VP1 sequencing of all human rhinovirus serotypes: insights into genus phylogeny and susceptibility to antiviral capsid-binding compounds

Rhinoviruses are the most common infectious agents of humans. They are the principal etiologic agents of afebrile viral upper-respiratory-tract infections (the common cold). Human rhinoviruses (HRVs) comprise a genus within the family Picornaviridae. There are >100 serotypically distinct members of this genus. In order to better understand their phylogenetic relationship, the nucleotide sequence for the major surface protein of the virus capsid, VP1, was determined for all known HRV serotypes and one untyped isolate (HRV-Hanks). Phylogenetic analysis of deduced amino acid sequence data support previous studies subdividing the genus into two species containing all but one HRV serotype (HRV-87). Seventy-five HRV serotypes and HRV-Hanks belong to species HRV-A, and twenty-five HRV serotypes belong to species HRV-B. Located within VP1 is a hydrophobic pocket into which small-molecule antiviral compounds such as pleconaril bind and inhibit functions associated with the virus capsid. Analyses of the amino acids that constitute this pocket indicate that the sequence correlates strongly with virus susceptibility to pleconaril inhibition. Further, amino acid changes observed in reduced susceptibility variant viruses recovered from patients enrolled in clinical trials with pleconaril were distinct from those that confer natural phenotypic resistance to the drug. These observations suggest that it is possible to differentiate rhinoviruses naturally resistant to capsid function inhibitors from those that emerge from susceptible virus populations as a result of antiviral drug selection pressure based on sequence analysis of the drug-binding pocket.     

Cross-neutralization of SARS-CoV-2 by HIV-1 specific broadly neutralizing antibodies and polyclonal plasma

Cross-reactive epitopes (CREs) are similar epitopes on viruses that are recognized or neutralized by same antibodies. The S protein of SARS-CoV-2, similar to type I fusion proteins of viruses such as HIV-1 envelope (Env) and influenza hemagglutinin, is heavily glycosylated. Viral Env glycans, though host derived, are distinctly processed and thereby recognized or accommodated during antibody responses. In recent years, highly potent and/or broadly neutralizing human monoclonal antibodies (bnAbs) that are generated in chronic HIV-1 infections have been defined. These bnAbs exhibit atypical features such as extensive somatic hypermutations, long complementary determining region (CDR) lengths, tyrosine sulfation and presence of insertions/deletions, enabling them to effectively neutralize diverse HIV-1 viruses despite extensive variations within the core epitopes they recognize. As some of the HIV-1 bnAbs have evolved to recognize the dense viral glycans and cross-reactive epitopes (CREs), we assessed if these bnAbs cross-react with SARS-CoV-2. Several HIV-1 bnAbs showed cross-reactivity with SARS-CoV-2 while one HIV-1 CD4 binding site bnAb, N6, neutralized SARS-CoV-2. Furthermore, neutralizing plasma antibodies of chronically HIV-1 infected children showed cross neutralizing activity against SARS-CoV-2 pseudoviruses. Collectively, our observations suggest that human monoclonal antibodies tolerating extensive epitope variability can be leveraged to neutralize pathogens with related antigenic profile.     

Inferring Social Status and Rich Club Effects in Enterprise Communication Networks

Social status, defined as the relative rank or position that an individual holds in a social hierarchy, is known to be among the most important motivating forces in social behaviors. In this paper, we consider the notion of status from the perspective of a position or title held by a person in an enterprise. We study the intersection of social status and social networks in an enterprise. We study whether enterprise communication logs can help reveal how social interactions and individual status manifest themselves in social networks. To that end, we use two enterprise datasets with three communication channels — voice call, short message, and email — to demonstrate the social-behavioral differences among individuals with different status. We have several interesting findings and based on these findings we also develop a model to predict social status. On the individual level, high-status individuals are more likely to be spanned as structural holes by linking to people in parts of the enterprise networks that are otherwise not well connected to one another. On the community level, the principle of homophily, social balance and clique theory generally indicate a “rich club” maintained by high-status individuals, in the sense that this community is much more connected, balanced and dense. Our model can predict social status of individuals with 93% accuracy.     

Exploring and exploiting disease interactions from multi-relational gene and phenotype networks

The availability of electronic health care records is unlocking the potential for novel studies on understanding and modeling disease co-morbidities based on both phenotypic and genetic data. Moreover, the insurgence of increasingly reliable phenotypic data can aid further studies on investigating the potential genetic links among diseases. The goal is to create a feedback loop where computational tools guide and facilitate research, leading to improved biological knowledge and clinical standards, which in turn should generate better data. We build and analyze disease interaction networks based on data collected from previous genetic association studies and patient medical histories, spanning over 12 years, acquired from a regional hospital. By exploring both individual and combined interactions among these two levels of disease data, we provide novel insight into the interplay between genetics and clinical realities. Our results show a marked difference between the well defined structure of genetic relationships and the chaotic co-morbidity network, but also highlight clear interdependencies. We demonstrate the power of these dependencies by proposing a novel multi-relational link prediction method, showing that disease co-morbidity can enhance our currently limited knowledge of genetic association. Furthermore, our methods for integrated networks of diverse data are widely applicable and can provide novel advances for many problems in systems biology and personalized medicine.     

TMBHMM: a frequency profile based HMM for predicting the topology of transmembrane beta barrel proteins and the exposure status of transmembrane residues

Transmembrane beta barrel (TMB) proteins are found in the outer membranes of bacteria, mitochondria and chloroplasts. TMBs are involved in a variety of functions such as mediating flux of metabolites and active transport of siderophores, enzymes and structural proteins, and in the translocation across or insertion into membranes. We present here TMBHMM, a computational method based on a hidden Markov model for predicting the structural topology of putative TMBs from sequence. In addition to predicting transmembrane strands, TMBHMM also predicts the exposure status (i.e., exposed to the membrane or hidden in the protein structure) of the residues in the transmembrane region, which is a novel feature of the TMBHMM method. Furthermore, TMBHMM can also predict the membrane residues that are not part of beta barrel forming strands. The training of the TMBHMM was performed on a non-redundant data set of 19 TMBs. The self-consistency test yielded Q(2) accuracy of 0.87, Q(3) accuracy of 0.83, Matthews correlation coefficient of 0.74 and SOV for beta strand of 0.95. In this self-consistency test the method predicted 83% of transmembrane residues with correct exposure status. On an unseen, non-redundant test data set of 10 proteins, the 2-state and 3-state TMBHMM prediction accuracies are around 73% and 72%, respectively, and are comparable to other methods from the literature. The TMBHMM web server takes an amino acid sequence or a multiple sequence alignment as an input and predicts the exposure status and the structural topology as output. The TMBHMM web server is available under the tmbhmm tab at: http://service.bioinformatik.uni-saarland.de/tmx-site/.     

Coxsackievirus A24 variant uses sialic acid-containing O-linked glycoconjugates as cellular receptors on human ocular cells

Coxsackievirus A24 variant (CVA24v) is a main causative agent of acute hemorrhagic conjunctivitis (AHC), which is a highly contagious eye infection. Previously it has been suggested that CVA24v uses sialic acid-containing glycoconjugates as attachment receptors on corneal cells, but the nature of these receptors is poorly described. Here, we set out to characterize and identify the cellular components serving as receptors for CVA24v. Binding and infection experiments using corneal cells treated with deglycosylating enzymes or metabolic inhibitors of de novo glycosylation suggested that the receptor(s) used by CVA24v are constituted by sialylated O-linked glycans that are linked to one or more cell surface proteins but not to lipids. CVA24v bound better to mouse L929 cells overexpressing human P-selectin glycoprotein ligand-1 (PSGL-1) than to mock-transfected cells, suggesting that PSGL-1 is a candidate receptor for CVA24v. Finally, binding competition experiments using a library of mono- and oligosaccharides mimicking known PSGL-1 glycans suggested that CVA24v binds to Neu5Acα2,3Gal disaccharides (Neu5Ac is N-acetylneuraminic acid). These results provide further insights into the early steps of the CVA24v life cycle.     

Combinatorial screening algorithm to engineer multiepitope subunit vaccine targeting human T-lymphotropic virus-1 infection

Human T-lymphotropic virus (HTLV), the first human retrovirus has been discovered which is known to cause the age-old assassinating disease HTLV-1 associated myelopathy. Cancer caused by this virus is adult T cell leukemia/lymphoma which targets 10–20 million throughout the world. The effect of this virus extends to the fact that it causes chronic disease to the spinal cord resulting in loss of sensation and further causes blood cancer. So, to overcome the complications, we designed a subunit vaccine by the assimilation of B-cell, cytotoxic T-lymphocyte , and helper T-lymphocyte epitopes. The epitopes were joined together along with adjuvant and linkers and a vaccine was fabricated which was further subjected to 3D modeling. The physiochemical properties, allergenicity, and antigenicity were evaluated. Molecular docking and dynamics were performed with the obtained 3D model against toll like receptor (TLR-3) immune receptor. Lastly, in silico cloning was performed to ensure the expression of the designed vaccine in pET28a(+) expression vector. The future prospects of the study entailed the in vitro and in vivo experimental analysis for evaluating the immune response of the designed vaccine construct.     

Allosteric Regulation of E-Cadherin Adhesion

Cadherins are transmembrane adhesion proteins that maintain intercellular cohesion in all tissues, and their rapid regulation is essential for organized tissue remodeling. Despite some evidence that cadherin adhesion might be allosterically regulated, testing of this has been hindered by the difficulty of quantifying altered E-cadherin binding affinity caused by perturbations outside the ectodomain binding site. Here, measured kinetics of cadherin-mediated intercellular adhesion demonstrated quantitatively that treatment with activating, anti-E-cadherin antibodies or the dephosphorylation of a cytoplasmic binding partner, p120^ctn, increased the homophilic binding affinity of E-cadherin. Results obtained with Colo 205 cells, which express inactive E-cadherin and do not aggregate, demonstrated that four treatments, which induced Colo 205 aggregation and p120^ctn dephosphorylation, triggered quantitatively similar increases in E-cadherin affinity. Several processes can alter cell aggregation, but these results directly demonstrated the allosteric regulation of cell surface E-cadherin by p120^ctn dephosphorylation.     

A genetic contribution to circulating cytokines and obesity in children

Cytokines are considered to be involved in obesity-related metabolic diseases. Study objectives are to determine the heritability of circulating cytokine levels, to investigate pleiotropy between cytokines and obesity traits, and to present genome scan results for cytokines in 1030 Hispanic children enrolled in VIVA LA FAMILIA Study. Cytokine phenotypes included monocyte chemoattractant protein-1 (MCP-1), tumor necrosis factor-alpha (TNF-alpha), leptin, adiponectin, soluble intercellular adhesion molecule-1 (sICAM-1), transforming growth factor beta 1 (TGF-beta1), C-reactive protein (CRP), regulated upon activation, normal T-cell expressed and secreted (RANTES) and eotaxin. Obesity-related phenotypes included body mass index (BMI), fat mass (FM), truncal FM and fasting serum insulin. Heritabilities ranged from 0.33 to 0.97. Pleiotropy was observed between cytokines and obesity traits. Positive genetic correlations were seen between CRP, leptin, MCP-1 and obesity traits, and negative genetic correlations with adiponectin, ICAM-1 and TGF-beta1. Genome-wide scan of sICAM-1 mapped to chromosome 3 (LOD=3.74) between markers D3S1580 and D3S1601, which flanks the adiponectin gene (ADIPOQ). Suggestive linkage signals were found in other chromosomal regions for other cytokines. In summary, significant heritabilities for circulating cytokines, pleiotropy between cytokines and obesity traits, and linkage for sICAM-1 on chromosome 3q substantiate a genetic contribution to circulating cytokine levels in Hispanic children.