A transcriptomic taxonomy of mouse brain-wide spinal projecting neurons.

The brain controls nearly all bodily functions via spinal projecting neurons (SPNs) that carry command signals from the brain to the spinal cord. However, a comprehensive molecular characterization of brain-wide SPNs is still lacking. Here we transcriptionally profiled a total of 65,002 SPNs, identified 76 region-specific SPN types, and mapped these types into a companion atlas of the whole mouse brain1. This taxonomy reveals a three-component organization of SPNs: (1) molecularly homogeneous excitatory SPNs from the cortex, red nucleus and cerebellum with somatotopic spinal terminations suitable for point-to-point communication; (2) heterogeneous populations in the reticular formation with broad spinal termination patterns, suitable for relaying commands related to the activities of the entire spinal cord; and (3) modulatory neurons expressing slow-acting neurotransmitters and/or neuropeptides in the hypothalamus, midbrain and reticular formation for 'gain setting' of brain-spinal signals. In addition, this atlas revealed a LIM homeobox transcription factor code that parcellates the reticulospinal neurons into five molecularly distinct and spatially segregated populations. Finally, we found transcriptional signatures of a subset of SPNs with large soma size and correlated these with fast-firing electrophysiological properties. Together, this study establishes a comprehensive taxonomy of brain-wide SPNs and provides insight into the functional organization of SPNs in mediating brain control of bodily functions.

Design of antimicrobial and cytolytic peptides by computational analysis of bacterial, algal, and invertebrate proteomes.

The increase of antibiotic resistance in bacterial species has raised the need to search for novel antimicrobial molecules. Antimicrobial peptides are molecules that commonly display an amphipathic character. In this work, we developed a computational strategy to search for new peptide sequences within the proteome of any organism that includes in-house developed software and the use of artificial intelligence tools available online. Eleven peptides were selected after analyzing 63,343 proteins from the proteomes of bacteria, algae and invertebrates. Then, we validated the results by means of several assays which were carried out against five (5) pathogenic bacterial species and two (2) cancer cell lines. As a result, we found that ten of the peptides were antimicrobial, with minimum inhibitory concentration values between 4 and [Formula: see text]. Furthermore, two of the more active peptides were also cytotoxic to human red blood cells and cancer cells. In general, the antimicrobial peptides we discovered produced damage on the bacterial cell membrane that included membrane wrinkling, cell blebbing, and leakage of cytoplasmic material. Based on these results, we concluded that the computational approach proposed for finding sequences encrypted in proteins is appropriate for the discovery of selective and non-selective antimicrobial and anticancer peptides.