After fertilization, zygotic genome activation (ZGA) marks the beginning of the embryonic program for a totipotent embryo, which further gives rise to the pluripotent embryonic lineages and extraembryonic trophectoderm after the first lineage commitment. While much has been learned about pluripotency regulation, how ZGA is connected to the pluripotency commitment in early embryos remains elusive. Here, we investigated the role of nuclear receptor 1 family transcription factors (TFs) in mouse pre-implantation embryos, whose motifs are highly enriched in accessible chromatin at the 2-cell (2C) to 8-cell (8C) stages after ZGA. We found NR5A2, an NR TF highly induced upon ZGA, is required for early development, as both the knockdown and knockout of Nr5a2 from 1C embryos led to morula arrest. While the zygotic genome was largely activated at the 2C stage, 4-8C-specific gene activation (mid-preimplantation activation) was substantially impaired. Genome-wide chromatin binding and RNA-seq analyses showed NR5A2 preferentially regulates its binding targets including a subset of key pluripotency genes (i.e., Nanog, Pou5f1, and Tdgf1). Finally, NR5A2-occupied sites at the 2C and 8C stages predominantly reside in accessible B1 elements where its motif is embedded. Taken together, these data identify NR5A2 as a key regulator that connects ZGA to the first lineage segregation in early mouse development.
Recent increases in vegetation cover, observed over much of the world, reflect increasing CO2 globally and warming in cold areas. However, the strength of the response to both CO2 and warming appears to be declining. Here we examine changes in vegetation cover on the Tibetan Plateau over the past 35 years. Although the climate trends are similar across the Plateau, drier regions have become greener by 0.31±0.14% yr −1 while wetter regions have become browner by 0.12±0.08% yr -1 . This divergent response is predicted by a universal model of primary production accounting for optimal carbon allocation to leaves, subject to constraint by water availability. Rising CO2 stimulates production in both greening and browning areas; increased precipitation enhances growth in dry regions, but growth is reduced in wetter regions because warming increases below-ground allocation costs. The declining sensitivity of vegetation to climate change reflects a shift from water to energy limitation. Main textA global increase in vegetation cover has been observed in recent decades 1-3 although this greening is not universal and some regions have experienced browning 4,5 . Greening has been attributed to human activities 1,2,6 . Recent increases in atmospheric CO2 concentration have had a positive impact on primary production and enhanced vegetation cover [7][8][9] . The impact of changes in climate has been more spatially heterogeneous 2,3 but it is thought that warming explains the marked greening trend observed in high northern latitudes [10][11][12] . There has been a 16% decline in the area of the northern extratropics where vegetation growth is limited by temperature over the past three decades, primarily at the southern margin of high-latitude ecosystems 10 . However, there is evidence that the thermal response of vegetation growth and carbon uptake has weakened over this period 13,14 for reasons that
<p>Recent increases in vegetation cover, observed over much of the world, reflect increasing CO<sub>2</sub> globally and warming in cold areas. However, the strength of the response to both CO<sub>2</sub> and warming appears to be declining. Here we examine changes in vegetation cover on the Tibetan Plateau over the past 35 years. Although the climate trends are similar across the Plateau, drier regions have become greener by 0.31&#177;0.14% yr<sup>&#8722;1</sup> while wetter regions have become browner by 0.12&#177;0.08% yr<sup>&#8211;1</sup>. This divergent response is predicted by a universal model of primary production accounting for optimal carbon allocation to leaves, subject to constraint by water availability. Rising CO<sub>2</sub> stimulates production in both greening and browning areas; increased precipitation enhances growth in dry regions, but growth is reduced in wetter regions because warming increases below-ground allocation costs. The declining sensitivity of vegetation to climate change reflects a shift from water to energy limitation.&#160;</p>
<p>Leaf phenology, often measured by the seasonal dynamics of leaf area index (LAI), is a key control on the exchanges of CO<sub>2</sub> and energy between land ecosystems and the atmosphere. It is therefore also a key target process for dynamic vegetation models. However, there is no agreement on how leaf phenology should be modelled. Much research has focused on the specific triggers for budburst&#8211; and, to a lesser extent, leaf senescence&#8211; in biomes characterized by distinct cold or dry seasons. Recent theoretical developments however suggest the existence of a more general, global relationship between leaf phenology and the seasonal time course of &#8220;steady-state LAI&#8221;: the LAI would be in equilibrium with GPP if weather conditions were held constant. This can be predicted from the time course of gross primary production (GPP) because LAI and GPP are mutually related, via the Beer&#8217;s law dependence of GPP on LAI, and the requirement for GPP to support LAI development. In our current research we are developing a new global phenology model, by combining this new theoretical approach with a terrestrial photosynthesis model (the P-model) that avoids the multiplicity of parameters required by more complex models, while achieving good fit to GPP derived from flux towers in all biomes. But whereas P-model applications to date have exploited satellite-derived green vegetation cover indices as input, our current research aims to predict the seasonal time course of both LAI and GPP. This is done in two steps. First, we predict seasonal maximum LAI as the lesser of an energy-limited value that maximizes GPP, and a water-limited value that allows vegetation to transpire a fraction of annual precipitation. Second, we model the time-course of LAI assuming that its derivative tracks the difference between current and steady-state LAI with some lag. We are testing this approach with data from a global phenocam network and using remotely sensed LAI. Results so far are promising, but point to challenges, especially in representing interannual variability and trends.</p>
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