AGEMAP: A Gene Expression Database for Aging in Mice

We present the AGEMAP (Atlas of Gene Expression in Mouse Aging Project) gene expression database, which is a resource that catalogs changes in gene expression as a function of age in mice. The AGEMAP database includes expression changes for 8,932 genes in 16 tissues as a function of age. We found great heterogeneity in the amount of transcriptional changes with age in different tissues. Some tissues displayed large transcriptional differences in old mice, suggesting that these tissues may contribute strongly to organismal decline. Other tissues showed few or no changes in expression with age, indicating strong levels of homeostasis throughout life. Based on the pattern of age-related transcriptional changes, we found that tissues could be classified into one of three aging processes: (1) a pattern common to neural tissues, (2) a pattern for vascular tissues, and (3) a pattern for steroid-responsive tissues. We observed that different tissues age in a coordinated fashion in individual mice, such that certain mice exhibit rapid aging, whereas others exhibit slow aging for multiple tissues. Finally, we compared the transcriptional profiles for aging in mice to those from humans, flies, and worms. We found that genes involved in the electron transport chain show common age regulation in all four species, indicating that these genes may be exceptionally good markers of aging. However, we saw no overall correlation of age regulation between mice and humans, suggesting that aging processes in mice and humans may be fundamentally different

Evolution of protein domain architectures

This chapter reviews current research on how protein domain architectures evolve. We begin by summarizing work on the phylogenetic distribution of proteins, as this will directly impact which domain architectures can be formed in different species. Studies relating domain family size to occurrence have shown that they generally follow power law distributions, both within genomes and larger evolutionary groups. These findings were subsequently extended to multi-domain architectures. Genome evolution models that have been suggested to explain the shape of these distributions are reviewed, as well as evidence for selective pressure to expand certain domain families more than others. Each domain has an intrinsic combinatorial propensity, and the effects of this have been studied using measures of domain versatility or promiscuity. Next, we study the principles of protein domain architecture evolution and how these have been inferred from distributions of extant domain arrangements. Following this, we review inferences of ancestral domain architecture and the conclusions concerning domain architecture evolution mechanisms that can be drawn from these. Finally, we examine whether all known cases of a given domain architecture can be assumed to have a single common origin (monophyly) or have evolved convergently (polyphyly). We end by a discussion of some available tools for computational analysis or exploitation of protein domain architectures and their evolution

Sequence Similarity Network Reveals Common Ancestry of Multidomain Proteins

We address the problem of homology identification in complex multidomain families with varied domain architectures. The challenge is to distinguish sequence pairs that share common ancestry from pairs that share an inserted domain but are otherwise unrelated. This distinction is essential for accuracy in gene annotation, function prediction, and comparative genomics. There are two major obstacles to multidomain homology identification: lack of a formal definition and lack of curated benchmarks for evaluating the performance of new methods. We offer preliminary solutions to both problems: 1) an extension of the traditional model of homology to include domain insertions; and 2) a manually curated benchmark of well-studied families in mouse and human. We further present Neighborhood Correlation, a novel method that exploits the local structure of the sequence similarity network to identify homologs with great accuracy based on the observation that gene duplication and domain shuffling leave distinct patterns in the sequence similarity network. In a rigorous, empirical comparison using our curated data, Neighborhood Correlation outperforms sequence similarity, alignment length, and domain architecture comparison. Neighborhood Correlation is well suited for automated, genome-scale analyses. It is easy to compute, does not require explicit knowledge of domain architecture, and classifies both single and multidomain homologs with high accuracy. Homolog predictions obtained with our method, as well as our manually curated benchmark and a web-based visualization tool for exploratory analysis of the network neighborhood structure, are available at http://www.neighborhoodcorrelation.org. Our work represents a departure from the prevailing view that the concept of homology cannot be applied to genes that have undergone domain shuffling. In contrast to current approaches that either focus on the homology of individual domains or consider only families with identical domain architectures, we show that homology can be rationally defined for multidomain families with diverse architectures by considering the genomic context of the genes that encode them. Our study demonstrates the utility of mining network structure for evolutionary information, suggesting this is a fertile approach for investigating evolutionary processes in the post-genomic era

The Compartmentalized Bacteria of the Planctomycetes-Verrucomicrobia-Chlamydiae Superphylum Have Membrane Coat-Like Proteins

Compartmentalized bacteria have proteins that are structurally related to eukaryotic membrane coats, and one of these proteins localizes at the membrane of vesicles formed inside bacterial cells

Teaching introductory programming: a quantitative evaluation of different approaches

© ACM, 2014. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in ACM Transactions on Computing Education, 2014, Vol. 14, No. 4, Article 26, DOI: http://dx.doi.org/10.1145/266241

Investigations on the diagnosis and retroviral aetiology of renal neoplasia in budgerigars (Melopsittacus undulatus)

The high susceptibility of budgerigars (Melopsittacus undulatus) to neoplasia, and specifically renal neoplasia, has often been reported. Further investigations led to a suspicion of a retrovirus as the causative agent for renal neoplasia in budgerigars, but definitive proof has yet to be found. In the present study, 32 budgerigars suspected of having renal neoplasia (based on the clinical presentation) were examined. The objectives were to investigate the use of different diagnostic methods for the ante-mortem diagnosis of this condition and to find more supporting evidence of a retroviral aetiology. The predominant clinical signs observed in budgerigars with renal neoplasia were lameness and absence of deep pain sensation of one leg. Alterations in haematology, plasma chemistry, and urine analyses could not pinpoint the cases of renal neoplasia. Contrast radiography of the intestinal tract proved to be diagnostically more useful compared with plain radiographic studies. Histology confirmed the renal neoplasia as adenocarcinoma. Investigations for virus identification included product-enhanced reverse transcriptase assay and enzyme-linked immunosorbent assay for the detection of avian leucosis virus group-specific antigen. Cell cultures and electron microscopy were performed on a limited number of patients. These investigations could find no presence of an exogenous, replicating retrovirus, neither could viral particles be detected by electron microscopy. Based on the current findings, it can be concluded that there is no evidence of retroviral involvement in the occurrence of renal neoplasia in budgerigars

ClinSV: clinical grade structural and copy number variant detection from whole genome sequencing data

Whole genome sequencing (WGS) has the potential to outperform clinical microarrays for the detection of structural variants (SV) including copy number variants (CNVs), but has been challenged by high false positive rates. Here we present ClinSV, a WGS based SV integration, annotation, prioritization, and visualization framework, which identified 99.8% of simulated pathogenic ClinVar CNVs > 10 kb and 11/11 pathogenic variants from matched microarrays. The false positive rate was low (1.5–4.5%) and reproducibility high (95–99%). In clinical practice, ClinSV identified reportable variants in 22 of 485 patients (4.7%) of which 35–63% were not detectable by current clinical microarray designs. ClinSV is available at https://github.com/KCCG/ClinSV