Scholarly Works, Academy of Integrated Science

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Anti-inflammatory cytokine stimulation of HMC3 cells: Proteome dataset
Ahuja, Shreya; Lazar, Iulia M. (Elsevier, 2023-07-20)
The immunoprotective functions of microglia in the brain are mediated by the inflammatory M1 phenotype. This phenotype is challenged by anti-inflammatory cytokines which polarize the microglia cells to an immunosuppressive M2 phenotype, a trait that is often exploited by cancer cells to evade immune recognition and promote tumor growth. Investigating the molecular determinants of this behavior is crucial for advancing the understanding of the mechanisms that cancer cells use to escape immune attack. In this article, we describe liquid chromatography (LC)-mass spectrometry (MS)/proteomic data acquired with an EASY-nanoLC 1200-Q ExactiveTM OrbitrapTM mass spectrometer that reflect the response of human microglia cells (HMC3) to stimulation with potential cancer-released anti-inflammatory cytokines known to be key players in promoting tumorigenesis in the brain (IL-4, IL-13, IL-10, TGFB and MCP-1). The MS files were processed with the Proteome Discoverer v.2.4 software package. The cell culture conditions, the sample preparation protocols, the MS acquisition parameters, and the data processing approach are described in detail. The RAW and processed MS files associated with this work were deposited in the PRIDE partner repository of the ProteomeXchange Consortium with the dataset identifiers PXD023163 and PXD023166, and the analyzed data in the Mendeley Data cloud-based repository with DOI 10.17632/fvhw2zwt5d.1. The biological interpretation of the data can be accessed in the research article “Systems-Level Proteomics Evaluation of Microglia Response to Tumor-Supportive Anti-inflammatory Cytokines” (Shreya Ahuja and Iulia M. Lazar, Frontiers in Immunology 2021 [1]). The proteome data described in this article will benefit researchers who are either interested in re-processing the data with alternative search engines and filtering criteria, and/or exploring the data in more depth to advance the understanding of cancer progression and the discovery of novel biomarkers or drug targets.
BubR1 recruitment to the kinetochore via Bub1 enhances spindle assembly checkpoint signaling
Banerjee, Anand; Chen, Chu; Humphrey, Lauren; Tyson, John J.; Joglekar, Ajit P. (American Society for Cell Biology, 2022-06-29)
During mitosis, unattached kinetochores in a dividing cell activate the spindle assembly checkpoint (SAC) and delay anaphase onset by generating the anaphase-inhibitory mitotic checkpoint complex (MCC). These kinetochores generate the MCC by recruiting its constituent proteins, including BubR1. In principle, BubR1 recruitment to signaling kinetochores should increase its local concentration and promote MCC formation. However, in human cells BubR1 is mainly thought to sensitize the SAC to silencing. Whether BubR1 localization to signaling kinetochores by itself enhances SAC signaling remains unknown. Therefore, we used ectopic SAC activation (eSAC) systems to isolate two molecules that recruit BubR1 to the kinetochore, the checkpoint protein Bub1 and the KI and MELT motifs in the kinetochore protein KNL1, and observed their contribution to eSAC signaling. Our quantitative analyses and mathematical modeling show that Bub1-mediated BubR1 recruitment to the human kinetochore promotes SAC signaling and highlight BubR1’s dual role of strengthening the SAC directly and silencing it indirectly.
Computationally efficient methods for large-scale atmospheric inverse modeling
Cho, Taewon; Chung, Julianne; Miller, Scot M.; Saibaba, Arvind K. (Copernicus, 2022-07-20)
Atmospheric inverse modeling describes the process of estimating greenhouse gas fluxes or air pollution emissions at the Earth's surface using observations of these gases collected in the atmosphere. The launch of new satellites, the expansion of surface observation networks, and a desire for more detailed maps of surface fluxes have yielded numerous computational and statistical challenges for standard inverse modeling frameworks that were often originally designed with much smaller data sets in mind. In this article, we discuss computationally efficient methods for large-scale atmospheric inverse modeling and focus on addressing some of the main computational and practical challenges. We develop generalized hybrid projection methods, which are iterative methods for solving large-scale inverse problems, and specifically we focus on the case of estimating surface fluxes. These algorithms confer several advantages. They are efficient, in part because they converge quickly, they exploit efficient matrix-vector multiplications, and they do not require inversion of any matrices. These methods are also robust because they can accurately reconstruct surface fluxes, they are automatic since regularization or covariance matrix parameters and stopping criteria can be determined as part of the iterative algorithm, and they are flexible because they can be paired with many different types of atmospheric models. We demonstrate the benefits of generalized hybrid methods with a case study from NASA's Orbiting Carbon Observatory 2 (OCO-2) satellite. We then address the more challenging problem of solving the inverse model when the mean of the surface fluxes is not known a priori; we do so by reformulating the problem, thereby extending the applicability of hybrid projection methods to include hierarchical priors. We further show that by exploiting mathematical relations provided by the generalized hybrid method, we can efficiently calculate an approximate posterior variance, thereby providing uncertainty information.
A continuous-time stochastic Boolean model provides a quantitative description of the budding yeast cell cycle
Laomettachit, Teeraphan; Kraikivski, Pavel; Tyson, John J. (Springer Nature, 2022-12-01)
The cell division cycle is regulated by a complex network of interacting genes and proteins. The control system has been modeled in many ways, from qualitative Boolean switching-networks to quantitative differential equations and highly detailed stochastic simulations. Here we develop a continuous-time stochastic model using seven Boolean variables to represent the activities of major regulators of the budding yeast cell cycle plus one continuous variable representing cell growth. The Boolean variables are updated asynchronously by logical rules based on known biochemistry of the cell-cycle control system using Gillespie’s stochastic simulation algorithm. Time and cell size are updated continuously. By simulating a population of yeast cells, we calculate statistical properties of cell cycle progression that can be compared directly to experimental measurements. Perturbations of the normal sequence of events indicate that the cell cycle is 91% robust to random ‘flips’ of the Boolean variables, but 9% of the perturbations induce lethal mistakes in cell cycle progression. This simple, hybrid Boolean model gives a good account of the growth and division of budding yeast cells, suggesting that this modeling approach may be as accurate as detailed reaction-kinetic modeling with considerably less demands on estimating rate constants.
Crosstalk between Plk1, p53, cell cycle, and G2/M DNA damage checkpoint regulation in cancer: computational modeling and analysis
Jung, Yongwoon; Kraikivski, Pavel; Shafiekhani, Sajad; Terhune, Scott S.; Dash, Ranjan K. (Nature Portfolio, 2021-12-09)
Different cancer cell lines can have varying responses to the same perturbations or stressful conditions. Cancer cells that have DNA damage checkpoint-related mutations are often more sensitive to gene perturbations including altered Plk1 and p53 activities than cancer cells without these mutations. The perturbations often induce a cell cycle arrest in the former cancer, whereas they only delay the cell cycle progression in the latter cancer. To study crosstalk between Plk1, p53, and G2/M DNA damage checkpoint leading to differential cell cycle regulations, we developed a computational model by extending our recently developed model of mitotic cell cycle and including these key interactions. We have used the model to analyze the cancer cell cycle progression under various gene perturbations including Plk1-depletion conditions. We also analyzed mutations and perturbations in approximately 1800 different cell lines available in the Cancer Dependency Map and grouped lines by genes that are represented in our model. Our model successfully explained phenotypes of various cancer cell lines under different gene perturbations. Several sensitivity analysis approaches were used to identify the range of key parameter values that lead to the cell cycle arrest in cancer cells. Our resulting model can be used to predict the effect of potential treatments targeting key mitotic and DNA damage checkpoint regulators on cell cycle progression of different types of cancer cells.
A Dynamic Mechanistic Model of Perceptual Binding
Kraikivski, Pavel (MDPI, 2022-04-01)
The brain’s ability to create a unified conscious representation of an object by integrating information from multiple perception pathways is called perceptual binding. Binding is crucial for normal cognitive function. Some perceptual binding errors and disorders have been linked to certain neurological conditions, brain lesions, and conditions that give rise to illusory conjunctions. However, the mechanism of perceptual binding remains elusive. Here, I present a computational model of binding using two sets of coupled oscillatory processes that are assumed to occur in response to two different percepts. I use the model to study the dynamic behavior of coupled processes to characterize how these processes can modulate each other and reach a temporal synchrony. I identify different oscillatory dynamic regimes that depend on coupling mechanisms and parameter values. The model can also discriminate different combinations of initial inputs that are set by initial states of coupled processes. Decoding brain signals that are formed through perceptual binding is a challenging task, but my modeling results demonstrate how crosstalk between two systems of processes can possibly modulate their outputs. Therefore, my mechanistic model can help one gain a better understanding of how crosstalk between perception pathways can affect the dynamic behavior of the systems that involve perceptual binding.
Mapping the cell-membrane proteome of the SKBR3/HER2+ cell line to the cancer hallmarks
Lazar, Iulia M.; Karcini, Arba; Haueis, Joshua R. S. (PLOS, 2022-08-01)
The hallmarks of biological processes that underlie the development of cancer have been long recognized, yet, existing therapeutic treatments cannot prevent cancer from continuing to be one of the leading causes of death worldwide. This work was aimed at exploring the extent to which the cell-membrane proteins are implicated in triggering cancer hallmark processes, and assessing the ability to pinpoint tumor-specific therapeutic targets through a combined membrane proteome/cancer hallmark perspective. By using GO annotations, a database of human proteins associated broadly with ten cancer hallmarks was created. Cell-membrane cellular subfractions of SKBR3/HER2+ breast cancer cells, used as a model system, were analyzed by high resolution mass spectrometry, and high-quality proteins (FDR<3%) identified by at least two unique peptides were mapped to the cancer hallmark database. Over 1,400 experimentally detected cell-membrane or cell-membrane associated proteins, representing ~18% of the human cell-membrane proteome, could be matched to the hallmark database. Representative membrane constituents such as receptors, CDs, adhesion and transport proteins were distributed over the entire genome and present in every hallmark category. Sustained proliferative signaling/cell cycle, adhesion/tissue invasion, and evasion of immune destruction emerged as prevalent hallmarks represented by the membrane proteins. Construction of protein-protein interaction networks uncovered a high level of connectivity between the hallmark members, with some receptor (EGFR, ERBB2, FGFR, MTOR, CSF1R), antigen (CD44), and adhesion (MUC1) proteins being implicated in most hallmark categories. An illustrative subset of 138 hallmark proteins that included 42 oncogenes, 24 tumor suppressors, 9 oncogene/tumor suppressor, and 45 approved drug targets was subjected to a more in-depth analysis. The existing drug targets were implicated mainly in signaling processes. Network centrality analysis revealed that nodes with high degree, rather than betweenness, represent a good resource for informing the selection of putative novel drug targets. Through heavy involvement in supporting cancer hallmark processes, we show that the functionally diverse and networked landscape of cancer cell-membrane proteins fosters unique opportunities for guiding the development of novel therapeutic interventions, including multi-agent, immuno-oncology and precision medicine applications.
Mathematical Modeling in Systems Biology
Kraikivski, Pavel (MDPI, 2023-09-25)
Mathematical modeling is a key tool used in the field of systems biology to determine the mechanisms with which the elements of biological systems interact to produce complex dynamic behavior [...]
Mathematical Models of Death Signaling Networks
Srinivasan, Madhumita; Clarke, Robert; Kraikivski, Pavel (MDPI, 2022-10-01)
This review provides an overview of the progress made by computational and systems biologists in characterizing different cell death regulatory mechanisms that constitute the cell death network. We define the cell death network as a comprehensive decision-making mechanism that controls multiple death execution molecular circuits. This network involves multiple feedback and feed-forward loops and crosstalk among different cell death-regulating pathways. While substantial progress has been made in characterizing individual cell death execution pathways, the cell death decision network is poorly defined and understood. Certainly, understanding the dynamic behavior of such complex regulatory mechanisms can be only achieved by applying mathematical modeling and system-oriented approaches. Here, we provide an overview of mathematical models that have been developed to characterize different cell death mechanisms and intend to identify future research directions in this field.
A Mechanistic Model of Perceptual Binding Predicts That Binding Mechanism Is Robust against Noise
Kraikivski, Pavel (MDPI, 2024-01-31)
The concept of the brain’s own time and space is central to many models and theories that aim to explain how the brain generates consciousness. For example, the temporo-spatial theory of consciousness postulates that the brain implements its own inner time and space for conscious processing of the outside world. Furthermore, our perception and cognition of time and space can be different from actual time and space. This study presents a mechanistic model of mutually connected processes that encode phenomenal representations of space and time. The model is used to elaborate the binding mechanism between two sets of processes representing internal space and time, respectively. Further, a stochastic version of the model is developed to investigate the interplay between binding strength and noise. Spectral entropy is used to characterize noise effects on the systems of interacting processes when the binding strength between them is varied. The stochastic modeling results reveal that the spectral entropy values for strongly bound systems are similar to those for weakly bound or even decoupled systems. Thus, the analysis performed in this study allows us to conclude that the binding mechanism is noise-resilient.
Modeling and analysis of the macronutrient signaling network in budding yeast
Jalihal, Amogh P.; Kraikivski, Pavel; Murali, T. M.; Tyson, John J. (American Society for Cell Biology, 2021-11-01)
Adaptive modulation of the global cellular growth state of unicellular organisms is crucial for their survival in fluctuating nutrient environments. Because these organisms must be able to respond reliably to ever varying and unpredictable nutritional conditions, their nutrient signaling networks must have a certain inbuilt robustness. In eukaryotes, such as the budding yeast Saccharomyces cerevisiae, distinct nutrient signals are relayed by specific plasma membrane receptors to signal transduction pathways that are interconnected in complex information-processing networks, which have been well characterized. However, the complexity of the signaling network confounds the interpretation of the overall regulatory "logic"of the control system. Here, we propose a literature-curated molecular mechanism of the integrated nutrient signaling network in budding yeast, focusing on early temporal responses to carbon and nitrogen signaling. We build a computational model of this network to reconcile literature-curated quantitative experimental data with our proposed molecular mechanism. We evaluate the robustness of our estimates of the model's kinetic parameter values. We test the model by comparing predictions made in mutant strains with qualitative experimental observations made in the same strains. Finally, we use the model to predict nutrient-responsive transcription factor activities in a number of mutant strains undergoing complex nutrient shifts.
Neurocognitive and radiological changes after cranial radiation therapy in humans and rodents: a systematic review
Perez, Whitney D.; Perez-Torres, Carlos J. (Taylor & Francis, 2022-05-24)
Background: Radiation-induced brain injury is a common long-term side effect for brain cancer survivors, leading to a reduced quality of life. Although there is growing research pertaining to this topic, the relationship between cognitive and radiologically detected lesions of radiation-induced brain injury in humans remains unclear. Furthermore, clinically translatable similarities between rodent models and human findings are also undefined. The objective of this review is to then identify the current evidence of radiation-induced brain injury in humans and to compare these findings to current rodent models of radiation-induced brain injury. Methods: This review includes an examination of the current literature on cognitive and radiological characteristics of radiation-induced brain injury in humans and rodents. A thorough search was conducted on PubMed, Web of Science, and Scopus to identify studies that performed cognitive assessments and magnetic resonance imaging techniques on either humans or rodents after cranial radiation therapy. A qualitative synthesis of the data is herein reported. Results: A total of 153 studies pertaining to cognitively or radiologically detected radiation injury of the brain are included in this systematic review; 106 studies provided data on humans while 47 studies provided data on rodents. Cognitive deficits in humans manifest across multiple domains after brain irradiation. Radiological evidence in humans highlight various neuroimaging-detectable changes post-irradiation. It is unclear, however, whether these findings reflect ground truth or research interests. Additionally, rodent models do not comprehensively reproduce characteristics of cognitive and radiological injury currently identified in humans. Conclusion: This systematic review demonstrates that associations between and within cognitive and radiological radiation-induced brain injuries often rely on the type of assessment. Well-designed studies that evaluate the spectrum of potential injury are required for a precise understanding of not only the clinical significance of radiation-induced brain injury in humans, but also how to replicate injury development in pre-clinical models.
The origin of impedance rise in Ni-Rich positive electrodes for lithium-ion batteries
Lee, Rung-Chuan; Franklin, Joseph; Tian, Chixia; Nordlund, Dennis; Doeff, Marca M.; Kostecki, Robert (2021-06-30)
The cycling performance of nickel-rich lithium nickel cobalt manganese oxide (NMC) electrodes in Li-ion batteries (LIBs) partially depends on the control of the kinetics of degradation processes that result in impedance rise. The impedance contribution from surface film formation at the NMC/electrolyte interfaces is highly dependent on the initial chemical composition and the structure of the NMC surfaces. Through comparison of film quantity and electrochemical performance of composite electrodes made of pristine- and surface treated-NMC materials, we are able to demonstrate that a simple surface treatment suppressed the subsequent film formation and reduced impedance rise of the Li/NMC half-cells during cycling. Detailed modelling of factors affecting cell impedance provide further insights to index individual interphase resistance, highlighting the underlying positive effects of the proposed surface treatment, and demonstrating the importance of homogeneous, electronically conducting matrices throughout the composite electrode.
Structure-preserving interpolation of bilinear control systems
Benner, Peter; Gugercin, Serkan; Werner, Steffen W. R. (2021-06)
In this paper, we extend the structure-preserving interpolatory model reduction framework, originally developed for linear systems, to structured bilinear control systems. Specifically, we give explicit construction formulae for the model reduction bases to satisfy different types of interpolation conditions. First, we establish the analysis for transfer function interpolation for single-input single-output structured bilinear systems. Then, we extend these results to the case of multi-input multi-output structured bilinear systems by matrix interpolation. The effectiveness of our structure-preserving approach is illustrated by means of various numerical examples.
Synchronization between Attractors: Genomic Mechanism of Cell-Fate Change
Tsuchiya, Masa; Brazhnik, Paul; Bizzarri, Mariano; Giuliani, Alessandro (MDPI, 2023-07-18)
Herein, we provide a brief overview of complex systems theory approaches to investigate the genomic mechanism of cell-fate changes. Cell trajectories across the epigenetic landscape, whether in development, environmental responses, or disease progression, are controlled by extensively coordinated genome-wide gene expression changes. The elucidation of the mechanisms underlying these coherent expression changes is of fundamental importance in cell biology and for paving the road to new therapeutic approaches. In previous studies, we pointed at dynamic criticality as a plausible characteristic of genome-wide transition dynamics guiding cell fate. Whole-genome expression develops an engine-like organization (genome engine) in order to establish an autonomous dynamical system, capable of both homeostasis and transition behaviors. A critical set of genes behaves as a critical point (CP) that serves as the organizing center of cell-fate change. When the system is pushed away from homeostasis, the state change that occurs at the CP makes local perturbation spread over the genome, demonstrating self-organized critical (SOC) control of genome expression. Oscillating-Mode genes (which normally keep genome expression on pace with microenvironment fluctuations), when in the presence of an effective perturbative stimulus, drive the dynamics of synchronization, and thus guide the cell-fate transition.
Widespread exposure to SARS-CoV-2 in wildlife communities
Goldberg, Amanda R.; Langwig, Kate E.; Brown, Katherine L.; Marano, Jeffrey M.; Rai, Pallavi; King, Kelsie M.; Sharp, Amanda K.; Ceci, Alessandro; Kailing, Christopher D.; Kailing, Macy J.; Briggs, Russell; Urbano, Matthew G.; Roby, Clinton; Brown, Anne M.; Weger-Lucarelli, James; Finkielstein, Carla V.; Hoyt, Joseph R. (Springer, 2024-07-29)
Pervasive SARS-CoV-2 infections in humans have led to multiple transmission events to animals. While SARS-CoV-2 has a potential broad wildlife host range, most documented infections have been in captive animals and a single wildlife species, the white-tailed deer. The full extent of SARS-CoV-2 exposure among wildlife communities and the factors that influence wildlife transmission risk remain unknown. We sampled 23 species of wildlife for SARS-CoV-2 and examined the effects of urbanization and human use on seropositivity. Here, we document positive detections of SARS-CoV-2 RNA in six species, including the deer mouse, Virginia opossum, raccoon, groundhog, Eastern cottontail, and Eastern red bat between May 2022–September 2023 across Virginia and Washington, D.C., USA. In addition, we found that sites with high human activity had three times higher seroprevalence than low human-use areas. We obtained SARS-CoV-2 genomic sequences from nine individuals of six species which were assigned to seven Pango lineages of the Omicron variant. The close match to variants circulating in humans at the time suggests at least seven recent human-to-animal transmission events. Our data support that exposure to SARS-CoV-2 has been widespread in wildlife communities and suggests that areas with high human activity may serve as points of contact for cross-species transmission.

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