Assessment of T-dependent and T-independent immune responses in cattle using a B cell ELISPOT assay

Understanding the mechanisms that maintain protective antibody levels after immunisation is important for vaccine design. In this study, we have determined the kinetics of plasma and memory B cells detectable in the blood of cattle immunised with model T-dependent or T-independent antigens. Immunisation with the T-D antigen resulted in an expansion of TNP-specific plasma cells post-TNP primary and booster immunisations, which was associated with increased titres of TNP-specific IgG antibodies. Although no TNP-specific memory B cells were detected in the T-D group following the primary immunisation, we detected an increase in the number of TNP-specific memory B cells post-TNP boost. In contrast, no TNP-specific plasma or memory B cells were detected after primary or secondary immunisation with the T-I antigen. We then investigated if immunisation with a third party antigen (tetanus toxin fragment C, TTC) would result in a bystander stimulation and increase the number of TNP-specific plasma and memory B cells in the T-D and/or T-I group. TTC immunisation in the T-D group resulted in a small increase in the number of TNP-specific plasma cells post-TTC primary immunisation and boost, and in an increase in the number of TNP-specific memory B cells post-TTC boost. This bystander effect was not observed in the animals previously immunised with the T-I antigen. In conclusion, the present study characterised for the first time the B cell response in cattle to immunisation with T-D and T-I antigens and showed that bystander stimulation of an established T-D B cell memory response may occur in cattle.

Infectiousness of pigs infected by the Porcine Reproductive and Respiratory Syndrome virus (PRRSV) is time-dependent

The time-dependent transmission rate of Porcine Reproductive and Respiratory Syndrome Virus (PRRSV) and the correlation between infectiousness, virological parameters and antibody responses of the infected pigs were studied in experimental conditions. Seven successive transmission trials involving a total of 77 specific pathogen-free piglets were carried out from 7 to 63 days post-inoculation (dpi). A semi-quantitative real time RT-PCR was developed to assess the evolution of the viral genome load in blood and nasal swabs from inoculated and contact pigs, with time. Virus genome in blood was detectable in inoculated pigs from 7 to 77 dpi, whereas viral genome shedding was detectable from nasal swabs from 2 to 48 dpi. The infectiousness of inoculated pigs, assessed from the frequency of occurrence of infected pigs in susceptible groups in each contact trial, increased from 7 to 14 dpi and then decreased slowly until 42 dpi (3, 7, 2, 1 and 0 pigs infected at 7, 14, 21, 28 and 42 dpi, respectively). These data were used to model the time-dependent infectiousness by a lognormal-like function with a latency period of 1 day and led to an estimated basic reproduction ratio, R 0 of 2.6 [1.8, 3.3]. The evolution of infectiousness was mainly correlated with the time-course of viral genome load in the blood whereas the decrease of infectiousness was strongly related to the increase in total antibodies.

Cluster analysis application identifies muscle characteristics of importance for beef tenderness

Background An important controversy in the relationship between beef tenderness and muscle characteristics including biochemical traits exists among meat researchers. The aim of this study is to explain variability in meat tenderness using muscle characteristics and biochemical traits available in the Integrated and Functional Biology of Beef (BIF-Beef) database. The BIF-Beef data warehouse contains characteristic measurements from animal, muscle, carcass, and meat quality derived from numerous experiments. We created three classes for tenderness (high, medium, and low) based on trained taste panel tenderness scores of all meat samples consumed (4,366 observations from 40 different experiments). For each tenderness class, the corresponding means for the mechanical characteristics, muscle fibre type, collagen content, and biochemical traits which may influence tenderness of the muscles were calculated. Results Our results indicated that lower shear force values were associated with more tender meat. In addition, muscles in the highest tenderness cluster had the lowest total and insoluble collagen contents, the highest mitochondrial enzyme activity (isocitrate dehydrogenase), the highest proportion of slow oxidative muscle fibres, the lowest proportion of fast-glycolytic muscle fibres, and the lowest average muscle fibre cross-sectional area. Results were confirmed by correlation analyses, and differences between muscle types in terms of biochemical characteristics and tenderness score were evidenced by Principal Component Analysis (PCA). When the cluster analysis was repeated using only muscle samples from m. Longissimus thoracis (LT), the results were similar; only contrasting previous results by maintaining a relatively constant fibre-type composition between all three tenderness classes. Conclusion Our results show that increased meat tenderness is related to lower shear forces, lower insoluble collagen and total collagen content, lower cross-sectional area of fibres, and an overall fibre type composition displaying more oxidative fibres than glycolytic fibres.

TranscriptomeBrowser 3.0: introducing a new compendium of molecular interactions and a new visualization tool for the study of gene regulatory networks

NetiNeti: discovery of scientific names from text using machine learning methods

CBDB: The codon bias database

Background In many genomes, a clear preference in the usage of particular codons exists. The mechanisms that induce codon biases remain an open question; studies have attributed codon usage to translational selection, mutational bias and drift. Furthermore, correlations between codon usage within host genomes and their viral pathogens have been observed for a myriad of host-virus systems. As such, numerous studies have investigated codon usage and codon bias in an effort to better understand how species evolve. Numerous metrics have been developed to identify biases in codon usage. In addition, a few data repositories of codon bias data are available, differing in the metrics reported as well as the number and taxonomy of strains examined. Description We have created a new web resource called the Codon Bias Database (CBDB) which provides information regarding the codon bias within the set of highly expressed genes for 300+ bacterial genomes. CBDB was developed to provide a resource for researchers investigating codon bias in bacteria, facilitating comparisons between strains and species. Furthermore, the site was created to serve those studying adaptation in phage; the genera selected for this first release of CBDB all have sequenced, annotated bacteriophages. The annotations and sequences for the highly expressed gene set are available for each strain in addition to the strain’s codon bias measurements. Conclusions Comparing species and strains provides a comprehensive look at how codon usage has been shaped over evolutionary time and can elucidate the putative mechanisms behind it. The Codon Bias Database provides a centralized repository of look-up tables and codon usage bias measures for a wide variety of genera, species and strains. Through our analysis of the variation in codon usage within the strains presently available, we find that most members of a genus have a codon composition most similar to other members of its genus, although not necessarily other members of its species.

Cancer bioinformatics: A new approach to systems clinical medicine

Cancer is one of the commonest causes of patient death in the clinic and a complex disease occurring in multiple organs per system, multiple systems per organ, or both, in the body. The poor diagnoses, therapies and prognoses of the disease could be mainly due to the variation of severities, durations, locations, sensitivity and resistance against drugs, cell differentiation and origin, and understanding of pathogenesis. With increasing evidence that the interaction and network between genes and proteins play an important role in investigation of cancer molecular mechanisms, it is necessary and important to introduce a new concept of Systems Clinical Medicine into cancer research, to integrate systems biology, clinical science, omics-based technology, bioinformatics and computational science to improve diagnosis, therapies and prognosis of diseases. Cancer bioinformatics is a critical and important part of the systems clinical medicine in cancer and the core tool and approach to carry out the investigations of cancer in systems clinical medicine. “Thematic Series on Cancer Bioinformatics” gather the strength of BMC Bioinformatics, BMC Cancer, Genome Medicine and Journal of Clinical Bioinformatics to headline the application of cancer bioinformatics for the development of bioinformatics methods, network biomarkers and precision medicine. The Series focuses on new developments in cancer bioinformatics and computational systems biology to explore the potential of clinical applications and improve the outcomes of patients with cancer.

Physicochemical property consensus sequences for functional analysis, design of multivalent antigens and targeted antivirals

Background Analysis of large sets of biological sequence data from related strains or organisms is complicated by superficial redundancy in the set, which may contain many members that are identical except at one or two positions. Thus a new method, based on deriving physicochemical property (PCP)-consensus sequences, was tested for its ability to generate reference sequences and distinguish functionally significant changes from background variability. Methods The PCP consensus program was used to automatically derive consensus sequences starting from sequence alignments of proteins from Flaviviruses (from the Flavitrack database) and human enteroviruses, using a five dimensional set of Eigenvectors that summarize over 200 different scalar values for the PCPs of the amino acids. A PCP-consensus protein of a Dengue virus envelope protein was produced recombinantly and tested for its ability to bind antibodies to strains using ELISA. Results PCP-consensus sequences of the flavivirus family could be used to classify them into five discrete groups and distinguish areas of the envelope proteins that correlate with host specificity and disease type. A multivalent Dengue virus antigen was designed and shown to bind antibodies against all four DENV types. A consensus enteroviral VPg protein had the same distinctive high pKa as wild type proteins and was recognized by two different polymerases. Conclusions The process for deriving PCP-consensus sequences for any group of aligned similar sequences, has been validated for sequences with up to 50% diversity. Ongoing projects have shown that the method identifies residues that significantly alter PCPs at a given position, and might thus cause changes in function or immunogenicity. Other potential applications include deriving target proteins for drug design and diagnostic kits.

Introduction: advanced intelligent computing theories and their applications in bioinformatics

The advancement of techniques in computer science and information technology witnessed the rapid growth of bioinformatics in various diverse areas such as sequence alignment, structure prediction, structure-function relationship, protein interactions, genome annotation, gene expression, microarray data analysis and so on. It is necessary and pertinent to discuss the issues on these topics and analyze the latest developments. The International Conference on Intelligent Computing (ICIC) provided a forum for discussing the recent investigations on bioinformatics related problems using high performance computing and efficient algorithms. Among the 832 submissions 33.7% were selected for presentations at ICIC 2011. Based on the novelty of the manuscripts, presentations and originality only 12 papers were selected as 'high quality', and the extended versions of them are included in the supplement. The supplement is broadly classified into five categories, structure-function relationship of proteins, protein-protein interactions, gene expression/interaction networks, microarray data analysis and visualization tools. The opening article by Gromiha et al. [1] related various physical, chemical, energetic and conformational properties of amino acid residues with the change of half maximal effective concentration (EC50) due to amino acid substitutions in olfactory receptors. Further, they utilized machine learning methods for discriminating the mutants, which enhance or reduce EC50 values upon mutation. Wang et al. [2] proposed a protein-protein dissimilarity learning algorithm for comparing protein structures using the contextual information of proteins. Lei et al. [3] developed a robust computational technique for assessing the reliability of protein-protein interactions and predicting the interacting pairs of proteins by integrating manifold embedding with various features. Wang et al. [4] described an algorithm for identifying overlapping modules in protein-protein interaction networks. Cui et al. [5] built a support vector machine model for predicting human proteins that interact with virus proteins and specifically human papillomavirus and hepatitis C virus. Liu et al. [6] constructed an integrated map of protein interaction network in Mycobacterium tuberculosis using machine learning and ortholog-based methods. Wang et al. [7] presented a network biology approach for investigating drug combinations and their target proteins in the context of genetic interaction networks and related human pathways with an aim to understand the underlying rules of effective drug combinations. Hsiao et al. [8] proposed an incremental evolutionary approach using network robustness for inferring gene regulatory networks with an application to deal with a large number of network parameters. Bevilacqua et al. [9] explored the issue of microarray data merging and used distant metastasis prediction for classifying three different sets of breast cancer data. Park et al. [10] analyzed the whole brain microarray data and physical connectivity of hippocampus with other brain regions to identify the genes related to Alzheimer's disease and their interactions with proteins. Ayadi et al. [11] described a stochastic pattern-driven neighborhood search algorithm for biclustering microarray data. In the last article, Jung et al. [12] described the development of a JAVA based stand-alone program for detecting and visualizing of genomic variants, which enables the manual exclusion of erroneous signals. It is also capable of visualizing genomic data from different sources such as data from comparative genomic hybridization arrays and sequence alignment format files. The guest editors of the supplement would like to thank the Executive Editor of BMC Bioinformatics Professor Kate Rice for providing an opportunity to publish some of the excellent papers presented in ICIC 2011. We also wish to thank Ms. Isobel Peters and Ms. Catherine Wells for their help and support in editing the supplement. Finally, our sincere thanks to all the authors of the papers selected for publication in this issue. This work was partially supported by the grants of the National Science Foundation of China, Nos. 61133010, & 31071168.

Evolutionary mechanisms driving the evolution of a large polydnavirus gene family coding for protein tyrosine phosphatases

Background Gene duplications have been proposed to be the main mechanism involved in genome evolution and in acquisition of new functions. Polydnaviruses (PDVs), symbiotic viruses associated with parasitoid wasps, are ideal model systems to study mechanisms of gene duplications given that PDV genomes consist of virulence genes organized into multigene families. In these systems the viral genome is integrated in a wasp chromosome as a provirus and virus particles containing circular double-stranded DNA are injected into the parasitoids’ hosts and are essential for parasitism success. The viral virulence factors, organized in gene families, are required collectively to induce host immune suppression and developmental arrest. The gene family which encodes protein tyrosine phosphatases (PTPs) has undergone spectacular expansion in several PDV genomes with up to 42 genes. Results Here, we present strong indications that PTP gene family expansion occurred via classical mechanisms: by duplication of large segments of the chromosomally integrated form of the virus sequences (segmental duplication), by tandem duplications within this form and by dispersed duplications. We also propose a novel duplication mechanism specific to PDVs that involves viral circle reintegration into the wasp genome. The PTP copies produced were shown to undergo conservative evolution along with episodes of adaptive evolution. In particular recently produced copies have undergone positive selection in sites most likely involved in defining substrate selectivity. Conclusion The results provide evidence about the dynamic nature of polydnavirus proviral genomes. Classical and PDV-specific duplication mechanisms have been involved in the production of new gene copies. Selection pressures associated with antagonistic interactions with parasitized hosts have shaped these genes used to manipulate lepidopteran physiology with evidence for positive selection involved in adaptation to host targets.