Shared and unique transcriptomic signatures of antidepressant and probiotics action in the mammalian brain.

Understanding the shared and divergent mechanisms across antidepressant (AD) classes and probiotics is critical for improving treatment for mood disorders. Here we examine the transcriptomic effects of bupropion (NDRI), desipramine (SNRI), fluoxetine (SSRI) and a probiotic formulation (Lacidofil®) on 10 regions across the mammalian brain. These treatments massively alter gene expression (on average, 2211 differentially expressed genes (DEGs) per region-treatment combination), highlighting the biological complexity of AD and probiotic action. Intersection of DEG sets against neuropsychiatric GWAS loci, sex-specific transcriptomic portraits of major depressive disorder (MDD), and mouse models of stress and depression reveals significant similarities and differences across treatments. Interestingly, molecular responses in the infralimbic cortex, basolateral amygdala and locus coeruleus are region-specific and highly similar across treatments, whilst responses in the Raphe, medial preoptic area, cingulate cortex, prelimbic cortex and ventral dentate gyrus are predominantly treatment-specific. Mechanistically, ADs concordantly downregulate immune pathways in the amygdala and ventral dentate gyrus. In contrast, protein synthesis, metabolism and synaptic signaling pathways are axes of variability among treatments. We use spatial transcriptomics to further delineate layer-specific molecular pathways and DEGs within the prefrontal cortex. Our study reveals complex AD and probiotics action on the mammalian brain and identifies treatment-specific cellular processes and gene targets associated with mood disorders.

A single-cell atlas identifies pretreatment features of primary imatinib resistance in chronic myeloid leukemia.

Primary resistance to tyrosine kinase inhibitors (TKIs) is a significant barrier to optimal outcomes in chronic myeloid leukemia (CML), but factors contributing to response heterogeneity remain unclear. Using single-cell RNA (scRNA) sequencing, we identified 8 statistically significant features in pretreatment bone marrow, which correlated with either sensitivity (major molecular response or MMR) or extreme resistance to imatinib (eventual blast crisis [BC] transformation). Employing machine-learning, we identified leukemic stem cell (LSC) and natural killer (NK) cell gene expression profiles predicting imatinib response with >80% accuracy, including no false positives for predicting BC. A canonical erythroid-specifying (TAL1/KLF1/GATA1) regulon was a hallmark of LSCs from patients with MMR and was associated with erythroid progenitor [ERP] expansion in vivo (P < .05), and a 2- to 10-fold (6.3-fold in group A vs 1.09-fold in group C) erythroid over myeloid bias in vitro. Notably, ERPs demonstrated exquisite TKI sensitivity compared with myeloid progenitors (P < .001). These LSC features were lost with progressive resistance, and MYC- and IRF1-driven inflammatory regulons were evident in patients who progressed to transformation. Patients with MMR also exhibited a 56-fold expansion (P < .01) of a normally rare subset of hyperfunctional adaptive-like NK cells, which diminished with progressive resistance, whereas patients destined for BC accumulated inhibitory NKG2A+ NK cells favoring NK cell tolerance. Finally, we developed antibody panels to validate our scRNA-seq findings. These panels may be useful for prospective studies of primary resistance, and in assessing the contribution of predetermined vs acquired factors in TKI response heterogeneity.

Integrative multi-omics landscape of fluoxetine action across 27 brain regions reveals global increase in energy metabolism and region-specific chromatin remodelling.

Depression and anxiety are major global health burdens. Although SSRIs targeting the serotonergic system are prescribed over 200 million times annually, they have variable therapeutic efficacy and side effects, and mechanisms of action remain incompletely understood. Here, we comprehensively characterise the molecular landscape of gene regulatory changes associated with fluoxetine, a widely-used SSRI. We performed multimodal analysis of SSRI response in 27 mammalian brain regions using 310 bulk RNA-seq and H3K27ac ChIP-seq datasets, followed by in-depth characterisation of two hippocampal regions using single-cell RNA-seq (20 datasets). Remarkably, fluoxetine induced profound region-specific shifts in gene expression and chromatin state, including in the nucleus accumbens shell, locus coeruleus and septal areas, as well as in more well-studied regions such as the raphe and hippocampal dentate gyrus. Expression changes were strongly enriched at GWAS loci for depression and antidepressant drug response, stressing the relevance to human phenotypes. We observed differential expression at dozens of signalling receptors and pathways, many of which are previously unknown. Single-cell analysis revealed stark differences in fluoxetine response between the dorsal and ventral hippocampal dentate gyri, particularly in oligodendrocytes, mossy cells and inhibitory neurons. Across diverse brain regions, integrative omics analysis consistently suggested increased energy metabolism via oxidative phosphorylation and mitochondrial changes, which we corroborated in vitro; this may thus constitute a shared mechanism of action of fluoxetine. Similarly, we observed pervasive chromatin remodelling signatures across the brain. Our study reveals unexpected regional and cell type-specific heterogeneity in SSRI action, highlights under-studied brain regions that may play a major role in antidepressant response, and provides a rich resource of candidate cell types, genes, gene regulatory elements and pathways for mechanistic analysis and identifying new therapeutic targets for depression and anxiety.

DUBStepR is a scalable correlation-based feature selection method for accurately clustering single-cell data.

Feature selection (marker gene selection) is widely believed to improve clustering accuracy, and is thus a key component of single cell clustering pipelines. Existing feature selection methods perform inconsistently across datasets, occasionally even resulting in poorer clustering accuracy than without feature selection. Moreover, existing methods ignore information contained in gene-gene correlations. Here, we introduce DUBStepR (Determining the Underlying Basis using Stepwise Regression), a feature selection algorithm that leverages gene-gene correlations with a novel measure of inhomogeneity in feature space, termed the Density Index (DI). Despite selecting a relatively small number of genes, DUBStepR substantially outperformed existing single-cell feature selection methods across diverse clustering benchmarks. Additionally, DUBStepR was the only method to robustly deconvolve T and NK heterogeneity by identifying disease-associated common and rare cell types and subtypes in PBMCs from rheumatoid arthritis patients. DUBStepR is scalable to over a million cells, and can be straightforwardly applied to other data types such as single-cell ATAC-seq. We propose DUBStepR as a general-purpose feature selection solution for accurately clustering single-cell data.

Single-cell analysis reveals androgen receptor regulates the ER-to-Golgi trafficking pathway with CREB3L2 to drive prostate cancer progression.

Androgen receptor (AR) plays a central role in driving prostate cancer (PCa) progression. How AR promotes this process is still not completely clear. Herein, we used single-cell transcriptome analysis to reconstruct the transcriptional network of AR in PCa. Our work shows AR directly regulates a set of signature genes in the ER-to-Golgi protein vesicle-mediated transport pathway. The expression of these genes is required for maximum androgen-dependent ER-to-Golgi trafficking, cell growth, and survival. Our analyses also reveal the signature genes are associated with PCa progression and prognosis. Moreover, we find inhibition of the ER-to-Golgi transport process with a small molecule enhanced antiandrogen-mediated tumor suppression of hormone-sensitive and insensitive PCa. Finally, we demonstrate AR collaborates with CREB3L2 in mediating ER-to-Golgi trafficking in PCa. In summary, our findings uncover a critical role for dysregulation of ER-to-Golgi trafficking expression and function in PCa progression, provide detailed mechanistic insights for how AR tightly controls this process, and highlight the prospect of targeting the ER-to-Golgi pathway as a therapeutic strategy for advanced PCa.

Single-Cell Transcriptome Analysis Reveals Estrogen Signaling Coordinately Augments One-Carbon, Polyamine, and Purine Synthesis in Breast Cancer.

Estrogen drives breast cancer (BCa) progression by directly activating estrogen receptor α (ERα). However, because of the stochastic nature of gene transcription, it is important to study the estrogen signaling pathway at the single-cell level to fully understand how ERα regulates transcription. Here, we performed single-cell transcriptome analysis on ERα-positive BCa cells following 17ß-estradiol stimulation and reconstructed the dynamic estrogen-responsive transcriptional network from discrete time points into a pseudotemporal continuum. Notably, differentially expressed genes show an estrogen-stimulated metabolic switch that favors biosynthesis but reduces estrogen degradation. Moreover, folate-mediated one-carbon metabolism is reprogrammed through the mitochondrial folate pathway and polyamine and purine synthesis are upregulated coordinately. Finally, we show AZIN1 and PPAT are direct ERα targets that are essential for BCa cell survival and growth. In summary, our study highlights the dynamic transcriptional heterogeneity in ERα-positive BCa cells upon estrogen stimulation and uncovers a mechanism of estrogen-mediated metabolic switch.

Identification of a Wells-Dawson polyoxometalate-based AP-2γ inhibitor with pro-apoptotic activity.

AP-2 gamma (AP-2γ) is a transcription factor that plays pivotal roles in breast cancer biology. To search for small molecule inhibitors of AP-2γ, we performed a high-throughput fluorescence anisotropy screen and identified a polyoxometalate compound with Wells-Dawson structure K6[P2Mo18O62] (Dawson-POM) that blocks the DNA-binding activity of AP-2γ. We showed that this blocking activity is due to the direct binding of Dawson-POM to AP-2γ. We also provided evidence to show that Dawson-POM decreases AP-2γ-dependent transcription similar to silencing the gene. Finally, we demonstrated that Dawson-POM contains anti-proliferative and pro-apoptotic effects in breast cancer cells. In summary, we identified the first small molecule inhibitor of AP-2γ and showed Dawson-POM-mediated inhibition of AP-2γ as a potential avenue for cancer therapy.

Studying forkhead box protein A1-DNA interaction and ligand inhibition using gold nanoparticles, electrophoretic mobility shift assay, and fluorescence anisotropy.

Forkhead box protein 1 (FoxA1) is a member of the forkhead family of winged helix transcription factors that plays pivotal roles in the development and differentiation of multiple organs and in the regulation of estrogen-stimulated genes. Conventional analytical methods-electrophoretic mobility shift assay (EMSA) and fluorescence anisotropy (FA)-as well as a gold nanoparticles (AuNPs)-based assay were used to study DNA binding properties of FoxA1 and ligand interruption of FoxA1-DNA binding. In the AuNPs assay, the distinct ability of protein-DNA complex to protect AuNPs against salt-induced aggregation was exploited to screen sequence selectivity and determine the binding affinity constant based on AuNPs color change and absorbance spectrum shift. Both conventional EMSA and FA and the AuNPs assay suggested that FoxA1 binds to DNA in a core sequence-dependent manner and the flanking sequence also played a role to influence the affinity. The EMSA and AuNPs were found to be more sensitive than FA in differentiation of sequence-dependent affinity. With the addition of a spin filtration step, AuNPs assay has been extended for studying small molecular ligand inhibition of FoxA1-DNA interactions enabling drug screening. The results correlate very well with those obtained using FA.