Population genetic structure and demographic history of the fat greenling Hexagrammos otakii

Fat greenling (Hexagrammos otakii) is an important commercial fish in the Northwestern Pacific, being distributed along the coastal waters of the East/Japan Sea and the Yellow Sea. To investigate population genetic structure and demographic history of this species, one hundred and fifty five individuals were collected from five localities in the distribution range of the species and sequence variations in the mitochondrial genes COI, COIII-ND3-ND4L, and cytochrome b were examined. For all the genes in every sampling location, the nucleotide diversities were very low (0.001 ～ 0.005) although the haplotype diversities were relatively high, 0.55 ～ 0.81 for COI, 0.79 ～ 0.84 for COIII-ND3-ND4L, and 0.95 ～ 0.97 for cytochrome b. Hierarchical analysis of molecular variance (AMOVA), the conventional population statistic FST, and exact test of population differentiation revealed no significant genetic structuring among the samples, indicating that fat greening is panmictic throughout the range of its distribution. Neutrality tests such as Tajima’s D and Fu’s FS statistics and mismatch distribution analyses suggested that fat greening has undergone the demographic history of population expansion during the late Pleistocene period approximately 91,000 ～ 327,000 years ago. The star-burst patterns of haplotype networks and low nucleotide diversities also indicated recent population expansion. These results help establish the fisheries management strategy for fat greenling in the Northwestern Pacific.     

NABIC marker database: A molecular markers information network of agricultural crops

In 2013, National Agricultural Biotechnology Information Center (NABIC) reconstructs a molecular marker database for useful genetic resources. The web-based marker database consists of three major functional categories: map viewer, RSN marker and gene annotation. It provides 7250 marker locations, 3301 RSN marker property, 3280 molecular marker annotation information in agricultural plants. The individual molecular marker provides information such as marker name, expressed sequence tag number, gene definition and general marker information. This updated marker-based database provides useful information through a user-friendly web interface that assisted in tracing any new structures of the chromosomes and gene positional functions using specific molecular markers.

Availability

The database is available for free at http://nabic.rda.go.kr/gere/rice/molecularMarkers/     

Direct measurement of DNA bending by type IIA topoisomerases: implications for non-equilibrium topology simplification

Type IIA topoisomerases modify DNA topology by passing one segment of duplex DNA (transfer or T–segment) through a transient double-strand break in a second segment of DNA (gate or G–segment) in an ATP-dependent reaction. Type IIA topoisomerases decatenate, unknot and relax supercoiled DNA to levels below equilibrium, resulting in global topology simplification. The mechanism underlying this non-equilibrium topology simplification remains speculative. The bend angle model postulates that non-equilibrium topology simplification scales with the bend angle imposed on the G–segment DNA by the binding of a type IIA topoisomerase. To test this bend angle model, we used atomic force microscopy and single-molecule Förster resonance energy transfer to measure the extent of bending imposed on DNA by three type IIA topoisomerases that span the range of topology simplification activity. We found that Escherichia coli topoisomerase IV, yeast topoisomerase II and human topoisomerase IIα each bend DNA to a similar degree. These data suggest that DNA bending is not the sole determinant of non-equilibrium topology simplification. Rather, they suggest a fundamental and conserved role for DNA bending in the enzymatic cycle of type IIA topoisomerases.     

Signal enhancement of surface plasmon-coupled emission (SPCE) with the evanescent field of surface plasmons on a bimetallic paraboloid biochip

We present a novel approach to the enhancement of surface plasmon-coupled emission (SPCE) using surface plasmon excitation in a bimetal (Ag/Au) layer and we validate the enhancement by presenting the results of a model human IgG immunoassay. Theoretical calculations using Fresnel's equations have been carried out to determine the optimum bimetallic composition and the resulting electric field enhancement. Signal enhancement of SPCE was confirmed using a range of bimetallic layers which were deposited on the surface of a high collection efficiency polymer array biochip and subsequently immobilized with Alexa Fluor 647 labeled anti-human IgG. The bimetallic film of Ag/Au (36/10nm) was determined as an optimum substrate for maximum SPCE signal which was a compromise between the long-term stability of the metal layer and the optimized evanescent field enhancement. An enhanced dose-dependent response was also demonstrated which was ～3 times greater than that detected with a pure gold layer. A human IgG immunoassay showed a dose-dependent response yielding a limit of detection of 1pg/ml by the 3σ rule. The improved performance of the bimetal layer compared to that of an assay carried out on a pure gold layer is attributed to the enhanced evanescent field intensity of surface plasmons in the bimetal combination which excites more fluorescence hence producing an enhanced SPCE signal. This result demonstrates the potential of the SPCE-based array biochips as a sensitive and high-throughput analysis platform for biomolecular interactions.     

Natural occurrence of Mycobacterium as an endosymbiont of Acanthamoeba isolated from a contact lens storage case

Recent in vitro studies have revealed that a certain Mycobacterium can survive and multiply within free-living amoebae. It is believed that protozoans function as host cells for the intracellular replication and evasion of Mycobacterium spp. under harmful conditions. In this study, we describe the isolation and characterization of a bacterium naturally observed within an amoeba isolate acquired from a contact lens storage case. The bacterium multiplied within Acanthamoeba, but exerted no cytopathic effects on the amoeba during a 6-year amoebic culture. Transmission electron microscopy showed that the bacteria were randomly distributed within the cytoplasm of trophozoites and cysts of Acanthamoeba. On the basis of the results of 18S rRNA gene analysis, the amoeba was identified as A. lugdunensis. A 16S rRNA gene analysis placed this bacterium within the genus Mycobacterium. The bacterium evidenced positive reactivity for acid-fast and fluorescent acid-fast stains. The bacterium was capable of growth on the Middlebrook 7H11-Mycobacterium-specific agar. The identification and characterization of bacterial endosymbionts of free-living protozoa bears significant implications for our understanding of the ecology and the identification of other atypical mycobacterial pathogens.     

Benchmarking mutation effect prediction algorithms using functionally validated cancer-related missense mutations

Background

Massively parallel sequencing studies have led to the identification of a large number of mutations present in a minority of cancers of a given site. Hence, methods to identify the likely pathogenic mutations that are worth exploring experimentally and clinically are required. We sought to compare the performance of 15 mutation effect prediction algorithms and their agreement. As a hypothesis-generating aim, we sought to define whether combinations of prediction algorithms would improve the functional effect predictions of specific mutations.

Results

Literature and database mining of single nucleotide variants (SNVs) affecting 15 cancer genes was performed to identify mutations supported by functional evidence or hereditary disease association to be classified either as non-neutral (n = 849) or neutral (n = 140) with respect to their impact on protein function. These SNVs were employed to test the performance of 15 mutation effect prediction algorithms. The accuracy of the prediction algorithms varies considerably. Although all algorithms perform consistently well in terms of positive predictive value, their negative predictive value varies substantially. Cancer-specific mutation effect predictors display no-to-almost perfect agreement in their predictions of these SNVs, whereas the non-cancer-specific predictors showed no-to-moderate agreement. Combinations of predictors modestly improve accuracy and significantly improve negative predictive values.

Conclusions

The information provided by mutation effect predictors is not equivalent. No algorithm is able to predict sufficiently accurately SNVs that should be taken forward for experimental or clinical testing. Combining algorithms aggregates orthogonal information and may result in improvements in the negative predictive value of mutation effect predictions.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-014-0484-1) contains supplementary material, which is available to authorized users.