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Background Cells orchestrate histone biogenesis with strict temporal and quantitative control. To efficiently regulate histone biogenesis, the repetitive Drosophila melanogaster replication-dependent histone genes are arrayed and clustered at a single locus. Regulatory factors concentrate in a nuclear body known as the histone locus body (HLB), which forms around the locus. Historically, HLB factors are largely discovered by chance, and few are known to interact directly with DNA. It is therefore unclear how the histone genes are specifically targeted for unique and coordinated regulation. Results To expand the list of known HLB factors, we performed a candidate-based screen by mapping 30 publicly available ChIP datasets of 27 unique factors to the Drosophila histone gene array. We identified novel transcription factor candidates, including the Drosophila Hox proteins Ultrabithorax (Ubx), Abdominal-A (Abd-A), and Abdominal-B (Abd-B), suggesting a new pathway for these factors in influencing body plan morphogenesis. Additionally, we identified six other factors that target the histone gene array: JIL-1, hormone-like receptor 78 (Hr78), the long isoform of female sterile homeotic (1) (fs(1)h) as well as the general transcription factors TBP associated factor 1 (TAF-1), Transcription Factor IIB (TFIIB), and Transcription Factor IIF (TFIIF). Conclusions Our foundational screen provides several candidates for future studies into factors that may influence histone biogenesis. Further, our study emphasizes the powerful reservoir of publicly available datasets, which can be mined as a primary screening technique.

Background Kv1.3 channels are promising therapeutic targets to modulate neuroinflammatory responses in neurodegenerative disease including Alzheimer’s disease (AD). Although the ability of Kv1.3 blockers to reduce neuropathology in AD mouse models has been demonstrated, the cellular mechanisms remain unclear. Method 5xFAD mice aged 6 months were treated with PAP‐1 (a small molecule Kv1.3 blocker, chow), ShK‐223 (peptide blocker, intra‐peritoneal twice‐weekly), control chow, or vehicle injections until 9 months age. We assessed neurobehavior, Aβ burden neuropathology and single‐nuclei transcriptomics (10x Chromium 3’). For Sn‐RNA‐seq, 137,375 cortical nuclei from n = 4 mice/group passed QC after alignment, data integration (Seurat) and batch effects removal (Harmony), followed by cell‐type annotation using a reference and its prediction score, pseudo‐bulk analysis was performed using cell‐type subsets based on the contrast PAP‐1 versus Control. DEGs were identified using (glmGamPoi) based on log2FC > < 0.25 and adjP <0.05, and magnitude of drug effect was compared across cell types. Utilized GSEA with ClusterProfiler and enrichGO to investigate Kv1.3 blockade effects on diverse cell populations. Result While both Kv1.3 blockers (PAP‐1 and ShK‐223) reduced Ab plaque burden in 5xFAD mice, lower Ab42 ELISA‐measured levels and improved fear conditioning behavior were only observed in the PAP‐1 group. snRNAseq revealed distinct cell type clusters, with consistent cell type proportions across all treatment groups. 5xFAD mice exhibited increased disease‐associated microglia compared to WT mice. In pseudo‐bulk analyses, PAP‐1 had the most significant impact on microglia compared to other cell types, evident from the proportion of DEGs in the sampled transcriptome. In microglia, PAP‐1 upregulated complement genes (C1qa, C1qb, C1qc), induced glial cell differentiation, transmembrane transport and voltage‐gated potassium channel. PAP‐1 treatment enhanced myelination (Plp1), oligodendrocyte differentiation and neuronal ensheathment in oligodendrocytes. These aligned with heightened activity of cognition and synapse‐related genes in astrocytes, glutamatergic and GABAergic neurons. Conclusion Our studies re‐affirm beneficial neuropathological effects of pharmacological Kv1.3 blockade in Ab models, and additionally reveal differential effects and mechanisms of membrane permeable and brain penetrant versus non‐permeant Kv1.3 blockers. PAP‐1 prominently affects microglia and oligodendrocytes, suggesting they may be potential targets of Kv1.3 blockade. Additionally, evidence indicates pro‐myelination and synaptic‐protective effects of Kv1.3 blockade.

Kv1.3 channels are promising therapeutic targets to modulate neuroinflammatory responses in neurodegenerative disease including Alzheimer's disease (AD). Although the ability of Kv1.3 blockers to reduce neuropathology in AD mouse models has been demonstrated, the cellular mechanisms remain unclear. 5xFAD mice aged 6 months were treated with PAP-1 (a small molecule Kv1.3 blocker, chow), ShK-223 (peptide blocker, intra-peritoneal twice-weekly), control chow, or vehicle injections until 9 months age. We assessed neurobehavior, Aβ burden neuropathology and single-nuclei transcriptomics (10x Chromium 3'). For Sn-RNA-seq, 137,375 cortical nuclei from n = 4 mice/group passed QC after alignment, data integration (Seurat) and batch effects removal (Harmony), followed by cell-type annotation using a reference and its prediction score, pseudo-bulk analysis was performed using cell-type subsets based on the contrast PAP-1 versus Control. DEGs were identified using (glmGamPoi) based on log2FC > < 0.25 and adjP <0.05, and magnitude of drug effect was compared across cell types. Utilized GSEA with ClusterProfiler and enrichGO to investigate Kv1.3 blockade effects on diverse cell populations. While both Kv1.3 blockers (PAP-1 and ShK-223) reduced Ab plaque burden in 5xFAD mice, lower Ab42 ELISA-measured levels and improved fear conditioning behavior were only observed in the PAP-1 group. snRNAseq revealed distinct cell type clusters, with consistent cell type proportions across all treatment groups. 5xFAD mice exhibited increased disease-associated microglia compared to WT mice. In pseudo-bulk analyses, PAP-1 had the most significant impact on microglia compared to other cell types, evident from the proportion of DEGs in the sampled transcriptome. In microglia, PAP-1 upregulated complement genes (C1qa, C1qb, C1qc), induced glial cell differentiation, transmembrane transport and voltage-gated potassium channel. PAP-1 treatment enhanced myelination (Plp1), oligodendrocyte differentiation and neuronal ensheathment in oligodendrocytes. These aligned with heightened activity of cognition and synapse-related genes in astrocytes, glutamatergic and GABAergic neurons. Our studies re-affirm beneficial neuropathological effects of pharmacological Kv1.3 blockade in Ab models, and additionally reveal differential effects and mechanisms of membrane permeable and brain penetrant versus non-permeant Kv1.3 blockers. PAP-1 prominently affects microglia and oligodendrocytes, suggesting they may be potential targets of Kv1.3 blockade. Additionally, evidence indicates pro-myelination and synaptic-protective effects of Kv1.3 blockade.

Cells orchestrate histone biogenesis with strict temporal and quantitative control. To efficiently regulate histone biogenesis, the repetitive Drosophila melanogaster replication-dependent histone genes are arrayed and clustered at a single locus. Regulatory factors concentrate in a nuclear body known as the histone locus body (HLB), which forms around the locus. Historically, HLB factors are largely discovered by chance, and few are known to interact directly with DNA. It is therefore unclear how the histone genes are specifically targeted for unique and coordinated regulation. To expand the list of known HLB factors, we performed a candidate-based screen by mapping 30 publicly available ChIP datasets of 27 unique factors to the Drosophila histone gene array. We identified novel transcription factor candidates, including the Drosophila Hox proteins Ultrabithorax (Ubx), Abdominal-A (Abd-A), and Abdominal-B (Abd-B), suggesting a new pathway for these factors in influencing body plan morphogenesis. Additionally, we identified six other factors that target the histone gene array: JIL-1, hormone-like receptor 78 (Hr78), the long isoform of female sterile homeotic (1) (fs(1)h) as well as the general transcription factors TBP associated factor 1 (TAF-1), Transcription Factor IIB (TFIIB), and Transcription Factor IIF (TFIIF). Our foundational screen provides several candidates for future studies into factors that may influence histone biogenesis. Further, our study emphasizes the powerful reservoir of publicly available datasets, which can be mined as a primary screening technique.

Cells orchestrate histone biogenesis with strict temporal and quantitative control. To efficiently regulate histone biogenesis, the repetitive replication-dependent histone genes are arrayed and clustered at a single locus. Regulatory factors concentrate in a nuclear body known as the histone locus body (HLB), which forms around the locus. Historically, HLB factors are largely discovered by chance, and few are known to interact directly with DNA. It is therefore unclear how the histone genes are specifically targeted for unique and coordinated regulation. To expand the list of known HLB factors, we performed a candidate-based screen by mapping 30 publicly available ChIP datasets and 27 factors to the histone gene array. We identified novel transcription factor candidates, including the Hox proteins Ultrabithorax, Abdominal-A and Abdominal-B, suggesting a new pathway for these factors in influencing body plan morphogenesis. Additionally, we identified six other transcription factors that target the histone gene array: JIL-1, Hr78, the long isoform of fs(1)h as well as the generalized transcription factors TAF-1, TFIIB, and TFIIF. Our foundational screen provides several candidates for future studies into factors that may influence histone biogenesis. Further, our study emphasizes the powerful reservoir of publicly available datasets, which can be mined as a primary screening technique.

Different brain cell types play distinct roles in brain development and disease. Molecular characterization of cell-specific mechanisms using cell type-specific approaches at the protein (proteomic) level, can provide biological and therapeutic insights. To overcome the barriers of conventional isolation-based methods for cell type-specific proteomics, in vivo proteomic labeling with proximity dependent biotinylation of cytosolic proteins using biotin ligase TurboID, coupled with mass spectrometry (MS) of labeled proteins, has emerged as a powerful strategy for cell type-specific proteomics in the native state of cells without need for cellular isolation. To complement in vivo proximity labeling approaches, in vitro studies are needed to ensure that cellular proteomes using the TurboID approach are representative of the whole cell proteome, and capture cellular responses to stimuli without disruption of cellular processes. To address this, we generated murine neuroblastoma (N2A) and microglial (BV2) lines stably expressing cytosolic TurboID to biotinylate the cellular proteome for downstream purification and analysis using MS. TurboID-mediated biotinylation captured 59% of BV2 and 65% of N2A proteomes under homeostatic conditions. TurboID expression and biotinylation minimally impacted homeostatic cellular proteomes of BV2 and N2A cells, and did not affect cytokine production or mitochondrial respiration in BV2 cells under resting or lipopolysaccharide (LPS)-stimulated conditions. These included endo-lysosome, translation, vesicle and signaling proteins in BV2 microglia, and synaptic, neuron projection and microtubule proteins in N2A neurons. The effect of LPS treatment on the microglial proteome was captured by MS analysis of biotinylated proteins (>500 differentially-abundant proteins) including increased canonical pro-inflammatory proteins (Il1a, Irg1, Oasl1) and decrease anti-inflammatory proteins (Arg1, Mgl2). Competing Interest Statement The authors have declared no competing interest. Footnotes * Updated abstract.

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t -distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings. This resource describes a collection of neurons from a variety of light microscopy-based datasets, which can serve as a gold standard for testing automated tracing algorithms, as shown by comparison of the performance of 35 algorithms.

BigNeuron is an open community bench-testing platform combining the expertise of neuroscientists and computer scientists toward the goal of setting open standards for accurate and fast automatic neuron reconstruction. The project gathered a diverse set of image volumes across several species representative of the data obtained in most neuroscience laboratories interested in neuron reconstruction. Here we report generated gold standard manual annotations for a selected subset of the available imaging datasets and quantified reconstruction quality for 35 automatic reconstruction algorithms. Together with image quality features, the data were pooled in an interactive web application that allows users and developers to perform principal component analysis t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and reconstruction data, and benchmarking of automatic reconstruction algorithms in user-defined data subsets. Our results show that image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. By benchmarking automatic reconstruction algorithms, we observed that diverse algorithms can provide complementary information toward obtaining accurate results and developed a novel algorithm to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms. Finally, to aid users in predicting the most accurate automatic reconstruction results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic reconstructions. Competing Interest Statement The authors have declared no competing interest. Footnotes * https://linusmg.shinyapps.io/BigNeuron_Gold166/ * https://neuroxiv.net/bigneuron/ * https://github.com/BigNeuron/BigNeuron-Wiki/wiki * https://github.com/lmanubens/BigNeuron