Multiple reassortment events in the evolutionary history of H1N1 influenza A virus since 1918

The H1N1 subtype of influenza A virus has caused substantial morbidity and mortality in humans, first documented in the global pandemic of 1918 and continuing to the present day. Despite this disease burden, the evolutionary history of the A/H1N1 virus is not well understood, particularly whether there is a virological basis for several notable epidemics of unusual severity in the 1940s and 1950s. Using a data set of 71 representative complete genome sequences sampled between 1918 and 2006, we show that segmental reassortment has played an important role in the genomic evolution of A/H1N1 since 1918. Specifically, we demonstrate that an A/H1N1 isolate from the 1947 epidemic acquired novel PB2 and HA genes through intra-subtype reassortment, which may explain the abrupt antigenic evolution of this virus. Similarly, the 1951 influenza epidemic may also have been associated with reassortant A/H1N1 viruses. Intra-subtype reassortment therefore appears to be a more important process in the evolution and epidemiology of H1N1 influenza A virus than previously realized

Influenza and Pneumonia Mortality in 66 Large Cities in the United States in Years Surrounding the 1918 Pandemic

The 1918 influenza pandemic was a major epidemiological event of the twentieth century resulting in at least twenty million deaths worldwide; however, despite its historical, epidemiological, and biological relevance, it remains poorly understood. Here we examine the relationship between annual pneumonia and influenza death rates in the pre-pandemic (1910–17) and pandemic (1918–20) periods and the scaling of mortality with latitude, longitude and population size, using data from 66 large cities of the United States. The mean pre-pandemic pneumonia death rates were highly associated with pneumonia death rates during the pandemic period (Spearman r = 0.64–0.72; P,0.001). By contrast, there was a weak correlation between pre-pandemic and pandemic influenza mortality rates. Pneumonia mortality rates partially explained influenza mortality rates in 1918 (r = 0.34, P = 0.005) but not during any other year. Pneumonia death counts followed a linear relationship with population size in all study years, suggesting that pneumonia death rates were homogeneous across the range of population sizes studied. By contrast, influenza death counts followed a power law relationship with a scaling exponent of ,0.81 (95%CI: 0.71, 0.91) in 1918, suggesting that smaller cities experienced worst outcomes during the pandemic. A linear relationship was observed for all other years. Our study suggests that mortality associated with the 1918–20 influenza pandemic was in part predetermined by pre-pandemic pneumonia death rates in 66 large US cities, perhaps through the impact of the physical and social structure of each city. Smaller cities suffered a disproportionately high per capita influenza mortality burden than larger ones in 1918, while city size did not affect pneumonia mortality rates in the pre-pandemic and pandemic periods

A cross-sectional survey to evaluate knowledge, attitudes and practices (KAP) regarding seasonal influenza vaccination among European travellers to resource-limited destinations

BACKGROUND: Influenza is one of the most common vaccine-preventable diseases in travellers. By performing two cross-sectional questionnaire surveys during winter 2009 and winter 2010 among European travellers to resource-limited destinations, we aimed to investigate knowledge, attitudes and practices (KAP) regarding seasonal influenza vaccination. METHODS: Questionnaires were distributed in the waiting room to the visitors of the University of Zurich Centre for Travel' Health (CTH) in January and February 2009 and January 2010 prior to travel health counselling (CTH09 and CTH10). Questions included demographic data, travel-related characteristics and KAP regarding influenza vaccination. Data were analysed by using SPSS version 14.0 for Windows. Differences in proportions were compared using the Chi-square test and the significance level was set at p 64 yrs (25, 21%) and recommendations of the family physician (27, 22.7%) were the most often reported reasons for being vaccinated. In the multiple logistic regression analyses of the pooled data increasing age (OR = 1.03, 95% CI 1.01 - 1.04), a business trip (OR = 0.39, 95% CI 0.17 - 0.92) and seasonal influenza vaccination in the previous winter seasons (OR = 12.91, 95% CI 8.09 - 20.58) were independent predictors for seasonal influenza vaccination in 2009 or 2010.Influenza vaccination recommended by the family doctor (327, 37.7%), travel to regions with known high risk of influenza (305, 35.1%), and influenza vaccination required for job purposes (233, 26.8%) were most frequently mentioned to consider influenza vaccination. CONCLUSIONS: Risk perception and vaccination coverage concerning seasonal and pandemic influenza was very poor among travellers to resource-limited destinations when compared to traditional at-risk groups. Previous access to influenza vaccination substantially facilitated vaccinations in the subsequent year. Information strategies about influenza should be intensified and include health professionals, e.g. family physicians, travel medicine practitioners and business enterprises

Using Non-Homogeneous Models of Nucleotide Substitution to Identify Host Shift Events: Application to the Origin of the 1918 ‘Spanish’ Influenza Pandemic Virus

Nonhomogeneous Markov models of nucleotide substitution have received scant attention. Here we explore the possibility of using nonhomogeneous models to identify host shift nodes along phylogenetic trees of pathogens evolving in different hosts. It has been noticed that influenza viruses show marked differences in nucleotide composition in human and avian hosts. We take advantage of this fact to identify the host shift event that led to the 1918 ‘Spanish’ influenza. This disease killed over 50 million people worldwide, ranking it as the deadliest pandemic in recorded history. Our model suggests that the eight RNA segments which eventually became the 1918 viral genome were introduced into a mammalian host around 1882–1913. The viruses later diverged into the classical swine and human H1N1 influenza lineages around 1913–1915. The last common ancestor of human strains dates from February 1917 to April 1918. Because pigs are more readily infected with avian influenza viruses than humans, it would seem that they were the original recipient of the virus. This would suggest that the virus was introduced into humans sometime between 1913 and 1918

Excess healthcare burden during 1918-1920 influenza pandemic in Taiwan: implications for post-pandemic preparedness

<p>Abstract</p> <p>Background</p> <p>It is speculated that the 2009 pandemic H1N1 influenza virus might fall into a seasonal pattern during the current post-pandemic period with more severe clinical presentation for high-risk groups identified during the 2009 pandemic. Hence the extent of likely excess healthcare needs during this period must be fully considered. We will make use of the historical healthcare record in Taiwan during and after the 1918 influenza pandemic to ascertain the scope of potential excess healthcare burden during the post-pandemic period.</p> <p>Methods</p> <p>To establish the healthcare needs after the initial wave in 1918, the yearly healthcare records (hospitalizations, outpatients, etc.) in Taiwan during 1918-1920 are compared with the corresponding data from the adjacent "baseline" years of 1916, 1917, 1921, and 1922 to estimate the excess healthcare burden during the initial outbreak in 1918 and in the years immediately after.</p> <p>Results</p> <p>In 1918 the number of public hospital outpatients exceeded the yearly average of the baseline years by 20.11% (95% CI: 16.43, 25.90), and the number of hospitalizations exceeded the corresponding yearly average of the baseline years by 12.20% (10.59, 14.38), while the excess number of patients treated by the public medics was statistically significant at 32.21% (28.48, 39.82) more than the yearly average of the baseline years. For 1920, only the excess number of hospitalizations was statistically significant at 19.83% (95% CI: 17.21, 23.38) more than the yearly average of the baseline years.</p> <p>Conclusions</p> <p>Considerable extra burden with significant loss of lives was reported in 1918 by both the public medics system and the public hospitals. In comparison, only a substantial number of excess hospitalizations in the public hospitals was reported in 1920, indicating that the population was relatively unprepared for the first wave in 1918 and did not fully utilize the public hospitals. Moreover, comparatively low mortality was reported by the public hospitals and the public medics during the second wave in 1920 even though significantly more patients were hospitalized, suggesting that there had been substantially less fatal illnesses among the hospitalized patients during the second wave. Our results provide viable parameters for assessing healthcare needs for post-pandemic preparedness.</p

Highly Pathogenic H5N1 Avian Influenza: Entry Pathways into North America via Bird Migration

Given the possibility of highly pathogenic H5N1 avian influenza arriving in North America and monitoring programs that have been established to detect and track it, we review intercontinental movements of birds. We divided 157 bird species showing regular intercontinental movements into four groups based on patterns of movement—one of these groups (breed Holarctic, winter Eurasia) fits well with the design of the monitoring programs (i.e., western Alaska), but the other groups have quite different movement patterns, which would suggest the importance of H5N1 monitoring along the Pacific, Atlantic, and Gulf coasts of North America

Identification of human-to-human transmissibility factors in PB2 proteins of influenza A by large-scale mutual information analysis

<p>Abstract</p> <p>Background</p> <p>The identification of mutations that confer unique properties to a pathogen, such as host range, is of fundamental importance in the fight against disease. This paper describes a novel method for identifying amino acid sites that distinguish specific sets of protein sequences, by comparative analysis of matched alignments. The use of mutual information to identify distinctive residues responsible for functional variants makes this approach highly suitable for analyzing large sets of sequences. To support mutual information analysis, we developed the AVANA software, which utilizes sequence annotations to select sets for comparison, according to user-specified criteria. The method presented was applied to an analysis of influenza A PB2 protein sequences, with the objective of identifying the components of adaptation to human-to-human transmission, and reconstructing the mutation history of these components.</p> <p>Results</p> <p>We compared over 3,000 PB2 protein sequences of human-transmissible and avian isolates, to produce a catalogue of sites involved in adaptation to human-to-human transmission. This analysis identified 17 characteristic sites, five of which have been present in human-transmissible strains since the 1918 Spanish flu pandemic. Sixteen of these sites are located in functional domains, suggesting they may play functional roles in host-range specificity. The catalogue of characteristic sites was used to derive sequence signatures from historical isolates. These signatures, arranged in chronological order, reveal an evolutionary timeline for the adaptation of the PB2 protein to human hosts.</p> <p>Conclusion</p> <p>By providing the most complete elucidation to date of the functional components participating in PB2 protein adaptation to humans, this study demonstrates that mutual information is a powerful tool for comparative characterization of sequence sets. In addition to confirming previously reported findings, several novel characteristic sites within PB2 are reported. Sequence signatures generated using the characteristic sites catalogue characterize concisely the adaptation characteristics of individual isolates. Evolutionary timelines derived from signatures of early human influenza isolates suggest that characteristic variants emerged rapidly, and remained remarkably stable through subsequent pandemics. In addition, the signatures of human-infecting H5N1 isolates suggest that this avian subtype has low pandemic potential at present, although it presents more human adaptation components than most avian subtypes.</p

Large-Scale Phylogenetic Analysis of Emerging Infectious Diseases

Microorganisms that cause infectious diseases present critical issues of national security, public health, and economic welfare.  For example, in recent years, highly pathogenic strains of avian influenza have emerged in Asia, spread through Eastern Europe and threaten to become pandemic. As demonstrated by the coordinated response to Severe Acute Respiratory Syndrome (SARS) and influenza, agents of infectious disease are being addressed via large-scale genomic sequencing.  The goal of genomic sequencing projects are to rapidly put large amounts of data in the public domain to accelerate research on disease surveillance, treatment, and prevention. However, our ability to derive information from large comparative genomic datasets lags far behind acquisition.  Here we review the computational challenges of comparative genomic analyses, specifically sequence alignment and reconstruction of phylogenetic trees.  We present novel analytical results on from two important infectious diseases, Severe Acute Respiratory Syndrome (SARS) and influenza.SARS and influenza have similarities and important differences both as biological and comparative genomic analysis problems.  Influenza viruses (Orthymxyoviridae) are RNA based.  Current evidence indicates that influenza viruses originate in aquatic birds from wild populations. Influenza has been studied for decades via well-coordinated international efforts.  These efforts center on surveillance via antibody characterization of the hemagglutinin (HA) and neuraminidase (N) proteins of the circulating strains to inform vaccine design. However we still do not have a clear understanding of: 1) various transmission pathways such as the role of intermediate hosts such as swine and domestic birds and 2) the key mutation and genomic recombination events that underlie periodic pandemics of influenza.  In the past 30 years, sequence data from HA and N loci has become an important data type. In the past year, full genomic data has become prominent.  These data present exciting opportunities to address unanswered questions in influenza pandemics.SARS is caused by a previously unrecognized lineage of coronavirus, SARS-CoV, which like influenza has an RNA based genome.  Although SARS-CoV is widely believed to have originated in animals there remains disagreement over the candidate animal source that lead to the original outbreak of SARS.  In contrast to the long history of the study of influenza, SARS was only recognized in late 2002 and the virus that causes SARS has been documented primarily by genomic sequencing.In the past, most studies of influenza were performed on a limited number of isolates and genes suited to a particular problem.  Major goals in science today are to understand emerging diseases in broad geographic, environmental, societal, biological, and genomic contexts. Synthesizing diverse information brought together by various researchers is important to find out what can be done to prevent future outbreaks {JON03}.  Thus comprehensive means to organize and analyze large amounts of diverse information are critical.  For example, the relationships of isolates and patterns of genomic change observed in large datasets might not be consistent with hypotheses formed on partial data.  Moreover when researchers rely on partial datasets, they restrict the range of possible discoveries.Phylogenetics is well suited to the complex task of understanding emerging infectious disease. Phylogenetic analyses can test many hypotheses by comparing diverse isolates collected from various hosts, environments, and points in time and organizing these data into various evolutionary scenarios.  The products of a phylogenetic analysis are a graphical tree of ancestor-descendent relationships and an inferred summary of mutations, recombination events, host shifts, geographic, and temporal spread of the viruses.  However, this synthesis comes at a price.  The cost of computation of phylogenetic analysis expands combinatorially as the number of isolates considered increases. Thus, large datasets like those currently produced are commonly considered intractable.  We address this problem with synergistic development of heuristics tree search strategies and parallel computing.Fil: Janies, D.. Ohio State University; Estados UnidosFil: Pol, Diego. Ohio State University; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin