Pratip Rana

Strain-specific Fibril Propagation by an Aβ Dodecamer

Low molecular weight oligomers of amyloid-β (Aβ) have emerged as the primary toxic agents in the etiology of Alzheimer disease (AD). Polymorphism observed within the aggregation end products of fibrils are known to arise due to microstructural differences among the oligomers. Diversity in aggregate morphology correlates with the differences in AD, cementing the idea that conformational strains of oligomers could be significant in phenotypic outcomes. Therefore, it is imperative to determine the ability of strains to faithfully propagate their structure. Here we report fibril propagation of an Aβ42 dodecamer called large fatty acid-derived oligomers (LFAOs). The LFAO oligomeric strain selectively induces acute cerebral amyloid angiopathy (CAA) in neonatally-injected transgenic CRND8 mice. Propagation in-vitro occurs as a three-step process involving the association of LFAO units. LFAO-seeded fibrils possess distinct morphology made of repeating LFAO units that could be regenerated upon sonication. Overall, these data bring forth an important mechanistic perspective into strain-specific propagation of oligomers that has remained elusive thus far.

A smart healthcare portal for clinical decision making and precision medicine

There has been an unprecedented generation of healthcare data at clinical practices. With the availability of advanced computing frameworks and the ability to electronically mine data from disparate sources (e.g. demographics, genetics, imaging, treatment, clinical decisions, and outcomes) big data research in medicine has become a very active field of interest. In this paper, we discuss the challenges associated with designing clinical decision support systems that try to leverage such disparate data sources and create smart healthcare tools to aid medical practitioners for better patient care and treatment plans. We next propose an integrated data curation, storage and analytics portal, called HINGE (the Health Information Gateway and Exchange application), that can effectively address many of the outstanding challenges in this domain. HINGE specifically caters to healthcare data from radiation oncology patients however, the underlying formalisms and principles, as discussed here, are readily extendible to other disease types making it an attractive tool for the design of next generation clinical decision support systems.

Propagation of an Aβ Dodecamer Strain Involves a Three-Step Mechanism and a Key Intermediate

Proteinaceous deposits composed of fibrillar amyloid-β (Aβ) are the primary neuropathological hallmarks in Alzheimer disease (AD) brains. The nucleation-dependent aggregation of Aβ is a stochastic process with frequently observed heterogeneity in aggregate size, structure, and conformation that manifests in fibril polymorphism. Emerging evidence indicates that polymorphic variations in Aβ fibrils contribute to phenotypic diversity and the rate of disease progression in AD. We recently demonstrated that a dodecamer strain derived from synthetic Aβ42 propagates to morphologically distinct fibrils and selectively induces cerebral amyloid angiopathy phenotype in transgenic mice. This report supports the growing contention that stable oligomer strains can influence phenotypic outcomes by faithful propagation of their structures. Although we determined the mechanism of dodecamer propagation on a mesoscopic scale, the molecular details of the microscopic reactions remained unknown. Here, we have dissected and evaluated individually the kinetics of macroscopic phases in aggregation to gain insight into the process of strain propagation. The bulk rates determined experimentally in each phase were used to build an ensemble kinetic simulation model, which confirmed our observation that dodecamer seeds initially grow by monomer addition toward the formation of a key intermediate. This is followed by conversion of the intermediate to fibrils by oligomer elongation and association mechanisms. Overall, this report reveals important insights into the molecular details of oligomer strain propagation involved in AD pathology.

Benchmarking the communication fidelity of biomolecular signaling cascades featuring pseudo-one-dimensional transport

Synthetic biologists endeavor to predict how the increasing complexity of multi-step signaling cascades impacts the fidelity of molecular signaling, whereby information about the cellular state is often transmitted with proteins that diffuse by a pseudo-one-dimensional stochastic process. This begs the question of how the cell leverages passive transport mechanisms to distinguish informative signals from the intrinsic noise of diffusion. We address this problem by using a one-dimensional drift-diffusion model to derive an approximate lower bound on the degree of facilitation needed to achieve single-bit informational efficiency in signaling cascades as a function of their length. Within the assumptions of our model, we find that a universal curve of the Shannon-Hartley form describes the information transmitted by a signaling chain of arbitrary length and depends upon only a small number of physically measurable parameters. This enables our model to be used in conjunction with experimental measurements to aid in the selective design of biomolecular systems that can overcome noise to function reliably, even at the single-cell level.Synthetic biologists endeavor to predict how the increasing complexity of multi-step signaling cascades impacts the fidelity of molecular signaling, whereby information about the cellular state is often transmitted with proteins that diffuse by a pseudo-one-dimensional stochastic process. This begs the question of how the cell leverages passive transport mechanisms to distinguish informative signals from the intrinsic noise of diffusion. We address this problem by using a one-dimensional drift-diffusion model to derive an approximate lower bound on the degree of facilitation needed to achieve single-bit informational efficiency in signaling cascades as a function of their length. Within the assumptions of our model, we find that a universal curve of the Shannon-Hartley form describes the information transmitted by a signaling chain of arbitrary length and depends upon only a small number of physically measurable parameters. This enables our model to be used in conjunction with experimental measurements to...

Determining causal miRNAs and their signaling cascade in diseases using an influence diffusion model

In recent studies, miRNAs have been found to be extremely influential in many of the essential biological processes. They exhibit a self-regulatory mechanism through which they act as positive/negative regulators of expression of genes and other miRNAs. This has direct implications in the regulation of various pathophysiological conditions, signaling pathways and different types of cancers. Studying miRNA-disease associations has been an extensive area of research; however deciphering miRNA-miRNA network regulatory patterns in several diseases remains a challenge. In this study, we use information diffusion theory to quantify the influence diffusion in a miRNA-miRNA regulation network across multiple disease categories. Our proposed methodology determines the critical disease specific miRNAs which play a causal role in their signaling cascade and hence may regulate disease progression. We extensively validate our framework using existing computational tools from the literature. Furthermore, we implement our framework on a comprehensive miRNA expression data set for alcohol dependence and identify the causal miRNAs for alcohol-dependency in patients which were validated by the phase-shift in their expression scores towards the early stages of the disease. Finally, our computational framework for identifying causal miRNAs implicated in diseases is available as a free online tool for the greater scientific community.

Fatty Acid Concentration and Phase Transitions Modulate Aβ Aggregation Pathways

Aggregation of amyloid β (Aβ) peptides is a significant event that underpins Alzheimer disease (AD) pathology. Aβ aggregates, especially the low-molecular weight oligomers, are the primary toxic agents in AD and hence, there is increasing interest in understanding their formation and behavior. Aggregation is a nucleation-dependent process in which the pre-nucleation events are dominated by Aβ homotypic interactions. Dynamic flux and stochasticity during pre-nucleation renders the reactions susceptible to perturbations by other molecules. In this context, we investigate the heterotypic interactions between Aβ and fatty acids (FAs) by two independent tool-sets such as reduced order modelling (ROM) and ensemble kinetic simulation (EKS). We observe that FAs influence Aβ dynamics distinctively in three broadly-defined FA concentration regimes containing non-micellar, pseudo-micellar or micellar phases. While the non-micellar phase promotes on-pathway fibrils, pseudo-micellar and micellar phases promote predominantly off-pathway oligomers, albeit via subtly different mechanisms. Importantly off-pathway oligomers saturate within a limited molecular size, and likely with a different overall conformation than those formed along the on-pathway, suggesting the generation of distinct conformeric strains of Aβ, which may have profound phenotypic outcomes. Our results validate previous experimental observations and provide insights into potential influence of biological interfaces in modulating Aβ aggregation pathways.