Neuregulin 3 promotes excitatory synapse formation on hippocampal interneurons

Hippocampal GABAergic interneurons are crucial for cortical network function and have been implicated in psychiatric disorders. We show here that Neuregulin 3 (Nrg3), a relatively little investigated low‐affinity ligand, is a functionally dominant interaction partner of ErbB4 in parvalbumin‐positive (PV) interneurons. Nrg3 and ErbB4 are located pre‐ and postsynaptically, respectively, in excitatory synapses on PV interneurons in vivo. Additionally, we show that ablation of Nrg3 results in a similar phenotype as the one described for ErbB4 ablation, including reduced excitatory synapse numbers on PV interneurons, altered short‐term plasticity, and disinhibition of the hippocampal network. In culture, presynaptic Nrg3 increases excitatory synapse numbers on ErbB4⁺ interneurons and affects short‐term plasticity. Nrg3 mutant neurons are poor donors of presynaptic terminals in the presence of competing neurons that produce recombinant Nrg3, and this bias requires postsynaptic ErbB4 but not ErbB4 kinase activity. Furthermore, when presented by non‐neuronal cells, Nrg3 induces postsynaptic membrane specialization. Our data indicate that Nrg3 provides adhesive cues that facilitate excitatory neurons to synapse onto ErbB4⁺ interneurons.     

Two new cytotypes reinforce that <Emphasis Type="Italic">Micronycteris hirsuta</Emphasis> Peters, 1869 does not represent a monotypic taxon

Background

The genus Micronycteris is a diverse group of phyllostomid bats currently comprising 11 species, with diploid number (2n) ranging from 26 to 40 chromosomes. The karyotypic relationships within Micronycteris and between Micronycteris and other phyllostomids remain poorly understood. The karyotype of Micronycteris hirsuta is of particular interest: three different diploid numbers were reported for this species in South and Central Americas with 2n?=?26, 28 and 30 chromosomes. Although current evidence suggests some geographic differentiation among populations of M. hirsuta based on chromosomal, morphological, and nuclear and mitochondrial DNA markers, the recognition of new species or subspecies has been avoided due to the need for additional data, mainly chromosomal data.

Results

We describe two new cytotypes for Micronycteris hirsuta (MHI) (2n?=?26 and 25, NF?=?32), whose differences in diploid number are interpreted as the products of Robertsonian rearrangements. C-banding revealed a small amount of constitutive heterochromatin at the centromere and the NOR was located in the interstitial portion of the short arm of a second pair, confirmed by FISH. Telomeric probes hybridized to the centromeric regions and weakly to telomeric regions of most chromosomes. The G-banding analysis and chromosome painting with whole chromosome probes from Carollia brevicauda (CBR) and Phyllostomus hastatus (PHA) enabled the establishment of genome-wide homologies between MHI, CBR and PHA.

Conclusions

The karyotypes of Brazilian specimens of Micronycteris hirsuta described here are new to Micronycteris and reinforce that M. hirsuta does not represent a monotypic taxon. Our results corroborate the hypothesis of karyotypic megaevolution within Micronycteris, and strong evidence for this is that the entire chromosome complement of M. hirsuta was shown to be derivative with respect to species compared in this study.

     

Comparison of normalisation methods for surface-enhanced laser desorption and ionisation (SELDI) time-of-flight (TOF) mass spectrometry data

Background  

Mass spectrometry for biological data analysis is an active field of research, providing an efficient way of high-throughput proteome screening. A popular variant of mass spectrometry is SELDI, which is often used to measure sample populations with the goal of developing (clinical) classifiers. Unfortunately, not only is the data resulting from such measurements quite noisy, variance between replicate measurements of the same sample can be high as well. Normalisation of spectra can greatly reduce the effect of this technical variance and further improve the quality and interpretability of the data. However, it is unclear which normalisation method yields the most informative result.