Biology subjects

Manolakos, E. S.

Publications and source records attributed to Manolakos, E. S..

6 recordsLinked to original sources

bioRxiv2025

Individual-Based Modeling of Microbial Communities Integrating Genetic Mechanisms: A Case Study of LuxS-Mediated Quorum Sensing in Salmonella Typhimurium

We present a digital twin framework for simulating microbial communities at the single-cell level, integrating genetic mechanisms through individual-based modeling (IBM). This in silico approach enables the study of bacterial populations with bio-sensing capabilities and stochastic virulence expression, facilitating the design of biotechnological applications such as targeted drug delivery. By combining kinetic modeling with IBM, we capture the regulatory interplay between quorum sensing (QS) and virulence, allowing for predictive simulations before costly wet-lab experiments with state-of-the-art technologies like organ-on-chip models. To demonstrate the power of this approach, we focus on Salmonella Typhimurium, where the LuxS/Autoinducer-2 QS system controls the expression of the type three secretion system (TTSS) encoded by Salmonella pathogenicity island-1 (SPI-1). TTSS-1 effectors are not only key virulence factors but also promising tools for precise intracellular delivery of therapeutic agents. These bacterial-derived effectors function as cell-penetrating effectors (CPEs), autonomously translocating into host cells and overcoming major hurdles in pharmacology by enabling targeted drug delivery. Our digital twin framework enables the simulation of Salmonella cells engineered to sense their environment and dynamically regulate virulence expression for the controlled secretion of effectors, including potential applications in delivering surrogate drugs to cancerous cells. This work establishes an advanced computational platform for optimizing bacterial therapies and accelerating the development of next-generation biomedical solutions.

systems biology↗

bioRxiv2022

A Microengineered Brain-Chip to Model Neuroinflammation in Humans

Species differences in the brain and the blood-brain barrier (BBB) biology hamper the translation from animal models to humans and impede the development of specific therapeutics for brain diseases. Here we present a human Brain-Chip engineered to recapitulate critical aspects of the complex brain cell-cell interactions that mediate neuroinflammation development. Our human organotypic microphysiological system (MPS) includes endothelial-like cells, pericytes, glia, and cortical neurons and maintains BBB permeability at in vivo relevant levels, providing a significant improvement in complexity and clinical mimicry compared to previous MPS models. This is the first report of a Brain-Chip with an RNA expression profile close to that of the adult human cortex and that demonstrates advantages over Transwell culture. Through perfusion of TNF-, we recreated key inflammatory features, such as glia activation, the release of proinflammatory cytokines, and increased barrier permeability. Our model may provide a reliable tool for mechanistic studies in neuron-glial interactions and dysregulation of BBB function during neuroinflammation.

bioengineering↗

bioRxiv2020

A Micro-engineered Human Colon Intestine-Chip Platform to Study Leaky Barrier

The intestinal epithelial barrier supports the symbiotic relationship between the microbiota colonizing the intestinal epithelium and the host immune system to maintain homeostasis. Leaky barrier is increasingly recognized as part of the pathogenesis of a number of chronic conditions in addition to inflammatory and infectious diseases. As our understanding on the regulation of the barrier remains limited, effective therapeutic targeting for the compromised barrier is still an unmet need. Here we combined advancements on the organoids and Organ-on-Chip technologies to establish a micro-engineered Colon Intestine-Chip for studying development and regulation of the human intestinal barrier. Our data demonstrate the significance of the endothelium in co-culture with the epithelial cells within a tissue-relevant microenvironment for the establishment of a tight epithelial barrier of polarized cells. Pathway analysis of the RNA sequencing (RNA-Seq), revealed significant upregulation of mechanisms relevant to the maturation of the intestinal epithelium in organoid-derived epithelial cells in co-culture with endothelium as compared to organoids maintained in suspension. We provide evidence that the Colon Intestine-Chip platform responds to interferon gamma (IFN{gamma}), a prototype cytokine utilized to model inflammation-induced barrier disruption, by induction of apoptosis and reorganization of the apical junctional complexes as shown with other systems. We also describe the mechanism of action of interleukin 22 (IL-22) on mature, organoid-derived intestinal epithelial cells that is consistent with barrier disruption. Overall we propose the Colon Intestine-Chip as a promising human organoid-derived platform to decipher mechanisms driving the development of leaky gut in patients and enable their translation for this unmet medical need.

bioengineering↗

bioRxiv2020

Modeling Alpha-Synuclein Pathology in a Human Brain-Chip to Assess Blood-Brain Barrier Disruption in Parkinson's Disease

Parkinsons disease and related synucleinopathies are characterized by the abnormal accumulation of alpha-synuclein aggregates, loss of dopaminergic neurons, and gliosis in the substantia nigra. Although clinical evidence and in vitro studies indicate disruption of the Blood-Brain Barrier in Parkinsons disease, the mechanisms mediating the endothelial dysfunction remain elusive. Lack of relevant models able to recapitulate the order of events driving the development of the disease in humans has been a significant bottleneck in the identification of specific successful druggable targets. Here we leveraged the Organs-on-Chips technology to engineer a human Brain-Chip representative of the substantia nigra area of the brain containing dopaminergic neurons, astrocytes, microglia, pericytes, and microvascular brain endothelial cells, cultured under fluid flow. Our Syn fibril-induced model was capable of reproducing several key aspects of Parkinsons disease, including accumulation of phosphorylated Syn (pSer129-Syn), mitochondrial impairment, neuroinflammation, and compromised barrier function. This model is poised to enable research into the dynamics of cell-cell interactions in human synucleinopathies and to serve as testing platform for novel therapeutic interventions, including target identification and target validation.

bioengineering↗

bioRxiv2020

An information-theoretic approach for measuring the distance of organ tissue samples using their transcriptomic signatures

MotivationRecapitulating aspects of human organ functions using in-vitro (e.g., plates, transwells, etc.), in-vivo (e.g., mouse, rat, etc.), or ex-vivo (e.g., organ chips, 3D systems, etc.) organ models are of paramount importance for precision medicine and drug discovery. It will allow us to identify potential side effects and test the effectiveness of therapeutic approaches early in their design phase and will inform the development of accurate disease models. Developing mathematical methods to reliably compare the "distance/similarity" of organ models from/to the real human organ they represent is an understudied problem with important applications in biomedicine and tissue engineering. ResultsWe introduce the Transctiptomic Signature Distance, TSD, an information-theoretic distance for assessing the transcriptomic similarity of two tissue samples, or two groups of tissue samples. In developing TSD, we are leveraging next-generation sequencing data and information retrieved from well-curated databases providing signature gene sets characteristic for human organs. We present the justification and mathematical development of the new distance and demonstrate its effectiveness in different scenarios of practical importance using several publicly available RNA-seq datasets. Contactdimitris.manatakis@emulatebio.com Supplementary informationSupplementary data are available at bioRxiv.

bioinformatics↗

bioRxiv2018

Machine learning methods to reverse engineer dynamic gene regulatory networks governing cell state transitions

Deciphering the dynamic gene regulatory mechanisms driving cells to make fate decisions remains elusive. We present a novel unsupervised machine learning methodology that can be used to analyze a dataset of heterogeneous single-cell gene expressions profiles, determine the most probable number of states (major cellular phenotypes) represented and extract the corresponding cell sub-populations. Most importantly, for any transition of interest from a source to a destination state, our methodology can zoom in, identify the cells most specific for studying the dynamics of this transition, order them along a trajectory of biological progression in posterior probabilities space, determine the \"key-player\" genes governing the transition dynamics, partition the trajectory into consecutive phases (transition \"micro-states\"), and finally reconstruct causal gene regulatory networks for each phase. Application of the end-to-end methodology provides new insights on key-player genes and their dynamic interactions during the important HSC-to-LMPP cell state transition involved in hematopoiesis. Moreover, it allows us to reconstruct a probabilistic representation of the \"epigenetic landscape\" of transitions and identify correctly the major ones in the hematopoiesis hierarchy of states.

bioinformatics↗