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

Abstract

SummaryThe 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.

Centrifuge: rapid and sensitive classification of metagenomic sequences

“…Centrifuge's principal advantage is that it provides a combination of fast classification speed and low memory requirements, making it possible to perform large metagenomics analyses on a desktop computer using p or p + h + v index. For example, Centrifuge took only 47 min on a standard desktop computer to analyze 130 paired-end RNA sequencing runs (a total of 26 GB) from patients infected with Ebola virus (Baize et al 2014;Gire et al 2014;Park et al 2015) as described below. Centrifuge's efficient indexing scheme makes it possible to index the NCBI nucleotide collection (nt) database, which is a comprehensive set of sequences (>36 million nonredundant sequences, ∼110 billion bp) collected from viruses, archaea, bacteria, and eukaryotes, and enables rapid and accurate classification of metagenomic samples.…”

“…The 2013-2015 EVD outbreak in West Africa cost the lives of over 11,000 people as of August 26, 2015 (WHO Ebola Situation Report, http://apps.who.int/ebola/ebolasituation-reports). In an international effort to research the disease and stop its spread, several groups sequenced the Ebola virus collected from patients' blood samples and released their data sets online (Baize et al 2014;Gire et al 2014;Park et al 2015). The genomic data were used to trace the disease and mutations in the Ebola genome and inform further public health and research efforts.…”

Essentials of filoviral load quantification

“…Variability in lethality might also relate to factors such as time from disease onset to presentation for care, quality of care available, patient demographics, or variant of infecting virus. 14,[22][23][24] Accurate viral load measurement is also important in interpreting Ebola virus persistence in and transmission risk from immuneprivileged body compartments and fl uids, 25 including the male gonads or semen, [25][26][27][28] eyes, 29 CNS, 30 breast milk, 31 and the intrauterine space in pregnant women. 32 Additionally, Ebola virus has occasionally been found in sweat and urine, 33 and in atypical or asymptomatic cases.…”

“…Measurement of viral load is an important parameter in Marburg virus disease and Ebola virus disease, because viral load correlates with severity of disease, survival, and infectivity. [9][10][11][12][13][14][15][16] Assessing the viral load, and thus the potential infectivity of a patient, can guide triage and admission placement to minimise risk of interperson trans mission. Viral load measurements are also important to better understand the clinical presentation and pathogenesis of fi lovirus disease, and to interpret the effi cacy of candidate therapies and vaccines in animal models and human beings.…”

“…42 For instance, a 1-2 log 10 diff erence in Ebola virus viral load, which may be within the margin of error of RT-PCR testing within and between many laboratories and assays, might correlate with signifi cant diff erences in lethality. 10,[12][13][14] Standard curves, generated by assessing multiple samples with known quantities of fi loviral RNA, can be used to measure the variability of results and off er a better understanding of the meaning of results across laboratories and time points. However, standard curves have rarely been generated under outbreak conditions, probably because of the high numbers of samples processed and to obtain and provide results rapidly.…”