Biology subjects

Kyono, Y.

Publications and source records attributed to Kyono, Y..

6 recordsLinked to original sources

bioRxiv2025

Massively parallel reporter assay reveals promoter-, position-, and strand-specific effects in transcription start sites

Massively parallel reporter assays (MPRA) are a high-throughput method of assessing the activity of candidate cis-regulatory sequences, and can be used to detect allelic differences at disease-associated variants. Previous MPRA studies have screened thousands of functional SNPs associated with various complex traits and conditions. Most MPRA libraries utilize a single plasmid configuration, a single minimal promoter to drive expression, and a single-strand orientation, which may fail to capture the context-dependent activity of disease-associated cis-regulatory elements. We interrogate the potential regulatory differences introduced by variable MPRA plasmid promoters and positions. We used an MPRA library to quantify the activity of 1,305 pancreatic islet-derived transcription start sites generated from CAP analysis of gene expression profiling. We cloned fragments upstream or downstream of a reporter gene along with either the human insulin (INS) promoter or a synthetic housekeeping promoter (SCP1). We used elastic net regression to predict position-specific fragment activity based on enrichment of transcription factor binding site motifs, and generalized linear models to predict position-specific fragment activity from tissue-specific chromatin state regulatory annotations. Our results support the use of MPRA strategies that account for context-dependent factors when assaying candidate regulatory elements in pursuit of understanding complex genetic diseases.

genomics↗

bioRxiv2023

Using a modular massively parallel reporter assay to discover context-specific regulatory grammars in type 2 diabetes

Most genome-wide association signals for complex disease reside in the noncoding genome, where defining function is nontrivial. MPRAs (massively parallel reporter assays) offer a scalable means to identify functional regulatory elements, but are typically conducted without regard to cell type, pairing cloned fragments with a generic housekeeping promoter. To explore the context-sensitivity of MPRAs, we screened enhancer activity across a panel of nearly 12,000 198-bp fragments spanning over 300 type 2 diabetes- and metabolic trait-associated regions in the 832/13 rat insulinoma beta cell line, a relevant model of pancreatic beta cells. We explored these fragments context sensitivity by comparing their activities when placed up- or downstream of a reporter gene, and in combination with either a synthetic housekeeping promoter (SCP1) or a more biologically relevant promoter corresponding to the human insulin (INS) gene. We identified clear effects of MPRA construct design on enhancer activity. Specifically, a subset of fragments (n = 702/11,656) displayed positional bias, evenly distributed across up- and downstream preference. Promoter choice also influenced MPRA activity (n = 698/11,656), mostly biased towards the cell-specific INS promoter (73.4%). To identify sequence features associated with promoter preference, we used Lasso regression with 562 genomic annotations and discovered that fragments with INS promoter-biased activity are enriched for HNF1 motifs. HNF1 family transcription factors are key regulators of glucose metabolism disrupted in maturity onset diabetes of the young (MODY), suggesting genetic convergence between rare coding variants that cause MODY and common T2D-associated regulatory regions. We designed a follow-up MPRA containing HNF1 motif-enriched fragments and observed several instances where deletion or mutation of HNF1 motifs disrupted the INS promoter-biased enhancer activity, specifically in the beta cell model but not in a skeletal muscle cell line, another diabetes-relevant cell type. Together, our study suggests that cell-specific regulatory activity is partially influenced by enhancer-promoter compatibility and indicates that careful attention should be paid when designing MPRA libraries to capture context-specific regulatory processes at disease-associated genetic signals.

genomics↗

bioRxiv2021

The Atypical Antipsychotic Quetiapine Induces Multiple Antibiotic Resistance in Escherichia coli

Atypical antipsychotic (AAP) medication is a critical tool for treating symptoms of psychiatric disorders. While AAPs primarily target dopamine (D2) and serotonin (5HT2A and 5HT1A) receptors, they also exhibit intrinsic antimicrobial activity as an off-target effect. Because AAPs are often prescribed to patients for many years, a potential risk associated with long-term AAP use is the unintended emergence of bacteria with antimicrobial resistance (AMR). Here, we show that exposure to the AAP quetiapine at estimated gut concentrations promotes AMR in Escherichia coli after six weeks. Quetiapine-exposed isolates exhibited an increase in minimal inhibitory concentrations (MICs) for ampicillin, tetracycline, ceftriaxone, and levofloxacin. By whole genome sequencing analysis, we identified mutations in genes that confer AMR, including the repressor for the multiple antibiotic resistance mar operon (marR), and real-time RT-qPCR analysis showed increased levels of marA, acrA, and tolC mRNAs and a reduced level of ompF mRNA in the isolates carrying marR mutations. To determine the contribution of each marR mutation to AMR, we constructed isogenic strains carrying individual mutant marR alleles in the parent background and re-evaluated their resistant phenotypes using MIC and RT-qPCR assays. While marR mutations induced a robust activity of the mar operon, they resulted in only a modest increase in MICs. Interestingly, although these marR mutations did not fully recapitulate the AMR phenotype of the quetiapine-exposed isolates, we show that marR mutations promote growth fitness in the presence of quetiapine. Our findings revealed an important link between the use of AAPs and AMR development in E. coli.

microbiology↗

bioRxiv2019

Integrating enhancer RNA signatures with diverse omics data identifies characteristics of transcription initiation in pancreatic islets

Identifying the tissue-specific molecular signatures of active regulatory elements is critical to understand gene regulatory mechanisms. Here, we identify transcription start sites (TSS) using cap analysis of gene expression (CAGE) across 57 human pancreatic islet samples. We identify 9,954 reproducible CAGE tag clusters (TCs), ~20% of which are islet-specific and occur mostly distal to known gene TSSs. We integrated islet CAGE data with histone modification and chromatin accessibility profiles to identify epigenomic signatures of transcription initiation. Using a massively parallel reporter assay, we validate transcriptional enhancer activity (5% FDR) for 2,279 of 3,378 (~68%) tested islet CAGE elements. TCs within accessible enhancers show higher enrichment to overlap type 2 diabetes genome-wide association study (GWAS) signals than existing islet annotations, which emphasizes the utility of mapping CAGE profiles in disease-relevant tissue. This work provides a high-resolution map of transcriptional initiation in human pancreatic islets with utility for dissecting functional enhancers at GWAS loci.

genomics↗

bioRxiv2019

Chromatin information content landscapes inform transcription factor and DNA interactions

Interactions between transcription factors (TFs) and chromatin are fundamental to genome organization and regulation and, ultimately, cell state. Here, we use information theory to measure signatures of TF-chromatin interactions encoded in the patterns of the accessible genome, which we call chromatin information enrichment (CIE). We calculate CIE for hundreds of TF motifs across human tissues and identify two classes: low and high CIE. The 10-20% of TF motifs with high CIE associate with higher protein-DNA residence time, including different binding sites subclasses of the same TF, increased nucleosome phasing, specific protein domains, and the genetic control of both gene expression and chromatin accessibility. These results show that variations in the information content of chromatin architecture reflect functional biological variation, with implications for cell state dynamics and memory.

genomics↗

bioRxiv2019

STARRPeaker: Uniform processing and accurate identification of whole human STARR-seq active regions

BackgroundHigh-throughput reporter assays, such as self-transcribing active regulatory region sequencing (STARR-seq), allow for unbiased and quantitative assessment of enhancers at a genome-wide scale. Recent advances in STARR-seq technology have employed progressively more complex genomic libraries and increased sequencing depths, to assay larger sized regions, up to the entire human genome. These advances necessitate a reliable processing pipeline and peak-calling algorithm. ResultsMost STARR-seq studies have relied on chromatin immunoprecipitation sequencing (ChIP-seq) processing pipelines. However, there are key differences in STARR-seq versus ChIP-seq. First, STARR-seq uses transcribed RNA to measure the activity of an enhancer, making an accurate determination of the basal transcription rate important. Second, STARR-seq coverage is highly non-uniform, overdispersed, and often confounded by sequencing biases, such as GC content and mappability. Lastly, here, we observed a clear correlation between RNA thermodynamic stability and STARR-seq readout, suggesting that STARR-seq may be sensitive to RNA secondary structure and stability. Considering these findings, we developed a negative-binomial regression framework for uniformly processing STARR-seq data, called STARRPeaker. In support of this, we generated whole-genome STARR-seq data from the HepG2 and K562 human cell lines and applied STARRPeaker to call enhancers. ConclusionsWe show STARRPeaker can unbiasedly detect active enhancers from both captured and whole-genome STARR-seq data. Specifically, we report [~]33,000 and [~]20,000 candidate enhancers from HepG2 and K562, respectively. Moreover, we show that STARRPeaker outperforms other peak callers in terms of identifying known enhancers with fewer false positives. Overall, we demonstrate an optimized processing framework for STARR-seq experiments can identify putative enhancers while addressing potential confounders.

bioinformatics↗