Contact replacement for NMR resonance assignment

Motivation: Complementing its traditional role in structural studies of proteins, nuclear magnetic resonance (NMR) spectroscopy is playing an increasingly important role in functional studies. NMR dynamics experiments characterize motions involved in target recognition, ligand binding, etc., while NMR chemical shift perturbation experiments identify and localize protein–protein and protein–ligand interactions. The key bottleneck in these studies is to determine the backbone resonance assignment, which allows spectral peaks to be mapped to specific atoms. This article develops a novel approach to address that bottleneck, exploiting an available X-ray structure or homology model to assign the entire backbone from a set of relatively fast and cheap NMR experiments

A robust SNP barcode for typing Mycobacterium tuberculosis complex strains

Strain-specific genomic diversity in the Mycobacterium tuberculosis complex (MTBC) is an important factor in pathogenesis that may affect virulence, transmissibility, host response and emergence of drug resistance. Several systems have been proposed to classify MTBC strains into distinct lineages and families. Here, we investigate single-nucleotide polymorphisms (SNPs) as robust (stable) markers of genetic variation for phylogenetic analysis. We identify ~92k SNP across a global collection of 1,601 genomes. The SNP-based phylogeny is consistent with the gold-standard regions of difference (RD) classification system. Of the ~7k strain-specific SNPs identified, 62 markers are proposed to discriminate known circulating strains. This SNP-based barcode is the first to cover all main lineages, and classifies a greater number of sublineages than current alternatives. It may be used to classify clinical isolates to evaluate tools to control the disease, including therapeutics and vaccines whose effectiveness may vary by strain type

Plant-Derived Polysaccharide Supplements Inhibit Dextran Sulfate Sodium-Induced Colitis in the Rat

Several plant-derived polysaccharides have been shown to have anti-inflammatory activity in animal models. Ambrotose complex and Advanced Ambrotose are dietary supplements that include aloe vera gel, arabinogalactan, fucoidan, and rice starch, all of which have shown such activity. This study was designed to evaluate these formulations against dextran sulfate sodium (DSS)-induced colitis in rats and to confirm their short-term safety after 14 days of daily dosing. Rats were dosed daily orally with vehicle, Ambrotose or Advanced Ambrotose. On day six groups of rats received tap water or 5% Dextran Sulfate sodium. Ambrotose and Advanced Ambrotose significantly lowered the disease scores and partially prevented the shortening of colon length. An increase in monocyte count was induced by dextran sulfate sodium and inhibited by Ambrotose and Advanced Ambrotose. There were no observable adverse effects after 14-day daily doses. The mechanism of action of the formulations against DSS-induced colitis may be related to its effect on monocyte count

Automated parameter estimation for biological models using Bayesian statistical model checking

Background: Probabilistic models have gained widespread acceptance in the systems biology community as a useful way to represent complex biological systems. Such models are developed using existing knowledge of the structure and dynamics of the system, experimental observations, and inferences drawn from statistical analysis of empirical data. A key bottleneck in building such models is that some system variables cannot be measured experimentally. These variables are incorporated into the model as numerical parameters. Determining values of these parameters that justify existing experiments and provide reliable predictions when model simulations are performed is a key research problem. Domain experts usually estimate the values of these parameters by fitting the model to experimental data. Model fitting is usually expressed as an optimization problem that requires minimizing a cost-function which measures some notion of distance between the model and the data. This optimization problem is often solved by combining local and global search methods that tend to perform well for the specific application domain. When some prior information about parameters is available, methods such as Bayesian inference are commonly used for parameter learning. Choosing the appropriate parameter search technique requires detailed domain knowledge and insight into the underlying system. Results: Using an agent-based model of the dynamics of acute inflammation, we demonstrate a novel parameter estimation algorithm by discovering the amount and schedule of doses of bacterial lipopolysaccharide that guarantee a set of observed clinical outcomes with high probability. We synthesized values of twenty-eight unknown parameters such that the parameterized model instantiated with these parameter values satisfies four specifications describing the dynamic behavior of the model. Conclusions: We have developed a new algorithmic technique for discovering parameters in complex stochastic models of biological systems given behavioral specifications written in a formal mathematical logic. Our algorithm uses Bayesian model checking, sequential hypothesis testing, and stochastic optimization to automatically synthesize parameters of probabilistic biological models

Improved annotation of 3' untranslated regions and complex loci by combination of strand-specific direct RNA sequencing, RNA-seq and ESTs

The reference annotations made for a genome sequence provide the framework for all subsequent analyses of the genome. Correct annotation is particularly important when interpreting the results of RNA-seq experiments where short sequence reads are mapped against the genome and assigned to genes according to the annotation. Inconsistencies in annotations between the reference and the experimental system can lead to incorrect interpretation of the effect on RNA expression of an experimental treatment or mutation in the system under study. Until recently, the genome-wide annotation of 3-prime untranslated regions received less attention than coding regions and the delineation of intron/exon boundaries. In this paper, data produced for samples in Human, Chicken and A. thaliana by the novel single-molecule, strand-specific, Direct RNA Sequencing technology from Helicos Biosciences which locates 3-prime polyadenylation sites to within +/- 2 nt, were combined with archival EST and RNA-Seq data. Nine examples are illustrated where this combination of data allowed: (1) gene and 3-prime UTR re-annotation (including extension of one 3-prime UTR by 5.9 kb); (2) disentangling of gene expression in complex regions; (3) clearer interpretation of small RNA expression and (4) identification of novel genes. While the specific examples displayed here may become obsolete as genome sequences and their annotations are refined, the principles laid out in this paper will be of general use both to those annotating genomes and those seeking to interpret existing publically available annotations in the context of their own experimental dataComment: 44 pages, 9 figure

Classification of ncRNAs using position and size information in deep sequencing data

Motivation: Small non-coding RNAs (ncRNAs) play important roles in various cellular functions in all clades of life. With next-generation sequencing techniques, it has become possible to study ncRNAs in a high-throughput manner and by using specialized algorithms ncRNA classes such as miRNAs can be detected in deep sequencing data. Typically, such methods are targeted to a certain class of ncRNA. Many methods rely on RNA secondary structure prediction, which is not always accurate and not all ncRNA classes are characterized by a common secondary structure. Unbiased classification methods for ncRNAs could be important to improve accuracy and to detect new ncRNA classes in sequencing data

Transcriptomics reveal an integrative role for maternal thyroid hormones during zebrafish embryogenesis

Thyroid hormones (THs) are essential for embryonic brain development but the genetic mechanisms involved in the action of maternal THs (MTHs) are still largely unknown. As the basis for understanding the underlying genetic mechanisms of MTHs regulation we used an established zebrafish monocarboxylic acid transporter 8 (MCT8) knock-down model and characterised the transcriptome in 25hpf zebrafish embryos. Subsequent mapping of differentially expressed genes using Reactome pathway analysis together with in situ expression analysis and immunohistochemistry revealed the genetic networks and cells under MTHs regulation during zebrafish embryogenesis. We found 4,343 differentially expressed genes and the Reactome pathway analysis revealed that TH is involved in 1681 of these pathways. MTHs regulated the expression of core developmental pathways, such as NOTCH and WNT in a cell specific context. The cellular distribution of neural MTH-target genes demonstrated their cell specific action on neural stem cells and differentiated neuron classes. Taken together our data show that MTHs have a role in zebrafish neurogenesis and suggest they may be involved in cross talk between key pathways in neural development. Given that the observed MCT8 zebrafish knockdown phenotype resembles the symptoms in human patients with Allan-Herndon-Dudley syndrome our data open a window into understanding the genetics of this human congenital condition.Portuguese Fundacao para Ciencia e Tecnologia (FCT) [PTDC/EXPL/MARBIO/0430/2013]; CCMAR FCT Plurianual financing [UID/Multi/04326/2013]; FCT [SFRH/BD/111226/2015, SFRH/BD/108842/2015, SFRH/BPD/89889/2012]; FCT-IF Starting Grant [IF/01274/2014]info:eu-repo/semantics/publishedVersio

Transkingdom Networks: A Systems Biology Approach to Identify Causal Members of Host-Microbiota Interactions

Improvements in sequencing technologies and reduced experimental costs have resulted in a vast number of studies generating high-throughput data. Although the number of methods to analyze these "omics" data has also increased, computational complexity and lack of documentation hinder researchers from analyzing their high-throughput data to its true potential. In this chapter we detail our data-driven, transkingdom network (TransNet) analysis protocol to integrate and interrogate multi-omics data. This systems biology approach has allowed us to successfully identify important causal relationships between different taxonomic kingdoms (e.g. mammals and microbes) using diverse types of data

Plant-RRBS, a bisulfite and next-generation sequencing-based methylome profiling method enriching for coverage of cytosine positions

Background: Cytosine methylation in plant genomes is important for the regulation of gene transcription and transposon activity. Genome-wide methylomes are studied upon mutation of the DNA methyltransferases, adaptation to environmental stresses or during development. However, from basic biology to breeding programs, there is a need to monitor multiple samples to determine transgenerational methylation inheritance or differential cytosine methylation. Methylome data obtained by sodium hydrogen sulfite (bisulfite)-conversion and next-generation sequencing (NGS) provide genome- wide information on cytosine methylation. However, a profiling method that detects cytosine methylation state dispersed over the genome would allow high-throughput analysis of multiple plant samples with distinct epigenetic signatures. We use specific restriction endonucleases to enrich for cytosine coverage in a bisulfite and NGS-based profiling method, which was compared to whole-genome bisulfite sequencing of the same plant material. Methods: We established an effective methylome profiling method in plants, termed plant-reduced representation bisulfite sequencing (plant-RRBS), using optimized double restriction endonuclease digestion, fragment end repair, adapter ligation, followed by bisulfite conversion, PCR amplification and NGS. We report a performant laboratory protocol and a straightforward bioinformatics data analysis pipeline for plant-RRBS, applicable for any reference-sequenced plant species. Results: As a proof of concept, methylome profiling was performed using an Oryza sativa ssp. indica pure breeding line and a derived epigenetically altered line (epiline). Plant-RRBS detects methylation levels at tens of millions of cytosine positions deduced from bisulfite conversion in multiple samples. To evaluate the method, the coverage of cytosine positions, the intra-line similarity and the differential cytosine methylation levels between the pure breeding line and the epiline were determined. Plant-RRBS reproducibly covers commonly up to one fourth of the cytosine positions in the rice genome when using MspI-DpnII within a group of five biological replicates of a line. The method predominantly detects cytosine methylation in putative promoter regions and not-annotated regions in rice. Conclusions: Plant-RRBS offers high-throughput and broad, genome- dispersed methylation detection by effective read number generation obtained from reproducibly covered genome fractions using optimized endonuclease combinations, facilitating comparative analyses of multi-sample studies for cytosine methylation and transgenerational stability in experimental material and plant breeding populations