Publications

Finding your inner modeler: An NSF-sponsored workshop to introduce cell biologists to modeling/computational approaches.
Finding Your Inner Modeler I

Abstract (Expand)

In classical Cell Biology, fundamental cellular processes are revealed empirically, one experiment at a time. While this approach has been enormously fruitful, our understanding of cells is far from … complete. In fact, the more we know, the more keenly we perceive our ignorance of the profoundly complex and dynamic molecular systems that underlie cell structure and function. Thus, it has become apparent to many cell biologists that experimentation alone is unlikely to yield major new paradigms, and that empiricism must be combined with theory and computational approaches to yield major new discoveries. To facilitate those discoveries, three workshops will convene annually for one day in three successive summers (2017-2019) to promote the use of computational modeling by cell biologists currently unconvinced of its utility or unsure how to apply it. The first of these workshops was held at the University of Illinois, Chicago in July 2017. Organized to facilitate interactions between traditional cell biologists and computational modelers, it provided a unique educational opportunity: a primer on how cell biologists with little or no relevant experience can incorporate computational modeling into their research. Here, we report on the workshop and describe how it addressed key issues that cell biologists face when considering modeling including: (1) Is my project appropriate for modeling? (2) What kind of data do I need to model my process? (3) How do I find a modeler to help me in integrating modeling approaches into my work? And, perhaps most importantly, (4) why should I bother?

Authors: D. E. Stone, E. S. Haswell, E. Sztul

Date Published: 4th Jan 2018

Publication Type: Journal

PACMANS: A bioinformatically informed algorithm to predict, design, and disrupt protease-on-protease hydrolysis.
Finding Your Inner Modeler IV

Abstract (Expand)

Multiple proteases in a system hydrolyze target substrates, but recent evidence indicates that some proteases will degrade other proteases as well. Cathepsin S hydrolysis of cathepsin K is one such … example. These interactions may be uni- or bi-directional and change the expected kinetics. To explore potential protease-on-protease interactions in silico, a program was developed for users to input two proteases: (1) the protease-ase that hydrolyzes (2) the substrate, protease. This program identifies putative sites on the substrate protease highly susceptible to cleavage by the protease-ase, using a sliding-window approach that scores amino acid sequences by their preference in the protease-ase active site, culled from MEROPS database. We call this PACMANS, Protease-Ase Cleavage from MEROPS ANalyzed Specificities, and test and validate this algorithm with cathepsins S and K. PACMANS cumulative likelihood scoring identified L253 and V171 as sites on cathepsin K subject to cathepsin S hydrolysis. Mutations made at these locations were tested to block hydrolysis and validate PACMANS predictions. L253A and L253V cathepsin K mutants significantly reduced cathepsin S hydrolysis, validating PACMANS unbiased identification of these sites. Interfamilial protease interactions between cathepsin S and MMP-2 or MMP-9 were tested after predictions by PACMANS, confirming its utility for these systems as well. PACMANS is unique compared to other putative site cleavage programs by allowing users to define the proteases of interest and target, and can also be employed for non-protease substrate proteins, as well as short peptide sequences.

Authors: M. C. Ferrall-Fairbanks, Z. T. Barry, M. Affer, M. A. Shuler, E. W. Moomaw, M. O. Platt

Date Published: 13th Jan 2017

Publication Type: Journal

Investigating the Life Expectancy and Proteolytic Degradation of Engineered Skeletal Muscle Biological Machines.
Finding Your Inner Modeler IV

Abstract (Expand)

A combination of techniques from 3D printing, tissue engineering and biomaterials has yielded a new class of engineered biological robots that could be reliably controlled via applied signals. These … machines are powered by a muscle strip composed of differentiated skeletal myofibers in a matrix of natural proteins, including fibrin, that provide physical support and cues to the cells as an engineered basement membrane. However, maintaining consistent results becomes challenging when sustaining a living system in vitro. Skeletal muscle must be preserved in a differentiated state and the system is subject to degradation by proteolytic enzymes that can break down its mechanical integrity. Here we examine the life expectancy, breakdown, and device failure of engineered skeletal muscle bio-bots as a result of degradation by three classes of proteases: plasmin, cathepsin L, and matrix metalloproteinases (MMP-2 and MMP-9). We also demonstrate the use of gelatin zymography to determine the effects of differentiation and inhibitor concentration on protease expression. With this knowledge, we are poised to design the next generation of complex biological machines with controllable function, specific life expectancy and greater consistency. These results could also prove useful for the study of disease-specific models, treatments of myopathies, and other tissue engineering applications.

Authors: C. Cvetkovic, M. C. Ferrall-Fairbanks, E. Ko, L. Grant, H. Kong, M. O. Platt, R. Bashir

Date Published: 19th Jun 2017

Publication Type: Journal

Computational predictions of cysteine cathepsin-mediated fibrinogen proteolysis.
Finding Your Inner Modeler IV

Abstract (Expand)

Fibrin clot formation is a proteolytic cascade of events with thrombin and plasmin identified as the main proteases cleaving fibrinogen precursor, and the fibrin polymer, respectively. Other proteases … may be involved directly in fibrin(ogen) cleavage, clot formation, and resolution, or in the degradation of fibrin-based scaffolds emerging as useful tools for tissue engineered constructs. Here, cysteine cathepsins are investigated for their putative ability to hydrolyze fibrinogen, since they are potent proteases, first identified in lysosomal protein degradation and known to participate in extracellular proteolysis. To further explore this, we used two independent computational technqiues, molecular docking and bioinformatics sequence analysis (PACMANS), to predict potential binding interactions and sites of hydrolysis between cathepsins K, L, and S and fibrinogen. By comparing the results from these two objective, computational methods, it was determined that cathepsins K, L, and S do bind and cleave fibrinogen alpha, beta, and gamma chains at similar and unique sites. These differences were visualized experimentally by the unique cleaved fibrinogen banding patterns after incubation with each of the cathepsins, separately. In conclusion, human cysteine cathepsins K, L, and S are a new class of proteases that should be considered during fibrin(ogen) degradation studies both for disease processes where coagulation is a concern, and also in the implementation and design of bioengineered systems.

Authors: M. C. Ferrall-Fairbanks, D. M. West, S. A. Douglas, R. D. Averett, M. O. Platt

Date Published: 22nd Dec 2017

Publication Type: Journal

Long-Term Cryopreservation and Revival of Tissue-Engineered Skeletal Muscle.
Finding Your Inner Modeler IV

Abstract (Expand)

IMPACT STATEMENT: The ability to freeze, revive, and prolong the lifetime of tissue-engineered skeletal muscle without incurring any loss of function represents a significant advancement in the field … of tissue engineering. Cryopreservation enables the efficient fabrication, storage, and shipment of these tissues. This in turn facilitates multidisciplinary collaboration between research groups, enabling advances in skeletal muscle regenerative medicine, organ-on-a-chip models of disease, drug testing, and soft robotics. Furthermore, the observation that freezing undifferentiated skeletal muscle enhances functional performance may motivate future studies developing stronger and more clinically relevant engineered muscle.

Authors: L. Grant, R. Raman, C. Cvetkovic, M. C. Ferrall-Fairbanks, G. J. Pagan-Diaz, P. Hadley, E. Ko, M. O. Platt, R. Bashir

Date Published: 10th Nov 2018

Publication Type: Journal

Leveraging Single-Cell RNA Sequencing Experiments to Model Intratumor Heterogeneity.
Finding Your Inner Modeler IV

Abstract (Expand)

PURPOSE: Many cancers can be treated with targeted therapy. Almost inevitably, tumors develop resistance to targeted therapy, either from pre-existence or by evolving new genotypes and traits. Intratumor … heterogeneity serves as a reservoir for resistance, which often occurs as a result of the selection of minor cellular subclones. On the level of gene expression, clonal heterogeneity can only be revealed using high-dimensional single-cell methods. We propose using a general diversity index (GDI) to quantify heterogeneity on multiple scales and relate it to disease evolution. MATERIALS AND METHODS: We focused on individual patient samples that were probed with single-cell RNA (scRNA) sequencing to describe heterogeneity. We developed a pipeline to analyze single-cell data via sample normalization, clustering, and mathematical interpretation using a generalized diversity measure, as well as to exemplify the utility of this platform using single-cell data. RESULTS: We focused on three sources of patient scRNA sequencing data: two healthy bone marrow (BM) donors, two patients with acute myeloid leukemia-each sampled before and after BM transplantation, four samples of presorted lineages-and six patients with lung carcinoma with multiregion sampling. While healthy/normal samples scored low in diversity overall, GDI further quantified the ways in which these samples differed. Whereas a widely used Shannon diversity index sometimes reveals fewer differences, GDI exhibits differences in the number of potential key drivers or clonal richness. Comparison of pre- and post-BM transplantation acute myeloid leukemia samples did not reveal differences in heterogeneity, although biological differences can exist. CONCLUSION: GDI can quantify cellular heterogeneity changes across a wide spectrum, even when standard measures, such as the Shannon index, do not. Our approach can be widely applied to quantify heterogeneity across samples and conditions.

Authors: M. C. Ferrall-Fairbanks, M. Ball, E. Padron, P. M. Altrock

Date Published: 18th Apr 2019

Publication Type: Journal

Re: Simulation analysis for tumor radiotherapy based on three-component mathematical models.
Finding Your Inner Modeler IV

Abstract

Not specified

Authors: M. C. Ferrall-Fairbanks, D. J. Glazar, R. J. Brady, G. J. Kimmel, M. U. Zahid, P. M. Altrock, H. Enderling

Date Published: 31st May 2019

Publication Type: Journal

Reassessing enzyme kinetics: Considering protease-as-substrate interactions in proteolytic networks.
Finding Your Inner Modeler IV

Abstract (Expand)

Enzymes are catalysts in biochemical reactions that, by definition, increase rates of reactions without being altered or destroyed. However, when that enzyme is a protease, a subclass of enzymes that … hydrolyze other proteins, and that protease is in a multiprotease system, protease-as-substrate dynamics must be included, challenging assumptions of enzyme inertness, shifting kinetic predictions of that system. Protease-on-protease inactivating hydrolysis can alter predicted protease concentrations used to determine pharmaceutical dosing strategies. Cysteine cathepsins are proteases capable of cathepsin cannibalism, where one cathepsin hydrolyzes another with substrate present, and misunderstanding of these dynamics may cause miscalculations of multiple proteases working in one proteolytic network of interactions occurring in a defined compartment. Once rates for individual protease-on-protease binding and catalysis are determined, proteolytic network dynamics can be explored using computational models of cooperative/competitive degradation by multiple proteases in one system, while simultaneously incorporating substrate cleavage. During parameter optimization, it was revealed that additional distraction reactions, where inactivated proteases become competitive inhibitors to remaining, active proteases, occurred, introducing another network reaction node. Taken together, improved predictions of substrate degradation in a multiple protease network were achieved after including reaction terms of autodigestion, inactivation, cannibalism, and distraction, altering kinetic considerations from other enzymatic systems, since enzyme can be lost to proteolytic degradation. We compiled and encoded these dynamics into an online platform (https://plattlab.shinyapps.io/catKLS/) for individual users to test hypotheses of specific perturbations to multiple cathepsins, substrates, and inhibitors, and predict shifts in proteolytic network reactions and system dynamics.

Authors: M. C. Ferrall-Fairbanks, C. A. Kieslich, M. O. Platt

Date Published: 11th Feb 2020

Publication Type: Journal

Investigating Inter- and Intrasample Diversity of Single-Cell RNA Sequencing Datasets.
Finding Your Inner Modeler IV

Abstract (Expand)

Tumor heterogeneity can arise from a variety of extrinsic and intrinsic sources and drives unfavorable outcomes. With recent technological advances, single-cell RNA sequencing has become a way for … researchers to easily assay tumor heterogeneity at the transcriptomic level with high resolution. However, ongoing research focuses on different ways to analyze this big data and how to compare across multiple different samples. In this chapter, we provide a practical guide to calculate inter- and intrasample diversity metrics from single-cell RNA sequencing datasets. These measures of diversity are adapted from commonly used metrics in statistics and ecology to quantify and compare sample heterogeneity at single-cell resolution.

Authors: M. C. Ferrall-Fairbanks, P. M. Altrock

Date Published: 14th Sep 2020

Publication Type: InBook

Spontaneous cell fusions as a mechanism of parasexual recombination in tumour cell populations.
Finding Your Inner Modeler IV

Abstract (Expand)

The initiation and progression of cancers reflect the underlying process of somatic evolution, in which the diversification of heritable phenotypes provides a substrate for natural selection, resulting … in the outgrowth of the most fit subpopulations. Although somatic evolution can tap into multiple sources of diversification, it is assumed to lack access to (para)sexual recombination-a key diversification mechanism throughout all strata of life. On the basis of observations of spontaneous fusions involving cancer cells, the reported genetic instability of polypoid cells and the precedence of fusion-mediated parasexual recombination in fungi, we asked whether cell fusions between genetically distinct cancer cells could produce parasexual recombination. Using differentially labelled tumour cells, we found evidence of low-frequency, spontaneous cell fusions between carcinoma cells in multiple cell line models of breast cancer both in vitro and in vivo. While some hybrids remained polyploid, many displayed partial ploidy reduction, generating diverse progeny with heterogeneous inheritance of parental alleles, indicative of partial recombination. Hybrid cells also displayed elevated levels of phenotypic plasticity, which may further amplify the impact of cell fusions on the diversification of phenotypic traits. Using mathematical modelling, we demonstrated that the observed rates of spontaneous somatic cell fusions may enable populations of tumour cells to amplify clonal heterogeneity, thus facilitating the exploration of larger areas of the adaptive landscape (relative to strictly asexual populations), which may substantially accelerate a tumour's ability to adapt to new selective pressures.

Authors: D. Miroshnychenko, E. Baratchart, M. C. Ferrall-Fairbanks, R. V. Velde, M. A. Laurie, M. M. Bui, A. C. Tan, P. M. Altrock, D. Basanta, A. Marusyk

Date Published: 20th Jan 2021

Publication Type: Journal

The harsh microenvironment in early breast cancer selects for a Warburg phenotype.
Finding Your Inner Modeler IV

Abstract (Expand)

The harsh microenvironment of ductal carcinoma in situ (DCIS) exerts strong evolutionary selection pressures on cancer cells. We hypothesize that the poor metabolic conditions near the ductal center … foment the emergence of a Warburg Effect (WE) phenotype, wherein cells rapidly ferment glucose to lactic acid, even in normoxia. To test this hypothesis, we subjected low-glycolytic breast cancer cells to different microenvironmental selection pressures using combinations of hypoxia, acidosis, low glucose, and starvation for many months and isolated single clones for metabolic and transcriptomic profiling. The two harshest conditions selected for constitutively expressed WE phenotypes. RNA sequencing analysis of WE clones identified the transcription factor KLF4 as potential inducer of the WE phenotype. In stained DCIS samples, KLF4 expression was enriched in the area with the harshest microenvironmental conditions. We simulated in vivo DCIS phenotypic evolution using a mathematical model calibrated from the in vitro results. The WE phenotype emerged in the poor metabolic conditions near the necrotic core. We propose that harsh microenvironments within DCIS select for a WE phenotype through constitutive transcriptional reprogramming, thus conferring a survival advantage and facilitating further growth and invasion.

Authors: M. Damaghi, J. West, M. Robertson-Tessi, L. Xu, M. C. Ferrall-Fairbanks, P. A. Stewart, E. Persi, B. L. Fridley, P. M. Altrock, R. A. Gatenby, P. A. Sims, A. R. A. Anderson, R. J. Gillies

Date Published: 19th Jan 2021

Publication Type: Journal

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