Q223R polymorphism of the LEPR and obesity

The problem of overweight and obesity is one of the most urgent health issues in the world. 13 % of girls and 21 % of boys aged 11 suffer from overweight in the Russian Federation. The main causes of pubertal obesity are endocrine pathology, lifestyle and genetic disorders including mutation and polymorphisms of different metabolic pathways. Leptin produced in adipose tissue participates in reproduction regulation, glucose homeostasis, bone formation, etc. These effects are provided by leptin receptors coding LEPR gene. Q223R (rs1137101) polymorphism is associated with an increased serum level of leptin and overweight. There is no exact information about association between this polymorphism and obesity of adolescent females. The objective was to reveal LEPR Q223R polymorphism association between overweight and obesity in adolescent females. 123 Caucasian adolescent females were involved in this study. All samples could be separated into two groups: the girls with normal weight (SDS BM1 ± 1.0; control group), girls with overweight and obesity (SDS BM1 > +1.0-2.0; studied group). Anthropometric measurements (weight, height, waist and hip circumference, body fat percentage) were taken, and genotyping was performed using polymerase chain reaction with electrophoresis detection. G-allele frequency was 43.1 % in control and 40 % in the clinical group. We found no significant differences of the prevalence of polymorphism Q223R between the studied groups (р = 0,862). Furthermore, there was no association between the carriage of AG and GG with weight, BM1, body fat percentage, waist and hip circumference in both groups (р > 0.05). We have not found any association between LEPR Q223R and overweight and obesity in adolescent females

Metabolism and obesity: role of leptin receptor gene

Background. Currently more than 119 obesity-related polymorphisms is known to participate in adult obesity. One of them is LEPR Q223R. Many researches shown association of this polymorphism with adult obesity. However, the role of LEPR Q223R in adolescent overweight and obesity is the matter of dispute. Aim: to determine association of polymorphism Q223R of LEPR gene with some biochemical and hormonal measurements of blood in female adolescents with normal weight and with overweight and obesity. Materials and methods. A total of 103 female adolescents (14-17 years of age) was examined. All girls were divided into 2 groups: 43 girls with normal weight (SDS BM 10.311 ± 0.585), and 65 girls with overweight and obesity (SDS BMI 2.255± 0.739) (р < 0.0001). Height, weight, BM1, SDS BM1 were measured. Laboratory tests included triglycerides, total cholesterol and its fraction, TTH, free thyroxin and leptin. All girls were genotyped on carrier of LEPR Q223R. Statistical analysis was provided by software Statistica 8.0 using nonparametric Mann - Whitney methods and Chi-square test with Yates correction. Results. Significant association of carrying RR-genotype with increase of SDS BM1 (p = 0.006), THS (p = 0.006) and decrease of free thyroxin was shown in control group. Conclusion. Our results showed the association of R-allele with increase of SDS BM1, THS and decrease T4 free in control group

The Effects of Alignment Quality, Distance Calculation Method, Sequence Filtering, and Region on the Analysis of 16S rRNA Gene-Based Studies

Pyrosequencing of PCR-amplified fragments that target variable regions within the 16S rRNA gene has quickly become a powerful method for analyzing the membership and structure of microbial communities. This approach has revealed and introduced questions that were not fully appreciated by those carrying out traditional Sanger sequencing-based methods. These include the effects of alignment quality, the best method of calculating pairwise genetic distances for 16S rRNA genes, whether it is appropriate to filter variable regions, and how the choice of variable region relates to the genetic diversity observed in full-length sequences. I used a diverse collection of 13,501 high-quality full-length sequences to assess each of these questions. First, alignment quality had a significant impact on distance values and downstream analyses. Specifically, the greengenes alignment, which does a poor job of aligning variable regions, predicted higher genetic diversity, richness, and phylogenetic diversity than the SILVA and RDP-based alignments. Second, the effect of different gap treatments in determining pairwise genetic distances was strongly affected by the variation in sequence length for a region; however, the effect of different calculation methods was subtle when determining the sample's richness or phylogenetic diversity for a region. Third, applying a sequence mask to remove variable positions had a profound impact on genetic distances by muting the observed richness and phylogenetic diversity. Finally, the genetic distances calculated for each of the variable regions did a poor job of correlating with the full-length gene. Thus, while it is tempting to apply traditional cutoff levels derived for full-length sequences to these shorter sequences, it is not advisable. Analysis of β-diversity metrics showed that each of these factors can have a significant impact on the comparison of community membership and structure. Taken together, these results urge caution in the design and interpretation of analyses using pyrosequencing data

XplorSeq: A software environment for integrated management and phylogenetic analysis of metagenomic sequence data

<p>Abstract</p> <p>Background</p> <p>Advances in automated DNA sequencing technology have accelerated the generation of metagenomic DNA sequences, especially environmental ribosomal RNA gene (rDNA) sequences. As the scale of rDNA-based studies of microbial ecology has expanded, need has arisen for software that is capable of managing, annotating, and analyzing the plethora of diverse data accumulated in these projects.</p> <p>Results</p> <p>XplorSeq is a software package that facilitates the compilation, management and phylogenetic analysis of DNA sequences. XplorSeq was developed for, but is not limited to, high-throughput analysis of environmental rRNA gene sequences. XplorSeq integrates and extends several commonly used UNIX-based analysis tools by use of a Macintosh OS-X-based graphical user interface (GUI). Through this GUI, users may perform basic sequence import and assembly steps (base-calling, vector/primer trimming, contig assembly), perform BLAST (Basic Local Alignment and Search Tool; <abbrgrp><abbr bid="B1">1</abbr><abbr bid="B2">2</abbr><abbr bid="B3">3</abbr></abbrgrp>) searches of NCBI and local databases, create multiple sequence alignments, build phylogenetic trees, assemble Operational Taxonomic Units, estimate biodiversity indices, and summarize data in a variety of formats. Furthermore, sequences may be annotated with user-specified meta-data, which then can be used to sort data and organize analyses and reports. A document-based architecture permits parallel analysis of sequence data from multiple clones or amplicons, with sequences and other data stored in a single file.</p> <p>Conclusion</p> <p>XplorSeq should benefit researchers who are engaged in analyses of environmental sequence data, especially those with little experience using bioinformatics software. Although XplorSeq was developed for management of rDNA sequence data, it can be applied to most any sequencing project. The application is available free of charge for non-commercial use at <url>http://vent.colorado.edu/phyloware</url>.</p

Large-Scale Neighbor-Joining with NINJA

Abstract Neighbor-joining is a well-established hierarchical clustering algorithm for inferring phylogenies. It begins with observed distances between pairs of sequences, and clustering order depends on a metric related to those distances. The canonical algorithm requires O(n3) time and O(n2) space for n sequences, which precludes application to very large sequence families, e.g. those containing 100,000 sequences. Datasets of this size are available today, and such phylogenies will play an increasingly important role in comparative genomics studies. Recent algorithmic advances have greatly sped up neighbor-joining for inputs of thousands of sequences, but are limited to fewer than 13,000 sequences on a system with 4GB RAM. In this paper, I describe an algorithm that speeds up neighbor-joining by dramatically reducing the number of distance values that are viewed in each iteration of the clustering procedure, while still computing a correct neighbor-joining tree. This algorithm can scale to inputs larger than 100,000 sequences because of external-memory-efficient data structures. A free implementation may by obtained fro

Comparative Analysis of Pyrosequencing and a Phylogenetic Microarray for Exploring Microbial Community Structures in the Human Distal Intestine

Background Variations in the composition of the human intestinal microbiota are linked to diverse health conditions. High-throughput molecular technologies have recently elucidated microbial community structure at much higher resolution than was previously possible. Here we compare two such methods, pyrosequencing and a phylogenetic array, and evaluate classifications based on two variable 16S rRNA gene regions. Methods and Findings Over 1.75 million amplicon sequences were generated from the V4 and V6 regions of 16S rRNA genes in bacterial DNA extracted from four fecal samples of elderly individuals. The phylotype richness, for individual samples, was 1,400–1,800 for V4 reads and 12,500 for V6 reads, and 5,200 unique phylotypes when combining V4 reads from all samples. The RDP-classifier was more efficient for the V4 than for the far less conserved and shorter V6 region, but differences in community structure also affected efficiency. Even when analyzing only 20% of the reads, the majority of the microbial diversity was captured in two samples tested. DNA from the four samples was hybridized against the Human Intestinal Tract (HIT) Chip, a phylogenetic microarray for community profiling. Comparison of clustering of genus counts from pyrosequencing and HITChip data revealed highly similar profiles. Furthermore, correlations of sequence abundance and hybridization signal intensities were very high for lower-order ranks, but lower at family-level, which was probably due to ambiguous taxonomic groupings. Conclusions The RDP-classifier consistently assigned most V4 sequences from human intestinal samples down to genus-level with good accuracy and speed. This is the deepest sequencing of single gastrointestinal samples reported to date, but microbial richness levels have still not leveled out. A majority of these diversities can also be captured with five times lower sampling-depth. HITChip hybridizations and resulting community profiles correlate well with pyrosequencing-based compositions, especially for lower-order ranks, indicating high robustness of both approaches. However, incompatible grouping schemes make exact comparison difficult

An Insect Herbivore Microbiome with High Plant Biomass-Degrading Capacity

Herbivores can gain indirect access to recalcitrant carbon present in plant cell walls through symbiotic associations with lignocellulolytic microbes. A paradigmatic example is the leaf-cutter ant (Tribe: Attini), which uses fresh leaves to cultivate a fungus for food in specialized gardens. Using a combination of sugar composition analyses, metagenomics, and whole-genome sequencing, we reveal that the fungus garden microbiome of leaf-cutter ants is composed of a diverse community of bacteria with high plant biomass-degrading capacity. Comparison of this microbiome's predicted carbohydrate-degrading enzyme profile with other metagenomes shows closest similarity to the bovine rumen, indicating evolutionary convergence of plant biomass degrading potential between two important herbivorous animals. Genomic and physiological characterization of two dominant bacteria in the fungus garden microbiome provides evidence of their capacity to degrade cellulose. Given the recent interest in cellulosic biofuels, understanding how large-scale and rapid plant biomass degradation occurs in a highly evolved insect herbivore is of particular relevance for bioenergy

Energy Conservation R. D. & D. Programs in High Temperature Processes

The potential for energy conservation in high temperature industrial processes is very large. Industrial processes are known to consume over 30 percent of the Nation's energy. In turn something less than one third of this estimated twenty quads of energy is actually required to produce the product. This broad sweeping statement covers many sins and many virtues. The blast furnace, for example, is the largest user of energy per net ton of steel produced and operates at approximately 67% of theoretical efficiency. The slot forge furnace, used to reheat steel fat hot forging, operates at approximately 10% of theoretical efficiency. Actually steel forging stock can also be reheated at about 50% efficiency and this has been done by a DOE sponsored contractor. The technology is, in fact, being commercialized by the contractor and its rapid diffusion by DOE will be actively encouraged

Evolving autonomous learning in cognitive networks

There are two common approaches for optimizing the performance of a machine: genetic algorithms and machine learning. A genetic algorithm is applied over many generations whereas machine learning works by applying feedback until the system meets a performance threshold. These methods have been previously combined, particularly in artificial neural networks using an external objective feedback mechanism. We adapt this approach to Markov Brains, which are evolvable networks of probabilistic and deterministic logic gates. Prior to this work MB could only adapt from one generation to the other, so we introduce feedback gates which augment their ability to learn during their lifetime. We show that Markov Brains can incorporate these feedback gates in such a way that they do not rely on an external objective feedback signal, but instead can generate internal feedback that is then used to learn. This results in a more biologically accurate model of the evolution of learning, which will enable us to study the interplay between evolution and learning and could be another step towards autonomously learning machines. © 2017 The Author(s)