Biology subjects

Kotov, A.

Publications and source records attributed to Kotov, A..

6 recordsLinked to original sources

bioRxiv2026

MicroRNA miR-219 is required for neural border and neural crest development in Xenopus neurulas

Neural crest (NC) multipotent stem cells give rise to many tissues including most of the peripheral nervous system, pigment cells and the craniofacial mesenchyme and skeleton. During gastrulation and early neurulation, cranial NC cells are specified in the ectoderm territory located between the anterior neural plate ectoderm and the future pre-placodal and lateral non-neural ectoderm. At the end of neurulation, NC cells undergo an epithelial-to-mesenchymal transition and migrate to various locations in the developing embryo where they differentiate. While the fine-tuning of NC specification is increasingly being elucidated, many questions remain, including how microRNAs may govern expression of gene programs during these processes. MicroRNAs are short non-coding 20-22 nucleotides-long RNAs which regulate gene expression through post-transcriptional repression. We have identified miR-219 as a candidate regulator of Xenopus NC development. Here, miR-219-dependent molecular pathways were investigated by morpholino knock-down and reveal NC phenotypes. The development of the NC and adjacent ectoderm was evaluated using whole mount in situ hybridization of key markers (pax3, zic1, xhe2, sox10, snai2, sox2), alcian blue cartilage staining, phenotype analysis, RNA sequencing of microdissected dorsal ectoderm and microRNA rescue experiments. While neural induction is mainly unaffected, miR-219 depletion alters gene expression programs associated with neural border development, resulting in loss of NC specification. HighlightsO_LImiR-219 depletion expands the neural border territory and disrupts neural crest specification. C_LIO_LImiR-219 depletion phenotypes are rescued with miRNA mimics. C_LIO_LImiR-219 morphant neural border expansion is rescued by pax3 depletion. C_LIO_LIRNA-seq reveals specific gene program modulation in miR-219 morphant neural crest. C_LIO_LImiR-219 is predicted to directly downregulate the neural gene Hes5.3. C_LI

developmental biology↗

bioRxiv2025

MicroRNA miR-196a controls neural crest patterning by repressing immature neural ectoderm programs in Xenopus embryos.

HighlightsmiR-196a promotes Neural Crest development: O_LIRNA sequencing of the developing neural border and neural crest reveals miR-196a-dependent gene programs. C_LIO_LImiR-196a represses early neural plate programs and reduces pluripotency gene expression. C_LIO_LImiR-196a may directly control neural crest gene sox10 in vivo. C_LI Neural crest (NC) cells form a multipotent stem cell population specified during neurulation, which undergo an epithelial-to-mesenchymal transition (EMT) and migrate extensively in the developing embryo, to generate numerous tissues and cell types including the craniofacial skeleton, the peripheral nervous system and pigment cells. The genetic and molecular details of NC specification are governed by a complex, yet still partially understood gene regulatory network (NC-GRN). In particular, the precise function of microRNAs (miRNA) in this network remains poorly characterized. miRNAs are short non-coding 20-22 nucleotides long RNAs, which control gene expression through post-transcriptional repression. Since miRNA-196a is expressed in the developing neural and NC cells of Xenopus laevis embryos, we here investigated miR-196a function in the NC-GRN, by knocking-down its expression using antisense morpholinos. Depletion of miR-196a revealed major NC and craniofacial phenotypes. These defects were preceded by the perturbed expression of key neural, neural border and NC markers such as sox2/3, zic1/3, pax3, sox10 and snail2. Using RNA sequencing of individual neural border and NC explants, we have identified a signature of genes up- and down-regulated by miR-196a and validate these with rescue experiments using a miRNA mimic. Our study identifies miR-196a as a key actor of early patterning in the dorsal ectoderm, balancing the extent of immature neural plate progenitors with NC and placode specification, while also promoting neuron differentiation within the neural plate.

developmental biology↗

bioRxiv2024

A revised single-cell transcriptomic atlas of Xenopus embryo reveals new differentiation dynamics.

This paper introduces an updated single-cell atlas for pivotal developmental stages in Xenopus, encompassing gastrulation, neurulation, and early tailbud. Notably surpassing its predecessor, the new atlas enhances gene mapping, read counts, and gene/cell type nomenclature. Leveraging the latest Xenopus tropicalis genome version, alongside advanced alignment pipelines and machine learning for cell type assignment, this release maintains consistency with previous cell type annotations while rectifying nomenclature issues. Employing an unbiased approach for cell type assignment proves especially apt for embryonic contexts, given the considerable number of non-terminally differentiated cell types. An alternative cell type attribution here adopts a fuzzy, non-deterministic stance, capturing the transient nature of early embryo progenitor cells by presenting an ensemble of types in superposition. The value of the new resource is emphasized through numerous examples, with a focus on previously unexplored germ cell populations where we uncover novel transcription onset features. Offering interactive exploration via a user-friendly web portal and facilitating complete data downloads, this atlas serves as a comprehensive and accessible reference.

developmental biology↗

bioRxiv2022

scEvoNet: a gradient boosting-based method for prediction of cell state evolution

* BackgroundExploring the function or the developmental history of cells in various organisms provides insights into a given cell types core molecular characteristics and putative evolutionary mechanisms. Numerous computational methods now exist for analyzing single-cell data and identifying cell states. These methods mostly rely on the expression of genes considered as markers for a given cell state. Yet, there is a lack of scRNA-seq computational tools to study the evolution of cell states, particularly how cell states change their molecular profiles. This can include novel gene activation or the novel deployment of programs already existing in other cell types, known as co-option. * ResultsHere we present scEvoNet, a Python tool for predicting cell type evolution in crossspecies or cancer-related scRNA-seq datasets. ScEvoNet builds the confusion matrix of cell states and a bipartite network connecting genes and cell states. It allows a user to obtain a set of genes shared by the characteristic signature of two cell states even between distantly-related datasets. These genes can be used as indicators of either evolutionary divergence or co-option occurring during organism or tumor evolution. Our results on cancer and developmental datasets indicate that scEvoNet is a helpful tool for the initial screening of such genes as well as for measuring cell state similarities. * ConclusionsThe scEvoNet package is implemented in Python and is freely available from https://github.com/monsoro/scEvoNet. Utilizing this framework and exploring the continuum of transcriptome states between developmental stages and species will help explain cell state dynamics.

bioinformatics↗

bioRxiv2022

From neural border to migratory stage: A comprehensive single cell roadmap of the timing and regulatory logic driving cranial and vagal neural crest emergence

Neural crest cells exemplify cellular diversification from a multipotent progenitor population. However, the full sequence of molecular choices orchestrating the emergence of neural crest heterogeneity from the embryonic ectoderm remains elusive. Gene-regulatory-networks (GRN) govern early development and cell specification towards definitive neural crest. Here, we combine ultra-dense single cell transcriptomes with machine-learning and large-scale experimental validation to provide a comprehensive GRN underlying neural crest fate diversification from induction to early migration stages in the frog Xenopus tropicalis. During gastrulation, a transient neural border zone state precedes the choice between neural crest and placodes which includes multiple and converging gene programs. Transcription factor connectome and bifurcation analyses demonstrate the early emergence of neural crest fates at the neural plate stage, alongside an unbiased multipotent lineage persisting until after epithelial-mesenchymal transition. We decipher the circuits driving cranial and vagal neural crest formation and provide a broadly applicable strategy for investigating SC transcriptomes in vertebrate GRNs in development, evolution, and disease.

developmental biology↗

bioRxiv2018

Computational Disease Subtyping based on Joint Analysis of Clinical and Genomic Data

ObjectiveTo investigate application of non-negative tensor decomposition for disease subtype discovery based on joint analysis of clinical and genomic data.\n\nData and MethodsSomatic mutation profiles including 11,996 genes of 503 breast cancer patients from the Cancer Genome Atlas (TCGA) along with 11 clinical variables and markers of these patients were used to construct a binary third-order tensor. CANDECOMP/PARAFAC method was applied to decompose the constructed tensor into rank-one component tensors. Definitions of breast cancer verotypes were constructed from the patient, gene and clinical vectors corresponding to each component tensor. Patient membership proportions in the identified verotypes were utilized in a Cox proportional hazards model to predict their survival.\n\nResultsQualitative evaluation of the verotypes obtained by tensor factorization indicates that they correspond to clinically meaningful breast cancer subtypes. While some components correspond to the known HER2- or ER-positive breast cancer subtypes, other components correspond to a variant of triple negative subtype and a cohort of patients with high mutation load of tumor suppressor genes. Quantitative evaluation indicates that the Cox model utilizing computationally discovered breast cancer verotypes is more accurate (AUC=0.5796) at predicting patient survival than the Cox models utilizing random patient membership proportions in cancer subtypes (AUC=0.4056) as well as patient membership proportions in genotypes (AUC=0.4731) and phenotypes (AUC=0.5047) obtained by non-negative factorization of the somatic mutation and clinical matrices.\n\nConclusionNon-negative factorization of a binary tensor constructed from clinical and genomic data enables high-throughput discovery of breast cancer verotypes that are effective at predicting patient survival.

bioinformatics↗