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Somatic motor neurons are selectively vulnerable in spinal muscular atrophy (SMA), which is caused by a deficiency of the ubiquitously expressed survival of motor neuron protein. However, some motor neuron groups, including oculomotor and trochlear (ocular), which innervate eye muscles, are for unknown reasons spared. To reveal mechanisms of vulnerability and resistance in SMA, we investigate the transcriptional dynamics in discrete neuronal populations using laser capture microdissection coupled with RNA sequencing (LCM-seq). Using gene correlation network analysis, we reveal a TRP53-mediated stress response that is intrinsic to all somatic motor neurons independent of their vulnerability, but absent in relatively resistant red nucleus and visceral motor neurons. However, the temporal and spatial expression analysis across neuron types shows that the majority of SMA-induced modulations are cell type-specific. Using Gene Ontology and protein network analyses, we show that ocular motor neurons present unique disease-adaptation mechanisms that could explain their resilience. Specifically, ocular motor neurons up-regulate (1) Syt1, Syt5, and Cplx2, which modulate neurotransmitter release; (2) the neuronal survival factors Gdf15, Chl1, and Lif; (3) Aldh4, that protects cells from oxidative stress; and (4) the caspase inhibitor Pak4. Finally, we show that GDF15 can rescue vulnerable human spinal motor neurons from degeneration. This confirms that adaptation mechanisms identified in resilient neurons can be used to reduce susceptibility of vulnerable neurons. In conclusion, this in-depth longitudinal transcriptomics analysis in SMA reveals novel cell type-specific changes that, alone and combined, present compelling targets, including Gdf15, for future gene therapy studies aimed toward preserving vulnerable motor neurons.

In this research, a novel combined cooling, heating and power (CCHP) system taking biomass and geothermal energy as co-feeds is proposed, which is mainly consist of four subsections: biomass gasification, compressed air energy storage (CAES), bio-gas turbine power generation and ground source heat pump (GSHP). The compressed air energy storage absorbs off-peak electricity from grid and the high pressure air is utilized to combusted with bio-gas derived from biomass gasification process, the waste heat is utilized by absorption chiller and ground source heat pump. Energy, exergy and economic performances of proposed system are investigated. In the simulation condition, the round trip efficiency and exergy efficiency are 90.06% and 31.52%, respectively; the total investment cost of system equipment is 908008$ and dynamic payback period can be reached at 3.032 year. In the parametric analysis, the effects of key parameters on system performances have been investigated. The system can be reached at an optimum performance when equivalence ratio is 0.275. The energy and exergy efficiency all improve with the increase in air outlet temperature of HX-01, while these criteria shows different tendency with the variations of inlet temperature of gas turbine, inlet and outlet pressure of air storage cavern. Moreover, the dynamic payback period of proposed system increases with the increase in off-peak electricity and biomass cost, whereas decreases with the increase in on-peak electricity cost, hot water cost and cooling cost. The proposed system provides a new way for renewable energy based integration system.

SUMMARY Defining transcriptional profiles of substantia nigra pars compacta (SNc) and ventral tegmental area (VTA) dopamine neurons is critical to understand their differential vulnerability in Parkinson’s disease. However, reported transcriptional profiles for these neuron populations rely on studies in rodents and display extensive variability across small sample sizes. Here, we use laser capture microdissection coupled with RNA sequencing to analyze single isolated SNc and VTA dopamine neurons in a large human cohort. By applying a unique iterative random pooling algorithm, with high utility to resolve differences across any two populations, we reveal 33 dopamine population-specific markers We also define the minimal cohort size required to identify these stably differentially expressed genes, explaining inconsistent results from previous studies. Finally, we identify human transcripts, including ZCCHC12 , CDH13 and SERPINE2 , that faithfully classify SNc or VTA dopamine neurons also in the mouse. The novel markers identified will be vital for future studies aiming to validate the identity of stem cell-derived dopamine neurons or to induce dopamine neuron resilience.

Molecular modeling was performed on a triazolo quinazoline lead compound to help develop a series of adenosine A2A receptor antagonists with improved hERG profile. Superposition of the lead compound onto MK-499, a benchmark hERG inhibitor, combined with pKa calculations and measurement, identified terminal fluorobenzene to be responsible for hERG activity. Docking of the lead compound into an A2A crystal structure suggested that this group is located at a flexible, spacious, and solvent-exposed opening of the binding pocket, making it possible to tolerate various functional groups. Transformation analysis (MMP, matched molecular pair) of in-house available experimental data on hERG provided suggestions for modifications in order to mitigate this liability. This led to the synthesis of a series of compounds with significantly reduced hERG activity. The strategy used in the modeling work can be applied to other medicinal chemistry programs to help improve hERG profile.

The synthesis and evaluation of small molecule antagonists of the G protein-coupled receptor NPBWR1 (GPR7) are reported for the first time. [4-(5-Chloropyridin-2-yl)piperazin-1-yl][(1S,2S,4R)-4-{[(1R)-1-(4-methoxyphenyl)ethyl]amino}-2-(thiophen-3-yl)cyclohexyl]methanone (1) emerged as a hit from a high-throughput screen. Examination of substituents that focused on replacing the 5-chloropyridine and 4-methoxybenzylamino groups of 1 led to the identification of compounds that exhibited subnanomolar potencies as low as 660pM (9k) in the functional assay and 200pM in the binding assay (9i).