Mate familiarity and social learning in a monogamous lizard

Social learning is thought to be advantageous as it allows an animal to gather information quickly without engaging in costly trial-and-error learning. However, animals should be selective about when and whom they learn from. Familiarity is predicted to positively inluence an animal’s reliance on social learning; yet, few studies have empirically tested this theory. We used a lizard (Liopholis whitii) that forms long-term monogamous pair bonds to examine the efects of partner familiarity on social learning in two novel foraging tasks, an association and reversal task. We allowed female lizards to observe trained conspeciics that were either familiar (social mate) or unfamiliar execute these tasks and compared these two groups with control females that did not receive social information. Lizards preferentially relied on trial-and-error learning in the association task. In the reversal task, lizards that were demonstrated by familiar partners learnt in fewer trials compared to control lizards and made more correct choices. Our results provide some evidence for context-dependent learning with lizards diferentiating between when they utilize social learning, and, to a limited degree, whom they learnt from. Understanding the role of the social context in which learning occurs provides important insight into the beneits of social learning and sociality more generally

Parameter estimation for robust HMM analysis of ChIP-chip data

Tiling arrays are an important tool for the study of transcriptional activity, protein-DNA interactions and chromatin structure on a genome-wide scale at high resolution. Although hidden Markov models have been used successfully to analyse tiling array data, parameter estimation for these models is typically ad hoc. Especially in the context of ChIP-chip experiments, no standard procedures exist to obtain parameter estimates from the data. Common methods for the calculation of maximum likelihood estimates such as the Baum-Welch algorithm or Viterbi training are rarely applied in the context of tiling array analysis. Results: Here we develop a hidden Markov model for the analysis of chromatin structure ChIP-chip tiling array data, using t emission distributions to increase robustness towards outliers. Maximum likelihood estimates are used for all model parameters. Two different approaches to parameter estimation are investigated and combined into an efficient procedure. Conclusion: We illustrate an efficient parameter estimation procedure that can be used for HMM based methods in general and leads to a clear increase in performance when compared to the use of ad hoc estimates. The resulting hidden Markov model outperforms established methods like TileMap in the context of histone modification studies.13 page(s

A reexamination of information theory-based methods for DNA-binding site identification

<p>Abstract</p> <p>Background</p> <p>Searching for transcription factor binding sites in genome sequences is still an open problem in bioinformatics. Despite substantial progress, search methods based on information theory remain a standard in the field, even though the full validity of their underlying assumptions has only been tested in artificial settings. Here we use newly available data on transcription factors from different bacterial genomes to make a more thorough assessment of information theory-based search methods.</p> <p>Results</p> <p>Our results reveal that conventional benchmarking against artificial sequence data leads frequently to overestimation of search efficiency. In addition, we find that sequence information by itself is often inadequate and therefore must be complemented by other cues, such as curvature, in real genomes. Furthermore, results on skewed genomes show that methods integrating skew information, such as <it>Relative Entropy</it>, are not effective because their assumptions may not hold in real genomes. The evidence suggests that binding sites tend to evolve towards genomic skew, rather than against it, and to maintain their information content through increased conservation. Based on these results, we identify several misconceptions on information theory as applied to binding sites, such as negative entropy, and we propose a revised paradigm to explain the observed results.</p> <p>Conclusion</p> <p>We conclude that, among information theory-based methods, the most unassuming search methods perform, on average, better than any other alternatives, since heuristic corrections to these methods are prone to fail when working on real data. A reexamination of information content in binding sites reveals that information content is a compound measure of search and binding affinity requirements, a fact that has important repercussions for our understanding of binding site evolution.</p

Phylogenetic Reconstruction and DNA Barcoding for Closely Related Pine Moth Species (Dendrolimus) in China with Multiple Gene Markers

Unlike distinct species, closely related species offer a great challenge for phylogeny reconstruction and species identification with DNA barcoding due to their often overlapping genetic variation. We tested a sibling species group of pine moth pests in China with a standard cytochrome c oxidase subunit I (COI) gene and two alternative internal transcribed spacer (ITS) genes (ITS1 and ITS2). Five different phylogenetic/DNA barcoding analysis methods (Maximum likelihood (ML)/Neighbor-joining (NJ), “best close match” (BCM), Minimum distance (MD), and BP-based method (BP)), representing commonly used methodology (tree-based and non-tree based) in the field, were applied to both single-gene and multiple-gene analyses. Our results demonstrated clear reciprocal species monophyly for three relatively distant related species, Dendrolimus superans, D. houi, D. kikuchii, as recovered by both single and multiple genes while the phylogenetic relationship of three closely related species, D. punctatus, D. tabulaeformis, D. spectabilis, could not be resolved with the traditional tree-building methods. Additionally, we find the standard COI barcode outperforms two nuclear ITS genes, whatever the methods used. On average, the COI barcode achieved a success rate of 94.10–97.40%, while ITS1 and ITS2 obtained a success rate of 64.70–81.60%, indicating ITS genes are less suitable for species identification in this case. We propose the use of an overall success rate of species identification that takes both sequencing success and assignation success into account, since species identification success rates with multiple-gene barcoding system were generally overestimated, especially by tree-based methods, where only successfully sequenced DNA sequences were used to construct a phylogenetic tree. Non-tree based methods, such as MD, BCM, and BP approaches, presented advantages over tree-based methods by reporting the overall success rates with statistical significance. In addition, our results indicate that the most closely related species D. punctatus, D. tabulaeformis, and D. spectabilis, may be still in the process of incomplete lineage sorting, with occasional hybridizations occurring among them

Brain type carnosinase in dementia: a pilot study

Research articl

A New Method for Species Identification via Protein-Coding and Non-Coding DNA Barcodes by Combining Machine Learning with Bioinformatic Methods

Species identification via DNA barcodes is contributing greatly to current bioinventory efforts. The initial, and widely accepted, proposal was to use the protein-coding cytochrome c oxidase subunit I (COI) region as the standard barcode for animals, but recently non-coding internal transcribed spacer (ITS) genes have been proposed as candidate barcodes for both animals and plants. However, achieving a robust alignment for non-coding regions can be problematic. Here we propose two new methods (DV-RBF and FJ-RBF) to address this issue for species assignment by both coding and non-coding sequences that take advantage of the power of machine learning and bioinformatics. We demonstrate the value of the new methods with four empirical datasets, two representing typical protein-coding COI barcode datasets (neotropical bats and marine fish) and two representing non-coding ITS barcodes (rust fungi and brown algae). Using two random sub-sampling approaches, we demonstrate that the new methods significantly outperformed existing Neighbor-joining (NJ) and Maximum likelihood (ML) methods for both coding and non-coding barcodes when there was complete species coverage in the reference dataset. The new methods also out-performed NJ and ML methods for non-coding sequences in circumstances of potentially incomplete species coverage, although then the NJ and ML methods performed slightly better than the new methods for protein-coding barcodes. A 100% success rate of species identification was achieved with the two new methods for 4,122 bat queries and 5,134 fish queries using COI barcodes, with 95% confidence intervals (CI) of 99.75–100%. The new methods also obtained a 96.29% success rate (95%CI: 91.62–98.40%) for 484 rust fungi queries and a 98.50% success rate (95%CI: 96.60–99.37%) for 1094 brown algae queries, both using ITS barcodes

RsmW, Pseudomonas aeruginosa small non-coding RsmA-binding RNA upregulated in biofilm versus planktonic growth conditions

BACKGROUND: Biofilm development, specifically the fundamentally adaptive switch from acute to chronic infection phenotypes, requires global regulators and small non-coding regulatory RNAs (sRNAs). This work utilized RNA-sequencing (RNA-seq) to detect sRNAs differentially expressed in Pseudomonas aeruginosa biofilm versus planktonic state. RESULTS: A computational algorithm was devised to detect and categorize sRNAs into 5 types: intergenic, intragenic, 5′-UTR, 3′-UTR, and antisense. Here we report a novel RsmY/RsmZ-type sRNA, termed RsmW, in P. aeruginosa up-transcribed in biofilm versus planktonic growth. RNA-Seq, 5’-RACE and Mfold predictions suggest RsmW has a secondary structure with 3 of 7 GGA motifs located on outer stem loops. Northern blot revealed two RsmW binding bands of 400 and 120 bases, suggesting RsmW is derived from the 3’-UTR of the upstream hypothetical gene, PA4570. RsmW expression is elevated in late stationary versus logarithmic growth phase in PB minimal media, at higher temperatures (37°C versus 28°C), and in both gacA and rhlR transposon mutants versus wild-type. RsmW specifically binds to RsmA protein in vitro and restores biofilm production and reduces swarming in an rsmY/rsmZ double mutant. PA4570 weakly resembles an RsmA/RsmN homolog having 49% and 51% similarity, and 16% and 17% identity to RsmA and RsmN amino acid sequences, respectively. PA4570 was unable to restore biofilm and swarming phenotypes in ΔrsmA deficient strains. CONCLUSION: Collectively, our study reveals an interesting theme regarding another sRNA regulator of the Rsm system and further unravels the complexities regulating adaptive responses for Pseudomonas species

The Causal Cascade to Multiple Sclerosis: A Model for MS Pathogenesis

BACKGROUND: MS pathogenesis seems to involve both genetic susceptibility and environmental risk factors. Three sequential factors are implicated in the environmental risk. The first acts near birth, the second acts during childhood, and the third acts long thereafter. Two candidate factors (vitamin D deficiency and Epstein-Barr viral infection) seem well suited to the first two environmental events. METHODOLOGY/PRINCIPAL FINDINGS: A mathematical Model for MS pathogenesis is developed, incorporating these environmental and genetic factors into a causal scheme that can explain some of the recent changes in MS-epidemiology (e.g., increasing disease prevalence, a changing sex-ratio, and regional variations in monozygotic twin concordance rates). CONCLUSIONS/SIGNIFICANCE: This Model suggests that genetic susceptibility is overwhelmingly the most important determinant of MS pathogenesis. Indeed, over 99% of individuals seem genetically incapable of developing MS, regardless of what environmental exposures they experience. Nevertheless, the contribution of specific genes to MS-susceptibility seems only modest. Thus, despite HLA DRB1*1501 being the most consistently identified genetic marker of MS-susceptibility (being present in over 50% of northern MS patient populations), only about 1% of individuals with this allele are even genetically susceptible to getting MS. Moreover, because genetic susceptibility seems so similar throughout North America and Europe, environmental differences principally determine the regional variations in disease characteristics. Additionally, despite 75% of MS-patients being women, men are 60% more likely to be genetically-susceptible than women. Also, men develop MS at lower levels of environmental exposure than women. Nevertheless, women are more responsive to the recent changes in environmental-exposure (whatever these have been). This explains both the changing sex-ratio and the increasing disease prevalence (which has increased by a minimum of 32% in Canada over the past 35 years). As noted, environmental risk seems to result from three sequential components of environmental exposure. The potential importance of this Model for MS pathogenesis is that, if correct, a therapeutic strategy, designed to interrupt one or more of these sequential factors, has the potential to markedly reduce or eliminate disease prevalence in the future