Evolution avoids a pathological stabilizing interaction in the immune protein S100A9

Changing expression system alters oligomerization and proinflammatory activity of recombinant human S100A9

S100A9 is a Damage Associated Molecular Pattern (DAMP) that activates the innate immune system via Toll-like receptor 4 (TLR4). Despite many years of study, the mechanism of activation remains unknown. To date, much of the biochemical characterization of S100A9 has been performed using recombinant S100A9 expressed in E. coli (S100A9ec). TLR4 is the canonical receptor for LPS, a molecule found in the outer membrane of E. coli, raising the possibility of artifacts due to LPS contamination. Here we report characterization of LPS-free recombinant S100A9 expressed in insect cells (S100A9in). We show that S100A9in does not activate TLR4. This difference does not appear to be due to LPS contamination, protein misfolding, purification artifacts, or differences in phosphorylation. We show instead that S100A9in adopts an altered oligomeric state compared to S100A9ec. Disrupting oligomer formation with the E. coli disaggregase SlyD restores activity to S100A9in. Our results also indicate that the oligomeric state of S100A9 is a major factor in its ability to activate TLR4 and that this can be altered in unexpected ways by the recombinant expression system used to produce the protein.

Ancestral Reconstruction and the Evolution of Protein Energy Landscapes

A protein's sequence determines its conformational energy landscape. This, in turn, determines the protein's function. Understanding the evolution of new protein functions therefore requires understanding how mutations alter the protein energy landscape. Ancestral sequence reconstruction (ASR) has proven a valuable tool for tackling this problem. In ASR, one phylogenetically infers the sequences of ancient proteins, allowing characterization of their properties. When coupled to biophysical, biochemical, and functional characterization, ASR can reveal how historical mutations altered the energy landscape of ancient proteins, allowing the evolution of enzyme activity, altered conformations, binding specificity, oligomerization, and many other protein features. In this article, we review how ASR studies have been used to dissect the evolution of energy landscapes. We also discuss ASR studies that reveal how energy landscapes have shaped protein evolution. Finally, we propose that thinking about evolution from the perspective of an energy landscape can improve how we approach and interpret ASR studies.

S100A9 interacts with a dynamic region on CD14 to activate Toll-like receptor 4

S100A9 is a Damage Associated Molecular Pattern (DAMP) that activates inflammatory pathways via Toll-like receptor 4 (TLR4). This activity plays important homeostatic roles in tissue repair, but can also contribute to inflammatory diseases. The mechanism of activation is unknown. Here, we follow up on a previous observation that the protein CD14 is an important co-receptor that enables S100A9 to activate TLR4. Using cell-based functional assays and a combination of mutations and pharmocological perturbations, we found that CD14 must be membrane bound to potentiate TLR4 activation by S100A9. Additionally, S100A9 is sensitive to inhibitors of pathways downstream of TLR4 internalization. Together, this suggests that S100A9 induces activity via CD14-dependent internalization of TLR4. We then used mutagenesis, structural modeling, and in vitro binding experiments to establish that S100A9 binds to CD14's N-terminus in a region that overlaps with, but is not identical to, the region where CD14 binds its canonical ligand, lipopolysaccharide (LPS). In molecular dynamics simulations, this region of the protein is dynamic, allowing it to reorganize to recognize both S100A9 (a soluble protein) and LPS (a small hydrophobic molecule). Our work is the first attempt at a molecular characterization of the S100A9/CD14 interaction, bringing us one step closer to unraveling the full mechanism by which S100A9 activates TLR4/MD-2.

In vivo functional phenotypes from a computational epistatic model of evolution

Computational models of evolution are valuable for understanding the dynamics of sequence variation, to infer phylogenetic relationships or potential evolutionary pathways and for biomedical and industrial applications. Despite these benefits, few have validated their propensities to generate outputs with in vivo functionality, which would enhance their value as accurate and interpretable evolutionary algorithms. We demonstrate the power of epistasis inferred from natural protein families to evolve sequence variants in an algorithm we developed called sequence evolution with epistatic contributions (SEEC). Utilizing the Hamiltonian of the joint probability of sequences in the family as fitness metric, we sampled and experimentally tested for in vivo [Formula: see text]-lactamase activity in Escherichia coli TEM-1 variants. These evolved proteins can have dozens of mutations dispersed across the structure while preserving sites essential for both catalysis and interactions. Remarkably, these variants retain family-like functionality while being more active than their wild-type predecessor. We found that depending on the inference method used to generate the epistatic constraints, different parameters simulate diverse selection strengths. Under weaker selection, local Hamiltonian fluctuations reliably predict relative changes to variant fitness, recapitulating neutral evolution. SEEC has the potential to explore the dynamics of neofunctionalization, characterize viral fitness landscapes, and facilitate vaccine development.

Targeting S100A9 protein affects mTOR-ER stress signaling and increases venetoclax sensitivity in Acute Myeloid Leukemia

Acute Myeloid Leukemia (AML) is a heterogeneous disease with limited treatment options and a high demand for novel targeted therapies. Since myeloid-related protein S100A9 is abundantly expressed in AML, we aimed to unravel the therapeutic impact and underlying mechanisms of targeting both intracellular and extracellular S100A9 protein in AML cell lines and primary patient samples. S100A9 silencing in AML cell lines resulted in increased apoptosis and reduced AML cell viability and proliferation. These therapeutic effects were associated with a decrease in mTOR and endoplasmic reticulum stress signaling. Comparable results on AML cell proliferation and mTOR signaling could be observed using the clinically available S100A9 inhibitor tasquinimod. Interestingly, while siRNA-mediated targeting of S100A9 affected both extracellular acidification and mitochondrial metabolism, tasquinimod only affected the mitochondrial function of AML cells. Finally, we found that S100A9-targeting approaches could significantly increase venetoclax sensitivity in AML cells, which was associated with a downregulation of BCL-2 and c-MYC in the combination group compared to single agent therapy. This study identifies S100A9 as a novel molecular target to treat AML and supports the therapeutic evaluation of tasquinimod in venetoclax-based regimens for AML patients.

In vivo functional phenotypes from a computational epistatic model of evolution

Computational models of evolution are valuable for understanding the dynamics of sequence variation, to infer phylogenetic relationships or potential evolutionary pathways and for biomedical and industrial applications. Despite these benefits, few have validated their propensities to generate outputs with in vivo functionality, which would enhance their value as accurate and interpretable evolutionary algorithms. We demonstrate the power of epistasis inferred from natural protein families to evolve sequence variants in an algorithm we developed called Sequence Evolution with Epistatic Contributions. Utilizing the Hamiltonian of the joint probability of sequences in the family as fitness metric, we sampled and experimentally tested for in vivo β -lactamase activity in E. coli TEM-1 variants. These evolved proteins can have dozens of mutations dispersed across the structure while preserving sites essential for both catalysis and interactions. Remarkably, these variants retain family-like functionality while being more active than their WT predecessor. We found that depending on the inference method used to generate the epistatic constraints, different parameters simulate diverse selection strengths. Under weaker selection, local Hamiltonian fluctuations reliably predict relative changes to variant fitness, recapitulating neutral evolution. SEEC has the potential to explore the dynamics of neofunctionalization, characterize viral fitness landscapes and facilitate vaccine development.

Disentangling contact and ensemble epistasis in a riboswitch

Mutations introduced into macromolecules often exhibit epistasis, where the effect of one mutation alters the effect of another. Knowing the mechanisms that lead to epistasis is important for understanding how macromolecules work and evolve, as well as for effective macromolecular engineering. Here, we investigate the interplay between "contact epistasis" (epistasis arising from physical interactions between mutated residues) and "ensemble epistasis" (epistasis that occurs when a mutation redistributes the conformational ensemble of a macromolecule, thus changing the effect of the second mutation). We argue that the two mechanisms can be distinguished in allosteric macromolecules by measuring epistasis at differing allosteric effector concentrations. Contact epistasis manifests as nonadditivity in the microscopic equilibrium constants describing the conformational ensemble. This epistatic effect is independent of allosteric effector concentration. Ensemble epistasis manifests as nonadditivity in thermodynamic observables-such as ligand binding-that are determined by the distribution of ensemble conformations. This epistatic effect strongly depends on allosteric effector concentration. Using this framework, we experimentally investigated the origins of epistasis in three pairwise mutant cycles introduced into the adenine riboswitch aptamer domain by measuring ligand binding as a function of allosteric effector concentration. We found evidence for both contact and ensemble epistasis in all cycles. Furthermore, we found that the two mechanisms of epistasis could interact with each other. For example, in one mutant cycle we observed 6 kcal/mol of contact epistasis in a microscopic equilibrium constant. In that same cycle, the maximum epistasis in ligand binding was only 1.5 kcal/mol: shifts in the ensemble masked the contribution of contact epistasis. Finally, our work yields simple heuristics for identifying contact and ensemble epistasis based on measurements of a biochemical observable as a function of allosteric effector concentration.