Telomeres: Linking stress and survival, ecology and evolution.

Telomeres are protective structures at the ends of eukaryotic chromosomes. The loss of telomeres through cell division and oxidative stress is related to cellular aging, organismal growth and disease. In this way, telomeres link molecular and cellular mechanisms with organismal processes, and may explain variation in a number of important life-history traits. Here, we discuss how telomere biology relates to the study of physiological ecology and life history evolution. We emphasize current knowledge on how telomeres may relate to growth, survival and lifespan in natural populations. We finish by examining interesting new connections between telomeres and the glucocorticoid stress response. Glucocorticoids are often employed as indices of physiological condition, and there is evidence that the glucocorticoid stress response is adaptive. We suggest that one way that glucocorticoids impact organismal survival is through elevated oxidative stress and telomere loss. Future work needs to establish and explore the link between the glucocorticoid stress response and telomere shortening in natural populations. If a link is found, it provides an explanatory mechanism by which environmental perturbation impacts life history trajectories.

Mining for Class-Specific Motifs in Protein Sequence Classification

Background: In protein sequence classification, identification of the sequence motifs or n-grams that can precisely discriminate between classes is a more interesting scientific question than the classification itself. A number of classification methods aim at accurate classification but fail to explain which sequence features indeed contribute to the accuracy. We hypothesize that sequences in lower denominations (n-grams) can be used to explore the sequence landscape and to identify class-specific motifs that discriminate between classes during classification. Discriminative n-grams are short peptide sequences that are highly frequent in one class but are either minimally present or absent in other classes. In this study, we present a new substitution-based scoring function for identifying discriminative n-grams that are highly specific to a class. Results: We present a scoring function based on discriminative n-grams that can effectively discriminate between classes. The scoring function, initially, harvests the entire set of 4- to 8-grams from the protein sequences of different classes in the dataset. Similar n-grams of the same size are combined to form new n-grams, where the similarity is defined by positive amino acid substitution scores in the BLOSUM62 matrix. Substitution has resulted in a large increase in the number of discriminatory n-grams harvested. Due to the unbalanced nature of the dataset, the frequencies of the n-grams are normalized using a dampening factor, which gives more weightage to the n-grams that appear in fewer classes and vice-versa. After the n-grams are normalized, the scoring function identifies discriminative 4- to 8-grams for each class that are frequent enough to be above a selection threshold. By mapping these discriminative n-grams back to the protein sequences, we obtained contiguous n-grams that represent short class-specific motifs in protein sequences. Our method fared well compared to an existing motif finding method known as Wordspy. We have validated our enriched set of class-specific motifs against the functionally important motifs obtained from the NLSdb, Prosite and ELM databases. We demonstrate that this method is very generic; thus can be widely applied to detect class-specific motifs in many protein sequence classification tasks. Conclusion: The proposed scoring function and methodology is able to identify class-specific motifs using discriminative n-grams derived from the protein sequences. The implementation of amino acid substitution scores for similarity detection, and the dampening factor to normalize the unbalanced datasets have significant effect on the performance of the scoring function. Our multipronged validation tests demonstrate that this method can detect class-specific motifs from a wide variety of protein sequence classes with a potential application to detecting proteome-specific motifs of different organisms.