Circadian transcription factor NPAS2 and the NAD+ -dependent deacetylase SIRT1 interact in the mouse nucleus accumbens and regulate reward.

Substance use disorders are associated with disruptions to both circadian rhythms and cellular metabolic state. At the molecular level, the circadian molecular clock and cellular metabolic state may be interconnected through interactions with the nicotinamide adenine dinucleotide (NAD+ )-dependent deacetylase, sirtuin 1 (SIRT1). In the nucleus accumbens (NAc), a region important for reward, both SIRT1 and the circadian transcription factor neuronal PAS domain protein 2 (NPAS2) are highly enriched, and both are regulated by the metabolic cofactor NAD+ . Substances of abuse, like cocaine, greatly disrupt cellular metabolism and promote oxidative stress; however, their effects on NAD+ in the brain remain unclear. Interestingly, cocaine also induces NAc expression of both NPAS2 and SIRT1, and both have independently been shown to regulate cocaine reward in mice. However, whether NPAS2 and SIRT1 interact in the NAc and/or whether together they regulate reward is unknown. Here, we demonstrate diurnal expression of Npas2, Sirt1 and NAD+ in the NAc, which is altered by cocaine-induced upregulation. Additionally, co-immunoprecipitation reveals NPAS2 and SIRT1 interact in the NAc, and cross-analysis of NPAS2 and SIRT1 chromatin immunoprecipitation sequencing reveals several reward-relevant and metabolic-related pathways enriched among shared gene targets. Notably, NAc-specific Npas2 knock-down or a functional Npas2 mutation in mice attenuates SIRT1-mediated increases in cocaine preference. Together, our data reveal an interaction between NPAS2 and SIRT1 in the NAc, which may serve to integrate cocaine's effects on circadian and metabolic factors, leading to regulation of drug reward.

Benchmark Evaluation of Protein-Protein Interaction Prediction Algorithms.

Protein-protein interactions (PPIs) perform various functions and regulate processes throughout cells. Knowledge of the full network of PPIs is vital to biomedical research, but most of the PPIs are still unknown. As it is infeasible to discover all of them experimentally due to technical and resource limitations, computational prediction of PPIs is essential and accurately assessing the performance of algorithms is required before further application or translation. However, many published methods compose their evaluation datasets incorrectly, using a higher proportion of positive class data than occuring naturally, leading to exaggerated performance. We re-implemented various published algorithms and evaluated them on datasets with realistic data compositions and found that their performance is overstated in original publications; with several methods outperformed by our control models built on 'illogical' and random number features. We conclude that these methods are influenced by an over-characterization of some proteins in the literature and due to scale-free nature of PPI network and that they fail when tested on all possible protein pairs. Additionally, we found that sequence-only-based algorithms performed worse than those that employ functional and expression features. We present a benchmark evaluation of many published algorithms for PPI prediction. The source code of our implementations and the benchmark datasets created here are made available in open source.