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.

Experimental promoter identification of a foodborne pathogen Salmonella enterica subsp. enterica serovar Typhimurium with near single base-pair resolution.

Salmonella enterica serovar Typhimurium (S. Typhimurium) is a common foodborne pathogen which is frequently used as the reference strain for Salmonella. Investigating the sigma factor network and protomers is crucial to understand the genomic and transcriptomic properties of the bacterium. Its promoters were identified using various methods such as dRNA-seq, ChIP-chip, or ChIP-Seq. However, validation using ChIP-exo, which exhibits higher-resolution performance compared to conventional ChIP, has not been conducted to date. In this study, using the representative strain S. Typhimurium LT2 (LT2), the ChIP-exo experiment was conducted to accurately determine the binding sites of catalytic RNA polymerase subunit RpoB and major sigma factors (RpoD, RpoN, RpoS, and RpoE) during exponential phase. Integrated with the results of RNA-Seq, promoters and sigmulons for the sigma factors and their association with RpoB have been discovered. Notably, the overlapping regions among binding sites of each alternative sigma factor were found. Furthermore, comparative analysis with Escherichia coli str. K-12 substr. MG1655 (MG1655) revealed conserved binding sites of RpoD and RpoN across different species. In the case of small RNAs (sRNAs), 50 sRNAs observed their expression during the exponential growth of LT2. Collectively, the integration of ChIP-exo and RNA-Seq enables genome-scale promoter mapping with high resolution and facilitates the characterization of binding events of alternative sigma factors, enabling a comprehensive understanding of the bacterial sigma factor network and condition-specific active promoters.

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.

Characterization of Klebsiella pneumoniae bacteriophages, KP1 and KP12, with deep learning-based structure prediction.

Concerns over Klebsiella pneumoniae resistance to the last-line antibiotic treatment have prompted a reconsideration of bacteriophage therapy in public health. Biotechnological application of phages and their gene products as an alternative to antibiotics necessitates the understanding of their genomic context. This study sequenced, annotated, characterized, and compared two Klebsiella phages, KP1 and KP12. Physiological validations identified KP1 and KP12 as members of Myoviridae family. Both phages showed that their activities were stable in a wide range of pH and temperature. They exhibit a host specificity toward K. pneumoniae with a broad intraspecies host range. General features of genome size, coding density, percentage GC content, and phylogenetic analyses revealed that these bacteriophages are distantly related. Phage lytic proteins (endolysin, anti-/holin, spanin) identified by the local alignment against different databases, were subjected to further bioinformatic analyses including three-dimensional (3D) structure prediction by AlphaFold. AlphaFold models of phage lysis proteins were consistent with the published X-ray crystal structures, suggesting the presence of T4-like and P1/P2-like bacteriophage lysis proteins in KP1 and KP12, respectively. By providing the primary sequence information, this study contributes novel bacteriophages for research and development pipelines of phage therapy that ultimately, cater to the unmet clinical and industrial needs against K. pneumoniae pathogens.

Comprehensive Genome Analysis on the Novel Species Sphingomonas panacis DCY99T Reveals Insights into Iron Tolerance of Ginseng.

Plant growth-promoting rhizobacteria play vital roles not only in plant growth, but also in reducing biotic/abiotic stress. Sphingomonas panacis DCY99T is isolated from soil and root of Panax ginseng with rusty root disease, characterized by raised reddish-brown root and this is seriously affects ginseng cultivation. To investigate the relationship between 159 sequenced Sphingomonas strains, pan-genome analysis was carried out, which suggested genomic diversity of the Sphingomonas genus. Comparative analysis of S. panacis DCY99T with Sphingomonas sp. LK11 revealed plant growth-promoting potential of S. panacis DCY99T through indole acetic acid production, phosphate solubilizing, and antifungal abilities. Detailed genomic analysis has shown that S. panacis DCY99T contain various heavy metals resistance genes in its genome and the plasmid. Functional analysis with Sphingomonas paucimobilis EPA505 predicted that S. panacis DCY99T possess genes for degradation of polyaromatic hydrocarbon and phenolic compounds in rusty-ginseng root. Interestingly, when primed ginseng with S. panacis DCY99T during high concentration of iron exposure, iron stress of ginseng was suppressed. In order to detect S. panacis DCY99T in soil, biomarker was designed using spt gene. This study brings new insights into the role of S. panacis DCY99T as a microbial inoculant to protect ginseng plants against rusty root disease.