The Role of Genomics in Tracking the Evolution of Influenza A Virus

Influenza A virus causes annual epidemics and occasional pandemics of short-term respiratory infections associated with considerable morbidity and mortality. The pandemics occur when new human-transmissible viruses that have the major surface protein of influenza A viruses from other host species are introduced into the human population. Between such rare events, the evolution of influenza is shaped by antigenic drift: the accumulation of mutations that result in changes in exposed regions of the viral surface proteins. Antigenic drift makes the virus less susceptible to immediate neutralization by the immune system in individuals who have had a previous influenza infection or vaccination. A biannual reevaluation of the vaccine composition is essential to maintain its effectiveness due to this immune escape. The study of influenza genomes is key to this endeavor, increasing our understanding of antigenic drift and enhancing the accuracy of vaccine strain selection. Recent large-scale genome sequencing and antigenic typing has considerably improved our understanding of influenza evolution: epidemics around the globe are seeded from a reservoir in East-Southeast Asia with year-round prevalence of influenza viruses; antigenically similar strains predominate in epidemics worldwide for several years before being replaced by a new antigenic cluster of strains. Future in-depth studies of the influenza reservoir, along with large-scale data mining of genomic resources and the integration of epidemiological, genomic, and antigenic data, should enhance our understanding of antigenic drift and improve the detection and control of antigenically novel emerging strains

Inference of Genotype–Phenotype Relationships in the Antigenic Evolution of Human Influenza A (H3N2) Viruses

Distinguishing mutations that determine an organism's phenotype from (near-) neutral ‘hitchhikers’ is a fundamental challenge in genome research, and is relevant for numerous medical and biotechnological applications. For human influenza viruses, recognizing changes in the antigenic phenotype and a strains' capability to evade pre-existing host immunity is important for the production of efficient vaccines. We have developed a method for inferring ‘antigenic trees’ for the major viral surface protein hemagglutinin. In the antigenic tree, antigenic weights are assigned to all tree branches, which allows us to resolve the antigenic impact of the associated amino acid changes. Our technique predicted antigenic distances with comparable accuracy to antigenic cartography. Additionally, it identified both known and novel sites, and amino acid changes with antigenic impact in the evolution of influenza A (H3N2) viruses from 1968 to 2003. The technique can also be applied for inference of ‘phenotype trees’ and genotype–phenotype relationships from other types of pairwise phenotype distances

A probabilistic model to recover individual genomes from metagenomes

Dröge J, Schönhuth A, McHardy AC. A probabilistic model to recover individual genomes from metagenomes. PeerJ Computer Science. 2017;3: e117.Shotgun metagenomics of microbial communities reveal information about strains of relevance for applications in medicine, biotechnology and ecology. Recovering their genomes is a crucial but very challenging step due to the complexity of the underlying biological system and technical factors. Microbial communities are heterogeneous, with oftentimes hundreds of present genomes deriving from different species or strains, all at varying abundances and with different degrees of similarity to each other and reference data. We present a versatile probabilistic model for genome recovery and analysis, which aggregates three types of information that are commonly used for genome recovery from metagenomes. As potential applications we showcase metagenome contig classification, genome sample enrichment and genome bin comparisons. The open source implementation MGLEX is available via the Python Package Index and on GitHub and can be embedded into metagenome analysis workflows and programs.</jats:p

Inferring functional modules of protein families with probabilistic topic models

<p>Abstract</p> <p>Background</p> <p>Genome and metagenome studies have identified thousands of protein families whose functions are poorly understood and for which techniques for functional characterization provide only partial information. For such proteins, the genome context can give further information about their functional context.</p> <p>Results</p> <p>We describe a Bayesian method, based on a probabilistic topic model, which directly identifies functional modules of protein families. The method explores the co-occurrence patterns of protein families across a collection of sequence samples to infer a probabilistic model of arbitrarily-sized functional modules.</p> <p>Conclusions</p> <p>We show that our method identifies protein modules - some of which correspond to well-known biological processes - that are tightly interconnected with known functional interactions and are different from the interactions identified by pairwise co-occurrence. The modules are not specific to any given organism and may combine different realizations of a protein complex or pathway within different taxa.</p

A probabilistic model to recover individual genomes from metagenomes

Shotgun metagenomics of microbial communities reveal information about strains of relevance for applications in medicine, biotechnology and ecology. Recovering their genomes is a crucial but very challenging step due to the complexity of the underlying biological system and technical factors. Microbial communities are heterogeneous, with oftentimes hundreds of present genomes deriving from different speci

The impact of seasonal and year-round transmission regimes on the evolution of influenza A virus

Punctuated antigenic change is believed to be a key element in the evolution of influenza A; clusters of antigenically similar strains predominate worldwide for several years until an antigenically distant mutant emerges and instigates a selective sweep. It is thought that a region of East–Southeast Asia with year-round transmission acts as a source of antigenic diversity for influenza A and seasonal epidemics in temperate regions make little contribution to antigenic evolution. We use a mathematical model to examine how different transmission regimes affect the evolutionary dynamics of influenza over the lifespan of an antigenic cluster. Our model indicates that, in non-seasonal regions, mutants that cause significant outbreaks appear before the peak of the wild-type epidemic. A relatively large proportion of these mutants spread globally. In seasonal regions, mutants that cause significant local outbreaks appear each year before the seasonal peak of the wild-type epidemic, but only a small proportion spread globally. The potential for global spread is strongly influenced by the intensity of non-seasonal circulation and coupling between non-seasonal and seasonal regions. Results are similar if mutations are neutral, or confer a weak to moderate antigenic advantage. However, there is a threshold antigenic advantage, depending on the non-seasonal transmission intensity, beyond which mutants can escape herd immunity in the non-seasonal region and there is a global explosion in diversity. We conclude that non-seasonal transmission regions are fundamental to the generation and maintenance of influenza diversity owing to their epidemiology. More extensive sampling of viral diversity in such regions could facilitate earlier identification of antigenically novel strains and extend the critical window for vaccine development

ANALISA PENGARUH VARIASI SUDUT MIXING CHAMBER TERHADAP ENTRAINMENT RATIO DAN DISTRIBUSI TEKANAN PADA STEAM EJECTOR DENGAN MENGGUNAKAN CFD

Pada situasi krisis energi seperti sekarang ini ejector refrigeration dapat sebagai solusi yang tepat, karena ejector merupakan alat yang digunakan untuk menggerakkan fluida dengan jalan memanfaatkan aliran fluida lain. Fluida yang digunakan untuk mendorong aliran fluida lain disebut primary fluid, sedangkan fluida yang terdorong disebut fluida isap/secondary fluid. Steam ejector banyak digunakan pada industri misalnya pada proses pendinginan fluida, proses pevakuman dan sebagainya. Keuntungan dari steam ejector di banding sistem-sistem yang lain adalah steam ejector memerlukan sedikit sumber energi, umur komponen tahan lama, karena tidak ada komponen yang bergerak, ramah lingkungan, karena menggunakan fluida air. Adapun kelemahan steam ejector adalah COP yang dihasilkan rendah. Entrainment ratio akan mempengaruhi nilai COP pada sistem, bila menginginkan nilai COP tinggi maka harus meningkatkan entrainment ratio dan untuk meningkatkan nilai entrainment ratio, dengan memvariasi geometri ejector. Tujuan penelitian ini adalah untuk mengetahui pengaruh variasi sudut mixing chamber terhadap entrainment ratio dan distribusi tekanan pada steam ejector dengan menggunakan CFD ( Computational Fluid Dynamics ) Geometri yang divariasikan adalah pada sudut Mixing Chamber yaitu: 3,5o,5o,7o dan 13o. Dari penelitian ini dihasilkan pada eksperimen nilai entrainment ratio (ω) paling tinggi pada sudut mixing chamber 7° menghasilkan entrainment ratio (ω)= 0,71 pada tekanan boiler 5 . Pada simulasi nilai entrainment ratio (ω) paling tinggi terjadi pada sudut mixing chamber 7° menghasilkan entrainment ratio (ω)= 3,32 pada tekanan boiler 5 . Semakin tinggi tekanan boiler, semakin tinggi kecepatan yang terjadi di sepanjang ejector maka semakin besar daerah vakum yang dihasilkan. Hal ini yang menyebabkan laju aliran massa secondary flow yang terhisap semakin banyak sehingga semakin besar nilai entrainment ratio yang dihasilkan. Pada Eksperimen Distribusi tekanan terjadi sepanjang ejector dan pada pengujian didapat nilai maximum pada titik 9 pada throat sebesar 104693 Pa yaitu pada sudut mixing chamber 7° pada tekanan boiler 5 . Pada simulasi distribusi tekanan terjadi sepanjang ejector dan pada pengujian didapat nilai maximum pada throat sebesar 101410,4 Pa yaitu pada sudut mixing chamber 7° pada tekanan boiler 5 .Kata kunci : COP, entrainment ratio, mixing chamber, steam ejecto