1
|
Zhao L, Guo X, Li L, Jing Q, Ma J, Xie T, Lin D, Li L, Yin Q, Wang Y, Zhang X, Li Z, Liu X, Hu T, Hu M, Ren W, Li J, Peng J, Yu L, Peng Z, Hong W, Leng X, Luo L, Ngobeh JJK, Tang X, Wu R, Zhao W, Shi B, Liu J, Yang Z, Chen XG, Zhou X, Zhang F. Phylodynamics unveils invading and diffusing patterns of dengue virus serotype-1 in Guangdong, China from 1990 to 2019 under a global genotyping framework. Infect Dis Poverty 2024; 13:43. [PMID: 38863070 PMCID: PMC11165891 DOI: 10.1186/s40249-024-01211-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Accepted: 05/29/2024] [Indexed: 06/13/2024] Open
Abstract
BACKGROUND The strong invasiveness and rapid expansion of dengue virus (DENV) pose a great challenge to global public health. However, dengue epidemic patterns and mechanisms at a genetic scale, particularly in term of cross-border transmissions, remain poorly understood. Importation is considered as the primary driver of dengue outbreaks in China, and since 1990 a frequent occurrence of large outbreaks has been triggered by the imported cases and subsequently spread to the western and northern parts of China. Therefore, this study aims to systematically reveal the invasion and diffusion patterns of DENV-1 in Guangdong, China from 1990 to 2019. METHODS These analyses were performed on 179 newly assembled genomes from indigenous dengue cases in Guangdong, China and 5152 E gene complete sequences recorded in Chinese mainland. The genetic population structure and epidemic patterns of DENV-1 circulating in Chinese mainland were characterized by phylogenetics, phylogeography, phylodynamics based on DENV-1 E-gene-based globally unified genotyping framework. RESULTS Multiple serotypes of DENV were co-circulating in Chinese mainland, particularly in Guangdong and Yunnan provinces. A total of 189 transmission clusters in 38 clades belonging to 22 subgenotypes of genotype I, IV and V of DENV-1 were identified, with 7 Clades of Concern (COCs) responsible for the large outbreaks since 1990. The epidemic periodicity was inferred from the data to be approximately 3 years. Dengue transmission events mainly occurred from Great Mekong Subregion-China (GMS-China), Southeast Asia (SEA), South Asia Subcontinent (SASC), and Oceania (OCE) to coastal and land border cities respectively in southeastern and southwestern China. Specially, Guangzhou was found to be the most dominant receipting hub, where DENV-1 diffused to other cities within the province and even other parts of the country. Genome phylogeny combined with epidemiological investigation demonstrated a clear local consecutive transmission process of a 5C1 transmission cluster (5C1-CN4) of DENV-1 in Guangzhou from 2013 to 2015, while the two provinces of Guangdong and Yunnan played key roles in ongoing transition of dengue epidemic patterns. In contextualizing within Invasion Biology theories, we have proposed a derived three-stage model encompassing the stages of invasion, colonization, and dissemination, which is supposed to enhance our understanding of dengue spreading patterns. CONCLUSIONS This study demonstrates the invasion and diffusion process of DENV-1 in Chinese mainland within a global genotyping framework, characterizing the genetic diversities of viral populations, multiple sources of importation, and periodic dynamics of the epidemic. These findings highlight the potential ongoing transition trends from epidemic to endemic status offering a valuable insight into early warning, prevention and control of rapid spreading of dengue both in China and worldwide.
Collapse
Affiliation(s)
- Lingzhai Zhao
- Institute of Infectious Diseases, Guangzhou Eighth People's Hospital, Guangzhou Medical University, Guangzhou, 510440, Guangdong, China
| | - Xiang Guo
- Department of Pathogen Biology, School of Public Health, Institute of Tropical Medicine, Southern Medical University; Guangdong Provincial Key Laboratory of Tropical Disease Research; Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes; Key Laboratory of Infectious Diseases Research in South China of Ministry of Education, Guangzhou, 510515, China
| | - Liqiang Li
- Department of Clinical Laboratory, The Third People's Hospital of Shenzhen, Southern University of Science and Technology, National Clinical Research Center for Infectious Diseases, Guangdong Provincial Clinical Research Center for Infectious Diseases (Tuberculosis), Shenzhen Clinical Research Center for Tuberculosis, Shenzhen, China
| | - Qinlong Jing
- Guangzhou Center for Disease Control and Prevention, Guangzhou, 510440, China
| | - Jinmin Ma
- BGI-Shenzhen, Shenzhen, 518083, China
| | - Tian Xie
- Department of Pathogen Biology, School of Public Health, Institute of Tropical Medicine, Southern Medical University; Guangdong Provincial Key Laboratory of Tropical Disease Research; Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes; Key Laboratory of Infectious Diseases Research in South China of Ministry of Education, Guangzhou, 510515, China
| | | | - Li Li
- Department of Biostatistics, School of Public Health, State Key Laboratory of Organ Failure Research, Guangdong Provincial Key Laboratory of Tropical Disease Research, Southern Medical University, Guangzhou, 510515, China
| | - Qingqing Yin
- Department of Pathogen Biology, School of Public Health, Institute of Tropical Medicine, Southern Medical University; Guangdong Provincial Key Laboratory of Tropical Disease Research; Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes; Key Laboratory of Infectious Diseases Research in South China of Ministry of Education, Guangzhou, 510515, China
| | - Yuji Wang
- Department of Pathogen Biology, School of Public Health, Institute of Tropical Medicine, Southern Medical University; Guangdong Provincial Key Laboratory of Tropical Disease Research; Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes; Key Laboratory of Infectious Diseases Research in South China of Ministry of Education, Guangzhou, 510515, China
| | - Xiaoqing Zhang
- Department of Pathogen Biology, School of Public Health, Institute of Tropical Medicine, Southern Medical University; Guangdong Provincial Key Laboratory of Tropical Disease Research; Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes; Key Laboratory of Infectious Diseases Research in South China of Ministry of Education, Guangzhou, 510515, China
| | - Ziyao Li
- Department of Pathogen Biology, School of Public Health, Institute of Tropical Medicine, Southern Medical University; Guangdong Provincial Key Laboratory of Tropical Disease Research; Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes; Key Laboratory of Infectious Diseases Research in South China of Ministry of Education, Guangzhou, 510515, China
| | - Xiaohua Liu
- Department of Pathogen Biology, School of Public Health, Institute of Tropical Medicine, Southern Medical University; Guangdong Provincial Key Laboratory of Tropical Disease Research; Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes; Key Laboratory of Infectious Diseases Research in South China of Ministry of Education, Guangzhou, 510515, China
| | - Tian Hu
- Department of Pathogen Biology, School of Public Health, Institute of Tropical Medicine, Southern Medical University; Guangdong Provincial Key Laboratory of Tropical Disease Research; Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes; Key Laboratory of Infectious Diseases Research in South China of Ministry of Education, Guangzhou, 510515, China
| | - Minling Hu
- Department of Pathogen Biology, School of Public Health, Institute of Tropical Medicine, Southern Medical University; Guangdong Provincial Key Laboratory of Tropical Disease Research; Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes; Key Laboratory of Infectious Diseases Research in South China of Ministry of Education, Guangzhou, 510515, China
| | - Wenwen Ren
- Department of Pathogen Biology, School of Public Health, Institute of Tropical Medicine, Southern Medical University; Guangdong Provincial Key Laboratory of Tropical Disease Research; Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes; Key Laboratory of Infectious Diseases Research in South China of Ministry of Education, Guangzhou, 510515, China
| | - Jun Li
- Guangdong Provincial Key Laboratory of Research On Emergency in TCM, Guangdong Provincial Hospital of Traditional Chinese Medicine, Guangzhou, 510120, China
| | - Jie Peng
- Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Lei Yu
- Institute of Infectious Diseases, Guangzhou Eighth People's Hospital, Guangzhou Medical University, Guangzhou, 510440, Guangdong, China
| | - Zhiqiang Peng
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, 511430, China
| | - Wenxin Hong
- Institute of Infectious Diseases, Guangzhou Eighth People's Hospital, Guangzhou Medical University, Guangzhou, 510440, Guangdong, China
| | - Xingyu Leng
- Institute of Infectious Diseases, Guangzhou Eighth People's Hospital, Guangzhou Medical University, Guangzhou, 510440, Guangdong, China
| | - Lei Luo
- Guangzhou Center for Disease Control and Prevention, Guangzhou, 510440, China
| | - Jone Jama Kpanda Ngobeh
- Department of Pathogen Biology, School of Public Health, Institute of Tropical Medicine, Southern Medical University; Guangdong Provincial Key Laboratory of Tropical Disease Research; Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes; Key Laboratory of Infectious Diseases Research in South China of Ministry of Education, Guangzhou, 510515, China
| | - Xiaoping Tang
- Institute of Infectious Diseases, Guangzhou Eighth People's Hospital, Guangzhou Medical University, Guangzhou, 510440, Guangdong, China
| | - Rangke Wu
- The School of Foreign Studies, Southern Medical University, Guangzhou, 510515, China
| | - Wei Zhao
- BSL-3 Laboratory(Guangdong), School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Benyun Shi
- College of Computer and Information Engineering, Nanjing Tech University, Nanjing, 211816, China
| | - Jiming Liu
- Department of Computer Science, Hong Kong Baptist University, Hong Kong, 999077, China
| | - Zhicong Yang
- Guangzhou Center for Disease Control and Prevention, Guangzhou, 510440, China.
| | - Xiao-Guang Chen
- Department of Pathogen Biology, School of Public Health, Institute of Tropical Medicine, Southern Medical University; Guangdong Provincial Key Laboratory of Tropical Disease Research; Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes; Key Laboratory of Infectious Diseases Research in South China of Ministry of Education, Guangzhou, 510515, China.
| | - Xiaohong Zhou
- Department of Pathogen Biology, School of Public Health, Institute of Tropical Medicine, Southern Medical University; Guangdong Provincial Key Laboratory of Tropical Disease Research; Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes; Key Laboratory of Infectious Diseases Research in South China of Ministry of Education, Guangzhou, 510515, China.
| | - Fuchun Zhang
- Institute of Infectious Diseases, Guangzhou Eighth People's Hospital, Guangzhou Medical University, Guangzhou, 510440, Guangdong, China.
- Guangzhou Medical Research Institute of Infectious Diseases, Infectious Disease Center, Guangzhou Eighth People's Hospital, Guangzhou Medical University, Guangzhou, 510440, China.
| |
Collapse
|
2
|
Castro LA, Leitner T, Romero-Severson E. Recombination smooths the time signal disrupted by latency in within-host HIV phylogenies. Virus Evol 2023; 9:vead032. [PMID: 37397911 PMCID: PMC10313349 DOI: 10.1093/ve/vead032] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 04/07/2023] [Accepted: 05/15/2023] [Indexed: 07/04/2023] Open
Abstract
Within-host Human immunodeficiency virus (HIV) evolution involves several features that may disrupt standard phylogenetic reconstruction. One important feature is reactivation of latently integrated provirus, which has the potential to disrupt the temporal signal, leading to variation in the branch lengths and apparent evolutionary rates in a tree. Yet, real within-host HIV phylogenies tend to show clear, ladder-like trees structured by the time of sampling. Another important feature is recombination, which violates the fundamental assumption that evolutionary history can be represented by a single bifurcating tree. Thus, recombination complicates the within-host HIV dynamic by mixing genomes and creating evolutionary loop structures that cannot be represented in a bifurcating tree. In this paper, we develop a coalescent-based simulator of within-host HIV evolution that includes latency, recombination, and effective population size dynamics that allows us to study the relationship between the true, complex genealogy of within-host HIV evolution, encoded as an ancestral recombination graph (ARG), and the observed phylogenetic tree. To compare our ARG results to the familiar phylogeny format, we calculate the expected bifurcating tree after decomposing the ARG into all unique site trees, their combined distance matrix, and the overall corresponding bifurcating tree. While latency and recombination separately disrupt the phylogenetic signal, remarkably, we find that recombination recovers the temporal signal of within-host HIV evolution caused by latency by mixing fragments of old, latent genomes into the contemporary population. In effect, recombination averages over extant heterogeneity, whether it stems from mixed time signals or population bottlenecks. Furthermore, we establish that the signals of latency and recombination can be observed in phylogenetic trees despite being an incorrect representation of the true evolutionary history. Using an approximate Bayesian computation method, we develop a set of statistical probes to tune our simulation model to nine longitudinally sampled within-host HIV phylogenies. Because ARGs are exceedingly difficult to infer from real HIV data, our simulation system allows investigating effects of latency, recombination, and population size bottlenecks by matching decomposed ARGs to real data as observed in standard phylogenies.
Collapse
Affiliation(s)
| | - Thomas Leitner
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
| | | |
Collapse
|
3
|
Skums P, Mohebbi F, Tsyvina V, Baykal PI, Nemira A, Ramachandran S, Khudyakov Y. SOPHIE: Viral outbreak investigation and transmission history reconstruction in a joint phylogenetic and network theory framework. Cell Syst 2022; 13:844-856.e4. [PMID: 36265470 PMCID: PMC9590096 DOI: 10.1016/j.cels.2022.07.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 07/05/2022] [Accepted: 07/19/2022] [Indexed: 01/26/2023]
Abstract
Genomic epidemiology is now widely used for viral outbreak investigations. Still, this methodology faces many challenges. First, few methods account for intra-host viral diversity. Second, maximum parsimony principle continues to be employed for phylogenetic inference of transmission histories, even though maximum likelihood or Bayesian models are usually more consistent. Third, many methods utilize case-specific data, such as sampling times or infection exposure intervals. This impedes study of persistent infections in vulnerable groups, where such information has a limited use. Finally, most methods implicitly assume that transmission events are independent, although common source outbreaks violate this assumption. We propose a maximum likelihood framework, SOPHIE, based on the integration of phylogenetic and random graph models. It infers transmission networks from viral phylogenies and expected properties of inter-host social networks modeled as random graphs with given expected degree distributions. SOPHIE is scalable, accounts for intra-host diversity, and accurately infers transmissions without case-specific epidemiological data.
Collapse
Affiliation(s)
- Pavel Skums
- Department of Computer Science, Georgia State University, Atlanta, GA, USA.
| | - Fatemeh Mohebbi
- Department of Computer Science, Georgia State University, Atlanta, GA, USA
| | - Vyacheslav Tsyvina
- Department of Computer Science, Georgia State University, Atlanta, GA, USA
| | - Pelin Icer Baykal
- Department of Biosystems Science & Engineering, ETH Zurich, Basel, Switzerland
| | - Alina Nemira
- Department of Computer Science, Georgia State University, Atlanta, GA, USA
| | - Sumathi Ramachandran
- Division of Viral Hepatitis, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Yury Khudyakov
- Division of Viral Hepatitis, Centers for Disease Control and Prevention, Atlanta, GA, USA
| |
Collapse
|
4
|
Lundgren E, Romero-Severson E, Albert J, Leitner T. Combining biomarker and virus phylogenetic models improves HIV-1 epidemiological source identification. PLoS Comput Biol 2022; 18:e1009741. [PMID: 36026480 PMCID: PMC9455879 DOI: 10.1371/journal.pcbi.1009741] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 09/08/2022] [Accepted: 08/02/2022] [Indexed: 01/07/2023] Open
Abstract
To identify and stop active HIV transmission chains new epidemiological techniques are needed. Here, we describe the development of a multi-biomarker augmentation to phylogenetic inference of the underlying transmission history in a local population. HIV biomarkers are measurable biological quantities that have some relationship to the amount of time someone has been infected with HIV. To train our model, we used five biomarkers based on real data from serological assays, HIV sequence data, and target cell counts in longitudinally followed, untreated patients with known infection times. The biomarkers were modeled with a mixed effects framework to allow for patient specific variation and general trends, and fit to patient data using Markov Chain Monte Carlo (MCMC) methods. Subsequently, the density of the unobserved infection time conditional on observed biomarkers were obtained by integrating out the random effects from the model fit. This probabilistic information about infection times was incorporated into the likelihood function for the transmission history and phylogenetic tree reconstruction, informed by the HIV sequence data. To critically test our methodology, we developed a coalescent-based simulation framework that generates phylogenies and biomarkers given a specific or general transmission history. Testing on many epidemiological scenarios showed that biomarker augmented phylogenetics can reach 90% accuracy under idealized situations. Under realistic within-host HIV-1 evolution, involving substantial within-host diversification and frequent transmission of multiple lineages, the average accuracy was at about 50% in transmission clusters involving 5-50 hosts. Realistic biomarker data added on average 16 percentage points over using the phylogeny alone. Using more biomarkers improved the performance. Shorter temporal spacing between transmission events and increased transmission heterogeneity reduced reconstruction accuracy, but larger clusters were not harder to get right. More sequence data per infected host also improved accuracy. We show that the method is robust to incomplete sampling and that adding biomarkers improves reconstructions of real HIV-1 transmission histories. The technology presented here could allow for better prevention programs by providing data for locally informed and tailored strategies.
Collapse
Affiliation(s)
- Erik Lundgren
- Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Ethan Romero-Severson
- Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Jan Albert
- Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, Sweden
- Department of Clinical Microbiology, Karolinska University Hospital, Stockholm, Sweden
| | - Thomas Leitner
- Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
- * E-mail:
| |
Collapse
|
7
|
Mahmoud NM, Mahmoud MH, Alamery S, Fouad H. Structural modeling and phylogenetic analysis for infectious disease transmission pattern based on maximum likelihood tree approach. JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021; 12:3479-3492. [PMID: 33425052 PMCID: PMC7778505 DOI: 10.1007/s12652-020-02702-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/22/2020] [Accepted: 11/16/2020] [Indexed: 06/12/2023]
Abstract
The contagious disease transmission pattern outbreak caused a massive human casualty and became a pandemic, as confirmed by the World Health Organization (WHO). The present research aims to understand the infectious disease transmission pattern outbreak due to molecular epidemiology. Hence, infected patients over time can spread infectious disease. The virus may develop further mutations, and that there might be a more toxic virulent strain, which leads to several environmental risk factors. Therefore, it is essential to monitor and characterize patient profiles, variants, symptoms, geographic locations, and treatment responses to analyze and evaluate infectious disease patterns among humans. This research proposes the Evolutionary tree analysis (ETA) for the molecular evolutionary genetic analysis to reduce medical risk factors. Furthermore, The Maximum likelihood tree method (MLTM) has been used to analyze the selective pressure, which is examined to identify a mutation that may influence the infectious disease transmission pattern's clinical progress. This study also utilizes ETA with Markov Chain Bayesian Statistics (MCBS) approach to reconstruct transmission trees with sequence information. The experimental shows that the proposed ETA-MCBS method achieves a 97.55% accuracy, prediction of 99.56%, and 98.55% performance compared to other existing methods.
Collapse
Affiliation(s)
- Nourelhoda M. Mahmoud
- Biomedical Engineering Department, Faculty of Engineering, Minia University, Minia, Egypt
| | - Mohamed H. Mahmoud
- Department of Biochemistry, College of Science, King Saud University, PO Box 22452, Riyadh, 11451 Saudi Arabia
| | - Salman Alamery
- Department of Biochemistry, College of Science, King Saud University, PO Box 22452, Riyadh, 11451 Saudi Arabia
| | - Hassan Fouad
- Biomedical Engineering Department, Faculty of Engineering, Helwan University, Cairo, Egypt
| |
Collapse
|