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Cryptococcus neoformans is one of the leading causes of invasive fungal infection in humans worldwide. C. neoformans uses macrophages as a proliferative niche to increase infective burden and avoid immune surveillance. However, the specific mechanisms by which C. neoformans manipulates host immunity to promote its growth during infection remain ill-defined. Here we demonstrate that eicosanoid lipid mediators manipulated and/or produced by C. neoformans play a key role in regulating pathogenesis. C. neoformans is known to secrete several eicosanoids that are highly similar to those found in vertebrate hosts. Using eicosanoid deficient cryptococcal mutants Δplb1 and Δlac1, we demonstrate that prostaglandin E2 is required by C. neoformans for proliferation within macrophages and in vivo during infection. Genetic and pharmacological disruption of host PGE2 synthesis is not required for promotion of cryptococcal growth by eicosanoid production. We find that PGE2 must be dehydrogenated into 15-keto-PGE2 to promote fungal growth, a finding that implicated the host nuclear receptor PPAR-γ. C. neoformans infection of macrophages activates host PPAR-γ and its inhibition is sufficient to abrogate the effect of 15-keto-PGE2 in promoting fungal growth during infection. Thus, we describe the first mechanism of reliance on pathogen-derived eicosanoids in fungal pathogenesis and the specific role of 15-keto-PGE2 and host PPAR-γ in cryptococcosis., Competing Interests: The authors have declared that no competing interests exist.

The National Highway Traffic Safety Administration (NHTSA) recently published a Request for Comments regarding a potential upgrade to the US New Car Assessment Program (US NCAP) - a star-rating program pertaining to vehicle crashworthiness. Therein, NHTSA (a) cited two metrics for assessing head risk: Head Injury Criterion (HIC15) and Brain Injury Criterion (BrIC), and (b) proposed to conduct risk assessment via its risk curves for those metrics, but did not prescribe a specific method for applying them. Recent studies, however, have indicated that the NHTSA risk curves for BrIC significantly overstate field-based head injury rates. Therefore, in the present three-part study, a new set of BrIC-based risk curves was derived, an overarching head risk equation involving risk curves for both BrIC and HIC15 was assessed, and some additional candidatepredictor- variable assessments were conducted. Part 1 pertained to the derivation. Specifically, data were pooled from various sources: Navy volunteers, amateur boxers, professional football players, simple-fall subjects, and racecar drivers. In total, there were 4,501 cases, with brain injury reported in 63. Injury outcomes were approximated on the Abbreviated Injury Scale (AIS). The statistical analysis was conducted subject to ordinal logistic regression analysis (OLR), such that the various levels of brain injury were cast as a function of BrIC. The resulting risk curves, with Goodman Kruksal Gamma=0.83, were significantly different than those from NHTSA. Part 2 pertained to the assessment relative to field data. Two perspectives were considered: "aggregate" (ΔV=0-56 km/h) and "point" (high-speed, regulatory focus). For the aggregate perspective, the new risk curves for BrIC were applied in field models pertaining to belted, mid-size, adult drivers in 11-1 o'clock, full-engagement frontal crashes in the National Automotive Sampling System (NASS, 1993-2014 calendar years). For the point perspective, BrIC data from tests were used. The assessments were conducted for minor, moderate, and serious injury levels for both Newer Vehicles (airbag-fitted) and Older Vehicles (not airbag-fitted). Curve-based injury rates and NASS-based injury rates were compared via average percent difference (AvgPctDiff). The new risk curves demonstrated significantly better fidelity than those from NHTSA. For example, for the aggregate perspective (n=12 assessments), the results were as follows: AvgPctDiff (present risk curves) = +67 versus AvgPctDiff (NHTSA risk curves) = +9378. Part 2 also contained a more comprehensive assessment. Specifically, BrIC-based risk curves were used to estimate brain-related injury probabilities, HIC15-based risk curves from NHTSA were used to estimate bone/other injury probabilities, and the maximum of the two resulting probabilities was used to represent the attendant headinjury probabilities. (Those HIC15-based risk curves yielded AvgPctDiff=+85 for that application.) Subject to the resulting 21 assessments, similar results were observed: AvgPctDiff (present risk curves) = +42 versus AvgPctDiff (NHTSA risk curves) = +5783. Therefore, based on the results from Part 2, if the existing BrIC metric is to be applied by NHTSA in vehicle assessment, we recommend that the corresponding risk curves derived in the present study be considered. Part 3 pertained to the assessment of various other candidate brain-injury metrics. Specifically, Parts 1 and 2 were revisited for HIC15, translation acceleration (TA), rotational acceleration (RA), rotational velocity (RV), and a different rotational brain injury criterion from NHTSA (BRIC). The rank-ordered results for the 21 assessments for each metric were as follows: RA, HIC15, BRIC, TA, BrIC, and RV. Therefore, of the six studied sets of OLR-based risk curves, the set for rotational acceleration demonstrated the best performance relative to NASS.