Failure of human rhombic lip differentiation underlies medulloblastoma formation

Medulloblastoma (MB) comprises a group of heterogeneous paediatric embryonal neoplasms of the hindbrain with strong links to early development of the hindbrain 1–4. Mutations that activate Sonic hedgehog signalling lead to Sonic hedgehog MB in the upper rhombic lip (RL) granule cell lineage 5–8. By contrast, mutations that activate WNT signalling lead to WNT MB in the lower RL 9,10. However, little is known about the more commonly occurring group 4 (G4) MB, which is thought to arise in the unipolar brush cell lineage 3,4. Here we demonstrate that somatic mutations that cause G4 MB converge on the core binding factor alpha (CBFA) complex and mutually exclusive alterations that affect CBFA2T2, CBFA2T3, PRDM6, UTX and OTX2. CBFA2T2 is expressed early in the progenitor cells of the cerebellar RL subventricular zone in Homo sapiens, and G4 MB transcriptionally resembles these progenitors but are stalled in developmental time. Knockdown of OTX2 in model systems relieves this differentiation blockade, which allows MB cells to spontaneously proceed along normal developmental differentiation trajectories. The specific nature of the split human RL, which is destined to generate most of the neurons in the human brain, and its high level of susceptible EOMES +KI67 + unipolar brush cell progenitor cells probably predisposes our species to the development of G4 MB

Data from: Signatures of human impact: size distributions and spatial organization of wetlands in the prairie pothole landscape

More than 50% of global wetland area has been lost over the last 200 years, resulting in losses of habitat and species diversity as well as decreased hydrologic and biogeochemical functionality. Recognition of the magnitude of wetland loss as well as the wide variety of ecosystem services provided by wetlands has in recent decades led to an increased focus on wetland restoration. Restoration activities, however, often proceed in an ad-hoc manner, with a focus on maximizing the total restored area rather than on other spatial attributes of the wetland network, which are less well understood. In this study, we have addressed the question of how human activities have altered the size distribution and spatial organization of wetlands over the Prairie Pothole Region of the Des Moines Lobe using high-resolution LIDAR data. Our results show that as well as the generally accepted 90% loss of depressional wetland area, there has been a disproportionate loss of both smaller and larger wetlands, with a marked alteration of the historical power-law relationship observed between wetland size and frequency and a resulting homogenization of the wetland size distribution. In addition, our results show significant decreases in perimeter-to-area ratios, increased mean distances between wetlands, particularly between smaller wetlands, and a reduced likelihood that current wetlands will be located in upland areas. Such patterns of loss can lead to disproportionate losses of ecosystem services, as smaller wetlands with larger perimeter-to-area ratios have been found to provide higher rates of biogeochemical processing and groundwater recharge, while increased mean distances between wetlands hinder species migration and thus negatively impact biodiversity. These results suggest the need to gear restoration efforts towards understanding and recreating the size distribution and spatial organization of historical wetlands, rather than focusing primarily on an increase in overall area

Catchment legacies and time lags: a parsimonious watershed model to predict the effects of legacy storage on nitrogen export.

Nutrient legacies in anthropogenic landscapes, accumulated over decades of fertilizer application, lead to time lags between implementation of conservation measures and improvements in water quality. Quantification of such time lags has remained difficult, however, due to an incomplete understanding of controls on nutrient depletion trajectories after changes in land-use or management practices. In this study, we have developed a parsimonious watershed model for quantifying catchment-scale time lags based on both soil nutrient accumulations (biogeochemical legacy) and groundwater travel time distributions (hydrologic legacy). The model accurately predicted the time lags observed in an Iowa watershed that had undergone a 41% conversion of area from row crop to native prairie. We explored the time scales of change for stream nutrient concentrations as a function of both natural and anthropogenic controls, from topography to spatial patterns of land-use change. Our results demonstrate that the existence of biogeochemical nutrient legacies increases time lags beyond those due to hydrologic legacy alone. In addition, we show that the maximum concentration reduction benefits vary according to the spatial pattern of intervention, with preferential conversion of land parcels having the shortest catchment-scale travel times providing proportionally greater concentration reductions as well as faster response times. In contrast, a random pattern of conversion results in a 1:1 relationship between percent land conversion and percent concentration reduction, irrespective of denitrification rates within the landscape. Our modeling framework allows for the quantification of tradeoffs between costs associated with implementation of conservation measures and the time needed to see the desired concentration reductions, making it of great value to decision makers regarding optimal implementation of watershed conservation measures

Normalized concentration reduction trajectories under different patterns of land-use change.

<p>(a) Normalized concentration trajectories at the catchment outlet plotted as a function of time (years) after land-use change for the frontal, random and distal patterns of conversion; fractional land-use conversion p = 0.5; (b) Concentration reduction fraction at infinite time as a function of land use conversion fraction p. In both figures, k = 0.18 ± 0.12, which corresponds to a range of “moderate” denitrification rates (Tesoriero et al. 2011). Other parameters used are lambda = 0.23 y^-1 and μ = 21.6 y. A 1:1 relationship between CR_inf and p, with no dependence on the k values is apparent for the random truncation.</p

Model Parameters for the Walnut Creek Watershed.

<p>Model Parameters for the Walnut Creek Watershed.</p

Normalized concentration reduction contours at t = 5 years (CR₅) plotted as a function of the fractional land-use conversion p and mean watershed travel time <i>μ</i>.

<p>Contours are plotted for the (a) frontal, (b) random and (c) distal truncation scenarios (k = 0.06 y^-1, <i>λ</i> = 0.16 y^-1).</p

Maximum normalized concentration reduction (CR_inf) contours plotted as a function of the fractional land-use conversion p and mean watershed travel time <i>μ</i>.

<p>Contours are plotted for the (a) frontal, (b) random and (c) distal truncation scenarios (k = 0.06 y^-1, <i>λ</i> = 0.16 y^-1).</p

Des Moines Lobe Wetland Data

This geodatabase contains lidar data and NWI wetland data for the Des Moines Lobe of the North American Prairie Pothole region

Site Information and Results for the Walnut Creek Case Study.

<p>(a) Subwatershed 5 (7.9 km²) of the Walnut Creek watershed, Jasper County, Iowa; (b) Data points correspond to groundwater nitrate concentrations in 19 monitoring wells across a chronosequence of restorations sites. Biogeochemical Legacy Depletion: Source Zone Nitrate-N Concentration as a function of time since land-use change; (c) Hydrologic and Biogeochemical Legacy Depletion: Data points correspond to mean annual nitrate concentrations measured at the outlet of subwatershed 5 as a function of time since land-use change. The grey shaded area in the figure corresponds to a range of values for the denitrification rate constant (k = 0.24 ± 0.08 y^-1).</p

Normalized concentration reduction contours at infinite time as a function of the allowable lag time and the fractional land-use conversion.

<p>The three rows represent different watershed mean travel times, while the three columns represent frontal, random and distal patterns of land-use change (k = 0.06 y^-1, <i>λ</i> = 0.16 y^-1).</p