Interplay of Extrinsic and Intrinsic Cues in Cell-Fate Decisions

A cell’s decision making process is coordinated by dynamic interplay between its extracellular environment and its intracellular milieu. For example, during stem cell differentiation, fate decisions are believed to be ultimately controlled by differential expression of lineage-specific transcription factors, but cytokine receptor signals also play a crucial instructive role in addition to providing permissive proliferation and survival cues. Here, we present a minimal computational framework that integrates the intrinsic and extrinsic regulatory elements implicated in the commitment of hematopoietic progenitor cells to mature red blood cells (Chapter 2). Our model highlights the importance of bidirectional interactions between cytokine receptors and transcription factors in conferring properties such as ultrasensitivity and bistability to differentiating cells. These system-level properties can induce a switch-like characteristic during differentiation and provide robustness to the mature state. We then experimentally test predictions from this lineage commitment model in a model system for studying erythropoiesis (Chapter 3). Our experiments show that hemoglobin synthesis is highly switch-like in response to cytokine and cells undergoing lineage commitment possess memory of earlier cytokine signals. We show that erythrocyte-specific receptor and transcription factor are indeed synchronously co-upregulated and the heterogeneity in their expression is positively correlated during differentiation, confirming the presence of autofeedback and receptor-mediated positive feedback loops. To evaluate the possibility of employing this minimal topology as a synthetic “memory module” for cell engineering applications, we constructed this topology synthetically in Saccharomyces cerevisiae by integrating Arabidopsis thaliana signaling components with an endogenous yeast pathway (Chapter 4). Our experiments show that any graded and unimodal signaling pathway can be rationally rewired to achieve our desired topology and the resulting network immediately attains high ultrasensitivity and bimodality without tweaking. We further show that this topology can be tuned to regulate system dynamics such as activation/deactivation kinetics, signal amplitude, switching threshold and sensitivity. We conclude with a computational study to explore the generality of this interplay between extrinsic and intrinsic cues in hematopoiesis. We extend our minimal model analysis in Chapter 2 to examine the more complex fate decisions in bipotent and multipotent progenitors, particularly how these cells can make robust decisions in the presence of multiple extrinsic cues and intrinsic noise (Chapter 5). Our model provides support to both the instructive and stochastic theories of commitment: cell fates are ultimately driven by lineage-specific transcription factors, but cytokine signaling can strongly bias lineage commitment by regulating these inherently noisy cell-fate decisions with complex, pertinent behaviors such as ligand-mediated ultrasensitivity and robust multistability. The simulations further suggest that the kinetics of differentiation to a mature cell state can depend on the starting progenitor state as well as on the route of commitment that is chosen. Lastly, our model shows good agreement with lineage-specific receptor expression kinetics from microarray experiments and provides a computational framework that can integrate both classical and alternative commitment paths in hematopoiesis that have been observed experimentally

Increased fat oxidation in 3T3-L1 adipocytes through forced expression of UCP 1

Obesity is a chronic condition that primarily develops from an increase in body fat in the form of white adipose tissue (WAT) mass. The resulting adiposity is a risk factor for many diseases, including type 2 diabetes (T2D), cardiovascular diseases, and some forms of cancer. Reducing WAT mass by targeted modulation of metabolic enzymes in fat cell metabolism is an attractive molecular therapeutic alternative to dietary approaches. In the present study, we exogenously up-regulate a novel respiratory uncoupling protein to increase substrate oxidation, and thereby control adipocyte fatty acid content. Increasing molecular evidence points to a family of uncoupling proteins (UCPs) playing an important role in adipocyte fat metabolism. Of specific interest is UCP1, which in brown adipocytes mediates energy dissipation as heat by de-coupling respiration and ATP synthesis. UCP1 is minimally expressed in white adipose tissue (WAT). We hypothesize that controlled expression of UCP1 in WAT will result in enhanced fatty acid oxidation to compensate for reduced ATP synthesis. We used a Tet-Off retroviral transfection system to express UCP1, with doxycycline being used to control the extent of expression. UCP1 cDNA was cloned into pRevTRE and was stably transfected into 3T3-L1 preadipocytes prior to differentiating them into adipocytes. A reporter gene (EGFP) was also transfected in parallel to optimize the transfection and preadipocyte differentiation conditions as well as to demonstrate regulated expression. Metabolite measurements showed that the UCP1-expressing adipocytes accumulated 83% less triglyceride and 85 % free fatty acids while maintaining constant ATP levels. These results suggest UCP1 and other metabolic enzymes as potential targets for development of pharmacological agents for the treatment of obesity and related disorders

Integrating Extrinsic and Intrinsic Cues into a Minimal Model of Lineage Commitment for Hematopoietic Progenitors

Autoregulation of transcription factors and cross-antagonism between lineage-specific transcription factors are a recurrent theme in cell differentiation. An equally prevalent event that is frequently overlooked in lineage commitment models is the upregulation of lineage-specific receptors, often through lineage-specific transcription factors. Here, we use a minimal model that combines cell-extrinsic and cell-intrinsic elements of regulation in order to understand how both instructive and stochastic events can inform cell commitment decisions in hematopoiesis. Our results suggest that cytokine-mediated positive receptor feedback can induce a “switch-like” response to external stimuli during multilineage differentiation by providing robustness to both bipotent and committed states while protecting progenitors from noise-induced differentiation or decommitment. Our model provides support to both the instructive and stochastic theories of commitment: cell fates are ultimately driven by lineage-specific transcription factors, but cytokine signaling can strongly bias lineage commitment by regulating these inherently noisy cell-fate decisions with complex, pertinent behaviors such as ligand-mediated ultrasensitivity and robust multistability. The simulations further suggest that the kinetics of differentiation to a mature cell state can depend on the starting progenitor state as well as on the route of commitment that is chosen. Lastly, our model shows good agreement with lineage-specific receptor expression kinetics from microarray experiments and provides a computational framework that can integrate both classical and alternative commitment paths in hematopoiesis that have been observed experimentally

Transient Noise Amplification and Gene Expression Synchronization in a Bistable Mammalian Cell-Fate Switch

Progenitor cells within a clonal population show variable proclivity toward lineage commitment and differentiation. This cell-to-cell variability has been attributed to transcriptome-wide gene expression noise generated by fluctuations in the amount of cellular machinery and stochasticity in the biochemical reactions involved in protein synthesis. It therefore remains unclear how a signaling network, in the presence of such noise, can execute unequivocal cell-fate decisions from external cues. Here, we use mathematical modeling and model-guided experiments to reveal functional interplay between instructive signaling and noise in erythropoiesis. We present evidence that positive transcriptional feedback loops in a lineage-specific receptor signaling pathway can generate ligand-induced memory to engender robust, switch-like responses. These same feedback loops can also transiently amplify gene expression noise in the signaling network, suggesting that external cues can actually bias seemingly stochastic decisions during cell-fate specification. Gene expression levels among key effectors in the signaling pathway are uncorrelated in the initial population of progenitor cells but become synchronized after addition of ligand, which activates the transcriptional feedback loops. Finally, we show that this transient noise amplification and gene expression synchronization induced by ligand can directly influence cell survival and differentiation kinetics within the population

Dose Schedule Optimization and the Pharmacokinetic Driver of Neutropenia

<div><p>Toxicity often limits the utility of oncology drugs, and optimization of dose schedule represents one option for mitigation of this toxicity. Here we explore the schedule-dependency of neutropenia, a common dose-limiting toxicity. To this end, we analyze previously published mathematical models of neutropenia to identify a pharmacokinetic (PK) predictor of the neutrophil nadir, and confirm this PK predictor in an <i>in vivo</i> experimental system. Specifically, we find total AUC and C_max are poor predictors of the neutrophil nadir, while a PK measure based on the moving average of the drug concentration correlates highly with neutropenia. Further, we confirm this PK parameter for its ability to predict neutropenia <i>in vivo</i> following treatment with different doses and schedules. This work represents an attempt at mechanistically deriving a fundamental understanding of the underlying pharmacokinetic drivers of neutropenia, and provides insights that can be leveraged in a translational setting during schedule selection.</p></div

sj-docx-1-aan-10.1177_02184923231171493 - Supplemental material for Machine learning algorithms for population-specific risk score in coronary artery bypass grafting

Supplemental material, sj-docx-1-aan-10.1177_02184923231171493 for Machine learning algorithms for population-specific risk score in coronary artery bypass grafting by Abhyuday Kumara Swamy, Vivek Rajagopal, Deepak Krishnan, Paramita Auddya Ghorai, Santhosh Rathnam Palani and Pradeep Narayan in Asian Cardiovascular and Thoracic Annals</p

Moving average of PK describes neutropenia.

<p>The maximum of the moving average concentration over the dosing interval was examined for its ability to predict the simulated median ANC nadir. (A) Example of the 16-day moving average calculated from concentration profile from 1on-9off dosing schedule. (B) The correlation between maximal moving average concentration and ANC nadir was calculated for sliding windows of 1 day to 28 days for low dose (blue squares), high dose (red circles), and combination (black line). The maximum ability to predict neutropenia for the combined low and high total doses occurs when the moving average is calculated over 16 days. (C) The maximum of the moving average concentration, max(C_avg,16day), accounts for most of the variability in median ANC nadir across total dose and schedule (R² = 0.86, <i>p</i><0.01).</p

Moving average PK describes rat neutrophil counts.

<p>Rat neutrophil counts were measured response to the investigational PLK inhibitor TAK-960 and the relationship between plasma PK and neutrophil nadir was assessed. A) Absolute Neutrophil Count (ANC) normalized to control group plotted over the course of 21 days on a variety of dosing regimens. B) Normalized ANC nadir plotted versus total cycle AUC for each schedule tested. C) Normalized ANC nadir plotted versus the C_max for each schedule tested. D) R² plotted for C_avg,ndays for n-days from 1 to 14 days. E) The corresponding p-values of the C_avg,ndays correlation showing correlation. F) Normalized ANC nadir is highly correlated with max(C_avg,4days) with R² = 0.70 (<i>p</i><0.05), a better correlation than either C_max or AUC.</p