Ryan N Gutenkunst

A promising direction in drug development is to exploit the ability of natural killer cells to kill antibody-labeled target cells. Monoclonal antibodies and drugs designed to elicit this effect typically bind cell-surface epitopes that are overexpressed on target cells but also present on other cells. Thus it is important to understand adhesion of cells by antibodies and similar molecules. We present an equilibrium model of such adhesion, incorporating heterogeneity in target cell epitope density, nonspecific adhesion forces, and epitope immobility. We compare with experiments on the adhesion of Jurkat T cells to bilayers containing the relevant natural killer cell receptor, with adhesion mediated by the drug alefacept. We show that a model in which all target cell epitopes are mobile and available is inconsistent with the data, suggesting that more complex mechanisms are at work. We hypothesize that the immobile epitope fraction may change with cell adhesion, and we find that such a model is more consistent with the data, although discrepancies remain. We also quantitatively describe the parameter space in which binding occurs. Our model elaborates substantially on previous work, and our results offer guidance for the refinement of therapeutic immunoadhesins. Furthermore, our comparison with data from Jurkat T cells also points toward mechanisms relating epitope immobility to cell adhesion.

Comparisons of whole-genome sequences from ancient and contemporary samples have pointed to several instances of archaic admixture through interbreeding between the ancestors of modern non-Africans and now extinct hominids such as Neanderthals and Denisovans. One implication of these findings is that some adaptive features in contemporary humans may have entered the population via gene flow with archaic forms in Eurasia. Within Africa, fossil evidence suggests that anatomically modern humans (AMH) and various archaic forms coexisted for much of the last 200,000 yr; however, the absence of ancient DNA in Africa has limited our ability to make a direct comparison between archaic and modern human genomes. Here, we use statistical inference based on high coverage whole-genome data (greater than 60×) from contemporary African Pygmy hunter-gatherers as an alternative means to study the evolutionary history of the genus Homo. Using whole-genome simulations that consider demographic histories that include both isolation and gene flow with neighboring farming populations, our inference method rejects the hypothesis that the ancestors of AMH were genetically isolated in Africa, thus providing the first whole genome-level evidence of African archaic admixture. Our inferences also suggest a complex human evolutionary history in Africa, which involves at least a single admixture event from an unknown archaic population into the ancestors of AMH, likely within the last 30,000 yr.

Accumulating evidence suggests that many tumors have a hierarchical organization, with the bulk of the tumor composed of relatively differentiated short-lived progenitor cells that are maintained by a small population of undifferentiated long-lived cancer stem cells. It is unclear, however, whether cancer stem cells originate from normal stem cells or from dedifferentiated progenitor cells. To address this, we mathematically modeled the effect of dedifferentiation on carcinogenesis. We considered a hybrid stochastic-deterministic model of mutation accumulation in both stem cells and progenitors, including dedifferentiation of progenitor cells to a stem cell-like state. We performed exact computer simulations of the emergence of tumor subpopulations with two mutations, and we derived semi-analytical estimates for the waiting time distribution to fixation. Our results suggest that dedifferentiation may play an important role in carcinogenesis, depending on how stem cell homeostasis is maintained. If the stem cell population size is held strictly constant (due to all divisions being asymmetric), we found that dedifferentiation acts like a positive selective force in the stem cell population and thus speeds carcinogenesis. If the stem cell population size is allowed to vary stochastically with density-dependent reproduction rates (allowing both symmetric and asymmetric divisions), we found that dedifferentiation beyond a critical threshold leads to exponential growth of the stem cell population. Thus, dedifferentiation may play a crucial role, the common modeling assumption of constant stem cell population size may not be adequate, and further progress in understanding carcinogenesis demands a more detailed mechanistic understanding of stem cell homeostasis.

Amino-terminal acetylation is among the most ubiquitous of protein modifications in eukaryotes. Although loss of N-terminal acetylation is associated with many abnormalities, the molecular basis of these effects is known for only a few cases, where acetylation of single factors has been linked to binding avidity or metabolic stability. In contrast, the impact of N-terminal acetylation for the majority of the proteome, and its combinatorial contributions to phenotypes, are unknown. Here, by studying the yeast prion [PSI(+)], an amyloid of the Sup35 protein, we show that loss of N-terminal acetylation promotes general protein misfolding, a redeployment of chaperones to these substrates, and a corresponding stress response. These proteostasis changes, combined with the decreased stability of unacetylated Sup35 amyloid, reduce the size of prion aggregates and reverse their phenotypic consequences. Thus, loss of N-terminal acetylation, and its previously unanticipated role in protein biogenesis, globally resculpts the proteome to create a unique phenotype.

The long-held principle that functionally important proteins evolve slowly has recently been challenged by studies in mice and yeast showing that the severity of a protein knockout only weakly predicts that protein's rate of evolution. However, the relevance of these studies to evolutionary changes within proteins is unknown, because amino acid substitutions, unlike knockouts, often only slightly perturb protein activity. To quantify the phenotypic effect of small biochemical perturbations, we developed an approach to use computational systems biology models to measure the influence of individual reaction rate constants on network dynamics. We show that this dynamical influence is predictive of protein domain evolutionary rate within networks in vertebrates and yeast, even after controlling for expression level and breadth, network topology, and knockout effect. Thus, our results not only demonstrate the importance of protein domain function in determining evolutionary rate, but also the power of systems biology modeling to uncover unanticipated evolutionary forces.