Glycan receptor specificity as a useful tool for characterization and surveillance of influenza A virus

Influenza A viruses are rapidly evolving pathogens with the potential for novel strains to emerge and result in pandemic outbreaks in humans. Some avian-adapted subtypes have acquired the ability to bind to human glycan receptors and cause severe infections in humans but have yet to adapt to and transmit between humans. The emergence of new avian strains and their ability to infect humans has confounded their distinction from circulating human virus strains through linking receptor specificity to human adaptation. Herein we review the various structural and biochemical analyses of influenza hemagglutinin–glycan receptor interactions. We provide our perspectives on how receptor specificity can be used to monitor evolution of the virus to adapt to human hosts so as to facilitate improved surveillance and pandemic preparedness.National Institutes of Health (U.S.) (Merit Award R37 GM057073-13)Singapore. National Research Foundation (Singapore-MIT Alliance for Research and Technology)Skolkovo Institute of Science and Technolog

Antigenically intact hemagglutinin in circulating avian and swine influenza viruses and potential for H3N2 pandemic

The 2009 swine-origin H1N1 influenza, though antigenically novel to the population at the time, was antigenically similar to the 1918 H1N1 pandemic influenza, and consequently was considered to be “archived” in the swine species before reemerging in humans. Given that the H3N2 is another subtype that currently circulates in the human population and is high on WHO pandemic preparedness list, we assessed the likelihood of reemergence of H3N2 from a non-human host. Using HA sequence features relevant to immune recognition, receptor binding and transmission we have identified several recent H3 strains in avian and swine that present hallmarks of a reemerging virus. IgG polyclonal raised in rabbit with recent seasonal vaccine H3 fail to recognize these swine H3 strains suggesting that existing vaccines may not be effective in protecting against these strains. Vaccine strategies can mitigate risks associated with a potential H3N2 pandemic in humans.National Institutes of Health (U.S.) (R37 GM057073-13

One-way trip: Influenza virus' adaptation to gallinaceous poultry may limit its pandemic potential

We hypothesise that some influenza virus adaptations to poultry may explain why the barrier for human-to-human transmission is not easily overcome once the virus has crossed from wild birds to chickens. Since the cluster of human infections with H5N1 influenza in Hong Kong in 1997, chickens have been recognized as the major source of avian influenza virus infection in humans. Although often severe, these infections have been limited in their subsequent human-to-human transmission, and the feared H5N1 pandemic has not yet occurred. Here we examine virus adaptations selected for during replication in chickens and other gallinaceous poultry. These include altered receptor binding and increased pH of fusion of the haemagglutinin as well as stalk deletions of the neuraminidase protein. This knowledge could aid the delivery of vaccines and increase our ability to prioritize research efforts on those viruses from the diverse array of avian influenza viruses that have greatest human pandemic potential

Finding sequence motifs with Bayesian models incorporating positional information: an application to transcription factor binding sites

<p>Abstract</p> <p>Background</p> <p>Biologically active sequence motifs often have positional preferences with respect to a genomic landmark. For example, many known transcription factor binding sites (TFBSs) occur within an interval [-300, 0] bases upstream of a transcription start site (TSS). Although some programs for identifying sequence motifs exploit positional information, most of them model it only implicitly and with <it>ad hoc </it>methods, making them unsuitable for general motif searches.</p> <p>Results</p> <p>A-GLAM, a user-friendly computer program for identifying sequence motifs, now incorporates a Bayesian model systematically combining sequence and positional information. A-GLAM's predictions with and without positional information were compared on two human TFBS datasets, each containing sequences corresponding to the interval [-2000, 0] bases upstream of a known TSS. A rigorous statistical analysis showed that positional information significantly improved the prediction of sequence motifs, and an extensive cross-validation study showed that A-GLAM's model was robust against mild misspecification of its parameters. As expected, when sequences in the datasets were successively truncated to the intervals [-1000, 0], [-500, 0] and [-250, 0], positional information aided motif prediction less and less, but never hurt it significantly.</p> <p>Conclusion</p> <p>Although sequence truncation is a viable strategy when searching for biologically active motifs with a positional preference, a probabilistic model (used reasonably) generally provides a superior and more robust strategy, particularly when the sequence motifs' positional preferences are not well characterized.</p

Scanning sequences after Gibbs sampling to find multiple occurrences of functional elements

BACKGROUND: Many DNA regulatory elements occur as multiple instances within a target promoter. Gibbs sampling programs for finding DNA regulatory elements de novo can be prohibitively slow in locating all instances of such an element in a sequence set. RESULTS: We describe an improvement to the A-GLAM computer program, which predicts regulatory elements within DNA sequences with Gibbs sampling. The improvement adds an optional "scanning step" after Gibbs sampling. Gibbs sampling produces a position specific scoring matrix (PSSM). The new scanning step resembles an iterative PSI-BLAST search based on the PSSM. First, it assigns an "individual score" to each subsequence of appropriate length within the input sequences using the initial PSSM. Second, it computes an E-value from each individual score, to assess the agreement between the corresponding subsequence and the PSSM. Third, it permits subsequences with E-values falling below a threshold to contribute to the underlying PSSM, which is then updated using the Bayesian calculus. A-GLAM iterates its scanning step to convergence, at which point no new subsequences contribute to the PSSM. After convergence, A-GLAM reports predicted regulatory elements within each sequence in order of increasing E-values, so users have a statistical evaluation of the predicted elements in a convenient presentation. Thus, although the Gibbs sampling step in A-GLAM finds at most one regulatory element per input sequence, the scanning step can now rapidly locate further instances of the element in each sequence. CONCLUSION: Datasets from experiments determining the binding sites of transcription factors were used to evaluate the improvement to A-GLAM. Typically, the datasets included several sequences containing multiple instances of a regulatory motif. The improvements to A-GLAM permitted it to predict the multiple instances

Differentiation of core promoter architecture between plants and mammals revealed by LDSS analysis

Mammalian promoters are categorized into TATA and CpG-related groups, and they have complementary roles associated with differentiated transcriptional characteristics. While the TATA box is also found in plant promoters, it is not known if CpG-type promoters exist in plants. Plant promoters contain Y Patches (pyrimidine patches) in the core promoter region, and the ubiquity of these beyond higher plants is not understood as well. Sets of promoter sequences were utilized for the analysis of local distribution of short sequences (LDSS), and approximately one thousand octamer sequences have been identified as promoter constituents from Arabidopsis, rice, human and mouse, respectively. Based on their localization profiles, the identified octamer sequences were classified into several major groups, REG (Regulatory Element Group), TATA box, Inr (Initiator), Kozak, CpG and Y Patch. Comparison of the four species has revealed three categories: (i) shared groups found in both plants and mammals (TATA box), (ii) common groups found in both kingdoms but the utilized sequence is differentiated (REG, Inr and Kozak) and (iii) specific groups found in either plants or mammals (CpG and Y Patch). Our comparative LDSS analysis has identified conservation and differentiation of promoter architectures between higher plants and mammals

The biological function of some human transcription factor binding motifs varies with position relative to the transcription start site

A number of previous studies have predicted transcription factor binding sites (TFBSs) by exploiting the position of genomic landmarks like the transcriptional start site (TSS). The studies’ methods are generally too computationally intensive for genome-scale investigation, so the full potential of ‘positional regulomics’ to discover TFBSs and determine their function remains unknown. Because databases often annotate the genomic landmarks in DNA sequences, the methodical exploitation of positional regulomics has become increasingly urgent. Accordingly, we examined a set of 7914 human putative promoter regions (PPRs) with a known TSS. Our methods identified 1226 eight-letter DNA words with significant positional preferences with respect to the TSS, of which only 608 of the 1226 words matched known TFBSs. Many groups of genes whose PPRs contained a common word displayed similar expression profiles and related biological functions, however. Most interestingly, our results included 78 words, each of which clustered significantly in two or three different positions relative to the TSS. Often, the gene groups corresponding to different positional clusters of the same word corresponded to diverse functions, e.g. activation or repression in different tissues. Thus, different clusters of the same word likely reflect the phenomenon of ‘positional regulation’, i.e. a word's regulatory function can vary with its position relative to a genomic landmark, a conclusion inaccessible to methods based purely on sequence. Further integrative analysis of words co-occurring in PPRs also yielded 24 different groups of genes, likely identifying cis-regulatory modules de novo. Whereas comparative genomics requires precise sequence alignments, positional regulomics exploits genomic landmarks to provide a ‘poor man's alignment’. By exploiting the phenomenon of positional regulation, it uses position to differentiate the biological functions of subsets of TFBSs sharing a common sequence motif