Stress-induced lipocalin-2 controls dendritic spine formation and neuronal activity in the amygdala.

This is a freely-available open access publication. Please cite the published version which is available via the DOI link in this record.Behavioural adaptation to psychological stress is dependent on neuronal plasticity and dysfunction at this cellular level may underlie the pathogenesis of affective disorders such as depression and post-traumatic stress disorder. Taking advantage of genome-wide microarray assay, we performed detailed studies of stress-affected transcripts in the amygdala - an area which forms part of the innate fear circuit in mammals. Having previously demonstrated the role of lipocalin-2 (Lcn-2) in promoting stress-induced changes in dendritic spine morphology/function and neuronal excitability in the mouse hippocampus, we show here that the Lcn-2 gene is one of the most highly upregulated transcripts detected by microarray analysis in the amygdala after acute restraint-induced psychological stress. This is associated with increased Lcn-2 protein synthesis, which is found on immunohistochemistry to be predominantly localised to neurons. Stress-naïve Lcn-2(-/-) mice show a higher spine density in the basolateral amygdala and a 2-fold higher rate of neuronal firing rate compared to wild-type mice. Unlike their wild-type counterparts, Lcn-2(-/-) mice did not show an increase in dendritic spine density in response to stress but did show a distinct pattern of spine morphology. Thus, amygdala-specific neuronal responses to Lcn-2 may represent a mechanism for behavioural adaptation to psychological stress.Marie Curie Excellence Grant from the European Commission.Medical Research Council Project GrantCOST Action ECMNe

Role of the N-Terminal Seven Residues of Surfactant Protein B (SP-B)

Breathing is enabled by lung surfactant, a mixture of proteins and lipids that forms a surface-active layer and reduces surface tension at the air-water interface in lungs. Surfactant protein B (SP-B) is an essential component of lung surfactant. In this study we probe the mechanism underlying the important functional contributions made by the N-terminal 7 residues of SP-B, a region sometimes called the “insertion sequence”. These studies employed a construct of SP-B, SP-B (1–25,63–78), also called Super Mini-B, which is a 41-residue peptide with internal disulfide bonds comprising the N-terminal 7-residue insertion sequence and the N- and C-terminal helices of SP-B. Circular dichroism, solution NMR, and solid state 2H NMR were used to study the structure of SP-B (1–25,63–78) and its interactions with phospholipid bilayers. Comparison of results for SP-B (8–25,63–78) and SP-B (1–25,63–78) demonstrates that the presence of the 7-residue insertion sequence induces substantial disorder near the centre of the lipid bilayer, but without a major disruption of the overall mechanical orientation of the bilayers. This observation suggests the insertion sequence is unlikely to penetrate deeply into the bilayer. The 7-residue insertion sequence substantially increases the solution NMR linewidths, most likely due to an increase in global dynamics

A computational framework to integrate high-throughput '-omics' datasets for the identification of potential mechanistic links

International audienceWe recently presented a three-pronged association study that integrated human intestinal microbiome data derived from shotgun-based sequencing with untargeted serum metabolome data and measures of host physiology. Metabolome and microbiome data are high dimensional, posing a major challenge for data integration. Here, we present a step-by-step computational protocol that details and discusses the dimensionality-reduction techniques used and methods for subsequent integration and interpretation of such heterogeneous types of data. Dimensionality reduction was achieved through a combination of data normalization approaches, binning of co-abundant genes and metabolites, and integration of prior biological knowledge. The use of prior knowledge to overcome functional redundancy across microbiome species is one central advance of our method over available alternative approaches. Applying this framework, other investigators can integrate various '-omics' readouts with variables of host physiology or any other phenotype of interest (e.g., connecting host and microbiome readouts to disease severity or treatment outcome in a clinical cohort) in a three-pronged association analysis to identify potential mechanistic links to be tested in experimental settings. Although we originally developed the framework for a human metabolome-microbiome study, it is generalizable to other organisms and environmental metagenomes, as well as to studies including other -omics domains such as transcriptomics and proteomics. The provided R code runs in similar to 1 h on a standard PC