Intraclonal Variation in Wood Density of Trembling Aspen in Alberta

Four trees from each of three putative clones of trembling aspen (Populus tremuloides Michx.) at one site in north-central Alberta were sampled to determine the patterns of wood density variation within stems and within clones. Sample disks were removed at five heights from each tree to examine variation among cardinal directions and across the southern radius at each height. Although only three clones were sampled, there were significant differences (0.05 level) among clones. Wood density tends to be high at the bottom of the tree, decreases to a minimum at midheight, then increases again near the top of the tree. In the radial direction, wood density is high near the pith (at all heights), decreases, then increases again in the mature wood zone (after rings 15-20+). Average wood density values within the twelve stems varied from 0.348 g/cc to 0.402 g/cc

Exact Asymptotic Results for a Model of Sequence Alignment

Finding analytically the statistics of the longest common subsequence (LCS) of a pair of random sequences drawn from c alphabets is a challenging problem in computational evolutionary biology. We present exact asymptotic results for the distribution of the LCS in a simpler, yet nontrivial, variant of the original model called the Bernoulli matching (BM) model which reduces to the original model in the large c limit. We show that in the BM model, for all c, the distribution of the asymptotic length of the LCS, suitably scaled, is identical to the Tracy-Widom distribution of the largest eigenvalue of a random matrix whose entries are drawn from a Gaussian unitary ensemble. In particular, in the large c limit, this provides an exact expression for the asymptotic length distribution in the original LCS problem.Comment: 4 pages Revtex, 2 .eps figures include

Bethe Ansatz in the Bernoulli Matching Model of Random Sequence Alignment

For the Bernoulli Matching model of sequence alignment problem we apply the Bethe ansatz technique via an exact mapping to the 5--vertex model on a square lattice. Considering the terrace--like representation of the sequence alignment problem, we reproduce by the Bethe ansatz the results for the averaged length of the Longest Common Subsequence in Bernoulli approximation. In addition, we compute the average number of nucleation centers of the terraces.Comment: 14 pages, 5 figures (some points are clarified

Comparison of Spectra in Unsequenced Species

International audienceWe introduce a new algorithm for the mass spectromet- ric identication of proteins. Experimental spectra obtained by tandem MS/MS are directly compared to theoretical spectra generated from pro- teins of evolutionarily closely related organisms. This work is motivated by the need of a method that allows the identication of proteins of unsequenced species against a database containing proteins of related organisms. The idea is that matching spectra of unknown peptides to very similar MS/MS spectra generated from this database of annotated proteins can lead to annotate unknown proteins. This process is similar to ortholog annotation in protein sequence databases. The difficulty with such an approach is that two similar peptides, even with just one mod- ication (i.e. insertion, deletion or substitution of one or several amino acid(s)) between them, usually generate very dissimilar spectra. In this paper, we present a new dynamic programming based algorithm: Packet- SpectralAlignment. Our algorithm is tolerant to modications and fully exploits two important properties that are usually not considered: the notion of inner symmetry, a relation linking pairs of spectrum peaks, and the notion of packet inside each spectrum to keep related peaks together. Our algorithm, PacketSpectralAlignment is then compared to SpectralAlignment [1] on a dataset of simulated spectra. Our tests show that PacketSpectralAlignment behaves better, in terms of results and execution tim

Elucidating the role of Agl in bladder carcinogenesis by generation and characterization of genetically engineered mice

Amylo-\u3b1-1,6-glucosidase,4-\u3b1-glucanotransferase (AGL) is an enzyme primarily responsible for glycogen debranching. Germline mutations lead to glycogen storage disease type III (GSDIII). We recently found AGL to be a tumor suppressor in xenograft models of human bladder cancer (BC) and low levels of AGL expression in BC are associated with poor patient prognosis. However, the impact of low AGL expression on the susceptibility of normal bladder to carcinogenesis is unknown. We address this gap by developing a germline Agl knockout (Agl-/-) mouse that recapitulates biochemical and histological features of GSDIII. Agl-/- mice exposed to N-butyl-N-(4-hydroxybutyl) nitrosamine (BBN) had a higher BC incidence compared with wild-type mice (Agl+/+). To determine if the increased BC incidence observed was due to decreased Agl expression in the urothelium specifically, we developed a urothelium-specific conditional Agl knockout (Aglcko) mouse using a Uroplakin II-Cre allele. BBN-induced carcinogenesis experiments repeated in Aglcko mice revealed that Aglcko mice had a higher BC incidence than control (Aglfl/fl) mice. RNA sequencing revealed that tumors from Agl-/- mice had 19 differentially expressed genes compared with control mice. An 'Agl Loss' gene signature was developed and found to successfully stratify normal and tumor samples in two BC patient datasets. These results support the role of AGL loss in promoting carcinogenesis and provide a rationale for evaluating Agl expression levels, or Agl Loss gene signature scores, in normal urothelium of populations at risk of BC development such as older male smokers

Computational Methods for Protein Identification from Mass Spectrometry Data

Protein identification using mass spectrometry is an indispensable computational tool in the life sciences. A dramatic increase in the use of proteomic strategies to understand the biology of living systems generates an ongoing need for more effective, efficient, and accurate computational methods for protein identification. A wide range of computational methods, each with various implementations, are available to complement different proteomic approaches. A solid knowledge of the range of algorithms available and, more critically, the accuracy and effectiveness of these techniques is essential to ensure as many of the proteins as possible, within any particular experiment, are correctly identified. Here, we undertake a systematic review of the currently available methods and algorithms for interpreting, managing, and analyzing biological data associated with protein identification. We summarize the advances in computational solutions as they have responded to corresponding advances in mass spectrometry hardware. The evolution of scoring algorithms and metrics for automated protein identification are also discussed with a focus on the relative performance of different techniques. We also consider the relative advantages and limitations of different techniques in particular biological contexts. Finally, we present our perspective on future developments in the area of computational protein identification by considering the most recent literature on new and promising approaches to the problem as well as identifying areas yet to be explored and the potential application of methods from other areas of computational biology

Human Genetics in Rheumatoid Arthritis Guides a High-Throughput Drug Screen of the CD40 Signaling Pathway

Although genetic and non-genetic studies in mouse and human implicate the CD40 pathway in rheumatoid arthritis (RA), there are no approved drugs that inhibit CD40 signaling for clinical care in RA or any other disease. Here, we sought to understand the biological consequences of a CD40 risk variant in RA discovered by a previous genome-wide association study (GWAS) and to perform a high-throughput drug screen for modulators of CD40 signaling based on human genetic findings. First, we fine-map the CD40 risk locus in 7,222 seropositive RA patients and 15,870 controls, together with deep sequencing of CD40 coding exons in 500 RA cases and 650 controls, to identify a single SNP that explains the entire signal of association (rs4810485, P = 1.4×10(−9)). Second, we demonstrate that subjects homozygous for the RA risk allele have ∼33% more CD40 on the surface of primary human CD19+ B lymphocytes than subjects homozygous for the non-risk allele (P = 10(−9)), a finding corroborated by expression quantitative trait loci (eQTL) analysis in peripheral blood mononuclear cells from 1,469 healthy control individuals. Third, we use retroviral shRNA infection to perturb the amount of CD40 on the surface of a human B lymphocyte cell line (BL2) and observe a direct correlation between amount of CD40 protein and phosphorylation of RelA (p65), a subunit of the NF-κB transcription factor. Finally, we develop a high-throughput NF-κB luciferase reporter assay in BL2 cells activated with trimerized CD40 ligand (tCD40L) and conduct an HTS of 1,982 chemical compounds and FDA–approved drugs. After a series of counter-screens and testing in primary human CD19+ B cells, we identify 2 novel chemical inhibitors not previously implicated in inflammation or CD40-mediated NF-κB signaling. Our study demonstrates proof-of-concept that human genetics can be used to guide the development of phenotype-based, high-throughput small-molecule screens to identify potential novel therapies in complex traits such as RA