Identification of DNA motifs within the 5’UTR of the ornithine decarboxylase gene (GiODC) in the red seaweed Grateloupia imbricata

[ES]El desarrollo de las estructuras reproductoras (cistocarpos) en macroalgas rojas se ve afectado por factores abióticos (fotoperiodo, salinidad…) y endógenos (reguladores del desarrollo como el etileno o las poliaminas (PAs)); siendo estas últimas las principales responsables de la inducción del cistocarpo. Las PAs son moléculas ubícuas sintetizadas a través de una reacción enzimática mediada por la enzima ODC; por lo que conocer los mecanismos reguladores a nivel transcripcional del gen ODC es clave para el conocimiento del proceso reproductivo. Así, en el presente estudio se han identificado in silico diversos motivos conservados en la región 5 'UTR del gen ODC en la macroalga roja Grateloupia imbricata (GiODC) y se discute brevemente su presencia y posible implicación en diversos procesos metabólicos.

Cross-reactions vs co-sensitization evaluated by in silico motifs and in vitro IgE microarray testing

Using an in silico allergen clustering method, we have recently shown that allergen extracts are highly cross-reactive. Here we used serological data from a multi-array IgE test based on recombinant or highly purified natural allergens to evaluate whether co-reactions are true cross-reactions or co-sensitizations by allergens with the same motifs.

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.

Targeting p-Conjugated Multiple Donor-Acceptor Motifs Exemplified by Tetrathiafulvalene-Linked Quinoxalines and Tetrabenz bc,ef,hi,uv ovalenes: Synthesis, Spectroscopic, Electrochemical, and Theoretical Characterization

An efficient synthetic approach to a symmetrically functionalized tetrathiafulvalene (TTF) derivative with two diamine moieties, 2-[5,6-diamino-4,7-bis(4-pentylphenoxy)-1,3-benzodithiol-2-ylidene]-4,7- bis(4-pentylphenoxy)-1,3-benzodithiole-5,6-diamine (2), is reported. The subsequent Schiff-base reactions of 2 afford large p-conjugated multiple donoracceptor (DA) arrays, for example, the triad 2-[4,9-bis(4-pentylphenoxy)-1,3-dithiolo[4,5-g]quinoxalin-2-ylidene]-4,9 -bis(4-pentylphenoxy)-1,3-dithiolo[4,5-g]quinoxaline (8) and the corresponding tetrabenz[bc,ef,hi,uv]ovalene-fused pentad 1, in good yields and high purity. The novel redox-active nanographene 1 is so far the largest known TTF-functionalized polycyclic aromatic hydrocarbon (PAH) with a well-resolved 1H NMR spectrum. The electrochemically highly amphoteric pentad 1 and triad 8 exhibit various electronically excited charge-transfer states in different oxidation states, thus leading to intense optical intramolecular charge-transfer (ICT) absorbances over a wide spectral range. The chemical and electrochemical oxidations of 1 result in an unprecedented TTF+ radical cation dimerization, thereby leading to the formation of [1+]2 at room temperature in solution due to the stabilizing effect, which arises from strong pp interactions. Moreover, ICT fluorescence is observed with large solvent-dependent Stokes shifts and quantum efficiencies of 0.05 for 1 and 0.035 for 8 in dichloromethane.