Human coronavirus (hCoV) HKU1 is one of six hCoVs identified to date and the only one with an unidentified cellular receptor. hCoV-HKU1 encodes a hemagglutinin-esterase (HE) protein that is unique to the group a betacoronaviruses (group 2a). The function of HKU1-HE remains largely undetermined. In this study, we examined binding of the S1 domain of hCoV-HKU1 spike to a panel of cells and found that the S1 could specifically bind on the cell surface of a human rhabdomyosarcoma cell line, RD. Pretreatment of RD cells with neuraminidase (NA) and trypsin greatly reduced the binding, suggesting that the binding was mediated by sialic acids on glycoproteins. However, unlike other group 2a CoVs, e.g., hCoV-OC43, for which 9-O-acetylated sialic acid (9-O-Ac-Sia) serves as a receptor determinant, HKU1-S1 bound with neither 9-O-Ac-Sia-containing glycoprotein(s) nor rat and mouse erythrocytes. Nonetheless, the HKU1-HE was similar to OC43-HE, also possessed sialate-O-acetylesterase activity, and acted as a receptor-destroying enzyme (RDE) capable of eliminating the binding of HKU1-S1 to RD cells, whereas the O-acetylesterase-inactive HKU1-HE mutant lost this capacity. Using primary human ciliated airway epithelial (HAE) cell cultures, the only in vitro replication model for hCoV-HKU1 infection, we confirmed that pretreatment of HAE cells with HE but not the enzymatically inactive mutant blocked hCoV-HKU1 infection. These results demonstrate that hCoV-HKU1 exploits O-Ac-Sia as a cellular attachment receptor determinant to initiate the infection of host cells and that its HE protein possesses the corresponding sialate-O-acetylesterase RDE activity. IMPORTANCEHuman coronaviruses (hCoV) are important human respiratory pathogens. Among the six hCoVs identified to date, only hCoV-HKU1 has no defined cellular receptor. It is also unclear whether hemagglutinin-esterase (HE) protein plays a role in viral entry. In this study, we found that, similarly to other members of the group 2a CoVs, sialic acid moieties on glycoproteins are critical receptor determinants for the hCoV-HKU1 infection. Interestingly, the virus seems to employ a type of sialic acid different from those employed by other group 2a CoVs. In addition, we determined that the HKU1-HE protein is an O-acetylesterase and acts as a receptor-destroying enzyme (RDE) for hCoV-HKU1. This is the first study to demonstrate that hCoV-HKU1 uses certain types of O-acetylated sialic acid residues on glycoproteins to initiate the infection of host cells and that the HKU1-HE protein possesses sialate-O-acetylesterase RDE activity.H uman coronaviruses (hCoVs) are enveloped RNA viruses. They are usually associated with mild to moderate respiratory tract illnesses but can also cause severe and highly lethal disease, depending on the virus strain (1). Six hCoV strains have been identified to date and belong to four different groups, including hCoV-229E and hCoV-NL63 in the alphacoronaviruses (group 1); hCoV-OC43 and hCoV-HKU1 in the group a betacoronaviruses (group 2a); severe acut...

We present a new de novo transcriptome assembler, Bridger, which takes advantage of techniques employed in Cufflinks to overcome limitations of the existing de novo assemblers. When tested on dog, human, and mouse RNA-seq data, Bridger assembled more full-length reference transcripts while reporting considerably fewer candidate transcripts, hence greatly reducing false positive transcripts in comparison with the state-of-the-art assemblers. It runs substantially faster and requires much less memory space than most assemblers. More interestingly, Bridger reaches a comparable level of sensitivity and accuracy with Cufflinks. Bridger is available at https://sourceforge.net/projects/rnaseqassembly/files/?source=navbar.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-015-0596-2) contains supplementary material, which is available to authorized users.

We develop new techniques for deriving strong computational lower bounds for a class of well-known NP-hard problems. This class includes WEIGHTED SATISFIABILITY, DOMINATING SET, HITTING SET, SET COVER, CLIQUE, and INDEPENDENT SET. For example, although a trivial enumeration can easily test in time O(n k ) if a given graph of n vertices has a clique of size k, we prove that unless an unlikely collapse occurs in parameterized complexity theory, the problem is not solvable in time f (k)n o(k) for any function f , even if we restrict the parameter values to be bounded by an arbitrarily small function of n. Under the same assumption, we prove that even if we restrict the parameter values k to be of the order Θ(μ(n)) for any reasonable function μ, no algorithm of running time n o(k) can test if a graph of n vertices has a clique of size k. Similar strong lower bounds on the computational complexity are also derived for other NP-hard problems in the above class. Our techniques can be further extended to derive computational lower bounds on polynomial time approximation schemes for NP-hard optimization problems. For example, we prove that the NP-hard DISTINGUISHING SUBSTRING SELECTION problem, for which a polynomial time approximation scheme has been recently developed, has no polynomial time approximation schemes of running time f (1/ )n o(1/ ) for any function f unless an unlikely collapse occurs in parameterized complexity theory. ✩ A preliminary version of this paper "Linear FPT reductions and computational lower bounds" was presented at

In fungi and metazoans, extracellular signals are often perceived by G-protein-coupled receptors (GPCRs) and transduced through heterotrimeric G-protein complexes to downstream targets. Plant heterotrimeric G proteins are also involved in diverse biological processes, but little is known about their upstream receptors. Moreover, the presence of bona fide GPCRs in plants is yet to be established. In Arabidopsis (Arabidopsis thaliana), heterotrimeric G protein consists of one Gα subunit (G PROTEIN α-SUBUNIT1), one Gβ subunit (ARABIDOPSIS G PROTEIN β-SUBUNIT1 [AGB1]), and three Gγs subunits (ARABIDOPSIS G PROTEIN γ-SUBUNIT1 [AGG1], AGG2, and AGG3). We identified AGB1 from a suppressor screen of BAK1-interacting receptor-like kinase1-1 (bir1-1), a mutant that activates cell death and defense responses mediated by the receptor-like kinase (RLK) SUPPRESSOR OF BIR1-1. Mutations in AGB1 suppress the cell death and defense responses in bir1-1 and transgenic plants overexpressing SUPPRESSOR OF BIR1-1. In addition, agb1 mutant plants were severely compromised in immunity mediated by three other RLKs, FLAGELLIN-SENSITIVE2 (FLS2), Elongation Factor-TU RECEPTOR (EFR), and CHITIN ELICITOR RECEPTOR KINASE1 (CERK1), respectively. By contrast, G PROTEIN α-SUBUNIT1 is not required for either cell death in bir1-1 or pathogen-associated molecular pattern-triggered immunity mediated by FLS2, EFR, and CERK1. Further analysis of agg1 and agg2 mutant plants indicates that AGG1 and AGG2 are also required for pathogen-associated molecular pattern-triggered immune responses mediated by FLS2, EFR, and CERK1, as well as cell death and defense responses in bir1-1. We hypothesize that the Arabidopsis heterotrimeric G proteins function as a converging point of plant defense signaling by mediating responses initiated by multiple RLKs, which may fulfill equivalent roles to GPCRs in fungi and animals.

Based on the framework of parameterized complexity theory, we derive tight lower bounds on the computational complexity for a number of well-known NP-hard problems. We start by proving a general result, namely that the parameterized weighted satisfiability problem on depth-t circuits cannot be solved in time n o(k) poly(m), where n is the circuit input length, m is the circuit size, and k is the parameter, unless the (t − 1)-st level W [t − 1] of the Whierarchy collapses to FPT. By refining this technique, we prove that a group of parameterized NP-hard problems, including weighted sat, dominating set, hitting set, set cover, and feature set, cannot be solved in time n o(k) poly(m), where n is the size of the universal set from which the k elements are to be selected and m is the instance size, unless the first level W [1] of the W-hierarchy collapses to FPT. We also prove that another group of parameterized problems which includes weighted q-sat (for any fixed q ≥ 2), clique, and independent set, cannot be solved in time n o(k) unless all search problems in the syntactic class SNP, introduced by Papadimitriou and Yannakakis, are solvable in subexponential time. Note that all these parameterized problems have trivial algorithms of running time either n k poly(m) or O(n k).

The advancements of information technology and related processing techniques have created a fertile base for progress in many scientific fields and industries. In the fields of drug discovery and development, machine learning techniques have been used for the development of novel drug candidates. The methods for designing drug targets and novel drug discovery now routinely combine machine learning and deep learning algorithms to enhance the efficiency, efficacy, and quality of developed outputs. The generation and incorporation of big data, through technologies such as high-throughput screening and high through-put computational analysis of databases used for both lead and target discovery, has increased the reliability of the machine learning and deep learning incorporated techniques. The use of these virtual screening and encompassing online information has also been highlighted in developing lead synthesis pathways. In this review, machine learning and deep learning algorithms utilized in drug discovery and associated techniques will be discussed. The applications that produce promising results and methods will be reviewed.

The Arabidopsis receptor-like kinase (RLK) BIR1 (BAK1-INTERACTING RECEPTOR-LIKE KINASE 1) functions as a negative regulator of plant immunity. Previous work showed that loss-of-function of BIR1 leads to constitutive activation of cell death and defense responses. These autoimmune phenotypes are partially dependent on another RLK, SOBIR1. In order to identify additional components involved in the BIR1-regulated plant defense signaling pathway, a suppressor screen was carried out in the bir1-1 pad4-1 mutant background. Mutations in the suppressor mutants were identified by genetic mapping and re-sequencing of the mutant genomes. A number of suppressor mutants were found to carry mutations in an additional RLK, BAK1, indicating that BAK1 is required for activation of cell death and defense responses in bir1-1. Co-immunoprecipitation analysis revealed that BAK1 and SOBIR1 associate with each other in planta when the function of BIR1 is compromised. Although BAK1 was previously characterized as a negative regulator of cell death, our study highlights a novel role of BAK1 in promoting cell death and defense responses in conjunction with SOBIR1.

Computed tomography (CT) examinations are commonly used to predict lung nodule malignancy in patients, which are shown to improve noninvasive early diagnosis of lung cancer. It remains challenging for computational approaches to achieve performance comparable to experienced radiologists. Here we present NoduleX, a systematic approach to predict lung nodule malignancy from CT data, based on deep learning convolutional neural networks (CNN). For training and validation, we analyze >1000 lung nodules in images from the LIDC/IDRI cohort. All nodules were identified and classified by four experienced thoracic radiologists who participated in the LIDC project. NoduleX achieves high accuracy for nodule malignancy classification, with an AUC of ~0.99. This is commensurate with the analysis of the dataset by experienced radiologists. Our approach, NoduleX, provides an effective framework for highly accurate nodule malignancy prediction with the model trained on a large patient population. Our results are replicable with software available at http://bioinformatics.astate.edu/NoduleX.