OpenFluDB, a database for human and animal influenza virus

Although research on influenza lasted for more than 100 years, it is still one of the most prominent diseases causing half a million human deaths every year. With the recent observation of new highly pathogenic H5N1 and H7N7 strains, and the appearance of the influenza pandemic caused by the H1N1 swine-like lineage, a collaborative effort to share observations on the evolution of this virus in both animals and humans has been established. The OpenFlu database (OpenFluDB) is a part of this collaborative effort. It contains genomic and protein sequences, as well as epidemiological data from more than 27 000 isolates. The isolate annotations include virus type, host, geographical location and experimentally tested antiviral resistance. Putative enhanced pathogenicity as well as human adaptation propensity are computed from protein sequences. Each virus isolate can be associated with the laboratories that collected, sequenced and submitted it. Several analysis tools including multiple sequence alignment, phylogenetic analysis and sequence similarity maps enable rapid and efficient mining. The contents of OpenFluDB are supplied by direct user submission, as well as by a daily automatic procedure importing data from public repositories. Additionally, a simple mechanism facilitates the export of OpenFluDB records to GenBank. This resource has been successfully used to rapidly and widely distribute the sequences collected during the recent human swine flu outbreak and also as an exchange platform during the vaccine selection procedure. Database URL: http://openflu.vital-it.ch

Influenza research database: an integrated bioinformatics resource for influenza research and surveillance.

BackgroundThe recent emergence of the 2009 pandemic influenza A/H1N1 virus has highlighted the value of free and open access to influenza virus genome sequence data integrated with information about other important virus characteristics.DesignThe Influenza Research Database (IRD, http://www.fludb.org) is a free, open, publicly-accessible resource funded by the U.S. National Institute of Allergy and Infectious Diseases through the Bioinformatics Resource Centers program. IRD provides a comprehensive, integrated database and analysis resource for influenza sequence, surveillance, and research data, including user-friendly interfaces for data retrieval, visualization and comparative genomics analysis, together with personal log in-protected 'workbench' spaces for saving data sets and analysis results. IRD integrates genomic, proteomic, immune epitope, and surveillance data from a variety of sources, including public databases, computational algorithms, external research groups, and the scientific literature.ResultsTo demonstrate the utility of the data and analysis tools available in IRD, two scientific use cases are presented. A comparison of hemagglutinin sequence conservation and epitope coverage information revealed highly conserved protein regions that can be recognized by the human adaptive immune system as possible targets for inducing cross-protective immunity. Phylogenetic and geospatial analysis of sequences from wild bird surveillance samples revealed a possible evolutionary connection between influenza virus from Delaware Bay shorebirds and Alberta ducks.ConclusionsThe IRD provides a wealth of integrated data and information about influenza virus to support research of the genetic determinants dictating virus pathogenicity, host range restriction and transmission, and to facilitate development of vaccines, diagnostics, and therapeutics

Modelling the species jump: towards assessing the risk of human infection from novel avian influenzas

The scientific understanding of the driving factors behind zoonotic and pandemic influenzas is hampered by complex interactions between viruses, animal hosts and humans. This complexity makes identifying influenza viruses of high zoonotic or pandemic risk, before they emerge from animal populations, extremely difficult and uncertain. As a first step towards assessing zoonotic risk of Influenza, we demonstrate a risk assessment framework to assess the relative likelihood of influenza A viruses, circulating in animal populations, making the species jump into humans. The intention is that such a risk assessment framework could assist decisionmakers to compare multiple influenza viruses for zoonotic potential and hence to develop appropriate strain-specific control measures. It also provides a first step towards showing proof of principle for an eventual pandemic risk model. We show that the spatial and temporal epidemiology is as important in assessing the risk of an influenza A species jump as understanding the innate molecular capability of the virus.We also demonstrate data deficiencies that need to be addressed in order to consistently combine both epidemiological and molecular virology data into a risk assessment framework

The Impact of Bioinformatics on Vaccine Design and Development

Vaccines are the pharmaceutical products that offer the best cost‐benefit ratio in the prevention or treatment of diseases. In that a vaccine is a pharmaceutical product, vaccine development and production are costly and it takes years for this to be accomplished. Several approaches have been applied to reduce the times and costs of vaccine development, mainly focusing on the selection of appropriate antigens or antigenic structures, carriers, and adjuvants. One of these approaches is the incorporation of bioinformatics methods and analyses into vaccine development. This chapter provides an overview of the application of bioinformatics strategies in vaccine design and development, supplying some successful examples of vaccines in which bioinformatics has furnished a cutting edge in their development. Reverse vaccinology, immunoinformatics, and structural vaccinology are described and addressed in the design and development of specific vaccines against infectious diseases caused by bacteria, viruses, and parasites. These include some emerging or re‐emerging infectious diseases, as well as therapeutic vaccines to fight cancer, allergies, and substance abuse, which have been facilitated and improved by using bioinformatics tools or which are under development based on bioinformatics strategies

Clade-level Spatial Modelling of HPAI H5N1 Dynamics in the Mekong Region Reveals New Patterns and Associations with Agro-Ecological Factors.

The highly pathogenic avian influenza (HPAI) H5N1 virus has been circulating in Asia since 2003 and diversified into several genetic lineages, or clades. Although the spatial distribution of its outbreaks was extensively studied, differences in clades were never previously taken into account. We developed models to quantify associations over time and space between different HPAI H5N1 viruses from clade 1, 2.3.4 and 2.3.2 and agro-ecological factors. We found that the distribution of clades in the Mekong region from 2004 to 2013 was strongly regionalised, defining specific epidemiological zones, or epizones. Clade 1 became entrenched in the Mekong Delta and was not supplanted by newer clades, in association with a relatively higher presence of domestic ducks. In contrast, two new clades were introduced (2.3.4 and 2.3.2) in northern Viet Nam and were associated with higher chicken density and more intensive chicken production systems. We suggest that differences in poultry production systems in these different epizones may explain these associations, along with differences in introduction pressure from neighbouring countries. The different distribution patterns found at the clade level would not be otherwise apparent through analysis treating all outbreaks equally, which requires improved linking of disease outbreak records and genetic sequence data

Virus Evol

Influenza databases now contain over 100,000 worldwide sequence records for strains influenza A(H3N2) and A(H1N1). Although these data facilitate global research efforts and vaccine development practices, they also represent a stumbling block for researchers because of their confusing and heterogeneous annotation. Unclear passaging annotations are particularly concerning given the recent work highlighting the presence and risk of false adaptation signals introduced by cell passaging of viral isolates. With this in mind, we aim to provide a concise outline of why viruses are passaged, a clear overview of passaging annotation nomenclature currently in use, and suggestions for a standardized nomenclature going forward. Our hope is that this summary will empower researchers and clinicians alike to more easily understand a virus sample's passage history when analyzing influenza sequences.2019-06-30T00:00:00Z31275610PMC6599686783

Análisis del genoma de influenza aviar H7N3 de la Epizootia 2012-2015 en México

El virus de la influenza aviar del tipo A H7N3 es una problemática que afecta con mayor impacto a las aves de corral donde pueden adquirir un fenotipo de influenza de alta patogenicidad -- La problemática se ve reflejada cuando estas aves de corral de muchas granjas se ven afectadas por este virus y mueren por la enfermedad o son sacrificadas por bioseguridad lo que se traduce a una pérdida económica y un riesgo en la seguridad alimentaria de muchas poblaciones, siendo los más vulnerables los pequeños productores que abastecen a mercados locales -- Para el caso del brote en México, se reportaron 4.9 millones de aves sacrificadas, donde 358 granjas estaban en riesgo de contagio de sus aves (Aproximadamente 17 millones de aves de corral), esto implicó una pérdida de US$750 millones que se destinaron para atender este caso en particular y los casos que se presentaron hasta el fin del año 2012 -- También hay importancia en el monitoreo a nivel genómico de las cepas H7N3 porque eventualmente pueden ocurrir mutaciones que favorezcan la infección a humanos -- El análisis bioinformático tiene el objetivo de estudiar las posibles mutaciones que desarrolla el virus y comprender bajo qué tipo de selección evolutiva está sometido, este estudio ayuda a entender cómo este agente patógeno desarrolla habilidades para escapar o tener un mayor éxito frente al sistema inmune de su hospedero, además de identificar las posibles diferencias que puedan existir a nivel de genoma entre cada una de los 10 aislamientos comparado con el genoma de referencia y el empleado para el desarrollo de la vacuna empleada durante el brote, adicionalmente se realiza análisis filogenético con estos 10 aislamientos -- Para el desarrollo de este trabajo de grado se emplearon herramientas como GS Mapper, clustal W, Swiss-Model, Pymol, Phypipe, RaxML y Mr Bayes, estas herramientas permiten el cumplimiento de los objetivos propuestos para cada uno de los aislamientos proporcionados por el Instituto de Salud pública de México -- Se obtuvo como resultado que existe una amplia diversificación de la cepa vacunal comparado con los genomas de este estudio, las mutaciones de hemaglutinina presentaron una selección positiva y hay indicios de que el brote del 2012 pudo provenir de los Estados Unido

INVESTIGATION ON THE IMMUNOLOGICAL SPECIFICITY OF CORONA VIRUS UTILIZING BIOINFORMATICS METHOD

학위논문 (석사)-- 서울대학교 보건대학원 : 보건학과 생명정보학전공, 2016. 8. 손현석.새로운 감염병의 창궐은 공중 보건에 심각한 위협이고 해결해야 할 과제이다. 2015년 한국에 출현한 중동 호흡기 증후군을 일으킨 코로나 바이러스(MERS-CoV)는 국민과 보건당국에 혼란을 주었고, 사회경제적으로 심각한 손실을 초래 하였다. 결과적으로, 한국의 감염병 대응 체계와 의료서비스 환경의 문제점을 인식하게 되었으며, 고위험성 바이러스 감염에 대처하는 한국의 보건 현황과 한계점을 보여 주었고, 치료제와 백신의 개발이라는 과제를 남겨 주었다. 60년전에 처음 발견된 코로나 바이러스는 2003년 사람에게 치명적인 호흡기 감염을 일으키는 SARS-CoV가 유행 되면서 세상의 주목을 받게 되었다. 그 후 코로나 바이러스의 병원성 및 숙주의 면역반응에 관한 이해가 필요함을 재조명하게 되었다. 전 세계적으로 코로나 바이러스에 대한 연구가 진행이 되고 있어서, 코로나 바이러스의 분자 생물학적인 결과와 면역 기전이 밝혀지고는 있지만, 아직 정보들이 산재되어 있고, 기존에 구축된 데이터베이스들 또한 접근과 이용이 용이하지 않다. 따라서 본 연구에서는 코로나 바이러스와 면역 관련 단백질들의 생물학적 특징 및 유전자 정보를 제공하는 HCoV-IMDB를 구축하여 연구자들 에게 유용한 정보를 제공해 주고자 한다. 구축된 데이터베이스의 자료를 기반으로 계통 발생 분석을 시행하여 기존에 알려지지 않았던 코로나 바이러스의 위험성을 예측한 결과, HCoV-OC43의 S 단백질이 MERS-CoV의 S단백질과 진화적으로 유사하였으며, 이를 통하여 HCoV-OC43의 변이 여부의 지속적인 감시가 필요함을 제시하였다. 본 연구는 새로운 바이러스의 창궐과 기존의 바이러스의 감시 등에 이용 할 수 있는 바이오인포매틱스적인 분석 방법을 제공함으로써 예방을 위한 보건학적 방법을 제시하였다. 또한, 코로나 바이러스의 면역 특이성 분석을 위하여, 네트워크 분석을 시행한 결과, 사람 단백질의 IC1 단백질과 SARS-CoV의 비구조 단백질들 사이의 상호 작용을 확인하였다. IC1은 면역기전 중 보체계의 활성, 혈액응고, 피브린의 분해와 키닌 합성 등의 물리적인 기전에 관여하는 단백질로써 앞으로 연구가 더 진행이 된다면, 아직 밝혀지지 않은 코로나 바이러스의 면역 기전의 이해에 대한 실마리를 제공해 주고 이는 항생제나 백신의 개발에 도움을 줄 수 있을 것이다. 더 나아가서는 개인의 유전적 차이에 적합한 개인 맞춤형 치료제의 개발에 기반이 되는 연구 자료가 될 것이다. 본 연구에서 이용한 네트워크 분석 방법은 기존의 면역학적 연구 결과들을 통합하여 보여 줌으로써 간과하고 넘어갈 수 있는 부분의 해석이 가능하며, 이러한 연구는 시스템적인 면역 기전의 이해에 기초로 사용될 수 있을 것이다.제 1장. 서 론 1 1.1 연구배경 1 1.1.1 코로나 바이러스의 특징 1 1.1.2 코로나 바이러스의 유행 5 1.1.3 코로나 바이러스의 연구현황 8 1.2 코로나 바이러스와 숙주의 상호작용 11 1.2.1 바이러스와 면역시스템의 작용 11 1.2.2 바이러스와 상호작용하는 면역 단백질 12 1.3 코로나 바이러스에 대한 생명정보학적 분석 17 1.3.1 바이러스와 면역 데이터베이스 구축 현황 17 1.3.2 계통수 분석법 19 1.3.3 네트워크 분석법 20 1.4 연구의 필요성 23 1.5 연구의 목적 26 제 2장. 연구 방법 36 2.1 데이터 수집 및 가공 36 2.2 시스템 개발 환경 및 BLAST 서버 구축 38 2.3 데이터베이스 구현 39 2.4 계통수 작성 41 2.5 네트워크 작성 45 제 3장. 연구 결과 50 3.1 면역 특이성 데이터베이스 구축 50 3.2 데이터베이스 기반 계통수 분석 52 3.3 데이터베이스 기반 네트워크 분석 53 제 4장. 고 찰 63 4.1 연구 결과의 고찰 63 4.2 연구의 한계점 및 향후 연구의 제안 69 4.2.1 연구의 한계점 69 4.2.2향후 연구의 제안 71 4.3 기대성과 73 4.4 보건학적 활용방안 74 제 5장. 결론 및 총론 76 5.1 결 론 76 5.2 총 론 79 참고 문헌 80 Abstract 95 부록 : 계통수 분석 결과 97Maste

Applications of large, heterogeneous datasets in understanding and treating pathogenic microbes

Major advances in a myriad of technologies over the past two decades have led to a remarkable increase in the generation of biological data. In response to this increase, researchers have developed methods to pool and analyze large, heterogeneous datasets for novel insights. Here I do just that, leveraging existing data to expand our understanding of therapeutic proteins and pathogenic microbes. In Chapter 2 I outline major shortcomings in existing viral annotation standards using metadata from all influenza A sequences submitted to the GISAID database between 2005 and 2018. I further establish updated nomenclature standards to improve annotation accuracy moving forward. In Chapter 3 I use published Vibrio cholerae sequencing data to derive a comprehensive gene coexpression network. This network provides direct insights into genes influencing pathogenicity, metabolism, and transcriptional regulation, further clarifies results from previous sequencing experiments in V. cholerae, and expands upon micro-array based findings in related gram-negative bacteria. In Chapter 4 I systematically probe all 49,000 unique beta hairpin substructures contained within the Protein Data Bank to uncover key characteristics correlated with stable beta hairpin structure, including amino acid biases and enriched inter-strand contacts. I also establish a set of broad design principles that can be applied to the generation of libraries encoding bioactive proteins. These findings highlight the untapped potential, promise, and power of pooled analyses using large, heterogeneous datasetsCellular and Molecular Biolog

Applications of Next Generation and Nanopore Sequencing for Infectious Disease Identification and Antimicrobial Resistance Detection

Infectious diseases are a major problem worldwide, in no small part due to the growing prominence of antimicrobial resistance. Current methods for species identification rely on microbiology culturing techniques and nucleic acid tests, which are time consuming, may require some a priori knowledge of infectious agents, and are limited in the information provided. To address some of these limitations, clinical diagnostics laboratories have been applying shotgun DNA sequencing for disease detection. Traditionally, metagenomic sequencing directly from clinical specimens has not been as widely used in infectious disease, due to the costs associated with producing and analyzing the data. However, sequencing is now becoming more affordable and integrated into the clinical setting. One such example is the recently released MinION sequencer from Oxford Nanopore, a portable, low-cost sequencer that connects to standard personal computers via USB. We are examining the application of the Oxford Nanopore MinION as a diagnostic aid for detecting pathogenic organisms in infectious disease, as well as acquisition of antimicrobial resistance. We hope to develop laboratory tests to identify and characterize infection causing organisms. The work for this master’s thesis focused specifically on developing and optimizing sequencing and computational techniques that can be applicable to infectious disease diagnostics. We illustrate our progress using three separate cases: 1) detection of vancomycin and carbapenem resistance in pathogens from remnant rectal swabs, 2) a clinical case study involving an extensively drug resistant strain of K. pneumoniae, and 3) long-read sequencing of clinical influenza samples. We hope to leverage the MinION’s versatility and sequence samples both from a clinical laboratory standpoint, as well as on site to locations of outbreaks