14,919 research outputs found
A conserved filamentous assembly underlies the structure of the meiotic chromosome axis.
The meiotic chromosome axis plays key roles in meiotic chromosome organization and recombination, yet the underlying protein components of this structure are highly diverged. Here, we show that 'axis core proteins' from budding yeast (Red1), mammals (SYCP2/SYCP3), and plants (ASY3/ASY4) are evolutionarily related and play equivalent roles in chromosome axis assembly. We first identify 'closure motifs' in each complex that recruit meiotic HORMADs, the master regulators of meiotic recombination. We next find that axis core proteins form homotetrameric (Red1) or heterotetrameric (SYCP2:SYCP3 and ASY3:ASY4) coiled-coil assemblies that further oligomerize into micron-length filaments. Thus, the meiotic chromosome axis core in fungi, mammals, and plants shares a common molecular architecture, and likely also plays conserved roles in meiotic chromosome axis assembly and recombination control
Regulatory motif discovery using a population clustering evolutionary algorithm
This paper describes a novel evolutionary algorithm for regulatory motif discovery in DNA promoter sequences. The algorithm uses data clustering to logically distribute the evolving population across the search space. Mating then takes place within local regions of the population, promoting overall solution diversity and encouraging discovery of multiple solutions. Experiments using synthetic data sets have demonstrated the algorithm's capacity to find position frequency matrix models of known regulatory motifs in relatively long promoter sequences. These experiments have also shown the algorithm's ability to maintain diversity during search and discover multiple motifs within a single population. The utility of the algorithm for discovering motifs in real biological data is demonstrated by its ability to find meaningful motifs within muscle-specific regulatory sequences
A fast and cost-effective approach to develop and map EST-SSR markers: oak as a case study
Background: Expressed Sequence Tags (ESTs) are a source of simple sequence repeats (SSRs) that can be used to develop molecular markers for genetic studies. The availability of ESTs for Quercus robur and Quercus petraea provided a unique opportunity to develop microsatellite markers to accelerate research aimed at studying adaptation of these long-lived species to their environment. As a first step toward the construction of a SSR-based linkage map of oak for quantitative trait locus (QTL) mapping, we describe the mining and survey of EST-SSRs as well as a fast and cost-effective approach (bin mapping) to assign these markers to an approximate map position. We also compared the level of polymorphism between genomic and EST-derived SSRs and address the transferability of EST-SSRs in Castanea sativa (chestnut). Results: A catalogue of 103,000 Sanger ESTs was assembled into 28,024 unigenes from which 18.6% presented one or more SSR motifs. More than 42% of these SSRs corresponded to trinucleotides. Primer pairs were designed for 748 putative unigenes. Overall 37.7% (283) were found to amplify a single polymorphic locus in a reference fullsib pedigree of Quercus robur. The usefulness of these loci for establishing a genetic map was assessed using a bin mapping approach. Bin maps were constructed for the male and female parental tree for which framework linkage maps based on AFLP markers were available. The bin set consisting of 14 highly informative offspring selected based on the number and position of crossover sites. The female and male maps comprised 44 and 37 bins, with an average bin length of 16.5 cM and 20.99 cM, respectively. A total of 256 EST-SSRs were assigned to bins and their map position was further validated by linkage mapping. EST-SSRs were found to be less polymorphic than genomic SSRs, but their transferability rate to chestnut, a phylogenetically related species to oak, was higher. Conclusion: We have generated a bin map for oak comprising 256 EST-SSRs. This resource constitutes a first step toward the establishment of a gene-based map for this genus that will facilitate the dissection of QTLs affecting complex traits of ecological importance
Disruption to control network function correlates with altered dynamic connectivity in the wider autism spectrum.
Autism is a common developmental condition with a wide, variable range of co-occurring neuropsychiatric symptoms. Contrasting with most extant studies, we explored whole-brain functional organization at multiple levels simultaneously in a large subject group reflecting autism's clinical diversity, and present the first network-based analysis of transient brain states, or dynamic connectivity, in autism. Disruption to inter-network and inter-system connectivity, rather than within individual networks, predominated. We identified coupling disruption in the anterior-posterior default mode axis, and among specific control networks specialized for task start cues and the maintenance of domain-independent task positive status, specifically between the right fronto-parietal and cingulo-opercular networks and default mode network subsystems. These appear to propagate downstream in autism, with significantly dampened subject oscillations between brain states, and dynamic connectivity configuration differences. Our account proposes specific motifs that may provide candidates for neuroimaging biomarkers within heterogeneous clinical populations in this diverse condition
Feedbacks from the metabolic network to the genetic network reveal regulatory modules in E. coli and B. subtilis
The genetic regulatory network (GRN) plays a key role in controlling the
response of the cell to changes in the environment. Although the structure of
GRNs has been the subject of many studies, their large scale structure in the
light of feedbacks from the metabolic network (MN) has received relatively
little attention. Here we study the causal structure of the GRNs, namely the
chain of influence of one component on the other, taking into account feedback
from the MN. First we consider the GRNs of E. coli and B. subtilis without
feedback from MN and illustrate their causal structure. Next we augment the
GRNs with feedback from their respective MNs by including (a) links from genes
coding for enzymes to metabolites produced or consumed in reactions catalyzed
by those enzymes and (b) links from metabolites to genes coding for
transcription factors whose transcriptional activity the metabolites alter by
binding to them. We find that the inclusion of feedback from MN into GRN
significantly affects its causal structure, in particular the number of levels
and relative positions of nodes in the hierarchy, and the number and size of
the strongly connected components (SCCs). We then study the functional
significance of the SCCs. For this we identify condition specific feedbacks
from the MN into the GRN by retaining only those enzymes that are essential for
growth in specific environmental conditions simulated via the technique of flux
balance analysis (FBA). We find that the SCCs of the GRN augmented by these
feedbacks can be ascribed specific functional roles in the organism. Our
algorithmic approach thus reveals relatively autonomous subsystems with
specific functionality, or regulatory modules in the organism. This automated
approach could be useful in identifying biologically relevant modules in other
organisms for which network data is available, but whose biology is less well
studied.Comment: 15 figure
Sustained-input switches for transcription factors and microRNAs are central building blocks of eukaryotic gene circuits
WaRSwap is a randomization algorithm that for the first time provides a practical network motif discovery method for large multi-layer networks, for example those that include transcription factors, microRNAs, and non-regulatory protein coding genes. The algorithm is applicable to systems with tens of thousands of genes, while accounting for critical aspects of biological networks, including self-loops, large hubs, and target rearrangements. We validate WaRSwap on a newly inferred regulatory network from Arabidopsis thaliana, and compare outcomes on published Drosophila and human networks. Specifically, sustained input switches are among the few over-represented circuits across this diverse set of eukaryotes
Subtle changes in chromatin loop contact propensity are associated with differential gene regulation and expression.
While genetic variation at chromatin loops is relevant for human disease, the relationships between contact propensity (the probability that loci at loops physically interact), genetics, and gene regulation are unclear. We quantitatively interrogate these relationships by comparing Hi-C and molecular phenotype data across cell types and haplotypes. While chromatin loops consistently form across different cell types, they have subtle quantitative differences in contact frequency that are associated with larger changes in gene expression and H3K27ac. For the vast majority of loci with quantitative differences in contact frequency across haplotypes, the changes in magnitude are smaller than those across cell types; however, the proportional relationships between contact propensity, gene expression, and H3K27ac are consistent. These findings suggest that subtle changes in contact propensity have a biologically meaningful role in gene regulation and could be a mechanism by which regulatory genetic variants in loop anchors mediate effects on expression
Origin of life in a digital microcosm
While all organisms on Earth descend from a common ancestor, there is no
consensus on whether the origin of this ancestral self-replicator was a one-off
event or whether it was only the final survivor of multiple origins. Here we
use the digital evolution system Avida to study the origin of self-replicating
computer programs. By using a computational system, we avoid many of the
uncertainties inherent in any biochemical system of self-replicators (while
running the risk of ignoring a fundamental aspect of biochemistry). We
generated the exhaustive set of minimal-genome self-replicators and analyzed
the network structure of this fitness landscape. We further examined the
evolvability of these self-replicators and found that the evolvability of a
self-replicator is dependent on its genomic architecture. We studied the
differential ability of replicators to take over the population when competed
against each other (akin to a primordial-soup model of biogenesis) and found
that the probability of a self-replicator out-competing the others is not
uniform. Instead, progenitor (most-recent common ancestor) genotypes are
clustered in a small region of the replicator space. Our results demonstrate
how computational systems can be used as test systems for hypotheses concerning
the origin of life.Comment: 20 pages, 7 figures. To appear in special issue of Philosophical
Transactions of the Royal Society A: Re-Conceptualizing the Origins of Life
from a Physical Sciences Perspectiv
Sequence-based Multiscale Model (SeqMM) for High-throughput chromosome conformation capture (Hi-C) data analysis
In this paper, I introduce a Sequence-based Multiscale Model (SeqMM) for the
biomolecular data analysis. With the combination of spectral graph method, I
reveal the essential difference between the global scale models and local scale
ones in structure clustering, i.e., different optimization on Euclidean (or
spatial) distances and sequential (or genomic) distances. More specifically,
clusters from global scale models optimize Euclidean distance relations. Local
scale models, on the other hand, result in clusters that optimize the genomic
distance relations. For a biomolecular data, Euclidean distances and sequential
distances are two independent variables, which can never be optimized
simultaneously in data clustering. However, sequence scale in my SeqMM can work
as a tuning parameter that balances these two variables and deliver different
clusterings based on my purposes. Further, my SeqMM is used to explore the
hierarchical structures of chromosomes. I find that in global scale, the
Fiedler vector from my SeqMM bears a great similarity with the principal vector
from principal component analysis, and can be used to study genomic
compartments. In TAD analysis, I find that TADs evaluated from different scales
are not consistent and vary a lot. Particularly when the sequence scale is
small, the calculated TAD boundaries are dramatically different. Even for
regions with high contact frequencies, TAD regions show no obvious consistence.
However, when the scale value increases further, although TADs are still quite
different, TAD boundaries in these high contact frequency regions become more
and more consistent. Finally, I find that for a fixed local scale, my method
can deliver very robust TAD boundaries in different cluster numbers.Comment: 22 PAGES, 13 FIGURE
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