Using reporter gene assays to identify cis regulatory differences between humans and chimpanzees

Most phenotypic differences between human and chimpanzee are likely to result from differences in gene regulation, rather than changes to protein-coding regions. To date, however, only a handful of human-chimpanzee nucleotide differences leading to changes in gene regulation have been identified. To hone in on differences in regulatory elements between human and chimpanzee, we focused on 10 genes that were previously found to be differentially expressed between the two species. We then designed reporter gene assays for the putative human and chimpanzee promoters of the 10 genes. Of seven promoters that we found to be active in human liver cell lines, human and chimpanzee promoters had significantly different activity in four cases, three of which recapitulated the gene expression difference seen in the microarray experiment. For these three genes, we were therefore able to demonstrate that a change in cis influences expression differences between humans and chimpanzees. Moreover, using site-directed mutagenesis on one construct, the promoter for the DDA3 gene, we were able to identify three nucleotides that together lead to a cis regulatory difference between the species. High-throughput application of this approach can provide a map of regulatory element differences between humans and our close evolutionary relatives.     

Ontology integration to identify protein complex in protein interaction networks

Background

Protein complexes can be identified from the protein interaction networks derived from experimental data sets. However, these analyses are challenging because of the presence of unreliable interactions and the complex connectivity of the network. The integration of protein-protein interactions with the data from other sources can be leveraged for improving the effectiveness of protein complexes detection algorithms.

Methods

We have developed novel semantic similarity method, which use Gene Ontology (GO) annotations to measure the reliability of protein-protein interactions. The protein interaction networks can be converted into a weighted graph representation by assigning the reliability values to each interaction as a weight. Following the approach of that of the previously proposed clustering algorithm IPCA which expands clusters starting from seeded vertices, we present a clustering algorithm OIIP based on the new weighted Protein-Protein interaction networks for identifying protein complexes.

Results

The algorithm OIIP is applied to the protein interaction network of Sacchromyces cerevisiae and identifies many well known complexes. Experimental results show that the algorithm OIIP has higher F-measure and accuracy compared to other competing approaches.

     

Using ensemble classifier to identify membrane protein types

Predicting membrane protein type is both an important and challenging topic in current molecular and cellular biology. This is because knowledge of membrane protein type often provides useful clues for determining, or sheds light upon, the function of an uncharacterized membrane protein. With the explosion of newly-found protein sequences in the post-genomic era, it is in a great demand to develop a computational method for fast and reliably identifying the types of membrane proteins according to their primary sequences. In this paper, a novel classifier, the so-called "ensemble classifier", was introduced. It is formed by fusing a set of nearest neighbor (NN) classifiers, each of which is defined in a different pseudo amino acid composition space. The type for a query protein is determined by the outcome of voting among these constituent individual classifiers. It was demonstrated through the self-consistency test, jackknife test, and independent dataset test that the ensemble classifier outperformed other existing classifiers widely used in biological literatures. It is anticipated that the idea of ensemble classifier can also be used to improve the prediction quality in classifying other attributes of proteins according to their sequences.     

Intrinsic connectivity reveals functionally distinct cortico-hippocampal networks in the human brain

Episodic memory depends on interactions between the hippocampus and interconnected neocortical regions. Here, using data-driven analyses of resting-state functional magnetic resonance imaging (fMRI) data, we identified the networks that interact with the hippocampus—the default mode network (DMN) and a “medial temporal network” (MTN) that included regions in the medial temporal lobe (MTL) and precuneus. We observed that the MTN plays a critical role in connecting the visual network to the DMN and hippocampus. The DMN could be further divided into 3 subnetworks: a “posterior medial” (PM) subnetwork comprised of posterior cingulate and lateral parietal cortices; an “anterior temporal” (AT) subnetwork comprised of regions in the temporopolar and dorsomedial prefrontal cortex; and a “medial prefrontal” (MP) subnetwork comprised of regions primarily in the medial prefrontal cortex (mPFC). These networks vary in their functional connectivity (FC) along the hippocampal long axis and represent different kinds of information during memory-guided decision-making. Finally, a Neurosynth meta-analysis of fMRI studies suggests new hypotheses regarding the functions of the MTN and DMN subnetworks, providing a framework to guide future research on the neural architecture of episodic memory.

Episodic memory depends on interactions between the hippocampus and interconnected neocortical regions. This study uses network analyses of intrinsic brain networks at rest to identify and characterize brain networks that interact with the hippocampus and have distinct functions during memory-guided decision making.     

Soluble CD14 discriminates slight structural differences between lipid as that lead to distinct host cell activation

Soluble CD14 (sCD14) in serum is known to sensitize host cells to LPS. In the present study, the contributions of sCD14 and LPS-binding protein to a lipid A moiety from LPS preparations of periodontopathogenic Fusobacterium nucleatum sp. nucleatum were compared with that of Escherichia coli-type synthetic lipid A (compound 506). F. nucleatum lipid A was identified to be a hexa-acylated fatty acid composed of tetradecanoate (C(14)) and hexadecanoate (C(16)), similar to dodecanoate (C(12)) and C(14) in compound 506. The two lipid A specimens exhibited nearly the same reactivity in Limulus amoebocyte lysate assays, though F. nucleatum lipid A showed a weaker lethal toxicity. Both lipid A specimens showed nearly the same activities toward host cells in the absence of FBS, though compound 506 exhibited much stronger activity in the presence of FBS, sCD14, or sCD14 together with LPS-binding protein. Furthermore, native PAGE/Western immunoblot assays demonstrated that F. nucleatum lipid A had a weaker binding to sCD14 as compared with compound 506. These results suggest that sCD14 is able to discriminate the slight structural differences between these lipid As, which causes their distinct host cell activation activities.     

Microtubule-tethering junctions are fine structural distinct entities that deserve attention

Microtubule tethering at cell junctions, as studied by light microscopy (Ligon and Holzbaur, Traffic 2007;8:808-819), exist as easily recognizable ultrastructural entities that are distinct from adherence junctions. Here, we encourage systematic analysis of cortical microtubule capture by electron microscopy.     

Using structural motif templates to identify proteins with DNA binding function

This work describes a method for predicting DNA binding function from structure using 3-dimensional templates. Proteins that bind DNA using small contiguous helix–turn–helix (HTH) motifs comprise a significant number of all DNA-binding proteins. A structural template library of seven HTH motifs has been created from non-homologous DNA-binding proteins in the Protein Data Bank. The templates were used to scan complete protein structures using an algorithm that calculated the root mean squared deviation (rmsd) for the optimal superposition of each template on each structure, based on C_α backbone coordinates. Distributions of rmsd values for known HTH-containing proteins (true hits) and non-HTH proteins (false hits) were calculated. A threshold value of 1.6 Å rmsd was selected that gave a true hit rate of 88.4% and a false positive rate of 0.7%. The false positive rate was further reduced to 0.5% by introducing an accessible surface area threshold value of 990 Ų per HTH motif. The template library and the validated thresholds were used to make predictions for target proteins from a structural genomics project.     

Ligand binding to distinct states diverts aggregation of an amyloid-forming protein

Although small molecules that modulate amyloid formation in vitro have been identified, significant challenges remain in determining precisely how these species act. Here we describe the identification of rifamycin SV as a potent inhibitor of β(2) microglobulin (β(2)m) fibrillogenesis when added during the lag time of assembly or early during fibril elongation. Biochemical experiments demonstrate that the small molecule does not act by a colloidal mechanism. Exploiting the ability of electrospray ionization-ion mobility spectrometry-mass spectrometry (ESI-IMS-MS) to resolve intermediates of amyloid assembly, we show instead that rifamycin SV inhibits β(2)m fibrillation by binding distinct monomeric conformers, disfavoring oligomer formation and diverting the course of assembly to the formation of spherical aggregates. The results demonstrate the power of ESI-IMS-MS to identify specific protein conformers as targets for intervention in fibrillogenesis using small molecules and reveal a mechanism of action in which ligand binding diverts unfolded protein monomers toward alternative assembly pathways.     

Semantic integration to identify overlapping functional modules in protein interaction networks

Background  

The systematic analysis of protein-protein interactions can enable a better understanding of cellular organization, processes and functions. Functional modules can be identified from the protein interaction networks derived from experimental data sets. However, these analyses are challenging because of the presence of unreliable interactions and the complex connectivity of the network. The integration of protein-protein interactions with the data from other sources can be leveraged for improving the effectiveness of functional module detection algorithms.     

Using UHF-dielectrometry to study protein structural transitions

UHF-dielectrometry method is based on the following facts: i) there is dispersion (i.e. dependence on frequency) of the dielectric permeability epsilon; ii) bound and free water have remarkable different epsilon, mobility and dispersion regions; iii) conformational changes in a macromolecule lead to redistribution of free and bound water and to change of the amount of free water molecules. Choosing the working frequency in the region of dispersion of free water molecules (9.2 GHz) we can detect conformational changes in proteins using free water as a marker. In this work the temperature dependencies of dielectric parameters of albumin and fibrinogen solutions were obtained in the temperature interval 5-40 degrees C. In contrast to dependencies for poor solvent, temperature dependencies of dielectric parameters for protein solutions are of non-monotonous character; they have a number of peculiarities in the temperature ranges of 8-10, 22-24 and 34-36 degrees C. At these temperatures redistribution of free and bound water in protein-water system occurs due to structural changes in protein molecules. In this work the mechanism of temperature changes of spatial organisation of protein molecules was proposed. Perhaps, this mechanism is responsible for maintenance of thermal stability of the functionally active conformation of native proteins.     

Three distinct actin-attached structural states of myosin in muscle fibers

We have used thiol cross-linking and electron paramagnetic resonance (EPR) to resolve structural transitions of myosin's light chain domain (LCD) and catalytic domain (CD) that are associated with force generation. Spin labels were incorporated into the LCD of muscle fibers by exchanging spin-labeled regulatory light chain for endogenous regulatory light chain, with full retention of function. To trap myosin in a structural state analogous to the elusive posthydrolysis ternary complex A.M'.D.P, we used pPDM to cross-link SH1 (Cys(707)) to SH2 (Cys(697)) on the CD. LCD orientation and dynamics were measured in three biochemical states: relaxation (A.M.T), SH1-SH2 cross-linked (A.M'.D.P analog), and rigor (A.M.D). EPR showed that the LCD of cross-linked fibers has an orientational distribution intermediate between relaxation and rigor, and saturation transfer EPR revealed slow rotational dynamics indistinguishable from that of rigor. Similar results were obtained for the CD using a bifunctional spin label to cross-link SH1-SH2, but the CD was more disordered than the LCD. We conclude that SH1-SH2 cross-linking traps a state in which both the CD and LCD are intermediate between relaxation (highly disordered and microsecond dynamics) and rigor (highly ordered and rigid), supporting the hypothesis that the cross-linked state is an A.M'D.P analog on the force generation pathway.     

Using multiple sequence correlation analysis to characterize functionally important protein regions

Protein co-evolution under structural and functional constraints necessitates the preservation of important interactions. Identifying functionally important regions poses many obstacles in protein engineering efforts. In this paper, we present a bioinformatics-inspired approach (residue correlation analysis, RCA) for predicting functionally important domains from protein family sequence data. RCA is comprised of two major steps: (i) identifying pairs of residue positions that mutate in a coordinated manner, and (ii) using these results to identify protein regions that interact with an uncommonly high number of other residues. We hypothesize that strongly correlated pairs result not only from contacting pairs, but also from residues that participate in conformational changes involved during catalysis or important interactions necessary for retaining functionality. The results show that highly mobile loops that assist in ligand association/dissociation tend to exhibit high correlation. RCA results exhibit good agreement with the findings of experimental and molecular dynamics studies for the three protein families that are analyzed: (i) DHFR (dihydrofolate reductase), (ii) cyclophilin, and (iii) formyl-transferase. Specifically, the specificity (percentage of correct predictions) in all three cases is substantially higher than those obtained by entropic measures or contacting residue pairs. In addition, we use our approach in a predictive fashion to identify important regions of a transmembrane amino acid transporter protein for which there is limited structural and functional information available.     

Using a residue clash map to functionally characterize protein recombination hybrids

In this article, we introduce a rapid, protein sequence database-driven approach to characterize all contacting residue pairs present in protein hybrids for inconsistency with protein family structural features. This approach is based on examining contacting residue pairs with different parental origins for different types of potentially unfavorable interactions (i.e. electrostatic repulsion, steric hindrance, cavity formation and hydrogen bond disruption). The identified clashing residue pairs between members of a protein family are then contrasted against functionally characterized hybrid libraries. Comparisons for five different protein recombination studies available in the literature: (i) glycinamide ribonucleotide transformylase (GART) from Escherichia coli (purN) and human (hGART), (ii) human Mu class glutathione S-transferase (GST) M1-1 and M2-2, (iii) beta-lactamase TEM-1 and PSE-4, (iv) catechol-2,3-oxygenase xylE and nahH, and (v) dioxygenases (toluene dioxygenase, tetrachlorobenzene dioxygenase and biphenyl dioxygenase) reveal that the patterns of identified clashing residue pairs are remarkably consistent with experimentally found patterns of functional crossover profiles. Specifically, we show that the proposed residue clash maps are on average 5.0 times more effective than randomly generated clashes and 1.6 times more effective than residue contact maps at explaining the observed crossover distributions among functional members of hybrid libraries. This suggests that residue clash maps can provide quantitative guidelines for the placement of crossovers in the design of protein recombination experiments.     

Using LogitBoost classifier to predict protein structural classes

Prediction of protein classification is an important topic in molecular biology. This is because it is able to not only provide useful information from the viewpoint of structure itself, but also greatly stimulate the characterization of many other features of proteins that may be closely correlated with their biological functions. In this paper, the LogitBoost, one of the boosting algorithms developed recently, is introduced for predicting protein structural classes. It performs classification using a regression scheme as the base learner, which can handle multi-class problems and is particularly superior in coping with noisy data. It was demonstrated that the LogitBoost outperformed the support vector machines in predicting the structural classes for a given dataset, indicating that the new classifier is very promising. It is anticipated that the power in predicting protein structural classes as well as many other bio-macromolecular attributes will be further strengthened if the LogitBoost and some other existing algorithms can be effectively complemented with each other.     

Using mechanistic Bayesian networks to identify downstream targets of the Sonic Hedgehog pathway

Background  

The topology of a biological pathway provides clues as to how a pathway operates, but rationally using this topology information with observed gene expression data remains a challenge.     

Mycobacterium smegmatis RecA protein is structurally similar to but functionally distinct from Mycobacterium tuberculosis RecA

In eubacteria, RecA proteins belong to a large superfamily of evolutionarily conserved, filament-forming, functional homologs of DNA strand exchange proteins. Here, we report the functional characterization of Mycobacterium smegmatis (Ms) and Mycobacterium tuberculosis (Mt) RecA proteins. Although in some respects Ms and Mt RecA proteins are structural and functional homologs of Escherichia coli (Ec) RecA, there are significant differences as well. The single-stranded DNA-binding property of RecA proteins was analyzed by electrophoretic mobility shift assays. We observed that Ms or Mt RecA proteins bound single-stranded DNA in a manner distinct from that of Ec RecA: The former two were able to form protein-DNA complexes in the presence of high salt. Further experiments indicated that Ms or Mt RecA proteins catalyzed adenosine triphosphate hydrolysis at approximately comparable rates across a wide range of pHs. Significantly, DNA strand invasion promoted by Ms or Mt RecA proteins displayed similar kinetics but distinctly different pH profiles. In contrast to MtRecA, MsRecA by itself was unable to form joint molecules across a wide range of pHs. However, regardless of the order in which SSB was added, it was able to stimulate MsRecA to form joint molecules within a narrow pH range, indicating that SSB is a required accessory factor. Together, these results provide a source of sharp contrast between EcRecA and mycobacterial RecAs on the one hand and Mt and Ms RecA proteins on the other.     

Using heterogeneous data to identify signatures of dengue outbreaks at fine spatio-temporal scales across Brazil

Dengue virus remains a significant public health challenge in Brazil, and seasonal preparation efforts are hindered by variable intra- and interseasonal dynamics. Here, we present a framework for characterizing weekly dengue activity at the Brazilian mesoregion level from 2010–2016 as time series properties that are relevant to forecasting efforts, focusing on outbreak shape, seasonal timing, and pairwise correlations in magnitude and onset. In addition, we use a combination of 18 satellite remote sensing imagery, weather, clinical, mobility, and census data streams and regression methods to identify a parsimonious set of covariates that explain each time series property. The models explained 54% of the variation in outbreak shape, 38% of seasonal onset, 34% of pairwise correlation in outbreak timing, and 11% of pairwise correlation in outbreak magnitude. Regions that have experienced longer periods of drought sensitivity, as captured by the “normalized burn ratio,” experienced less intense outbreaks, while regions with regular fluctuations in relative humidity had less regular seasonal outbreaks. Both the pairwise correlations in outbreak timing and outbreak trend between mesoresgions were best predicted by distance. Our analysis also revealed the presence of distinct geographic clusters where dengue properties tend to be spatially correlated. Forecasting models aimed at predicting the dynamics of dengue activity need to identify the most salient variables capable of contributing to accurate predictions. Our findings show that successful models may need to leverage distinct variables in different locations and be catered to a specific task, such as predicting outbreak magnitude or timing characteristics, to be useful. This advocates in favor of “adaptive models” rather than “one-size-fits-all” models. The results of this study can be applied to improving spatial hierarchical or target-focused forecasting models of dengue activity across Brazil.     

Experimental approaches to identify genetic networks

Systems biology offers the promise of a fully integrated view of cellular physiology. To realize this potential requires the analysis of diverse genome-wide datasets and the incorporation of these analyses into integrated networks. In the past decade, the budding yeast Saccharomyces cerevisiae has provided the benchmark for the design of such large-scale experiments. Many of these experimental approaches have been adopted and adapted to study other systems, including worm, fly, fish and mammalian cultured cells, using an ingenious set of molecular tools.     

Crystal structures of the Synechocystis photoreceptor Slr1694 reveal distinct structural states related to signaling

Crystal structures of the Synechocystis BLUF phototaxis photoreceptor Slr1694 have been determined in two crystal forms, a monoclinic form at 1.8 A resolution and an orthorhombic form at 2.1 A resolution. In both forms, the photoreceptor is comprised of two pentamer rings stacked face to face. Twenty total subunits in the two asymmetric units of these crystal forms display three distinct tertiary structures that differ in the length of the fifth beta-strand and in the orientation of Trp91, a conserved Trp residue near the FMN chromophore. Fluorescence spectroscopic analysis on Slr1694 in solution is consistent with motion of Trp91 from a hydrophobic environment in the dark state to a more hydrophilic environment in the light-excited state. Mutational analysis indicates that movement of Trp91 is dependent on the occupancy of the hydrophobic Trp binding pocket with a nearby Met. These different tertiary structures may be associated with absorption changes in the blue region of the spectrum.