Classification of neocortical interneurons using affinity propagation

In spite of over a century of research on cortical circuits, it is still unknown how many classes of cortical neurons exist. Neuronal classification has been a difficult problem because it is unclear what a neuronal cell class actually is and what are the best characteristics are to define them. Recently, unsupervised classifications using cluster analysis based on morphological, physiological or molecular characteristics, when applied to selected datasets, have provided quantitative and unbiased identification of distinct neuronal subtypes. However, better and more robust classification methods are needed for increasingly complex and larger datasets. We explored the use of affinity propagation, a recently developed unsupervised classification algorithm imported from machine learning, which gives a representative example or exemplar for each cluster. As a case study, we applied affinity propagation to a test dataset of 337 interneurons belonging to four subtypes, previously identified based on morphological and physiological characteristics. We found that affinity propagation correctly classified most of the neurons in a blind, non-supervised manner. In fact, using a combined anatomical/physiological dataset, our algorithm differentiated parvalbumin from somatostatin interneurons in 49 out of 50 cases. Affinity propagation could therefore be used in future studies to validly classify neurons, as a first step to help reverse engineer neural circuits.

Humoral Immunity Links Candida albicans Infection and Celiac Disease

Objective The protein Hwp1, expressed on the pathogenic phase of Candida albicans, presents sequence analogy with the gluten protein gliadin and is also a substrate for transglutaminase. This had led to the suggestion that C. albicans infection (CI) may be a triggering factor for Celiac disease (CeD) onset. We investigated cross-immune reactivity between CeD and CI. Methods Serum IgG levels against recombinant Hwp1 and serological markers of CeD were measured in 87 CeD patients, 41 CI patients, and 98 healthy controls (HC). IgA and IgG were also measured in 20 individuals from each of these groups using microchips sensitized with 38 peptides designed from the N-terminal of Hwp1. Results CI and CeD patients had higher levels of anti-Hwp1 (p= 0.0005 and p= 0.004) and anti-gliadin (p= 0.002 and p= 0.0009) antibodies than HC but there was no significant difference between CeD and CI patients. CeD and CI patients had higher levels of anti-transglutaminase IgA than HC (p= 0.0001 and p= 0.0039). During CI, the increase in anti-Hwp1 paralleled the increase in anti-gliadin antibodies. Microchip analysis showed that CeD patients were more reactive against some Hwp1 peptides than CI patients, and that some deamidated peptides were more reactive than their native analogs. Binding of IgG from CeD patients to Hwp1 peptides was inhibited by gamma III gliadin peptides. Conclusions Humoral cross-reactivity between Hwp1 and gliadin was observed during CeD and CI. Increased reactivity to Hwp1 deamidated peptide suggests that transglutaminase is involved in this interplay. These results support the hypothesis that CI may trigger CeD onset in genetically-susceptible individuals.