A Multi-Omic Huntington's Disease Transgenic Sheep-Model Database for Investigating Disease Pathogenesis.

BACKGROUND: The pathological mechanism of cellular dysfunction and death in Huntington's disease (HD) is not well defined. Our transgenic HD sheep model (OVT73) was generated to investigate these mechanisms and for therapeutic testing. One particular cohort of animals has undergone focused investigation resulting in a large interrelated multi-omic dataset, with statistically significant changes observed comparing OVT73 and control 'omic' profiles and reported in literature. OBJECTIVE: Here we make this dataset publicly available for the advancement of HD pathogenic mechanism discovery. METHODS: To enable investigation in a user-friendly format, we integrated seven multi-omic datasets from a cohort of 5-year-old OVT73 (n = 6) and control (n = 6) sheep into a single database utilising the programming language R. It includes high-throughput transcriptomic, metabolomic and proteomic data from blood, brain, and other tissues. RESULTS: We present the 'multi-omic' HD sheep database as a queryable web-based platform that can be used by the wider HD research community (https://hdsheep.cer.auckland.ac.nz/). The database is supported with a suite of simple automated statistical analysis functions for rapid exploratory analyses. We present examples of its use that validates the integrity relative to results previously reported. The data may also be downloaded for user determined analysis. CONCLUSION: We propose the use of this online database as a hypothesis generator and method to confirm/refute findings made from patient samples and alternate model systems, to expand our understanding of HD pathogenesis. Importantly, additional tissue samples are available for further investigation of this cohort.

Racing Fast and Slow: Defining the Tactical Behavior That Differentiates Medalists in Elite Men's 1,500 m Championship Racing.

Background: 1,500 m running has long been a blue ribbon event of track championship racing. The eventual medalists employ common tactical behaviors such as a fast sustained pace from the start (gun-to-tape), or, slow initial laps that precede a precisely timed race kick. Before the kick, there are positional changes caused by surging, that can go uncharacterized. The inter-relationship of surge events, tactical positioning, and kick execution may have important implications for eventual medal winning outcomes and require further definition. Methods: In a randomized order, three middle-distance running experts were provided publically available video (YouTube) of 16 men's 1,500 m championship races across, European, World and Olympic championships. Each expert determined the occurrence of surges (defined as any point in the 1,500 m after the first 300 m where an athlete repositions by ≥3 places; or noticeably dictates a raise in the pace from the front) and the race kick. Following a second level verification of expert observations, tactical behaviors (quantity and distance marker within each race) mean distance from the finish were compared between fast (≤3:34.00, n = 5), medium (>3:34.00- ≤3:41.99, n = 7) and slow (≥3:42.00, n = 4) race categories. Results: Before the race kick, there were more surges in slow (5 ± 1.7, mean ±90% confidence limits) vs. fast races (1 ± 0.4, very large difference, very likely). The final surge before the race kick occurred earlier in fast (704 ± 133 m from the finish) vs. medium (427 ± 83 m, large difference, most likely), and slow races (370 ± 137 m, large difference, most likely). At initiation of the race kick in fast races, large positional differences were found between eventual gold (2 ± 1.2; likely) and silver (2.2 ± 1.6; likely) vs. bronze medalists (4.4 ± 1.2). In slow races, positional differences were unclear between eventual gold (4.3 ± 4.7), silver (4.8 ± 4.8) and bronze medalists (5.3 ± 1.5). Regardless of category, the race kick occurred on the last lap, with unclear differences between fast 244 ± 92 m medium 243 ± 56 m and slow 236 ± 142 m races. Conclusions: Presenting tactical behaviors by race categorization (slow, medium, fast race times), provides a novel understanding of the nuance of racing tactics. The present findings highlight the importance of considering within race athlete decision making across multiple-race scenarios during championship preparation.