of MeV. Phylogenetic analysis highlighted a higher number of supported internal clusters in MeV genotype H1, followed by D8, B3 and finally by D4. Intermixing was detected both among different locations and years of collection. The MF-NCR of MeV genotype D8 had the highest proportion of variable sites (47%), followed by B3 (31.8%), H1 (29%), and D4 (13.4%). The estimates of the average evolutionary divergences showed that the MF-NCR of MeV genotypes D8, B3 and H1 have higher variability (3%) compared to MF-NCR of MeV genotype D4 (1%). In conclusion, this study provides a comprehensive overview of the evolution of the MF-NCR and its clustering. It highlights changes in sequence variability across the most prevalent genotypes, as well as across different locations and over time.

Keywords

1 Introduction

Measles is a highly contagious systemic disease caused by the measles virus (MeV), a member of the Morbillivirus genus in the Paramyxoviridae family (1). Transmission mainly occurs through aerosols and respiratory droplets produced by infected individuals during coughing or sneezing, while the disease is often associated with severe respiratory, gastrointestinal and neurological manifestations, including high fever, cough, runny nose and a rash all over the body (2, 3). Complications occur more frequently in children under 5 years, adults over 20 years, pregnant women, and individuals who are immunocompromised or malnourished (4, 5). Despite the availability of a safe and effective vaccine, measles is still endemic in many countries, resulting in high morbidity and mortality (6).

A resurgence of measles has been observed worldwide following the global decline in vaccination rates between 2020 and 2023, which was largely due to disruptions in routine immunization during the SARS-CoV-2 pandemic (7). Notably, approximately 10.3 million cases were reported in 2023, representing a 20% increase from 2022 (8). Notification rates in the European Union/European Economic Area (EU/EEA) rose from 9.1 cases per million in 2023 to 77.4 in 2024, surpassing pre-pandemic levels (9). Significant outbreaks were also recorded in the United States in early 2025 (10). According to the European Centre for Disease Prevention and Control (ECDC), 10,825 measles cases were observed across 30 EU/EEA countries between 1 September 2024 and 31 August 2025, with Romania most affected. The majority of cases occurred in unvaccinated individuals, mainly among young children, and measles-related deaths were documented (11). These findings are concerning and highlight primarily vaccination gaps and the need for a comprehensive investigation of MeV evolution, transmission dynamics, and mechanisms of immune evasion. As widely established by the global measles elimination plan coordinated by the World Health Organization (WHO), continuous genomic surveillance is crucial for tracing transmission chains, assessing genetic variability, supporting public health interventions, and preventing future outbreaks (12, 13).

To date, 24 genotypes of MeV have been identified: A, B1–3, C1–2, D1–11, E, F, G1–3 and H1–2. However, global molecular surveillance data indicate that MeV genetic diversity declined markedly during the decade preceding the COVID-19 pandemic, with only four genotypes (B3, D4, D8 and H1) reported worldwide in routine surveillance during this period. Since 2021, only genotypes B3 and D8 have been detected globally (14, 15). Studies have suggested that circulating MeVs with genotypes B3, D4, D8, or H1 are more prone to adaptive mutations to escape immunity, exhibit antigenic variations that may affect vaccine effectiveness through reduced recognition by neutralizing antibodies (16–19). Therefore, molecular monitoring is essential.

Following this significant decline, molecular surveillance of MeV has shifted to a more detailed analysis of sequence variants within the 450-nucleotide (nt) fragment encoding the C-terminal region of the nucleoprotein (N450) (20, 21). Nevertheless, due to the limited variability of the N450 region, the accurate reconstruction of transmission chains, the identification of case origins, and the distinction between endemic and imported MeV cases may be hindered (22, 23). To overcome these limitations, complementary sequencing approaches, such as analysis of the highly variable non-coding region (NCR) between the matrix (M) and fusion (F) protein-coding sequences (MF-NCR) (24–26) or whole-genome sequencing (23), are increasingly employed in specific cases to enhance molecular resolution and improve the accuracy of transmission tracking. The MF-NCR is the longest non-coding region of the MeV genome, GC-rich, highly variable and has recently been proposed as a new target and complementary for MeV molecular characterization (24, 25), although standard protocols are still not available. While not essential for viral replication, the MF-NCR has been linked with cytopathogenicity and modulation of M and F protein expression (27). This may be achieved through RNA secondary structures that affect translation or mRNA localization (27, 28).

In this study, we present a comprehensive phylogenetic analysis aimed at better characterizing the MF-NCR of the four MeV genotypes most widely distributed before the COVID-19 pandemic (B3, D4, D8 and H1) across different locations and collection periods. Our analysis, combining maximum likelihood phylogenetic reconstruction with divergence time estimates, provides insights into the genetic diversity and evolutionary dynamics of circulating MeV strains and supports molecular surveillance efforts.

2 Materials and methods

2.1 Dataset and sequence alignment

The MF-NCR sequences of MeV included in this study were collected from human clinical samples (host: Homo sapiens according to the NCBI terminology) and were obtained from the NCBI nucleotide database (https://www.ncbi.nlm.nih.gov/genbank/) adopting the filter “measles or morbillivirus”. In particular, the MF-NCR was extracted for each sequence in the downloaded GenBank file by a Python script using the upper limit of the CDS matrix protein product + 1 as the starting point and the lower limit of the CDS fusion protein product - 1 as the end point for extraction (https://biopython.org/). The sequences with known available information on country and year of collection were included.

The genotypes that were predominant before the onset of the COVID-19 pandemic (B3, D4, D8 and H1) were considered. Therefore, four different MF-NCR datasets, respectively for B3, D4, D8 and H1 MeV genotypes, were examined including the sequences from human cases and from different locations. The first consisted of 168 MeV (genotype B3) MF-NCR sequences (sampling years 1997 - 2024). The second one included 112 MeV (genotype D4) MF-NCR sequences (sampling years 2003 - 2017). The third contained 387 MeV (genotype D8) MF-NCR sequences (sampling years 2006 - 2024) and finally the fourth consisted of 224 MeV (genotype H1) MF-NCR sequences (sampling years 1997 - 2018). The accession numbers of the sequences included in each dataset, as well as the number of sequences per country and the correspondence of the accession numbers with taxa names, are listed in Supplementary Tables 1a–c. All the sequence alignments were performed using the MAFFT v.7 program (29) under the Galaxy platform (30, 31) by using the FFT-NS-2 (fast, progressive) algorithm.

2.2 Phylogenetic analysis

The maximum likelihood phylogenetic trees together with the best fitting evolutionary models of all the dataset of MF-NCR sequences of MeV were estimated through Phyml 3.0 (32) with Smart Model selection (SMS) (33) and by using the aLRT SH-like for statistical branch support. In particular, the GTR +I+G was selected for the first, third and fourth dataset, meanwhile the TN93+I+G was the best fitting model for the second dataset. The proportion of variable and constant sites in the different datasets were estimated with MEGA X (34).

Evolutionary divergence estimates (mean genetic distances) were conducted with MEGA X (34) and both total, intergroup and intragroup distances (based on the location and year of collection) were performed using TN93+I+G (35).

3 Results

3.1 Phylogenetic analysis of the MF-NCR of MeV genotype B3

The maximum likelihood phylogenetic tree of the first dataset (MF-NCR sequences of MeV genotype B3, n=168) showed two statistically supported clades (Figure 1). The first clade (A), located in the upper part of the tree, included one sequence from Sudan collected in 1997 externally located to one from USA, one from United Kingdom and seven from Uganda. The sequences from different collection years were mixed. Within the lower clade, 5 supported internal clusters/clades were detected (B1- B5 in Figure 1, Supplementary Figure 1). Sixty percent of them (3/5 clusters B1, B3, B5) were composed of sequences from mixed locations, while clusters B2 and B4 were composed of sequences from the same location (Supplementary Figure 1). The MF-NCR sequences collected in different years resulted intermixed. Of note, a large group of Italian sequences collected in 2017 were inside B1, with two sequences from Netherlands and one from France located externally. Other two strains from Italy, collected in 2022, were found inside cluster B5 related to a strain from Canada. Alignment analysis using MEGA X showed that 68.2% of sites were conserved and 31.8% were variable in this dataset. The overall mean distance estimate for the MF-NCR of MeV genotype B3 (first dataset) was 3%. The mean genetic distances when sequences were grouped by location (within-group mean distances) ranged from 0.4% to 3% (Table 1a). Italy 0.4% (as shown in Table 1a), France (1.3%) and United Kingdom (1.4%) had the lowest divergence, whereas Uganda 3.1% (as shown in Table 1a) and Russia 2.5% (as shown in Table 1a) had the highest distance (Table 1a).

Maximum likelihood phylogenetic analysis of the MF-NCR sequences of MeV genotype B3 (first dataset). Branch lengths were estimated with the GTR +I+G nucleotide substitution model. The scale bar at the bottom of the tree shows the number of nucleotide substitutions per site. An asterisk along a branch represents significant statistical support for the clade/cluster subtending that branch (SH-aLRT > 95%). The main clades and clusters are highlighted in different colors.
Figure 1. Maximum likelihood phylogenetic analysis of the MF-NCR sequences of MeV genotype B3 (first dataset). Branch lengths were estimated with the GTR +I+G nucleotide substitution model. The scale bar at the bottom of the tree shows the number of nucleotide substitutions per site. An asterisk along a branch represents significant statistical support for the clade/cluster subtending that branch (SH-aLRT > 95%). The main clades and clusters are highlighted in different colors.

Table 1a. Estimates of average evolutionary divergence over sequence pairs within groups according to the locations, first dataset.

Country coded
UG3.1%
CNn/c
FR1.3%
NL1.6%
USA1.8%
RU2.5%
IT0.4%
SDn/c
MAn/c
CA1.9%
LVn/c
SOUTH KRn/c
UK1.4%

Analyses were conducted using the TN93+I+G model.

UG, Uganda; CN, China; FR, France; NL, Netherlands; USA, United States; RU, Russia; IT, Italy; SD, Sudan; MA, Morocco; CA, Canada; LV, Latvia; SOUTH KR, South Korea; UK, United Kingdom.

The mean genetic distances performed between sequences grouped according to their location showed high heterogeneity (Table 1b). In particular, the lowest divergences (0% - 1.5%) were highlighted between (Table 1b in yellow): Netherlands and China; USA and China; Italy and China; Canada and China; South Korea and China; United Kingdom and China; Italy and Netherlands; South Korea and Netherlands; South Korea and USA; South Korea and Italy; South Korea and Canada; United Kingdom and South Korea. The highest distances (6.7% – 9.3%) were observed between Morocco and Uganda; Sudan and France; Sudan and Netherlands; Sudan and USA; Sudan and Russia; Sudan and Italy; Morocco and Sudan; Canada and Sudan; Latvia and Sudan (Table 1b in dark pink). The table also shows the mean genetic distances between all the other countries examined.

Table 1b. Estimates of evolutionary divergence over sequence pairs between groups, first dataset.

Country codeUGCNFRNLUSARUITSDMACALVSOUTH KR
UG
CN4.0%
FR5.0%1.7%
NL4.9%1.3%2.1%
USA5.0%1.5%2.4%2.2%
RU5.4%2.0%2.3%2.3%2.6%
IT5.1%1.5%2.4%1.2%2.6%2.7%
SD6.5%6.1%6.9%6.7%6.9%7.2%7.0%
MA7.4%3.9%4.6%4.2%4.4%4.6%4.9%9.3%
CA4.8%1.3%2.4%2.1%2.2%2.7%2.3%6.7%4.7%
LV5.5%1.8%3.4%3.0%3.1%3.5%3.3%7.3%5.6%2.8%
SOUTH KR4.0%0.0%1.7%1.3%1.5%2.0%1.5%6.1%3.9%1.3%1.8%
UK4.1%0.8%2.3%1.9%2.2%2.6%2.2%6.1%4.5%1.9%2.3%0.8%

Analyses were conducted using the TN93+I+G model.

UG, Uganda; CN, China; FR, France; NL, Netherlands; USA, United States; RU, Russia; IT, Italy; SD, Sudan; MA, Morocco; CA, Canada; LV, Latvia; SOUTH KR, South Korea; UK, United Kingdom.

Yellow shading indicates the lowest mean genetic distance values, while dark pink shading indicates the highest mean genetic distance values observed among the compared groups.

Mean within-group genetic distances, performed by grouping the sequences by year of collection (Supplementary Table 2a), showed the lowest variability in 2017 (0.20%), 2014 (0.38%) and 2021 (0.72%). Meanwhile, the highest intra-group distances were in 2013 (3.33%) and 2020 (3.10%).

Regarding the divergences computed between groups according to different years of collection, the lowest (0.7% - 1.6%) are highlighted in yellow, the highest (6.8% - 8.5%) distances are highlighted in dark pink (Supplementary Table 2b).

3.2 Phylogenetic analysis of the MF-NCR of MeV genotype D4

The maximum likelihood phylogenetic tree of the second dataset (MF-NCR sequences of MeV genotype D4, n=112) showed two statistically supported clades, A and B (Figure 2). Clade A included five sequences from India collected in different time periods (2010, 2013, 2014, 2017). Clade B was composed by a sub-clade B1 (Figure 2) including several sequences from different locations and sampling years intermixed. Among the outermost sequences it is possible to find two sequences from India collected in 2007, externally located to two strains from USA (2009), one from Italy (collected in 2010), one from Croatia sampled in 2008, several strains from Canada (2011), one from Netherlands (2011), one from France (2011) and five from United Kingdom (collected in 2011 and 2012).

Maximum likelihood phylogenetic analysis of the MF-NCR sequences of MeV genotype D4 (second dataset). Branch lengths were estimated with the TN93+I+G nucleotide substitution model. The scale bar at the bottom of the tree shows the number of nucleotide substitutions per site. An asterisk along a branch represents significant statistical support for the clade/cluster subtending that branch (SH-aLRT > 95%). The main clades and clusters are highlighted in different colors.
Figure 2. Maximum likelihood phylogenetic analysis of the MF-NCR sequences of MeV genotype D4 (second dataset). Branch lengths were estimated with the TN93+I+G nucleotide substitution model. The scale bar at the bottom of the tree shows the number of nucleotide substitutions per site. An asterisk along a branch represents significant statistical support for the clade/cluster subtending that branch (SH-aLRT > 95%). The main clades and clusters are highlighted in different colors.

Inside the subclade B1, it is possible to highlight two clusters (defined b; bb). Cluster b included a large number of sequences from Canada, all of them collected in 2011, and one from USA (2013). Cluster bb included other nine sequences from Canada (sampling year: 2011).

The proportion of variable and constant sites for MF-NCR of MeV genotype D4 was 13.4% and 86.6% respectively, with an overall mean genetic distance (second dataset) of 1%.

The mean genetic distances of the sequences grouped by location (within-group mean distances) ranged from 0.08% to 3.36% (Table 2a). United Kingdom, Canada, USA had the lowest divergence, whereas Croatia (3.36%) and India 3.27% (as shown in Table 2a) had the highest distance (Table 2a).

Table 2a. Estimates of average evolutionary divergence over sequence pairs within groups according to the locations, second dataset.

Country coded
IN3.27%
NLn/c
CA0.18%
USA0.84%
FRn/c
UK0.08%
HR3.36%
ITn/c

Analyses were conducted using the TN93+I+G model.

IN, India; NL, Netherlands; CA, Canada; USA, United States; FR, France; UK, United Kingdom; HR, Croatia; IT, Italy.

The mean genetic distances performed between sequences grouped according to their location showed that the lowest divergences (0.1% - 1.7%) were highlighted between (Table 2b in yellow): France and Netherlands, United Kingdom and Netherlands, United Kingdom and France, Canada and Netherlands, France and Canada, USA and Netherlands, France and USA, United Kingdom and USA, Italy and USA, USA and Canada, Italy and Netherlands, Italy and France, Italy and United Kingdom, Italy and Canada, Italy and Croatia. The highest distances (4.3% - 4.6%) were observed between USA and India, Netherlands and India, France and India, United Kingdom and India, Canada and India (Table 2b in dark pink). The table shows the mean genetic distances between all the other countries examined. Mean within-group genetic distances, performed by grouping the sequences by year of collection (Supplementary Table 3a), showed the lowest variability in 2009 (0.3%), 2011 (0.2%) and 2012 (0.1%). Meanwhile, the highest intra-group distances were detected in 2013 (3.9%) and 2010 (3.4%). The lowest divergences between groups according to the year of collection are highlighted in yellow (Supplementary Table 3b). The highest mean distances between groups, according to the years of collection, are highlighted in dark pink (Supplementary Table 3b).

Table 2b. Estimates of evolutionary divergence over sequence pairs between groups, second dataset.

Country codeINNLCAUSAFRUKHR
IN
NL4.4%
CA4.6%0.3%
USA4.3%0.5%0.7%
FR4.5%0.1%0.4%0.6%
UK4.5%0.1%0.2%0.6%0.2%
HR3.9%2.3%2.5%2.2%2.2%2.4%
IT3.8%0.7%0.9%0.6%0.8%0.8%1.7%

Analyses were conducted using the TN93+I+G model.

IN, India; NL, Netherlands; CA, Canada; USA, United States; FR, France; UK, United Kingdom; HR, Croatia; IT, Italy.

Yellow shading indicates the lowest mean genetic distance values, while dark pink shading indicates the highest mean genetic distance values observed among the compared groups.

3.3 Phylogenetic analysis of the MF-NCR of MeV genotype D8

The maximum likelihood phylogenetic tree (Figure 3) of the third dataset (MF-NCR sequences of MeV genotype D8, n=387) showed two main supported clades A (the smallest) and B (the larger).

Maximum likelihood phylogenetic analysis of the MF-NCR sequences of MeV genotype D8 (third dataset). Branch lengths were estimated with the GTR +I+G nucleotide substitution model. The scale bar at the bottom of the tree shows the number of nucleotide substitutions per site. An asterisk along a branch represents significant statistical support for the clade/cluster subtending that branch (SH-aLRT > 95%). The main clades and clusters are highlighted in different colors.
Figure 3. Maximum likelihood phylogenetic analysis of the MF-NCR sequences of MeV genotype D8 (third dataset). Branch lengths were estimated with the GTR +I+G nucleotide substitution model. The scale bar at the bottom of the tree shows the number of nucleotide substitutions per site. An asterisk along a branch represents significant statistical support for the clade/cluster subtending that branch (SH-aLRT > 95%). The main clades and clusters are highlighted in different colors.

Clade A was composed by four sequences from Hungary (one sampled in 2023 and three in 2024) related to 32 sequences from Austria (eight of them collected in 2023 and 24 sequences sampled in 2024). Clade B included four clusters (defined B1, B2, B3 and B4) all composed of intermixed locations and collection years (Supplementary Figure 2). Cluster B1 included six sequences from Italy, sampled in 2017, related to five sequences from China (collected in 2018 and 2019), two from USA (2019) and one from Canada (2018). Cluster B2 included one sequence from Thailand sampled in 2024 related to seven sequences from Japan (one sampled in 2023 and the other in 2024), one from Austria and one from Brazil (both collected in 2024). B3 included 11 sequences from France collected between 2017 and 2018, and one from USA collected in 2019. Inside B4 it is possible to highlight, in addition to small, supported clusters of two or three sequences, four main supported sub-clusters (a, b, c, d). In particular, sub-cluster a included several strains collected from 2018 to 2020 derived from different intermixed locations. Sub-cluster b was composed by six strains from Russia collected in 2021 and 2023. Sub-cluster c included several strains from Austria (collected in 2023 and 2024) related to one from Russia (2023), one from Hungary (2024) and one from France (2023). Finally, sub-cluster d included sequences from different locations and sampling years intermixed (Supplementary Figure 2).

The proportion of variable and constant sites for the third dataset (MeV genotype D8) was respectively 47% and 53%. The overall mean distance estimate for the MF-NCR of MeV genotype D8 (third dataset) was 3%. The mean genetic distances of the sequences grouped by location (within-group mean distances) ranged from 0.2% to 4.6% (Table 3a).

Table 3a. Estimates of average evolutionary divergence over sequence pairs within groups according to the locations, third dataset.

Country Coded
BR1.9%
CN1.3%
IN3.3%
CA1.0%
NL2.8%
AUn/c
NZ2.2%
RU0.8%
IT1.8%
KR0.8%
FR0.8%
THn/c
VN0.2%
USA1.1%
AT1.8%
JP1.2%
HR4.6%
UK0.6%
DEn/c
HU4.3%

Analyses were conducted using the TN93+I+G model.

BR, Brazil; CN, China; IN, India; CA, Canada; NL, Netherlands; AU, Australia; NZ, New Zealand; RU, Russia; IT, Italy; KR, South Korea; FR, France; TH, Thailand; VN, Vietnam; USA, United States; AT, Austria; JP, Japan; HR, Croatia; UK, United Kingdom; DE, Germany; HU, Hungary.

Vietnam (0.2%), United Kingdom (0.6%), Russia (0.8%), South Korea (0.8%), France (0.8%), Canada (1%), USA (1.1%), Japan (1.2%), China (1.3%) had the lowest divergence, whereas Croatia (4.6%), Hungary (4.3%) and India (3.3%) had the highest distance (Table 3a).

The mean genetic distances performed between sequences grouped according to their location showed that the lowest divergences (0.2% - 1.5%) were highlighted between Germany and Vietnam, Japan and Thailand, France and South Korea, Japan and Australia, Thailand and Australia, France and Australia, South Korea and Australia, USA and Canada, USA and China, Canada and China (Table 3b highlighted in yellow). The highest distances (5.1% – 7.2%) were observed between Hungary and Germany (7%), Hungary and United Kingdom (7%), Hungary and Vietnam (7.2%), Hungary and India (6.2%), Hungary and Croatia (5.6%), United Kingdom and Austria (5.1%), Austria and Vietnam (5.2%), Germany and Thailand (5.1%), United Kingdom and Thailand (5.2%), Vietnam and Thailand (5.3), Hungary and Netherlands (5.1%), United Kingdom and Brazil (5.2%), Germany and Brazil (5.1%), Hungary and Brazil (5.1%) (Table 3b highlighted in dark pink). The table shows the mean genetic distances also between all the other countries examined.

Table 3b. Estimates of evolutionary divergence over sequence pairs between groups, third dataset.

Country codeBRCNINCANLAUNZRUITKRFRTHVNUSAATJPHRUKDE
BR
CN2.1%
IN4.4%3.4%
CA1.9%1.2%3.4%
NL2.8%2.0%3.1%1.9%
AU3.0%2.2%3.6%2.4%3.0%
NZ3.1%2.4%3.9%2.4%2.9%2.3%
RU2.6%2.0%3.5%1.8%2.4%2.6%2.5%
IT3.4%2.4%4.0%2.8%3.3%2.2%3.0%3.3%
KR2.6%1.9%3.5%2.1%2.7%1.3%1.9%2.2%1.8%
FR2.7%2.1%3.8%2.2%2.8%1.5%2.2%2.4%2.2%1.1%
TH3.7%2.8%4.3%3.1%3.7%1.0%2.9%3.3%2.7%1.8%2.1%
VN5.3%4.3%3.1%4.2%3.4%4.6%4.9%4.3%4.9%4.3%4.6%5.3%
USA1.9%1.2%3.3%1.0%1.9%2.3%2.3%1.8%2.6%2.0%2.1%3.0%4.2%
AT3.4%2.8%4.4%2.7%3.4%3.0%3.3%2.3%3.5%2.7%2.8%3.6%5.2%2.7%
JP3.0%2.3%3.7%2.4%3.0%0.7%2.4%2.5%2.5%1.6%1.8%1.5%4.7%2.3%3.1%
HR4.1%3.2%3.4%3.2%3.1%3.2%3.6%3.4%3.6%2.6%3.1%3.8%2.5%3.2%4.1%3.4%
UK5.2%4.1%3.0%4.1%2.8%4.4%4.6%4.2%4.7%4.2%4.5%5.2%1.8%4.2%5.1%4.5%3.2%
DE5.1%4.1%3.0%4.0%3.2%4.4%4.7%4.1%4.7%4.1%4.4%5.1%0.2%4.0%5.0%4.5%2.3%1.7%
HU5.1%4.4%6.2%4.5%5.1%4.1%4.7%4.7%4.5%3.7%3.9%4.7%7.2%4.4%4.2%4.4%5.6%7.0%7.0%

Analyses were conducted using the TN93+I+G model. BR, Brazil; CN, China; IN, India; CA, Canada; NL, Netherlands; AU, Australia; NZ, New Zealand; RU, Russia; IT, Italy; KR, South Korea; FR, France; TH, Thailand; VN, Vietnam; USA, United States; AT, Austria; JP, Japan; HR, Croatia; UK, United Kingdom; DE, Germany; HU, Hungary.

Yellow shading indicates the lowest mean genetic distance values, while dark pink shading indicates the highest mean genetic distance values observed among the compared groups.

The computation of the mean within group distances by years of collection (Supplementary Table 4a), showed the lowest variability in 2019 (0.71%) and 2017 (0.77%), followed by 2013 (0.97%), 2012 (1.02%), 2020 (1.22%) and 2016 (1.36%).

The highest intra-group distances were detected in 2010 (4.53%), followed by 2006 (3.15%) and 2014 (3.12%). Other intermediate distances were reported in Supplementary Table 4a.

The computation of the divergences between groups according to the year of collection were reported in Supplementary Table 4b with the lower distances highlighted in yellow and the highest mean distances highlighted in dark pink.

3.4 Phylogenetic analysis of the MF-NCR of MeV genotype H1

The maximum likelihood phylogenetic tree (Figure 4) of the fourth dataset (MF-NCR sequences of MeV genotype H1, n=224) showed two main supported clades A and B.

Maximum likelihood phylogenetic analysis of the MF-NCR sequences of MeV genotype H1 (fourth dataset). Branch lengths were estimated with the GTR +I+G nucleotide substitution model. The scale bar at the bottom of the tree shows the number of nucleotide substitutions per site. An asterisk along a branch represents significant statistical support for the clade/cluster subtending that branch (SH-aLRT > 95%). The main clades and clusters are highlighted in different colors.
Figure 4. Maximum likelihood phylogenetic analysis of the MF-NCR sequences of MeV genotype H1 (fourth dataset). Branch lengths were estimated with the GTR +I+G nucleotide substitution model. The scale bar at the bottom of the tree shows the number of nucleotide substitutions per site. An asterisk along a branch represents significant statistical support for the clade/cluster subtending that branch (SH-aLRT > 95%). The main clades and clusters are highlighted in different colors.

Clade A included a sub-clade composed by sequences from China, inside which it is possible to highlight 5 supported clusters, defined A1-A5, all of them from China (Supplementary Figure 3). Interestingly, outside the sub-clade, in addition to some sequences from China it is possible to find one from Netherlands (1997), two from South Korea (sampled in 2007 and 2010) and one from USA (2009). Clade B is made up entirely of sequences from China, six internal clusters were highlighted (Figure 4, Supplementary Figure 3), 50% of them were composed by intermixing among different sampling years.

For the fourth dataset (MeV genotype H1), 29% of sites were variable and 71% were conserved, with an overall mean genetic distance of 3% across the MF-NCR sequences. The mean genetic distances of the sequences grouped by location (within-group mean distances) ranged from 1.2% to 3.5% (Table 4a). South Korea had the lowest divergence (1.2%), whereas China (3.5%) had the highest distance (Table 4a). The mean genetic distances performed between sequences grouped according to their location showed that the lowest divergences (1.1% - 3.6%) were highlighted between USA and South Korea (1.1%), South Korea and Netherlands (3.5%), USA and Netherlands (3.6%) (Table 4b in yellow). The highest distances (4.3% - 4.8%) were observed between USA and China (4.3%), South Korea and China (4.4%), Netherlands and China (4.8%), (Table 4b highlighted in dark pink). The table shows the mean genetic distances between all the other countries examined. The computation of the mean within group distances by years of collection (Supplementary Table 5a), showed the lowest variability in 2016 (0.7%) and 2018 (1%). The highest intra-group distances were detected in 2007 (4.5%), followed by 2013, 2014 (3.2%) and 2012 (3.1%). Other intermediate distances were reported in Supplementary Table 5a. The divergences between groups according to the year of collection were reported in Supplementary Table 5b (lower distances in yellow and higher in dark pink).

Table 4a. Estimates of average evolutionary divergence over sequence pairs within groups according to the locations, fourth dataset.

Country Coded
CN3.5%
NLn/c
KR1.2%
USn/c

CN, China; NL, Netherlands; US, USA; KR, South Korea.

Table 4b. Estimates of evolutionary divergence over sequence pairs between groups, fourth dataset.

Country codeCNNLKR
CN
NL4.8%
KR4.4%3.5%
US4.3%3.6%1.1%

Analyses were conducted using the TN93+I+G model.

CN, China; NL, Netherlands; US, USA; KR, South Korea.

Yellow shading indicates the lowest mean genetic distance values, while dark pink shading indicates the highest mean genetic distance values observed among the compared groups.

4 Discussion

MeV, the causative agent of measles, represents a significant global health concern and a major contributor to morbidity worldwide. Despite substantial progress toward its elimination, measles continues to be endemic in several regions, particularly in Africa, the Middle East, and Asia. Outbreaks can also occur in countries that have previously interrupted MeV transmission, such as the United States and EU/EEA member states, often due to imported cases associated with international travels (11, 36).

Therefore, comprehensive monitoring of the evolutionary dynamics, genetic diversity and spatiotemporal circulation of MeV is essential to provide information for public health interventions.

Recently, the MF-NCR has been increasingly analyzed to investigate molecular aspects of MeV. The MF-NCR is the longest and most GC-rich non-coding region within the MeV genome (22–25, 37, 38). It has been associated with the regulation of mRNA translation and localization, the modulation of cytopathogenicity and the control of viral replication through the differential expression of the M and F proteins (27). However, no recent evolutionary studies have been conducted based on a large dataset of measles MF-NCR sequences collected from different countries and time periods, comparing the genetic variability on such a large scale within and between genotypes.

Hence, the present study concerned an in-depth evolutionary and phylogenetic analysis based on the MF-NCR of MeV. Combining maximum likelihood analysis with divergence estimates provided insights into genetic diversity and evolution. This showed that the B3, D4, D8 and H1 MF-NCR MeV datasets had different topologies and levels of cluster richness. Based on our data, a higher number of supported internal clusters were highlighted in MeV genotype H1, consistent with findings by Song J. et al. (38), followed by D8, B3 and finally by D4. The latter is characterized by very similar strains inside cluster b. The presence of higher number of supported internal clusters could suggest strains with similar evolutionary histories. In terms of cluster composition, intermixing was detected both among different locations and among years of collection, except few internal clusters in B3, D4 and H1 dataset (as the latter was composed mainly by sequences from China). Our data might indicate closer genetic relatedness and spread or transmission among different countries.

In addition, this study allowed comparison of variability across datasets, showing that measles MF-NCR genotype D8 had the highest proportion of variable sites (47%), followed by B3 (31.8%), H1 (29%), and D4 (13.4%). The marked diversity in D8 and B3 is particularly relevant as these are the only genotypes still circulating globally and have been linked to outbreaks in highly vaccinated populations, raising the possibility of immune escape (39). Consistently, recent investigations found that sera from vaccinated children showed reduced neutralizing activity against B3 compared to D4, H1 and the vaccine strain (40). Furthermore, the MF intergenic region shows high variability and harbors the greatest number of substitutions when comparing non-coding regions between wild-type and vaccine strains (41). This analysis can be useful to delve deeper into specific molecular traits or to identify indicators of variability. Investigating the evolution over time of the MeV strains belonging to the circulating genotypes is essential to monitor the dynamics and variability patterns, implement appropriate targeted control measures, prevent the spread of strains that might escape vaccine-induced immune response and identify links or importations.

Estimates of the average evolutionary divergence over all sequence pairs allowed comparison and understanding of viral genetic diversity in the MF-NCR dataset. These estimates showed that the MF-NCR of MeV genotype D8, B3, and H1 have higher variability (3%, i.e. an average of 3 bp substitutions per 100 bp) compared to MF-NCR of MeV genotype D4 (1%). The intragroup diversity estimation allowed to identify the locations with more variability, i.e. Uganda and Russia for genotype B3; Croatia and India for genotype D4; Croatia, Hungary and India for genotype D8; China for genotype H1. These results are consistent with data reporting genotype B3 widespread and endemic throughout the African continent till the mid-2000s (42) and later spread through the world. Multiple outbreaks in the Indian subcontinent were associated to genotype D4, which is widely distributed there, but also reported to be widespread in several European countries, i.e. Russia in 2000-2006, Croatia in 2003-2004, Romania in 2004-2007, Germany in 2005 and 2006, and Denmark, France and Italy in 2006, among others (43). The similar intragroup genetic diversity observed in the MF-NCR sequences of MeV genotype D4 strains from Croatia and India is consistent with previous studies reporting the circulation in Croatia of a D4 strain closely related to the “India-like” subgroup of genotype D4 (44). Meanwhile, the genotype D8, which is endemic to India, has spread to the Americas and Europe (45). Genotype H1 was endemic only in China (46).

The highest distances between locations identified in this study were mainly between localities that were very far apart in all the datasets, except for a few specific cases in genotype D8, where the countries were relatively close together, such as Hungary and Germany, Hungary and Croatia, and Vietnam and Thailand. Our data might suggest that the diversity, accumulation of substitutions and the distances tend to increase over space. This aspect has been supported by many studies reporting a strong correlation between genetic and geographic distance (47–49).

MF-NCR has been used to improve the resolution of measles surveillance, to infer chains of transmission, outbreaks and importations in combination with phylogenetic approaches (22).

Overall, this study provides a comprehensive overview of the evolution of the MF-NCR and its clustering. It delineates variations in genetic diversity among the four most prevalent genotypes present before the onset of the COVID-19 pandemic (B3, D4, D8, and H1), as well as their distribution across different geographic regions and over time. A potential limit of the study should be mentioned i.e. the genomes available for the analysis, that can be subject to potential sampling bias in collection dates and geographic sampling due to the availability in database, however the findings here reported gave us the possibility to reveal a snapshot of the MF-NCR genetic diversity with identification of supported clusters and phylogenetic relationships. Due to its higher variability compared to other regions of the MeV genome, including the most studied N450, the MF-NCR could provide greater resolution in distinguishing transmission chains. Hence, our findings highlight the potential of this region as a complementary and reliable molecular marker for epidemiological investigations. However, further studies integrating experimental and functional analyses will be needed to clarify how MeV genetic diversity may influence viral phenotypic traits, evolution, and control strategies.

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Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fviro.2026.1815667/full#supplementary-material

Supplementary material Supplementary material at the source

Supplementary material Supplementary material at the source

Supplementary material Supplementary material at the source

Supplementary material Supplementary material at the source

Supplementary material Supplementary material at the source

The main clades and clusters highlighted in Figure 1 (ML tree of the MF-NCR sequences of MeV genotype B3) are reported enlarged. Supplementary material at the source

The main clades and clusters highlighted in Figure 3 (ML tree of the MF-NCR sequences of MeV genotype D8) are reported enlarged. Supplementary material at the source

The main clades and clusters highlighted in Figure 4 (ML tree of the MF-NCR sequences of MeV genotype H1) are reported enlarged. Supplementary material at the source

Footnotes

Edited by: Marina Siljic, University of Belgrade, Serbia

Reviewed by: Ivana Grgic, University Hospital for Infectious Diseases “Dr Fran Mihaljevic”, Croatia

Robert Hohan, The National Institute of Infectious Diseases prof.dr Matei Bals, Romania

Footnotes

ECDC, European Centre for Disease Prevention and Control; EU/EEA, European Union/European Economic Area; F, Fusion; M, Matrix; MeV, Measles Virus; NCR, Non-Coding Region; SMS, Smart Model Selection; WHO, World Health Organization.

Statements

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The authors ALP and CS declared that they were an editorial board member of Frontiers at the time of submission. This had no impact on the peer review process and the final decision.

Funding

The author(s) declared that financial support was received for this work and/or its publication. This research was supported by EU funding within the NextGenerationEU-MUR PNRR Extended Partnership initiative on Emerging Infectious Diseases (Project no. PE00000007, INF-ACT).

Data availability

The accession numbers of the datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.

Author contributions

MF: Data curation, Formal analysis, Investigation, Writing – original draft, Writing – review & editing. AK: Data curation, Investigation, Writing – review & editing. EP: Investigation, Writing – review & editing. DDC: Investigation, Writing – review & editing. GC: Investigation, Writing – review & editing. MG: Investigation, Writing – review & editing. LM: Investigation, Writing – review & editing. AP: Investigation, Writing – original draft, Writing – review & editing. CS: Conceptualization, Investigation, Supervision, Validation, Writing – original draft, Writing – review & editing. ALP: Conceptualization, Funding acquisition, Investigation, Supervision, Validation, Writing – original draft, Writing – review & editing.

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Citation

Fracella, M., et al. (2026). Tracking the phylogenetic profile of the MF-non-coding region of measles virus. Frontiers in Virology, Volume 6. https://doi.org/
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Copyright © 2026 Fracella, Knijn, Palermo, Di Carlo, Colonnelli, Gentile, Maddaloni, Pierangeli, Scagnolari and Lo Presti.

License: https://creativecommons.org/licenses/by/4.0/

*Correspondence: Carolina Scagnolari; Alessandra Lo Presti

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