Sequence-based Search Research Articles

De novo transcriptome assembly databases for the butterfly orchid Phalaenopsis equestris.

Orchids are renowned for their spectacular flowers and ecological adaptations. After the sequencing of the genome of the tropical epiphytic orchid Phalaenopsis equestris, we combined Illumina HiSeq2000 for RNA-Seq and Trinity for de novo assembly to characterize the transcriptomes for 11 diverse P. equestris tissues representing the root, stem, leaf, flower buds, column, lip, petal, sepal and three developmental stages of seeds. Our aims were to contribute to a better understanding of the molecular mechanisms driving the analysed tissue characteristics and to enrich the available data for P. equestris. Here, we present three databases. The first dataset is the RNA-Seq raw reads, which can be used to execute new experiments with different analysis approaches. The other two datasets allow different types of searches for candidate homologues. The second dataset includes the sets of assembled unigenes and predicted coding sequences and proteins, enabling a sequence-based search. The third dataset consists of the annotation results of the aligned unigenes versus the Nonredundant (Nr) protein database, Kyoto Encyclopaedia of Genes and Genomes (KEGG) and Clusters of Orthologous Groups (COG) databases with low e-values, enabling a name-based search.

Identification of Essential Genes in the Salmonella Phage SPN3US Reveals Novel Insights into Giant Phage Head Structure and Assembly.

ABSTRACTGiant tailed bacterial viruses, or phages, such as Pseudomonas aeruginosa phage ϕKZ, have long genomes packaged into large, atypical virions. Many aspects of ϕKZ and related phage biology are poorly understood, mostly due to the fact that the functions of the majority of their proteins are unknown. We hypothesized that the Salmonella enterica phage SPN3US could be a useful model phage to address this gap in knowledge. The 240-kb SPN3US genome shares a core set of 91 genes with ϕKZ and related phages, ∼61 of which are virion genes, consistent with the expectation that virion complexity is an ancient, conserved feature. Nucleotide sequencing of 18 mutants enabled assignment of 13 genes as essential, information which could not have been determined by sequence-based searches for 11 genes. Proteome analyses of two SPN3US virion protein mutants with knockouts in 64 and 241 provided new insight into the composition and assembly of giant phage heads. The 64 mutant analyses revealed all the genetic determinants required for assembly of the SPN3US head and a likely head-tail joining role for gp64, and its homologs in related phages, due to the tailless-particle phenotype produced. Analyses of the mutation in 241, which encodes an RNA polymerase β subunit, revealed that without this subunit, no other subunits are assembled into the head, and enabled identification of a “missing” β′ subunit domain. These findings support SPN3US as an excellent model for giant phage research, laying the groundwork for future analyses of their highly unusual virions, host interactions, and evolution.IMPORTANCE In recent years, there has been a paradigm shift in virology with the realization that extremely large viruses infecting prokaryotes (giant phages) can be found in many environments. A group of phages related to the prototype giant phage ϕKZ are of great interest due to their virions being among the most complex of prokaryotic viruses and their potential for biocontrol and phage therapy applications. Our understanding of the biology of these phages is limited, as a large proportion of their proteins have not been characterized and/or have been deemed putative without any experimental verification. In this study, we analyzed Salmonella phage SPN3US using a combination of genomics, genetics, and proteomics and in doing so revealed new information regarding giant phage head structure and assembly and virion RNA polymerase composition. Our findings demonstrate the suitability of SPN3US as a model phage for the growing group of phages related to ϕKZ.

Carbohydrate-binding module 74 is a novel starch-binding domain associated with large and multidomain α-amylase enzymes.

Microbacterium aurum B8.A is a bacterium that originates from a potato starch-processing plant and employs a GH13 α-amylase (MaAmyA) enzyme that forms pores in potato starch granules. MaAmyA is a large and multi-modular protein that contains a novel domain at its C terminus (Domain 2). Deletion of Domain 2 from MaAmyA did not affect its ability to degrade starch granules but resulted in a strong reduction in granular pore size. Here, we separately expressed and purified this Domain 2 in Escherichia coli and determined its likely function in starch pore formation. Domain 2 independently binds amylose, amylopectin, and granular starch but does not have any detectable catalytic (hydrolytic or oxidizing) activity on α-glucan substrates. Therefore, we propose that this novel starch-binding domain is a new carbohydrate-binding module (CBM), the first representative of family CBM74 that assists MaAmyA in efficient pore formation in starch granules. Protein sequence-based BLAST searches revealed that CBM74 occurs widespread, but in bacteria only, and is often associated with large and multi-domain α-amylases containing family CBM25 or CBM26 domains. CBM74 may specifically function in binding to granular starches to enhance the capability of α-amylase enzymes to degrade resistant starches (RSs). Interestingly, the majority of family CBM74 representatives are found in α-amylases originating from human gut-associated Bifidobacteria, where they may assist in resistant starch degradation. The CBM74 domain thus may have a strong impact on the efficiency of RS digestion in the mammalian gastrointestinal tract.

GCGene: a gene resource for gastric cancer with literature evidence.

Gastric cancer (GC) is the fifth most common cancer and third leading cause of cancer-related deaths worldwide. Its lethality primarily stems from a lack of detection strategies for early stages of GC and a lack of noninvasive detection strategies for advanced stages. The development of early diagnostic biomarkers largely depends on understanding the biological pathways and regulatory mechanisms associated with putative GC genes. Unfortunately, the GC-implicated genes that have been identified thus far are scattered among thousands of published studies, and no systematic summary is available, which hinders the development of a large-scale genetic screen. To provide a publically accessible resource tool to meet this need, we constructed a literature-based database GCGene (Gastric Cancer Gene database) with comprehensive annotations supported by a user-friendly website. In the current release, we have collected 1,815 unique human genes including 1,678 protein-coding and 137 non-coding genes curated from extensive examination of 3,142 PubMed abstracts. The resulting database has a convenient web-based interface to facilitate both textual and sequence-based searches. All curated genes in GCGene are downloadable for advanced bioinformatics data mining. Gene prioritization was performed to rank the relative relevance of these genes in GC development. The 100 top-ranked genes are highly mutated according to the cohort of published studies we reviewed. By conducting a network analysis of these top-ranked GC-associated genes in the human interactome, we were able to identify strong links between 8 highly connected genes with low expression and patient survival time. GCGene is freely available to academic users at http://gcgene.bioinfo-minzhao.org/.

Bioinformatics analysis identifies several intrinsically disordered human E3 ubiquitin-protein ligases.

The ubiquitin-proteasome system targets misfolded proteins for degradation. Since the accumulation of such proteins is potentially harmful for the cell, their prompt removal is important. E3 ubiquitin-protein ligases mediate substrate ubiquitination by bringing together the substrate with an E2 ubiquitin-conjugating enzyme, which transfers ubiquitin to the substrate. For misfolded proteins, substrate recognition is generally delegated to molecular chaperones that subsequently interact with specific E3 ligases. An important exception is San1, a yeast E3 ligase. San1 harbors extensive regions of intrinsic disorder, which provide both conformational flexibility and sites for direct recognition of misfolded targets of vastly different conformations. So far, no mammalian ortholog of San1 is known, nor is it clear whether other E3 ligases utilize disordered regions for substrate recognition. Here, we conduct a bioinformatics analysis to examine >600 human and S. cerevisiae E3 ligases to identify enzymes that are similar to San1 in terms of function and/or mechanism of substrate recognition. An initial sequence-based database search was found to detect candidates primarily based on the homology of their ordered regions, and did not capture the unique disorder patterns that encode the functional mechanism of San1. However, by searching specifically for key features of the San1 sequence, such as long regions of intrinsic disorder embedded with short stretches predicted to be suitable for substrate interaction, we identified several E3 ligases with these characteristics. Our initial analysis revealed that another remarkable trait of San1 is shared with several candidate E3 ligases: long stretches of complete lysine suppression, which in San1 limits auto-ubiquitination. We encode these characteristic features into a San1 similarity-score, and present a set of proteins that are plausible candidates as San1 counterparts in humans. In conclusion, our work indicates that San1 is not a unique case, and that several other yeast and human E3 ligases have sequence properties that may allow them to recognize substrates by a similar mechanism as San1.

An Efficient Minimum Free Energy Structure-Based Search Method for Riboswitch Identification Based on Inverse RNA Folding.

Riboswitches are RNA genetic control elements that were originally discovered in bacteria and provide a unique mechanism of gene regulation. They work without the participation of proteins and are believed to represent ancient regulatory systems in the evolutionary timescale. One of the biggest challenges in riboswitch research is to find additional eukaryotic riboswitches since more than 20 riboswitch classes have been found in prokaryotes but only one class has been found in eukaryotes. Moreover, this single known class of eukaryotic riboswitch, namely the TPP riboswitch class, has been found in bacteria, archaea, fungi and plants but not in animals. The few examples of eukaryotic riboswitches were identified using sequence-based bioinformatics search methods such as a combination of BLAST and pattern matching techniques that incorporate base-pairing considerations. None of these approaches perform energy minimization structure predictions. There is a clear motivation to develop new bioinformatics methods, aside of the ongoing advances in covariance models, that will sample the sequence search space more flexibly using structural guidance while retaining the computational efficiency of sequence-based methods. We present a new energy minimization approach that transforms structure-based search into a sequence-based search, thereby enabling the utilization of well established sequence-based search utilities such as BLAST and FASTA. The transformation to sequence space is obtained by using an extended inverse RNA folding problem solver with sequence and structure constraints, available within RNAfbinv. Examples in applying the new method are presented for the purine and preQ1 riboswitches. The method is described in detail along with its findings in prokaryotes. Potential uses in finding novel eukaryotic riboswitches and optimizing pre-designed synthetic riboswitches based on ligand simulations are discussed. The method components are freely available for use.

Molecular identification and functional characterization of the first Nα-acetyltransferase in plastids by global acetylome profiling.

Protein Nα-terminal acetylation represents one of the most abundant protein modifications of higher eukaryotes. In humans, six Nα-acetyltransferases (Nats) are responsible for the acetylation of approximately 80% of the cytosolic proteins. N-terminal protein acetylation has not been evidenced in organelles of metazoans, but in higher plants is a widespread modification not only in the cytosol but also in the chloroplast. In this study, we identify and characterize the first organellar-localized Nat in eukaryotes. A primary sequence-based search in Arabidopsis thaliana revealed seven putatively plastid-localized Nats of which AT2G39000 (AtNAA70) showed the highest conservation of the acetyl-CoA binding pocket. The chloroplastic localization of AtNAA70 was demonstrated by transient expression of AtNAA70:YFP in Arabidopsis mesophyll protoplasts. Homology modeling uncovered a significant conservation of tertiary structural elements between human HsNAA50 and AtNAA70. The in vivo acetylation activity of AtNAA70 was demonstrated on a number of distinct protein Nα-termini with a newly established global acetylome profiling test after expression of AtNAA70 in E. coli. AtNAA70 predominately acetylated proteins starting with M, A, S and T, providing an explanation for most protein N-termini acetylation events found in chloroplasts. Like HsNAA50, AtNAA70 displays Nε-acetyltransferase activity on three internal lysine residues. All MS data have been deposited in the ProteomeXchange with identifier PXD001947 (http://proteomecentral.proteomexchange.org/dataset/PXD001947).

Using homology relations within a database markedly boosts protein sequence similarity search

Inference of homology from protein sequences provides an essential tool for analyzing protein structure, function, and evolution. Current sequence-based homology search methods are still unable to detect many similarities evident from protein spatial structures. In computer science a search engine can be improved by considering networks of known relationships within the search database. Here, we apply this idea to protein-sequence-based homology search and show that it dramatically enhances the search accuracy. Our new method, COMPADRE (COmparison of Multiple Protein sequence Alignments using Database RElationships) assesses the relationship between the query sequence and a hit in the database by considering the similarity between the query and hit's known homologs. This approach increases detection quality, boosting the precision rate from 18% to 83% at half-coverage of all database homologs. The increased precision rate allows detection of a large fraction of protein structural relationships, thus providing structure and function predictions for previously uncharacterized proteins. Our results suggest that this general approach is applicable to a wide variety of methods for detection of biological similarities. The web server is available at prodata.swmed.edu/compadre.

The solution structure of the MANEC-type domain from hepatocyte growth factor activator inhibitor-1 reveals an unexpected PAN/apple domain-type fold.

A decade ago, motif at N-terminus with eight-cysteines (MANEC) was defined as a new protein domain family. This domain is found exclusively at the N-terminus of >400 multi-domain type-1 transmembrane proteins from animals. Despite the large number of MANEC-containing proteins, only one has been characterized at the protein level: hepatocyte growth factor activator inhibitor-1 (HAI-1). HAI-1 is an essential protein, as knockout mice die in utero due to placental defects. HAI-1 is an inhibitor of matriptase, hepsin and hepatocyte growth factor (HGF) activator, all serine proteases with important roles in epithelial development, cell growth and homoeostasis. Dysregulation of these proteases has been causatively implicated in pathological conditions such as skin diseases and cancer. Detailed functional understanding of HAI-1 and other MANEC-containing proteins is hampered by the lack of structural information on MANEC. Although many MANEC sequences exist, sequence-based database searches fail to predict structural homology. In the present paper, we present the NMR solution structure of the MANEC domain from HAI-1, the first three-dimensional (3D) structure from the MANEC domain family. Unexpectedly, MANEC is a new subclass of the PAN/apple domain family, with its own unifying features, such as two additional disulfide bonds, two extended loop regions and additional α-helical elements. As shown for other PAN/apple domain-containing proteins, we propose a similar active role of the MANEC domain in intramolecular and intermolecular interactions. The structure provides a tool for the further elucidation of HAI-1 function as well as a reference for the study of other MANEC-containing proteins.

KAPPA, a simple algorithm for discovery and clustering of proteins defined by a key amino acid pattern: a case study of the cysteine-rich proteins.

Proteins defined by a key amino acid pattern are key players in the exchange of signals between bacteria, animals and plants, as well as important mediators for cell-cell communication within a single organism. Their description and characterization open the way to a better knowledge of molecular signalling in a broad range of organisms, and to possible application in medical and agricultural research. The contrasted pattern of evolution in these proteins makes it difficult to detect and cluster them with classical sequence-based search tools. Here, we introduce Key Aminoacid Pattern-based Protein Analyzer (KAPPA), a new multi-platform program to detect them in a given set of proteins, analyze their pattern and cluster them by comparison to reference patterns (ab initio search) or internal pairwise comparison (de novo search). In this study, we use the concrete example of cysteine-rich proteins (CRPs) to show that the similarity of two cysteine patterns can be precisely and efficiently assessed by a quantitative tool created for KAPPA: the κ-score. We also demonstrate the clear advantage of KAPPA over other classical sequence search tools for ab initio search of new CRPs. Eventually, we present de novo clustering and subclustering functionalities that allow to rapidly generate consistent groups of CRPs without a seed reference. KAPPA executables are available for Linux, Windows and Mac OS at http://kappa-sequence-search.sourceforge.net.

The evolution of compositionally and functionally distinct actin filaments.

The actin filament is astonishingly well conserved across a diverse set of eukaryotic species. It has essentially remained unchanged in the billion years that separate yeast, Arabidopsis and man. In contrast, bacterial actin-like proteins have diverged to the extreme, and many of them are not readily identified from sequence-based homology searches. Here, we present phylogenetic analyses that point to an evolutionary drive to diversify actin filament composition across kingdoms. Bacteria use a one-filament-one-function system to create distinct filament systems within a single cell. In contrast, eukaryotic actin is a universal force provider in a wide range of processes. In plants, there has been an expansion of the number of closely related actin genes, whereas in fungi and metazoa diversification in tropomyosins has increased the compositional variety in actin filament systems. Both mechanisms dictate the subset of actin-binding proteins that interact with each filament type, leading to specialization in function. In this Hypothesis, we thus propose that different mechanisms were selected in bacteria, plants and metazoa, which achieved actin filament compositional variation leading to the expansion of their functional diversity.

Interactive exploration of integrated biological datasets using context-sensitive workflows

Network inference utilizes experimental high-throughput data for the reconstruction of molecular interaction networks where new relationships between the network entities can be predicted. Despite the increasing amount of experimental data, the parameters of each modeling technique cannot be optimized based on the experimental data alone, but needs to be qualitatively assessed if the components of the resulting network describe the experimental setting. Candidate list prioritization and validation builds upon data integration and data visualization. The application of tools supporting this procedure is limited to the exploration of smaller information networks because the display and interpretation of large amounts of information is challenging regarding the computational effort and the users' experience. The Ondex software framework was extended with customizable context-sensitive menus which allow additional integration and data analysis options for a selected set of candidates during interactive data exploration. We provide new functionalities for on-the-fly data integration using InterProScan, PubMed Central literature search, and sequence-based homology search. We applied the Ondex system to the integration of publicly available data for Aspergillus nidulans and analyzed transcriptome data. We demonstrate the advantages of our approach by proposing new hypotheses for the functional annotation of specific genes of differentially expressed fungal gene clusters. Our extension of the Ondex framework makes it possible to overcome the separation between data integration and interactive analysis. More specifically, computationally demanding calculations can be performed on selected sub-networks without losing any information from the whole network. Furthermore, our extensions allow for direct access to online biological databases which helps to keep the integrated information up-to-date.

Microbial alkyl- and aryl-sulfatases: mechanism, occurrence, screening and stereoselectivities.

This review gives an overview on the occurrence of sulfatases in Prokaryota, Eukaryota and Archaea. The mechanism of enzymes acting with retention or inversion of configuration during sulfate ester hydrolysis is discussed taking two complementary examples. Methods for the discovery of novel alkyl sulfatases are described by way of sequence-based search and enzyme induction. A comprehensive list of organisms with their respective substrate scope regarding prim- and sec-alkyl sulfate esters allows to assess the capabilities and limitations of various biocatalysts employed as whole cell systems or as purified enzymes with respect to their activities and enantioselectivities. Methods for immobilization and selectivity enhancement by addition of metal ions or organic (co)solvents are summarised.

Filling-in Void and Sparse Regions in Protein Sequence Space by Protein-Like Artificial Sequences Enables Remarkable Enhancement in Remote Homology Detection Capability

Protein functional annotation relies on the identification of accurate relationships, sequence divergence being a key factor. This is especially evident when distant protein relationships are demonstrated only with three-dimensional structures. To address this challenge, we describe a computational approach to purposefully bridge gaps between related protein families through directed design of protein-like “linker” sequences. For this, we represented SCOP domain families, integrated with sequence homologues, as multiple profiles and performed HMM-HMM alignments between related domain families. Where convincing alignments were achieved, we applied a roulette wheel-based method to design 3,611,010 protein-like sequences corresponding to 374 SCOP folds. To analyze their ability to link proteins in homology searches, we used 3024 queries to search two databases, one containing only natural sequences and another one additionally containing designed sequences. Our results showed that augmented database searches showed up to 30% improvement in fold coverage for over 74% of the folds, with 52 folds achieving all theoretically possible connections. Although sequences could not be designed between some families, the availability of designed sequences between other families within the fold established the sequence continuum to demonstrate 373 difficult relationships. Ultimately, as a practical and realistic extension, we demonstrate that such protein-like sequences can be “plugged-into” routine and generic sequence database searches to empower not only remote homology detection but also fold recognition. Our richly statistically supported findings show that complementary searches in both databases will increase the effectiveness of sequence-based searches in recognizing all homologues sharing a common fold.

Heparin-binding motifs and biofilm formation by Candida albicans.

Candida albicans is a leading pathogen in infections of central venous catheters, which are frequently infused with heparin. Binding of C. albicans to medically relevant concentrations of soluble and plate-bound heparin was demonstrable by confocal microscopy and enzyme-linked immunosorbent assay (ELISA). A sequence-based search identified 34 C. albicans surface proteins containing ≥1 match to linear heparin-binding motifs. The virulence factor Int1 contained the most putative heparin-binding motifs (n = 5); peptides encompassing 2 of 5 motifs bound to heparin-Sepharose. Alanine substitution of lysine residues K805/K806 in 804QKKHQIHK811 (motif 1 of Int1) markedly attenuated biofilm formation in central venous catheters in rats, whereas alanine substitution of K1595/R1596 in 1593FKKRFFKL1600 (motif 4 of Int1) did not impair biofilm formation. Affinity-purified immunoglobulin G (IgG) recognizing motif 1 abolished biofilm formation in central venous catheters; preimmune IgG had no effect. After heparin treatment of C. albicans, soluble peptides from multiple C. albicans surface proteins were detected, such as Eno1, Pgk1, Tdh3, and Ssa1/2 but not Int1, suggesting that heparin changes candidal surface structures and may modify some antigens critical for immune recognition. These studies define a new mechanism of biofilm formation for C. albicans and a novel strategy for inhibiting catheter-associated biofilms.

Spectral Library Generating Function for Assessing Spectrum-Spectrum Match Significance

Tandem mass spectrometry (MS/MS) continues to be the technology of choice for high-throughput analysis of complex proteomics samples. While MS/MS spectra are commonly identified by matching against a database of known protein sequences, the complementary approach of spectral library searching against collections of reference spectra consistently outperforms sequence-based searches by resulting in significantly more identified spectra. However, while spectral library searches benefit from the advance knowledge of the expected peptide fragmentation patterns recorded in library spectra, estimation of the statistical significance of spectrum-spectrum matches (SSMs) continues to be hindered by difficulties in finding an appropriate definition of "random" SSMs to use as a null model when estimating the significance of true SSMs. We propose to avoid this problem by changing the null hypothesis: instead of determining the probability of observing a high SSM score between randomly matched spectra, we estimate the probability of observing a low SSM score between replicate spectra of the same molecule. To this end, we explicitly model the variation in instrument measurements of MS/MS peak intensities and show how these models can be used to determine a theoretical distribution of SSM scores between reference and query spectra of the same molecule. While the proposed spectral library generating function (SLGF) approach can be used to calculate theoretical distributions for any additive SSM score (e.g., any dot product), we further show how it can be used to calculate the distribution of expected cosines between reference and query spectra. We developed a spectral library search tool, Tremolo, and demonstrate that this SLGF-based search tool significantly outperforms current state-of-the-art spectral library search tools and provide a detailed discussion of the multiple reasons behind the observed differences in the sets of identified MS/MS spectra.

Functional Knowledge Transfer for High-accuracy Prediction of Under-studied Biological Processes

A key challenge in genetics is identifying the functional roles of genes in pathways. Numerous functional genomics techniques (e.g. machine learning) that predict protein function have been developed to address this question. These methods generally build from existing annotations of genes to pathways and thus are often unable to identify additional genes participating in processes that are not already well studied. Many of these processes are well studied in some organism, but not necessarily in an investigator's organism of interest. Sequence-based search methods (e.g. BLAST) have been used to transfer such annotation information between organisms. We demonstrate that functional genomics can complement traditional sequence similarity to improve the transfer of gene annotations between organisms. Our method transfers annotations only when functionally appropriate as determined by genomic data and can be used with any prediction algorithm to combine transferred gene function knowledge with organism-specific high-throughput data to enable accurate function prediction.We show that diverse state-of-art machine learning algorithms leveraging functional knowledge transfer (FKT) dramatically improve their accuracy in predicting gene-pathway membership, particularly for processes with little experimental knowledge in an organism. We also show that our method compares favorably to annotation transfer by sequence similarity. Next, we deploy FKT with state-of-the-art SVM classifier to predict novel genes to 11,000 biological processes across six diverse organisms and expand the coverage of accurate function predictions to processes that are often ignored because of a dearth of annotated genes in an organism. Finally, we perform in vivo experimental investigation in Danio rerio and confirm the regulatory role of our top predicted novel gene, wnt5b, in leftward cell migration during heart development. FKT is immediately applicable to many bioinformatics techniques and will help biologists systematically integrate prior knowledge from diverse systems to direct targeted experiments in their organism of study.

SPPS: A Sequence-Based Method for Predicting Probability of Protein-Protein Interaction Partners

BackgroundThe molecular network sustained by different types of interactions among proteins is widely manifested as the fundamental driving force of cellular operations. Many biological functions are determined by the crosstalk between proteins rather than by the characteristics of their individual components. Thus, the searches for protein partners in global networks are imperative when attempting to address the principles of biology.ResultsWe have developed a web-based tool “Sequence-based Protein Partners Search” (SPPS) to explore interacting partners of proteins, by searching over a large repertoire of proteins across many species. SPPS provides a database containing more than 60,000 protein sequences with annotations and a protein-partner search engine in two modes (Single Query and Multiple Query). Two interacting proteins of human FBXO6 protein have been found using the service in the study. In addition, users can refine potential protein partner hits by using annotations and possible interactive network in the SPPS web server.ConclusionsSPPS provides a new type of tool to facilitate the identification of direct or indirect protein partners which may guide scientists on the investigation of new signaling pathways. The SPPS server is available to the public at http://mdl.shsmu.edu.cn/SPPS/.

Identification of CCR2‐binding features in Cytl1 by a CCL2‐like chemokine model

Chemokines are small secreted proteins that play an important role in immune responses and have also been shown to be involved in cartilage development and contributing to pathogenesis of a variety of diseases. They present a conserved 3D structure, so-called IL8-like chemokine fold, which is supported by conserved cysteines forming intra-molecular disulfide bonds. These cysteine sequence motifs have often been used to find new chemokine family members by sequence-based database searches. However, it has been shown that different patterns can provide disulfide bonds fitting into an IL8-like architecture, which has been the key to identify new remote homologues of the IL8-like chemokine family. We report a structural-functional characterization of cytokine-like protein 1 (Cytl1) by a combination of different computational structure-based techniques. Previous studies based on sequence analysis and secondary structure predictions reported that Cytl1 might adopt a 4-helical cytokine fold. However, our detailed molecular modeling studies and structure-based functional analysis strongly suggest that Cytl1 is more likely to adopt an IL8-like chemokine fold, in particular similar to CCL2 (monocyte chemoattractant protein 1, MCP-1). Moreover, we identify in a CCL2-like 3D model of Cytl1 the necessary reported features to signal through the chemokine receptor CCR2. Those discovered structural features of Cytl1 as CCL2-like chemokine, together with the fact that both, CCL2 and Cytl1, are known to be involved in cartilage development and pathogenesis of osteoarthritis and rheumatoid arthritis, make us hypothesize that Cytl1 could be a structurally and functionally related analog of CCL2 signaling through the chemokine receptor CCR2.

Systematic analysis of human microRNA divergence based on evolutionary emergence

MicroRNAs (miRNAs) play important roles in post-transcriptional gene expression control. To gain new insight into human miRNAs, we performed comprehensive sequence-based homology search for known human miRNAs to study the evolutionary distribution of human miRNAs. Furthermore, we carried out a series of studies to compare various features for different lineage-specific human miRNAs. Our results showed that major expansions of human miRNA genes coincide with the advent of vertebrates, mammals and primates. Further system-level analysis revealed significant differences in human miRNAs that arose from different evolutionary time points for a number of characteristics, implicating genetic and functional diversification for different human miRNAs during evolution. Our finds provide more useful knowledge for further exploring origins and evolution of human miRNA genes.