One-step-one-pot hydrothermally derived metal-organic-framework-nanohybrids for integrated point-of-care diagnostics of SARS-CoV-2 viral antigen/pseudovirus utilizing electrochemical biosensor chip.

The COVID-19 pandemic has become a global catastrophe, affecting the health and economy of the human community. It is required to mitigate the impact of pandemics by developing rapid molecular diagnostics for SARS-CoV-2 virus detection. In this context, developing a rapid point-of-care (POC) diagnostic test is a holistic approach to the prevention of COVID-19. In this context, this study aims at presenting a real-time, biosensor chip for improved molecular diagnostics including recombinant SARS-CoV-2 spike glycoprotein and SARS-CoV-2 pseudovirus detection based on one-step-one-pot hydrothermally derived CoFeBDCNH2-CoFe2O4 MOF-nanohybrids. This study was tested on a PalmSens-EmStat Go POC device, showing a limit of detection (LOD) for recombinant SARS-CoV-2 spike glycoprotein of 6.68 fg/mL and 6.20 fg/mL in buffer and 10% serum-containing media, respectively. To validate virus detection in the POC platform, an electrochemical instrument (CHI6116E) was used to perform dose dependent studies under similar experimental conditions to the handheld device. The results obtained from these studies were comparable indicating the capability and high detection electrochemical performance of MOF nanocomposite derived from one-step-one-pot hydrothermal synthesis for SARS-CoV-2 detection for the first time. Further, the performance of the sensor was tested in the presence of Omicron BA.2 and wild-type D614G pseudoviruses.

Eradicating mesothelin-positive human gastric and pancreatic tumors in xenograft models with optimized anti-mesothelin antibody-drug conjugates from synthetic antibody libraries.

Mesothelin (MSLN) is an attractive candidate of targeted therapy for several cancers, and hence there are increasing needs to develop MSLN-targeting strategies for cancer therapeutics. Antibody-drug conjugates (ADCs) targeting MSLN have been demonstrated to be a viable strategy in treating MSLN-positive cancers. However, developing antibodies as targeting modules in ADCs for toxic payload delivery to the tumor site but not to normal tissues is not a straightforward task with many potential hurdles. In this work, we established a high throughput engineering platform to develop and optimize anti-MSLN ADCs by characterizing more than 300 scFv CDR-variants and more than 50 IgG CDR-variants of a parent anti-MSLN antibody as candidates for ADCs. The results indicate that only a small portion of the complementarity determining region (CDR) residues are indispensable in the MSLN-specific targeting. Also, the enhancement of the hydrophilicity of the rest of the CDR residues could drastically increase the overall solubility of the optimized anti-MSLN antibodies, and thus substantially improve the efficacies of the ADCs in treating human gastric and pancreatic tumor xenograft models in mice. We demonstrated that the in vivo treatments with the optimized ADCs resulted in almost complete eradication of the xenograft tumors at the treatment endpoints, without detectable off-target toxicity because of the ADCs' high specificity targeting the cell surface tumor-associated MSLN. The technological platform can be applied to optimize the antibody sequences for more effective targeting modules of ADCs, even when the candidate antibodies are not necessarily feasible for the ADC development due to the antibodies' inferior solubility or affinity/specificity to the target antigen.

A panel of anti-influenza virus nucleoprotein antibodies selected from phage-displayed synthetic antibody libraries with rapid diagnostic capability to distinguish diverse influenza virus subtypes.

Immunoassays based on sandwich immuno-complexes of capture and detection antibodies simultaneously binding to the target analytes have been powerful technologies in molecular analyses. Recent developments in single molecule detection technologies enable the detection limit of the sandwich immunoassays approaching femtomolar (10-15 M), driving the needs of developing sensitive and specific antibodies for ever-increasingly broad applications in detecting and quantifying biomarkers. The key components underlying the sandwich immunoassays are antibody-based affinity reagents, for which the conventional sources are mono- or poly-clonal antibodies from immunized animals. The downsides of the animal-based antibodies as affinity reagents arise from the requirement of months of development timespan and limited choices of antibody candidates due to immunodominance of humoral immune responses in animals. Hence, developing animal antibodies capable of distinguishing highly related antigens could be challenging. To overcome the limitation imposed by the animal immune systems, we developed an in vitro methodology based on phage-displayed synthetic antibody libraries for diverse antibodies as affinity reagents against closely related influenza virus nucleoprotein (NP) subtypes, aiming to differentiating avian influenza virus (H5N1) from seasonal influenza viruses (H1N1 and H3N2), for which the NPs are closely related by 90-94% in terms of pairwise amino acid sequence identity. We applied the methodology to attain, within four weeks, a panel of IgGs with distinguishable specificities against a group of representative NPs with pairwise amino acid sequence identities up to more than 90%, and the antibodies derived from the antibody libraries without further affinity refinement had comparable affinity of mouse antibodies to the NPs with the detection limit less than 1 nM of viral NP from lysed virus with sandwich ELISA. The panel of IgGs were capable of rapidly distinguishing infections due to virulent avian influenza virus from infections of seasonal flu, in responding to a probable emergency scenario where avian influenza virus would be transmissible among humans overlapping with the seasonal influenza infections. The results indicate that the in vitro antibody development methodology enables developing diagnostic antibodies that would not otherwise be available from animal-based antibody technologies.

Evaluating BRCA mutation risk predictive models in a Chinese cohort in Taiwan.

Accurate estimation of carrier probabilities of cancer susceptibility gene mutations is an important part of pre-test genetic counselling. Many predictive models are available but their applicability in the Asian population is uncertain. We evaluated the performance of five BRCA mutation risk predictive models in a Chinese cohort of 647 women, who underwent germline DNA sequencing of a cancer susceptibility gene panel. Using areas under the curve (AUCs) on receiver operating characteristics (ROC) curves as performance measures, the models did comparably well as in western cohorts (BOADICEA 0.75, BRCAPRO 0.73, Penn II 0.69, Myriad 0.68). For unaffected women with family history of breast or ovarian cancer (n = 144), BOADICEA, BRCAPRO, and Tyrer-Cuzick models had excellent performance (AUC 0.93, 0.92, and 0.92, respectively). For women with both personal and family history of breast or ovarian cancer (n = 241), all models performed fairly well (BOADICEA 0.79, BRCAPRO 0.79, Penn II 0.75, Myriad 0.70). For women with personal history of breast or ovarian cancer but no family history (n = 262), most models did poorly. Between the two well-performed models, BOADICEA underestimated mutation risks while BRCAPRO overestimated mutation risks (expected/observed ratio 0.67 and 2.34, respectively). Among 424 women with personal history of breast cancer and available tumor ER/PR/HER2 data, the predictive models performed better for women with triple negative breast cancer (AUC 0.74 to 0.80) than for women with luminal or HER2 overexpressed breast cancer (AUC 0.63 to 0.69). However, incorporating ER/PR/HER2 status into the BOADICEA model calculation did not improve its predictive accuracy.

Effective binding to protein antigens by antibodies from antibody libraries designed with enhanced protein recognition propensities.

Antibodies provide immune protection by recognizing antigens of diverse chemical properties, but elucidating the amino acid sequence-function relationships underlying the specificity and affinity of antibody-antigen interactions remains challenging. We designed and constructed phage-displayed synthetic antibody libraries with enriched protein antigen-recognition propensities calculated with machine learning predictors, which indicated that the designed single-chain variable fragment variants were encoded with enhanced distributions of complementarity-determining region (CDR) hot spot residues with high protein antigen recognition propensities in comparison with those in the human antibody germline sequences. Antibodies derived directly from the synthetic antibody libraries, without affinity maturation cycles comparable to those in in vivo immune systems, bound to the corresponding protein antigen through diverse conformational or linear epitopes with specificity and affinity comparable to those of the affinity-matured antibodies from in vivo immune systems. The results indicated that more densely populated CDR hot spot residues were sustainable by the antibody structural frameworks and could be accompanied by enhanced functionalities in recognizing protein antigens. Our study results suggest that synthetic antibody libraries, which are not limited by the sequences found in antibodies in nature, could be designed with the guidance of the computational machine learning algorithms that are programmed to predict interaction propensities to molecules of diverse chemical properties, leading to antibodies with optimal characteristics pertinent to their medical applications.

Antibody-drug conjugates with HER2-targeting antibodies from synthetic antibody libraries are highly potent against HER2-positive human gastric tumor in xenograft models.

HER2-ECD (human epidermal growth factor receptor 2 - extracellular domain) is a prominent therapeutic target validated for treating HER2-positive breast and gastric cancer, but HER2-specific therapeutic options for treating advanced gastric cancer remain limited. We have developed antibody-drug conjugates (ADCs), comprising IgG1 linked via valine-citrulline to monomethyl auristatin E, with potential to treat HER2-positive gastric cancer in humans. The antibodies optimally selected from the ADC discovery platform, which was developed to discover antibody candidates suitable for immunoconjugates from synthetic antibody libraries designed using antibody-antigen interaction principles, were demonstrated to be superior immunoconjugate targeting modules in terms of efficacy and off-target toxicity. In comparison with the two control humanized antibodies (trastuzumab and H32) derived from murine antibody repertoires, the antibodies derived from the synthetic antibody libraries had enhanced receptor-mediated internalization rate, which could result in ADCs with optimal efficacies. Along with the ADCs, two other forms of immunoconjugates (scFv-PE38KDEL and IgG1-AL1-PE38KDEL) were used to test the antibodies for delivering cytotoxic payloads to xenograft tumor models in vivo and to cultured cells in vitro. The in vivo experiments with the three forms of immunoconjugates revealed minimal off-target toxicities of the selected antibodies from the synthetic antibody libraries; the off-target toxicities of the control antibodies could have resulted from the antibodies' propensity to target the liver in the animal models. Our ADC discovery platform and the knowledge gained from our in vivo tests on xenograft models with the three forms of immunoconjugates could be useful to anyone developing optimal ADC cancer therapeutics.

Germline breast cancer susceptibility gene mutations and breast cancer outcomes.

BACKGROUND: It is unclear whether germline breast cancer susceptibility gene mutations affect breast cancer related outcomes. We wanted to evaluate mutation patterns in 20 breast cancer susceptibility genes and correlate the mutations with clinical characteristics to determine the effects of these germline mutations on breast cancer prognosis. METHODS: The study cohort included 480 ethnic Chinese individuals in Taiwan with at least one of the six clinical risk factors for hereditary breast cancer: family history of breast or ovarian cancer, young age of onset for breast cancer, bilateral breast cancer, triple negative breast cancer, both breast and ovarian cancer, and male breast cancer. PCR-enriched amplicon-sequencing on a next generation sequencing platform was used to determine the germline DNA sequences of all exons and exon-flanking regions of the 20 genes. Protein-truncating variants were identified as pathogenic. RESULTS: We detected a 13.5% carrier rate of pathogenic germline mutations, with BRCA2 being the most prevalent and the non-BRCA genes accounting for 38.5% of the mutation carriers. BRCA mutation carriers were more likely to be diagnosed of breast cancer with lymph node involvement (66.7% vs 42.6%; P = 0.011), and had significantly worse breast cancer specific outcomes. The 5-year disease-free survival was 73.3% for BRCA mutation carriers and 91.1% for non-carriers (hazard ratio for recurrence or death 2.42, 95% CI 1.29-4.53; P = 0.013). After adjusting for clinical prognostic factors, BRCA mutation remained an independent poor prognostic factor for cancer recurrence or death (adjusted hazard ratio 3.04, 95% CI 1.40-6.58; P = 0.005). Non-BRCA gene mutation carriers did not exhibit any significant difference in cancer characteristics or outcomes compared to those without detected mutations. Among the risk factors for hereditary breast cancer, the odds of detecting a germline mutation increased significantly with having bilateral breast cancer (adjusted odds ratio 3.27, 95% CI 1.64-6.51; P = 0.0008) or having more than one risk factor (odds ratio 2.07, 95% CI 1.22-3.51; P = 0.007). CONCLUSIONS: Without prior knowledge of the mutation status, BRCA mutation carriers had more advanced breast cancer on initial diagnosis and worse cancer-related outcomes. Optimal approach to breast cancer treatment for BRCA mutation carriers warrants further investigation.

Predicting Ligand Binding Sites on Protein Surfaces by 3-Dimensional Probability Density Distributions of Interacting Atoms.

Predicting ligand binding sites (LBSs) on protein structures, which are obtained either from experimental or computational methods, is a useful first step in functional annotation or structure-based drug design for the protein structures. In this work, the structure-based machine learning algorithm ISMBLab-LIG was developed to predict LBSs on protein surfaces with input attributes derived from the three-dimensional probability density maps of interacting atoms, which were reconstructed on the query protein surfaces and were relatively insensitive to local conformational variations of the tentative ligand binding sites. The prediction accuracy of the ISMBLab-LIG predictors is comparable to that of the best LBS predictors benchmarked on several well-established testing datasets. More importantly, the ISMBLab-LIG algorithm has substantial tolerance to the prediction uncertainties of computationally derived protein structure models. As such, the method is particularly useful for predicting LBSs not only on experimental protein structures without known LBS templates in the database but also on computationally predicted model protein structures with structural uncertainties in the tentative ligand binding sites.

Discovering neutralizing antibodies targeting the stem epitope of H1N1 influenza hemagglutinin with synthetic phage-displayed antibody libraries.

Broadly neutralizing antibodies developed from the IGHV1-69 germline gene are known to bind to the stem region of hemagglutinin in diverse influenza viruses but the sequence determinants for the antigen recognition, including neutralization potency and binding affinity, are not clearly understood. Such understanding could inform designs of synthetic antibody libraries targeting the stem epitope on hemagglutinin, leading to artificially designed antibodies that are functionally advantageous over antibodies from natural antibody repertoires. In this work, the sequence space of the complementarity determining regions of a broadly neutralizing antibody (F10) targeting the stem epitope on the hemagglutinin of a strain of H1N1 influenza virus was systematically explored; the elucidated antibody-hemagglutinin recognition principles were used to design a phage-displayed antibody library, which was then used to discover neutralizing antibodies against another strain of H1N1 virus. More than 1000 functional antibody candidates were selected from the antibody library and were shown to neutralize the corresponding strain of influenza virus with up to 7 folds higher potency comparing with the parent F10 antibody. The antibody library could be used to discover functionally effective antibodies against other H1N1 influenza viruses, supporting the notion that target-specific antibody libraries can be designed and constructed with systematic sequence-function information.

Predominant structural configuration of natural antibody repertoires enables potent antibody responses against protein antigens.

Humoral immunity against diverse pathogens is rapidly elicited from natural antibody repertoires of limited complexity. But the organizing principles underlying the antibody repertoires that facilitate this immunity are not well-understood. We used HER2 as a model immunogen and reverse-engineered murine antibody response through constructing an artificial antibody library encoded with rudimentary sequence and structural characteristics learned from high throughput sequencing of antibody variable domains. Antibodies selected in vitro from the phage-displayed synthetic antibody library bound to the model immunogen with high affinity and specificities, which reproduced the specificities of natural antibody responses. We conclude that natural antibody structural repertoires are shaped to allow functional antibodies to be encoded efficiently, within the complexity limit of an individual antibody repertoire, to bind to diverse protein antigens with high specificity and affinity. Phage-displayed synthetic antibody libraries, in conjunction with high-throughput sequencing, can thus be designed to replicate natural antibody responses and to generate novel antibodies against diverse antigens.

Origins of specificity and affinity in antibody-protein interactions.

Natural antibodies are frequently elicited to recognize diverse protein surfaces, where the sequence features of the epitopes are frequently indistinguishable from those of nonepitope protein surfaces. It is not clearly understood how the paratopes are able to recognize sequence-wise featureless epitopes and how a natural antibody repertoire with limited variants can recognize seemingly unlimited protein antigens foreign to the host immune system. In this work, computational methods were used to predict the functional paratopes with the 3D antibody variable domain structure as input. The predicted functional paratopes were reasonably validated by the hot spot residues known from experimental alanine scanning measurements. The functional paratope (hot spot) predictions on a set of 111 antibody-antigen complex structures indicate that aromatic, mostly tyrosyl, side chains constitute the major part of the predicted functional paratopes, with short-chain hydrophilic residues forming the minor portion of the predicted functional paratopes. These aromatic side chains interact mostly with the epitope main chain atoms and side-chain carbons. The functional paratopes are surrounded by favorable polar atomistic contacts in the structural paratope-epitope interfaces; more that 80% these polar contacts are electrostatically favorable and about 40% of these polar contacts form direct hydrogen bonds across the interfaces. These results indicate that a limited repertoire of antibodies bearing paratopes with diverse structural contours enriched with aromatic side chains among short-chain hydrophilic residues can recognize all sorts of protein surfaces, because the determinants for antibody recognition are common physicochemical features ubiquitously distributed over all protein surfaces.

Antibody variable domain interface and framework sequence requirements for stability and function by high-throughput experiments.

Protein structural stability and biological functionality are dictated by the formation of intradomain cores and interdomain interfaces, but the intricate sequence-structure-function interrelationships in the packing of protein cores and interfaces remain difficult to elucidate due to the intractability of enumerating all packing possibilities and assessing the consequences of all the variations. In this work, groups of ß strand residues of model antibody variable domains were randomized with saturated mutagenesis and the functional variants were selected for high-throughput sequencing and high-throughput thermal stability measurements. The results show that the sequence preferences of the intradomain hydrophobic core residues are strikingly flexible among hydrophobic residues, implying that these residues are coupled indirectly with antigen binding through energetic stabilization of the protein structures. By contrast, the interdomain interface residues are directly coupled with antigen binding. The interdomain interface should be treated as an integral part of the antigen-binding site.

Loop-sequence features and stability determinants in antibody variable domains by high-throughput experiments.

Protein loops are frequently considered as critical determinants in protein structure and function. Recent advances in high-throughput methods for DNA sequencing and thermal stability measurement have enabled effective exploration of sequence-structure-function relationships in local protein regions. Using these data-intensive technologies, we investigated the sequence-structure-function relationships of six complementarity-determining regions (CDRs) and ten non-CDR loops in the variable domains of a model vascular endothelial growth factor (VEGF)-binding single-chain antibody variable fragment (scFv) whose sequence had been optimized via a consensus-sequence approach. The results show that only a handful of residues involving long-range tertiary interactions distant from the antigen-binding site are strongly coupled with antigen binding. This implies that the loops are passive regions in protein folding; the essential sequences of these regions are dictated by conserved tertiary interactions and the consensus local loop-sequence features contribute little to protein stability and function.

Prediction of carbohydrate binding sites on protein surfaces with 3-dimensional probability density distributions of interacting atoms.

Non-covalent protein-carbohydrate interactions mediate molecular targeting in many biological processes. Prediction of non-covalent carbohydrate binding sites on protein surfaces not only provides insights into the functions of the query proteins; information on key carbohydrate-binding residues could suggest site-directed mutagenesis experiments, design therapeutics targeting carbohydrate-binding proteins, and provide guidance in engineering protein-carbohydrate interactions. In this work, we show that non-covalent carbohydrate binding sites on protein surfaces can be predicted with relatively high accuracy when the query protein structures are known. The prediction capabilities were based on a novel encoding scheme of the three-dimensional probability density maps describing the distributions of 36 non-covalent interacting atom types around protein surfaces. One machine learning model was trained for each of the 30 protein atom types. The machine learning algorithms predicted tentative carbohydrate binding sites on query proteins by recognizing the characteristic interacting atom distribution patterns specific for carbohydrate binding sites from known protein structures. The prediction results for all protein atom types were integrated into surface patches as tentative carbohydrate binding sites based on normalized prediction confidence level. The prediction capabilities of the predictors were benchmarked by a 10-fold cross validation on 497 non-redundant proteins with known carbohydrate binding sites. The predictors were further tested on an independent test set with 108 proteins. The residue-based Matthews correlation coefficient (MCC) for the independent test was 0.45, with prediction precision and sensitivity (or recall) of 0.45 and 0.49 respectively. In addition, 111 unbound carbohydrate-binding protein structures for which the structures were determined in the absence of the carbohydrate ligands were predicted with the trained predictors. The overall prediction MCC was 0.49. Independent tests on anti-carbohydrate antibodies showed that the carbohydrate antigen binding sites were predicted with comparable accuracy. These results demonstrate that the predictors are among the best in carbohydrate binding site predictions to date.

Protein-protein interaction site predictions with three-dimensional probability distributions of interacting atoms on protein surfaces.

Protein-protein interactions are key to many biological processes. Computational methodologies devised to predict protein-protein interaction (PPI) sites on protein surfaces are important tools in providing insights into the biological functions of proteins and in developing therapeutics targeting the protein-protein interaction sites. One of the general features of PPI sites is that the core regions from the two interacting protein surfaces are complementary to each other, similar to the interior of proteins in packing density and in the physicochemical nature of the amino acid composition. In this work, we simulated the physicochemical complementarities by constructing three-dimensional probability density maps of non-covalent interacting atoms on the protein surfaces. The interacting probabilities were derived from the interior of known structures. Machine learning algorithms were applied to learn the characteristic patterns of the probability density maps specific to the PPI sites. The trained predictors for PPI sites were cross-validated with the training cases (consisting of 432 proteins) and were tested on an independent dataset (consisting of 142 proteins). The residue-based Matthews correlation coefficient for the independent test set was 0.423; the accuracy, precision, sensitivity, specificity were 0.753, 0.519, 0.677, and 0.779 respectively. The benchmark results indicate that the optimized machine learning models are among the best predictors in identifying PPI sites on protein surfaces. In particular, the PPI site prediction accuracy increases with increasing size of the PPI site and with increasing hydrophobicity in amino acid composition of the PPI interface; the core interface regions are more likely to be recognized with high prediction confidence. The results indicate that the physicochemical complementarity patterns on protein surfaces are important determinants in PPIs, and a substantial portion of the PPI sites can be predicted correctly with the physicochemical complementarity features based on the non-covalent interaction data derived from protein interiors.

Rationalization and design of the complementarity determining region sequences in an antibody-antigen recognition interface.

Protein-protein interactions are critical determinants in biological systems. Engineered proteins binding to specific areas on protein surfaces could lead to therapeutics or diagnostics for treating diseases in humans. But designing epitope-specific protein-protein interactions with computational atomistic interaction free energy remains a difficult challenge. Here we show that, with the antibody-VEGF (vascular endothelial growth factor) interaction as a model system, the experimentally observed amino acid preferences in the antibody-antigen interface can be rationalized with 3-dimensional distributions of interacting atoms derived from the database of protein structures. Machine learning models established on the rationalization can be generalized to design amino acid preferences in antibody-antigen interfaces, for which the experimental validations are tractable with current high throughput synthetic antibody display technologies. Leave-one-out cross validation on the benchmark system yielded the accuracy, precision, recall (sensitivity) and specificity of the overall binary predictions to be 0.69, 0.45, 0.63, and 0.71 respectively, and the overall Matthews correlation coefficient of the 20 amino acid types in the 24 interface CDR positions was 0.312. The structure-based computational antibody design methodology was further tested with other antibodies binding to VEGF. The results indicate that the methodology could provide alternatives to the current antibody technologies based on animal immune systems in engineering therapeutic and diagnostic antibodies against predetermined antigen epitopes.

Body-mass index and progression of hepatitis B: a population-based cohort study in men.

PURPOSE: To determine prospectively whether body-mass index (BMI) is associated with liver-related morbidity and mortality among male hepatitis B virus (HBV) carriers. PATIENTS AND METHODS: We performed a prospective study of 2,903 male HBV surface antigen-positive government employees who were free of cancer at enrollment between 1989 and 1992. Main outcome measures included ultrasonography, biochemical tests, incident hepatocellular carcinoma (HCC), and liver-related death. RESULTS: During mean follow-up of 14.7 years, 134 developed HCC and 92 died as a result of liver-related causes. In Cox proportional hazards models adjusting for age, number of visits, diabetes, and use of alcohol and tobacco, the hazard ratios for incident HCC were 1.48 (95% CI, 1.04 to 2.12) in overweight men (BMI between 25.0 and 29.9 kg/m(2)) and 1.96 (95% CI, 0.72 to 5.38) in obese men (BMI >or= 30.0 kg/m(2)), compared with normal-weight men (BMI between 18.5 and 24.9 kg/m(2)). Liver-related mortality had adjusted hazard ratios of 1.74 (95% CI, 1.15 to 2.65) in overweight men and 1.50 (95% CI, 0.36 to 6.19) in obese men. Excess BMI was also associated with the occurrence of fatty liver and cirrhosis detected by ultrasonography, as well as elevated ALT and gamma-glutamyltransferase (GGT) activity during follow-up. The association of BMI with GGT was stronger than with ALT, and elevated GGT activity and cirrhosis were the strongest predictors for incident HCC and liver-related death. CONCLUSION: This longitudinal cohort study indicates that excess body weight is involved in the transition from healthy HBV carrier state to HCC and liver-related death among men.

Genetic and epidemiological analysis of influenza virus epidemics in Taiwan during 2003 to 2006.

The genetic characterization of Taiwanese influenza A and B viruses on the basis of analyses of pairwise amino acid variations, genetic clustering, and phylogenetics was performed. A total of 548, 2,123, and 1,336 sequences of the HA1 genes of influenza A virus subtypes H1 and H3 and influenza B virus, respectively, collected during 2003 to 2006 from an island-wide surveillance network were determined. Influenza A virus H3 showed activity during all periods, although it was dominant only in the winters of 2002-2003 and 2003-2004. Instead, influenza B virus and influenza A virus H1 were dominant in the winters of 2004-2005 and 2005-2006, respectively. Additionally, two influenza A virus H3 peaks were found in the summers of 2004 and 2005. From clustering analysis, similar characteristics of high sequence diversity and short life spans for the influenza A virus H1 and H3 clusters were observed, despite their distinct seasonal patterns. In contrast, clusters with longer life spans and fewer but larger clusters were found among the influenza B viruses. We also noticed that more amino acid changes at antigenic sites, especially at sites B and D in the H3 viruses, were found in 2003 and 2004 than in the following 2 years. The only epidemic of the H1 viruses, which occurred in the winter of 2005-2006, was caused by two genetically distinct lineages, and neither of them showed apparent antigenic changes compared with the antigens of the vaccine strain. For the influenza B viruses, the multiple dominant lineages of Yamagata-like strains with large genetic variations observed reflected the evolutionary pressure caused by the Yamagata-like vaccine strain. On the other hand, only one dominant lineage of Victoria-like strains circulated from 2004 to 2006.

Genetic analysis and evaluation of the reassortment of influenza B viruses isolated in Taiwan during the 2004-2005 and 2006-2007 epidemics.

Influenza B viruses were predominant in Taiwan during the 2004-2005 epidemic and both Victoria and Yamagata lineage viruses co-circulated. A reassortant influenza B virus that contained a Victoria lineage hemagglutinin (HA) gene and Yamagata lineage neuraminidase (NA) gene appeared first in 2002 and became predominant during the 2004-2005 epidemic. During the 2006-2007 epidemic, an influenza B outbreak occurred in Taiwan and only Victoria lineage viruses circulated. We characterized the viruses isolated in the 2006-2007 epidemic and found that the HA genes of influenza B viruses from that epidemic were highly similar to those from the 2004-2005 epidemic. We also analyzed the NA genes of isolates from the 2006-2007 epidemic and found that they all belonged to the Yamagata lineage and formed a new genetic subclade. Comparison of isolates from the 2004-2005 and 2006-2007 epidemics revealed four substitutions, N220K, E320D, K343R and E404K in NA genes. Although the HA sequences from the 2006-2007 epidemic were similar to those from the 2004-2005 epidemic, the NA sequences differed, suggesting distinct patterns of evolution of the HA and NA genes from 2004-2007 in Taiwan. This study emphasizes that the evolution of the NA genes may contribute to reemergence of influenza B viruses.

Epidemiological survey of enterovirus infections occurring in Taiwan between 2000 and 2005: analysis of sentinel physician surveillance data.

Enterovirus (EV) infections are common. There are more than 60 known serotypes, and each has different epidemiologic or medical importance. Over 700 physicians from 75% of basic administrative units of Taiwan participated in the "Sentinel Physician Surveillance of Infectious Disease" and reported weekly to the Center for Disease Control-Taiwan with data on various infections. Data of laboratory-confirmed EV infections from this surveillance between 2000 and 2005 was analyzed. EV serotypes were determined by immunofluorescence staining and/or viral VP1 sequence analysis. A total of 12,236 EV cases, or approximately 1,300-2,500 per year, were identified, and 52% of the cases occurred between April and July. The median age was 3 years, and 57.6% of patients were male. Coxsackievirus A (CA) 16 and EV71, which primarily manifest as hand-foot-and-mouth disease, were the most prevalent serotypes every year except 2004. Other prevalent serotypes and associated symptoms varied from year to year. Echovirus (E) 30 and E6, which are associated with aseptic meningitis, were prevalent in 2001 and 2002, CA4 and CA10, which cause herpangina, were predominant in 2004, and coxsackievirus B (CB) 4 and CB3, which are associated with neonatal febrile disease, were most common in 2004 and 2005, respectively. Some of these epidemics overlapped with outbreaks of the same serotypes in other Asian Pacific countries. Of all serotypes, EV71 was associated with the highest number of severe complications in patients. Surveying the epidemic pattern, disease spectra, and severity associated with each EV serotype provided important information for public health and medical personnel.