Did a Single Amino Acid Change Make Ebola Virus More Virulent?

A mutation in the Ebola virus glycoprotein arose early during the 2013-2016 epidemic and dominated the viral population. Two studies by Diehl et al. and Urbanowicz et al. now reveal that this mutation is associated with higher infectivity to human cells, representing the clearest example of Ebola's functional adaptation to human hosts.

Roots, Not Parachutes: Research Collaborations Combat Outbreaks.

Recent infectious disease epidemics illustrate how health systems failures anywhere can create disease vulnerabilities everywhere. We must therefore prioritize investments in health care infrastructure in outbreak-prone regions of the world. We describe how "rooted" research collaborations can establish capacity for pathogen surveillance and facilitate rapid outbreak responses.

Human Adaptation of Ebola Virus during the West African Outbreak.

The 2013-2016 outbreak of Ebola virus (EBOV) in West Africa was the largest recorded. It began following the cross-species transmission of EBOV from an animal reservoir, most likely bats, into humans, with phylogenetic analysis revealing the co-circulation of several viral lineages. We hypothesized that this prolonged human circulation led to genomic changes that increased viral transmissibility in humans. We generated a synthetic glycoprotein (GP) construct based on the earliest reported isolate and introduced amino acid substitutions that defined viral lineages. Mutant GPs were used to generate a panel of pseudoviruses, which were used to infect different human and bat cell lines. These data revealed that specific amino acid substitutions in the EBOV GP have increased tropism for human cells, while reducing tropism for bat cells. Such increased infectivity may have enhanced the ability of EBOV to transmit among humans and contributed to the wide geographic distribution of some viral lineages.

Ebola Virus Glycoprotein with Increased Infectivity Dominated the 2013-2016 Epidemic.

The magnitude of the 2013-2016 Ebola virus disease (EVD) epidemic enabled an unprecedented number of viral mutations to occur over successive human-to-human transmission events, increasing the probability that adaptation to the human host occurred during the outbreak. We investigated one nonsynonymous mutation, Ebola virus (EBOV) glycoprotein (GP) mutant A82V, for its effect on viral infectivity. This mutation, located at the NPC1-binding site on EBOV GP, occurred early in the 2013-2016 outbreak and rose to high frequency. We found that GP-A82V had heightened ability to infect primate cells, including human dendritic cells. The increased infectivity was restricted to cells that have primate-specific NPC1 sequences at the EBOV interface, suggesting that this mutation was indeed an adaptation to the human host. GP-A82V was associated with increased mortality, consistent with the hypothesis that the heightened intrinsic infectivity of GP-A82V contributed to disease severity during the EVD epidemic.

Ebola Virus Epidemiology, Transmission, and Evolution during Seven Months in Sierra Leone.

The 2013-2015 Ebola virus disease (EVD) epidemic is caused by the Makona variant of Ebola virus (EBOV). Early in the epidemic, genome sequencing provided insights into virus evolution and transmission and offered important information for outbreak response. Here, we analyze sequences from 232 patients sampled over 7 months in Sierra Leone, along with 86 previously released genomes from earlier in the epidemic. We confirm sustained human-to-human transmission within Sierra Leone and find no evidence for import or export of EBOV across national borders after its initial introduction. Using high-depth replicate sequencing, we observe both host-to-host transmission and recurrent emergence of intrahost genetic variants. We trace the increasing impact of purifying selection in suppressing the accumulation of nonsynonymous mutations over time. Finally, we note changes in the mucin-like domain of EBOV glycoprotein that merit further investigation. These findings clarify the movement of EBOV within the region and describe viral evolution during prolonged human-to-human transmission.

Ebola virus antibody decay-stimulation in a high proportion of survivors.

Neutralizing antibody function provides a foundation for the efficacy of vaccines and therapies1-3. Here, using a robust in vitro Ebola virus (EBOV) pseudo-particle infection assay and a well-defined set of solid-phase assays, we describe a wide spectrum of antibody responses in a cohort of healthy survivors of the Sierra Leone EBOV outbreak of 2013-2016. Pseudo-particle virus-neutralizing antibodies correlated with total anti-EBOV reactivity and neutralizing antibodies against live EBOV. Variant EBOV glycoproteins (1995 and 2014 strains) were similarly neutralized. During longitudinal follow-up, antibody responses fluctuated in a 'decay-stimulation-decay' pattern that suggests de novo restimulation by EBOV antigens after recovery. A pharmacodynamic model of antibody reactivity identified a decay half-life of 77-100 days and a doubling time of 46-86 days in a high proportion of survivors. The highest antibody reactivity was observed around 200 days after an individual had recovered. The model suggests that EBOV antibody reactivity declines over 0.5-2 years after recovery. In a high proportion of healthy survivors, antibody responses undergo rapid restimulation. Vigilant follow-up of survivors and possible elective de novo antigenic stimulation by vaccine immunization should be considered in order to prevent EBOV viral recrudescence in recovering individuals and thereby to mitigate the potential risk of reseeding an outbreak.

Resurgence of Ebola virus in 2021 in Guinea suggests a new paradigm for outbreaks.

Seven years after the declaration of the first epidemic of Ebola virus disease in Guinea, the country faced a new outbreak-between 14 February and 19 June 2021-near the epicentre of the previous epidemic1,2. Here we use next-generation sequencing to generate complete or near-complete genomes of Zaire ebolavirus from samples obtained from 12 different patients. These genomes form a well-supported phylogenetic cluster with genomes from the previous outbreak, which indicates that the new outbreak was not the result of a new spillover event from an animal reservoir. The 2021 lineage shows considerably lower divergence than would be expected during sustained human-to-human transmission, which suggests a persistent infection with reduced replication or a period of latency. The resurgence of Zaire ebolavirus from humans five years after the end of the previous outbreak of Ebola virus disease reinforces the need for long-term medical and social care for patients who survive the disease, to reduce the risk of re-emergence and to prevent further stigmatization.

Recent advances in the treatment of Ebola disease: A brief overview.

Ebola disease (EBOD) remains a significant and ongoing threat to African countries, characterized by a mortality rate of 25% to 90% in patients with high viral load and significant transmissibility. The most recent outbreak, reported in Uganda in September 2022, was declared officially over in January 2023. However, it was caused by the Sudan Ebola virus (SUDV), a culprit species not previously reported for a decade. Since its discovery in 1976, the management of EBOD has primarily relied on supportive care. Following the devastating outbreak in West Africa from 2014 to 2016 secondary to the Zaire Ebola virus (EBOV), where over 28,000 lives were lost, dedicated efforts to find effective therapeutic agents have resulted in considerable progress in treating and preventing disease secondary to EBOV. Notably, 2 monoclonal antibodies-Ebanga and a cocktail of monoclonal antibodies, called Inmazeb-received Food and Drug Administration (FDA) approval in 2020. Additionally, multiple vaccines have been approved for EBOD prevention by various regulatory bodies, with Ervebo, a recombinant vesicular stomatitis virus-vectored vaccine against EBOV being the first vaccine to receive approval by the FDA in 2019. This review covers the key signs and symptoms of EBOD, its modes of transmission, and the principles guiding supportive care. Furthermore, it explores recent advancements in treating and preventing EBOD, highlighting the unique properties of each therapeutic agent and the ongoing progress in discovering new treatments.

Scalable gradients enable Hamiltonian Monte Carlo sampling for phylodynamic inference under episodic birth-death-sampling models.

Birth-death models play a key role in phylodynamic analysis for their interpretation in terms of key epidemiological parameters. In particular, models with piecewise-constant rates varying at different epochs in time, to which we refer as episodic birth-death-sampling (EBDS) models, are valuable for their reflection of changing transmission dynamics over time. A challenge, however, that persists with current time-varying model inference procedures is their lack of computational efficiency. This limitation hinders the full utilization of these models in large-scale phylodynamic analyses, especially when dealing with high-dimensional parameter vectors that exhibit strong correlations. We present here a linear-time algorithm to compute the gradient of the birth-death model sampling density with respect to all time-varying parameters, and we implement this algorithm within a gradient-based Hamiltonian Monte Carlo (HMC) sampler to alleviate the computational burden of conducting inference under a wide variety of structures of, as well as priors for, EBDS processes. We assess this approach using three different real world data examples, including the HIV epidemic in Odesa, Ukraine, seasonal influenza A/H3N2 virus dynamics in New York state, America, and Ebola outbreak in West Africa. HMC sampling exhibits a substantial efficiency boost, delivering a 10- to 200-fold increase in minimum effective sample size per unit-time, in comparison to a Metropolis-Hastings-based approach. Additionally, we show the robustness of our implementation in both allowing for flexible prior choices and in modeling the transmission dynamics of various pathogens by accurately capturing the changing trend of viral effective reproductive number.

Rhabdomyolysis, Acute Kidney Injury, and Mortality in Ebola Virus Disease: Retrospective Analysis of Cases From the Eastern Democratic Republic of the Congo, 2019.

BACKGROUND: Skeletal muscle injury in Ebola virus disease (EVD) has been reported, but its association with morbidity and mortality remains poorly defined. METHODS: This retrospective study included patients admitted to 2 EVD treatment units over an 8-month period in 2019 during an EVD epidemic in the Democratic Republic of the Congo. RESULTS: An overall 333 patients (median age, 30 years; 58% female) had at least 1 creatine kinase (CK) measurement (n = 2229; median, 5/patient [IQR, 1-11]). Among patients, 271 (81%) had an elevated CK level (>380 U/L); 202 (61%) had rhabdomyolysis (CK >1000 IU/L); and 45 (14%) had severe rhabdomyolysis (≥5000 U/L). Among survivors, the maximum CK level was a median 1600 (IQR, 550-3400), peaking 3.4 days after admission (IQR, 2.3-5.5) and decreasing thereafter. Among fatal cases, the CK rose monotonically until death, with a median maximum CK level of 2900 U/L (IQR, 1500-4900). Rhabdomyolysis at admission was an independent predictor of acute kidney injury (adjusted odds ratio, 2.2 [95% CI, 1.2-3.8]; P = .0065) and mortality (adjusted hazard ratio, 1.7 [95% CI, 1.03-2.9]; P = .037). CONCLUSIONS: Rhabdomyolysis is associated with acute kidney injury and mortality in patients with EVD. These findings may inform clinical practice by identifying laboratory monitoring priorities and highlighting the importance of fluid management.

Geographic Disparities in Domestic Pig Population Exposure to Ebola Viruses, Guinea, 2017-2019.

Although pigs are naturally susceptible to Reston virus and experimentally to Ebola virus (EBOV), their role in Orthoebolavirus ecology remains unknown. We tested 888 serum samples collected from pigs in Guinea during 2017-2019 (between the 2013-16 epidemic and its resurgence in 2021) by indirect ELISA against the EBOV nucleoprotein. We identified 2 hotspots of possible pig exposure by IgG titer levels: the northern coast had 48.7% of positive serum samples (37/76), and Forest Guinea, bordering Sierra Leone and Liberia, where the virus emerged and reemerged, had 50% of positive serum samples (98/196). The multitarget Luminex approach confirms ELISA results against Ebola nucleoprotein and highlights cross-reactivities to glycoprotein of EBOV, Reston virus, and Bundibugyo virus. Those results are consistent with previous observations of the circulation of Orthoebolavirus species in pig farming regions in Sierra Leone and Ghana, suggesting potential risk for Ebola virus disease in humans, especially in Forest Guinea.

Ebola Virus Transmission Initiated by Relapse of Systemic Ebola Virus Disease.

During the 2018-2020 Ebola virus disease (EVD) outbreak in North Kivu province in the Democratic Republic of Congo, EVD was diagnosed in a patient who had received the recombinant vesicular stomatitis virus-based vaccine expressing a ZEBOV glycoprotein (rVSV-ZEBOV) (Merck). His treatment included an Ebola virus (EBOV)-specific monoclonal antibody (mAb114), and he recovered within 14 days. However, 6 months later, he presented again with severe EVD-like illness and EBOV viremia, and he died. We initiated epidemiologic and genomic investigations that showed that the patient had had a relapse of acute EVD that led to a transmission chain resulting in 91 cases across six health zones over 4 months. (Funded by the Bill and Melinda Gates Foundation and others.).

Molecular characterization of the 2022 Sudan virus disease outbreak in Uganda.

IMPORTANCE: Ebola disease (EBOD) is a public health threat with a high case fatality rate. Most EBOD outbreaks have occurred in remote locations, but the 2013-2016 Western Africa outbreak demonstrated how devastating EBOD can be when it reaches an urban population. Here, the 2022 Sudan virus disease (SVD) outbreak in Mubende District, Uganda, is summarized, and the genetic relatedness of the new variant is evaluated. The Mubende variant exhibited 96% amino acid similarity with historic SUDV sequences from the 1970s and a high degree of conservation throughout the outbreak, which was important for ongoing diagnostics and highly promising for future therapy development. Genetic differences between viruses identified during the Mubende SVD outbreak were linked with epidemiological data to better interpret viral spread and contact tracing chains. This methodology should be used to better integrate discrete epidemiological and sequence data for future viral outbreaks.

Fast and Accurate Maximum-Likelihood Estimation of Multi-Type Birth-Death Epidemiological Models from Phylogenetic Trees.

Multi-type birth-death (MTBD) models are phylodynamic analogies of compartmental models in classical epidemiology. They serve to infer such epidemiological parameters as the average number of secondary infections Re and the infectious time from a phylogenetic tree (a genealogy of pathogen sequences). The representatives of this model family focus on various aspects of pathogen epidemics. For instance, the birth-death exposed-infectious (BDEI) model describes the transmission of pathogens featuring an incubation period (when there is a delay between the moment of infection and becoming infectious, as for Ebola and SARS-CoV-2), and permits its estimation along with other parameters. With constantly growing sequencing data, MTBD models should be extremely useful for unravelling information on pathogen epidemics. However, existing implementations of these models in a phylodynamic framework have not yet caught up with the sequencing speed. Computing time and numerical instability issues limit their applicability to medium data sets (≤ 500 samples), while the accuracy of estimations should increase with more data. We propose a new highly parallelizable formulation of ordinary differential equations for MTBD models. We also extend them to forests to represent situations when a (sub-)epidemic started from several cases (e.g., multiple introductions to a country). We implemented it for the BDEI model in a maximum likelihood framework using a combination of numerical analysis methods for efficient equation resolution. Our implementation estimates epidemiological parameter values and their confidence intervals in two minutes on a phylogenetic tree of 10,000 samples. Comparison to the existing implementations on simulated data shows that it is not only much faster but also more accurate. An application of our tool to the 2014 Ebola epidemic in Sierra-Leone is also convincing, with very fast calculation and precise estimates. As MTBD models are closely related to Cladogenetic State Speciation and Extinction (ClaSSE)-like models, our findings could also be easily transferred to the macroevolution domain.

Ebola virus disease in children: epidemiology, pathogenesis, management, and prevention.

Ebola disease is a severe disease with extremely high case-fatality rates ranging from 28-100%. Observations made during the 2013-2016 West African epidemic improved our understanding of the clinical course of Ebola disease and accelerated the study of therapeutic and preventative strategies. The epidemic also highlighted the unique challenges associated with providing optimal care for children during Ebola disease outbreaks. In this review, we outline current understanding of Ebola disease epidemiology, pathogenesis, management, and prevention, highlighting data pertinent to the care of children. IMPACT: In this review, we summarize recent advancements in our understanding of Ebola disease epidemiology, clinical presentation, and therapeutic and preventative strategies. We highlight recent data pertinent to the care of children and pregnant women and identify research gaps for this important emerging viral infection in children.

Use of Ebola Vaccines - Worldwide, 2021-2023.

Ebola virus disease (Ebola) is a rare but severe illness in humans, with an average case fatality rate of approximately 50%. Two licensed vaccines are currently available against Orthoebolavirus zairense, the virus that causes Ebola: the 1-dose rVSVΔG-ZEBOV-GP (ERVEBO [Merck]) and the 2-dose regimen of Ad26.ZEBOV and MVA-BN-Filo (Zabdeno/Mvabea [Johnson & Johnson]). The Strategic Advisory Group of Experts on Immunization recommends the use of 1-dose ERVEBO during Ebola outbreaks, and in 2021, a global stockpile of ERVEBO was established to ensure equitable, timely, and targeted access to vaccine doses for future Ebola outbreaks. This report describes the use of Ebola vaccines and the role of the stockpile developed and managed by the International Coordinating Group (ICG) on Vaccine Provision during 2021-2023. A total of 145,690 doses have been shipped from the ICG stockpile since 2021. However, because outbreaks since 2021 have been limited and rapidly contained, most doses (139,120; 95%) shipped from the ICG stockpile have been repurposed for preventive vaccination of high-risk groups, compared with 6,570 (5%) used for outbreak response. Repurposing doses for preventive vaccination could be prioritized in the absence of Ebola outbreaks to prevent transmission and maximize the cost-efficiency and benefits of the stockpile.

Towards a genomics-informed, real-time, global pathogen surveillance system.

The recent Ebola and Zika epidemics demonstrate the need for the continuous surveillance, rapid diagnosis and real-time tracking of emerging infectious diseases. Fast, affordable sequencing of pathogen genomes - now a staple of the public health microbiology laboratory in well-resourced settings - can affect each of these areas. Coupling genomic diagnostics and epidemiology to innovative digital disease detection platforms raises the possibility of an open, global, digital pathogen surveillance system. When informed by a One Health approach, in which human, animal and environmental health are considered together, such a genomics-based system has profound potential to improve public health in settings lacking robust laboratory capacity.

SpatialWavePredict: a tutorial-based primer and toolbox for forecasting growth trajectories using the ensemble spatial wave sub-epidemic modeling framework.

BACKGROUND: Dynamical mathematical models defined by a system of differential equations are typically not easily accessible to non-experts. However, forecasts based on these types of models can help gain insights into the mechanisms driving the process and may outcompete simpler phenomenological growth models. Here we introduce a friendly toolbox, SpatialWavePredict, to characterize and forecast the spatial wave sub-epidemic model, which captures diverse wave dynamics by aggregating multiple asynchronous growth processes and has outperformed simpler phenomenological growth models in short-term forecasts of various infectious diseases outbreaks including SARS, Ebola, and the early waves of the COVID-19 pandemic in the US. RESULTS: This tutorial-based primer introduces and illustrates a user-friendly MATLAB toolbox for fitting and forecasting time-series trajectories using an ensemble spatial wave sub-epidemic model based on ordinary differential equations. Scientists, policymakers, and students can use the toolbox to conduct real-time short-term forecasts. The five-parameter epidemic wave model in the toolbox aggregates linked overlapping sub-epidemics and captures a rich spectrum of epidemic wave dynamics, including oscillatory wave behavior and plateaus. An ensemble strategy aims to improve forecasting performance by combining the resulting top-ranked models. The toolbox provides a tutorial for forecasting time-series trajectories, including the full uncertainty distribution derived through parametric bootstrapping, which is needed to construct prediction intervals and evaluate their accuracy. Functions are available to assess forecasting performance, estimation methods, error structures in the data, and forecasting horizons. The toolbox also includes functions to quantify forecasting performance using metrics that evaluate point and distributional forecasts, including the weighted interval score. CONCLUSIONS: We have developed the first comprehensive toolbox to characterize and forecast time-series data using an ensemble spatial wave sub-epidemic wave model. As an epidemic situation or contagion occurs, the tools presented in this tutorial can facilitate policymakers to guide the implementation of containment strategies and assess the impact of control interventions. We demonstrate the functionality of the toolbox with examples, including a tutorial video, and is illustrated using daily data on the COVID-19 pandemic in the USA.

Understanding the delay in identifying Sudan Virus Disease: gaps in integrated disease surveillance and response and community-based surveillance to detect viral hemorrhagic fever outbreaks in Uganda, September 2022.

BACKGROUND: Early detection of outbreaks requires robust surveillance and reporting at both community and health facility levels. Uganda implements Integrated Disease Surveillance and Response (IDSR) for priority diseases and uses the national District Health Information System (DHIS2) for reporting. However, investigations after the first case in the 2022 Uganda Sudan virus outbreak was confirmed on September 20, 2022 revealed many community deaths among persons with Ebola-like symptoms as far back as August. Most had sought care at private facilities. We explored possible gaps in surveillance that may have resulted in late detection of the Sudan virus disease (SVD) outbreak in Uganda. METHODS: Using a standardized tool, we evaluated core surveillance capacities at public and private health facilities at the hospital level and below in three sub-counties reporting the earliest SVD cases in the outbreak. Key informant interviews (KIIs) were conducted with 12 purposively-selected participants from the district local government. Focus group discussions (FGDs) were conducted with community members from six villages where early probable SVD cases were identified. KIIs and FGDs focused on experiences with SVD and Viral Hemorrhagic Fever (VHF) surveillance in the district. Thematic data analysis was used for qualitative data. RESULTS: Forty-six (85%) of 54 health facilities surveyed were privately-owned, among which 42 (91%) did not report to DHIS2 and 39 (85%) had no health worker trained on IDSR; both metrics were 100% in the eight public facilities. Weak community-based surveillance, poor private facility engagement, low suspicion index for VHF among health workers, inability of facilities to analyze and utilize surveillance data, lack of knowledge about to whom to report, funding constraints for surveillance activities, lack of IDSR training, and lack of all-cause mortality surveillance were identified as gaps potentially contributing to delayed outbreak detection. CONCLUSION: Both systemic and knowledge-related gaps in IDSR surveillance in SVD-affected districts contributed to the delayed detection of the 2022 Uganda SVD outbreak. Targeted interventions to address these gaps in both public and private facilities across Uganda could help avert similar situations in the future.