PirB regulates asymmetries in hippocampal circuitry.

Left-right asymmetry is a fundamental feature of higher-order brain structure; however, the molecular basis of brain asymmetry remains unclear. We recently identified structural and functional asymmetries in mouse hippocampal circuitry that result from the asymmetrical distribution of two distinct populations of pyramidal cell synapses that differ in the density of the NMDA receptor subunit GluRε2 (also known as NR2B, GRIN2B or GluN2B). By examining the synaptic distribution of ε2 subunits, we previously found that ß2-microglobulin-deficient mice, which lack cell surface expression of the vast majority of major histocompatibility complex class I (MHCI) proteins, do not exhibit circuit asymmetry. In the present study, we conducted electrophysiological and anatomical analyses on the hippocampal circuitry of mice with a knockout of the paired immunoglobulin-like receptor B (PirB), an MHCI receptor. As in ß2-microglobulin-deficient mice, the PirB-deficient hippocampus lacked circuit asymmetries. This finding that MHCI loss-of-function mice and PirB knockout mice have identical phenotypes suggests that MHCI signals that produce hippocampal asymmetries are transduced through PirB. Our results provide evidence for a critical role of the MHCI/PirB signaling system in the generation of asymmetries in hippocampal circuitry.

Detecting network communities beyond assortativity-related attributes.

In network science, assortativity refers to the tendency of links to exist between nodes with similar attributes. In social networks, for example, links tend to exist between individuals of similar age, nationality, location, race, income, educational level, religious belief, and language. Thus, various attributes jointly affect the network topology. An interesting problem is to detect community structure beyond some specific assortativity-related attributes ρ, i.e., to take out the effect of ρ on network topology and reveal the hidden community structures which are due to other attributes. An approach to this problem is to redefine the null model of the modularity measure, so as to simulate the effect of ρ on network topology. However, a challenge is that we do not know to what extent the network topology is affected by ρ and by other attributes. In this paper, we propose a distance modularity, which allows us to freely choose any suitable function to simulate the effect of ρ. Such freedom can help us probe the effect of ρ and detect the hidden communities which are due to other attributes. We test the effectiveness of distance modularity on synthetic benchmarks and two real-world networks.