Cflec-5, a pattern recognition receptor in scallop Chlamys farreri agglutinating yeast Pichia pastoris

C-type lectins are a superfamily of carbohydrate-recognition proteins which play crucial roles as pattern recognition receptors (PRRs) in the innate immunity. In this study, the full-length cDNA of a C-type lectin was cloned from scallop Chlamys farreri (designated as Cflec-5) by expression sequence tag (EST) analysis and rapid amplification of cDNA ends (RACE) approach The full-length cDNA of Cflec-5 was of 1412 bp. The open reading frame encoded a polypeptide of 153 amino acids, including a signal sequence and a conserved carbohydrate-recognition domain with the EPN motif determining the mannose-binding specificity The deduced amino acid sequence of Cflec-5 showed high similarity to members of C-type lectin superfamily. The quantitative real-time PCR was performed to investigate the tissue distribution of Cflec-5 mRNA and its temporal expression profiles in hemocytes post pathogen-associated molecular patterns (PAMPs) stimulation. In healthy scallops, the Cflec-5 mRNA was mainly detected in gill and mantle, and marginally in other tissues The mRNA expression of Cflec-5 could be significantly induced by lipopolysaccharide (LPS) and glucan stimulation and reached the maximum level at 6 h and 12 h, respectively But its expression level did not change significantly during peptidoglycan (PGN) stimulation The function of Cflec-5 was investigated by recombination and expression of the cDNA fragment encoding its mature peptide in Escherichia coli Rosetta Gami (DE3) The recombinant Cflec-5 agglutinated Pichia pastoris in a calcium-independent way The agglutinating activity could be inhibited by D-mannose. LPS and glucan, but not by D-galactose or PGN. These results collectively suggested that Cflec-5 was involved in the innate Immune response of scallops and might contribute to nonself-recognition through its interaction with various PAMPs (C) 2010 Elsevier Ltd All rights reserved

A fibrinogen-related protein from bay scallop Argopecten irradians involved in innate immunity as pattern recognition receptor

The family of fibrinogen-related proteins (FREPs) is a group of proteins with fibrinogen-like domains. Many members of this family play important roles as pattern recognition receptors in innate immune responses. The cDNA of bay scallop Argopecten irradians FREP (designated as AiFREP) was cloned by rapid amplification of cDNA ends (RACE) method based on the expressed sequence tag (EST). The full-length cDNA of AiFREP was of 990 bp. The open reading frame encoded a polypeptide of 251 amino acids, including a signal sequence and a 213 amino acids fibrinogen-like domain. The fibrinogen-like domain of AiFREP was highly similar to those of mammalian ficolins and other FREPs. The temporal expression of AiFREP mRNA in hemolymph was examined by fluorescent quantitative real-time PCR. The mRNA level of scallops challenged by Listonella anguillarum was significantly up-regulated, peaked to 9.39-fold at 9 h after stimulation, then dropped back to 4.37-fold at 12 h, while there was no significant change in the Micrococcus luteus challenged group in all periods of treatment. The function of AiFREP was investigated by recombination and expression of the cDNA fragment encoding its mature peptide in Escherichia coli Rosetta gami (DE3). The recombinant AiFREP (rAiFREP) agglutinated chicken erythrocytes and human A, B, O-type erythrocytes. The agglutinating activities were calcium-dependent and could be inhibited by acetyl group-containing carbohydrates. rAiFREP also agglutinated Gram-negative bacteria E. coli JM109, L anguillarum and Gram-positive bacteria M. luteus in the presence of calcium ions. These results collectively suggested that AiFREP functions as a pattern recognition receptor in the immune response of bay scallop and contributed to nonself recognition in invertebrates, which would also provide clues for elucidating the evolution of the lectin pathway of the complement system. (C) 2008 Elsevier Ltd. All rights reserved.

Molecular cloning and characterization of a short type peptidoglycan recognition protein (CfPGRP-S1) cDNA from Zhikong scallop Chlamys farreri

Peptidoglycan recognition protein (PGRP) specifically binds to peptidoglycan and plays a crucial role in the innate immune responses as a pattern recognition receptor (PRR). The cDNA of a short type PGRP was cloned from scallop Chlamys farreri (named CfPGRP-SI) by homology cloning with degenerate primers, and confirmed by virtual Northern blots. The full length of CfPGRP-SI cDNA was 1073 bp in length, including a 5 ' untranslated region (UTR) of 59 bp, a 3 ' UTR of 255 bp, and an open reading frame (ORF) of 759 bp encoding a polypeptide of 252 amino acids with an estimated molecular mass of 27.88 kDa and a predicted isoelectric point of 8.69. BLAST analysis revealed that CfPGRP-S1 shared high identities with other known PGRPs. A conserved PGRP domain and three zinc-binding sites were present at its C-terminus. The temporal expression of QPGRP-S1 gene in healthy, Vibrio anguillarum-challenged and Micrococcus lysodeikticus-challenged scallops was measured by RT-PCR analysis. The expression of CfPGRP-S1 was upregulated initially in the first 12 h or 24 h either by M. lysodeikticus or V. anguillarum challenge and reached the maximum level at 24 h or 36 h, then dropped progressively, and recovered to the original level as the stimulation decreased at 72 h. There was no significant difference between V. anguillarum and M. lysodeikticus challenge. The results indicated that the CfPGRP-S1 was a constitutive and inducible acute-phase protein which was involved in the immune response against bacterial infection. (c) 2007 Elsevier Ltd. All rights reserved.

Origin and evolution of the RIG-I like RNA helicase gene family

Background: The DExD/H domain containing RNA helicases such as retinoic acid-inducible gene I (RIG-I) and melanoma differentiation-associated gene 5 (MDA5) are key cytosolic pattern recognition receptors (PRRs) for detecting nucleotide pathogen associated molecular patterns (PAMPs) of invading viruses. The RIG-I and MDA5 proteins differentially recognise conserved PAMPs in double stranded or single stranded viral RNA molecules, leading to activation of the interferon system in vertebrates. They share three core protein domains including a RNA helicase domain near the C terminus (HELICc), one or more caspase activation and recruitment domains (CARDs) and an ATP dependent DExD/H domain. The RIG-I/MDA5 directed interferon response is negatively regulated by laboratory of genetics and physiology 2 (LGP2) and is believed to be controlled by the mitochondria antiviral signalling protein (MAVS), a CARD containing protein associated with mitochondria. Results: The DExD/H containing RNA helicases including RIG-I, MDA5 and LGP2 were analysed in silico in a wide spectrum of invertebrate and vertebrate genomes. The gene synteny of MDA5 and LGP2 is well conserved among vertebrates whilst conservation of the gene synteny of RIG-I is less apparent. Invertebrate homologues had a closer phylogenetic relationship with the vertebrate RIG-Is than the MDA5/LGP2 molecules, suggesting the RIG-I homologues may have emerged earlier in evolution, possibly prior to the appearance of vertebrates. Our data suggest that the RIG-I like helicases possibly originated from three distinct genes coding for the core domains including the HELICc, CARD and ATP dependent DExD/H domains through gene fusion and gene/domain duplication. Furthermore, presence of domains similar to a prokaryotic DNA restriction enzyme III domain (Res III), and a zinc finger domain of transcription factor (TF) IIS have been detected by bioinformatic analysis. Conclusion: The RIG-I/MDA5 viral surveillance system is conserved in vertebrates. The RIG-I like helicase family appears to have evolved from a common ancestor that originated from genes encoding different core functional domains. Diversification of core functional domains might be fundamental to their functional divergence in terms of recognition of different viral PAMPs.

Cloning and characterization of a novel C-type lectin gene from shrimp Litopenaeus vannamei

In invertebrates, C-type lectins play crucial roles in innate immunity responses by mediating the recognition of host cells to pathogens and clearing microinvaders, which interact with carbohydrates and function as pattern recognition receptors (PRRs). A novel C-type lectin gene (LvLec) cDNA was cloned from hemocytes of Litopenaeus vannamei by expressed sequence tag (EST) and rapid amplification of cDNA ends (RACE) PCR. The full-length cDNA of LvLec was of 618 bp, consisting of a 5'-terminal untranslated region (UTR) of 60 bp and a 3'-UTR of 87 bp with a poly (A) tail. The deduced amino acid sequence of LvLec possessed all conserved features critical for the fundamental structure, such as the four cysteine residues (Cys(53), Cys(128), Cys(144), Cys(152)) involved in the formation of disulfides bridges and the potential Ca2+/carbohydrate-binding sites. The high similarity and the close phylogenetic relationship of LvLec shared with C-type lectins from vertebrates and invertebrates. The structural features of LvLec indicated that it was an invertebrate counterpart of the C-type lectin family. The cDNA fragment encoding the mature peptide of LvLec was recombined and expressed in Escherichia coli BL21(DE3)-pLysS. The recombinant protein (rLvLec) could agglutinate bacteria E. coli JM109 depending on Ca2+, and the agglutination could be inhibited by mannose and EDTA. These results indicated that LvLec was a new member of C-type lectin family and involved in the immune defence response to Gram negative bacteria in Litopenaeus vannamei. (C) 2008 Elsevier Ltd. All rights reserved.

A toll receptor from Chinese shrimp Fenneropenaeus chinensis is responsive to Vibrio anguillarum infection

Toll-like receptors (TLRs) are an evolutionarily ancient family of pattern recognition receptors (PRRs), playing a crucial role in innate immune responses. Here we present a Toll homolog from Chinese shrimp Fenneropenaeus chinensis, designated FcToll. The full-length cDNA of FcToll is 4115 bp including a poly A-tail of 16 bp, encoding a putative protein of 931 amino acids. The predicted protein consists of an extracellular domain with a potential signal peptide, 16 leucine-rich repeats (LRR), two LRR-C-terminal (LRR-CT) motifs, and two LRR-N-terminal (LRR-NT) motifs, followed by a transmembrane segment of 23 amino acids, and a cytoplasmic Toll/Interteukin-IR (TIR) domain of 139 residues. Genomic structure of FcToll gene contains five exons and four introns. Phylogenetic analysis revealed that it belongs to insect-type invertebrate Toll family. Transcripts of FcToll gene were constitutively expressed in various tissues, with predominant level in lymphoid organ. Real-time PCR assays demonstrated that expression patterns of FcToll were distinctly modulated after bacterial or viral stimulation, with significant enhancement after 5 h post-Vibrio anguillorum challenge but markedly reduced levels immediately after white spot syndrome virus (WSSV) exposure. These results suggest that FcToll might be involved in innate host defense, especially against the pathogen V. anguillarum. (c) 2008 Elsevier Ltd. All rights reserved.

Pattern Recognition With Weighted Complex Networks

In this paper we introduce a weighted complex networks model to investigate and recognize structures of patterns. The regular treating in pattern recognition models is to describe each pattern as a high-dimensional vector which however is insufficient to express the structural information. Thus, a number of methods are developed to extract the structural information, such as different feature extraction algorithms used in pre-processing steps, or the local receptive fields in convolutional networks. In our model, each pattern is attributed to a weighted complex network, whose topology represents the structure of that pattern. Based upon the training samples, we get several prototypal complex networks which could stand for the general structural characteristics of patterns in different categories. We use these prototypal networks to recognize the unknown patterns. It is an attempt to use complex networks in pattern recognition, and our result shows the potential for real-world pattern recognition. A spatial parameter is introduced to get the optimal recognition accuracy, and it remains constant insensitive to the amount of training samples. We have discussed the interesting properties of the prototypal networks. An approximate linear relation is found between the strength and color of vertexes, in which we could compare the structural difference between each category. We have visualized these prototypal networks to show that their topology indeed represents the common characteristics of patterns. We have also shown that the asymmetric strength distribution in these prototypal networks brings high robustness for recognition. Our study may cast a light on understanding the mechanism of the biologic neuronal systems in object recognition as well.

Visual pattern recognition network: Its training algorithm and its optoelectronic architecture

A visual pattern recognition network and its training algorithm are proposed. The network constructed of a one-layer morphology network and a two-layer modified Hamming net. This visual network can implement invariant pattern recognition with respect to image translation and size projection. After supervised learning takes place, the visual network extracts image features and classifies patterns much the same as living beings do. Moreover we set up its optoelectronic architecture for real-time pattern recognition. (C) 1996 Optical Society of America

Study on Closed-set Speaker Identification Based on Biomimetic Pattern Recognition

In this paper, a new classifier of speaker identification has been proposed, which is based on Biomimetic pattern recognition (BPR). Distinguished from traditional speaker recognition methods, such as DWT, HMM, GMM, SVM and so on, the proposed classifier is constructed by some finite sub-space which is reasonable covering of the points in high dimensional space according to distributing characteristic of speech feature points. It has been used in the system of speaker identification. Experiment results show that better effect could be obtained especially with lesser samples. Furthermore, the proposed classifier employs a much simpler modeling structure as compared to the GMM. In addition, the basic idea "cognition" of Biomimetic pattern recognition (BPR) results in no requirement of retraining the old system for enrolling new speakers.

An Automatic Classification Method of Metabolic Cycle Based on Biomimetic Pattern Recognition

Correct classification of different metabolic cycle stages to identification cell cycle is significant in both human development and clinical diagnostics. However, it has no perfect method has been reached in classification of metabolic cycle yet. This paper exploringly puts forward an automatic classification method of metabolic cycle based on Biomimetic pattern recognition (BPR). As to the three phases of yeast metabolic cycle, the correct classification rate reaches 90%, 100% and 100% respectively.

Biomimetic pattern recognition theory and its applications

Biomimetic pattern recogntion (BPR), which is based on "cognition" instead of "classification", is much closer to the function of human being. The basis of BPR is the Principle of homology-continuity (PHC), which means the difference between two samples of the same class must be gradually changed. The aim of BPR is to find an optimal covering in the feature space, which emphasizes the "similarity" among homologous group members, rather than "division" in traditional pattern recognition. Some applications of BPR are surveyed, in which the results of BPR are much better than the results of Support Vector Machine. A novel neuron model, Hyper sausage neuron (HSN), is shown as a kind of covering units in BPR. The mathematical description of HSN is given and the 2-dimensional discriminant boundary of HSN is shown. In two special cases, in which samples are distributed in a line segment and a circle, both the HSN networks and RBF networks are used for covering. The results show that HSN networks act better than RBF networks in generalization, especially for small sample set, which are consonant with the results of the applications of BPR. And a brief explanation of the HSN networks' advantages in covering general distributed samples is also given.

Geometrical learning, descriptive geometry, and biomimetic pattern recognition

Studies on learning problems from geometry perspective have attracted an ever increasing attention in machine learning, leaded by achievements on information geometry. This paper proposes a different geometrical learning from the perspective of high-dimensional descriptive geometry. Geometrical properties of high-dimensional structures underlying a set of samples are learned via successive projections from the higher dimension to the lower dimension until two-dimensional Euclidean plane, under guidance of the established properties and theorems in high-dimensional descriptive geometry. Specifically, we introduce a hyper sausage like geometry shape for learning samples and provides a geometrical learning algorithm for specifying the hyper sausage shapes, which is then applied to biomimetic pattern recognition. Experimental results are presented to show that the proposed approach outperforms three types of support vector machines with either a three degree polynomial kernel or a radial basis function kernel, especially in the cases of high-dimensional samples of a finite size. (c) 2005 Elsevier B.V. All rights reserved.

Manifold covering theory in biomimetic pattern recognition

We describe a new model which is based on the concept of cognizing theory. The method identifies subsets of the data which are embedded in arbitrary oriented lower dimensional space. We definite manifold covering in biomimetic pattern recognition, and study its property. Furthermore, we propose this manifold covering algorithm based on Biomimetic Pattern Recognition. At last, the experimental results for face recognition demonstrates that the correct rejection rate of the test samples excluded in the classes of training samples is very high and effective.

Research on continuous speech recognition with small vocabulary based on biomimetic pattern recognition

Based on biomimetic pattern recognition theory, we proposed a novel speaker-independent continuous speech keyword-spotting algorithm. Without endpoint detection and division, we can get the minimum distance curve between continuous speech samples and every keyword-training net through the dynamic searching to the feature-extracted continuous speech. Then we can count the number of the keywords by investigating the vale-value and the numbers of the vales in the curve. Experiments of small vocabulary continuous speech with various speaking rate have got good recognition results and proved the validity of the algorithm.