Xiuwei Zhang

T helper 2 (Th2) cells regulate helminth infections, allergic disorders, tumor immunity, and pregnancy by secreting various cytokines. It is likely that there are undiscovered Th2 signaling molecules. Although steroids are known to be immunoregulators, de novo steroid production from immune cells has not been previously characterized. Here, we demonstrate production of the steroid pregnenolone by Th2 cells in vitro and in vivo in a helminth infection model. Single-cell RNA sequencing and quantitative PCR analysis suggest that pregnenolone synthesis in Th2 cells is related to immunosuppression. In support of this, we show that pregnenolone inhibits Th cell proliferation and B cell immunoglobulin class switching. We also show that steroidogenic Th2 cells inhibit Th cell proliferation in a Cyp11a1 enzyme-dependent manner. We propose pregnenolone as a "lymphosteroid," a steroid produced by lymphocytes. We speculate that this de novo steroid production may be an intrinsic phenomenon of Th2-mediated immune responses to actively restore immune homeostasis.

BACKGROUND: Differentiation of lymphocytes is frequently accompanied by cell cycle changes, interplay that is of central importance for immunity but is still incompletely understood. Here, we interrogate and quantitatively model how proliferation is linked to differentiation in CD4+ T cells. RESULTS: We perform ex vivo single-cell RNA-sequencing of CD4+ T cells during a mouse model of infection that elicits a type 2 immune response and infer that the differentiated, cytokine-producing cells cycle faster than early activated precursor cells. To dissect this phenomenon quantitatively, we determine expression profiles across consecutive generations of differentiated and undifferentiated cells during Th2 polarization in vitro. We predict three discrete cell states, which we verify by single-cell quantitative PCR. Based on these three states, we extract rates of death, division and differentiation with a branching state Markov model to describe the cell population dynamics. From this multi-scale modelling, we infer a significant acceleration in proliferation from the intermediate activated cell state to the mature cytokine-secreting effector state. We confirm this acceleration both by live imaging of single Th2 cells and in an ex vivo Th1 malaria model by single-cell RNA-sequencing. CONCLUSION: The link between cytokine secretion and proliferation rate holds both in Th1 and Th2 cells in vivo and in vitro, indicating that this is likely a general phenomenon in adaptive immunity.

Introduction Evolution and design of protein complexes are frequently viewed through the lens of amino acid mutations at protein interfaces, but we showed previously that residues distant from interfaces are also commonly involved in the evolution of alternative quaternary structures. We hypothesized that in these protein families, the difference in oligomeric state is due to a change in intersubunit geometry. The indirect mutations would act by changing protein conformation and dynamics, similar to the way in which allosteric small molecules introduce functional conformational change. We refer to these substitutions as “allosteric mutations.” Rationale In this work, we investigate the mechanism of action of allosteric mutations on oligomeric state in the PyrR family of pyrimidine operon attenuators. In this family, an entirely sequence-conserved helix that forms a tetrameric interface in the thermophilic ortholog (BcPyrR) switches to being solvent-exposed in the mesophilic ortholog (BsPyrR). This results in a homodimeric structure in which the two subunits are clearly rotated relative to their orientation in the tetramer. What is the origin of this rotation and the change in quaternary structure? To dissect the role of the 49 substitutions between BsPyrR and BcPyrR, we used ancestral sequence reconstruction in combination with structural and biophysical methods to identify a set of allosteric mutations that are responsible for this shift in conformation. We compared the conformational changes introduced by the mutations to the protein motion during allosteric regulation by guanosine monophosphate (GMP). Results We identified 11 key mutations controlling oligomeric state, all distant from the interfaces and outside ligand-binding pockets. We confirmed the role of these allosteric mutations by engineering a shift in oligomeric state in an inferred ancestral PyrR protein (intermediate in sequence between the extant orthologs). We further used the inferred ancestral states and their mutants to show that the allosteric mutations are part of a downhill adaptation of the PyrR proteins to lower temperatures. We compared the x-ray crystal structures of ancestral and engineered PyrR proteins to the free and GMP-bound structure of the mesophilic BsPyrR, which shifts its equilibrium from dimer to tetramer upon ligand binding. Binding of the allosteric molecule introduces a change in intersubunit geometry that is equivalent to the evolutionary difference in intersubunit geometry between the dimeric and tetrameric homologs. We further find that the difference in oligomeric state is coupled to the difference in intrinsic dynamics of the dimers. Finally, we used the residue-residue contact network approach to show that the residues corresponding to the allosteric mutations undergo large contact rewiring when the intersubunit geometry and, in turn, oligomeric state change, either by GMP binding or by the introduction of allosteric mutations. Conclusion We show that evolution employs the intrinsic dynamics of this protein to toggle a conformational switch in a manner similar to that of small molecules. Shifting the relative populations of different states by subtle modifications is a process central to protein function and, as shown here, also to protein evolution. This suggests that we can learn from evolution and design proteins with multiple conformational states.

The experimental determination of transcriptional regulatory networks in the laboratory remains difficult and timeconsuming, while computational methods to infer these networks provide only modest accuracy. The latter can be attributed partly to the limitations of a single-organism approach. Computational biology has long used comparative and evolutionary approaches to extend the reach and accuracy of its analyses. In this paper, we describe ProPhyC, a probabilistic phylogenetic model and associated inference algorithms, designed to improve the inference of regulatory networks for a family of organisms by using known evolutionary relationships among these organisms. ProPhyC can be used with various network evolutionary models and any existing inference method. Extensive experimental results on both biological and synthetic data confirm that our model (through its associated refinement algorithms) yields substantial improvement in the quality of inferred networks over all current methods. We also compare ProPhyC with a transfer learning approach we design. This approach also uses phylogenetic relationships while inferring regulatory networks for a family of organisms. Using similar input information but designed in a very different framework, this transfer learning approach does not perform better than ProPhyC, which indicates that ProPhyC makes good use of the evolutionary information.