Ham Ching Lam

Identification and Complete Genome of Seneca Valley Virus in Vesicular Fluid and Sera of Pigs Affected with Idiopathic Vesicular Disease, Brazil

Numerous, ongoing outbreaks in Brazilian swine herds have been characterized by vesicular lesions in sows and acute losses of neonatal piglets. The complete genome of Seneca Valley virus (SVV) was identified in vesicular fluid and sera of sows, providing evidence of association between SVV and vesicular disease and viraemia in affected animals.

Genomic and evolutionary inferences between American and global strains of porcine epidemic diarrhea virus

Abstract Porcine epidemic diarrhea virus (PEDV) has caused severe economic losses both recently in the United States (US) and historically throughout Europe and Asia. Traditionally, analysis of the spike gene has been used to determine phylogenetic relationships between PEDV strains. We determined the complete genomes of 93 PEDV field samples from US swine and analyzed the data in conjunction with complete genome sequences available from GenBank (n =126) to determine the most variable genomic areas. Our results indicate high levels of variation within the ORF1 and spike regions while the C-terminal domains of structural genes were highly conserved. Analysis of the Receptor Binding Domains in the spike gene revealed a limited number of amino acid substitutions in US strains compared to Asian strains. Phylogenetic analysis of the complete genome sequence data revealed high rates of recombination, resulting in differing evolutionary patterns in phylogenies inferred for the spike region versus whole genomes. These finding suggest that significant genetic events outside of the spike region have contributed to the evolution of PEDV.

Characterization and evolution of porcine deltacoronavirus in the United States

Abstract Porcine deltacoronavirus (PDCoV) was identified in multiple states across the United States (US) in 2014. In this study, we investigate the presence of PDCoV in diagnostic samples, which were further categorized by case identification (ID), and the association between occurrence, age, specimen and location between March and September 2014. Approximately, 7% of the case IDs submitted from the US were positive for PDCoV. Specimens were categorized into eight groups, and the univariate analysis indicated that oral fluids had 1.89 times higher odds of detecting PDCoV compared to feces. While the 43–56 day age group had the highest percentage of PDCoV positives (8.4%), the univariate analysis indicated no significant differences between age groups. However, multivariable analysis for age adjusted by specimen indicated the >147 day age group had 59% lower odds than suckling pigs of being positive for PDCoV. The percentage of PDCoV in diagnostic samples decreased to <1% in September 2014. In addition, 19 complete PDCoV genomes were sequenced, and Bayesian analysis was conducted to estimate the emergence of the US clade. The evolutionary rate of the PDCoV genome is estimated to be 3.8×10−4 substitutions/site/year (2.3×10−4–5.4×10−4, 95% HPD). Our results indicate that oral fluids continue to be a valuable specimen to monitor swineherd health, and PDCoV has been circulating in the US prior to 2014.

Comparative In Vitro and In Vivo Studies of Porcine Rotavirus G9P[13] and Human Rotavirus Wa G1P[8]

ABSTRACT The changing epidemiology of group A rotavirus (RV) strains in humans and swine, including emerging G9 strains, poses new challenges to current vaccines. In this study, we comparatively assessed the pathogenesis of porcine RV (PRV) G9P[13] and evaluated the short-term cross-protection between this strain and human RV (HRV) Wa G1P[8] in gnotobiotic pigs. Complete genome sequencing demonstrated that PRV G9P[13] possessed a human-like G9 VP7 genotype but shared higher overall nucleotide identity with historic PRV strains. PRV G9P[13] induced longer rectal virus shedding and RV RNAemia in pigs than HRV Wa G1P[8] and generated complete short-term cross-protection in pigs challenged with HRV or PRV, whereas HRV Wa G1P[8] induced only partial protection against PRV challenge. Moreover, PRV G9P[13] replicated more extensively in porcine monocyte-derived dendritic cells (MoDCs) than did HRV Wa G1P[8]. Cross-protection was likely not dependent on serum virus-neutralizing (VN) antibodies, as the heterologous VN antibody titers in the sera of G9P[13]-inoculated pigs were low. Thus, our results suggest that heterologous protection by the current monovalent G1P[8] HRV vaccine against emerging G9 strains should be evaluated in clinical and experimental studies to prevent further dissemination of G9 strains. Differences in the pathogenesis of these two strains may be partially attributable to their variable abilities to replicate and persist in porcine immune cells, including dendritic cells (DCs). Additional studies are needed to evaluate the emerging G9 strains as potential vaccine candidates and to test the susceptibility of various immune cells to infection by G9 and other common HRV/PRV genotypes. IMPORTANCE The changing epidemiology of porcine and human group A rotaviruses (RVs), including emerging G9 strains, may compromise the efficacy of current vaccines. An understanding of the pathogenesis and genetic, immunological, and biological features of the new emerging RV strains will contribute to the development of new surveillance and prevention tools. Additionally, studies of cross-protection between the newly identified emerging G9 porcine RV strains and a human G1 RV vaccine strain in a susceptible host (swine) will allow evaluation of G9 strains as potential novel vaccine candidates to be included in porcine or human vaccines.

Complete Genome Sequence of Porcine Epidemic Diarrhea Virus Strain COL/Cundinamarca/2014 from Colombia

ABSTRACT Porcine epidemic diarrhea virus (PEDV) has been found throughout Europe and Asia, and has emerged in North and South America. A whole genome sequence was obtained from a paraffin-embedded tissue sample from the Instituto Colombiano Agropecuario (ICA), Colombia through Next Generation Sequencing techniques to further understand the evolution of PEDV.

Evolution and Vaccination of Influenza Virus

In this study, we present an application paradigm in which an unsupervised machine learning approach is applied to the high-dimensional influenza genetic sequences to investigate whether vaccine is a driving force to the evolution of influenza virus. We first used a visualization approach to visualize the evolutionary paths of vaccine-controlled and non-vaccine-controlled influenza viruses in a low-dimensional space. We then quantified the evolutionary differences between their evolutionary trajectories through the use of within- and between-scatter matrices computation to provide the statistical confidence to support the visualization results. We used the influenza surface Hemagglutinin (HA) gene for this study as the HA gene is the major target of the immune system. The visualization is achieved without using any clustering methods or prior information about the influenza sequences. Our results clearly showed that the evolutionary trajectories between vaccine-controlled and non-vaccine-controlled influenza viruses are different and vaccine as an evolution driving force cannot be completely eliminated.

High throughput genetic sequence analysis

We present an application paradigm in which an unsupervised machine learning approach is applied to high dimensional influenza sequence datasets: (1) human A/H3N2, (2) avian H5, and (3) North American swine influenza H3N2 virus. Interesting visual patterns observed in the A/H3N2 influenza virus led us to hypothesize that vaccination could be one of the driving forces in the evolution of the human A/H3N2 influenza virus. We provide simulation study and statistical results to support our finding that the influenza virus evolves differently in a protected environment than it evolves in the wild. In the swine H3N2 case, our result suggests that the diversification of North American swine influenza virus can be attributed to the mutations at two positively selected sites on the hemaggluttinin protein.

Analyzing influenza virus sequences using binary encoding approach

Capturing mutation patterns of each individual influenza virus sequence is often challenging; in this paper, we demonstrated that using a binary encoding scheme coupled with dimension reduction technique, we were able to capture the intrinsic mutation pattern of the virus. Our approach looks at the variance between sequences instead of the commonly used p-distance or Hamming distance. We first convert the influenza genetic sequences to a binary strings and form a binary sequence alignment matrix and then apply Principal Component Analysis PCA to this matrix. PCA also provides identification power to identify reassortant virus by using data projection technique. Due to the sparsity of the binary string, we were able to analyze large volume of influenza sequence data in a very short time. For protein sequences, our scheme also allows the incorporation of biophysical properties of each amino acid. Here, we present various encouraging results from analyzing influenza nucleotide, protein and genome sequences using the proposed approach.

Poster: H3N2 influenza vaccine: A historical perspective

We analyzed the dN/dS ratios of the vaccine strains against the immuno-escape mutant strain circulatings. Our preliminary analysis suggested the following: (1) Using graphical technique (see figure), we use sites (blue) with highest dN/dS ratios within the hyper-variable regions (red) (sites 100–200) of each flu season plotted against vaccine introduction with new vaccines (green) and carried-over vaccines (black). Our result shows that the position of the antigenic sites are shifting to avoid mutation in previously targeted sites by the vaccine. This suggests that the influenza virus shows a first order memory effect from the previous exposure to the antibody immune response. (2) We see that the hyper-variability regions are under much larger mutation pressure than the rest of the sequence. (3) Synonymous mutation appears to be closing the gap on the non-synonymous mutation which suggests that synonymous changes may have a major impact on fitness, contrary to a base hypothesis of being selectively neutral.

Analyze influenza virus sequences using binary encoding approach

Capturing mutation patterns of each individual influenza virus sequence is often challenging; in this paper, we demonstrated that using a binary encoding scheme coupled with dimension reduction technique, we were able to capture the intrinsic mutation pattern of the virus. Our approach looks at the variance between sequences instead of the commonly used p-distance or Hamming distance. We first convert the influenza genetic sequence to a binary string and then apply Principal Component Analysis (PCA) to the converted sequence. PCA also provides a prediction capability for detecting reassortant virus by using data projection technique. Due to the sparsity of the binary string, we were able to analyze large volume of influenza sequence data in a very short time. For protein sequences, our scheme also allows the incorporation of biophysical properties of each amino acid. Here, we present various results from analyzing influenza nucleotide, protein and genome sequences using the proposed approach. With the Next-Generation Sequencing (NGS) promises of sequencing DNA at unprecedented speed and production of massive quantity of data, it is imperative that new technique needs to be developed to provide quick and reliable analysis of any sequence data. Here, we believe our approach can be used at the upstream stage of sequence data analysis pipeline to gain insight as to which direction should be continued on in analyzing the available data.