Neurohypophyseal hormone-like peptides in the ovine pineal gland using reverse-phase liquid chromatography and radioimmunoassay

A method is described for the determination of the neurohormone contents of ovine pineal tissue by radioimmunoassay (RIA) after successive fractionation on gel filtration in formic acid and reverse-phase liquid chromatography (HPLC). This method gives a good resolution for the neurohormones vasopressin, vasotocin and oxytocin, without a significant interference of aspecific cross-reacting of peptides with the RIA. An acid extract from ovine pineal tissue was found to contain amounts of immunoreactive AVP- and OXT-like peptides, whereas an AVT-like peptide was not detectable over background levels after HPLC with post-column RIA. It is concluded from our results that an AVT-like peptide is not present in ovine pineal tissue, and the pineal AVP- and OXT-like peptides appeared to be associated to neurophysin molecules.     

Reconstruction of ancestral RNA sequences under multiple structural constraints

Background

Secondary structures form the scaffold of multiple sequence alignment of non-coding RNA (ncRNA) families. An accurate reconstruction of ancestral ncRNAs must use this structural signal. However, the inference of ancestors of a single ncRNA family with a single consensus structure may bias the results towards sequences with high affinity to this structure, which are far from the true ancestors.

Methods

In this paper, we introduce achARNement, a maximum parsimony approach that, given two alignments of homologous ncRNA families with consensus secondary structures and a phylogenetic tree, simultaneously calculates ancestral RNA sequences for these two families.

Results

We test our methodology on simulated data sets, and show that achARNement outperforms classical maximum parsimony approaches in terms of accuracy, but also reduces by several orders of magnitude the number of candidate sequences. To conclude this study, we apply our algorithms on the Glm clan and the FinP-traJ clan from the Rfam database.

Conclusions

Our results show that our methods reconstruct small sets of high-quality candidate ancestors with better agreement to the two target structures than with classical approaches. Our program is freely available at: http://csb.cs.mcgill.ca/acharnement.

     

Presence of immunoreactive neurophysins of a higher molecular weight in addition to the 10.000 form in the ovine pineal gland

Using an aqueous extraction followed by ultrafiltration through Amicon Diaflo membranes, two ovine pineal fractions were obtained, which contain immunoreactive neurophysin. The presence of neurophysin was monitored by radioimmunoassay, employing an antiserum raised against pituitary bovine neurophysin and selected because it reacts with neurophysins of many other mammals. From 50 g of wet ovine pineal glands 552 micrograms of immunoreactive neurophysins were obtained. About 5% of these immunoreactive neurophysins are eluted from three different Sephadex columns with an elution volume corresponding to Mr above 10,000 between bovine serum albumin and pituitary neurophysin. The remaining 95% of ovine immunoreactive pineal neurophysin (Mr 10,000) shares immunological and physico-chemical properties with highly purified bovine pituitary neurophysin used as a reference. From the results of gel filtration and affinity chromatography on LVP-Sepharose it was concluded that ovine pineal gland may contain a neurophysin precursor molecule in addition to the neurophysin Mr 10,000.     

Finding recurrent RNA structural networks with fast maximal common subgraphs of edge-colored graphs

RNA tertiary structure is crucial to its many non-coding molecular functions. RNA architecture is shaped by its secondary structure composed of stems, stacked canonical base pairs, enclosing loops. While stems are precisely captured by free-energy models, loops composed of non-canonical base pairs are not. Nor are distant interactions linking together those secondary structure elements (SSEs). Databases of conserved 3D geometries (a.k.a. modules) not captured by energetic models are leveraged for structure prediction and design, but the computational complexity has limited their study to local elements, loops. Representing the RNA structure as a graph has recently allowed to expend this work to pairs of SSEs, uncovering a hierarchical organization of these 3D modules, at great computational cost. Systematically capturing recurrent patterns on a large scale is a main challenge in the study of RNA structures. In this paper, we present an efficient algorithm to compute maximal isomorphisms in edge colored graphs. We extend this algorithm to a framework well suited to identify RNA modules, and fast enough to considerably generalize previous approaches. To exhibit the versatility of our framework, we first reproduce results identifying all common modules spanning more than 2 SSEs, in a few hours instead of weeks. The efficiency of our new algorithm is demonstrated by computing the maximal modules between any pair of entire RNA in the non-redundant corpus of known RNA 3D structures. We observe that the biggest modules our method uncovers compose large shared sub-structure spanning hundreds of nucleotides and base pairs between the ribosomes of Thermus thermophilus, Escherichia Coli, and Pseudomonas aeruginosa.