Systems Biology of Blood Coagulation and Platelet Activation

Blood clotting is a highly conserved physiological response that prevents excessive blood loss following vessel injury. It involves a sequence of plasma reactions leading to the formation of thromin (the coagulation cascade) as well as tightly controlled intracellular reactions mediating platelet activation. These two events are inextricably coupled, with the active platelet surface serving as a cofactor for coagulation factor assembly and thrombin serving as a potent platelet agonist. Using the technologies of automated liquid handling, high throughput experimental systems were developed that allowed individual exploration of these two components of the thrombotic response under diverse initial conditions. Based on this high dimensional experimental exploration, a “bottom-up” mechanism based Ordinary Differential Equation (ODE) description of thrombin generation kinetics and a “top-down” data driven Neural Network model of platelet activation were developed. In the first study, “contact activation” (and not “blood-borne TF” alone) despite the best available inhibitor to prevent it, was found build up enough autocatalytic strength to trigger coagulation in the absence of exogenous tissue factor, particularly upon activated platelets. Further, the “Platelet-Plasma model” successfully predicted the stability of blood under multiple perturbations with active enzymes at various physiologically realizable conditions. In the second study, “Pairwise Agonist Scanning” (PAS), a strategy that trains a Neural Network model based on measurements of cellular responses to individual and all pairwise combinations of input signals is described. PAS was used to predict calcium signaling responses of human platelets in EDTA-treated plasma to six different agonists (ADP, Convulxin, U46619, SFLLRN, AYPGKF and PGE2). The model predicted responses to sequentially added agonists, to ternary combinations of agonists and to 45 different combinations of four to six agonists (R=0.88). Furthermore, PAS was used to distinguish between the phenotypic responses of platelets from healthy human donors. Taken together, these two studies lay the groundwork for integration of coagulation reaction kinetics and donor specific descriptions of platelet function with models of convection and diffusion to simulate thrombosis under flow

Systems Biology of Coagulation Initiation: Kinetics of Thrombin Generation in Resting and Activated Human Blood

Blood function defines bleeding and clotting risks and dictates approaches for clinical intervention. Independent of adding exogenous tissue factor (TF), human blood treated in vitro with corn trypsin inhibitor (CTI, to block Factor XIIa) will generate thrombin after an initiation time (Ti) of 1 to 2 hours (depending on donor), while activation of platelets with the GPVI-activator convulxin reduces Ti to ∼20 minutes. Since current kinetic models fail to generate thrombin in the absence of added TF, we implemented a Platelet-Plasma ODE model accounting for: the Hockin-Mann protease reaction network, thrombin-dependent display of platelet phosphatidylserine, VIIa function on activated platelets, XIIa and XIa generation and function, competitive thrombin substrates (fluorogenic detector and fibrinogen), and thrombin consumption during fibrin polymerization. The kinetic model consisting of 76 ordinary differential equations (76 species, 57 reactions, 105 kinetic parameters) predicted the clotting of resting and convulxin-activated human blood as well as predicted Ti of human blood under 50 different initial conditions that titrated increasing levels of TF, Xa, Va, XIa, IXa, and VIIa. Experiments with combined anti-XI and anti-XII antibodies prevented thrombin production, demonstrating that a leak of XIIa past saturating amounts of CTI (and not “blood-borne TF” alone) was responsible for in vitro initiation without added TF. Clotting was not blocked by antibodies used individually against TF, VII/VIIa, P-selectin, GPIb, protein disulfide isomerase, cathepsin G, nor blocked by the ribosome inhibitor puromycin, the Clk1 kinase inhibitor Tg003, or inhibited VIIa (VIIai). This is the first model to predict the observed behavior of CTI-treated human blood, either resting or stimulated with platelet activators. CTI-treated human blood will clot in vitro due to the combined activity of XIIa and XIa, a process enhanced by platelet activators and which proceeds in the absence of any evidence for kinetically significant blood borne tissue factor

Etiology of Late-Onset Alzheimer’s Disease, Biomarker Efficacy, and the Role of Machine Learning in Stage Diagnosis

Late-onset Alzheimer’s disease (LOAD) is a subtype of dementia that manifests after the age of 65. It is characterized by progressive impairments in cognitive functions, behavioral changes, and learning difficulties. Given the progressive nature of the disease, early diagnosis is crucial. Early-onset Alzheimer’s disease (EOAD) is solely attributable to genetic factors, whereas LOAD has multiple contributing factors. A complex pathway mechanism involving multiple factors contributes to LOAD progression. Employing a systems biology approach, our analysis encompassed the genetic, epigenetic, metabolic, and environmental factors that modulate the molecular networks and pathways. These factors affect the brain’s structural integrity, functional capacity, and connectivity, ultimately leading to the manifestation of the disease. This study has aggregated diverse biomarkers associated with factors capable of altering the molecular networks and pathways that influence brain structure, functionality, and connectivity. These biomarkers serve as potential early indicators for AD diagnosis and are designated as early biomarkers. The other biomarker datasets associated with the brain structure, functionality, connectivity, and related parameters of an individual are broadly categorized as clinical-stage biomarkers. This study has compiled research papers on Alzheimer’s disease (AD) diagnosis utilizing machine learning (ML) methodologies from both categories of biomarker data, including the applications of ML techniques for AD diagnosis. The broad objectives of our study are research gap identification, assessment of biomarker efficacy, and the most effective or prevalent ML technology used in AD diagnosis. This paper examines the predominant use of deep learning (DL) and convolutional neural networks (CNNs) in Alzheimer’s disease (AD) diagnosis utilizing various types of biomarker data. Furthermore, this study has addressed the potential scope of using generative AI and the Synthetic Minority Oversampling Technique (SMOTE) for data augmentation

Machine Learning-Based Alzheimer’s Disease Stage Diagnosis Utilizing Blood Gene Expression and Clinical Data: A Comparative Investigation

Background/Objectives: This study presents a comparative analysis of the multistage diagnosis of Alzheimer’s disease (AD), including mild cognitive impairment (MCI), utilizing two distinct types of biomarkers: blood gene expression and clinical biomarker samples. Both of these samples, obtained from participants in the Alzheimer’s Disease Neuroimaging Initiative (ADNI), were independently analyzed utilizing machine learning (ML)-based multiclassifiers. This study applied novel machine learning-based data augmentation techniques to gene expression profile data that are high-dimensional, low-sample-size (HDLSS) and inherently highly imbalanced. The investigation obtained the highest multiclassification performance to date in the multistage diagnosis of Alzheimer’s disease utilizing the blood gene expression profiles of Alzheimer’s Disease Neuroimaging Initiative (ADNI) participants. Based on the performance results obtained, and other factors such as early prediction capabilities, this study compares the efficacies of the two types of biomarkers for multistage diagnosis. This study presents the sole investigation in which multiclassification-based AD stage diagnosis was conducted utilizing blood gene expression data. We obtained the best multiclassification result in both modalities of the ADNI data in terms of F1-score and were able to identify new genetic biomarkers. Methods: The combination of the XGBoost and SFBS (Sequential Floating Backward Selection) methods was used to select the features. We were able to select the 95 most effective gene probe sets out of 49,386. For the clinical study data, eight of the most effective biomarkers were selected using SFBS. A deep learning (DL) classifier was used to identify the stages—cognitive normal (CN), mild cognitive impairment (MCI), and Alzheimer’s disease (AD)/dementia. DL, support vector machine (SVM), gradient boosting (GB), and random forest (RF) classifiers were used for the AD stage detection from gene expression profile data. Because of the high data imbalance in genomic data, borderline oversampling/data augmentation was applied in the model training and original samples for validation. Results: Utilizing clinical data, the highest ROC AUC scores attained were 0.989, 0.927, and 0.907 for the identification of the CN, MCI, and dementia stages, respectively. The highest F1 scores achieved were 0.971, 0.939, and 0.886. Employing gene expression data, we obtained ROC AUC scores of 0.763, 0.761, and 0.706 for the CN, MCI, and dementia stages, respectively, and F1 scores of 0.71, 0.77, and 0.53 for CN, MCI, and dementia, respectively. Conclusions: This represents the best outcome to date for AD stage diagnosis from ADNI blood gene expression profile data utilizing multiclassification techniques. The results indicated that our multiclassification model effectively manages the imbalanced data of a high-dimension, low-sample-size (HDLSS) nature to identify samples of the minority class. MAPK14, PLG, FZD2, FXYD6, and TEP1 are among the novel genes identified as being associated with AD risk

‘Machine Learning’ multiclassification for stage diagnosis of Alzheimer’s disease utilizing augmented blood gene expression and feature fusion

Abstract Objective The present study explores the classification of Alzheimer’s disease (AD) stages, encompassing cognitive normalcy, Mild Cognitive Impairment (MCI), and AD/Dementia, through the application of Machine Learning (ML) multiclassification algorithms. This investigation utilizes blood gene expression datasets obtained from participants in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the National Center for Biotechnology Information (NCBI). Three blood gene expression datasets of high dimensionality and low sample size (HDLSS) have been utilized in this study, with one dataset exhibiting significant class imbalance. This study integrates clinical data from electronic health records (EHRs) with gene expression datasets, which has been found to significantly enhance the accuracy of stage diagnosis. Methods A combination of XGBoost and SFBS (“sequential floating backward selection”) methods is utilized to select features. Our research identified a subset of 95 gene transcripts exhibiting optimal efficacy from an extensive collection of over 49,000 transcripts within the ADNI gene expression dataset. Furthermore, our analysis of two integrated NCBI datasets revealed 125 gene transcripts demonstrating superior effectiveness among more than 30,000 potential candidates. These findings resulted in the development of two distinct model categories: one derived from the ADNI dataset and the other from the integrated NCBI dataset. DL classifier is used for developing models of both categories while GB (Gradient Boost), SVM (Support Vector Machine) classifier based models are built to identify AD stages from NCBI participants. Because of high data imbalance in genomic data, border line oversampling is explored for model training and original data for validation. We have conducted a multimodal analysis and stage classification by integrating the ADNI gene expression and clinical datasets using ‘Feature-Level Fusion’. Result In the case of ADNI study participants, we obtained best multi-classification performance with ‘ROC AUC’ scores of 0. 76, 0.76, 0.71 for the CN, MCI, and Dementia stages, respectively. We achieved F1 scores of 0.71, 0.77, 0.53 for these same categories. For the NCBI-based model, the best AUC scores of 0.82, 0.74, and 0.79 (for CN, MCI, and AD, respectively) and F1 scores of 0.75, 0.60, and 0.77 were attained when evaluated using GSE3060 test data. When assessed with GSE3061 test data, the model achieved optimal AUC scores of 0.81, 0.75, and 0.78, and F1 scores of 0.74, 0.67, and 0.73.This research identified MAPK14, MID1, TEP1, PLG, DRAXIN, USP47 as genes associated with AD. In the context of ADNI data, the integration of clinical data with gene expression data led to an enhancement of the best F1 scores to 0.85, 0.86, and 0.83 for CN, MCI, and AD, respectively. Additionally, the ROC AUC scores were improved to 0.90, 0.85, and 0.89. Conclusion Using machine learning multiclassification techniques on blood gene expression profile data from ADNI and NCBI, we achieved the most promising results to date for diagnosing multiple stages of Alzheimer’s disease. This proves that the efficacy of our feature selection techniques that could find essential genes associated with AD. Highly accurate of diagnosis of stages that include MCI from genetic data can potentially provide timely alert for individuals susceptible/predisposed to AD

Systems Biology of Platelet-Vessel Wall Interactions.

Blood Systems Biology seeks to quantify outside-in signaling as platelets respond to numerous external stimuli, typically under flow conditions. Platelets can activate via GPVI collagen receptor and numerous G-protein coupled receptors (GPCRs) responsive to ADP, thromboxane, thrombin, and prostacyclin. A bottom-up ODE approach allowed prediction of platelet calcium and phosphoinositides following P2Y1 activation with ADP, either for a population average or single cell stochastic behavior. The homeostasis assumption (i.e. a resting platelet stays resting until activated) was particularly useful in finding global steady states for these large metabolic networks. Alternatively, a top-down approach involving Pairwise Agonist Scanning allowed large data sets of measured calcium mobilization to predict an individual’s platelet responses. The data was used to train Neural Network (NN) models of signaling to predict patient-specific responses to combinatorial stimulation. A kinetic description of platelet signaling then allows prediction of inside-out activation of platelets as they experience the complex biochemical milieu at the site of thrombosis. Multiscale lattice kinetic Monte Carlo (LKMC) utilizes these detailed descriptions of platelet signaling under flow conditions where released soluble species are solved by finite element method and the flow field around the growing thrombus is updated using computational fluid dynamics or lattice Boltzmann method. Since hemodynamic effects are included in a multiscale approach, thrombosis can then be predicted under arterial and venous thrombotic conditions for various anatomical geometries. Such systems biology approaches accommodate the effect of anti-platelet pharmacological intervention where COX1 pathways or ADP signaling are modulated in a patient-specific manner