Publications

Metagenome-wide characterization of shared antimicrobial resistance genes in sympatric people and lemurs in rural Madagascar. PeerJ, 12, p.e17805

Talbot, B.M., Clennon, J.A., Rakotoarison, M.F.N., Rautman, L., Durry, S., Ragazzo, L.J., Wright, P.C., Gillespie, T.R. and Read, T.D., 2024

Tracking the spread of antibiotic resistant bacteria is critical to reduce global morbidity and mortality associated with human and animal infections. There is a need to understand the role that wild animals in maintenance and transfer of antibiotic resistance genes. This study, led by Emory University researchers Brooke Talbot and Dr. Tim Reade used metagenomics to identify and compare the abundance of bacterial species and ARGs detected in the gut microbiomes from sympatric humans and wild mouse lemurs, and identified unique AMR profiles between humans and wildlife, emphasizing a One Health approach to surveillance efforts in monitoring the movement of AMR in a community.

Characterizing SARS-CoV-2 Transmission Heterogeneity Between Urban and Rural Populations in Texas, USA, Using a Novel Spatial Transmission Count Statistic. medRxiv. 2024:2023.12.28.23300535

Lyu L, Veytsel G, Stott G, Fox S, Dailey C, Damodaran L, et. al.

This study explores how population heterogeneity, particularly between urban and rural areas, affects viral spread and epidemic trajectories, focusing on SARS-CoV-2 in Texas. By using a novel spatial transmission count statistic, Lyu et al. analyzed over 12,000 viral genomes to trace the spread of the Delta variant, revealing that urban centers were the primary sources of the epidemic and were closely linked to the global outbreak, while rural areas experienced repeated introductions from urban centers. The findings highlight the distinct roles of urban and rural areas in the spatial diffusion of the virus.

Sequence-based detection of emerging antigenically novel influenza A viruses. Proceedings of the Royal Society B: Biological Sciences. 2024;291(2028):20240790

Forna A, Weedop KB, Damodaran L, Hassell N, Kondor R, Bahl J, et al.

This study introduces NIAViD, an unsupervised machine learning tool designed to detect evolutionary transitions in influenza A (H3N2) viruses’ antigenicity using HA1 sequences and physico-chemical properties, without relying on traditional lab assays. NIAViD demonstrated high sensitivity in training (88.9%) and validation (72.7%), outperforming a null model, and effectively identifies novel antigenic clusters and potential sites of significant antigenic change. The tool holds potential to enhance surveillance networks and support the development of updated influenza vaccines.

Beyond scale-free networks: integrating multilayer social networks with molecular clusters in the local spread of COVID-19. Scientific Reports. 2023;13(1):21861

Fujimoto K, Kuo J, Stott G, Lewis R, Chan HK, Lyu L, et al.

This study assesses the validity of the scale-free network assumption in COVID-19 epidemiology using social network data from SARS-CoV-2 Delta variant clusters in Houston. By constructing genome-informed social networks and comparing various distribution models, Fujimoto et al. found that while the power-law model initially failed the goodness of fit test, it performed comparably or better after incorporating social network ties, suggesting both hub and non-hub mechanisms in viral transmission. These insights improve our understanding of the social dynamics influencing SARS-CoV-2 spread, aiding in the development of more effective public health interventions.

Tracing SARS-CoV-2 Clusters Across Local-scales Using Genomic Data. medRxiv 2024.09.18.24313896.

Leke Lyu, Guppy Stott, Cody Dailey, Sachin Subedi, Kayo Fujimoto, Ryker Penn, Pamela Brown, Roger Sealy, Justin Bahl.

The growing recognition of genome sequencing as critical for outbreak response has led to a rapid increase in the availability of sequence data. In this context, we put forward an analytical workflow within the Bayesian phylodynamic framework to identify and trace imported SARS-CoV-2 clusters using large-scale genome datasets. By utilizing metrics such as the Source-Sink Score, Local Import Score, and Persistent Time, our approach characterizes transmission patterns in each subregion and elucidates transmission heterogeneity. As new variants continue to emerge, the insights provided by our analysis are crucial for addressing the challenges of current and future pandemics effectively. This work