Genome-wide Identification of DNA-protein Interaction to Reconstruct Bacterial Transcription Regulatory Network

ChIP-mini: a low-input ChIP-exo protocol for elucidating DNA-binding protein dynamics in intracellular pathogens

Genome-wide identification of binding profiles for DNA-binding proteins from the limited number of intracellular pathogens in infection studies is crucial for understanding virulence and cellular processes but remains challenging, as the current ChIP-exo is designed for high-input bacterial cells (>1010). Here, we developed an optimized ChIP-mini method, a low-input ChIP-exo utilizing a 5,000-fold reduced number of initial bacterial cells and an analysis pipeline, to identify genome-wide binding dynamics of DNA-binding proteins in host-infected pathogens. Applying ChIP-mini to intracellular Salmonella Typhimurium, we identified 642 and 1,837 binding sites of H-NS and RpoD, respectively, elucidating changes in their binding position and binding intensity during infection. Post-infection, we observed 21 significant reductions in H-NS binding at intergenic regions, exposing the promoter region of virulence genes, such as those in Salmonella pathogenicity islands-2, 3 and effectors. Furthermore, we revealed the crucial phenomenon that novel and significantly increased RpoD bindings were found within regions exhibiting diminished H-NS binding, thereby facilitating substantial upregulation of virulence genes. These findings markedly enhance our understanding of how H-NS and RpoD simultaneously coordinate the transcription initiation of virulence genes within macrophages. Collectively, this work demonstrates a broadly adaptable tool that will enable the elucidation of DNA-binding protein dynamics in diverse intracellular pathogens during infection.

Deep-learning optimized DEOCSU suite provides an iterable pipeline for accurate ChIP-exo peak calling

Recognizing binding sites of DNA-binding proteins is a key factor for elucidating transcriptional regulation in organisms. ChIP-exo enables researchers to delineate genome-wide binding landscapes of DNA-binding proteins with near single base-pair resolution. However, the peak calling step hinders ChIP-exo application since the published algorithms tend to generate false-positive and false-negative predictions. Here, we report the development of DEOCSU (DEep-learning Optimized ChIP-exo peak calling SUite), a novel machine learning-based ChIP-exo peak calling suite. DEOCSU entails the deep convolutional neural network model which was trained with curated ChIP-exo peak data to distinguish the visualized data of bona fide peaks from false ones. Performance validation of the trained deep-learning model indicated its high accuracy, high precision and high recall of over 95%. Applying the new suite to both in-house and publicly available ChIP-exo datasets obtained from bacteria, eukaryotes and archaea revealed an accurate prediction of peaks containing canonical motifs, highlighting the versatility and efficiency of DEOCSU. Furthermore, DEOCSU can be executed on a cloud computing platform or the local environment. With visualization software included in the suite, adjustable options such as the threshold of peak probability, and iterable updating of the pre-trained model, DEOCSU can be optimized for users' specific needs.

Model-driven experimental design workflow expands understanding of regulatory role of Nac in Escherichia coli

The establishment of experimental conditions for transcriptional regulator network (TRN) reconstruction in bacteria continues to be impeded by the limited knowledge of activating conditions for transcription factors (TFs). Here, we present a novel genome-scale model-driven workflow for designing experimental conditions, which optimally activate specific TFs. Our model-driven workflow was applied to elucidate transcriptional regulation under nitrogen limitation by Nac and NtrC, in Escherichia coli. We comprehensively predict alternative nitrogen sources, including cytosine and cytidine, which trigger differential activation of Nac using a model-driven workflow. In accordance with the prediction, genome-wide measurements with ChIP-exo and RNA-seq were performed. Integrative data analysis reveals that the Nac and NtrC regulons consist of 97 and 43 genes under alternative nitrogen conditions, respectively. Functional analysis of Nac at the transcriptional level showed that Nac directly down-regulates amino acid biosynthesis and restores expression of tricarboxylic acid (TCA) cycle genes to alleviate nitrogen-limiting stress. We also demonstrate that both TFs coherently modulate α-ketoglutarate accumulation stress due to nitrogen limitation by co-activating amino acid and diamine degradation pathways. A systems-biology approach provided a detailed and quantitative understanding of both TF's roles and how nitrogen and carbon metabolic networks respond complementarily to nitrogen-limiting stress.

Characterization of an Entner-Doudoroff pathway-activated Escherichia coli

BACKGROUND

Escherichia coli have both the Embden-Meyerhof-Parnas pathway (EMPP) and Entner-Doudoroff pathway (EDP) for glucose breakdown, while the EDP primarily remains inactive for glucose metabolism. However, EDP is a more favorable route than EMPP for the production of certain products.

RESULTS

EDP was activated by deleting the pfkAB genes in conjunction with subsequent adaptive laboratory evolution (ALE). The evolved strains acquired mutations in transcriptional regulatory genes for glycolytic process (crp, galR, and gntR) and in glycolysis-related genes (gnd, ptsG, and talB). The genotypic, transcriptomic and phenotypic analyses of those mutations deepen our understanding of their beneficial effects on cellulosic biomass bio-conversion. On top of these scientific understandings, we further engineered the strain to produce higher level of lycopene and 3-hydroxypropionic acid.

CONCLUSIONS

These results indicate that the E. coli strain has innate capability to use EDP in lieu of EMPP for glucose metabolism, and this versatility can be harnessed to further engineer E. coli for specific biotechnological applications.

Bacterial Transcription Factors Bind to Coding Regions and Regulate Internal Cryptic Promoters

Transcription factors (TFs) regulate transcription by binding to the specific sequences at the promoter region. However, the mechanisms and functions of TFs binding within the coding sequences (CDS) remain largely elusive in prokaryotes. To this end, we collected 409 data sets for bacterial TFs, including 104 chromatin immunoprecipitation sequencing (ChIP-seq) assays and 305 data sets from the systematic evolution of ligands by exponential enrichment (SELEX) in seven model bacteria. Interestingly, these TFs displayed the same binding capabilities for both coding and intergenic regions. Subsequent biochemical and genetic experiments demonstrated that several TFs bound to the coding regions and regulated the transcription of the binding or adjacent genes. Strand-specific RNA sequencing revealed that these CDS-binding TFs regulated the activity of the cryptic promoters, resulting in the altered transcription of the corresponding antisense RNA. TF RhpR hindered the transcriptional elongation of a subgenic transcript within a CDS. A ChIP-seq and Ribo-seq coanalysis revealed that RhpR influenced the translational efficiency of binding genes. Taken together, the present study reveals three regulatory mechanisms of CDS-bound TFs within individual genes, operons, and antisense RNAs, which demonstrate the variability of the regulatory mechanisms of TFs and expand upon the complexity of bacterial transcriptomes.

Comprehensive and Integrated Analysis Identifies ZEB1 as a Key Novel Gene in Oral Squamous Cell Carcinoma

Oral squamous cell carcinoma (OSCC) is the most common head and neck cancer with a poor prognosis. Therefore, it is crucial to explore molecular prognostic biomarkers for OSCC. ZEB1 (also known as δEF1) is a member of the zinc finger E-box binding protein family of transcription factors involved in various biological processes, including tumorigenesis, progression, and metastasis. Recent evidence suggests that ZEB1 has a role in the tumorigenicity of oral epithelial cells, although its mode of action needs to be investigated further. To better understand the relationship between ZEB1 and OSCC, we transfected the ZEB1-overexpressing oral squamous cell lines SCC9 and SCC25 with lentivirus and then extracted RNA from the cells for gene expression analysis. Furthermore, the GSE30784 dataset was downloaded from the Gene Expression Omnibus (GEO) database to identify potential biomarkers of OSCC and to assess the potential mechanisms. The criteria for identification of their DEGs were |logFC| > 1 and $P$  < 0.05. Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) analyses were also carried out. Integrating the data from the PPI network and survival analysis identified that ZEB1 might be an independent prognostic biomarker in OSCC. In conclusion, integrated bioinformatics and microarray analysis identified the critical gene ZEB1 linked to the overall survival (OS) of patients with OSCC. ZEB1 could be applied as a prognostic biomarker to forecast the survival of patients with OSCC and might indicate innovative therapeutic indicators for OSCC.