protein occupancy
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2022 ◽  
Author(s):  
Ethan Ashby ◽  
Lucinda Paddock ◽  
Hannah L Betts ◽  
Geneva Miller ◽  
Anya Porter ◽  
...  

Trypanosoma brucei , the causative agent of Human and Animal African trypanosomiasis, cycles between a mammalian host and a tsetse fly vector. The parasite undergoes huge changes in morphology and metabolism as it adapts to each host environment. These changes are reflected in the differing transcriptomes of parasites living in each host. While changes in the transcriptome have been well catalogued for parasites differentiating from the mammalian bloodstream to the insect stage, it remains unclear whether chromatin interacting proteins mediate transcriptomic changes during life cycle adaptation. We and others have shown that chromatin interacting bromodomain proteins localize to transcription start sites in bloodstream parasites, but whether the localization of bromodomain proteins changes as parasites differentiate from bloodstream to insect stage parasites remains unknown. To address this question, we performed Cleavage Under Target and Release Using Nuclease (CUT&RUN) timecourse experiments using a tagged version of Bromodomain Protein 3 (Bdf3) in parasites differentiating from bloodstream to insect stage forms. We found that Bdf3 occupancy at most loci increased at 3 hours following onset of differentiation and decreased thereafter. A number of sites with increased bromodomain protein occupancy lie proximal to genes known to have altered transcript levels during differentiation, such as procyclins, procyclin associated genes, and invariant surface glycoproteins. While most Bdf3 occupied sites are observed throughout differentiation, a very small number appear de novo as differentiation progresses. Notably, one such site lies proximal to the procyclin gene locus, which contains genes essential for remodeling surface proteins following transition to the insect stage. Overall, these studies indicate that occupancy of chromatin interacting proteins is dynamic during life cycle stage transitions, and provides the groundwork for future studies aimed at uncovering whether changes in bromodomain protein occupancy affect transcript levels of neighboring genes. Additionally, the optimization of CUT&RUN for use in Trypanosoma brucei may prove helpful for other researchers as an alternative to Chromatin Immunoprecipitation (ChIP).


2021 ◽  
Author(s):  
Nataliya Petryk ◽  
Nazaret Reverón-Gómez ◽  
Cristina González-Aguilera ◽  
Maria Dalby ◽  
Robin Andersson ◽  
...  

2021 ◽  
Author(s):  
Alexander Krohannon ◽  
Mansi Srivastava ◽  
Simone Rauch ◽  
Rajneesh Srivastava ◽  
Bryan Dickinson ◽  
...  

Recent discovery of the gene editing system - CRISPR (Clustered Regularly Interspersed Short Palindromic Repeats) associated proteins (Cas), has resulted in its widespread use for improved understanding of a variety of biological systems. Cas13, a lesser studied Cas protein, has been repurposed to allow for efficient and precise editing of RNA molecules. The Cas13 system utilizes base complementarity between a crRNA/sgRNA (crispr RNA or single guide RNA) and a target RNA transcript, to preferentially bind to only the target transcript. Unlike targeting the upstream regulatory regions of protein coding genes on the genome, the transcriptome is significantly more redundant, leading to many transcripts having wide stretches of identical nucleotide sequences. Transcripts also exhibit complex three-dimensional structures and interact with an array of RBPs (RNA Binding Proteins), both of which further limit the scope of effective target sequences. As a result, there currently exists no method to predict whether a specific sgRNA will effectively knockdown a transcript. Here we present a novel machine learning and computational tool, CASowary, to predict the efficacy of a sgRNA. We used publicly available RNA knockdown data from Cas13 characterization experiments for 555 sgRNAs targeting the transcriptome in HEK293 cells, in conjunction with transcriptome-wide protein occupancy information on RNA. Our model utilizes a Decision Tree architecture with a set of 112 sequence and target availability features, to classify sgRNA efficacy into one of four classes, based upon expected level of target transcript knockdown. After accounting for noise in the training data set, the noise-normalized accuracy exceeds 70%. Additionally, highly effective sgRNA predictions have been experimentally validated using an independent RNA targeting Cas system - CIRTS, confirming the robustness and reproducibility of our model's sgRNA predictions. Utilizing transcriptome wide protein occupancy map generated using POP-seq in Hela cells against publicly available protein-RNA interaction map in Hek293 cells, we show that CASowary can predict high quality guides for numerous transcripts in a cell line specific manner. Application of CASowary to whole transcriptomes should enable rapid deployment of CRISPR/Cas13 systems, facilitating the development of therapeutic interventions linked with aberrations in RNA regulatory processes.


PLoS Biology ◽  
2021 ◽  
Vol 19 (6) ◽  
pp. e3001306
Author(s):  
Peter L. Freddolino ◽  
Haley M. Amemiya ◽  
Thomas J. Goss ◽  
Saeed Tavazoie

Free-living bacteria adapt to environmental change by reprogramming gene expression through precise interactions of hundreds of DNA-binding proteins. A predictive understanding of bacterial physiology requires us to globally monitor all such protein–DNA interactions across a range of environmental and genetic perturbations. Here, we show that such global observations are possible using an optimized version of in vivo protein occupancy display technology (in vivo protein occupancy display—high resolution, IPOD-HR) and present a pilot application to Escherichia coli. We observe that the E. coli protein–DNA interactome organizes into 2 distinct prototypic features: (1) highly dynamic condition-dependent transcription factor (TF) occupancy; and (2) robust kilobase scale occupancy by nucleoid factors, forming silencing domains analogous to eukaryotic heterochromatin. We show that occupancy dynamics across a range of conditions can rapidly reveal the global transcriptional regulatory organization of a bacterium. Beyond discovery of previously hidden regulatory logic, we show that these observations can be utilized to computationally determine sequence specificity models for the majority of active TFs. Our study demonstrates that global observations of protein occupancy combined with statistical inference can rapidly and systematically reveal the transcriptional regulatory and structural features of a bacterial genome. This capacity is particularly crucial for non-model bacteria that are not amenable to routine genetic manipulation.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mansi Srivastava ◽  
Rajneesh Srivastava ◽  
Sarath Chandra Janga

AbstractInteraction between proteins and RNA is critical for post-transcriptional regulatory processes. Existing high throughput methods based on crosslinking of the protein–RNA complexes and poly-A pull down are reported to contribute to biases and are not readily amenable for identifying interaction sites on non poly-A RNAs. We present Protein Occupancy Profile-Sequencing (POP-seq), a phase separation based method in three versions, one of which does not require crosslinking, thus providing unbiased protein occupancy profiles on whole cell transcriptome without the requirement of poly-A pulldown. Our study demonstrates that ~ 68% of the total POP-seq peaks exhibited an overlap with publicly available protein–RNA interaction profiles of 97 RNA binding proteins (RBPs) in K562 cells. We show that POP-seq variants consistently capture protein–RNA interaction sites across a broad range of genes including on transcripts encoding for transcription factors (TFs), RNA-Binding Proteins (RBPs) and long non-coding RNAs (lncRNAs). POP-seq identified peaks exhibited a significant enrichment (p value < 2.2e−16) for GWAS SNPs, phenotypic, clinically relevant germline as well as somatic variants reported in cancer genomes, suggesting the prevalence of uncharacterized genomic variation in protein occupied sites on RNA. We demonstrate that the abundance of POP-seq peaks increases with an increase in expression of lncRNAs, suggesting that highly expressed lncRNA are likely to act as sponges for RBPs, contributing to the rewiring of protein–RNA interaction network in cancer cells. Overall, our data supports POP-seq as a robust and cost-effective method that could be applied to primary tissues for mapping global protein occupancies.


2020 ◽  
Author(s):  
Mansi Srivastava ◽  
Rajneesh Srivastava ◽  
Sarath Chandra Janga

AbstractInteraction between proteins and RNA is critical for post-transcriptional regulatory processes. Existing high throughput methods based on crosslinking of the protein-RNA complexes and polyA pull down are reported to contribute to biases and are not readily amenable for identifying interaction sites on non polyA RNAs. We present Protein Occupancy Profile-Sequencing (POP-seq), a phase separation based method in three versions, one of which does not require crosslinking, thus providing unbiased protein occupancy profiles on whole cell transcriptome without the requirement of polyA pulldown. Our study demonstrates that ~68% of the total POP-seq peaks exhibited an overlap with publicly available protein-RNA interaction profiles of 97 RNA binding proteins (RBPs) in K562 cells. We show that POP-seq variants consistently capture protein-RNA interaction sites across a broad range of genes including on transcripts encoding for transcription factors (TFs), RNA-Binding Proteins (RBPs) and long non-coding RNAs (lncRNAs). POP-seq identified peaks exhibited a significant enrichment (p value < 2.2e-16) for GWAS SNPs, phenotypic, clinically relevant germline as well as somatic variants reported in cancer genomes, suggesting the prevalence of uncharacterized genomic variation in protein occupied sites on RNA. We demonstrate that the abundance of POP-seq peaks increases with an increase in expression of lncRNAs, suggesting that highly expressed lncRNA are likely to act as sponges for RBPs, contributing to the rewiring of protein-RNA interaction network in cancer cells. Overall, our data supports POP-seq as a robust and cost-effective method that could be applied to primary tissues for mapping global protein occupancies.


2020 ◽  
Author(s):  
James Vasta ◽  
Cesear Corona ◽  
Jennifer Wilkinson ◽  
Morgan R. Ingold ◽  
Chad Zimprich ◽  
...  

Author(s):  
Peter L. Freddolino ◽  
Thomas J. Goss ◽  
Haley M. Amemiya ◽  
Saeed Tavazoie

AbstractFree living bacteria adapt to environmental change by reprogramming gene expression through precise interactions of hundreds of DNA-binding proteins. A predictive understanding of bacterial physiology requires us to globally monitor all such protein-DNA interactions across a range of environmental and genetic perturbations. Here, we show that such global observations are possible using a modification of in vivo protein occupancy display technology (IPOD-HR) applied to E. coli. We observe that the E. coli protein-DNA interactome organizes into two distinct prototypic features: (1) highly dynamic condition-dependent transcription factor occupancy by dedicated transcriptional regulators, and (2) condition-invariant kilobase scale occupancy by nucleoid factors, forming silencing domains analogous to eukaryotic heterochromatin. We show that occupancy dynamics across a range of conditions can rapidly reveal the global transcriptional regulatory organization of a bacterium. Beyond discovery of previously hidden regulatory logic, we show that these observations can be utilized to computationally determine sequence-specificity models for the majority of active transcription factors. Our study demonstrates that global observations of protein occupancy combined with statistical inference can rapidly and systematically reveal the transcriptional regulatory and structural features of a bacterial genome. This capacity is particularly crucial for non-model bacteria which are not amenable to routine genetic manipulation.


2018 ◽  
Vol 62 (6) ◽  
Author(s):  
Dhruvitkumar S. Sutaria ◽  
Bartolome Moya ◽  
Kari B. Green ◽  
Tae Hwan Kim ◽  
Xun Tao ◽  
...  

ABSTRACTPenicillin-binding proteins (PBPs) are the high-affinity target sites of all β-lactam antibiotics in bacteria. It is well known that each β-lactam covalently binds to and thereby inactivates different PBPs with various affinities. Despite β-lactams serving as the cornerstone of our therapeutic armamentarium againstKlebsiella pneumoniae, PBP binding data are missing for this pathogen. We aimed to generate the first PBP binding data on 13 chemically diverse and clinically relevant β-lactams and β-lactamase inhibitors inK. pneumoniae. PBP binding was determined using isolated membrane fractions fromK. pneumoniaestrains ATCC 43816 and ATCC 13883. Binding reactions were conducted using β-lactam concentrations from 0.0075 to 256 mg/liter (or 128 mg/liter). After β-lactam exposure, unbound PBPs were labeled by Bocillin FL. Binding affinities (50% inhibitory concentrations [IC50]) were reported as the β-lactam concentrations that half-maximally inhibited Bocillin FL binding. PBP occupancy patterns by β-lactams were consistent across both strains. Carbapenems bound to all PBPs, with PBP2 and PBP4 as the highest-affinity targets (IC50, <0.0075 mg/liter). Preferential PBP2 binding was observed by mecillinam (amdinocillin; IC50, <0.0075 mg/liter) and avibactam (IC50, 2 mg/liter). Aztreonam showed high affinity for PBP3 (IC50, 0.06 to 0.12 mg/liter). Ceftazidime bound PBP3 at low concentrations (IC50, 0.06 to 0.25 mg/liter) and PBP1a/b at higher concentrations (4 mg/liter), whereas cefepime bound PBPs 1 to 4 at more even concentrations (IC50, 0.015 to 2 mg/liter). These PBP binding data on a comprehensive set of 13 clinically relevant β-lactams and β-lactamase inhibitors inK. pneumoniaeenable, for the first time, the rational design and optimization of double β-lactam and β-lactam–β-lactamase inhibitor combinations.


PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e3742 ◽  
Author(s):  
Tomasz Dzida ◽  
Mudassar Iqbal ◽  
Iryna Charapitsa ◽  
George Reid ◽  
Henk Stunnenberg ◽  
...  

We have developed a machine learning approach to predict stimulation-dependent enhancer-promoter interactions using evidence from changes in genomic protein occupancy over time. The occupancy of estrogen receptor alpha (ERα), RNA polymerase (Pol II) and histone marks H2AZ and H3K4me3 were measured over time using ChIP-Seq experiments in MCF7 cells stimulated with estrogen. A Bayesian classifier was developed which uses the correlation of temporal binding patterns at enhancers and promoters and genomic proximity as features to predict interactions. This method was trained using experimentally determined interactions from the same system and was shown to achieve much higher precision than predictions based on the genomic proximity of nearest ERα binding. We use the method to identify a genome-wide confident set of ERα target genes and their regulatory enhancers genome-wide. Validation with publicly available GRO-Seq data demonstrates that our predicted targets are much more likely to show early nascent transcription than predictions based on genomic ERα binding proximity alone.


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