SignificanceWe report the development of peptidomimetic antibiotics derived from a natural antimicrobial peptide, human α-defensin 5. By engaging multiple bacterial targets, the lead compound is efficacious in vitro and in vivo against bacteria with highly inducible antibiotic resistance, promising a useful therapeutic agent for the treatment of infections caused by antibiotic-resistant bacteria.

Disruptions in mitochondrial dynamics may contribute to the selective degeneration of dopamine (DA) neurons in Parkinson's disease (PD). However, little is known about the normal functions of mitochondrial dynamics in these neurons, especially in axons where degeneration begins, and this makes it difficult to understand the disease process. To study one aspect of mitochondrial dynamics-mitochondrial fission-in mouse DA neurons, we deleted the central fission protein dynamin-related protein 1 (Drp1). Drp1 loss rapidly eliminates the DA terminals in the caudate-putamen and causes cell bodies in the midbrain to degenerate and lose α-synuclein. Without Drp1, mitochondrial mass dramatically decreases, especially in axons, where the mitochondrial movement becomes uncoordinated. However, in the ventral tegmental area (VTA), a subset of midbrain DA neurons characterized by small hyperpolarization-activated cation currents (Ih) is spared, despite near complete loss of their axonal mitochondria. Drp1 is thus critical for targeting mitochondria to the nerve terminal, and a disruption in mitochondrial fission can contribute to the preferential death of nigrostriatal DA neurons.

The suprachiasmatic nucleus (SCN) is the mammalian central circadian pacemaker with heterogeneous neurons acting in concert while each neuron harbors a self-sustained molecular clockwork. Nevertheless, how system-level SCN signals encode time of the day remains enigmatic. Here we show that population-level Ca2+ signals predict hourly time, via a group decision-making mechanism coupled with a spatially modular time feature representation in the SCN. Specifically, we developed a high-speed dual-view two-photon microscope for volumetric Ca2+ imaging of up to 9000 GABAergic neurons in adult SCN slices, and leveraged machine learning methods to capture emergent properties from multiscale Ca2+ signals as a whole. We achieved hourly time prediction by polling random cohorts of SCN neurons, reaching 99.0% accuracy at a cohort size of 900. Further, we revealed that functional neuron subtypes identified by contrastive learning tend to aggregate separately in the SCN space, giving rise to bilaterally symmetrical ripple-like modular patterns. Individual modules represent distinctive time features, such that a module-specifically learned time predictor can also accurately decode hourly time from random polling of the same module. These findings open a new paradigm in deciphering the design principle of the biological clock at the system level.