Divergent signaling pathways in phagocytic cells during sepsis

Neutrophil accumulation in the lung plays a pivotal role in the pathogenesis of acute lung injury during sepsis. Directed movement of neutrophils is mediated by a group of chemoattractants, especially CXC chemokines. Local lung production of CXC chemokines is intensified during experimental sepsis induced by cecal ligation and puncture (CLP), as reflected by rising levels of MIP-2 and cytokine-induced neutrophil chemoattractant-1 in bronchoalveolar lavage fluids. Alveolar macrophages are primed and blood neutrophils are down-regulated for production of MIP-2 and cytokine-induced neutrophil chemoattractant production in response to LPS and C5a. Under these conditions of stimulation, activation of MAPKs (p38, p42/p44) occurs in sham neutrophils but not in CLP neutrophils, while under the same conditions phosphorylation of p38 and p42/p44 occurs in both sham and CLP alveolar macrophages. These data indicate that, under septic conditions, there is impaired signaling in neutrophils and enhanced signaling in alveolar macrophages, resulting in CXC chemokine production, and C5a appears to play a pivotal role in this process. As a result, CXC chemokines increase in lung, setting the stage for neutrophil accumulation in lung during sepsis.     

Identifying overrepresented concepts in gene lists from literature: a statistical approach based on Poisson mixture model

Background  

Large-scale genomic studies often identify large gene lists, for example, the genes sharing the same expression patterns. The interpretation of these gene lists is generally achieved by extracting concepts overrepresented in the gene lists. This analysis often depends on manual annotation of genes based on controlled vocabularies, in particular, Gene Ontology (GO). However, the annotation of genes is a labor-intensive process; and the vocabularies are generally incomplete, leaving some important biological domains inadequately covered.     

Conformational features of 2′-O-methyl-adenosylyl-adenosine

In order to obtain information about the conformational features of a 2′-O-methylated polyribonucleotide at the nearest neighbor level, a detailed nuclear magnetic resonance study of AmpA was undertaken. AmpA was isolated from alkali hydrolysates of yeast RNA, and proton spectra were recorded at 100 MHz in the Fourier transform mode in D₂O solutions, 0.01 M, pH 5.4 and 1.5 at 25°C. ³¹P spectra were recorded at 40.48 MHz. Complete, accurate sets of nmr parameters derived for each nucleotidyl unit by simulation iteration methods. The nmr data were translated into conformational parameters for all the bonds using procedures developed in earlier studies from these laboratories. It is shown that AmpA exists in aqueous solution with a flexible molecular framework, which shows preferences for certain orientations. The ribose rings exist as a ²E ? ³E equilibrium with the —pA ribose showing a bias for the ³E pucker. The C(4′)—C(5′) bonds of both nucleotidyl units show significant preference (75–80%) to exist in gg conformation. The dominant conformer (80%) about C(5′)—O(5′) of the 5′-nucleotidyl unit is g′g′. Even though an unambiguous determination of the orientation of the 3′-phosphate group cannot be made, tentative evidence shows that it preferentially occupies g⁺ domains [O(3′)—P trans to C(3′)—C(2′)] in which the H(3′) —C(3′)—O(3′)—P(3′) dihedral angle is about 31°. There is reasonable evidence that the 2′-O-methyl preferentially occupies the domain in which the O(2′)—CH₃ bond is trans to C(2′)—C(1′). Lowering of pH to 1.5, which results in protonation of both the adenine moieties, causes destacking of AmpA. Such destacking is accompanied by small, but real, perturbations in the conformations about most of the bonds in the backbone. A detailed comparison of the solution conformations of ApA and AmpA clearly shows that 2′-O-methylation strongly influences the conformational preference about the C(3′)—O(3′) bond of the 3′-nucleotidyl unit, in addition to inducing small changes in the overall ribophosphate backbone conformational equilibria. The effect of 2′-O-methylation is such that the C(3′)—O(3′) is forced to occupy preferentially the g⁺ domain rather than the normally preferred g^? domain [O(3′)—P trans to C(3′)—C(4′)] in ApA. The data on ApA and AmpA further reveal that the extent of stacking interaction is less in AmpA compared to ApA. It is suggested that stacked species of AmpA exist as right-handed stacks where the magnitude of ω and ω′ about O(5′)—P and P—O(3′) is about 290°. The reason for the lesser degree of stacking in AmpA compared to ApA is intramolecular interaction between 2′-O-methyl and the flexible O(3′)—P—O(5′) bridge, the interaction causing some perturbation in the magnitudes of ω/ω′, causing destacking. The destacking will lead to an increase in _χCN by a few degrees, causing an increase in ²E populations; the latter in turn will shift the 3′ phosphate group from g^? to g⁺ domains. In short, a coupled series of conformational events is envisioned at the onset of destacking, made feasible by the interaction between the 2′-O-methyl group and the swivel O(3′)—P—O(5′) bridge.     

Tracking 20 Years of Compound-to-Target Output from Literature and Patents

The statistics of drug development output and declining yield of approved medicines has been the subject of many recent reviews. However, assessing research productivity that feeds development is more difficult. Here we utilise an extensive database of structure-activity relationships extracted from papers and patents. We have used this database to analyse published compounds cumulatively linked to nearly 4000 protein target identifiers from multiple species over the last 20 years. The compound output increases up to 2005 followed by a decline that parallels a fall in pharmaceutical patenting. Counts of protein targets have plateaued but not fallen. We extended these results by exploring compounds and targets for one large pharmaceutical company. In addition, we examined collective time course data for six individual protease targets, including average molecular weight of the compounds. We also tracked the PubMed profile of these targets to detect signals related to changes in compound output. Our results show that research compound output had decreased 35% by 2012. The major causative factor is likely to be a contraction in the global research base due to mergers and acquisitions across the pharmaceutical industry. However, this does not rule out an increasing stringency of compound quality filtration and/or patenting cost control. The number of proteins mapped to compounds on a yearly basis shows less decline, indicating the cumulative published target capacity of global research is being sustained in the region of 300 proteins for large companies. The tracking of six individual targets shows uniquely detailed patterns not discernible from cumulative snapshots. These are interpretable in terms of events related to validation and de-risking of targets that produce detectable follow-on surges in patenting. Further analysis of the type we present here can provide unique insights into the process of drug discovery based on the data it actually generates.