Biology subjects

Zou, F.

Publications and source records attributed to Zou, F..

At least 37 records · Page 2Linked to original sources

bioRxiv2024

The dual resistance mechanism of CYP325G4 and CYP6AA9 in Culex pipiens pallens legs according to transcriptome and proteome analysis

Mosquitoes within the Culex pipiens complex play a crucial role in human disease transmission. Insecticides, especially pyrethroids, are used to control these vectors. Mosquito legs are the main entry point and barrier for insecticides to gain their neuronal targets. However, the resistance mechanism in mosquito legs is unclear. Herein, we employed transcriptomic analyses and isobaric tags for relative and absolute quantitation techniques to investigate the resistance mechanism, focusing on Cx. pipiens legs. We discovered 2346 differentially expressed genes (DEGs) between deltamethrin-resistant (DR) and deltamethrin-sensitive (DS) mosquito legs, including 41 cytochrome P450 genes. In the same comparison, we identified 228 differentially expressed proteins (DEPs), including six cytochrome P450 proteins). Combined transcriptome and proteome analysis revealed only two upregulated P450 genes, CYP325G4 and CYP6AA9. The main clusters of DEGs and DEPs were associated with metabolic processes, such as cytochrome P450-mediated metabolism of drugs and xenobiotics. Transcription analysis revealed high CYP325G4 and CYP6AA9 expression in the DR strain at 72 hours post-eclosion compared with that in the DS strain, particularly in the legs. Mosquitoes knocked down for CYP325G4 were more sensitive to deltamethrin than the controls. CYP325G4 knockdown reduced the expression of several chlorinated hydrocarbon (CHC)-related genes, which altered the cuticle thickness and structure. Conversely, CYP6AA9 knockdown increased CHC gene expression without altering cuticle thickness and structure. P450 activity analysis demonstrated that CYP325G4 and CYP6AA9 contributed to metabolic resistance in the midgut and legs. This study identified CYP325G4 as a novel mosquito deltamethrin resistance factor, being involved in both metabolic and cuticular resistance mechanisms. The previously identified CYP6AA9 was investigated for its involvement in metabolic resistance and potential cuticular resistance in mosquito legs. These findings enhance our comprehension of resistance mechanisms, identifying P450s as promising targets for the future management of mosquito vector resistance, and laying a theoretical groundwork for mosquito resistance management. Author SummaryCulex pipiens mosquitoes are the primary vector of the filamentous nematode, Wuchereria bancrofti and also involved in the transmission of other pathogens, such as West Nile virus (WNV), avian malarias, and avian pox virus. Insecticides, particularly pyrethroids, continue to be the primary method to control these significant vectors. Worryingly, resistance to insecticides has become widespread and is rapidly intensifying in Culex mosquitoes throughout China, posing a threat to the efficacy of insecticides. Legs are the main sites of contact with ITNs and sprayed insecticides, and the insecticides have to penetrate the leg cuticle to reach their targets.Therefore, the resistance mechanisms in mosquito legs deserve further investigation. Several reports have found a certain amount of P450 in insect legs. Unfortunately, none of the above reports have conducted further functional studies on P450s in the legs. Here, we have identified two P450 enzymes, CYP325G4 and CYP6AA9, through the integrated analysis of transcriptomics and proteomics. CYP325G4 enriched in the cuticle of resistant mosquitoes might possess a dual resistance mechanism involving metabolic resistance and cuticle resistance. CYP6AA9 was slightly different, possibly exerting metabolic resistance as its main function and also being involved in cuticle synthesis. Understanding the dual resistance mechanism of P450s in the metabolism of pyrethroid insecticides will have an important role in optimizing vector control strategies.

physiology↗

bioRxiv2024

A novel VCP modulator, KUS121, attenuates atherosclerosis progression by maintaining intracellular ATP and mitigating ER stress in endothelial cells

BackgroundEndoplasmic reticulum (ER) stress signaling pathways have pivotal roles in atherosclerosis progression. Recently, we have developed Kyoto University Substance (KUS) 121, which selectively inhibits ATPase activities of valosin-containing protein (VCP) and consequently saves intracellular ATP consumption and mitigates ER stress. Methods and ResultsWe assessed the efficacy of KUS121 against atherosclerosis by its daily injection into Apoe-/- mice fed with Western Diet (WD) for 8 weeks. Consequently, KUS121 treatment reduced atherosclerosis progression by approximately 40% in atherosclerotic plaques. Interestingly, we found that C/EBP homologous protein (CHOP), an established ER stress marker, was mainly expressed in plaque endothelium. Therefore, we assessed the action of KUS121 in endothelial cells using the human endothelial cell line (EA.hy926 cells). As a result, KUS121 prevented ER stress-induced apoptosis and downregulated the IRE1 (Inositol-requiring enzyme) -associated inflammatory pathways. Consistent with these in vitro findings, KUS121 treatment also significantly reduced endothelial apoptosis assessed by TUNEL staining and inflammation examined by immunostaining of Nuclear factor kappa B (NF-{kappa}B) and Intercellular adhesion molecule (ICAM) 1 at plaque endothelium. We also demonstrated that KUS121 maintained ATP levels in EA.hy926 cells and atherosclerotic plaque lesions using the single-wavelength or the FRET-based fluorescent ATP sensor. Supplementation of intracellular ATP by Methyl pyruvate (MePyr) attenuated ER stress-induced apoptotic and inflammatory pathways in endothelial cells, which could be the main mechanism how KUS121 reduces ER stress. ConclusionsKUS121 can be a new therapeutic option for atherosclerotic diseases by maintaining intracellular ATP levels and attenuating ER stress-induced apoptosis and inflammation in plaque endothelium.

pathology↗

bioRxiv2024

Transcription Factor Condensates Mediate Clustering of MET Regulon and Enhancement in Gene Expression

Some transcription factors (TFs) can form liquid-liquid phase separated (LLPS) condensates. However, the functions of these TF condensates in 3D genome organization and gene regulation remain elusive. In response to methionine (met) starvation, budding yeast TF Met4 and a few co-activators, including Met32, induce a set of genes involved in met biosynthesis. Here, we show that the endogenous Met4 and Met32 form co-localized puncta-like structures in yeast nuclei upon met depletion. Recombinant Met4 and Met32 form mixed droplets with LLPS properties in vitro. In relation to chromatin, Met4 puncta co-localize with target genes, and at least a subset of these target genes is clustered in 3D in a Met4-dependent manner. A MET3pr-GFP reporter inserted near several native Met4 binding sites becomes co-localized with Met4 puncta and displays enhanced transcriptional activity. A Met4 variant with a partial truncation of an intrinsically disordered region (IDR) shows less puncta formation, and this mutant selectively reduces the reporter activity near Met4 binding sites to the basal level. Overall, these results support a model where Met4 and co-activators form condensates to bring multiple target genes into a vicinity with higher local TF concentrations, which facilitates a strong response to methionine depletion.

genetics↗

bioRxiv2023

Adaptive Regularized Tri-Factor Non-Negative Matrix Factorization for Cell Type Deconvolution

Accurate deconvolution of cell types from bulk gene expression is crucial for understanding cellular compositions and uncovering cell-type specific differential expression and physiological states of diseased tissues. Existing deconvolution methods have limitations, such as requiring complete cellular gene expression signatures or neglecting partial biological information. Moreover, these methods often overlook varying cell-type mRNA amounts, leading to biased proportion estimates. Additionally, they do not effectively utilize valuable reference information from external studies, such as means and ranges of population cell-type proportions. To address these challenges, we introduce an Adaptive Regularized Tri-factor non-negative matrix factorization approach for deconvolution (ARTdeConv). We rigorously establish the numerical convergence of our algorithm. Through benchmark simulations, we demonstrate the superior performance of ARTdeConv compared to state-of-the-art semi-reference-based and reference-free methods. In a real-world application, our method accurately estimates cell proportions, as evidenced by the nearly perfect Pearsons correlation between ARTdeConv estimates and flow cytometry measurements in a dataset from a trivalent influenza vaccine study. Moreover, our analysis of ARTdeConv estimates in COVID-19 patients reveals patterns consistent with important immunological phenomena observed in other studies. The proposed method, ARTdeConv, is implemented as an R package and can be accessed on GitHub for researchers and practitioners.

bioinformatics↗

bioRxiv2023

A Deep Neural Network Two-part Model and Feature Importance Test for Semi-continuous Data

Semi-continuous data frequently arise in clinical practice. For example, while many surgical patients suffer from varying degrees of acute postoperative pain (POP) post surgery (i.e., POP score > 0), others experience none (i.e., POP score = 0), indicating the existence of two distinct data processes at play. Existing parametric or semi-parametric two-part modeling methods for this type of semicontinuous data can fail to appropriately model these two underlying data processes as such methods rely heavily on (generalized) linear additive assumptions. However, many factors may interact to jointly influence the experience of POP non-additively and non-linearly. Motivated by this challenge and inspired by the flexibility of deep neural networks (DNN) to accurately approximate complex functions universally, we derive a DNN-based two-part model by adapting the conventional DNN methods by adding two additional components: a bootstrapping procedure along with a filtering algorithm to boost the stability of the conventional DNN, an approach we denote as sDNN. To improve the interpretability and transparency of sDNN, we further derive a feature importance testing procedure to identify important features contributing to the outcome measurements of the two data processes, denoting this approach fsDNN. We show that fsDNN not only offers a valid feature importance test but also that using the identified features can further improve the predictive performance of sDNN. The proposed sDNN- and fsDNN-based twopart models are applied to the analysis of real data from a POP study, in which application they clearly demonstrate advantages over the existing parametric and semi-parametric two-part models. Further, we conduct extensive numerical studies to demonstrate that sDNN and fsDNN consistently outperform the existing two-part models regardless of the data complexity. An R package implementing the proposed methods has been developed and deposited on GitHub (https://github.com/SkadiEye/fsDNN).

bioinformatics↗

bioRxiv2022

Re-expression of CA1 and entorhinal activity patterns preserves temporal context memory at long timescales

Converging, cross-species evidence indicates that memory for time is supported by hippocampal area CA1 and entorhinal cortex. However, limited evidence characterizes how these regions preserve temporal memories over long timescales (e.g., months). At long timescales, memoranda may be encountered in multiple temporal contexts, potentially creating interference. Here, using 7T fMRI, we measured CA1 and entorhinal activity patterns as human participants viewed thousands of natural scene images distributed, and repeated, across many months. We show that memory for an images original temporal context was predicted by the degree to which CA1/entorhinal activity patterns from the first encounter with an image were re-expressed during re-encounters occurring minutes to months later. Critically, temporal memory signals were dissociable from predictors of recognition confidence, which were carried by distinct medial temporal lobe expressions. These findings suggest that CA1 and entorhinal cortex preserve temporal memories across long timescales by coding for and reinstating temporal context information.

neuroscience↗

bioRxiv2022

A histone deacetylase network regulates epigenetic reprogramming and viral silencing in HIV infected cells

Approximately 70% of the HIV-1 latent reservoir originates from infections of CD4 T cells that occur in the months near the time of ART initiation, raising the possibility that interventions during this period might prevent reservoir seeding and reduce reservoir size. We identify class 1 histone deacetylase inhibitors (HDACi) as potent agents of latency prevention. Inhibiting HDACs in productively infected cells caused extended maintenance of HIV expression and this activity was associated with persistently elevated H3K9 acetylation and reduced H3K9 methylation at the viral LTR promoter region. HDAC inhibition in HIV-infected CD4 T cells during effector-to-memory transition led to striking changes in the memory phenotype of infected cells. Proviral silencing is accomplished through distinct activities of HDAC1/2 and HDAC3. Thus HDACs regulate a critical gateway process for HIV latency establishment and are required for the development of CD4 T-cell memory subsets that preferentially harbor long-lived, latent provirus.

molecular biology↗

bioRxiv2022

TwinEQTL: Ultra Fast and Powerful Association Analysis for eQTL and GWAS in Twin Studies

We develop a computationally efficient alternative, TwinEQTL, to a linear mixed-effects model (LMM) for twin genome-wide association study (GWAS) data. Instead of analyzing all twin samples together with LMM, TwinEQTL first splits twin samples into two independent groups on which multiple linear regression analysis can be validly performed separately, followed by an appropriate meta-analysis-like approach to combine the two non-independent test results. Through mathematical derivations, we prove the validity of TwinEQTL algorithm and show that the correlation between two dependent test statistics at each single-nucleotide polymorphism (SNP) are independent of its minor allele frequency (MAF). Thus the correlation is constant across all SNPs. Through simulations, we show empirically that TwinEQTL has well controlled type I error with negligible power loss compared to the gold-standard linear mixed effects models. To accommodate eQTL analysis with twin subjects, we further implement TwinEQTL into a R package with much improved computational efficiency. Our approaches provide a significant leap in terms of computing speed for GWAS and eQTL analysis with twin samples.

genetics↗

bioRxiv2022

Inadvertent Transfer of Murine VL30 Retrotransposons to CAR-T Cells.

For more than a decade genetically engineered autologous T-cells have been successfully employed as immunotherapy drugs for patients with incurable blood cancers. The active component in some of these game-changing medicines are autologous T-cells that express viral vector-delivered chimeric antigen receptors (CARs), which specifically target proteins that are preferentially expressed on cancer cells. Some of these therapeutic CAR expressing T-cells (CAR-Ts) are engineered via transduction with {gamma}-retroviral vectors ({gamma}-RVVs) produced in a stable producer cell line that was derived from murine PG13 packaging cells (ATCC CRL-10686). Earlier studies reported on the co-packaging of murine virus-like 30S RNA (VL30) genomes with {gamma}-retroviral vectors generated in murine stable packaging cells. In an earlier study VL30 mRNA was found to enhance the metastatic potential of human melanoma cells. These findings raise biosafety concerns regarding the possibility that therapeutic CAR-Ts have been inadvertently contaminated with potentially oncogenic VL30 retrotransposons. In this study, we demonstrated the presence of infectious VL30 particles in PG13 cells conditioned media and observed the ability of these particles to deliver transcriptionally active VL30 genomes to human cells. Notably, VL30 genomes packaged by HIV-1-based vector particles transduced naive human cells in culture. Furthermore, we detected transfer and expression of VL30 genomes in clinical-grade CAR-Ts generated by transduction with PG13 cells-derived {gamma}-retroviral vectors. Our findings raise biosafety concerns regarding the use of murine packaging cell lines in ongoing clinical applications.

microbiology↗

bioRxiv2021

High-throughput Evaluation of Epilepsy-associated KCNQ2 Variants Reveals Functional and Pharmacological Heterogeneity

Hundreds of KCNQ2 variants have been identified by genetic testing of children with early onset epilepsy and/or developmental disability. Voltage-clamp recording from heterologous cells has proved useful for establishing deleterious functional effects of KCNQ2 variants, but procedures adapting these assays for standardized, higher throughput data collection and reporting are lacking. In this study, we employed automated patch clamp recording to assess in parallel the functional and pharmacological properties of 79 missense and 2 in-frame deletion variants of KCNQ2. Among the variants we studied were a training set of 18 pathogenic variants previously studied by voltage-clamp recording, 24 mostly rare population variants, and 39 disease-associated variants with unclear functional effects. Variant KCNQ2 subunits were transiently expressed in a cell line stably expressing KCNQ3 to reconstitute the physiologically relevant channel complex. Variants with severe loss-of-function were also co-expressed 1:1 with WT KCNQ2 in the KCNQ3 cell line to mimic the heterozygous genotype and assess dominant-negative behavior. In total, we analyzed electrophysiological data recorded from 9,480 cells. The functional properties of WT KCNQ2/KCNQ3 channels and pharmacological responses to known blockers and activators determined by automated patch clamp recording were highly concordant with previous findings. Similarly, functional properties of 18 known pathogenic variants largely matched previously published results and the validated automated patch clamp assay. Many of the 39 previously unstudied disease-associated KCNQ2 variants exhibited prominent loss-of-function and dominant-negative effects, providing strong evidence in support of pathogenicity. All variants, exhibit response to retigabine (10 {micro}M), although there were differences in maximal responses. Variants within the ion selectivity filter exhibited the weakest responses whereas retigabine had the strongest effect on gain-of-function variants in the voltage-sensor domain. Our study established a high throughput method to detect deleterious functional consequences of KCNQ2 variants. We demonstrated that dominant-negative loss-of-function is a common mechanism associated with missense KCNQ2 variants but this does not occur with rare population variation in this gene. Importantly, we observed genotype-dependent differences in the response of KCNQ2 variants to retigabine.

neuroscience↗

bioRxiv2021

Prioritizing candidate peptides for cancer vaccines by PEPPRMINT: a statistical model to predict peptide presentation by HLA-I proteins

Recent development of cancer immunotherapy has opened unprecedented avenues to eliminate tumor cells using the human immune system. Cancer vaccines composed of neoantigens, or peptides unique to tumor cells due to somatic mutations, have emerged as a promising approach to activate or strengthen the immune response against cancer. A key step to identifying neoantigens is computationally predicting which somatically mutated peptides are presented on the cell surface by a human leukocyte antigen (HLA). Computational prediction relies on large amounts of high-quality training data, such as mass spectrometry data of peptides presented by one of several HLAs in living cells. We developed a complete pipeline to prioritize neoantigens for cancer vaccines. A key step of our pipeline is PEPPRMINT (PEPtide PResentation using a MIxture model and Neural neTwork), a model designed to exploit mass spectrometry data to predict peptide presentation by HLAs. We applied our pipeline to DNA sequencing data of 60 melanoma patients and identified a group of neoantigens that were more immunogenic in tumor cells than in normal cells. Additionally, the neoantigen burden estimated by PEPPRMINT was significantly associated with activity of the immune system, suggesting these neoantigens could induce an immune response.

genomics↗

bioRxiv2021

Joint Gene Network Construction by Single-Cell RNA Sequencing Data

SO_SCPLOWUMMARYC_SCPLOWIn contrast to differential gene expression analysis at single gene level, gene regulatory networks (GRN) analysis depicts complex transcriptomic interactions among genes for better understandings of underlying genetic architectures of human diseases and traits. Recently, single-cell RNA sequencing (scRNA-seq) data has started to be used for constructing GRNs at a much finer resolution than bulk RNA-seq data and microarray data. However, scRNA-seq data are inherently sparse which hinders direct application of the popular Gaussian graphical models (GGMs). Furthermore, most existing approaches for constructing GRNs with scRNA-seq data only consider gene networks under one condition. To better understand GRNs under different but related conditions with single-cell resolution, we propose to construct Joint Gene Networks with scRNA-seq data (JGNsc) using the GGMs framework. To facilitate the use of GGMs, JGNsc first proposes a hybrid imputation procedure that combines a Bayesian zero-inflated Poisson (ZIP) model with an iterative low-rank matrix completion step to efficiently impute zero-inflated counts resulted from technical artifacts. JGNsc then transforms the imputed data via a nonparanormal transformation, based on which joint GGMs are constructed. We demonstrate JGNsc and assess its performance using synthetic data. The application of JGNsc on two cancer clinical studies of medulloblastoma and glioblastoma identifies novel findings in addition to confirming well-known biological results.

genetics↗

bioRxiv2021

eQTL mapping using allele-specific gene expression

Using information from allele-specific gene expression (ASE) can sub-stantially improve the power to map gene expression quantitative trait loci (eQTLs). However, such practice has been limited, partly due to high computational cost and the requirement to access raw data that can take a large amount of storage space. To address these computational challenges, we have developed a computational framework that uses a statistical method named TReCASE as its computational engine, and it is computationally feasible for large scale analysis. We applied it to map eQTLs in 28 human tissues using the data from the Genotype-Tissue Expression (GTEx) project. Compared with a popular linear regression method that does not use ASE data, TReCASE can double the number of eGenes (i.e., genes with at least one significant eQTL) when sample size is relatively small, e.g., n = 200. We also demonstrated how to use the ASE data that we have collected to study dynamic eQTLs whose effect sizes vary with respect to another variable, such as age. We find the majority of such dynamic eQTLs are due to some underlying latent factors, such as cell type proportions. We further compare TReCASE versus another method RASQUAL. TReCASE is ten times or more faster than RASQUAL and it provides more robust type I error control.

genomics↗

bioRxiv2021

Improved Metabolite Prediction Using Microbiome Data-Based Elastic Net Models

Microbiome data are becoming increasingly available in large health cohorts yet metabolomics data are still scant. While many studies generate microbiome data, they lack matched metabolomics data or have considerable missing proportions of metabolites. Since metabolomics is key to understanding microbial and general biological activities, the possibility of imputing individual metabolites or inferring metabolomics pathways from microbial taxonomy or metagenomics is intriguing. Importantly, current metabolomics profiling methods such as the HMP Unified Metabolic Analysis Network (HUMAnN) have unknown accuracy and are limited in their ability to predict individual metabolites. To address this gap, we developed a novel metabolite prediction method, and we present its application and evaluation in an oral microbiome study. We developed ENVIM based on the Elastic Net Model (ENM) to predict metabolites using micorbiome data. ENVIM introduces an extra step to ENM to consider variable importance scores and thus achieve better prediction power. We investigate the metabolite prediction performance of ENVIM using metagenomic and metatranscriptomic data in a supragingival biofilm multi-omics dataset of 297 children ages 3-5 who were participants of a community-based study of early childhood oral health (ZOE 2.0) in North Carolina, United States. We further validate ENVIM in two additional publicly available multi-omics datasets generated from studies of gut health and vagina health. We select gene-family sets based on variable importance scores and modify the existing ENM strategy used in the MelonnPan prediction software to accommodate the unique features of microbiome and metabolome data. We evaluate metagenomic and metatranscriptomic predictors and compare the prediction performance of ENVIM to the standard ENM employed in MelonnPan. The newly-developed ENVIM method showed superior metabolite predictive accuracy than MelonnPan using metatranscriptomics data only, metagenomics data only, or both of these two. Both methods perform better prediction using gut or vagina microbiome data than using oral microbiome data for the samples corresponding metabolites. The top predictable compounds have been reported in all these three datasets from three different body sites. Enrichment of prediction some contributing species has been detected.

microbiology↗

bioRxiv2021

Distribution and vulnerability of transcriptional outputs across the genome in Myc-amplified medulloblastoma cells

Myc plays a central role in tumorigenesis by orchestrating the expression of genes essential to numerous cellular processes1-4. While it is well established that Myc functions by binding to its target genes to regulate their transcription5, the distribution of the transcriptional output across the human genome in Myc-amplified cancer cells, and the susceptibility of such transcriptional outputs to therapeutic interferences remain to be fully elucidated. Here, we analyze the distribution of transcriptional outputs in Myc-amplified medulloblastoma (MB) cells by profiling nascent total RNAs within a temporal context. This profiling reveals that a major portion of transcriptional action in these cells was directed at the genes fundamental to cellular infrastructure, including rRNAs and particularly those in the mitochondrial genome (mtDNA). Notably, even when Myc protein was depleted by as much as 80%, the impact on transcriptional outputs across the genome was limited, with notable reduction mostly only in genes involved in ribosomal biosynthesis, genes residing in mtDNA or encoding mitochondria-localized proteins, and those encoding histones. In contrast to the limited direct impact of Myc depletion, we found that the global transcriptional outputs were highly dependent on the activity of Inosine Monophosphate Dehydrogenases (IMPDHs), rate limiting enzymes for de novo guanine nucleotide synthesis and whose expression in tumor cells was positively correlated with Myc expression. Blockage of IMPDHs attenuated the global transcriptional outputs with a particularly strong inhibitory effect on infrastructure genes, which was accompanied by the abrogation of MB cells proliferation in vitro and in vivo. Together, our findings reveal a real time action of Myc as a transcriptional factor in tumor cells, provide new insight into the pathogenic mechanism underlying Myc-driven tumorigenesis, and support IMPDHs as a therapeutic vulnerability in cancer cells empowered by a high level of Myc oncoprotein.

cancer biology↗

bioRxiv2021

CBX4 regulates long-form thymic stromal lymphopoietin-mediated airway inflammation through SUMOylation in HDM-induced asthma

RationaleThymic stromal lymphopoietin (TSLP) is present in two distinct isoforms, short-form (sfTSLP) and long-form (lfTSLP). lfTSLP promotes inflammation while sfTSLP inhibits inflammation in allergic asthma. However, little is known about the regulation of lfTSLP and sfTSLP during allergic attack in asthma airway epithelium. Methods and ResultsHere, we report that SUMOylation was enhanced in HDM-induced allergic asthma airway epithelium. Inhibition of SUMOylation significantly alleviated airway Th2 inflammation and lfTSLP expression. Mechanistically, CBX4, a SUMOylation E3 ligase, enhanced lfTSLP, but not sfTSLP, mRNA translation through the RNA binding protein, MEX-3B. MEX-3B promoted lfTSLP translation through binding of its KH domains to the lfTSLP mRNA. Furthermore, CBX4 regulated MEX-3B transcription in HBE through enhancing SUMOylation levels of the transcription factor, TFII-I. ConclusionWe demonstrate an important mechanism whereby CBX4 promotes MEX-3B transcription through enhancing TFII-I SUMOylation, and MEX-3B enhances the expression of lfTSLP through binding to the lfTSLP mRNA and promoting its translation. Our findings uncover a novel target of CBX4 for therapeutic agents to lfTSLP-mediated asthma.

molecular biology↗

bioRxiv2021

Distinct generation of subjective vividness and confidence during naturalistic memory retrieval in angular gyrus

Our subjective experience of remembering guides and monitors the reconstruction of past and simulation of the future, which enables us to identify mistakes and adjust our behavior accordingly. However, it remains incompletely understood what underlies the process of subjective mnemonic experience. Here, we combined behavior, repetitive transcranial magnetic stimulation (rTMS), and functional neuroimaging to probe whether vividness and confidence are generated differently during retrieval. We found that preretrieval rTMS targeting the left angular gyrus (AnG) selectively attenuated the vividness efficiency compared to control stimulation while keeping metacognitive efficiency and objective memory accuracy unaffected. Using trial-wise data, we showed that AnG stimulation altered the mediating role of vividness in confidence in the accuracy of memory judgment. Moreover, resting-state functional connectivity of hippocampus and AnG was specifically associated with vividness efficiency, but not metacognitive efficiency across individuals. Together, these results identify the causal involvement of AnG in gauging the vividness, but not the confidence, of memory, thereby suggesting a differentiation account of conscious assessment of memory by functionally and anatomically dissociating the monitoring of vividness from confidence.

neuroscience↗

bioRxiv2020

Testcrosses are an efficient strategy for identifying cis regulatory variation: Bayesian analysis of allele specific expression (BASE)

Allelic imbalance (AI) occurs when alleles in a diploid individual are differentially expressed and indicates cis acting regulatory variation. What is the distribution of allelic effects in a natural population? Are all alleles the same? Are all alleles distinct? Tests of allelic effect are performed by crossing individuals and comparing expression between alleles directly in the F1. However, a crossing scheme that compares alleles pairwise is a prohibitive cost for more than a handful of alleles as the number of crosses is at least (n2-n)/2 where n is the number of alleles. We show here that a testcross design followed by a hypothesis test of AI between testcrosses can be used to infer differences between non-tester alleles, allowing n alleles to be compared with n crosses. Using a mouse dataset where both testcrosses and direct comparisons have been performed, we show that [~]75% of the predicted differences between non-tester alleles are validated in a background of [~]10% differences in AI. The testing for AI involves several complex bioinformatics steps. BASE is a complete bioinformatics pipeline that incorporates state-of-the-art error reduction techniques and a flexible Bayesian approach to estimating AI and formally comparing levels of AI between conditions. The modular structure of BASE has been packaged in Galaxy, made available in Nextflow and sbatch. (https://github.com/McIntyre-Lab/BASE_2020). In the mouse data, the direct test identifies more cis effects than the testcross. Cis-by-trans interactions with trans-acting factors on the X contributing to observed cis effects in autosomal genes in the direct cross remains a possible explanation for the discrepancy.

bioinformatics↗