Intra-tumoral phenotypic heterogeneity promotes tumor relapse and therapeutic resistance and remains an unsolved clinical challenge. Decoding the interconnections among different biological axes of plasticity is crucial to understand the molecular origins of phenotypic heterogeneity. Here, we use multi-modal transcriptomic data-bulk, single-cell, and spatial transcriptomics-from breast cancer cell lines and primary tumor samples, to identify associations between epithelial-mesenchymal transition (EMT) and luminal-basal plasticity-two key processes that enable heterogeneity. We show that luminal breast cancer strongly associates with an epithelial cell state, but basal breast cancer is associated with hybrid epithelial/mesenchymal phenotype(s) and higher phenotypic heterogeneity. Mathematical modeling of core underlying gene regulatory networks representative of the crosstalk between the luminal-basal and epithelial-mesenchymal axes elucidate mechanistic underpinnings of the observed associations from transcriptomic data. Our systems-based approach integrating multi-modal data analysis with mechanism-based modeling offers a predictive framework to characterize intra-tumor heterogeneity and identify interventions to restrict it.

A major challenge in therapeutic approaches applying hematopoietic stem cells (HSCs) is the cell quantity. The primary objective of this study was to predict the miRNAs and anti-miRNAs using bioinformatics tools and investigate their effects on the expression levels of key genes predicted in the improvement of proliferation, and the inhibition of differentiation in HSCs isolated from Human umbilical cord blood (HUCB). A network including genes related to the differentiation and proliferation stages of HSCs was constructed by enriching data of text (PubMed) and StemChecker server with KEGG signaling pathways, and was improved using GEO datasets. Bioinformatics tools predicted a profile from miRNAs containing miR-20a-5p, miR-423-5p, and chimeric anti-miRNA constructed from 5\'-miR-340/3\'-miR-524 for the high-score genes (RB1, SMAD4, STAT1, CALML4, GNG13, and CDKN1A/CDKN1B genes) in the network. The miRNAs and anti-miRNA were transferred into HSCs using polyethylenimine (PEI). The gene expression levels were estimated using the RT-qPCR technique in the PEI + (miRNA/anti-miRNA)-contained cell groups (n = 6). Furthermore, CD markers (90, 16, and 45) were evaluated using flow cytometry. Strong relationships were found between the high-score genes, miRNAs, and chimeric anti-miRNA. The RB1, SMAD4, and STAT1 gene expression levels were decreased by miR-20a-5p (P < 0.05). Additionally, the anti-miRNA increased the gene expression level of GNG13 (P < 0.05), whereas the miR-423-5p decreased the CDKN1A gene expression level (P < 0.01). The cellular count also increased significantly (P < 0.05) but the CD45 differentiation marker did not change in the cell groups. The study revealed the predicted miRNA/anti-miRNA profile expands HSCs isolated from HUCB. While miR-20a-5p suppressed the RB1, SMAD4, and STAT1 genes involved in cellular differentiation, the anti-miRNA promoted the GNG13 gene related to the proliferation process. Notably, the mixed miRNA/anti-miRNA group exhibited the highest cellular expansion. This approach could hold promise for enhancing the cell quantity in HSC therapy.

Prostate cancer (PCa) is a complex and biologically diverse disease with no curative treatment options at present. This study aims to utilize computational methods to explore potential anti-PCa compounds based on differentially expressed genes (DEGs), with the goal of identifying novel therapeutic indications or repurposing existing drugs. The methods employed in this study include DEGs-to-drug prediction, pharmacokinetics prediction, target prediction, network analysis, and molecular docking. The findings revealed a total of 79 upregulated DEGs and 110 downregulated DEGs in PCa, which were used to identify drug compounds capable of reversing the dysregulated conditions (dexverapamil, emetine, parthenolide, dobutamine, terfenadine, pimozide, mefloquine, ellipticine, and trifluoperazine) at a threshold probability of 20% on several molecular targets, such as serotonin receptors 2a/2b/2c, HERG protein, adrenergic receptors alpha-1a/2a, dopamine D3 receptor, inducible nitric oxide synthase (iNOS), epidermal growth factor receptor erbB1 (EGFR), tyrosine-protein kinases, and C-C chemokine receptor type 5 (CCR5). Molecular docking analysis revealed that terfenadine binding to inducible nitric oxide synthase (-7.833 kcal.mol-1) and pimozide binding to HERG (-7.636 kcal.mol-1). Overall, binding energy ΔGbind (Total) at 0 ns was lower than that of 100 ns for both the Terfenadine-iNOS complex (-101.707 to -103.302 kcal.mol-1) and Ellipticine-TOPIIα complex (-42.229 to -58.780 kcal.mol-1). In conclusion, this study provides insight on molecular targets that could possibly contribute to the molecular mechanisms underlying PCa. Further preclinical and clinical studies are required to validate the therapeutic effectiveness of these identified drugs in PCa disease.

The root system plays a decisive role in the growth and development of plants. The water requirement of a root system depends strongly on the plant species. Potatoes are an important food and vegetable crop grown worldwide, especially under irrigation in arid and semi-arid regions. However, the expected impact of global warming on potato yields calls for an investigation of genes related to root development and drought resistance signaling pathways in potatoes. In this study, we investigated the molecular mechanisms of different drought-tolerant potato root systems in response to drought stress under controlled water conditions, using potato as a model. We analyzed the transcriptome and proteome of the drought-sensitive potato cultivar Atlantic (Atl) and the drought-tolerant cultivar Qingshu 9 (Q9) under normal irrigation (CK) and weekly drought stress (D). The results showed that a total of 14,113 differentially expressed genes (DEGs) and 5596 differentially expressed proteins (DEPs) were identified in the cultivars. A heat map analysis of DEGs and DEPs showed that the same genes and proteins in Atl and Q9 exhibited different expression patterns under drought stress. Weighted gene correlation network analysis (WGCNA) showed that in Atl, Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG)-enriched pathways were related to pyruvate metabolism and glycolysis, as well as cellular signaling and ion transmembrane transporter protein activity. However, GO terms and KEGG-enriched pathways related to phytohormone signaling and the tricarboxylic acid cycle were predominantly enriched in Q9. The present study provides a unique genetic resource to effectively explore the functional genes and uncover the molecular regulatory mechanism of the potato root system in response to drought stress.

Biomarker screening is critical for precision oncology. However, one of the main challenges in precision oncology is that the screened biomarkers often fail to achieve the expected clinical effects and are rarely approved by regulatory authorities. Considering the close association between cancer pathogenesis and the evolutionary events of organisms, we first explored the evolutionary feature underlying clinically approved biomarkers, and two evolutionary features of approved biomarkers (Ohnologs and specific evolutionary stages of genes) were identified. Subsequently, we utilized evolutionary features for screening potential prognostic biomarkers in four common cancers: head and neck squamous cell carcinoma, liver hepatocellular carcinoma, lung adenocarcinoma, and lung squamous cell carcinoma. Finally, we constructed an evolution-strengthened prognostic model (ESPM) for cancers. These models can predict cancer patients\' survival time across different cancer cohorts effectively and perform better than conventional models. In summary, our study highlights the application potentials of evolutionary information in precision oncology biomarker screening.

Cells respond divergently to drugs due to the heterogeneity among cell populations. Thus, it is crucial to identify drug-responsive cell populations in order to accurately elucidate the mechanism of drug action, which is still a great challenge. Here, we address this problem with scRank, which employs a target-perturbed gene regulatory network to rank drug-responsive cell populations via in silico drug perturbations using untreated single-cell transcriptomic data. We benchmark scRank on simulated and real datasets, which shows the superior performance of scRank over existing methods. When applied to medulloblastoma and major depressive disorder datasets, scRank identifies drug-responsive cell types that are consistent with the literature. Moreover, scRank accurately uncovers the macrophage subpopulation responsive to tanshinone IIA and its potential targets in myocardial infarction, with experimental validation. In conclusion, scRank enables the inference of drug-responsive cell types using untreated single-cell data, thus providing insights into the cellular-level impacts of therapeutic interventions.

The process of domestication, despite its short duration as it compared with the time scale of the natural evolutionary process, has caused rapid and substantial changes in the phenotype of domestic animal species. Nonetheless, the genetic mechanisms underlying these changes remain poorly understood. The present study deals with an analysis of the transcriptomes from four brain regions of gray rats (Rattus norvegicus), serving as an experimental model object of domestication. We compared gene expression profiles in the hypothalamus, hippocampus, periaqueductal gray matter, and the midbrain tegmental region between tame domesticated and aggressive gray rats and revealed subdivisions of differentially expressed genes by principal components analysis that explain the main part of differentially gene expression variance. Functional analysis (in the DAVID (Database for Annotation, Visualization and Integrated Discovery) Bioinformatics Resources database) of the differentially expressed genes allowed us to identify and describe the key biological processes that can participate in the formation of the different behavioral patterns seen in the two groups of gray rats. Using the STRING- DB (search tool for recurring instances of neighboring genes) web service, we built a gene association network. The genes engaged in broad network interactions have been identified. Our study offers data on the genes whose expression levels change in response to artificial selection for behavior during animal domestication.

Breast cancers (BRCA) exhibit substantial transcriptional heterogeneity, posing a significant clinical challenge. The global transcriptional changes in a disease context, however, are likely mediated by few key genes which reflect disease etiology better than the differentially expressed genes (DEGs). We apply our network-based tool PathExt to 1,059 BRCA tumors across 4 subtypes to identify key mediator genes in each subtype. Compared to conventional differential expression analysis, PathExt-identified genes exhibit greater concordance across tumors, revealing shared and subtype-specific biological processes; better recapitulate BRCA-associated genes in multiple benchmarks, and are more essential in BRCA subtype-specific cell lines. Single-cell transcriptomic analysis reveals a subtype-specific distribution of PathExt-identified genes in multiple cell types from the tumor microenvironment. Application of PathExt to a TNBC chemotherapy response dataset identified subtype-specific key genes and biological processes associated with resistance. We described putative drugs that target key genes potentially mediating drug resistance.

Central precocious puberty (CPP) is a developmental disorder caused by early activation of the hypothalamic-pituitary-gonadal axis. The incidence of CPP is rapidly increasing, but the underlying mechanisms are not fully understood. Previous studies have shown that gain-of-function mutations in the KISS1R and KISS1 genes and loss-of-function mutations in the MKRN3, LIN28, and DLK1 genes may lead to early initiation of pubertal development. Recent research has also revealed the significant role of epigenetic factors such as DNA methylation and microRNAs in the regulation of gonadotropin-releasing hormone neurons, as well as the modulating effect of gene networks involving multiple variant genes on pubertal initiation. This review summarizes the genetic etiology and pathogenic mechanisms underlying CPP.
中枢性性早熟（central precocious puberty, CPP）是下丘脑-垂体-性腺轴提早激活所导致的发育异常性疾病，其发病率快速增加，但发病机制尚未完全明确。既往研究发现KISS1R、KISS1基因的功能获得性突变，以及MKRN3、LIN28和DLK1基因的功能缺失性突变可导致青春发育期启动时间提前。新近研究发现表观遗传因素如DNA甲基化、微小核糖核酸在促性腺激素释放激素神经元的调控中起重要作用；基因网络中多个变异基因的协同作用也可影响青春发育启动。该文综述了导致CPP的遗传学病因进展及其致病机制。.

The heart is the first organ to form during embryonic development, establishing the circulatory infrastructure necessary to sustain life and enable downstream organogenesis. Critical to the heart\'s function is its ability to initiate and propagate electrical impulses that allow for the coordinated contraction and relaxation of its chambers, and thus, the movement of blood and nutrients. Several specialized structures within the heart, collectively known as the cardiac conduction system (CCS), are responsible for this phenomenon. In this review, we discuss the discovery and scientific history of the mammalian cardiac conduction system as well as the key genes and transcription factors implicated in the formation of its major structures. We also describe known human diseases related to CCS development and explore existing challenges in the clinical context.