Evolutionary conservation and network structure characterize genes of phenotypic relevance for mitosis in human

Genome Sequencing and Analysis of Thraustochytriidae sp. SZU445 Provides Novel Insights into the Polyunsaturated Fatty Acid Biosynthesis Pathway

Thraustochytriidae sp. have broadly gained attention as a prospective resource for the production of omega-3 fatty acids production in significant quantities. In this study, the whole genome of Thraustochytriidae sp. SZU445, which produces high levels of docosapentaenoic acid (DPA) and docosahexaenoic acid (DHA), was sequenced and subjected to protein annotation. The obtained clean reads (63.55 Mb in total) were assembled into 54 contigs and 25 scaffolds, with maximum and minimum lengths of 400 and 0.0054 Mb, respectively. A total of 3513 genes (24.84%) were identified, which could be classified into six pathways and 44 pathway groups, of which 68 genes (1.93%) were involved in lipid metabolism. In the Gene Ontology database, 22,436 genes were annotated as cellular component (8579 genes, 38.24%), molecular function (5236 genes, 23.34%), and biological process (8621 genes, 38.42%). Four enzymes corresponding to the classic fatty acid synthase (FAS) pathway and three enzymes corresponding to the classic polyketide synthase (PKS) pathway were identified in Thraustochytriidae sp. SZU445. Although PKS pathway-associated dehydratase and isomerase enzymes were not detected in Thraustochytriidae sp. SZU445, a putative DHA- and DPA-specific fatty acid pathway was identified.

The drug target genes show higher evolutionary conservation than non-target genes

Although evidence indicates that drug target genes share some common evolutionary features, there have been few studies analyzing evolutionary features of drug targets from an overall level. Therefore, we conducted an analysis which aimed to investigate the evolutionary characteristics of drug target genes. We compared the evolutionary conservation between human drug target genes and non-target genes by combining both the evolutionary features and network topological properties in human protein-protein interaction network. The evolution rate, conservation score and the percentage of orthologous genes of 21 species were included in our study. Meanwhile, four topological features including the average shortest path length, betweenness centrality, clustering coefficient and degree were considered for comparison analysis. Then we got four results as following: compared with non-drug target genes, 1) drug target genes had lower evolutionary rates; 2) drug target genes had higher conservation scores; 3) drug target genes had higher percentages of orthologous genes and 4) drug target genes had a tighter network structure including higher degrees, betweenness centrality, clustering coefficients and lower average shortest path lengths. These results demonstrate that drug target genes are more evolutionarily conserved than non-drug target genes. We hope that our study will provide valuable information for other researchers who are interested in evolutionary conservation of drug targets.

A generic methodological framework for studying single cell motility in high-throughput time-lapse data

Motivation: Motility is a fundamental cellular attribute, which plays a major part in processes ranging from embryonic development to metastasis. Traditionally, single cell motility is often studied by live cell imaging. Yet, such studies were so far limited to low throughput. To systematically study cell motility at a large scale, we need robust methods to quantify cell trajectories in live cell imaging data.

Results: The primary contribution of this article is to present Motility study Integrated Workflow (MotIW), a generic workflow for the study of single cell motility in high-throughput time-lapse screening data. It is composed of cell tracking, cell trajectory mapping to an original feature space and hit detection according to a new statistical procedure. We show that this workflow is scalable and demonstrates its power by application to simulated data, as well as large-scale live cell imaging data. This application enables the identification of an ontology of cell motility patterns in a fully unsupervised manner.

Availability and implementation: Python code and examples are available online (http://cbio.ensmp.fr/∼aschoenauer/motiw.html)

Contact:thomas.walter@mines-paristech.fr

Supplementary information:Supplementary data are available at Bioinformatics online.

The diminishing role of hubs in dynamical processes on complex networks

It is notoriously difficult to predict the behaviour of a complex self-organizing system, where the interactions among dynamical units form a heterogeneous topology. Even if the dynamics of each microscopic unit is known, a real understanding of their contributions to the macroscopic system behaviour is still lacking. Here, we develop information-theoretical methods to distinguish the contribution of each individual unit to the collective out-of-equilibrium dynamics. We show that for a system of units connected by a network of interaction potentials with an arbitrary degree distribution, highly connected units have less impact on the system dynamics when compared with intermediately connected units. In an equilibrium setting, the hubs are often found to dictate the long-term behaviour. However, we find both analytically and experimentally that the instantaneous states of these units have a short-lasting effect on the state trajectory of the entire system. We present qualitative evidence of this phenomenon from empirical findings about a social network of product recommendations, a protein–protein interaction network and a neural network, suggesting that it might indeed be a widespread property in nature.