Motifs and cis-regulatory modules mediating the expression of genes co-expressed in presynaptic neurons

Background  

Hundreds of proteins modulate neurotransmitter release and synaptic plasticity during neuronal development and in response to synaptic activity. The expression of genes in the pre- and post-synaptic neurons is under stringent spatio-temporal control, but the mechanism underlying the neuronal expression of these genes remains largely unknown.     

Automated detection of problem restraints in NMR data sets using the FINGAR genetic algorithm method

The recently described FINGAR genetic algorithm method for NMR refinement [D.A. Pearlman (1996) J. Biomol. NMR, 8, 67–76] has been extended so that it can be used to detect problem restraints in an NMR-derived set of data. A problem restraint is defined as a restraint in a generally well-behaved set where the associated target value is in error, due to inaccuracies in the data, misassignment, etc. The method described here, FINGAR.RWF, locates problem restraints by finding those restraints that, if removed from the data set, result in a disproportionate improvement in the scoring function. The method is applied to several test cases of simulated data, as well as to real data for the FK506 macrocycle, with excellent results.     

Evolution of lineage-specific functions in ancient cis-regulatory modules

Morphological evolution is driven both by coding sequence variation and by changes in regulatory sequences. However, how cis-regulatory modules (CRMs) evolve to generate entirely novel expression domains is largely unknown. Here, we reconstruct the evolutionary history of a lens enhancer located within a CRM that not only predates the lens, a vertebrate innovation, but bilaterian animals in general. Alignments of orthologous sequences from different deuterostomes sub-divide the CRM into a deeply conserved core and a more divergent flanking region. We demonstrate that all deuterostome flanking regions, including invertebrate sequences, activate gene expression in the zebrafish lens through the same ancient cluster of activator sites. However, levels of gene expression vary between species due to the presence of repressor motifs in flanking region and core. These repressor motifs are responsible for the relatively weak enhancer activity of tetrapod flanking regions. Ray-finned fish, however, have gained two additional lineage-specific activator motifs which in combination with the ancient cluster of activators and the core constitute a potent lens enhancer. The exploitation and modification of existing regulatory potential in flanking regions but not in the highly conserved core might represent a more general model for the emergence of novel regulatory functions in complex CRMs.     

A multi-objective genetic algorithm to find active modules in multiplex biological networks

The identification of subnetworks of interest—or active modules—by integrating biological networks with molecular profiles is a key resource to inform on the processes perturbed in different cellular conditions. We here propose MOGAMUN, a Multi-Objective Genetic Algorithm to identify active modules in MUltiplex biological Networks. MOGAMUN optimizes both the density of interactions and the scores of the nodes (e.g., their differential expression). We compare MOGAMUN with state-of-the-art methods, representative of different algorithms dedicated to the identification of active modules in single networks. MOGAMUN identifies dense and high-scoring modules that are also easier to interpret. In addition, to our knowledge, MOGAMUN is the first method able to use multiplex networks. Multiplex networks are composed of different layers of physical and functional relationships between genes and proteins. Each layer is associated to its own meaning, topology, and biases; the multiplex framework allows exploiting this diversity of biological networks. We applied MOGAMUN to identify cellular processes perturbed in Facio-Scapulo-Humeral muscular Dystrophy, by integrating RNA-seq expression data with a multiplex biological network. We identified different active modules of interest, thereby providing new angles for investigating the pathomechanisms of this disease.Availability: MOGAMUN is available at https://github.com/elvanov/MOGAMUN and as a Bioconductor package at https://bioconductor.org/packages/release/bioc/html/MOGAMUN.html. Contact: rf.uma-vinu@toduab.siana     

BEAM: a beam search algorithm for the identification of cis-regulatory elements in groups of genes.

The identification of potential protein binding sites (cis-regulatory elements) in the upstream regions of genes is key to understanding the mechanisms that regulate gene expression. To this end, we present a simple, efficient algorithm, BEAM (beam-search enumerative algorithm for motif finding), aimed at the discovery of cis-regulatory elements in the DNA sequences upstream of a related group of genes. This algorithm dramatically limits the search space of expanded sequences, converting the problem from one that is exponential in the length of motifs sought to one that is linear. Unlike sampling algorithms, our algorithm converges and is capable of finding statistically overrepresented motifs with a low failure rate. Further, our algorithm is not dependent on the objective function or the organism used. Limiting the space of candidate motifs enables the algorithm to focus only on those motifs that are most likely to be biologically relevant and enables the algorithm to use direct evaluations of background frequencies instead of resorting to probabilistic estimates. In addition, limiting the space of candidate motifs makes it possible to use computationally expensive objective functions that are able to correctly identify biologically relevant motifs.     

Novel algorithm for coexpression detection in time-varying microarray data sets

When analyzing the results of microarray experiments, biologists generally use unsupervised categorization tools. However, such tools regard each time point as an independent dimension and utilize the Euclidean distance to compute the similarities between expressions. Furthermore, some of these methods require the number of clusters to be determined in advance, which is clearly impossible in the case of a new dataset. Therefore, this study proposes a novel scheme, designated as the Variation-based Coexpression Detection (VCD) algorithm, to analyze the trends of expressions based on their variation over time. The proposed algorithm has two advantages. First, it is unnecessary to determine the number of clusters in advance since the algorithm automatically detects those genes whose profiles are grouped together and creates patterns for these groups. Second, the algorithm features a new measurement criterion for calculating the degree of change of the expressions between adjacent time points and evaluating their trend similarities. Three real-world microarray datasets are employed to evaluate the performance of the proposed algorithm.     

Condition-specific coregulation with cis-regulatory motifs and modules in the mouse genome

Deciphering genetic regulatory codes remains a challenge. Here, we present an effective approach to identifying in vivo condition-specific coregulation with cis-regulatory motifs and modules in the mouse genome. A resampling-based algorithm was adopted to cluster our microarray data of a stress response, which generated 35 tight clusters with unique expression patterns containing 811 genes of 5652 genes significantly altered. Database searches identified many known motifs within the 3-kb regulatory regions of 40 genes from 3 clusters and modules with six to nine motifs that were commonly shared by 60-100% of these genes. The upstream regulatory region contained the highest frequency of these common motifs. CisModule program predictions were comparable with the results from database searches and found four potentially novel motifs. This result indicates that these motifs and modules could be responsible for gene coregulation of the stress response in the lacrimal gland.     

A new detection algorithm for implantable cardioverter defibrillator.

One of the most important subsystems of implantable cardioverter defibrillator (ICD) is the sensing stage, since it determines the sensitivity and specificity of the device to detect the heart rate and the underlying arrhythmia. This paper aims to investigate a new detection algorithm for ICD, which operates fully automatically. The algorithm ARGUS was implemented as a computer model and tested with intracardiac electrograms recorded (band-pass: 0.05 to 500 Hz; sampling rate: 1-4 kHz) under different rhythm condition like sinus rhythm (n = 18), atrial tachycardia (n = 16), and ventricular tachycardia as well as fibrillation (n = 139) during electrophysiological tests or ICD implantation. The results of the tests were visually inspected on a beat-to-beat basis. In total 31,934 events were classified by the algorithm (18,758 as long intervals (LI) with cycle length > 300 ms; 13,176 as short intervals (SI)). 195 out of the 13,176 SI and 572 out of 18,758 LI were incorrectly classified (SI: 1.48%; LI: 3.05%). In conclusion the new algorithm yield high sensitivity (99.9%) and specificity (97.0%) as known from conventional ICD algorithms but need no manual adjustments.