Find research datasets worth reusing

opencc-by-4.0Jul 2023View details →

Human OPRM1 and murine Oprm1 promoter driven viral constructs for genetic access to μ-opioidergic cell-types

With concurrent global epidemics of chronic pain and opioid use disorders, there is a critical need to identify, target and manipulate specific cell populations expressing the mu-opioid receptor (MOR). However, available tools and transgenic models for gaining long-term genetic access to MOR+ neural cell-types and circuits involved in modulating pain, analgesia and addiction across species are limited. To address this, we developed a catalog of MOR promoter (MORp) based constructs packaged into adeno-associated viral vectors that drive transgene expression in MOR+ cells. MORp constructs designed from promoter regions upstream of the mouse Oprm1 gene (mMORp) were validated for transduction efficiency and selectivity in endogenous MOR+ neurons in the brain, spinal cord, and periphery of mice, with additional studies revealing robust expression in rats, shrews, and human induced pluripotent stem cell (iPSC)-derived nociceptors. The use of mMORp for in vivo fiber photometry, behavioral chemogenetics, and intersectional genetic strategies is also demonstrated. Lastly, a human designed MORp (hMORp) efficiently transduced macaque cortical OPRM1+ cells. Together, our MORp toolkit provides researchers cell-type specific genetic access to target and functionally manipulate mu-opioidergic neurons across a range of vertebrate species and translational models for pain, addiction, and neuropsychiatric disorders.

opencc-by-4.0Aug 2023View details →

ChromBPNet models and data: Transcription factor stoichiometry, motif affinity and syntax regulate single cell chromatin dynamics during fibroblast reprogramming to pluripotency

This record contains ChromBPNet models and data used to train the models for the paper "Transcription factor stoichiometry, motif affinity and syntax regulate single cell chromatin dynamics during fibroblast reprogramming to pluripotency" by Nair, Ameen et al. `data` contains bigwigs and regions (peaks + non-peaks) used for training each of the models. See `data/README.txt` for more details. Models: Loading the model: The models were trained using tf1.14. The models are provided in h5 format for tf1.14 (py3.7) and SavedModel format for tf2.X. tf2.X tested only for py3.8-11, tf2.8-13. To load the models in tf1.14: <pre><code class="language-python">model = tf.keras.models.load_model("path/to/model.h5")</code></pre> In tf2: <pre><code class="language-python">model = tf.keras.models.load_model("path/to/model_dir")</code></pre> If all fails, you can load the architecture as provided in `model_arch.py` with default parameters (`bpnet_seq` for bias model and `chrombpnet` for chrombpnet model), and then load the weights using `model.load_weights` from the weights provided in the `weights` directory.   Usage: The bias models take as input one-hot sequence of length 2000. It has 2 outputs, a vector of logits of length 2000, and 1 logcounts scalar: <pre><code class="language-python"># seq_one_hot of length B x 2000 x 4 out_bias_logits, out_bias_logcounts = bias_model.predict(seq_one_hot) # out_bias_logits: B x 2000 # out_bias_logcounts: B x 1</code></pre> The ChromBPNet model takes as input a one-hot sequence of length 2000, bias logits of length 2000 and bias log-counts scalar. It has the same output types as the bias model. To run the chrombpnet model to obtain predictions: <pre><code class="language-python">pred_profile, pred_logcounts = chrombpnet_model.predict([seq_one_hot, out_bias_logits, out_bias_logcounts]) # pred_profile: B x 2000 # pred_logcounts: B x 1 </code></pre> If you wish to obtain the "de-biased" predictions (see Methods), simply pass in zeros instead of the bias model predictions as: <pre><code class="language-python">pred_profile_debiased, pred_logcounts_debiased = chrombpnet_model.predict([seq_one_hot, np.zeros((seq_one_hot.shape[0], 2000)), np.zeros((seq_one_hot.shape[0], 1))])</code></pre> To obtain predicted per-base predicted counts (with or without bias): <pre><code class="language-python">pred_per_base_counts = scipy.special.softmax(pred_profile, axis=-1) * (np.exp(pred_logcounts)-1) # pred_per_base_counts: B x 2000 </code></pre> Note that in general predicted counts can't be compared across models as they are not corrected for sequencing depth.   Note: All bias models used across folds are identical, except for the final intercept term in the counts output (see Methods), that is specific to each cell state, fold combination.   Folds: The splits used for training the different folds are as below: Fold Test Chromosomes Validation Chromosomes 0 chr1 chr8, chr10 1 chr2, chr19 chr1 2 chr3, chr20 chr2, chr19 3 chr6, chr13, chr22 chr3, chr20 4 chr5, chr16, chrY chr6, chr13, chr22 5 chr4, chr15, chr21 chr5, chr16, chrY 6 chr7, chr18, chr14 chr4, chr15, chr21 7 chr11, chr17, chrX chr7, chr18, chr14 8 chr9, chr12 chr11, chr17, chrX 9 chr8, chr10 chr9, chr12 Remaining chromosomes were used as the training chromosome for each fold.

opencc-zeroAug 2023View details →

Phage-antibiotic synergy: cell filamentation is a key driver of successful phage predation

<div class="t-landing__text-wall "> Phages are promising tools to fight antibiotic-resistant bacteria, and as for now, phage therapy is essentially performed in combination with antibiotics. Interestingly, combined treatments including phages and a wide range of antibiotics lead to an increased bacterial killing, a phenomenon called phage-antibiotic synergy (PAS), suggesting that antibiotic-induced changes in bacterial physiology alter the dynamics of phage propagation. Using single-phage and single-cell techniques, each step of the lytic cycle of phage HK620 was studied in E. coli cultures treated with either ciprofloxacin or cephalexin, two filamentation-inducing antibiotics. In the presence of sublethal doses of antibiotics, multiple stress tolerance and DNA repair pathways are triggered following activation of the SOS response. One of the most notable effects is the inhibition of bacterial division. As a result, a significant fraction of cells forms filaments that stop dividing but have higher rates of mutagenesis. Antibiotic-induced filaments become easy targets for phages due to their enlarged surface areas, as demonstrated by fluorescence microscopy and flow cytometry techniques. Adsorption, infection and lysis occur more often in filamentous cells compared to regular-sized bacteria. In addition, the reduction in bacterial numbers caused by impaired cell division may account for the faster elimination of bacteria during PAS. We developed a mathematical model to capture the interaction between sublethal doses of antibiotics and exposition to phages. This model shows that the induction of filamentation by sublethal doses of antibiotics can amplify the replication of phages and therefore yield PAS. We also use this model to study the consequences of PAS on the emergence of antibiotic resistance. A significant percentage of hyper-mutagenic filamentous bacteria are effectively killed by phages due to their increased susceptibility to infection. As a result, the addition of even a very low number of bacteriophages produced a strong reduction of the mutagenesis rate of the entire bacterial population. We confirm this prediction experimentally using reporters for bacterial DNA repair. Our work highlights the multiple benefits associated with the combination of sublethal doses of antibiotics with bacteriophages. </div>

opencc-by-4.0Sep 2023View details →

ALA induced transcriptome and single cell microscopy of M.tuberculosis

Single cell microscopy of porphyeins in vegetative and dormant Mycobacterium tuberculosis. Confocal fluorescence microscopy, life-time measurements, and microspectrofluorimetry. Fluorescence lifetime measurements were performed on a PicoQuant MicroTime 200 confocal scanning system (Pico-Quant GmbH, Berlin, Germany) based on an Olympus IX-71 inverted fluorescence microscope (Japan). SymphoTime® software was used for data collection. Fluorescence spectra were recorded in the confocal mode of the MicroTime 200 system using a Shamrock 163 spectrograph with a Newton DU-970 camera (Andor, UK). Transcriptomic analysis of cells of M. tuberculosis in a vegetative state and under transition into dormant state upon administration of exogenous ALA. The quality of the resulting libraries was checked using the Fragment Analyzer. Quantitative analysis was performed by qPCR. After quality control and assessment of DNA quantity, the pool of libraries was sequenced on an Illumina NovaSeq 6000 instrument (length of reads - 150 bp on both sides of the fragments). FASTQ files were generated using bcl2fastq v2.20 Conversion Software (Illumina). The quality data string record format is Phred 33. As a result, 1,107,614,502 reads were received.  <pre>This study was funded by Russian Science Foundation grant 19-15-00324.</pre>

opencc-by-nc-nd-4.0Sep 2023View details →

Models and Data associated with: Single-cell gene expression prediction from DNA sequence at large contexts

This archive holds trained models and associated data for the <a href="https://www.biorxiv.org/content/10.1101/2023.07.26.550634v1">manuscript</a>: "Single-cell gene expression prediction from DNA sequence at large contexts" Structure: <ul> <li>configs - example configs for the workflows to produce publication data </li> <li>data_* - pre-processed single cell data used for publication</li> <li>models_* - model checkpoints, hyperparameters and training progress in tensorboard logs</li> <li>preprocessing - additional data required to reproduce the pre-processing workflow</li> </ul>   "Copyright 2023 GlaxoSmithKline Research & Development Limited. All rights reserved."

opencc-by-4.0Sep 2023View details →

Spatially-resolved single-cell atlas of ascidian endostyle provides insights into the origin of vertebrate pharyngeal organs

This repository contains the data and code needed to recreate figures in the main and supplementary text of the paper "Spatially-resolved single-cell atlas of ascidian endostyle provides insights into the origin of vertebrate pharyngeal organs" by the same authors. Instructions for running the code are provided in the README.md file. See also <a href="https://github.com/lskfs/ascidian-endostyle">https://github.com/lskfs/ascidian-endostyle</a>.

Data from: Significant differences in capsid properties and potency between AAV vectors produced in Sf9 and HEK293 cells

For successful vector-based gene therapy manufacturing, the selected adeno-associated virus (AAV) vector production system must produce vector at sufficient scale. However, concerns have arisen regarding the quality of vector produced using different systems. In this study, we compared AAV serotypes 1, 8, and 9 produced by two different systems (Sf9/baculovirus and HEK293/transfection) and purified by two separate processes. We evaluated capsid properties including protein composition, post-translational modification, particle content profiles, and in vitro and in vivo vector potency. Vectors produced in the Sf9/baculovirus system displayed reduced incorporation of viral protein 1 and 2 into the capsid, increased capsid protein deamidation, increased empty and partially packaged particles in vector preparations, and an overall reduced potency. The differences observed were largely independent of the harvest method and purification process. These findings illustrate the need for careful consideration when choosing an AAV vector production system for clinical production.

opencc-by-4.0Aug 2023View details →

Enhanced Mapping of Small Molecule Binding Sites in Cells

Selected docking poses for Wozniak et al manuscript. File name include the structure id (PDBID or Alphafold model) and the probe id, separated by an underscore (i.e., 6tjk_4.pdb, or AF-P10620-F1-model_6.pdb).

opencc-by-4.0Jul 2023View details →

AFM Dataset on HeLa cells

Dataset acquired using a NanoWizard III (Bruker, Santa Barbara, CA, USA) on 40 HeLa cells using a SAA-SPH-5UM AFM probe (Bruker, Santa Barbara, CA, USA). The dataset includes force maps (20 cells) and microrheological measurements (20 cells) acquired on HeLa cells. Additionally this dataset includes: - Force settings used to acquired the data - Data used to characterize the piezo lag - Data used to characterize the viscous drag - Results obtained from PyFMLab, MATLAB routines and JPKDP

opencc-zeroMay 2023View details →

Cell Health Data Supplementary Files

This dataset contains supplementary files related to the <a href="https://github.com/WayScience/cell-health-data">cell-health-data</a> repository that were too large to upload on GitHub. Each of these files were saved to and loaded from an external hard drive during Cell Health Data Processing. These files include: <ul> <li>single_cell_classification_probabilities/ : Single cell classification probabilities derived with each model trained in <a href="https://github.com/WayScience/phenotypic_profiling_model">phenotypic_profiling_model</a>. Each compressed csv file in single_cell_classification_probabilities/ corresponds to single-cell classifications from a particular plate as derived with a particular model. The model used to derive the probabilities is indicated by the file's parent folders. Each compressed csv file includes the metadata (location and perturbation) and phenotypic class probabilities for each cell in the plate.</li> <li>classification_profiles/ : This folder contains aggregated data known as "classification profiles". These profiles are generated from the single-cell classification probabilities found in the 'single_cell_classification_probabilities/' folder. Specifically, for each model, we find the mean of the single-cell classification probabilities across each perturbation and cell line to create a composite profile. This aggregated data provides a summarized view of cell behavior for each perturbation/cell line combination, as predicted by each model.</li> </ul> For more information regarding the generation of this data, please see <a href="https://github.com/WayScience/cell-health-data/tree/master/4.classify-single-cell-phenotypes">cell-health-data/4.classify-single-cell-phenotypes</a>.

opencc-bySep 2022View details →

v0.92 single cell Eye in a Disk (scEiaD) dataset for PLAE.nei.nih.gov

Unified (integrated) pan study ocular single cell transcriptome

Artificial space weathering to mimic solar wind enhances the toxicity of lunar dust simulants in human lung cells

During NASA's Apollo missions, inhalation of dust particles from lunar regolith was identified as a potential occupational hazard for astronauts. These fine particles adhered tightly to spacesuits and were unavoidably brought into the living areas of the spacecraft. Apollo astronauts reported that exposure to the dust caused intense respiratory and ocular irritation. This problem is a potential challenge for the Artemis Program, which aims to return humans to the Moon for extended stays in this decade. Since lunar dust is "weathered" by space radiation, solar wind, and the incessant bombardment of micrometeorites, we investigated whether treatment of lunar regolith simulants to mimic space weathering enhanced their toxicity. Two such simulants were employed in this research, Lunar Mare Simulant-1 (LMS-1), and Lunar Highlands Simulant-1 (LHS-1), which were added to cultures of human lung epithelial cells (A549) to simulate lung exposure to the dusts. In addition to pulverization, previously shown to increase dust toxicity sharply, the simulants were exposed to hydrogen gas at high temperature as a proxy for solar wind exposure. This treatment further increased the toxicity of both simulants, as measured by the disruption of mitochondrial function, and damage to DNA both in mitochondria and in the nucleus. By testing the effects of supplementing the cells with an antioxidant (N-acetylcysteine), we showed that a substantial component of this toxicity arises from free radicals. It remains to be determined to what extent the radicals arise from the dust itself, as opposed to their active generation by inflammatory processes in the treated cells.

Data for: A cell surface-binding antibody atlas nominates a MUC18-directed antibody-drug conjugate for targeting melanoma

Recent advances in targeted therapy and immunotherapy have substantially improved the treatment of melanoma. However, therapeutic strategies are still needed for unresponsive or treatment-relapsed melanoma patients. To discover antibody-drug conjugate (ADC)-tractable cell surface targets for melanoma, we developed an atlas of melanoma cell surface binding antibodies (pAbs) using a proteome-scale antibody array platform (PETAL). Target identification of pAbs led to development of melanoma cell killing ADCs against LGR6, TRPM1, ASAP1, and MUC18, among others. MUC18 was overexpressed in both tumor cells and tumor-infiltrating blood vessels across major melanoma subtypes, making it a potential dual-compartment and universal melanoma therapeutic target. AMT-253, an MUC18-directed ADC based on topoisomerase I inhibitor exatecan and a self-immolative T moiety, had a higher therapeutic index compared to its microtubule inhibitor-based counterpart and favorable pharmacokinetics and tolerability in monkeys. AMT-253 exhibited MUC18-specific cytotoxicity through DNA damage and apoptosis and a strong bystander killing effect, leading to potent antitumor activities against melanoma cell line and patient-derived xenograft models. Tumor vasculature-targeting by a mouse MUC18-specific antibody-T1000-exatecan conjugate inhibited tumor growth in human melanoma xenografts. Combination therapy of AMT-253 with an anti-angiogenic agent generated higher efficacy than single agent in a mucosal melanoma model. Beyond melanoma, AMT-253 was also efficacious in a wide range of MUC18-expressing solid tumors. Efficient target/antibody discovery in combination with the T moiety-exatecan linker-payload exemplified here may facilitate discovery of new ADC to improve cancer treatment.

Quantification of hair cell number, ribeye b and nuclei in the zebrafish inner ear endorgans

The three otolithic endorgans of the inner ear are known to be involved in sound detection in different teleost fishes, yet their relative roles for auditory-vestibular functions within the same species remain unclear. In zebrafish (Danio rerio), saccule and utricle are thought to play key functions in encoding auditory and vestibular information, respectively, but the biological function of the lagena is not clear. We hypothesized that the zebrafish saccule serves as a primary auditory endorgan, making it more vulnerable to noise exposure, and that lagena might have an auditory function given its connectivity to the saccule and dominant vestibular function of the utricle. In this study, we compared the impact of acoustic trauma (continuous white noise at 168 dB for 24 h) between the sensory epithelia of the three otolithic endorgans. Noise treatment caused hair cell loss in both the saccule and lagena but not in the utricle. This effect was identified immediately after acoustic treatment and did not increase 24 h post trauma. Furthermore, hair cell loss was accompanied by a reduction in presynaptic activity measured based on Ribeye b presence but mainly in the saccule, supporting its main contribution for noise-induced hearing loss. Our findings support the hypothesis that the saccule plays a major role in sound detection and that lagena is also acoustically affected extending the species hearing dynamic range.

Computationally defined and in vitro validated putative genomic safe harbour loci for transgene expression in human cells

Selection of the target site is an inherent question for any project aiming for directed transgene integration. Genomic safe harbour (GSH) loci have been proposed as safe sites in the human genome for transgene integration. Although several sites have been characterised for transgene integration in the literature, most of these do not meet criteria set out for a GSH, and the limited set that do have not been characterised extensively. Here, we conducted a computational analysis using publicly available data to identify 25 unique putative GSH loci that reside in active chromosomal compartments. We validated stable transgene expression and minimal disruption of the native transcriptome in three GSH sites in vitro using human embryonic stem cells (hESCs) and their differentiated progeny. Furthermore, for easily targeted transgene expression, we have engineered constitutive landing pad expression constructs into the three validated GSH in hESCs.

opencc-by-nc-4.0Jan 2024View details →

Combining graph neural networks and computer vision methods for cell nuclei classification in lung tissue

Database of the article "Combining graph neural networks and computer vision methods for cell nuclei classification in lung tissue ".

opencc-by-4.0Jan 2023View details →

COVID-19 vaccination single cell datasets

The datasets presented here comprise the sequencing data featured in the research paper titled: "Multimodal single-cell datasets characterize antigen-specific CD8+ T cells across SARS-CoV-2 vaccination and infection": https://www.nature.com/articles/s41590-023-01608-9 Peripheral Blood Mononuclear Cell (PBMC) samples utilized for both CITE-seq and ASAP-seq were systematically collected at four distinct time intervals: <ul> <li>Pre-vaccination (Day 0)</li> <li>Post-primary vaccination (Day 2 and Day 10.</li> <li>Seven days post-boost vaccination (Day 28).</li> </ul> The count matrix folder contains count matrices for each experimental type, specifically CITE-seq, ASAP-seq, and ECCITE-seq. In addition, we have included the fully integrated, processed Seurat objects for downstream analysis. Details of the content within the count matrix folder are as follows: <ul> <li>The RNA, ATAC, and TCR modality outputs were generated using the 10x Cellranger pipeline.</li> <li>HTO and ADT modalities were mapped with Alevin.</li> </ul> Outlined below are the three processed single-cell datasets: <ol> <li>PBMC_vaccine_CITE.rds: 3' RNA and surface proteins (173 TotalSeq-A antibodies)</li> <li>PBMC_vaccine_ASAP.rds: Chromatin accessibility and surface proteins (173 TotalSeq-A antibodies)</li> <li>PBMC_vaccine_ECCITE_TCR.rds: 5' RNA, surface proteins (137 TotalSeq-C antibodies), TCR and dextramer loaded with peptides of SARS-CoV-2 spike protein.</li> </ol> <ul> <li>antigen_module_genes.rds: This file contains the vaccine-induced gene sets.</li> <li>antigen_module_peaks.rds: This file contains the DE peaks specific for vaccine-induced cells. </li> </ul> To map the scRNA-seq query dataset onto our CITE-seq reference: <pre><code>library(Seurat) PBMC_CITE <- readRDS("/zenedo/PBMC_vaccine_CITE.rds") query_scRNA <- readRDS("/home/xx/your_own_data.rds") anchors <- FindTransferAnchors( reference = PBMC_CITE, query = query_scRNA, normalization.method = "SCT", k.anchor = 5, reference.reduction = "spca", dims = 1:50) query_scRNA <- MapQuery( anchorset = anchors, query = query_scRNA, reference = PBMC_CITE, refdata = list( l1 = "celltypel1", l2 = "celltypel2", l3 = "celltypel3"), reference.reduction = "spca", reduction.model = "wnn.umap") </code></pre> To use the scATAC-seq data, please run the commands below to update the path of the fragment file for the object.   <pre><code>Vaccine_ASAP <- readRDS("PBMC_vaccine_ASAP.rds") # remove fragment file information Fragments(Vaccine_ASAP) <- NULL # Update the path of the fragment file Fragments(Vaccine_ASAP) <- CreateFragmentObject(path = "download/PBMC_vaccine_ASAP_fragments.tsv.gz", cells = Cells(Vaccine_ASAP))</code></pre>

opencc-by-4.0Sep 2023View details →

"CausalXtract: a flexible pipeline to extract causal effects from live-cell time-lapse imaging data" datasets

Datasets from the article: CausalXtract: a flexible pipeline to extract causal effects from live-cell time-lapse imaging data by Franck Simon, Maria Colomba Comes, Tiziana Tocci, Louise Dupuis, Vincent Cabeli, Nikita Lagrange, Arianna Mencattini, Maria Carla Parrini, Eugenio Martinelli, Hervé Isambert.   The original videos are uploaded as: "20161230.zip", "20170105.rar", "Video_2017_0517.zip". Details for each experiment can be found in: "Experiments' details.zip". The ROIs (ROI: Region of Interest) are uploaded as .tif files in: "EXTRACTED ROIs.zip". The MATLAB data is uploaded in "MATLAB_DATA.rar" and includes: the cancer cells' trajectories (subfolder: "TUMOR TRAJECTORIES"), the immune cells' trajectories (subfolder: "IMMUNE TRAJECTORIES"), the ROIs videos as .mat files for the detection of cells (subfolder: "ROI MAT"), the ROIs further cropped for the extraction of shape descriptors (folder: "ROI_TU MAT"). The ROIs videos .mat files included in the last two subfolders are stopped after their apoptosis has been detected.

opencc-by-4.0Dec 2022View details →

Atomistic, Macromolecular Model of the Populus Secondary Cell Wall Informed by Solid-State NMR

Solid-state NMR dataset used to inform molecular model of the Populus secondary cell wall. Contains: - 2D through-bond and through-space ssNMR data on 13C-enriched Populus wood. Data used to inform spectral deconvolution of selective 1D spin-diffusion data - 2D gelHSQC NMR data for understanding of lignin composition - MultiCP-1D-DARR datasets on 5 replicates. Includes raw NMR data and ascii files of processed spectra. For each replicate, three experiments are conducted: non-selective 1D MultiCP-DARR, 22ppm selective MultiCP-DARR, and 150ppm selective MultiCP-DARR. 13C-13C spin-diffusion mixing times ranged from 0.001 to 5000 ms. - Excel files containing tabulated deconvoluted signal areas, and averages for key signal groups. - Excel files containing tabulated magnetization recovery values for each signal and signal groups for all replicates. Averaged values for X2C, X2L, L2C and L2X at the longest mixing times (see manuscript) are used as key metrics for evaluating molecular models. - Excel files containing tabulated T1-adjusted spin-diffusion rate constants for each replicate, and key averages.

PHRF1 promotes the class switch recombination of IgA in CH12F3-2A cells-Dataset

PHRF1 is an E3 ligase that promotes TGF-B signaling by ubiquitinating a homeodomain repressor TG-interacting factor (TGIF). The suppression of PHRF1 activity by PML-RAR facilitates the progression of acute promyelocytic leukemia (APL). PHRF1 also contributes to non-homologous end-joining in response to DNA damage by linking H3K36me3 and NBS1 with DNA repair machinery. However, its role in class switch recombination (CSR) is not well understood. In this study, we report the importance of PHRF1 in IgA switching in CH12F3-2A cells and CD19-Cre mice. Our studies revealed that Crispr-Cas9 mediated PHRF1 knockout and shRNA-silenced CH12F3-2A cells reduced IgA production, as well as decreased the amounts of PARP1, NELF-A, and NELF-D. The introduction of PARP1 could partially restore IgA production in PHRF1 knockout cells. Intriguingly, IgA, as well as IgG1, IgG2a, and IgG3, switchings were not significantly decreased in PHRF1 deficient splenic B lymphocytes isolated from CD19-Cre mice. The levels of PARP1 and NELF-D were not decreased in PHRF1-depleted primary splenic B cells. Overall, our findings suggest that PHRF1 may modulate IgA switching in CH12F3-2A cells.