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Computational Biology

Artificial intelligence-enabled automated analysis of transmission electron micrographs to evaluate chemotherapy impact on mitochondrial morphology in triple negative breast cancer

Abstract

Mitochondrial dynamics play a crucial role in cancer cell metabolism and response to therapy, but quantitative analysis of mitochondrial morphology remains challenging due to the complexity and variability of these organelles. In this study, we developed an artificial intelligence (AI)-based computational pipeline for automated segmentation and morphological analysis of mitochondria in transmission electron micrographs (TEMs) of triple negative breast cancer (TNBC) cells. Using deep learning semantic segmentation techniques, our approach achieves high accuracy in identifying and delineating mitochondria across diverse cellular contexts. We applied this method to investigate mitochondrial morphological changes in TNBC cells in response to chemotherapy treatment, revealing significant alterations in size, shape, cristae architecture, and spatial distribution that correlate with treatment response and resistance development. Our results demonstrate that resistant cells exhibit distinct mitochondrial phenotypes characterized by increased networking, altered cristae density, and positional reorganization within the cell. The computational pipeline we developed provides a robust and objective methodology for quantitative analysis of mitochondrial morphology that can be applied to various experimental conditions and cell types. This approach enables new insights into the role of mitochondrial dynamics in cancer biology and therapy response, with potential implications for identifying novel therapeutic targets and biomarkers.

Single cell expression and chromatin accessibility of the Toxoplasma gondii lytic cycle identifies AP2XII-8 as an essential ribosome regulon driver

Abstract

The apicomplexan parasite Toxoplasma gondii undergoes a lytic replication cycle that is characterized by a coordinated gene expression program orchestrated by stage-specific transcription factors. Here, we generated and analyzed comprehensive single-cell multi-omic profiles (scRNA-seq and scATAC-seq) across the lytic cycle to understand the regulatory mechanisms governing this complex developmental process. Our analysis identified distinct cell states corresponding to G1, S/M, and cytokinesis phases with specific transcriptional signatures. By integrating chromatin accessibility and gene expression data, we identified key regulatory elements and transcription factor binding sites. We characterized the essential ApiAP2 transcription factor AP2XII-8 using CUT&RUN and conditional knockdown approaches, revealing its role as a master regulator of ribosomal gene expression. AP2XII-8 depletion causes severe growth defects and dysregulation of hundreds of genes, particularly affecting ribosome biogenesis. This study provides unprecedented insights into the regulatory mechanisms underlying T. gondii development and highlights AP2XII-8 as a critical regulator of ribosome biosynthesis, establishing a foundation for understanding apicomplexan developmental biology through multi-omic approaches.

A Bayesian noisy logic model for inference of transcription factor activity from single cell and bulk transcriptomic data

Abstract

Transcription factors (TFs) play a crucial role in regulating gene expression, but inferring their activities from transcriptomic data remains challenging. Here, we present NLBayes, a Bayesian noisy logic framework for the inference of TF activities from both bulk and single-cell RNA sequencing data. Our approach models the complex regulatory interactions between TFs and their target genes using a probabilistic Boolean logic framework that accounts for the inherent noise and uncertainty in gene expression data. NLBayes incorporates prior knowledge of TF-target relationships and uses Markov Chain Monte Carlo (MCMC) sampling to infer the posterior distributions of TF activities. Through extensive validation on simulated data and applications to real datasets, we demonstrate that NLBayes outperforms existing methods in accuracy, robustness to noise, and interpretability. We apply NLBayes to analyze TF activities in prostate cancer progression and treatment response, revealing key regulators and their dynamics. Furthermore, our model's capability to handle single-cell data allows for the identification of cell type-specific TF activities and regulatory patterns. NLBayes provides a powerful and versatile tool for the systems-level analysis of transcriptional regulation in complex biological processes and diseases.

Comparative single-cell transcriptional atlases of Babesia species reveal conserved and species-specific expression profiles

Abstract

Babesia is a genus of apicomplexan parasites that infect red blood cells in vertebrate hosts. Pathology occurs during rapid replication cycles in the asexual blood stage of infection. Current knowledge of Babesia replication cycle progression and regulation is limited and relies mostly on comparative studies with related parasites. Due to limitations in synchronizing Babesia parasites, fine-scale time-course transcriptomic resources are not readily available. Single-cell transcriptomics provides a powerful unbiased alternative for profiling asynchronous cell populations. Here, we applied single-cell RNA sequencing to 3 Babesia species (B. divergens, B. bovis, and B. bigemina). We used analytical approaches and algorithms to map the replication cycle and construct pseudo-synchronized time-course gene expression profiles. We identify clusters of co-expressed genes showing "just-in-time" expression profiles, with gradually cascading peaks throughout asexual development. Moreover, clustering analysis of reconstructed gene curves reveals coordinated timing of peak expression in epigenetic markers and transcription factors. Using a regularized Gaussian graphical model, we reconstructed co-expression networks and identified conserved and species-specific nodes. Motif analysis of a co-expression interactome of AP2 transcription factors identified specific motifs previously reported to play a role in DNA replication in Plasmodium species. Finally, we present an interactive web application to visualize and interactively explore the datasets.

The Extracellular Milieu of Toxoplasma's Lytic Cycle Drives Lab Adaptation, Primarily by Transcriptional Reprogramming

Abstract

Evolve and resequencing (E&R) was applied to lab adaptation of Toxoplasma gondii for over 1,500 generations with the goal of mapping host-independent in vitro virulence traits. Phenotypic assessments of steps across the lytic cycle revealed that only traits needed in the extracellular milieu evolved. Nonsynonymous single-nucleotide polymorphisms (SNPs) in only one gene, a P4 flippase, fixated across two different evolving populations, whereas dramatic changes in the transcriptional signature of extracellular parasites were identified. Newly developed computational tools correlated phenotypes evolving at different rates with specific transcriptomic changes. A set of 300 phenotype-associated genes was mapped, of which nearly 50% is annotated as hypothetical. Validation of a select number of genes by knockouts confirmed their role in lab adaptation and highlights novel mechanisms underlying in vitro virulence traits. Further analyses of differentially expressed genes revealed the development of a "pro-tachyzoite" profile as well as the upregulation of the fatty acid biosynthesis (FASII) pathway. The latter aligned with the P4 flippase SNP and aligned with a low abundance of medium-chain fatty acids at low passage, indicating this is a limiting factor in extracellular parasites. In addition, partial overlap with the bradyzoite differentiation transcriptome in extracellular parasites indicated that stress pathways are involved in both situations. This was reflected in the partial overlap between the assembled ApiAP2 and Myb transcription factor network underlying the adapting extracellular state with the bradyzoite differentiation program. Overall, E&R is a new genomic tool successfully applied to map the development of polygenic traits underlying in vitro virulence of T. gondii.