Making genomic surveillance deliver: A lineage classification and nomenclature system to inform rabies elimination.

The availability of pathogen sequence data and use of genomic surveillance is rapidly increasing. Genomic tools and classification systems need updating to reflect this. Here, rabies virus is used as an example to showcase the potential value of updated genomic tools to enhance surveillance to better understand epidemiological dynamics and improve disease control. Previous studies have described the evolutionary history of rabies virus, however the resulting taxonomy lacks the definition necessary to identify incursions, lineage turnover and transmission routes at high resolution. Here we propose a lineage classification system based on the dynamic nomenclature used for SARS-CoV-2, defining a lineage by phylogenetic methods for tracking virus spread and comparing sequences across geographic areas. We demonstrate this system through application to the globally distributed Cosmopolitan clade of rabies virus, defining 96 total lineages within the clade, beyond the 22 previously reported. We further show how integration of this tool with a new rabies virus sequence data resource (RABV-GLUE) enables rapid application, for example, highlighting lineage dynamics relevant to control and elimination programmes, such as identifying importations and their sources, as well as areas of persistence and routes of virus movement, including transboundary incursions. This system and the tools developed should be useful for coordinating and targeting control programmes and monitoring progress as countries work towards eliminating dog-mediated rabies, as well as having potential for broader application to the surveillance of other viruses.

Introduction

Rabies virus (RABV) causes around 59,000 deaths and costs in excess of $8.6 billion per year [1], with a near 100% mortality rate after the onset of symptoms [2]. Post-exposure prophylaxis is highly effective in preventing rabies if administered quickly following exposure [3] but this is not always possible given its high cost and limited accessibility. Rabies can occur in all species of mammal, but up to 99% of human rabies cases arise from bites from infected domestic dogs [4,5]. Vaccinating dogs to interrupt transmission is therefore paramount [6] and a major focus of ‘Zero by 30’, the global strategy to eliminate human deaths from dog-mediated rabies by 2030 [7]. The focus of ‘Zero by 30’ is on dog-mediated rabies, but spillover from dogs into other carnivores often occurs, generally causing only short-lived chains of transmission [8]. Genomic surveillance is therefore vital to identify and monitor any wildlife reservoirs that may emerge [9].

To achieve the ‘Zero by 30’ goal, effective and coordinated surveillance is essential. Genomic surveillance can complement routine epidemiological surveillance through the insights it can provide on the lineages circulating in an area and any sources of incursions [10]. Sequence data proved useful in understanding rabies dynamics in Bangui, the capital of the Central African Republic [11]. Instead of sustained transmission in the city, local extinction occurred on three occasions with introductions from surrounding areas reseeding circulation, showing the need to expand control efforts across a larger geographic area [11]. Genomic surveillance can also reveal host shifts and other unusual dynamics. When rabies in Arctic foxes underwent a host shift to infect red foxes (Vulpes vulpes), the virus spread rapidly throughout Canada, resulting in epidemics in the 1950s and 1960s before being apparently eliminated in 1990 [12]. However, between 2015 and 2017 a number of rabies cases occurred in wildlife which were genetically similar to sequences from these historic fox rabies epidemics. Analysis revealed that although several lineages were eliminated, one persisted and underwent a host switch to skunks [12].

To monitor how an epidemiological situation is changing first requires characterization of the current situation. Several well-defined RABV clades circulate globally, within two major phylogenetic groups; bat-related and dog-related [13]. The dog-related group is split into between 5 and 7 different clades, depending upon the classification [13,14]. This includes the Cosmopolitan clade for which there are over 9500 publicly available sequences (including sequences of all lengths) split into 22 subclades, present in over 100 countries [13,15]. These discrepancies in clade numbers, however, are illustrative of issues surrounding the interpretation and classification of RABV phylogenetic data as a universal naming system does not extend past high-level classification, with no set rules for defining lineages. Additionally, genomic data availability varies. Increasingly studies are focusing on whole genome sequences (WGS) given the greater resolution they provide, but the vast majority of studies thus far have generated shorter, partial gene sequences [15-17]. A lineage designation system therefore needs to be able to incorporate all of these data to provide maximal contextual information. Relevant terms needed to understand a lineage designation system are defined in Box 1.

Box 1

Definitions

Clade–Monophyletic group of sequences with a single ancestor; already defined by various studies for RABV [13,18].

Subclade–A smaller monophyletic group contained within a larger clade; again, defined in previous RABV studies [13].

Cosmopolitan Clade—A globally distributed, diverse clade of rabies virus that contains 22 subclades (Fig 1) [13].

Fig 1

Illustration of rabies virus clades, subclades and lineages.

Maximum likelihood tree of all publicly available whole genome rabies virus sequences (n = 650) coloured by previously identified subclades [13]. The NEE subclade has been expanded to show major and minor lineages from the updated MADDOG classification system.

Lineage–A group of genetically related sequences defined by statistical support of their placement in a phylogeny and genetic differences from a common ancestor. Clades and subclades (illustrated in Fig 1) can be split further into lineages which is typically a higher resolution classification. These are not yet defined for RABV—but Cosmopolitan clade lineages are defined in this study.

Major lineage–A lineage named with a letter—e.g. A1 or Cosmopolitan AF1b_A1, that can be the first iteration of a lineage, or a lineage that has undergone significant evolution to become a new major lineage (Fig 1).

Minor lineage–A lineage named with numbers (following the major clade nomenclature)—e.g. A1.1.1 or Cosmopolitan AF1b_A1.1.1 This is a major lineage that has undergone evolution, but not enough to become a new major lineage.

Lineage designation—An initial step to designate lineages based on a set of reference sequences, or the first set of data from an area, defined by an existing set of rules, and to name them accordingly. This is completed once to form a reference set of sequences to be used for lineage assignment, and may need to be updated as genetic diversity accumulates or is generated by sequencing efforts.

Lineage assignment—Identifying which existing lineage, defined by the initial lineage designation step, a new sequence belongs to.

MAD DOG—Method for Assignment, Definition and Designation Of Global rabies virus lineages; the method and corresponding tools presented in this study.

Although the current phylogenetic classifications generally work well at a global level, they lack resolution for surveillance at more local or regional scales. The existing system generally represents where the different clades originally emerged and their early geographical distribution [13]. However, these clades do not always remain isolated to particular areas, and their dispersal is affected by human movements, leading to frequent introductions of lineages to new areas and their subsequent co-circulation [10]. Moreover, control and elimination efforts will further affect the distribution and diversity of circulating lineages. In many regions, a limited number of subclades appear to circulate, therefore, without a higher resolution classification it becomes difficult to identify patterns like lineage extinction that are to be expected, and therefore to monitor the impacts of control.

Rambaut et al. (2020) propose a universal virus nomenclature system to address these issues, which they apply to SARS-CoV-2 [18]. The system outlines a set of rules for classifying and naming viral lineages to produce a standardised tool to identify viral diversity on global and local scales, to track lineage emergence and transmission, and to allow for coherent updates as lineage turnover occurs [18]. Here, we adapt and apply these rules to a large diverse major clade of RABV to produce an updated, dynamic, classification system for the virus. The increased definition provided allows for more detailed genomic surveillance that can be used to monitor the circulation of viral lineages and identify incursions, unusual transmission routes and potential host shift events.

Methods

Data collection and processing

All available RABV sequences (n = 23,386), irrespective of sequence length and clade, and their associated metadata were downloaded from the RABV-GLUE (Box 2) resource via the website (http://rabv.glue.cvr.ac.uk). Many metadata entries stripped from Genbank and added to the RABV-GLUE database were missing information necessary for analysis, including the sample collection year, host, and location (n = 9650). Records with missing information were searched manually and any information that could be found from primary publications was updated into RABV-GLUE. The dataset of all sequences was then filtered to only include sequences designated to the Cosmopolitan clade by RABV-GLUE, excluding vaccine strains. For the purposes of these analyses we considered WGS, covering >90% of the genome (at least 10,000 nucleotides (nt)), and nucleoprotein (N) gene sequences (1300 nt), thus excluding smaller partial gene fragments.

Box 2

RABV-GLUE

RABV-GLUE is a ‘sequence data resource’ developed to support rabies elimination efforts by enabling efficient dissemination and utilisation of RABV sequence data using GLUE—a bioinformatics environment for managing and interpreting virus sequence data [15,19]. GLUE supports development of virus-specific ‘projects’ comprising curated sets of sequences, genome feature annotations, alignments, reference clades and phylogenies with associated metadata, with options to upload sequences to GenBank (Fig 2). Loading projects into the GLUE ‘engine’ creates a relational database representing complex semantic links between data items so computational analyses can be precisely and widely replicated.

Fig 2

Schematic of the online capabilities of RABV-GLUE.

RABV-GLUE can be used to obtain sequences from GenBank and has options to filter and download sequences of interest, and for phylogenetic interpretation of user input sequences. The metadata for these sequences has been manually curated to correct mistakes and fill in information missing from GenBank to allow users access to cleaner, more documented sequence data. As additional corrections are identified, RABV-GLUE will be curated live. RABV-GLUE projects can be installed locally on all commonly used computing platforms and are fully containerised via Docker [20]. GLUE’s command layer provides a mechanism for retrieving and analysing data by coordinating interactions between the RABV-GLUE database and commonly used bioinformatics software tools (e.g. MAAFT, RAXML). This allows experienced bioinformaticians to quickly establish local RABV sequence databases and integrate these resources into existing bioinformatic pipelines, tailoring functionalities to their specific needs. Hosting the RABV-GLUE project in an openly accessible online version control system (e.g., GitHub) provides a mechanism for managing its ongoing development by multiple collaborators, following practices established in the software industry. GLUE projects can also be developed into interactive, user-friendly web services through a graphical user interface. An online interface to RABV-GLUE is available at http://rabv-glue.cvr.gla.ac.uk/ (Fig 2). Here we use sequence data and classifications from RABV-GLUE, to provide the basis for the development of an updated classification system which will be integrated into RABV-GLUE, enhancing its capacity to provide detailed, high resolution lineage information about user input sequences.

Lineage designation

Sequences were aligned using MAFFT with the FFT-NS-2 algorithm [21]. Maximum Likelihood trees were constructed using IQTREE2 with model selection [22] and 100 bootstrap replicates [23]. Ancestral sequence reconstruction was then undertaken using Treetime ancestral [24].

A custom R function for lineage designation was developed which requires the tree, corresponding alignment, ancestral sequences and metadata. This returns summary statistics about each sequence, its lineage designation, and details about each of the designated lineages. Lineage defining nodes are identified according to thresholds on bootstrap support (>70) and cluster size (>10 descendants of >95% coverage at genome level or of the gene of interest, excluding gaps and ambiguous bases). For each node still in consideration, the ancestral sequence is extracted. To define a new lineage, there must be at least one common mutation between all descendants that is different to the ancestral sequence. Having at least 10 descendants also helps to ensure the robustness of designations, preventing the incorrect designation of lineages as a result of sequencing errors. The algorithm for lineage designation is summarised in Fig 3. The parameters for designation of partial genome sequences were refined to optimise the comparability with the whole genome designations, testing various bootstrap support thresholds and numbers of sequences needed to designate a lineage.

Fig 3

Summary of lineage designation steps.

Existing phylogenetic groupings are already in place for RABV [13,25] and sequences are automatically defined by these clades in RABV-GLUE which acts as a useful baseline for further classification. Subsequent MAD DOG lineage designations are named according to the rules in Rambaut et al. [18], starting from lineage A1 (which in this instance would be Cosmopolitan A1). Any lineages descended from this become A1.1 or A1.2 etc and any descended from these become A1.1.1, A1.2.1 etc. After 3 iterations a lineage becomes a new major lineage–A1.1.1.1 = B1.

Lineage assignment

Once the designation step has been performed to construct a set of reference sequences with defined lineages, new sequences can be compared to the reference set and assigned to lineages. New sequences (imported individually or in bulk) are added to the existing reference alignment using MAFFT and for each new sequence the two closest references are identified and used to transfer a lineage assignment.

Emerging and undersampled lineages

The requirement for a lineage to have 10 sequences was used to ensure that sequencing errors do not translate to artificial lineages. However, this criterion may result in emerging lineages, or undersampled lineages being initially overlooked. Therefore, we also include both an Emerging or Undersampled Lineages, and a Singletons of Interest output.

For emerging/undersampled lineages, any nodes that fulfil all the requirements for defining a lineage but only have between 5 and 9 sequences, not the 10 required for a full lineage designation, are extracted. To ensure that lineages are distinct, and not just a cluster of sequences within the parent lineage, the patristic distance between the defining node and its parent node are calculated. If this distance exceeds the 95th percentile of patristic distances between sequences within the parent lineage, the node is marked as defining a potentially emerging or undersampled lineage. Furthermore, to ensure the lineage is emerging/undersampled, the country and year of collection for each sequence in the potentially emerging lineage are extracted. If all the sequences in the potentially emerging lineage are from the same country within a 5-year period, the lineage is marked as emerging, named with an ‘_Ex’ suffix (where x is a number indicating multiple distinct emerging/undersampled lineages within the same parent lineage), and added to the ‘Emerging or Undersampled Lineages’ table.

To identify singletons of interest, branch lengths are extracted for each tip and the longest 5% are identified. For each of the tips that have the longest 5% of branch lengths, their closest related sequence is identified. The patristic distance between the query sequence and its closest relative are calculated. If the distance exceeds the 95th percentile of patristic distances between sequences within the parent lineage, the sequence is marked as a singleton of interest. Information about the number and metadata of these sequences is collated for each lineage and added to the ‘Singletons of Interest’ table, to identify which may be of interest to investigate further.

MAD DOG

MAD DOG has been developed as a command-line based tool to undertake all the steps for lineage designation and assignment outlined above, given a set of input sequences (and corresponding metadata for designation). The assignment tool also outputs information about the assigned lineage including which countries it has been sampled in and when the first and most recent sequences for that lineage were recorded. Additionally, this can be implemented through an R package (MADDOG), full details of which can be found at DOI: 10.5281/zenodo.5503916.

MAD DOG was used to designate lineages for sequence subsets from the Cosmopolitan clade, with its performance tested on whole and partial genome sequences. The resulting designations were explored on a global scale and on a specific geographic area (Tanzania) to test functionality and interpret lineage assignments.

Results

Cosmopolitan dataset

The Cosmopolitan dataset comprised 650 WGS, excluding vaccine strains, spanning 46 countries from 1950–2018. The N gene data comprised an additional 1958 sequences spanning an additional 25 countries from 1939–2019. Running the lineage designation on Cosmopolitan WGS resulted in 52 lineages, more than a 2-fold increase in definition compared to the 22 previously defined subclades (Fig 4). The baseline MAD DOG designation agreed with the previously determined global phylogenetic groupings, with the exception of clusters containing <10 sequences (a required threshold for MAD DOG lineage designation). However, our tool provided a much deeper characterisation beyond the subclade level [13]. Some subclades are split into multiple lineages providing increased definition; four showed at least a 4-fold increase in definition, and one showed a 10-fold increase, in line with sampling density (Fig 4A and Table 1). The AF1b subclade (purple in Figs 4 and 5) is split into 10 lineages, and the ME1a (orange) subclade is split into 4, indicating more than a 4-fold increase in definition in both cases. Some subclades show no further definition due to the small number of sequences available to represent this subset of RABV diversity.

Fig 4

Lineage designations of whole genome and N gene sequences from the RABV Cosmopolitan clade.

A: Maximum likelihood tree showing the lineage positions, rooted using an outgroup of 10 Asian SEA1b sequences, with scale indicating substitutions per site per year, and hierarchical relationships with dashed lines indicating 5 lineage iterations (n = 2608 total sequences for tree and sunburst plot, combing both WGS and N gene sequences). B: Progression of classification definition from 22 previously defined subclades (left) to 52 lineages using only whole genome sequences (middle) to 147 lineages adding N gene sequences, with lineages seen at whole genome level in blue and additional lineages only seen at N gene level in red (right).

Table 1

Details of numbers of lineages and sequences available for each Cosmopolitan subclade at whole genome and N gene level.

	Whole genome			N gene
Subclade	Lineages	Number of sequences	Year of first sequence	Lineages	Number of sequences	Year of first sequence
AF1a	2	24	1984	10	377	1982
AF1b	10	251	1981	10	471	1981
AF1c	0	3	1986	0	6	1986
AF4	0	8	1950	1	13	1950
AM1	0	4	1982	6	58	1981
AM2a	1	10	1991	4	136	1990
AM2b	0	2	2009	0	24	1986
AM3a	0	5	1986	20	172	1985
AM3b	0	4	1986	10	108	1986
AM4	0	4	1974	1	19	1974
CA1	1	25	1974	9	224	1974
CA2	1	15	1993	7	106	1993
CA3	0	7	1991	6	45	1991
CE	1	15	1990	1	19	1985
EE	1	15	1986	1	47	1977
ME1a	4	101	1976	4	195	1976
ME1b	0	2	1993	0	7	1993
ME2	4	38	1989	6	66	1989
NEE	6	71	1986	6	78	1985
WE	1	14	1986	1	148	1974
YUGCOW	0	2	1978	0	6	1978
YUGFOX	0	1	1972	0	12	1972

Fig 5

Distribution of RABV lineages from the Cosmopolitan clade determined from WGS and N gene sequences.

Circle size indicates the number of sequences (sum of both WGS and N gene sequences), with circles plotted to the centroid of each country. Sunburst plot from Fig 4A included to indicate colour scheme. Base layer of map from Natural Earth (https://www.naturalearthdata.com/).

Lineage designation was performed on an N gene subset of the same data (the 650 sequences used for WGS designation) to allow a direct comparison between lineage designations at different levels of sequence data resolution. This resulted in a 25% overall decrease compared to the definition achieved using WGS, highlighting that the sequence length impacts the designations. The reduced definition applies to some subclades more than others. Various numbers of minimum sequences and minimum bootstrap support values to define a lineage were tested on the N gene subset and the WGS designations to optimise the comparability of the two. Once the parameters for designation at N gene level were refined, a minimum of 10 sequences and bootstrap support of 70 was used for this subset, and applied to all available Cosmopolitan N gene sequences; resulting in an increase from 650 to 2608 sequences. Applying this modified algorithm to the N gene sequences resulted in lineage designations that did not entirely match up with the WGS designations, due to differences in sequence numbers and tree topology. This caused problems when attempting to analyse and compare lineages, and therefore some extra steps were required to ensure the N gene designations corresponded to the WGS designations. N gene sequences from any subclades that were not seen in the WGS designation, such as AM3a (Table 1), were extracted and lineage designation was run as normal on these. For those subclades that were split further into lineages at WGS level, the N gene sequences first underwent lineage assignment using the WGS designations as a reference. N gene lineages could then be designated, using the WGS lineage assignment as a starting point. This resulted in an additional 44 designations above the WGS designations due to the increased volume of sequences (Figs 4 and 5, Table 1), highlighting how data volume and coverage also affects the designations.

24 emerging lineages were identified, with multiple lineages emerging within some lineages, for example AM3a_A1 and CA1_A1 (S1 and S4 Tables). 16 of the designated lineages are identified as being potentially undersampled (S5 Table). Some of these contain multiple singleton sequences, such as 4 singletons within the AM3b_A1.1.1 lineage, indicating there may be multiple descendent lineages missed due to undersampling.

The geographic distribution of lineages can be seen in Fig 5, with full details in S1 Table. It appears that Europe, North America and Southern Africa are well represented. However, only the Cosmopolitan clade is included here and therefore the overall global RABV sequencing coverage of RABV lineages cannot be assessed from this alone.

WGS capture greater variation, and are able to differentiate between samples that are identical at a partial genome level [16]. In this study, although all sequences were unique at the whole genome level, only 80% of these were unique at an N gene level (n = 509 from 1958 N gene sequences). While whole genomes offer better phylogenetic resolution, the N gene is commonly targeted by diagnostic laboratories providing a greater number of sequences and wider spatio-temporal coverage to detect and define lineages not captured by the still limited number of WGS. In total, there are 1469 WGS of RABV (650 from the Cosmopolitan clade) from 82 countries available on RABV-GLUE but 7304 N gene sequences (1958 from the Cosmopolitan clade) from 109 countries. This highlights the volume of additional data available when using partial genome sequences. However, the proportion of studies sequencing RABV whole genomes is increasing, as are studies using at least N gene length sequences instead of smaller fragments (S1 Fig).

Local lineage designation case study—Tanzania

The Tanzania dataset comprised 224 WGS from 1996–2018. Running the lineage designation on these sequences alone, i.e. without wider geographic context, limited the designation of less commonly sampled lineages that may be more prevalent in other countries (S2 Table). The inclusion of sequences from Eastern and Southern Africa (known to belong to the same global subclade) resulted in an additional lineage designation that could not be defined using Tanzania sequences alone. Therefore, additional relevant context to provide an informative reference set for initial lineage designation proved to be important for consistent, higher resolution lineage designations. This revealed 15 lineages present in Tanzania (S3 Table) from the Cosmopolitan clade, in contrast to only 14 defined with Tanzania data alone. Five lineages are specific to the Serengeti District and the remaining 10 are present in multiple areas (Fig 7). Of the 15 lineages, 13 have been seen in the Serengeti District. In 2010, there were 11 lineages circulating that had been detected in the Serengeti District. By 2017 following considerable dog vaccination efforts in the district but not in the wider region, only 6 were still evident.

Fig 7

Lineage designations of 224 rabies virus genomes from Tanzania.

Maximum likelihood tree with scale in substitutions per site per year, hierarchical relationships, and distribution of lineages. Sequences obtained from RABV-GLUE. Base layer of map from GADM (https://gadm.org/download_country.html).

There do not appear to be any singletons of interest present in Tanzania, however there is a lineage that may be undersampled; Cosmopolitan_A1.2.2_E1, for which 5 sequences are present from Tanzania between 2003–2004 (S4 and S5 Tables). Additional, more recent sequences are likely to show this lineage has become a fully designated lineage.

The increased use of whole genome sequencing is pronounced in Tanzania (S2 Fig); most Tanzanian sequences available on RABV-GLUE for the last decade are whole genome, with all sequences since 2005 being at least N gene.

Case studies identifying an imported human rabies case and reconstructing historical spread of RABV into Tanzania.

Dated points represent first records of the lineage in the country, with arrows suggesting the likely source of introductions. Scale indicates substitutions per site per year. Base layer of maps from Natural Earth (https://www.naturalearthdata.com/). A: Left: Countries where AM3a_A1.3 has been isolated over time. Bright red = information from WGS lineage designation, darker red = additional information from N gene lineage designation. The star indicates the human rabies case. Right: Maximum likelihood Subtree of AM3a_A1.3 lineage, with icons to indicate country of origin, B: Left: Countries where lineage A1.2.1 has been isolated over time. Bright green = information from WGS lineage designation, darker green = additional information from N gene lineage designation. The star indicates case of interest. Right: Maximum likelihood subtree of A1.2.1 lineage and descendents, with icons to indicate country of origin, corresponding to map, and dated tips.

Discussion

We aimed to apply the dynamic nomenclature system proposed by Rambaut et al. (2020) to RABV and to investigate how this classification system could be useful in surveillance to support control and elimination programmes. Implementing this classification system allows for much greater resolution of circulating RABV lineages at both the whole genome and N gene levels. Whole genome level designations and assignments provide most resolution (doubling the resolution of analysis possible, with an over 10-fold increase in some areas) and accurate assignments. However, unlike SARS-CoV-2, which is a new pathogen with limited accumulated diversity over a recent time frame, rabies virus is an endemic pathogen that has circulated in some areas for decades. Therefore, our analysis highlighted the importance of considering partial genome data (N gene sequences) to incorporate the considerable historical data available for RABV (and more generally to other endemic pathogens with partial genome data). The case studies and examples presented here illustrate how higher resolution lineage designations enable greater epidemiological interpretation, for example, in identifying the origins of cases and for surveillance to monitor lineage introductions and extinctions as elimination is approached (Box 3, Table 2).

Table 2

Case Studies using the MADDOG lineage classification system.

Example 1—Identifying the origin of cases

Example 2—Reconstructing historical RABV spread

The higher resolution achieved through this classification system can be used to ‘zoom in’ on unusual cases and look at the historical and geographical context of a lineage. Here, we illustrate the example of a human rabies case (GenBank accession: KC737850) reported from the USA in 2011 [26], where dog-associated rabies has been eliminated but wildlife variants are endemic.The case report explains that this individual moved from Brazil to the USA, and many years later developed rabies [26]. In this situation the most likely cause of rabies was either exposure to wildlife rabies in the USA, or exposure to rabies in Brazil, which seemed unlikely given the long incubation period. Sequence data shows that the latter scenario is correct (Fig 6A), with the RABV sequence from the patient showing high similarity to dog RABV sequences in Brazil as reported by Boland et al. [26]. Using additional N gene sequences, we can examine this lineage in more detail. A sequence from Peru in 1999 (accession: KU938904) (Fig 6A), was listed in RABV-GLUE as being from the common vampire bat, Desmodus rotundus but the original GenBank record states ‘livestock case infected by bat’. In this case it appears to have been misassigned as a bat host by GLUE’s automatic curation from Genbank. However, the identification of a dog lineage in a bat is unusual. Follow-up with researchers from the original study revealed this was mislabelled on Genbank and was in fact most likely a livestock case infected by a dog [29]. These mislabelled entries are subsequently updated in RABV-GLUE. In this way, the metadata for sequences available in RABV-GLUE is constantly updated and corrected, providing additional and improved information beyond what is available on GenBank. The lineage classification system allows resolution of these unusual cases, and identification of sequences that are incorrectly labelled.

Rabies was first documented in Tanzania in the 1930’s [27]. Although present in North Africa for centuries, the virus is thought to have become endemic and spread within sub-Saharan Africa following European colonisation in the twentieth century [27]. The literature suggests at least two historical introductions to Tanzania; from the North and the South of the country [28]. With the classification system, it is possible to identify spread over time on a finer scale, and therefore evaluate potential support for these theories.The lineage A1.2.1 appears to support the spread of RABV into Tanzania from southern Africa (Fig 6B), with the first sequence from this ancestral lineage found in South Africa in 1981 (KX148103). By 1992 the lineage was detected in Zimbabwe (KT336434), as part of a study looking at potential incursions between South Africa and Zimbabwe [30], before being sequenced in Tanzania in 2010 [28]. This tracing of the lineage demonstrates the movement route across the continent has been followed at least once historically. Routine monitoring also allows us to identify that the lineage has persisted in South Africa, as the sequence MT454644 from 2017 is designated to the A1.2.1 lineage [31]. Additional information provided by inclusion of N gene data shows the lineage was also detected in Zambia from 1999, likely due to spread from neighbouring Zimbabwe.

Box 3

There are challenges in adapting the universal classification system developed for SARS-CoV-2 to RABV [18]. The spatial and temporal density of sequences is very different. More than 4 million WGS from over 185 countries are available for SARS-CoV-2 in a time span of less than two years [32,33], whereas RABV sequences span 7 decades but are much sparser. Moreover, SARS-CoV-2 lineages were designated from the emergence of this pathogen in human populations and have been updated as variation has accumulated. In contrast, the RABV lineage designation is retrospective; accounting for greater diversity and time. As the naming system calls for a new major lineage after 4 iterations of minor lineages (A1.1.1.1 = B1), the names do not fully reflect the depth of diversity and evolutionary time frame (e.g. that B1 is a descendent of A1), which can make it hard to see lineage turnover. For this reason, the full evolutionary path of each lineage is listed as an output i.e. for the AF1a_A1.2 lineage, AF1a_A1<-A1<-Cosmopolitan is listed (S1 Table). Likewise, it can be difficult to identify co-circulating lineages with this system as sequences are assigned to later iterations. For example, although A1 may no longer appear to be circulating, enough variation may have accumulated to designate more recent sequences to descendant lineage A1.1. However, these limitations are primarily to do with the naming of lineages, and analyses should be designed to incorporate the iterative nature of lineage designations, to represent the full extent of viral diversity. Additionally, the lineage designation steps implemented into the MAD DOG tool are not specific to RABV. The tool can therefore be used for any virus with just an input set of sequences and metadata. This may be valuable for other viruses with existing naming systems that need updating to show the appropriate definition for surveillance purposes, as the tool can incorporate existing naming systems and build from these. Additionally, we caution that the phylogenetic analyses reported here are not time-measured. MAD DOG is not intended as a tool for detailed phylodynamic analysis and more sophisticated inference, using BEAST or other analyses that incorporate the molecular clock are required to address important evolutionary and ecological questions e.g [12,34-36]. MADDOG is designed to serve as a rapid tool to provide a starting point for further investigations, assessing the number and diversity of sequenced lineages. Thus MADDOG may provide valuable insight into the direction subsequent analyses should focus on or rapidly actionable information in the event of identifying sources of incursions (or sustained but undetected local transmission) and for informing control policies accordingly.

One of the most recent and widely cited global phylogenomic analyses of RABV (Troupin et al. (2016)), used 321 WGS to capture and represent a level of geographic and temporal RABV diversity that was previously unexplored, covering all 6 major clades (Cosmopolitan, Asian, Africa-2, Africa-3, Arctic and Indian Subcontinent) [13]. However, since then publicly available RABV genomes have tripled and the way in which genomic surveillance is utilised has advanced from (usually) retrospective studies defining broad phylogenetic patterns, towards informing local control efforts in real-time. Therefore, we used this study as the baseline for designating coarse phylogenetic groups (using the existing clade names) but showcase the added value of our high resolution lineage designation tool for more specific epidemiological investigations. Sequence data has become increasingly accessible and we use 650 WGS just from the Cosmopolitan clade (over 50% of total dog-related RABV WGS available), to provide an improved characterisation of RABV diversity that supports our aim of interpreting sequence data for local and national surveillance. This approach follows from other local studies, for example, Brunker et al. (2015) identify 5 lineages circulating in Tanzania using phylogenetic methods and genetic distances, showing their co-circulation and identifying historical introductions and human-mediated movement of infected animals [28]. However, different studies define and name lineages in different ways. A study from Cameroon used geographic area as part of its definition of viral lineages [37] while Talbi et al. [38] identify 8 “subtypes” within the Africa 2 clade (named A-H) defined by phylogenetic placement with Bayesian posterior support; thus splitting the subclade further, but not to a high resolution [38]. These examples highlight how the lack of a universal lineage definition and naming system makes comparison between studies challenging, as the scale and method for lineage assignment and designation are not comparable.

An area that highlights the use of MAD DOG is the whole genome analysis of the Africa-1b subclade (AF1b), which is seen to be present in 9 African countries, with sequences spanning 1981–2018. Eight of the 10 AF1b lineages have only been seen in Tanzania, while the remainder have been detected in multiple countries (S1 Table). This subclade has good definition due to the large number of sequences, with over 10% of these WGS from a large South African study [31] and several studies in Tanzania [10,28,39] that generated over 80% of the available AF1b WGS. When incorporating N gene data for AF1b, the limited diversity compared to WGS means additional N gene sequences do not translate into designated lineages, as also evident in the ME1a and WE subclades (Fig 4 and Table 1). The definition of designated lineages therefore depends both on the breadth of the data (the length of the sequences) and the depth of the data (number of sequences). Overall, a 25% decrease in definition, and in some instances up to 50%, is seen when using N gene sequences alone compared to WGS of the same sequence set. Despite the lower resolution provided by N gene data, the large volume of partial genome sequences makes their inclusion essential for representing previously identified diversity. As with the WGS designated lineages, those subclades not seen are due to having fewer than 10 sequences available (Table 1).

A significant area of difference between the whole genome and N gene designations, that highlights the importance of using all available data, is found in the Africa-1a (AF1a) subclade. When only using whole genomes (21 WGS), the AF1a subclade splits into 2 lineages seen across multiple countries. With all the available N gene data (310 sequences) AF1a is split into an additional 3 lineages, with distinct lineages present in Ethiopia (1) and Tunisia (1). The first N gene sequence in the AF1a subclade is from 1982, whereas the first WGS is from 2 years later. Although the difference here is only two years, in other subclades, such as AM2b, the earliest available N gene sequences are from two decades earlier, thus excluding N gene sequences would exclude considerable historical data, and limit inference about historical patterns of dispersal.

Of the 24 emerging/undersampled lineages, some appear to be nascent lineages that did not persist, such as Cosmopolitan_B1.1_E2 which contains 7 sequences from France in 1991, but no further sequences. It may be that this lineage was emerging but halted due to an outbreak that was controlled. Alternatively, it may be that this is an undersampled lineage that was only not designated as a full lineage due to lack of sequences. In contrast, Cosmopolitan CA1_B1.1_E1 contains 7 sequences from Mongolia detected in 2017–2018 and therefore is likely to be an emerging lineage that could still be identified as a full lineage with the addition of more recent sequence data (S4 Table). Of the 16 singletons of interest, singletons are seen both in deeper lineages, such as AF1b_A1, and more terminal lineages, such as AM3b_A1.1.1. These singletons could be indicative of sequencing errors, or could be true significant diversity, perhaps indicating very sparse sequencing failing to capture the extent of the diversity within the lineage or the very beginning of a new, divergent lineage. Lineage AM3b_A1.1.1 contains 4 singletons of interest (all sequenced from Brazil in 2006), which could indicate multiple distinct descendent lineages that cannot be designated due to undersampling (S5 Table). These singletons likely represent sparse sequencing of samples during this time. Investigating undersampled contemporary lineages in detail is beyond the scope of this study, but could provide insight into where viral diversity is accumulating or overlooked, and monitoring may allow the identification of areas where new lineages are emerging.

Applying the MAD DOG lineage designation on a local scale allows in-country transmission routes and persistence of lineages to be seen at new depth. Tanzania is used as an example, which has a large number of WGS with detailed metadata. This provides greater inference to identify the uses and potential issues of this classification system on a local scale, such as local lineage dynamics and identification of areas needing improved vaccination efforts. 152 of the 205 Tanzanian sequences are from the Serengeti District, with 14 of the 15 Tanzanian lineages being detected there. This reflects greater sampling density enabling detection of circulating lineages, which also may be present elsewhere but remain undetected due to limited sampling. The sequence data point to the impact of improved dog vaccination in Serengeti district [40], with the apparent reduction in lineages from 11 in 2010 to just 6 by 2017. However, more sequences from 2017 onwards will be needed to determine whether lineage extinctions have truly occurred, or if limited ongoing sequencing has affected the detection of older lineages that are persisting in the district or elsewhere in the country.

While we provide proof of concept by applying MAD DOG to only the Cosmopolitan clade, the lineage classification and nomenclature system that we have developed can be easily extended to update all the RABV clades, and has potential to be extended to other viruses. The development of RABV-GLUE, and the incorporation of the updated classifications, provides an accessible platform for interpreting RABV sequence data with detailed genotyping that incorporates these high resolution lineage designations even for users unfamiliar with genomic analysis. This functionality is important in rapid sequencing, and to inform policy, as it should allow detailed, accessible interpretations about a sequence to be given quickly, which may for example be used to identify sustained transmission or the source of an incursion. As well as the improved access to and interpretation of sequence data through RABV-GLUE, the increased resolution provided by this updated system can be used to better understand the spread and persistence of RABV and resulting lineage dynamics, as highlighted by the examples presented here. These tools should prove valuable for monitoring the progress of rabies control programmes as they strive to achieve the 2030 goal.

Lineage Information for WGS and N gene lineages.

Details of the 147 designated lineages from 2608 rabies virus whole genome (10,000nt) and N gene (1300nt) sequences from the Cosmopolitan clade, obtained from RABV-GLUE. Includes all countries sequences assigned to each lineage have been seen in, the first and most recent collection years of those sequences, the number of sequences assigned to each lineage for both whole genome and N gene sequences. The evolutionary path of each lineage is also listed.

(CSV)

Click here for additional data file.

Tanzania Lineage Designation.

Details of the 14 designated lineages from lineage designation run exclusively on whole genome (10000nt) rabies virus sequences from Tanzania, obtained from RABV-GLUE. Includes all places sequences assigned each lineage have been seen in, the first and most recent collection years of those sequences, the maximum and mean patristic distance of all sequences in each lineage and the lineages descended from it, and the number of sequences assigned to each lineage. Where place = NA, no information is publicly available for sequence locations. Mara region contains the Serengeti District.

(DOCX)

Click here for additional data file.

Tanzania Subset of Global Lineage Information.

Details of the 15 global MAD DOG lineages that have been detected in Tanzania. Includes all places sequences assigned to each lineage have been seen in, the first and most recent collection years of those sequences and the number of sequences assigned to each lineage.

(DOCX)

Click here for additional data file.

Cosmopolitan Emerging or Undersampled Lineages.

Details of potentially emerging or undersampled lineages in the Cosmopolitan clade that do not yet have enough sequences to be defined as a new lineage. Lineages are named with an ‘_Ex’ suffix (where x is a number indicating multiple distinct emerging/undersampled lineages within the same parent lineage). The number of tips for each lineage, as well as the country and time period the sequences are from are also listed, as is the patristic distance between the node defining the lineage and its parent node.

(DOCX)

Click here for additional data file.

Cosmopolitan Singletons of Interest.

Details of singleton sequences with long branch lengths (longest 5%), indicating potentially undersampled lineages, sequencing errors, or newly emerging divergent lineages that need to be monitored. The number of singleton sequences in each lineage is listed, plus collection years and counties of those singleton sequences where this information is publicly available.

(DOCX)

Click here for additional data file.

Global Sequence Length Maps and Time Series.

Left: Map of collection locations of all available whole genome and N gene sequences. Base layer of map from Natural Earth (https://www.naturalearthdata.com/). Right: Time series of number of whole genome, N gene, and shorter sequences collected.

(TIF)

Click here for additional data file.

Tanzanian Sequence Length Time Series.

Timeseries of the number of whole genome, N gene, and shorter partial genome sequences from Tanzania available on RABV-GLUE.

(TIF)

Click here for additional data file.

6 Jan 2022

Dear Miss Campbell,

Thank you very much for submitting your manuscript "Making Genomic Surveillance Deliver: A Lineage Classification and Nomenclature System to Inform Rabies Elimination" for consideration at PLOS Pathogens. As with all papers reviewed by the journal, your manuscript was reviewed by members of the editorial board and by several independent reviewers. In light of the reviews (below this email), we would like to invite the resubmission of a significantly-revised version that takes into account the reviewers' comments.

Thank you for this submission, and again my apologies for the extreme delays noted in my prior correspondence.

All three reviewers were enthusiastic about this work. All three make suggestions for improving the presentation of the data and demonstrating the utility of your new system. Reviewer 1 and 3 have important constructive critiques that would need to be addressed in a revision.

We cannot make any decision about publication until we have seen the revised manuscript and your response to the reviewers' comments. Your revised manuscript is also likely to be sent to reviewers for further evaluation.

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Kasturi Haldar

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Michael Malim

Editor-in-Chief

PLOS Pathogens

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***********************

Thank you for this submission, and again my apologies for the extreme delays noted in my prior correspondence.

All three reviewers were enthusiastic about this work. All three make suggestions for improving the presentation of the data and demonstrating the utility of your new system. Reviewer 1 and 3 have important constructive critiques that would need to be addressed in a revision.

Reviewer's Responses to Questions

Part I - Summary

Please use this section to discuss strengths/weaknesses of study, novelty/significance, general execution and scholarship.

Reviewer #1: The manuscript „Making Genomic Surveillance Deliver: A Lineage Classification and Nomenclature System to Inform Rabies Elimination“ by Campbell and coauthors describes a high resolution phylogenetic classification system for rabies to identify incursions and transmission routes. The proposed model is based on the experiences with SARS-CoV-2 and could also be used for other epizootics/enzootics.

Optimization of the classification of outbreak strains is important and improvements seem helpful.

A major strength of the presented workflow is the inclusion of WGS and partial sequences (like the N gene) as well as a more structured and more detailed classification which could help to compare genetic epidemiology analysis of different working groups in the future.

However, it remains unclear, what the advantages for the genomic epidemiology and control measures are. Statements about the need of improved classifications should be more clear and more detailed. Today, analysis of transmission routes is done for rabies in numerous studies (e.g. Denise A Marston, Daniel L Horton, Javier Nunez, Richard J Ellis, Richard J Orton, Nicholas Johnson, Ashley C Banyard, Lorraine M McElhinney, Conrad M Freuling, Müge Fırat, Nil Ünal, Thomas Müller, Xavier de Lamballerie, Anthony R Fooks, Genetic analysis of a rabies virus host shift event reveals within-host viral dynamics in a new host, Virus Evolution, Volume 3, Issue 2, July 2017, vex038, https://doi.org/10.1093/ve/vex038). In those studies, phylogenetic or BEAST analyses allowed highly detailed data and conclusions.

In the manuscript, it must therefore be made even clearer what the advantages of the new methodology are. Overall, the structure of the manuscript takes some getting used to and should be reconsidered.

Reviewer #2: Campbell et al. develop a system to classify rabies virus sequences into lineages, adapting the existing PANGO lineage method developed for SARS-CoV-2. The authors describe this new method and provide example cases that highlight the utility of more fine-grained classifications for rabies surveillance and transmission analysis. Overall, the paper is clearly written, the classification system makes sense, and the examples provided are a nice addition that really highlights the method concretely. Additionally, the comparison between resolution from WGS compared to N gene is helpful. I have only minor comments.

Reviewer #3: In this work, Campbell et al. applied a dynamic lineage classification and nomenclature system previously used for SARS-CoV-2 on rabies virus (RABV) sequence data, specifically for the Cosmopolitan clade of viruses. Compared to the present system which only provides a high-level subspecies classification, the finer delineations produced by the authors facilitate epidemiological interpretations such as origins identification and surveillance of global as well as local circulating strains. The manuscript is generally well-written with a clear presentation on how the newly applied system could be used to partially describe the spatiotemporal dynamics of RABV.

**********

Part II – Major Issues: Key Experiments Required for Acceptance

Please use this section to detail the key new experiments or modifications of existing experiments that should be absolutely required to validate study conclusions.

Generally, there should be no more than 3 such required experiments or major modifications for a "Major Revision" recommendation. If more than 3 experiments are necessary to validate the study conclusions, then you are encouraged to recommend "Reject".

Reviewer #1: 1. Please explain the term „…we find an over 200% increase in the definition…“. What does the 200% refer to (- Line 57 and 318)? Is this changing the resolution of the analysis possible today or does this just change and unify the classifications?

2. How was it proven, that there were areas of persistence and transmission that were not previously apparent? Please provide data and explanations (- Line 58)

3. Would a „standard analysis“ using Blast, MEGA and BEAST result in the same outcome (as e.g. in Figure 6)? Please provide a direct comparison to convince the reader to use the new classification tool. What is the advantage of the strain designation for this type of analysis of a standard tree and BEAST analysis (e.g. Figure 7)? Please provide more detailed explanations/data of the advantages.

4. It is not clear why the proposed new classification system is supporting the „Zero by 30“ aim. Is this view supported by WHO/FAO and rabies reference laboratories?

Reviewer #2: (No Response)

Reviewer #3: However, there are a couple of concerns I hope the authors will find helpful in improving their work:

1) I am presuming that the phylogenetic trees shown in Figure 4 are patristic distance trees and not temporally-resolved ones. While there isn’t a scale to denote the branch lengths, a fair proportion of tips appear as singletons with extended branch lengths from the common ancestor of the virus clade they have been assigned to. These tips likely represent undersampled diversity. While these singletons may share common mutations with the larger clades that they are assigned to, they should also encode multiple mutations that would otherwise be deemed genetically distinct should there be more similar sequences sampled. Such singletons are less likely to appear for SARS-CoV-2 given its recent emergence and the unprecedented volume of sequence data that have been generated in substantially shorter timescales. As such, I feel that there is a need to tweak the current lineage classification system to flag and distinguish these singletons, which in itself may help inform on where there might be surveillance gaps.

Although the authors did not address this specifically, they note that the sparse availability of RABV sequences meant that the new nomenclature “do not fully reflect the depth of diversity and evolutionary time frame” of the viruses. The authors then made the argument that this limitation would only affect the naming of lineages, and that “analyses should be designed to incorporate the iterative nature of lineage designations”. However, if the aim of this proposed system is to be a new standard to capture underlying evolutionary and/or epidemiological processes which the authors assert, more care should be taken into the precision of what evolutionary/epidemiological bounds are embedded within these names.

2) I am also curious as to the choice of outgroup used to root the tree. Is the tree currently rooted by temporal structure? If so, how robust is the temporal structure? If the authors claim that the new nomenclature system can help identify case origins and reconstruct historical spread, the rooting choice isn’t a trivial one as it will impact interpretations on the directionality of evolutionary events and reconstruction of ancestral states.

**********

Part III – Minor Issues: Editorial and Data Presentation Modifications

Please use this section for editorial suggestions as well as relatively minor modifications of existing data that would enhance clarity.

Reviewer #1: 1. Box 1: what is the difference/relation of clade and lineage should be explained. Please define also clade designation and clade assignment.

2. Figure 3/step 3: how to deal with new outbreaks with novel lineages and less than 10 sequences in the early phase?

3. The keywords listed should be checked. Words like phylogenetics or lineage are missing.

4. The boxes seem to be missplaced in the introduction section.

5. Figure 3 seems to be more part of the results section

6. Figure 3/step 4: Single nucleotide changes are used for classification. How to exclude sequencing errors as a reason for new „lineages“? How robust is the analysis concerning sequencing errors?

7. Define „cosmopolitan clade“

8. Several of the supplemental datasets (figures/tabes) are not mentioned within the text.

Reviewer #2: 1. The authors mention in the Methods that MADDOG is available via an R package, which is great. However, the authors do not provide a link to the code, installation instructions, an example dataset, or any sort of documentation. Because this paper primarily introduces a new method, it would be helpful if the authors provided a direct link to the method, even if it is just a github page. I was not able to find such a page from a google search, and I think it would be helpful to potential users to provide at minimum a link to a web page with some documentation, an example dataset, and installation instructions.

2. Figure 5 is a bit challenging to interpret as currently colored and described in the legend. From reading the legend, I had inferred that when a given country had 2 circles overlaid on its centroid, that the darker/more vibrant one represented lineages inferred from WGS, while the lighter one represented those inferred from N gene sequences. However, some countries have multiple overlaid circles that are all light (Brazil, US, Mexico). For these countries, it was not clear to me what the interior vs. exterior circles represented. Additionally, the colors in the less vibrant circles were challenging to distinguish. I would suggest clarifying this in the legend, and perhaps altering the figure in some way to make the colors easier to distinguish for the light circles.

3. The case examples detailed in figures 6 and 7 were excellent and very helpful. However, showing some additional data would be helpful in showing how the sequence data and lineage designations allow for the inferences shown in the map and arrow graphics. For figure 6, it would be helpful to include the tree or at least a partial snippet of the tree, so that the data used for the inference shown on the map is clear. For figure 7b, it would be helpful to show on country-level information for the tips shown on the tree. This could be added as additional text in the strain name, as shapes or colors for the tips, or some other method. Having this information directly on the tree would make it more clear how the tree was used to infer the transmission history shown in 7a.

This is just a compliment: I thought it was very cool that the authors followed up with the original authors of the case report described in example 1 to confirm their inferences! That was a fun example to read.

Reviewer #3: Lastly, a minor point - can the authors provide a main or supplementary figure showing the difference between the current and proposed lineage designation? I think it will be impactful to show how the new nomenclature system yield more informative delineations compared to the currently used one.

**********

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

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9 Mar 2022

Submitted filename: Review responses (1).docx

Click here for additional data file.

30 Mar 2022

Dear Miss Campbell,

We are pleased to inform you that your manuscript 'Making Genomic Surveillance Deliver: A Lineage Classification and Nomenclature System to Inform Rabies Elimination' has been provisionally accepted for publication in PLOS Pathogens.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. A member of our team will be in touch with a set of requests.

Please note that your manuscript will not be scheduled for publication until you have made the required changes, so a swift response is appreciated.

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Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Pathogens.

Best regards,

Adam S. Lauring

Section Editor

PLOS Pathogens

Adam Lauring

Section Editor

PLOS Pathogens

Kasturi Haldar

Editor-in-Chief

PLOS Pathogens

​orcid.org/0000-0001-5065-158X

Michael Malim

Editor-in-Chief

PLOS Pathogens

orcid.org/0000-0002-7699-2064

***********************************************************

Reviewer Comments (if any, and for reference):

Reviewer's Responses to Questions

Part I - Summary

Please use this section to discuss strengths/weaknesses of study, novelty/significance, general execution and scholarship.

Reviewer #1: The revised version has much improved and all critical/open points were well addressed.

Reviewer #2: The authors have been quite responsive to reviewer comments. I had only minor comments to begin with, and they have all been appropriately addressed.

Reviewer #3: The authors have responded to all of my previous comments satisfactorily. It is good to see the integration between the WGS and N genes by using the latter for further lineage delineation based on comments from other reviewers and great to see that the authors took care to differentiate sampled viruses that may be emerging/under-sampled/likely resulted from sequencing errors. Nice piece of work, congratulations.

**********

Part II – Major Issues: Key Experiments Required for Acceptance

Please use this section to detail the key new experiments or modifications of existing experiments that should be absolutely required to validate study conclusions.

Generally, there should be no more than 3 such required experiments or major modifications for a "Major Revision" recommendation. If more than 3 experiments are necessary to validate the study conclusions, then you are encouraged to recommend "Reject".

Reviewer #1: No further modifications are requested from my site.

Reviewer #2: (No Response)

Reviewer #3: I have no other major issues.

**********

Part III – Minor Issues: Editorial and Data Presentation Modifications

Please use this section for editorial suggestions as well as relatively minor modifications of existing data that would enhance clarity.

Reviewer #1: No further minor issues.

Reviewer #2: (No Response)

Reviewer #3: None.

**********

PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

25 Apr 2022

Dear Miss Campbell,

We are delighted to inform you that your manuscript, "Making Genomic Surveillance Deliver: A Lineage Classification and Nomenclature System to Inform Rabies Elimination," has been formally accepted for publication in PLOS Pathogens.

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Thank you again for supporting open-access publishing; we are looking forward to publishing your work in PLOS Pathogens.

Best regards,

Kasturi Haldar

Editor-in-Chief

PLOS Pathogens

​orcid.org/0000-0001-5065-158X

Michael Malim

Editor-in-Chief

PLOS Pathogens

orcid.org/0000-0002-7699-2064