Fine-Tuning Multi-Gene Clusters via Well-Characterized Gene Expression Regulatory Elements: Case Study of the Arginine Synthesis Pathway in C. glutamicum

2020 ◽  
Author(s):  
Yanting Duan ◽  
Weiji Zhai ◽  
Weijia Liu ◽  
Xiaomei Zhang ◽  
Jin-Song Shi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Clusters ◽  
Regulatory Elements ◽  
Fine Tuning

scholarly journals Biodegradation of Tetralin: Genomics, Gene Function and Regulation

Genes ◽  
2019 ◽  
Vol 10 (5) ◽  
pp. 339 ◽  
Author(s):  
Belén Floriano ◽  
Eduardo Santero ◽  
Francisca Reyes-Ramírez
Keyword(s):  
Gene Expression ◽  
Current Knowledge ◽  
Gene Clusters ◽  
Sole Source ◽  
Regulatory Elements ◽  
Catabolic Pathway ◽  
Regulatory Systems ◽  
Biochemical Genetic ◽  
Complex Regulation ◽  
Rhodococcus Sp

Tetralin (1,2,3,4-tetrahydonaphthalene) is a recalcitrant compound that consists of an aromatic and an alicyclic ring. It is found in crude oils, produced industrially from naphthalene or anthracene, and widely used as an organic solvent. Its toxicity is due to the alteration of biological membranes by its hydrophobic character and to the formation of toxic hydroperoxides. Two unrelated bacteria, Sphingopyxis granuli strain TFA and Rhodococcus sp. strain TFB were isolated from the same niche as able to grow on tetralin as the sole source of carbon and energy. In this review, we provide an overview of current knowledge on tetralin catabolism at biochemical, genetic and regulatory levels in both strains. Although they share the same biodegradation strategy and enzymatic activities, no evidences of horizontal gene transfer between both bacteria have been found. Moreover, the regulatory elements that control the expression of the gene clusters are completely different in each strain. A special consideration is given to the complex regulation discovered in TFA since three regulatory systems, one of them involving an unprecedented communication between the catabolic pathway and the regulatory elements, act together at transcriptional and posttranscriptional levels to optimize tetralin biodegradation gene expression to the environmental conditions.

scholarly journals In silico analysis of promoter region and regulatory elements of mitogenome co-expressed trn gene clusters encoding for bio-pesticide in entomopathogenic fungus, Metarhizium anisopliae: strain ME1

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Getachew Bantihun ◽  
Mulugeta Kebede
Keyword(s):  
Gene Expression ◽  
Gene Regulation ◽  
Entomopathogenic Fungi ◽  
Metarhizium Anisopliae ◽  
In Silico ◽  
Promoter Region ◽  
Gene Clusters ◽  
Regulatory Elements ◽  
Predictive Score ◽  
Promoter Regions

Abstract Background Pest control strategies almost entirely rely on chemical insecticides, which cause environmental problems such as biosphere deterioration and emergence of resistant pests. Bio-pesticide is an alternative approach, which uses organisms such as entomopathogenic fungi, Metarhizium anisopliae, to control pests. Screening such potential organism at a molecular level and understanding their gene regulation mechanism is an important approach to reduce emergence of pesticide resistance and worsening of the biosphere. Understanding promoter regions which play a pivotal role in gene regulation is crucial. In particular, identification of the promoter regions in M. anisopliae Strain ME1 remains poorly understood. To our knowledge, the mitogenome trn gene clusters of M. anisopliae Strain ME1 were not characterized. Here, we used machine learning approach to identify and characterize the promoter regions, regulatory elements, and CpG island densities of 15 protein coding genes of entomopathogenic fungi, M. anisolpliae Strain ME1. Results The current analysis revealed multiple transcription start sites (TSS) for all utilized sequences, except for promoter region genes of Pro-cob and Pro-nad5. With reference to the start codon (ATG), 85.3% of TSS was located above – 500 bp. Based on the standard predictive score at cut off value of 0.8a, the current study revealed 54.7% of predictive score greater than or equal from 0.9 promoter prediction score. Expectation maximization algorithm output identified five candidate motifs. Nonetheless, of all candidate motifs, MtrnI was revealed as the common promoter region motif with a value of 76.9% both in terms of size of binding sites and with an E value of 9.1E−054. Accordingly, we perceived that MtrnI serve as the binding site for tryptophan cluster with P value 0.0044 and C4 type zinc fingers functions as the binding site to regulate gene expression of M. anisopliae Strain ME1. The analysis revealed that mitogenome trn gene clusters of M. anisopliae Strain ME1 showed homologues evolutionary ancestor supported with a bootstrap value of 100%. Conclusion Identified common candidate motifs and binding transcription factors through in silico approach are likely expected to contribute for better understanding of gene expression and strain improvement of M. anisopliae Strain ME1 for its bio-pesticides role.

scholarly journals Epromoters function as a hub to recruit key transcription factors required for the inflammatory response

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
David Santiago-Algarra ◽  
Charbel Souaid ◽  
Himanshu Singh ◽  
Lan T. M. Dao ◽  
Saadat Hussain ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcription Factors ◽  
Inflammatory Response ◽  
Gene Clusters ◽  
Regulatory Elements ◽  
Interferon Response ◽  
Type I ◽  
Gene Promoters ◽  
Bona Fide ◽  
Distal Gene

AbstractGene expression is controlled by the involvement of gene-proximal (promoters) and distal (enhancers) regulatory elements. Our previous results demonstrated that a subset of gene promoters, termed Epromoters, work as bona fide enhancers and regulate distal gene expression. Here, we hypothesized that Epromoters play a key role in the coordination of rapid gene induction during the inflammatory response. Using a high-throughput reporter assay we explored the function of Epromoters in response to type I interferon. We find that clusters of IFNa-induced genes are frequently associated with Epromoters and that these regulatory elements preferentially recruit the STAT1/2 and IRF transcription factors and distally regulate the activation of interferon-response genes. Consistently, we identified and validated the involvement of Epromoter-containing clusters in the regulation of LPS-stimulated macrophages. Our findings suggest that Epromoters function as a local hub recruiting the key TFs required for coordinated regulation of gene clusters during the inflammatory response.

scholarly journals Fine-tuning biosensor dynamic range based on rational design of cross-ribosome-binding sites in bacteria

2020 ◽  
Author(s):  
Nana Ding ◽  
Shenghu Zhou ◽  
Zhenqi Yuan ◽  
Xiaojuan Zhang ◽  
Jing Chen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Deep Learning ◽  
Translation Initiation ◽  
Rational Design ◽  
Dynamic Range ◽  
Regulatory Elements ◽  
Fine Tuning ◽  
Initiation Rate ◽  
Ribosome Binding

ABSTRACTCurrently, predictive translation tuning of regulatory elements to the desired output of transcription factor based biosensors remains a challenge. The gene expression of a biosensor system must exhibit appropriate translation intensity, which is controlled by the ribosome-binding site (RBS), to achieve fine-tuning of its dynamic range (i.e., fold change in gene expression between the presence and absence of inducer) by adjusting the translation initiation rate of the transcription factor and reporter. However, existing genetically encoded biosensors generally suffer from unpredictable translation tuning of regulatory elements to dynamic range. Here, we elucidated the connections and partial mechanisms between RBS, translation initiation rate, protein folding and dynamic range, and presented a rational design platform that predictably tuned the dynamic range of biosensors based on deep learning of large datasets cross-RBSs (cRBSs). A library containing 24,000 semi-rationally designed cRBSs was constructed using DNA microarray, and was divided into five sub-libraries through fluorescence-activated cell sorting. To explore the relationship between cRBSs and dynamic range, we established a classification model with the cRBSs and average dynamic range of five sub-libraries to accurately predict the dynamic range of biosensors based on convolutional neural network in deep learning. Thus, this work provides a powerful platform to enable predictable translation tuning of RBS to the dynamic range of biosensors.

scholarly journals Analysing structural diversity of seed storage protein gene promoters: Buckwheat a case study

Fagopyrum ◽  
2018 ◽  
Vol 35 (1) ◽  
pp. 5-17 ◽  
Author(s):  
Upasna Chettry ◽  
Lashaihun Dohtdong ◽  
N. K. Chrungoo
Keyword(s):  
Gene Expression ◽  
Binding Sites ◽  
Regulatory Elements ◽  
Fine Tuning ◽  
Tata Box ◽  
Proximal Promoter ◽  
Specific Gene ◽  
Gene Promoters ◽  
Seed Storage Protein Gene ◽  
Nucleosome Binding

Multiple sequence alignment of 5’UTR of SSP genes from accessions of Fagopyrum esculentumrevealed the invariant nature of sequences with the transcription start site at P761and TATA box located -30bp upstream the TSS. Other cis-elements identified in the sequences included the legumin box (-581, -524, -184, -135, -91), the -131 prolamin box, DOF element (-718, -649, -540,-432, -272,-225, -128) and CAAT box (-692, -530, -475, -411, -282, -168, -54). Other elements identified included those involved in abscisic acid signallingviz., ABI3 at P-470,-95,-68,RAV1 at P-694and -543and AGL15 at P-671. A comparative analysis of regulatory elements of SSP gene promoters of distantly related species the presence of five cis-regulatory elements viz. TATA BOX, E-BOX, RY- element, CAAT box and the Endosperm box, which interplay in seed specific SSP gene expression. Other modulators influencing seed specific gene expression detected in the sequences included the  ABA-responsive elements ABI3, RAV1 and AGL15 which play an integral role in seed maturation. Identification of potential nucleosome binding sites in SSP gene promoters of Cicer arietinum, Brassica napus, B. campestris, Vicia faba, and Pisum sativumat positions 78, 635, 195, 112 and 152 respectively surmises the spatial fine tuning of SSP gene transcriptional regulation in these species. On the other hand, absence of nucleosome binding sites in the promoters of Fagopyrum esculentum, Zea mays, Avena sativa, Triticum aestivum and Oryza sativamay indicate relatively easier access of transcription factors to the proximal promoter, thereby providing higher level of gene expression.

scholarly journals Programmable cross-ribosome-binding sites to fine-tune the dynamic range of transcription factor-based biosensor

2020 ◽  
Vol 48 (18) ◽  
pp. 10602-10613
Author(s):  
Nana Ding ◽  
Zhenqi Yuan ◽  
Xiaojuan Zhang ◽  
Jing Chen ◽  
Shenghu Zhou ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Deep Learning ◽  
Dynamic Range ◽  
Regulatory Elements ◽  
Fine Tuning ◽  
Classification Model ◽  
Ribosome Binding ◽  
Ribosome Binding Sites ◽  
Biosensor System

Abstract Currently, predictive translation tuning of regulatory elements to the desired output of transcription factor (TF)-based biosensors remains a challenge. The gene expression of a biosensor system must exhibit appropriate translation intensity, which is controlled by the ribosome-binding site (RBS), to achieve fine-tuning of its dynamic range (i.e. fold change in gene expression between the presence and absence of inducer) by adjusting the translation level of the TF and reporter. However, existing TF-based biosensors generally suffer from unpredictable dynamic range. Here, we elucidated the connections and partial mechanisms between RBS, translation level, protein folding and dynamic range, and presented a design platform that predictably tuned the dynamic range of biosensors based on deep learning of large datasets cross-RBSs (cRBSs). In doing so, a library containing 7053 designed cRBSs was divided into five sub-libraries through fluorescence-activated cell sorting to establish a classification model based on convolutional neural network in deep learning. Finally, the present work exhibited a powerful platform to enable predictable translation tuning of RBS to the dynamic range of biosensors.

scholarly journals CRISPR-assisted multi-dimensional regulation for fine-tuning gene expression in Bacillus subtilis

2019 ◽  
Vol 47 (7) ◽  
pp. e40-e40 ◽  
Author(s):  
Zhenghui Lu ◽  
Shihui Yang ◽  
Xin Yuan ◽  
Yunyun Shi ◽  
Li Ouyang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Bacillus Subtilis ◽  
Fine Tuning

scholarly journals Mapping chromatin accessibility and active regulatory elements reveals pathological mechanisms in human gliomas

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Karolina Stępniak ◽  
Magdalena A. Machnicka ◽  
Jakub Mieczkowski ◽  
Anna Macioszek ◽  
Bartosz Wojtaś ◽  
...  
Keyword(s):  
Gene Expression ◽  
Chromatin Structure ◽  
Molecular Mechanisms ◽  
Genome Mapping ◽  
Malignant Gliomas ◽  
Regulatory Elements ◽  
Chromatin Accessibility ◽  
Multiple Tumor ◽  
Genes Encoding ◽  
Methylation Patterns

AbstractChromatin structure and accessibility, and combinatorial binding of transcription factors to regulatory elements in genomic DNA control transcription. Genetic variations in genes encoding histones, epigenetics-related enzymes or modifiers affect chromatin structure/dynamics and result in alterations in gene expression contributing to cancer development or progression. Gliomas are brain tumors frequently associated with epigenetics-related gene deregulation. We perform whole-genome mapping of chromatin accessibility, histone modifications, DNA methylation patterns and transcriptome analysis simultaneously in multiple tumor samples to unravel epigenetic dysfunctions driving gliomagenesis. Based on the results of the integrative analysis of the acquired profiles, we create an atlas of active enhancers and promoters in benign and malignant gliomas. We explore these elements and intersect with Hi-C data to uncover molecular mechanisms instructing gene expression in gliomas.

scholarly journals DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome

2021 ◽  
Author(s):  
Yanrong Ji ◽  
Zhihan Zhou ◽  
Han Liu ◽  
Ramana V Davuluri
Keyword(s):  
Dna Sequences ◽  
Regulatory Elements ◽  
Ease Of Use ◽  
Fine Tuning ◽  
Supplementary Information ◽  
Sequence Motifs ◽  
Semantic Relationship ◽  
Accurate Identification ◽  
Conserved Sequence ◽  
Genome Wide

Abstract Motivation Deciphering the language of non-coding DNA is one of the fundamental problems in genome research. Gene regulatory code is highly complex due to the existence of polysemy and distant semantic relationship, which previous informatics methods often fail to capture especially in data-scarce scenarios. Results To address this challenge, we developed a novel pre-trained bidirectional encoder representation, named DNABERT, to capture global and transferrable understanding of genomic DNA sequences based on up and downstream nucleotide contexts. We compared DNABERT to the most widely used programs for genome-wide regulatory elements prediction and demonstrate its ease of use, accuracy and efficiency. We show that the single pre-trained transformers model can simultaneously achieve state-of-the-art performance on prediction of promoters, splice sites and transcription factor binding sites, after easy fine-tuning using small task-specific labeled data. Further, DNABERT enables direct visualization of nucleotide-level importance and semantic relationship within input sequences for better interpretability and accurate identification of conserved sequence motifs and functional genetic variant candidates. Finally, we demonstrate that pre-trained DNABERT with human genome can even be readily applied to other organisms with exceptional performance. We anticipate that the pre-trained DNABERT model can be fined tuned to many other sequence analyses tasks. Availability and implementation The source code, pretrained and finetuned model for DNABERT are available at GitHub (https://github.com/jerryji1993/DNABERT). Supplementary information Supplementary data are available at Bioinformatics online.

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