SARS Coronavirus Detection

We developed a set of three real-time reverse transcription–polymerase chain reaction (PCR) assays that amplify three different regions of the SARS-associated coronavirus (SARS-CoV), can be run in parallel or in a single tube, and can detect <10 genome equivalents of SARS-CoV. The assays consider all currently available SARS-CoV sequences and are optimized for two prominent real-time PCR platforms

Dr. Yang Zhong: an explorer on the road forever

On the morning of September 25th 2017, grievous news spread from the remote Ordos region of Inner Mongolia to Fudan University campus in Shanghai. Professor Yang Zhong, a famous botanist and the Dean of Fudan University’s graduate school, passed away in a tragic car accident while on a business trip

Design of Wide-Spectrum Inhibitors Targeting Coronavirus Main Proteases

The genus Coronavirus contains about 25 species of coronaviruses (CoVs), which are important pathogens causing highly prevalent diseases and often severe or fatal in humans and animals. No licensed specific drugs are available to prevent their infection. Different host receptors for cellular entry, poorly conserved structural proteins (antigens), and the high mutation and recombination rates of CoVs pose a significant problem in the development of wide-spectrum anti-CoV drugs and vaccines. CoV main proteases (M(pro)s), which are key enzymes in viral gene expression and replication, were revealed to share a highly conservative substrate-recognition pocket by comparison of four crystal structures and a homology model representing all three genetic clusters of the genus Coronavirus. This conclusion was further supported by enzyme activity assays. Mechanism-based irreversible inhibitors were designed, based on this conserved structural region, and a uniform inhibition mechanism was elucidated from the structures of M(pro)-inhibitor complexes from severe acute respiratory syndrome-CoV and porcine transmissible gastroenteritis virus. A structure-assisted optimization program has yielded compounds with fast in vitro inactivation of multiple CoV M(pro)s, potent antiviral activity, and extremely low cellular toxicity in cell-based assays. Further modification could rapidly lead to the discovery of a single agent with clinical potential against existing and possible future emerging CoV-related diseases

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