Function of microRNA-146a and NF-κB in physiologic and pathologic hematopoiesis

During inflammation and infection, hematopoietic stem and progenitor cells (HSPCs) are stimulated to proliferate and differentiate into mature immune cells, especially of the myeloid lineage. MicroRNA-146a (miR-146a) is a critical negative regulator of inflammation. Deletion of the gene encoding miR-146a—expressed in all blood cell types—produces effects that appear as dysregulated inflammatory hematopoiesis, leading to a decline in the number and quality of hematopoietic stem cells (HSCs), excessive myeloproliferation, and, ultimately, to exhaustion of the HSCs and hematopoietic neoplasms. Six-week-old deleted mice are normal, with no effect on cell numbers, but by 4 months bone marrow hypercellularity can be seen, and by 8 months marrow exhaustion is becoming evident. The ability of HSCs to replenish the entire hematopoietic repertoire in a myelo-ablated mouse also declines precipitously as miR-146a-deficient mice age. In the absence of miR-146a, LPS-mediated serial inflammatory stimulation accelerates the effects of aging. This chronic inflammatory stress on HSCs in deleted mice involves a molecular axis consisting of upregulation of the signaling protein TRAF6 leading to excessive activity of the transcription factor NF-κB and overproduction of the cytokine IL-6. At the cellular level, transplant studies show that the defects are attributable to both an intrinsic problem in the miR-146a-deficient HSCs and extrinsic effects of miR-146a-deficient lymphocytes and non-hematopoietic cells. This study has identified a microRNA, miR-146a, to be a critical regulator of HSC homeostasis during chronic inflammatory challenge in mice and has provided a molecular connection between chronic inflammation and the development of bone marrow failure and myeloproliferative neoplasms. This may have implications for human hematopoietic malignancies, such as myelodysplastic syndrome, which frequently displays downregulated miR-146a expression.

Robust Control of Evolutionary Dynamics

The application of principles from evolutionary biology has long been used to gain new insights into the progression and clinical control of both infectious diseases and neoplasms. This iterative evolutionary process consists of expansion, diversification and selection within an adaptive landscape - species are subject to random genetic or epigenetic alterations that result in variations; genetic information is inherited through asexual reproduction and strong selective pressures such as therapeutic intervention can lead to the adaptation and expansion of resistant variants. These principles lie at the center of modern evolutionary synthesis and constitute the primary reasons for the development of resistance and therapeutic failure, but also provide a framework that allows for more effective control.

A model system for studying the evolution of resistance and control of therapeutic failure is the treatment of chronic HIV-1 infection by broadly neutralizing antibody (bNAb) therapy. A relatively recent discovery is that a minority of HIV-infected individuals can produce broadly neutralizing antibodies, that is, antibodies that inhibit infection by many strains of HIV. Passive transfer of human antibodies for the prevention and treatment of HIV-1 infection is increasingly being considered as an alternative to a conventional vaccine. However, recent evolution studies have uncovered that antibody treatment can exert selective pressure on virus that results in the rapid evolution of resistance. In certain cases, complete resistance to an antibody is conferred with a single amino acid substitution on the viral envelope of HIV.

The challenges in uncovering resistance mechanisms and designing effective combination strategies to control evolutionary processes and prevent therapeutic failure apply more broadly. We are motivated by two questions: Can we predict the evolution to resistance by characterizing genetic alterations that contribute to modified phenotypic fitness? Given an evolutionary landscape and a set of candidate therapies, can we computationally synthesize treatment strategies that control evolution to resistance?

To address the first question, we propose a mathematical framework to reason about evolutionary dynamics of HIV from computationally derived Gibbs energy fitness landscapes -- expanding the theoretical concept of an evolutionary landscape originally conceived by Sewall Wright to a computable, quantifiable, multidimensional, structurally defined fitness surface upon which to study complex HIV evolutionary outcomes.

To design combination treatment strategies that control evolution to resistance, we propose a methodology that solves for optimal combinations and concentrations of candidate therapies, and allows for the ability to quantifiably explore tradeoffs in treatment design, such as limiting the number of candidate therapies in the combination, dosage constraints and robustness to error. Our algorithm is based on the application of recent results in optimal control to an HIV evolutionary dynamics model and is constructed from experimentally derived antibody resistant phenotypes and their single antibody pharmacodynamics. This method represents a first step towards integrating principled engineering techniques with an experimentally based mathematical model in the rational design of combination treatment strategies and offers predictive understanding of the effects of combination therapies of evolutionary dynamics and resistance of HIV. Preliminary in vitro studies suggest that the combination antibody therapies predicted by our algorithm can neutralize heterogeneous viral populations despite containing resistant mutations.