Genomic characterization of the 2024/2025 Mpox outbreak in Uganda

This study characterizes the 2024/2025 Mpox outbreak in Uganda through genomic analysis of 511 samples, revealing substantial viral diversification, multiple cross-border introductions from the Democratic Republic of Congo, and the co-circulation of distinct lineages that underscore the critical need for integrated regional surveillance.

The Gist

Imagine the Mpox virus as a traveler with a very specific passport: its genetic code. Just like a passport tells you where someone has been and who they've met, this virus's DNA tells scientists where it came from, how it's changing, and how it's spreading.

This paper is like a detective story written by a team of Ugandan scientists and their international partners. They wanted to solve the mystery of the 2024/2025 Mpox outbreak in Uganda. Here is the story in simple terms:

1. The Big Picture: A Virus on the Move

For a long time, Mpox was like a quiet neighbor living in the deep forests of Central and West Africa. But recently, it started moving into new neighborhoods, including East Africa. The scientists noticed that while we knew a lot about the virus in other parts of the world, we were "flying blind" in East Africa. We didn't have enough maps (genetic data) to see exactly what the virus was doing.

2. The Investigation: Gathering the Clues

To fix this, the team in Uganda went on a massive scavenger hunt.

• The Samples: They collected 511 "fingerprints" (virus samples) from sick people across 44 different districts in Uganda. They used high-tech microscopes (sequencers) to read the virus's genetic code.
• The Global Context: They didn't just look at Uganda; they grabbed 895 more fingerprints from neighbors like the Democratic Republic of Congo (DRC) and other countries from a global library of data.
• The Goal: They wanted to see if the virus was just hopping in from the outside repeatedly, or if it had moved in, set up camp, and started spreading locally.

3. The Discovery: Two Big Families

When they put all these fingerprints together to build a family tree, they found something interesting. The virus wasn't just one big blob; it had split into two main families (Clusters), and each family had two smaller branches (Subclusters).

• Family 1 (The DRC Branch): This group was mostly made up of viruses from the Democratic Republic of Congo. It was a bit more "stiff" and less diverse, like a family that hasn't moved around much.
• Family 2 (The Uganda Branch): This was the most exciting part. The majority of the Ugandan viruses belonged to a very diverse, busy branch of this family. This is like finding a bustling city where the virus has been living, evolving, and spreading from person to person for a while.

4. The Plot Twist: Cross-Border Travel

The scientists used a technique called "phylogeography," which is like tracking a traveler's GPS history. They found that the virus didn't just appear in Uganda by magic.

• The Border Hopping: The virus made several trips across the border from the DRC into Uganda.
• The Takeover: Once it crossed the border, it didn't just visit and leave. It found a way to stay, spread, and evolve. The data showed that the virus was successfully passing from human to human within Uganda, creating long chains of transmission.

5. Why This Matters: The "Fire" Analogy

Think of the virus like a fire.

• Old View: We used to think the fire in Uganda was just a few sparks flying in from a forest fire next door (the DRC) and dying out quickly.
• New View: This study shows that the sparks didn't just die out; they caught a dry bush in Uganda and started their own forest fire. The fire is now burning on its own, changing shape, and spreading through the local trees.

The Takeaway

The main lesson is that neighbors are connected. Because people, goods, and animals move freely across borders in East and Central Africa, a virus can easily hop from one country to another.

What should we do?
The scientists are saying we need to stop building walls between our data. If Uganda finds a new "spark," the DRC needs to know immediately, and vice versa. By sharing information and testing more people, we can put out the fire before it becomes a wildfire.

In short: The virus is no longer just a visitor; it has moved in. To stop it, we need to work together across borders and keep a close watch on how it changes.

Technical Summary

1. Problem Statement

• Geographic Shift: While Monkeypox virus (MPXV) has historically been endemic to Central and West Africa, recent outbreaks in East Africa (specifically Uganda) indicate an expanding geographic footprint and a shift toward urban/peri-urban transmission.
• Data Gap: Despite the severity of the 2024/2025 outbreak in Uganda, genomic data from East Africa remains scarce compared to global datasets. This lack of local data hinders the understanding of viral evolutionary trajectories, transmission dynamics, and the ability to distinguish between zoonotic spillovers and sustained human-to-human transmission.
• Public Health Risk: The potential for accelerated microevolution (driven by host APOBEC3 activity) and cross-border transmission necessitates high-resolution genomic surveillance to inform outbreak response, diagnostic accuracy, and regional containment strategies.

2. Methodology

The study employed a comprehensive genomic epidemiology approach combining local sequencing with global public data:

• Sample Collection:
  • Source: 511 PCR-confirmed MPXV positive samples collected from symptomatic individuals across 44 districts in Uganda between July 2024 and December 2025.
  • Sample Types: Diverse clinical specimens including lesion swabs, blood, genital swabs, oropharyngeal swabs, saliva, and semen.
  • Inclusion Criteria: Only samples achieving ≥70% genome coverage were included.
• Sequencing Workflow:
  • Extraction: DNA extracted using QIAamp DNA Kit.
  • Amplification: Tiled amplicon PCR using the Yale Mpox primer scheme combined with the Illumina Microbial Amplicon Prep (iMAP) Kit.
  • Platforms: Dual-platform sequencing was utilized:
    • Illumina MiSeq: For short-read high-accuracy sequencing.
    • Oxford Nanopore (GridION): Using R10 Flow Cells and Rapid Barcoding Kit 24 V14 for long-read sequencing.
• Bioinformatics Pipeline:
  • Assembly: Raw reads processed via the ARTIC (v1.4.4) Nextflow pipeline to generate consensus genomes.
  • Clade Assignment: Sequences assigned to clades using Nextclade.
  • Quality Control & Alignment: Performed using Squirrel, which flags SNPs near ambiguous bases, unique clusters, and reversions.
  • Mutation Analysis: The apobec3-phylo mode in Squirrel was used to characterize APOBEC3-like mutation signatures.
  • Phylogenetics: Maximum-likelihood phylogenetic trees constructed in Squirrel, rooted with the historical DRC 1970 strain (KJ642613).
• Comparative Dataset:
  • Integrated 895 publicly available Clade Ib genomes (2023–2025) from GISAID, Pathoplexus, and NCBI, primarily from the Democratic Republic of Congo (DRC), Uganda, and Burundi.

3. Key Contributions

• First Large-Scale Genomic Dataset for Uganda: Generated and analyzed the largest set of Ugandan MPXV genomes to date (511 sequences), providing a critical baseline for East African MPXV surveillance.
• Regional Contextualization: Merged local data with regional public datasets to create a robust phylogenetic framework covering the East and Central African transmission landscape.
• Methodological Standardization: Demonstrated the efficacy of a dual-platform (Illumina + Nanopore) sequencing strategy combined with the ARTIC pipeline for rapid, high-coverage MPXV surveillance in a resource-limited setting.
• APOBEC3 Characterization: Systematically analyzed mutation patterns to detect signatures of host-mediated viral evolution (APOBEC3 editing) within the outbreak.

4. Key Results

• Demographics: The 511 sequenced cases had a mean age of 27 years (range 1–60). Sex distribution was 51.3% male and 41.9% female.
• Phylogenetic Structure:
  • The analysis resolved Clade Ib into two major clusters (Cluster 1 and Cluster 2), each subdivided into two subclusters.
  • Cluster 1: Dominated by sequences from the DRC, showing lower genetic diversity.
  • Cluster 2: Exhibited higher genetic diversity and broader geographic distribution.
• Uganda's Viral Landscape:
  • Dominance: The majority of Ugandan sequences (n=574, including the 511 local + 63 from public data) clustered within Subcluster 2 of Cluster 2, the most genetically diverse lineage in the dataset.
  • Co-circulation: A minority of Ugandan isolates were found in Subcluster 1 of Cluster 2 (n=9) and Subcluster 2 of Cluster 1 (n=64), confirming the co-circulation of multiple lineages.
• Transmission Dynamics:
  • Sustained Transmission: The high genetic diversity within the dominant Ugandan subcluster indicates sustained local human-to-human transmission rather than repeated, isolated zoonotic spillovers.
  • Cross-Border Introductions: Phylogeographic analysis confirmed multiple independent cross-border introductions into Uganda, primarily from the DRC.
  • Seeding: These introductions successfully seeded local transmission chains rather than acting as epidemiological dead ends.

5. Significance and Implications

• Regional Connectivity: The study highlights that porous borders and population mobility between Uganda and the DRC are primary drivers of the outbreak, facilitating repeated viral introductions and subsequent local spread.
• Surveillance Strategy: The findings underscore the necessity of integrated, cross-border genomic surveillance. Relying solely on local data is insufficient; harmonized data sharing between East and Central African nations is critical for early detection of lineage expansion.
• Public Health Response:
  • The evidence of sustained transmission suggests that containment strategies must focus on interrupting human-to-human chains, not just preventing zoonotic spillover.
  • The identification of multiple co-circulating lineages implies that diagnostic assays and vaccine strategies must be robust against diverse viral variants.
• Limitations: The authors note a sampling bias heavily skewed toward Uganda and the DRC due to varying sequencing capacities. This may obscure unsampled diversity in neighboring countries, though the data strongly supports the conclusion of cross-border mobility as a key driver.

Conclusion: This study provides the first high-resolution genomic map of the 2024/2025 Mpox outbreak in Uganda, revealing a complex landscape of multiple introductions and sustained local transmission driven by regional connectivity. It calls for immediate strengthening of regional sequencing capacity and data sharing to effectively manage future outbreaks in the region.