The Dok-3/Grb2 adaptor module promotes inducible association of the lipid phosphatase SHIP with the BCR in a coreceptor-independent manner.

The SH2 domain-containing inositol 5'-phosphatase (SHIP) plays a key role in preventing autoimmune phenomena by limiting antigen-mediated B cell activation. SHIP function is thought to require the dual engagement of the BCR and negative regulatory coreceptors as only the latter appear capable of recruiting SHIP from the cytosol to the plasma membrane by virtue of phosphorylated immunoreceptor tyrosine-based inhibitory motifs. Here we demonstrate a coreceptor-independent membrane recruitment and function of SHIP in B cells. In the absence of coreceptor ligation, SHIP translocates to sites of BCR activation through a concerted action of the protein adaptor unit Dok-3/Grb2 and phosphorylated BCR signaling components. Our data reveal auto-inhibitory SHIP activation by the activated BCR and suggest an unexpected negative-regulatory capacity of immunoreceptor tyrosine-based activation motifs in Igα and Igβ

The Dok-3/Grb2 protein signal module attenuates Lyn kinase-dependent activation of Syk kinase in B cell antigen receptor microclusters.

Recruitment of the growth factor receptor-bound protein 2 (Grb2) by the plasma membrane-associated adapter protein downstream of kinase 3 (Dok-3) attenuates signals transduced by the B cell antigen receptor (BCR). Here we describe molecular details of Dok-3/Grb2 signal integration and function, showing that the Lyn-dependent activation of the BCR transducer kinase Syk is attenuated by Dok-3/Grb2 in a site-specific manner. This process is associated with the SH3 domain-dependent translocation of Dok-3/Grb2 complexes into BCR microsignalosomes and augmented phosphorylation of the inhibitory Lyn target SH2 domain-containing inositol 5′ phosphatase. Hence, our findings imply that Dok-3/Grb2 modulates the balance between activatory and inhibitory Lyn functions with the aim to adjust BCR signaling efficiency

Predicting Geothermal Heat Flow in Antarctica With a Machine Learning Approach

We present a machine learning approach to statistically derive geothermal heat flow (GHF) for Antarctica. The adopted approach estimates GHF from multiple geophysical and geological data sets, assuming that GHF is substantially related to the geodynamic setting of the plates. We apply a Gradient Boosted Regression Tree algorithm to find an optimal prediction model relating GHF to the observables. The geophysical and geological features are primarily global data sets, which are often unreliable in polar regions due to limited data coverage. Quality and reliability of the data sets are reviewed and discussed in line with the estimated GHF model. Predictions for Australia, where an extensive database of GHF measurements exists, demonstrate the validity of the approach. In Antarctica, only a sparse number of direct GHF measurements are available. Therefore, we explore the use of regional data sets of Antarctica and its tectonic Gondwana neighbors to refine the predictions. With this, we demonstrate the need for adding reliable data to the machine learning approach. Finally, we present a new geothermal heat flow map, which exhibits intermediate values compared to previous models, ranging from 35 to 156 mW/m2, and visible connections to the conjugate margins in Australia, Africa, and India.Plain Language Summary: The heat energy transferred from the Earth's interior to the surface (geothermal heat flow) can substantially affect the dynamics of an overlying ice sheet. It can lead to melting at the base and hence, decouple the ice sheet from the bedrock. In Antarctica, this parameter is poorly constrained, and only a sparse number of thermal gradient measurements exist. Indirect methods, therefore, try to estimate the continental Antarctic heat flow. Here, we use a machine learning approach to combine multiple information on geology, tectonic setting, and heat flow measurements from all continents to predict Antarctic values. We further show that using reliable data is crucial for the resulting prediction and a mindful choice of features is recommendable. The final result exhibits values within the range of previously proposed heat flow maps and shows local similarities to the continents once connected to East Antarctica within the supercontinent Gondwana. We suggest a minimum and maximum heat flow map, which can be used as input for ice sheet modeling and sea level rise predictions.Key Points: A new geothermal heat flow map of Antarctica is established by adopting a machine learning approach. Input features include both global and regional geological and tectonic information, and heat flow observations. A Gondwana reconstruction shows connections of heat flow at the conjugate margins of East Antarctica.Deutsche Forschungsgemeinschaft (DFG) http://dx.doi.org/10.13039/50110000165