Tracking a serial killer: Integrating phylogenetic relationships, epidemiology, and geography for two invasive meningococcal disease outbreaks

Invasive Meningococcal Disease and COVID-19 Co-Infection: A Case Report

This case report presents a 53-year-old male patient infected with COVID-19 who developed acute respiratory distress syndrome (ARDS) and septic shock due to meningococcemia, despite the absence of clinical signs of meningitis. This patient's condition was complicated by pneumonia in the setting of myocardial failure. In the curse of the disease, it is remarked that the importance of early recognition of sepsis symptoms is crucial in distinguishing patients with COVID-19 from those with other infections and preventing fatal outcomes. The case presented an excellent opportunity to review meningococcal disease's intrinsic and extrinsic risk factors. With the identified risk factors, we propose different measures to be considered to diminish and recognize this fatal disease early.

Reactive vaccination as control strategy for an outbreak of invasive meningococcal disease caused by Neisseria meningitidis C:P1.5-1,10-8:F3-6:ST-11(cc11), Bergamo province, Italy, December 2019 to January 2020

In Italy, serogroup C meningococci of the clonal complex cc11 (MenC/cc11) have caused several outbreaks of invasive meningococcal disease (IMD) during the past 20 years. Between December 2019 and January 2020, an outbreak of six cases of IMD infected with MenC/cc11 was identified in a limited area in the northern part of Italy. All cases presented a severe clinical picture, and two of them were fatal. This report is focused on the microbiological and molecular analysis of meningococcal isolates with the aim to reconstruct the chain of transmission. It further presents the vaccination strategy adopted to control the outbreak. The phylogenetic evaluation demonstrated the close genetic proximity between the strain involved in this outbreak and a strain responsible for a larger epidemic that had occurred in 2015 and 2016 in the Tuscany Region. The rapid identification and characterisation of IMD cases and an extensive vaccination campaign contributed to the successful control of this outbreak caused by a hyperinvasive meningococcal strain.

Methods Combining Genomic and Epidemiological Data in the Reconstruction of Transmission Trees: A Systematic Review

In order to better understand transmission dynamics and appropriately target control and preventive measures, studies have aimed to identify who-infected-whom in actual outbreaks. Numerous reconstruction methods exist, each with their own assumptions, types of data, and inference strategy. Thus, selecting a method can be difficult. Following PRISMA guidelines, we systematically reviewed the literature for methods combing epidemiological and genomic data in transmission tree reconstruction. We identified 22 methods from the 41 selected articles. We defined three families according to how genomic data was handled: a non-phylogenetic family, a sequential phylogenetic family, and a simultaneous phylogenetic family. We discussed methods according to the data needed as well as the underlying sequence mutation, within-host evolution, transmission, and case observation. In the non-phylogenetic family consisting of eight methods, pairwise genetic distances were estimated. In the phylogenetic families, transmission trees were inferred from phylogenetic trees either simultaneously (nine methods) or sequentially (five methods). While a majority of methods (17/22) modeled the transmission process, few (8/22) took into account imperfect case detection. Within-host evolution was generally (7/8) modeled as a coalescent process. These practical and theoretical considerations were highlighted in order to help select the appropriate method for an outbreak.

Evolution of Sequence Type 4821 Clonal Complex Hyperinvasive and Quinolone-Resistant Meningococci

Expansion of quinolone-resistant Neisseria meningitidis clone China^{CC4821-R1-C/B} from sequence type (ST) 4821 clonal complex (CC4821) caused a serogroup shift from serogroup A to serogroup C invasive meningococcal disease (IMD) in China. To determine the relationship among globally distributed CC4821 meningococci, we analyzed whole-genome sequence data from 173 CC4821 meningococci isolated from 4 continents during 1972–2019. These meningococci clustered into 4 sublineages (1–4); sublineage 1 primarily comprised of IMD isolates (41/50, 82%). Most isolates from outside China (40/49, 81.6%) formed a distinct sublineage, the Europe–USA cluster, with the typical strain designation B:P1.17-6,23:F3-36:ST-3200(CC4821), harboring mutations in penicillin-binding protein 2. These data show that the quinolone-resistant clone China^{CC4821-R1-C/B} has expanded to other countries. The increasing distribution worldwide of serogroup B CC4821 raises the concern that CC4821 has the potential to cause a pandemic that would be challenging to control, despite indirect evidence that the Trumenba vaccine might afford some protection.

Using Neisseria meningitidis genomic diversity to inform outbreak strain identification

Meningococcal disease is a life-threatening illness caused by the human-restricted bacterium Neisseria meningitidis. Outbreaks in the USA involve at least two cases in an organization or community caused by the same serogroup within three months. Genome comparisons, including phylogenetic analysis and quantification of genome distances can provide confirmatory evidence of pathogen transmission during an outbreak. Interpreting genome distances depends on understanding their distribution both among isolates from outbreaks and among those not from outbreaks. Here, we identify outbreak strains based on phylogenetic relationships among 141 N. meningitidis isolates collected from 28 outbreaks in the USA during 2010-2017 and 1516 non-outbreak isolates collected through contemporaneous meningococcal surveillance. We show that genome distance thresholds based on the maximum SNPs and allele distances among isolates in the phylogenetically defined outbreak strains are sufficient to separate most pairs of non-outbreak isolates into separate strains. Non-outbreak isolate pairs that could not be distinguished from each other based on genetic distances were concentrated in the clonal complexes CC11, CC103, and CC32. Within each of these clonal complexes, phylodynamic analysis identified a group of isolates with extremely low diversity, collected over several years and multiple states. Clusters of isolates with low genetic diversity could indicate increased pathogen transmission, potentially resulting in local outbreaks or nationwide clonal expansions.

Fundamentals of genomic epidemiology, lessons learned from the coronavirus disease 2019 (COVID-19) pandemic, and new directions

The coronavirus disease 2019 (COVID-19) pandemic was one of the significant causes of death worldwide in 2020. The disease is caused by severe acute coronavirus syndrome (SARS) coronavirus 2 (SARS-CoV-2), an RNA virus of the subfamily Orthocoronavirinae related to 2 other clinically relevant coronaviruses, SARS-CoV and MERS-CoV. Like other coronaviruses and several other viruses, SARS-CoV-2 originated in bats. However, unlike other coronaviruses, SARS-CoV-2 resulted in a devastating pandemic. The SARS-CoV-2 pandemic rages on due to viral evolution that leads to more transmissible and immune evasive variants. Technology such as genomic sequencing has driven the shift from syndromic to molecular epidemiology and promises better understanding of variants. The COVID-19 pandemic has exposed critical impediments that must be addressed to develop the science of pandemics. Much of the progress is being applied in the developed world. However, barriers to the use of molecular epidemiology in low- and middle-income countries (LMICs) remain, including lack of logistics for equipment and reagents and lack of training in analysis. We review the molecular epidemiology literature to understand its origins from the SARS epidemic (2002-2003) through influenza events and the current COVID-19 pandemic. We advocate for improved genomic surveillance of SARS-CoV and understanding the pathogen diversity in potential zoonotic hosts. This work will require training in phylogenetic and high-performance computing to improve analyses of the origin and spread of pathogens. The overarching goals are to understand and abate zoonosis risk through interdisciplinary collaboration and lowering logistical barriers.

Phylogenetic Concepts and Tools Applied to Epidemiologic Investigations of Infectious Diseases

In this review, which is a part of the Microbiology Spectrum Curated Collection: Advances in Molecular Epidemiology of Infectious Diseases, I present an overview of the principles used to classify organisms in the field of phylogenetics, highlight the methods used to infer the interrelationships of organisms, and summarize how these concepts are applied to molecular epidemiologic analyses. I present steps in analyses that come downstream of the assembly of a set of genomes or genes and the production of a multiple-sequence alignment or other matrices of putative orthologs for comparison. I focus on the history of the problem of phylogenetic reconstruction and debates within the field about the most appropriate methods. I illustrate methods that bridge the gap between molecular epidemiology and traditional epidemiology, including phylogenetic character evolution and geographic visualization. Finally, I provide practical advice on how to conduct an example analysis in the appendix. *This article is part of a curated collection.