The spindle checkpoint proteins BUB1 and BUBR1: (SLiM)ming down to the basics.

Benzimidazole 1 (BUB1) and budding uninhibited by benzimidazole 1-related 1 (BUBR1) are multidomain paralogs with key roles in chromosome alignment during mitosis and the spindle assembly checkpoint (SAC), an evolutionarily conserved signaling pathway that monitors errors in chromosome segregation during cell division in eukaryotes. Although BUB1 and BUBR1 share a similar domain organization and short linear interaction motifs (SLiMs), they control distinct aspects of chromosome congression and the SAC. Here we discuss the roles of BUB1 and BUBR1 SLiMs in mitosis and complement this with additional insights gleamed from studying their evolution. We show that BUB1 and BUBR1 SLiMs form highly specific interactions that are carefully orchestrated in space and time and contend that they define BUB1 and BUBR1 as organizing hubs that drive SAC signaling and ensure genome stability.

Radiation necrosis after radiation therapy treatment of brain metastases: A computational approach.

Metastasis is the process through which cancer cells break away from a primary tumor, travel through the blood or lymph system, and form new tumors in distant tissues. One of the preferred sites for metastatic dissemination is the brain, affecting more than 20% of all cancer patients. This figure is increasing steadily due to improvements in treatments of primary tumors. Stereotactic radiosurgery (SRS) is one of the main treatment options for patients with a small or moderate number of brain metastases (BMs). A frequent adverse event of SRS is radiation necrosis (RN), an inflammatory condition caused by late normal tissue cell death. A major diagnostic problem is that RNs are difficult to distinguish from BM recurrences, due to their similarities on standard magnetic resonance images (MRIs). However, this distinction is key to choosing the best therapeutic approach since RNs resolve often without further interventions, while relapsing BMs may require open brain surgery. Recent research has shown that RNs have a faster growth dynamics than recurrent BMs, providing a way to differentiate the two entities, but no mechanistic explanation has been provided for those observations. In this study, computational frameworks were developed based on mathematical models of increasing complexity, providing mechanistic explanations for the differential growth dynamics of BMs relapse versus RN events and explaining the observed clinical phenomenology. Simulated tumor relapses were found to have growth exponents substantially smaller than the group in which there was inflammation due to damage induced by SRS to normal brain tissue adjacent to the BMs, thus leading to RN. ROC curves with the synthetic data had an optimal threshold that maximized the sensitivity and specificity values for a growth exponent ß* = 1.05, very close to that observed in patient datasets.

Overcoming chemotherapy resistance in low-grade gliomas: A computational approach.

Low-grade gliomas are primary brain tumors that arise from glial cells and are usually treated with temozolomide (TMZ) as a chemotherapeutic option. They are often incurable, but patients have a prolonged survival. One of the shortcomings of the treatment is that patients eventually develop drug resistance. Recent findings show that persisters, cells that enter a dormancy state to resist treatment, play an important role in the development of resistance to TMZ. In this study we constructed a mathematical model of low-grade glioma response to TMZ incorporating a persister population. The model was able to describe the volumetric longitudinal dynamics, observed in routine FLAIR 3D sequences, of low-grade glioma patients acquiring TMZ resistance. We used the model to explore different TMZ administration protocols, first on virtual clones of real patients and afterwards on virtual patients preserving the relationships between parameters of real patients. In silico clinical trials showed that resistance development was deferred by protocols in which individual doses are administered after rest periods, rather than the 28-days cycle standard protocol. This led to median survival gains in virtual patients of more than 15 months when using resting periods between two and three weeks and agreed with recent experimental observations in animal models. Additionally, we tested adaptive variations of these new protocols, what showed a potential reduction in toxicity, but no survival gain. Our computational results highlight the need of further clinical trials that could obtain better results from treatment with TMZ in low grade gliomas.

The shape of cancer relapse: Topological data analysis predicts recurrence in paediatric acute lymphoblastic leukaemia.

Although children and adolescents with acute lymphoblastic leukaemia (ALL) have high survival rates, approximately 15-20% of patients relapse. Risk of relapse is routinely estimated at diagnosis by biological factors, including flow cytometry data. This high-dimensional data is typically manually assessed by projecting it onto a subset of biomarkers. Cell density and "empty spaces" in 2D projections of the data, i.e. regions devoid of cells, are then used for qualitative assessment. Here, we use topological data analysis (TDA), which quantifies shapes, including empty spaces, in data, to analyse pre-treatment ALL datasets with known patient outcomes. We combine these fully unsupervised analyses with Machine Learning (ML) to identify significant shape characteristics and demonstrate that they accurately predict risk of relapse, particularly for patients previously classified as 'low risk'. We independently confirm the predictive power of CD10, CD20, CD38, and CD45 as biomarkers for ALL diagnosis. Based on our analyses, we propose three increasingly detailed prognostic pipelines for analysing flow cytometry data from ALL patients depending on technical and technological availability: 1. Visual inspection of specific biological features in biparametric projections of the data; 2. Computation of quantitative topological descriptors of such projections; 3. A combined analysis, using TDA and ML, in the four-parameter space defined by CD10, CD20, CD38 and CD45. Our analyses readily extend to other haematological malignancies.

A Mathematical Model for Fibrous Dysplasia: The Role of the Flow of Mutant Cells.

Fibrous dysplasia (FD) is a mosaic non-inheritable genetic disorder of the skeleton in which normal bone is replaced by structurally unsound fibro-osseous tissue. There is no curative treatment for FD, partly because its pathophysiology is not yet fully known. We present a simple mathematical model of the disease incorporating its basic known biology, to gain insight on the dynamics of the involved bone-cell populations, and shed light on its pathophysiology. We develop an analytical study of the model and study its basic properties. The existence and stability of steady states are studied, an analysis of sensitivity on the model parameters is done, and different numerical simulations provide findings in agreement with the analytical results. We discuss the model dynamics match with known facts on the disease, and how some open questions could be addressed using the model.

Efficacy of liver free and Chitosan against Eimeria tenella in chickens.

Eimeria spp. are the pathogen that causes coccidiosis, a significant disease that affects intensively reared livestock, especially poultry. Anticoccidial feed additives, chemicals, and ionophores have routinely been employed to reduce Eimeria infections in broiler production. Therefore, the shift to antibiotic-free and organic farming necessitates novel coccidiosis preventive strategies. The present study evaluated the effects of potential feed additives, liver free and chitosan, against Eimeria tenella infection in White Leghorn broiler female chickens. One hundred sixty-five 1-day-old White Leghorn broiler female chicks were divided into 11 groups (15 female chicks per group), including the positive control group (G1), the negative control group (G2), a chitosan-treated group (G3), a chitosan-treated-infected group (G4), the liver free-treated group (G5), the liver free-treated-infected group (G6), the liver free-and-chitosan-treated group (G7), the liver free-and-chitosan-infected group (G8), the therapeutic liver free-and-chitosan-treated-infected group (G9), the sulfaquinoxaline-treated group (G10), and the sulfaquinoxaline-treated-infected group (G11). Chitosan was fed to the chicks in G3 and G4 as a preventative measure at a dose of 250 mg/kg. The G5 and G6 groups received 1.5 mg/kg of Liverfree. The G7 and G8 groups received chitosan and Liverfree. The G10 and G11 groups were administered 2 g/L of sulfaquinoxaline. From the moment the chicks arrived at Foshan University (one-day-old chicks) until the completion of the experiment, all medications were given to them as a preventative measure. G8 did; however, receive chitosan and liver free as therapeutic supplements at 7 dpi. The current study showed that the combination of liver free and chitosan can achieve better prophylactic and therapeutic effects than either alone. In E. tenella challenged chickens, G8 and G9 chickens showed reduced oocyst shedding and lesion score, improved growth performance (body weight, body weight gain, feed intake, feed conversion ratio, and mortality rate), and cecal histology. The current study demonstrates that combining liver free and chitosan has superior preventive and therapeutic benefits than either alone, and they could also be used as alternative anticoccidial agents.

Evolutionary dynamics at the tumor edge reveal metabolic imaging biomarkers.

Human cancers are biologically and morphologically heterogeneous. A variety of clonal populations emerge within these neoplasms and their interaction leads to complex spatiotemporal dynamics during tumor growth. We studied the reshaping of metabolic activity in human cancers by means of continuous and discrete mathematical models and matched the results to positron emission tomography (PET) imaging data. Our models revealed that the location of increasingly active proliferative cellular spots progressively drifted from the center of the tumor to the periphery, as a result of the competition between gradually more aggressive phenotypes. This computational finding led to the development of a metric, normalized distance from 18F-fluorodeoxyglucose (18F-FDG) hotspot to centroid (NHOC), based on the separation from the location of the activity (proliferation) hotspot to the tumor centroid. The NHOC metric can be computed for patients using 18F-FDG PET-computed tomography (PET/CT) images where the voxel of maximum uptake (standardized uptake value [SUV]max) is taken as the activity hotspot. Two datasets of 18F-FDG PET/CT images were collected, one from 61 breast cancer patients and another from 161 non-small-cell lung cancer patients. In both cohorts, survival analyses were carried out for the NHOC and for other classical PET/CT-based biomarkers, finding that the former had a high prognostic value, outperforming the latter. In summary, our work offers additional insights into the evolutionary mechanisms behind tumor progression, provides a different PET/CT-based biomarker, and reveals that an activity hotspot closer to the tumor periphery is associated to a worst patient outcome.

Understanding the role of B cells in CAR T-cell therapy in leukemia through a mathematical model.

Chimeric antigen receptor T (CAR T) cell therapy has been proven to be successful against a variety of leukemias and lymphomas. This paper undertakes an analytical and numerical study of a mathematical model describing the competition of CAR T, leukemia, tumor, and B cells. Considering its significance in sustaining anti-CD19 CAR T-cell stimulation, a B-cell source term is integrated into the model. Through stability and bifurcation analyses, the potential for tumor eradication, contingent on the continuous influx of B cells, has been revealed, showing a transcritical bifurcation at a critical B-cell input. Additionally, an almost heteroclinic cycle between equilibrium points is identified, providing a theoretical basis for understanding disease relapse. Analyzing the oscillatory behavior of the system, the time-dependent dynamics of CAR T cells and leukemic cells can be approximated, shedding light on the impact of initial tumor burden on therapeutic outcomes. In conclusion, the study provides insights into CAR T-cell therapy dynamics for acute lymphoblastic leukemias, offering a theoretical foundation for clinical observations and suggesting avenues for future immunotherapy modeling research.

On the Regulation of Mitosis by the Kinetochore, a Macromolecular Complex and Organising Hub of Eukaryotic Organisms.

The kinetochore is the multiprotein complex of eukaryotic organisms that is assembled on mitotic or meiotic centromeres to connect centromeric DNA with microtubules. Its function involves the coordinated action of more than 100 different proteins. The kinetochore acts as an organiser hub that establishes physical connections with microtubules and centromere-associated proteins and recruits central protein components of the spindle assembly checkpoint (SAC), an evolutionarily conserved surveillance mechanism of eukaryotic organisms that detects unattached kinetochores and destabilises incorrect kinetochore-microtubule attachments. The molecular communication between the kinetochore and the SAC is highly dynamic and tightly regulated to ensure that cells can progress towards anaphase until each chromosome is properly bi-oriented on the mitotic spindle. This is achieved through an interplay of highly cooperative interactions and concerted phosphorylation/dephosphorylation events that are organised in time and space.This contribution discusses our current understanding of the function, structure and regulation of the kinetochore, in particular, how its communication with the SAC results in the amplification of specific signals to exquisitely control the eukaryotic cell cycle. This contribution also addresses recent advances in machine learning approaches, cell imaging and proteomics techniques that have enhanced our understanding of the molecular mechanisms that ensure the high fidelity and timely segregation of the genetic material every time a cell divides as well as the current challenges in the study of this fascinating molecular machine.

A mesoscopic simulator to uncover heterogeneity and evolutionary dynamics in tumors.

Increasingly complex in silico modeling approaches offer a way to simultaneously access cancerous processes at different spatio-temporal scales. High-level models, such as those based on partial differential equations, are computationally affordable and allow large tumor sizes and long temporal windows to be studied, but miss the discrete nature of many key underlying cellular processes. Individual-based approaches provide a much more detailed description of tumors, but have difficulties when trying to handle full-sized real cancers. Thus, there exists a trade-off between the integration of macroscopic and microscopic information, now widely available, and the ability to attain clinical tumor sizes. In this paper we put forward a stochastic mesoscopic simulation framework that incorporates key cellular processes during tumor progression while keeping computational costs to a minimum. Our framework captures a physical scale that allows both the incorporation of microscopic information, tracking the spatio-temporal emergence of tumor heterogeneity and the underlying evolutionary dynamics, and the reconstruction of clinically sized tumors from high-resolution medical imaging data, with the additional benefit of low computational cost. We illustrate the functionality of our modeling approach for the case of glioblastoma, a paradigm of tumor heterogeneity that remains extremely challenging in the clinical setting.

SUVmax to tumor perimeter distance: a robust radiomics prognostic biomarker in resectable non-small cell lung cancer patients.

OBJECTIVE: The purpose of this study was to evaluate the prognostic value of novel geometric variables obtained from pre-treatment [18F]FDG PET/CT with respect to classical ones in patients with non-small cell lung cancer (NSCLC). METHODS: Retrospective study including stage I-III NSCLC patients with baseline [18F]FDG PET/CT. Clinical, histopathologic, and metabolic parameters were obtained. After tumor segmentation, SUV and volume-based variables, global texture, sphericity, and two novel parameters, normalized SUVpeak to centroid distance (nSCD) and normalized SUVmax to perimeter distance (nSPD), were obtained. Early recurrence (ER) and short-term mortality (STM) were used as end points. Univariate logistic regression and multivariate logistic regression with respect to ER and STM were performed. RESULTS: A cohort of 173 patients was selected. ER was detected in 49/104 of patients with recurrent disease. Additionally, 100 patients died and 53 had STM. Age, pathologic lymphovascular invasion, lymph nodal infiltration, TNM stage, nSCD, and nSPD were associated with ER, although only age (aOR = 1.06, p = 0.002), pathologic lymphovascular invasion (aOR = 3.40, p = 0.022), and nSPD (aOR = 0.02, p = 0.018) were significant independent predictors of ER in multivariate analysis. Age, lymph nodal infiltration, TNM stage, nSCD, and nSPD were predictors of STM. Age (aOR = 1.05, p = 0.006), lymph nodal infiltration (aOR = 2.72, p = 0.005), and nSPD (aOR = 0.03, p = 0.022) were significantly associated with STM in multivariate analysis. Coefficient of variation (COV) and SUVmean/SUVmax ratio did not show significant predictive value with respect to ER or STM. CONCLUSION: The geometric variables, nSCD and nSPD, are robust biomarkers of the poorest outcome prediction of patients with NSCLC with respect to classical PET variables. KEY POINTS: • In NSCLC patients, it is crucial to find prognostic parameters since TNM system alone cannot explain the variation in lung cancer survival. • Age, lymphovascular invasion, lymph nodal infiltration, and metabolic geometrical parameters were useful as prognostic parameters. • The displacement grade of the highest point of metabolic activity towards the periphery assessed by geometric variables obtained from [18F]FDG PET/CT was a robust biomarker of the poorest outcome prediction of patients with NSCLC.

In vitro biomimetic models for glioblastoma-a promising tool for drug response studies.

The poor response of glioblastoma to current treatment protocols is a consequence of its intrinsic drug resistance. Resistance to chemotherapy is primarily associated with considerable cellular heterogeneity, and plasticity of glioblastoma cells, alterations in gene expression, presence of specific tumor microenvironment conditions and blood-brain barrier. In an attempt to successfully overcome chemoresistance and better understand the biological behavior of glioblastoma, numerous tri-dimensional (3D) biomimetic models were developed in the past decade. These novel advanced models are able to better recapitulate the spatial organization of glioblastoma in a real time, therefore providing more realistic and reliable evidence to the response of glioblastoma to therapy. Moreover, these models enable the fine-tuning of different tumor microenvironment conditions and facilitate studies on the effects of the tumor microenvironment on glioblastoma chemoresistance. This review outlines current knowledge on the essence of glioblastoma chemoresistance and describes the progress achieved by 3D biomimetic models. Moreover, comprehensive literature assessment regarding the influence of 3D culturing and microenvironment mimicking on glioblastoma gene expression and biological behavior is also provided. The contribution of the blood-brain barrier as well as the blood-tumor barrier to glioblastoma chemoresistance is also reviewed from the perspective of 3D biomimetic models. Finally, the role of mathematical models in predicting 3D glioblastoma behavior and drug response is elaborated. In the future, technological innovations along with mathematical simulations should create reliable 3D biomimetic systems for glioblastoma research that should facilitate the identification and possibly application in preclinical drug testing and precision medicine.

Computational Biology Dynamics of Mps1 Kinase Molecular Interactions with Isoflavones Reveals a Chemical Scaffold with Potential to Develop New Therapeutics for the Treatment of Cancer.

The protein kinase Mps1 (monopolar spindle 1) is an important regulator of the Spindle Assembly Checkpoint (SAC), the evolutionary conserved checkpoint system of higher organisms that monitors the proper bipolar attachment of all chromosomes to the mitotic spindle during cell division. Defects in the catalytic activity and the transcription regulation of Mps1 are associated with genome instability, aneuploidy, and cancer. Moreover, multiple Mps1 missense and frameshift mutations have been reported in a wide range of types of cancer of different tissue origin. Due to these features, Mps1 arises as one promising drug target for cancer therapy. In this contribution, we developed a computational biology approach to study the dynamics of human Mps1 kinase interaction with isoflavones, a class of natural flavonoids, and compared their predicted mode of binding with that observed in the crystal structure of Mps1 in complex with reversine, a small-sized inhibitor of Mps1 and Aurora B kinases. We concluded that isoflavones define a chemical scaffold that can be used to develop new Mps1 inhibitors for the treatment of cancer associated with Mps1 amplification and aberrant chromosome segregation. In a broader context, the present report illustrates how modern chemoinformatics approaches can accelerate drug development in oncology.

Cdc20: At the Crossroads between Chromosome Segregation and Mitotic Exit.

Cell-division cycle protein 20 homologue (Cdc20) has important functions in chromosome segregation and mitotic exit. Cdc20 is the target of the spindle assembly checkpoint (SAC) and a key cofactor of the anaphase-promoting complex or cyclosome (APC/C) E3 ubiquitin ligase, thus regulating APC/C ubiquitin activity on specific substrates for their subsequent degradation by the proteasome. Here we discuss the roles of Cdc20 in SAC signalling and mitotic exit, describe how the integration of traditional approaches with emerging technologies has revealed new details of Cdc20 functions, comment about the potential of Cdc20 as a therapeutic target for the treatment of human malignancies, and discuss recent advances and controversies in the mechanistic understanding of the control of chromosome segregation during cell division.

Dynamical properties of feedback signalling in B lymphopoiesis: A mathematical modelling approach.

Haematopoiesis is the process of generation of blood cells. Lymphopoiesis generates lymphocytes, the cells in charge of the adaptive immune response. Disruptions of this process are associated with diseases like leukaemia, which is especially incident in children. The characteristics of self-regulation of this process make them suitable for a mathematical study. In this paper we develop mathematical models of lymphopoiesis using currently available data. We do this by drawing inspiration from existing structured models of cell lineage development and integrating them with paediatric bone marrow data, with special focus on regulatory mechanisms. A formal analysis of the models is carried out, giving steady states and their stability conditions. We use this analysis to obtain biologically relevant regions of the parameter space and to understand the dynamical behaviour of B-cell renovation. Finally, we use numerical simulations to obtain further insight into the influence of proliferation and maturation rates on the reconstitution of the cells in the B line. We conclude that a model including feedback regulation of cell proliferation represents a biologically plausible depiction for B-cell reconstitution in bone marrow. Research into haematological disorders could benefit from a precise dynamical description of B lymphopoiesis.

Nonlinear science against the COVID-19 pandemic.

This special issue showcases recent uses of mathematical and nonlinear science methods in the study of different problems arising in the context of the COVID-19 pandemic. The sixteen original research papers included in this collection span a wide spectrum of studies including classical epidemiological models, new models accounting for COVID-19 specificities, non-pharmaceutical control measures, network models and other problems related to the pandemic.

A Mathematical Description of the Bone Marrow Dynamics during CAR T-Cell Therapy in B-Cell Childhood Acute Lymphoblastic Leukemia.

Chimeric Antigen Receptor (CAR) T-cell therapy has demonstrated high rates of response in recurrent B-cell Acute Lymphoblastic Leukemia in children and young adults. Despite this success, a fraction of patients' experience relapse after treatment. Relapse is often preceded by recovery of healthy B cells, which suggests loss or dysfunction of CAR T-cells in bone marrow. This site is harder to access, and thus is not monitored as frequently as peripheral blood. Understanding the interplay between B cells, leukemic cells, and CAR T-cells in bone marrow is paramount in ascertaining the causes of lack of response. In this paper, we put forward a mathematical model representing the interaction between constantly renewing B cells, CAR T-cells, and leukemic cells in the bone marrow. Our model accounts for the maturation dynamics of B cells and incorporates effector and memory CAR T-cells. The model provides a plausible description of the dynamics of the various cellular compartments in bone marrow after CAR T infusion. After exploration of the parameter space, we found that the dynamics of CAR T product and disease were independent of the dose injected, initial B-cell load, and leukemia burden. We also show theoretically the importance of CAR T product attributes in determining therapy outcome, and have studied a variety of possible response scenarios, including second dosage schemes. We conclude by setting out ideas for the refinement of the model.

Novel Biochemical Properties and Physiological Role of the Flavin Mononucleotide Oxidoreductase YhdA from Bacillus subtilis.

Cr(VI) is mutagenic and teratogenic and considered an environmental pollutant of increasing concern. The use of microbial enzymes that convert this ion into its less toxic reduced insoluble form, Cr(III), represents a valuable bioremediation strategy. In this study, we examined the Bacillus subtilis YhdA enzyme, which belongs to the family of NADPH-dependent flavin mononucleotide oxide reductases and possesses azo-reductase activity as a factor that upon overexpression confers protection on B. subtilis from the cytotoxic effects promoted by Cr(VI) and counteracts the mutagenic effects of the reactive oxygen species (ROS)-promoted lesion 8-OxoG. Further, our in vitro assays unveiled catalytic and biochemical properties of biotechnological relevance in YhdA; a pure recombinant His10-YhdA protein efficiently catalyzed the reduction of Cr(VI) employing NADPH as a cofactor. The activity of the pure oxidoreductase YhdA was optimal at 30°C and at pH 7.5 and displayed Km and Vmax values of 7.26 mM and 26.8 µmol·min-1·mg-1 for Cr(VI), respectively. Therefore, YhdA can be used for efficient bioremediation of Cr(VI) and counteracts the cytotoxic and genotoxic effects of oxygen radicals induced by intracellular factors and those generated during reduction of hexavalent chromium.IMPORTANCE Here, we report that the bacterial flavin mononucleotide/NADPH-dependent oxidoreductase YhdA, widely distributed among Gram-positive bacilli, conferred protection to cells from the cytotoxic effects of Cr(VI) and prevented the hypermutagenesis exhibited by a MutT/MutM/MutY-deficient strain. Additionally, a purified recombinant His10-YhdA protein displayed a strong NADPH-dependent chromate reductase activity. Therefore, we postulate that in bacterial cells, YhdA counteracts the cytotoxic and genotoxic effects of intracellular and extracellular inducers of oxygen radicals, including those caused by hexavalent chromium.

Computational design of improved standardized chemotherapy protocols for grade II oligodendrogliomas.

Here we put forward a mathematical model describing the response of low-grade (WHO grade II) oligodendrogliomas (LGO) to temozolomide (TMZ). The model describes the longitudinal volumetric dynamics of tumor response to TMZ of a cohort of 11 LGO patients treated with TMZ. After finding patient-specific parameters, different therapeutic strategies were tried computationally on the 'in-silico twins' of those patients. Chemotherapy schedules with larger-than-standard rest periods between consecutive cycles had either the same or better long-term efficacy than the standard 28-day cycles. The results were confirmed in a large trial of 2000 virtual patients. These long-cycle schemes would also have reduced toxicity and defer the appearance of resistances. On the basis of those results, a combination scheme consisting of five induction TMZ cycles given monthly plus 12 maintenance cycles given every three months was found to provide substantial survival benefits for the in-silico twins of the 11 LGO patients (median 5.69 years, range: 0.67 to 68.45 years) and in a large virtual trial including 2000 patients. We used 220 sets of experiments in-silico to show that a clinical trial incorporating 100 patients per arm (standard intensive treatment versus 5 + 12 scheme) could demonstrate the superiority of the novel scheme after a follow-up period of 10 years. Thus, the proposed treatment plan could be the basis for a standardized TMZ treatment for LGO patients with survival benefits.

The Anaphase Promoting Complex/Cyclosome (APC/C): A Versatile E3 Ubiquitin Ligase.

In the present chapter we discuss the essential roles of the human E3 ubiquitin ligase Anaphase Promoting Complex/Cyclosome (APC/C) in mitosis as well as the emerging evidence of important APC/C roles in cellular processes beyond cell division control such as regulation of genomic integrity and cell differentiation of the nervous system. We consider the potential incipient role of APC/C dysregulation in the pathophysiology of the neurological disorder Alzheimer's disease (AD). We also discuss how certain Deoxyribonucleic Acid (DNA) and Ribonucleic Acid (RNA) viruses take control of the host's cell division regulatory system through harnessing APC/C ubiquitin ligase activity and hypothesise the plausible molecular mechanisms underpinning virus manipulation of the APC/C. We also examine how defects in the function of this multisubunit protein assembly drive abnormal cell proliferation and lastly argue the potential of APC/C as a promising therapeutic target for the development of innovative therapies for the treatment of chronic malignancies such as cancer.