Session M52: Physics of disease states and normal physiology

Proliferative advantage of specific aneuploid cells drives evolution of tumor karyotypes

Most tumors have abnormal karyotypes, which arise from mistakes during mitotic division of healthy euploid cells and evolve through numerous complex mechanisms. In a recent mouse model with high levels of chromosome missegregation, chromosome gains dominate over losses both in pretumor and tumor tissues, whereas tumors are characterized by gains of chromosomes 14 and 15. However, the mechanisms driving clonal selection leading to tumor karyotype evolution remain unclear. Here we show, by introducing a mathematical model based on a concept of a macro-karyotype, that tumor karyotypes can be explained by proliferation-driven evolution of aneuploid cells. In pretumor cells, increased apoptosis and slower proliferation of cells with monosomies lead to predominant chromosome gains over losses. Tumor karyotypes with gain of one chromosome can be explained by karyotype-dependent proliferation, while for those with two chromosomes an interplay with karyotype-dependent apoptosis is an additional possible pathway. Thus, evolution of tumor-specific karyotypes requires proliferative advantage of specific aneuploid karyotypes.

    

Impact of the cancer endocycle on cell survival after therapy

We characterized nuclear morphology and function as cancer cells underwent chemotherapeutic treatment and recovery to identify the unique characteristics that are associated with treatment resistance and successful survival. We found that one way cancer cells can survive systemic therapy is to enter the polyaneuploid cancer cell (PACC) state, a recently-described mechanism of therapy resistance. Cancer cells in this state are physically enlarged, undergo whole-genome doubling resulting in polyaneuploid genomes, and are associated with worse prognosis in cancer patients.  The PACC state is accessed when a cancer cell experiences external stress, such as genotoxic chemotherapy; after a period of recovery, cells exit the PACC state and resume proliferation to repopulate the tumor cell pool. We discovered that cells that survive after therapy are predominantly mononucleated and have increasing nuclear size, enabled by continuous endocycling.  We further found that cells in the PACC state are likely to employ more efficient DNA damage repair. Lastly, while many cancer cells treated with a genotoxic agent succumb to apoptosis shortly after treatment, our data suggest that surviving cancer cells must strike a balance of autophagy to restore cellular homeostasis while avoiding autophagic cell death. Overall, our findings demonstrate the fate of cancer cells following chemotherapy treatment and define key characteristics of the resistant PACC state that is accessed via endocycling. This work is essential for understanding and, ultimately, targeting, cancer resistance and recurrence.  

    

Modeling and Computer simulation of a possible prognostic analysis of the biomechanical dynamics of cardiovascular diseases using electrocardiogram

Modeling is the attempt to mimic reality in a controlled scenario and therefore has become a powerful multidisciplinary tool of computer approaches in various fields. In studying diseases, one modeling strategy is to assume that the biological behaviour of the presence of the disease in the infected person (IP) can be physically described as the superposition of the characteristic wave of the disease on the characteristic wave of the healthy person (HP); characteristic wave here means a wave that has all the wave characteristics of the source wave. For cardiovascular diseases (CVA), the resultant superposed wave which encompasses their biomechanical dynamics are depicted in the electrocardiograms (ECG) of the IPs. In this study, we demonstrated that by simulating the decomposition of these ECG into the characteristic waves of the HP and CVA, it is possible to determine the prognostic analysis of the latter from their biomechanical dynamics. The implication of this computer approach is then discussed.   

A possible prognostic analysis of the biomechanical dynamics of SARS-CoV-2 using electrocardiogram

In studying SARS-CoV-2, we use the modeling strategy of assuming that the biological behaviour of the presence of the virus in the infected person (IP) can be physically described as the superposition of its characteristic wave on the characteristic wave of the healthy person (HP). We then propose that the Electrocardiogram (ECG) can be used as diagnostic tool since it provides the most clinically helpful information from the electrical signals of the characteristic wave of the heart in a cardiac cycle. For this study, 120 ECG strips of IPs with SARS-CoV-2 are examined and the following ECG parameters such as the QT interval, QRS duration, PR interval, heart rate, rhythm and the amplitude of P, Q, R and T waves were measured on lead II which is the normal direction of the signal flow as depolarized from the Sinu atria node. Attenuation of the vibration and varying abnormalities are observed in these parameters when compared with the standard ECG parameters of HP and the red line benchmark for the IPs delineated. It is observed from further analysis of the data that the myocardial injury is the signature of the effect of the SARS-CoV-2. Therefore, a biomechanical dynamics of the SARS-CoV-2 on the IP is that the virus decreases the blood flow in the cardiac cycle of the heart. This opens the possibility to strategize the possible cure to this deadly virus.   

Harnessing the Power of Magnetic Resonance Physics to Study Cerebral Aging and Dementias

Water molecules within biological tissues undergo interactions with their environment through several processes including nuclear relaxation, magnetization transfer, chemical exchange, and diffusion. These processes are sensitive to various underlying local tissue properties such as density, microstructure, temperature, acidity, composition, and geometry. Through use of virtually unlimited combinations of static and spatially varying magnetic fields, or radiofrequency pulses formalisms, magnetic resonance imaging (MRI) provides unique sensitivity and specificity to probe these mechanisms in all biological tissues. Recent advances in acquisition strategies, hardware designs, computational analyses, signal modeling, and sequence pulse paradigms have positioned MRI as a powerful emerging noninvasive modality to studying biological tissue, from muscle to central nervous system, from mice to human, and from in-vitro to in-vivo, exhibiting extraordinary sensitivity and specificity in differentiating normal from abnormal cell-level processes. These techniques are based, but not limited to, on multicomponent relaxometry or diffusion, magnetization transfer, high-dimensional imaging, susceptibility imaging, cerebral functioning, etc. Using this technology to examine changes in tissue microstructure and function in aging or pathology has the potential to provide a window into the underlying age-related diseases’ biology and mechanisms, and to nominate new MR biomarkers for longitudinal assessment and targets for intervention. The purpose of my talk is to provide an overview about some of these advanced MRI techniques, and their application to provide new insights into white matter maturation and degenerations in normative aging and dementias.

    

A comparative study on the effects of ammonia on the growth and metabolism of two glioblastoma cell lines probed by NMR spectroscopy

One of the most lethal and aggressive types of cancer is glioblastoma. Despite the rigorous research regarding this malady, there is still no cure, and survival still does not last long-term. Hence, mimicking glioblastoma in vitro as accurately as possible through cell culture is an important aspect of research to generate reliable results. Ammonia concentration on cell culture media is shown to decrease viability, thwart glucose consumption, heighten reactive oxygen species production, change the morphology of cells, etc. In this study, we investigated how different concentrations of ammonium chloride affect two cell lines of glioblastoma: SfxL and U87. We administered 0mM, 5 mM, and 10 mM of ammonium chloride to both cell lines for 48 hrs. Preliminary trials show highly different results between SfxL and U87. After 24 hrs, SFxL cells metabolized glucose into lactate however, after 48 hrs, the prominent metabolic byproduct was not lactate but acetate, and as the ammonium concentration increased, the lactate peak decreased. Furthermore, alanine peaks were also observed, and they showed a positive relationship with ammonium concentration. On the contrary, U87 after 48 hrs only have a significant increase in lactate production. These preliminary NMR results will be discussed along with the other supporting data. This study is supported by the Welch Foundation grant AT-2111-20220331, US Department of Defense CDMRP grants W81XWH-21-1-0176, W81XWH-22-1-0105, W81XWH-19-1-0741, and W81XWH-22-1-0003.   

A Hand-Held, Fast, Small Volume Blood Diagnostics Device for MethylGlyoxal, a Biomarker for Alzheimer's Disease's Mild Cognitive Impairment Stage

Around 44 million people worldwide suffer from Alzheimer's Disease (AD). AD is a progressive, neurodegenerative disease that destroys critical brain functions. AD is the 6th leading cause of US deaths and one of the most expensive conditions to care for, at about $0.3 B/year. Diagnosing AD early and monitoring its progression are key for potential treatments to be effective. Early detection of the Mild Cognitive Impairment (MCI) stage, the clinical stage preceding early AD, is critical for drugs and cognitive therapy to be impactful. AD diagnostics are presently limited. Most commonly, self-reporting or reporting by those close to the patient is often inefficient and delays treatment past the critical phase of MCI. AD can be confirmed by spinal tap and advanced brain imaging, which are invasive and expensive, respectively. In 2019, methylglyoxal (MGO) levels in blood serum were found to correlate significantly with MCI preceding AD, making MGO a potential biomarker for the diagnosis of MCI when it is a precursor to AD. The present work prototypes a novel, hand-held, fast, inexpensive, and accurate Small Volume Blood Diagnostics (SVBD) device-akin to glucometers- called Alz-BioSs™, to measure MGO levels when MCI is present and also potentially help monitor the effect of treatments on a regular basis. Alz-BioSs™ aims to help diagnose and then monitor MCI and AD during the MCI precursor phase to provide patients with a greater chance for early and effective treatments. Alz-BioSs™ measures MGO in blood plasma by collecting a 0.5 mL blood drop into a single-use microfluidic chip for rapid, passive separation of plasma from blood. The resulting 0.2-0.3 mL of plasma then reacts with an adjacent chip coated with bio-reagent o-phenylenediamine (OPD) and gold nanoparticles (AuNP). The resulting colorimetric reaction is detected via a miniature optical cage system (OCS) using LEDs and photodetectors to quantify MGO levels via photo-absorption.   

Detection and prediction of chemotherapy-related cognitive impairment using quantitative neuroimaging and innovative AI tools

Chemotherapy-related cognitive impairment (CRCI), coined "chemo-brain," is experienced by up to 75% of cancer patients and can degrade quality-of-life for years post-treatment. Yet it lacks objective diagnostic criteria or predictive markers. Information-rich quantitative neuroimaging may reveal relevant biomarkers which AI approaches can efficiently utilize for CRCI detection and prediction. Using 2,011 T1-weighted MRI brain scans from 1,349 patients, we explored several binary classifier models for two purposes. (1) To distinguish which patients have CRCI at the time of the scan using images obtained during or after chemotherapy (N=1,442). (2) To distinguish which patients will develop CRCI using pre-treatment scans (N=569) and comorbidity data. We improved upon a convolutional neural network (CNN) which previously showed strong performance in the classification of Alzheimer's disease, adding uncertainty estimation and out-of-distribution techniques. We explored manual feature extraction and connectivity analyses to add interpretability to the black box CNN. We evaluated performance using ROC and precision-recall analyses. This work demonstrates successful preliminary development of new, reliable, patient-specific informatics to improve long-term care of chemotherapy patients.   

P301L Tau Mediates Increased Glutamate Release in Hippocampal Neurons Via Increased VGLUT1 in Tauopathy Mouse Model

The molecular pathways that contribute to the onset of symptoms in tauopathy models, including Alzheimer’s Disease (AD), are difficult to distinguish because multiple changes can happen simultaneously at different stages of disease progression. Understanding the mechanistic pathways of early synaptic alterations is essential in order to understand how different processes can have confounding affects and still lead to neurodegeneration in AD and other neurodegeneratic diseases. Here we focus on an early onset rTg(Tau_P301L)4510 tauopathy mouse model that exhibits hyperexcitability in hippocampal neurons of adult mice that is correlated with presynaptic changes and increased extracellular glutamate levels. However, it is not clear if increased extracellular glutamate is caused by presynaptic changes alone, or if presynaptic changes are a contributing factor among other factors. We show that tau_P301L positive neurons exhibit a 40% increase in VGLUT1 per vesicle compared to tau_P301L negative littermates. Further, we use the extracellular glutamate reporter iGluSnFR to show that increased VGLUT1 per vesicle directly translates into a 40% increase in extracellular glutamate. Together, these results show that increased extracellular glutamate levels observed in tau_P301L mice are not caused by increased vesicle exocytosis probability but rather are directly related to increased VGLUT1 transporters per synaptic vesicle.

    

Multinomial logistic regression algorithm for the classification of patients with common parkinsonian syndromes

Parkinsonian syndromes are common neurodegenerative brain disorders. Due to overlapping clinical presentation, their early differentiatiation is challenging, but very important. We developed and tested a multinomial logistic regression algorithm (MLR) for the classification of 18F-fluorodeoxyglucose positron emission tomography images of healthy subjects (CN) and patients with Parkinson disease (PD), multiple system atrophy (MSA) or progressive supranuclear palsy (PSP).

The MLR is based on already established multivariate spatial covariance technique, known as scaled subprofile model with principal component analysis. In MLR we included three classes of patients instead of a single one and used MLR instead of standard logistic regression. We analysed 20 CN, 20 PD, 20 MSA and 20 PSP subjects in MLR and tested it using leave-one-out approach.

The great majority of the subjects were classified correctly. Identification performance was the best for MSA and PSP with AUC = 0.94, while it was slightly lower (AUC=0.91) for PD and the lowest (AUC=0.87) for CN.

We demonstrated the possibility of using MLR for differential diagnosis of parkinsonisms based on subjects’ metabolic brain images. Its good performance warrants a study on a larger cohort and subsequent adaption in clinical practice.   

Tracking of the metabolic effects of dichloroacetate in colorectal cancer cells using NMR

Colorectal cancer, also known as colon cancer is the fourth most common cause of cancer-related deaths in the United States and its incidence is growing. In this study, the effects of dichloroacetate on the metabolism in cultured colorectal cancer cells have been studied. Cancer cells utilize glucose at a higher rate under the glycolytic pathway for ATP production as compared to normal cells. In particular, this study used carbon-13 nuclear magnetic resonance (NMR) spectroscopy to track the glucose metabolism in the presence of dichloroacetate at different concentrations in cultured Colo-205 and LoVo colorectal cancer cell lines. The details of these results will be presented.