Domain Expansion and Functional Diversification in Vertebrate Reproductive Proteins

The rapid evolution of fertilization proteins has generated remarkable diversity in molecular structure and function. Glycoproteins of vertebrate egg coats contain multiple zona pellucida (ZP)-N domains (1–6 copies) that facilitate multiple reproductive functions, including species-specific sperm recognition. In this report, we integrate phylogenetics and machine learning to investigate how ZP-N domains diversify in structure and function. The most C-terminal ZP-N domain of each paralog is associated with another domain type (ZP-C), which together form a “ZP module.” All modular ZP-N domains are phylogenetically distinct from nonmodular or free ZP-N domains. Machine learning–based classification identifies eight residues that form a stabilizing network in modular ZP-N domains that is absent in free domains. Positive selection is identified in some free ZP-N domains. Our findings support that strong purifying selection has conserved an essential structural core in modular ZP-N domains, with the relaxation of this structural constraint allowing free N-terminal domains to functionally diversify.     

Network-assisted investigation of combined causal signals from genome-wide association studies in schizophrenia

With the recent success of genome-wide association studies (GWAS), a wealth of association data has been accomplished for more than 200 complex diseases/traits, proposing a strong demand for data integration and interpretation. A combinatory analysis of multiple GWAS datasets, or an integrative analysis of GWAS data and other high-throughput data, has been particularly promising. In this study, we proposed an integrative analysis framework of multiple GWAS datasets by overlaying association signals onto the protein-protein interaction network, and demonstrated it using schizophrenia datasets. Building on a dense module search algorithm, we first searched for significantly enriched subnetworks for schizophrenia in each single GWAS dataset and then implemented a discovery-evaluation strategy to identify module genes with consistent association signals. We validated the module genes in an independent dataset, and also examined them through meta-analysis of the related SNPs using multiple GWAS datasets. As a result, we identified 205 module genes with a joint effect significantly associated with schizophrenia; these module genes included a number of well-studied candidate genes such as DISC1, GNA12, GNA13, GNAI1, GPR17, and GRIN2B. Further functional analysis suggested these genes are involved in neuronal related processes. Additionally, meta-analysis found that 18 SNPs in 9 module genes had P _meta<1×10⁻⁴, including the gene HLA-DQA1 located in the MHC region on chromosome 6, which was reported in previous studies using the largest cohort of schizophrenia patients to date. These results demonstrated our bi-directional network-based strategy is efficient for identifying disease-associated genes with modest signals in GWAS datasets. This approach can be applied to any other complex diseases/traits where multiple GWAS datasets are available.     

Comparative protein structure modeling by combining multiple templates and optimizing sequence-to-structure alignments

MOTIVATION: Two major bottlenecks in advancing comparative protein structure modeling are the efficient combination of multiple template structures and the generation of a correct input target-template alignment. RESULTS: A novel method, Multiple Mapping Method with Multiple Templates (M4T) is introduced that implements an algorithm to automatically select and combine Multiple Template structures (MT) and an alignment optimization protocol (Multiple Mapping Method, MMM). The MT module of M4T selects and combines multiple template structures through an iterative clustering approach that takes into account the 'unique' contribution of each template, their sequence similarity among themselves and to the target sequence, and their experimental resolution. MMM is a sequence-to-structure alignment method that optimally combines alternatively aligned regions according to their fit in the structural environment of the template structure. The resulting M4T alignment is used as input to a comparative modeling module. The performance of M4T has been benchmarked on CASP6 comparative modeling target sequences and on a larger independent test set, and showed favorable performance to current state of the art methods.     

Fast algorithms for large-scale genome alignment and comparison

We describe a suffix-tree algorithm that can align the entire genome sequences of eukaryotic and prokaryotic organisms with minimal use of computer time and memory. The new system, MUMmer 2, runs three times faster while using one-third as much memory as the original MUMmer system. It has been used successfully to align the entire human and mouse genomes to each other, and to align numerous smaller eukaryotic and prokaryotic genomes. A new module permits the alignment of multiple DNA sequence fragments, which has proven valuable in the comparison of incomplete genome sequences. We also describe a method to align more distantly related genomes by detecting protein sequence homology. This extension to MUMmer aligns two genomes after translating the sequence in all six reading frames, extracts all matching protein sequences and then clusters together matches. This method has been applied to both incomplete and complete genome sequences in order to detect regions of conserved synteny, in which multiple proteins from one organism are found in the same order and orientation in another. The system code is being made freely available by the authors.     

A Peptide Motif from the Second Fibronectin Module of the Neural Cell Adhesion Molecule,NCAM, NLIKQDDGGSPIRHY,is a Binding Site for the FGF Receptor

The mechanism of fibroblast growth factor receptor (FGFR) activation by the neural cell adhesion molecule (NCAM) is not well understood. A motif in the second NCAM fibronectin type III (FN3) module, termed FGL, has by means of nuclear magnetic resonance (NMR) and surface plasmon resonance (SPR) analyses been demonstrated to be involved in NCAM–FGFR interactions. An FGFR activation motif (FRM) in the first NCAM FN3 module also has been suggested to take part in NCAM interactions with FGFR. Here, we show for the first time that a peptide motif in the second NCAM FN3 module, different from the previously described FGL motif (NLIKQDDGGSPIRHY; termed BCL) binds and activates FGFR and induces FGFR-dependent neurite outgrowth in cultures of cerebellar granule neurons. Our results provide evidence that the BCL motif is one of the multiple FGFR binding sites in NCAM. Special issue article in honor of Dr. Anna Maria Giuffrida-Stella.     

Aging effects on DNA methylation modules in human brain and blood tissue

Background

Several recent studies reported aging effects on DNA methylation levels of individual CpG dinucleotides. But it is not yet known whether aging-related consensus modules, in the form of clusters of correlated CpG markers, can be found that are present in multiple human tissues. Such a module could facilitate the understanding of aging effects on multiple tissues.

Results

We therefore employed weighted correlation network analysis of 2,442 Illumina DNA methylation arrays from brain and blood tissues, which enabled the identification of an age-related co-methylation module. Module preservation analysis confirmed that this module can also be found in diverse independent data sets. Biological evaluation showed that module membership is associated with Polycomb group target occupancy counts, CpG island status and autosomal chromosome location. Functional enrichment analysis revealed that the aging-related consensus module comprises genes that are involved in nervous system development, neuron differentiation and neurogenesis, and that it contains promoter CpGs of genes known to be down-regulated in early Alzheimer's disease. A comparison with a standard, non-module based meta-analysis revealed that selecting CpGs based on module membership leads to significantly increased gene ontology enrichment, thus demonstrating that studying aging effects via consensus network analysis enhances the biological insights gained.

Conclusions

Overall, our analysis revealed a robustly defined age-related co-methylation module that is present in multiple human tissues, including blood and brain. We conclude that blood is a promising surrogate for brain tissue when studying the effects of age on DNA methylation profiles.     

WASH, WHAMM and JMY: regulation of Arp2/3 complex and beyond

Arp2/3 complex mediates the nucleation of actin filaments in multiple subcellular processes, and is activated by nucleation-promoting factors (NPFs) from the Wiskott-Aldrich Syndrome family. In exciting new developments, this family has grown by three members: WASH, WHAMM and JMY, which extend the repertoire of dynamic membrane structures that are remodeled following Arp2/3 activation in vivo. These novel NPFs share an actin- and Arp2/3-interacting WCA module, and combine Arp2/3 activation with additional biochemical functions, including capping protein inhibition, microtubule engagement or Arp2/3-independent actin nucleation, none of which had been previously associated with canonical WCA-harboring proteins. Uncovering the physiological relevance of these unique activities will require concerted efforts from multiple disciplines, and is sure to impact our understanding of how the cytoskeleton controls so many dynamic subcellular events.     

Localization and characterization of the hyaluronan-binding site on the link module from human TSG-6

BACKGROUND: The interactions of hyaluronan (HA) with proteins are important in extracellular matrix integrity and leukocyte migration and are usually mediated by a domain termed a Link module. Although the tertiary structure of a Link module has been determined, the molecular basis of HA-protein interactions remains poorly understood. RESULTS: Isothermal titration calorimetry was used to characterize the interaction of the Link module from human TSG-6 (Link_TSG6) with HA oligosaccharides of defined length (HA(4)-HA(16)). All oligomers bound (except HA(4)) with K(d) values ranging from 0.2-0.5 microM at 25 degrees C. The reaction is exothermic with a favourable entropy and the thermodynamic profile is similar to those of other glycosaminoglycan-protein interactions. The HA(8) recognition site on Link_TSG6 was localized by comparing nuclear magnetic resonance (NMR) spectra from a 1:1 complex with free protein. Residues perturbed on HA binding include both amino acids that are likely to be directly involved in the interaction (i.e., Lys11, Tyr59, Asn67, Phe70, Lys72 and Tyr78) and those affected by a ligand-induced conformational change in the beta4/beta5 loop. The sidechain of Asn67 becomes more rigid in the complex suggesting that it is in close proximity to the binding site. CONCLUSIONS: In TSG-6 a single Link module is sufficient for a high-affinity interaction with HA. The HA-binding surface on Link_TSG6 is found in a similar position to that suggested previously for CD44, indicating that its location might be conserved across the Link module superfamily. Here we find no evidence for the involvement of linear sequence motifs in HA binding.     

Novel miniature SPR immunosensor equipped with all-in-one multi-microchannel sensor chip for detecting low-molecular-weight analytes

A simple and versatile miniaturized surface plasmon resonance (SPR) immunosensor enabling parallel analysis of multiple analytes or multiple samples of an analyte has been investigated for detection of a low-molecular-weight (lmw) toxin, 2,4-dichlorophenoxyacetic acid (2,4-D). A specially designed multi-microchannel SPR sensor module, integrating an optical-prism coated with an array of thin Au-films, a multi-microchannel plate (eight channels) and a flow-cell together, has been fabricated. The sensing surface was fabricated simply by physical adsorption of a protein conjugate of 2,4-D, and an indirect competitive immunoassay principle has been applied for the quantification of 2,4-D. Multiple 2,4-D samples were analyzed in a single step and a low-detection-limit (LDL) of 0.1 ppb (ng ml(-1)) 2,4-D was established. Competence of the portable SPR immunosensor for selective detection of 2,4-D despite the presence of various structurally resemblant interferents and from river-water samples has been demonstrated. The independent all-in-one sensor module highly favors shelf-storage between multiple determinations, and reusability of a same multi-microchannel flow-module for more than 35 days with intermittent storage (4-8 degrees C) has been confirmed. The LDL of 2,4-D could be enhanced further by introducing a simple avidin-biotin interaction-based sandwich immunoassay, with which the sensor signal multiplied enormously by a factor of ca. 10 and the LDL enhanced to 0.008 ppb. The miniature SPR sensor demonstrated here for simultaneous analysis of multiple samples with reusability and good storage ability is an important consideration for the advancement of biosensor technology.     

Host discrimination in modular mutualisms: a theoretical framework for meta-populations of mutualists and exploiters

Plants in multiple symbioses are exploited by symbionts that consume their resources without providing services. Discriminating hosts are thought to stabilize mutualism by preferentially allocating resources into anatomical structures (modules) where services are generated, with examples of modules including the entire inflorescences of figs and the root nodules of legumes. Modules are often colonized by multiple symbiotic partners, such that exploiters that co-occur with mutualists within mixed modules can share rewards generated by their mutualist competitors. We developed a meta-population model to answer how the population dynamics of mutualists and exploiters change when they interact with hosts with different module occupancies (number of colonists per module) and functionally different patterns of allocation into mixed modules. We find that as module occupancy increases, hosts must increase the magnitude of preferentially allocated resources in order to sustain comparable populations of mutualists. Further, we find that mixed colonization can result in the coexistence of mutualist and exploiter partners, but only when preferential allocation follows a saturating function of the number of mutualists in a module. Finally, using published data from the fig–wasp mutualism as an illustrative example, we derive model predictions that approximate the proportion of exploiter, non-pollinating wasps observed in the field.     

Bayesian module identification from multiple noisy networks

Background and motivations

Module identification has been studied extensively in order to gain deeper understanding of complex systems, such as social networks as well as biological networks. Modules are often defined as groups of vertices in these networks that are topologically cohesive with similar interaction patterns with the rest of the vertices. Most of the existing module identification algorithms assume that the given networks are faithfully measured without errors. However, in many real-world applications, for example, when analyzing protein-protein interaction networks from high-throughput profiling techniques, there is significant noise with both false positive and missing links between vertices. In this paper, we propose a new model for more robust module identification by taking advantage of multiple observed networks with significant noise so that signals in multiple networks can be strengthened and help improve the solution quality by combining information from various sources.

Methods

We adopt a hierarchical Bayesian model to integrate multiple noisy snapshots that capture the underlying modular structure of the networks under study. By introducing a latent root assignment matrix and its relations to instantaneous module assignments in all the observed networks to capture the underlying modular structure and combine information across multiple networks, an efficient variational Bayes algorithm can be derived to accurately and robustly identify the underlying modules from multiple noisy networks.

Results

Experiments on synthetic and protein-protein interaction data sets show that our proposed model enhances both the accuracy and resolution in detecting cohesive modules, and it is less vulnerable to noise in the observed data. In addition, it shows higher power in predicting missing edges compared to individual-network methods.

     

Sequence similarity is more relevant than species specificity in probabilistic backtranslation

Background  

Backtranslation is the process of decoding a sequence of amino acids into the corresponding codons. All synthetic gene design systems include a backtranslation module. The degeneracy of the genetic code makes backtranslation potentially ambiguous since most amino acids are encoded by multiple codons. The common approach to overcome this difficulty is based on imitation of codon usage within the target species.     

Experimental Peptide Identification Repository (EPIR): an integrated peptide-centric platform for validation and mining of tandem mass spectrometry data

LC MS/MS has become an established technology in proteomic studies, and with the maturation of the technology the bottleneck has shifted from data generation to data validation and mining. To address this bottleneck we developed Experimental Peptide Identification Repository (EPIR), which is an integrated software platform for storage, validation, and mining of LC MS/MS-derived peptide evidence. EPIR is a cumulative data repository where precursor ions are linked to peptide assignments and protein associations returned by a search engine (e.g. Mascot, Sequest, or PepSea). Any number of datasets can be parsed into EPIR and subsequently validated and mined using a set of software modules that overlay the database. These include a peptide validation module, a protein grouping module, a generic module for extracting quantitative data, a comparative module, and additional modules for extracting statistical information. In the present study, the utility of EPIR and associated software tools is demonstrated on LC MS/MS data derived from a set of model proteins and complex protein mixtures derived from MCF-7 breast cancer cells. Emphasis is placed on the key strengths of EPIR, including the ability to validate and mine multiple combined datasets, and presentation of protein-level evidence in concise, nonredundant protein groups that are based on shared peptide evidence.     

Divided we stand: splitting synthetic cells for their proliferation

With the recent dawn of synthetic biology, the old idea of man-made artificial life has gained renewed interest. In the context of a bottom-up approach, this entails the de novo construction of synthetic cells that can autonomously sustain themselves and proliferate. Reproduction of a synthetic cell involves the synthesis of its inner content, replication of its information module, and growth and division of its shell. Theoretical and experimental analysis of natural cells shows that, whereas the core synthesis machinery of the information module is highly conserved, a wide range of solutions have been realized in order to accomplish division. It is therefore to be expected that there are multiple ways to engineer division of synthetic cells. Here we survey the field and review potential routes that can be explored to accomplish the division of bottom-up designed synthetic cells. We cover a range of complexities from simple abiotic mechanisms involving splitting of lipid-membrane-encapsulated vesicles due to physical or chemical principles, to potential division mechanisms of synthetic cells that are based on prokaryotic division machineries.     

Regulating the dynamics of EGF receptor signaling in space and time

The epidermal growth factor receptor (EGFR) signaling cascade represents one of the cardinal pathways that transmits information between cells during development in a broad range of multicellular organisms. Most of the elements that constitute the core EGFR signaling module, as well as a variety of negative and positive modulators, have been identified. Although this molecular pathway is utilized multiple times during development, the spatial and temporal features of its signaling can be modified to fit a particular developmental setting. Recent work has unraveled the various mechanisms by which the EGFR pathway can be modulated.