Biology subjects

Ikonomou, L.

Publications and source records attributed to Ikonomou, L..

6 recordsLinked to original sources

bioRxiv2026

Higher-order metabolic interactions among oral commensals gate pathobiont expansion through succinate-driven pH modulation

Despite advances in defining microbiome shifts associated with inflammatory mucosal diseases, the ecological interactions that constrain or facilitate pathobiont expansion remain poorly understood. Using a six-species synthetic oral microbial community and factorial interaction screening, we identified a higher-order interaction governing the growth of the periodontal pathobiont Porphyromonas gingivalis. The core community member Veillonella parvula was the strongest positive determinant of P. gingivalis growth, but this effect was substantially reduced by the health-associated commensal Actinomyces oris. A. oris-mediated antagonism resulted from extracellular acidification and was enhanced under anaerobic conditions. Genetic dissection of A. oris fermentation revealed that lactate dehydrogenase (ldh1) contributes to anaerobic lactate production, but ldh1 deletion only partially relieved P. gingivalis inhibition. Instead, succinate, produced through a succinate dehydrogenase/fumarate reductase pathway (sdh2), emerged as the principal driver of acidification and P. gingivalis suppression. At high cell densities, however, V. parvula consumed both lactate and succinate, reversing acidification and restoring conditions permissive for P. gingivalis growth. To assess the relevance of these interactions in vivo, we analyzed subgingival microbiome from 1,355 adults, including 555 with detectable P. gingivalis. Among these individuals, P. gingivalis abundance was negatively associated with Actinomyces abundance after adjustment for periodontitis severity, whereas this association was attenuated at high V. parvula abundance, consistent with the interaction observed in vitro. Together, these findings identify succinate as a previously unrecognized mediator of interbacterial antagonism and reveal how higher-order metabolic interactions among oral commensals regulate pathobiont expansion through pH modulation, with protective effects shaped by oxygen availability and metabolic cross-feeding.

microbiology↗

bioRxiv2026

Chemotherapy-Induced Oral Mucosal Injury Is Defined by p53 Activation, Cell Cycle Arrest and Diverse Epithelial Progenitor Dynamics

Chemotherapy-induced oral mucositis is a common and debilitating complication, yet the mechanisms underlying oral mucosa injury and repair remain poorly defined. Using a mouse model of 5-fluorouracil (5-FU)-induced mucositis, we define gene networks and oral mucosal cellular landscape dynamics in response to chemotoxic stress. We show that 5-FU-induced epithelial atrophy is driven primarily by cell cycle arrest rather than apoptosis, despite concurrent activation of p53-dependent transcriptional programs linked to both cell fates within individual cells. Relative to intestine, the recovering oral mucosa exhibits a more effective cell cycle checkpoint response and uniquely undergoes metabolic reprogramming toward lipid oxidation. Single-cell RNA sequencing revealed putative epithelial progenitor populations with distinct responses to chemotherapy, including chemoresistant cells with a p53- and AP-1 complex gene signature, reminiscent of lung transitional cell states. These findings define diverse progenitor dynamics and p53-driven responses as key determinants of oral mucosal injury and repair following chemotherapy.

cell biology↗

bioRxiv2025

Finding signatures of low-dimensional geometric landscapes in high-dimensional cell fate transitions

Multicellular organisms develop a wide variety of highly-specialized cell types. The consistency and robustness of developmental cell fate trajectories suggests that complex gene regulatory networks effectively act as low-dimensional cell fate landscapes. A complementary set of works draws on the theory of dynamical systems to argue that cell fate transitions can be categorized into universal decision-making classes. However, the theory connecting geometric landscapes and decision-making classes to high-dimensional gene expression space is still in its infancy. Here, we introduce a phenomenological model that allows us to identify gene expression signatures of decision-making classes from single-cell RNA-sequencing time-series data. Our model combines low-dimensional gradient-like dynamical systems and high-dimensional Hopfield networks to capture the interplay between cell fate, gene expression, and signaling pathways. We apply our model to the maturation of alveolar cells in mouse lungs to show that the transient appearance of a mixed alveolar type 1/type 2 state suggests the triple cusp decision-making class. We also analyze lineage-tracing data on hematopoetic differentiation and show that bipotent neutrophil-monocyte progenitors likely undergo a heteroclinic flip bifurcation. Our results suggest it is possible to identify universal decision-making classes for cell fate transitions directly from data.

biophysics↗

bioRxiv2023

scTOP: physics-inspired order parameters for cellular identification and visualization

Advances in single-cell RNA-sequencing (scRNA-seq) provide an unprecedented window into cellular identity. The increasing abundance of data requires new theoretical and computational frameworks for understanding cell fate determination, accurately classifying cell fates from expression data, and integrating knowledge from cell atlases. Here, we present single-cell Type Order Parameters (scTOP): a statistical-physics-inspired approach for constructing "order parameters" for cell fate given a reference basis of cell types. scTOP can quickly and accurately classify cells at a single-cell resolution, generate interpretable visualizations of developmental trajectories, and assess the fidelity of engineered cells. Importantly, scTOP does this without using feature selection, statistical fitting, or dimensional reduction (e.g., UMAP, PCA, etc.). We illustrate the power of scTOP utilizing a wide variety of human and mouse datasets (both in vivo and in vitro). By reanalyzing mouse lung alveolar development data, we characterize a transient perinatal hybrid alveolar type 1/alveolar type 2 (AT1/AT2) cell population that disappears by 15 days post-birth and show that it is transcriptionally distinct from previously identified adult AT2-to-AT1 transitional cell types. Visualizations of lineage tracing data on hematopoiesis using scTOP confirm that a single clone can give rise to as many as three distinct differentiated cell types. We also show how scTOP can quantitatively assess the transcriptional similarity between endogenous and transplanted cells in the context of murine pulmonary cell transplantation. Finally, we provide an easy-to-use Python implementation of scTOP. Our results suggest that physics-inspired order parameters can be an important tool for understanding development and characterizing engineered cells.

biophysics↗

bioRxiv2022

Directed differentiation of mouse pluripotent stem cells into functional lung-specific mesenchyme

The successful generation of endodermal, ectodermal, and most mesodermal lineages from pluripotent stem cells has resulted in basic discoveries and regenerative medicine clinical trials of cell-based therapies. In contrast, the derivation of tissue-specific mesenchyme via directed differentiation in vitro has markedly lagged, due in part to a limited understanding of the signaling pathways regulating in vivo mesenchymal development and a lack of specific markers or reporters able to purify such lineages. The derivation of lung-specific mesenchyme is a particularly important goal since this tissue plays important roles in lung development and respiratory disease pathogenesis. Here we generate a mouse induced pluripotent stem cell (iPSC) line carrying a lung-specific mesenchymal reporter/lineage tracer facilitating the tracking and purification of engineered lung-specific mesenchyme. We identify the key signaling pathways (RA and Shh) necessary to specify lung mesenchyme from lateral plate mesodermal precursors and find that mouse iPSC-derived lung mesenchyme (iLM) expresses the molecular and functional phenotypes of primary developing lung mesenchyme. Purified iLM can be recombined with separately engineered lung epithelial progenitors, self-organizing into 3-dimensional organoids featuring significantly augmented structural complexity and lineage purity, including interacting juxtaposed layers of epithelium and mesenchyme. Co-culture with iLM increases the yield of lung epithelial progenitors and impacts epithelial and mesenchymal differentiation programs, suggesting functional epithelial-mesenchymal crosstalk. Our iPSC-derived population thus expresses key features of developing lung mesenchyme, providing an inexhaustible source of cells for studying lung development, modeling diseases, and developing therapeutics.

developmental biology↗

bioRxiv2020

Human iPSC-derived alveolar and airway epithelial cells can be cultured at air-liquid interface and express SARS-CoV-2 host factors

Development of an anti-SARS-CoV-2 therapeutic is hindered by the lack of physiologically relevant model systems that can recapitulate host-viral interactions in human cell types, specifically the epithelium of the lung. Here, we compare induced pluripotent stem cell (iPSC)-derived alveolar and airway epithelial cells to primary lung epithelial cell controls, focusing on expression levels of genes relevant for COVID-19 disease modeling. iPSC-derived alveolar epithelial type II-like cells (iAT2s) and iPSC-derived airway epithelial lineages express key transcripts associated with lung identity in the majority of cells produced in culture. They express ACE2 and TMPRSS2, transcripts encoding essential host factors required for SARS-CoV-2 infection, in a minor subset of each cell sub-lineage, similar to frequencies observed in primary cells. In order to prepare human culture systems that are amenable to modeling viral infection of both the proximal and distal lung epithelium, we adapt iPSC-derived alveolar and airway epithelial cells to two-dimensional air-liquid interface cultures. These engineered human lung cell systems represent sharable, physiologically relevant platforms for SARS-CoV-2 infection modeling and may therefore expedite the development of an effective pharmacologic intervention for COVID-19.

molecular biology↗