Probability density function (PDF) calculated for (b) sites along the backbone of each SCs within the nuclear volume for various 4Pi-microscopy samples as well as (a) for our SC model sytems at high and low bending rigidity, and , respectively.

<p>The behavior of the experimental PDF fits qualitatively to our (double-tethered) SC polymer model, where the probability density is high for middle-chain regions and drops quickly towards the polymer end regions. This is in contrast to the untethered model, where random coil formation induces a less steep decrease of the probability density function towards both polymer end regions.</p

Figure 4

<p>(a) Intrachain entanglement (“self-entanglement”) and (b) interchain entanglement measured by the mean average crossing number mACN as a function of chain rigidity . Tethering the SC polymer's ends to the borders of the confining cavity induces fewer chain overcrossings than the “null model” consisting of free semiflexible polymers in confinement, which suggests that the interplay between tethering and confinement might help to prevent an excess of chain overcrossings. However, semiflexibility induces a trade-off in both polymer systems between the amount of intrachain- and interchain-entanglement which has to be balanced with respect to interlock resolution. Notably, we find a surprisingly low amount of both types of chain overcrossings for the 4Pi microscopy dataset, mACN and mACN, which cannot be explained within our SC polymer model.</p

Figure 1

<p>(a) Sketch of the applied SC polymer model with the tethered polymer ends being able to diffuse along the envelope of the confining geometry. (b) “Snapshot” of SC polymers in confinement based on the double-tethered SC polymer model. (c) “Snapshot” of synaptonemal complexes in spermatocyte nuclei based on 4Pi-microscopy data. Visual inspection of structural characteristics between the 4Pi-microscopy images and the SC model results such as their end-to-end distance as well as their orientation with respect to each other and the confining cavity indicate that entropy might be one driving force for structural SC organization complementing the dedicated action of specific proteins or actin cables <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0036282#pone.0036282-Kleckner1" target="_blank">[2]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0036282#pone.0036282-Koszul1" target="_blank">[10]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0036282#pone.0036282-Storlazzi1" target="_blank">[25]</a>.</p

Summary of values obtained from 4Pi data.

<p>Summary of values obtained from 4Pi data.</p

Mean squared end-to-end distance <b> as a function of chain rigidity </b> for the double-tethered SC polymer model (“Two tethers”) as well as for the “null” model of free polymers (“Untethered”).

<p>The insets show the probability density function (PDF) of the end-to-end distance for the flexible regime as well as for the stiff case . Semiflexible end-tethered polymers are forces to stretch out between the (moving) attachment sites, leading to larger mean end-to-end distances in agreement with visual inspection of the SC's end-to-end distances in 4Pi-microscopy images. The shaded region indicates the range of bending rigidity that generates the experimentally observed mean squared end-to-end distance of .</p

Tissue-Specific Functional Networks for Prioritizing Phenotype and Disease Genes

<div><p>Integrated analyses of functional genomics data have enormous potential for identifying phenotype-associated genes. Tissue-specificity is an important aspect of many genetic diseases, reflecting the potentially different roles of proteins and pathways in diverse cell lineages. Accounting for tissue specificity in global integration of functional genomics data is challenging, as “functionality” and “functional relationships” are often not resolved for specific tissue types. We address this challenge by generating tissue-specific functional networks, which can effectively represent the diversity of protein function for more accurate identification of phenotype-associated genes in the laboratory mouse. Specifically, we created 107 tissue-specific functional relationship networks through integration of genomic data utilizing knowledge of tissue-specific gene expression patterns. Cross-network comparison revealed significantly changed genes enriched for functions related to specific tissue development. We then utilized these tissue-specific networks to predict genes associated with different phenotypes. Our results demonstrate that prediction performance is significantly improved through using the tissue-specific networks as compared to the global functional network. We used a testis-specific functional relationship network to predict genes associated with male fertility and spermatogenesis phenotypes, and experimentally confirmed one top prediction, <em>Mbyl1</em>. We then focused on a less-common genetic disease, ataxia, and identified candidates uniquely predicted by the cerebellum network, which are supported by both literature and experimental evidence. Our systems-level, tissue-specific scheme advances over traditional global integration and analyses and establishes a prototype to address the tissue-specific effects of genetic perturbations, diseases and drugs.</p> </div

Example enriched Gene Ontology terms in the tissue MA:0000016 nervous system.

<p>Example enriched Gene Ontology terms in the tissue MA:0000016 nervous system.</p

Tissue-specific networks are more accurate than the global network in reflecting protein functional relationships.

<p><b>A.</b> 107 tissues were grouped into major body systems according to the anatomical hierarchical structure maintained in GXD <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002694#pcbi.1002694-Smith1" target="_blank">[20]</a>. Through three-fold cross-validation, the performance of tissue-specific networks was compared against the global network and the percentage improvement of tissue-specific networks over the global network was plotted. All tissue-specific networks out-performed the global network in this cross-validation analysis. Improvements were consistent across tissues belonging to all major organ systems. Candle-stick plots (minimum, 25%, median, 75% and maximum) represent the distribution of percentage AUC improvement for all tissues in a specific system. <b>B.</b> Example precision recall curves of tissue-specific and the global network, generated using three-fold cross-validation. Across the entire precision-recall space, tissue-specific networks performed better than the global network. Complete precision-recall figures for all networks are included in <b><a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002694#pcbi.1002694.s002" target="_blank">Dataset S2</a></b>.</p

Strategy for constructing tissue-specific networks and predicting phenotype-associated genes.

<p>Diverse functional genomic datasets such as expression, protein-protein interactions and phenotype information were integrated in a Bayesian framework to generate tissue-specific networks. Input datasets were probabilistically “weighted” based on how informative they were in reflecting known co-functional proteins that are both expressed in a given tissue. To account for overlap in information in multiple datasets (especially the large number of gene expression microarray datasets), mutual information-based regularization was used to down-weight datasets showing significant overlap with each other. These networks were then used as input into a Support Vector Machine classifier to predict phenotype related genes. Finally, we implemented a web interface that allows network comparison between tissues.</p

Top connected genes to <i>Atcay</i> in the cerebellum-specific network reveals likely ataxia candidates.

<p>Edges with weight greater than 0.9 are shown. In the cerebellum network (<b>A</b>), <i>Grm1</i> and <i>Cacn1a</i> are the top predicted connections to <i>Atcay</i>, with confidences of 0.902 and 0.943, respectively. Both genes are closely connected to <i>Atcay</i> and its top 10 neighbors. In the global network (<b>B</b>), <i>Grm1</i> and <i>Cacn1a</i> are much more weakly connected to <i>Atcay</i> (0.763 and 0.647, respectively), and are not identified as top connectors to <i>Atcay</i>. <i>Grm1</i> and Cacn1a are not connected to <i>Atcay</i> or any of its top 10 neighbors in the global network.</p