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.

Design of synthetic promoters for cyanobacteria with generative deep-learning model.

Deep generative models, which can approximate complex data distribution from large datasets, are widely used in biological dataset analysis. In particular, they can identify and unravel hidden traits encoded within a complicated nucleotide sequence, allowing us to design genetic parts with accuracy. Here, we provide a deep-learning based generic framework to design and evaluate synthetic promoters for cyanobacteria using generative models, which was in turn validated with cell-free transcription assay. We developed a deep generative model and a predictive model using a variational autoencoder and convolutional neural network, respectively. Using native promoter sequences of the model unicellular cyanobacterium Synechocystis sp. PCC 6803 as a training dataset, we generated 10 000 synthetic promoter sequences and predicted their strengths. By position weight matrix and k-mer analyses, we confirmed that our model captured a valid feature of cyanobacteria promoters from the dataset. Furthermore, critical subregion identification analysis consistently revealed the importance of the -10 box sequence motif in cyanobacteria promoters. Moreover, we validated that the generated promoter sequence can efficiently drive transcription via cell-free transcription assay. This approach, combining in silico and in vitro studies, will provide a foundation for the rapid design and validation of synthetic promoters, especially for non-model organisms.

The Escherichia coli Fur pan-regulon has few conserved but many unique regulatory targets.

While global transcription factors (TFs) have been studied extensively in Escherichia coli model strains, conservation and diversity in TF regulation between strains is still unknown. Here we use a combination of ChIP-exo-to define ferric uptake regulator (Fur) binding sites-and differential gene expression-to define the Fur regulon in nine E. coli strains. We then define a pan-regulon consisting of 469 target genes that includes all Fur target genes in all nine strains. The pan-regulon is then divided into the core regulon (target genes found in all the strains, n = 36), the accessory regulon (target found in two to eight strains, n = 158) and the unique regulon (target genes found in one strain, n = 275). Thus, there is a small set of Fur regulated genes common to all nine strains, but a large number of regulatory targets unique to a particular strain. Many of the unique regulatory targets are genes unique to that strain. This first-established pan-regulon reveals a common core of conserved regulatory targets and significant diversity in transcriptional regulation amongst E. coli strains, reflecting diverse niche specification and strain history.

Use of acetate as substrate for sustainable production of homoserine and threonine by Escherichia coli W3110: A modular metabolic engineering approach.

Acetate, a promising yet underutilized carbon source for biological production, was explored for the efficient production of homoserine and threonine in Escherichia coli W. A modular metabolic engineering approach revealed the crucial roles of both acetate assimilation pathways (AckA/Pta and Acs), optimized TCA cycle flux and glyoxylate shunt activity, and enhanced CoA availability, mediated by increased pantothenate kinase activity, for efficient homoserine production. The engineered strain W-H22/pM2/pR1P exhibited a high acetate assimilation rate (5.47 mmol/g cell/h) and produced 44.1 g/L homoserine in 52 h with a 53% theoretical yield (0.18 mol/mol) in fed-batch fermentation. Similarly, strain W-H31/pM2/pR1P achieved 45.8 g/L threonine in 52 h with a 65% yield (0.22 mol/mol). These results represent the highest reported levels of amino acid production using acetate, highlighting its potential as a valuable and sustainable feedstock for biomanufacturing.

proChIPdb: a chromatin immunoprecipitation database for prokaryotic organisms.

The transcriptional regulatory network in prokaryotes controls global gene expression mostly through transcription factors (TFs), which are DNA-binding proteins. Chromatin immunoprecipitation (ChIP) with DNA sequencing methods can identify TF binding sites across the genome, providing a bottom-up, mechanistic understanding of how gene expression is regulated. ChIP provides indispensable evidence toward the goal of acquiring a comprehensive understanding of cellular adaptation and regulation, including condition-specificity. ChIP-derived data's importance and labor-intensiveness motivate its broad dissemination and reuse, which is currently an unmet need in the prokaryotic domain. To fill this gap, we present proChIPdb (prochipdb.org), an information-rich, interactive web database. This website collects public ChIP-seq/-exo data across several prokaryotes and presents them in dashboards that include curated binding sites, nucleotide-resolution genome viewers, and summary plots such as motif enrichment sequence logos. Users can search for TFs of interest or their target genes, download all data, dashboards, and visuals, and follow external links to understand regulons through biological databases and the literature. This initial release of proChIPdb covers diverse organisms, including most major TFs of Escherichia coli, and can be expanded to support regulon discovery across the prokaryotic domain.

Gene Expression and Metabolome Analysis Reveals Anti-Inflammatory Impacts of 11,17diHDoPE on PM10-Induced Mouse Lung Inflammation.

Oxylipins, the metabolites of polyunsaturated fatty acids, are vital in regulating cell proliferation and inflammation. Among these oxylipins, specialized pro-resolving mediators notably contribute to inflammation resolution. Previously, we showed that the specialized pro-resolving mediators isomer 11,17dihydroxy docosapentaenoic acid (11,17diHDoPE) can be synthesized in bacterial cells and exhibits anti-inflammatory effects in mammalian cells. This study investigates the in vivo impact of 11,17diHDoPE in mice exposed to particulate matter 10 (PM10). Our results indicate that 11,17diHDoPE significantly mitigates PM10-induced lung inflammation in mice, as evidenced by reduced pro-inflammatory cytokines and pulmonary inflammation-related gene expression. Metabolomic analysis reveals that 11,17diHDoPE modulates inflammation-related metabolites such as threonine, 2-keto gluconic acid, butanoic acid, and methyl oleate in lung tissues. In addition, 11,17diHDoPE upregulates the LA-derived oxylipin pathway and downregulates arachidonic acid- and docosahexaenoic acid-derived oxylipin pathways in serum. Correlation analyses between gene expression and metabolite changes suggest that 11,17diHDoPE alleviates inflammation by interfering with macrophage differentiation. These findings underscore the in vivo role of 11,17diHDoPE in reducing pulmonary inflammation, highlighting its potential as a therapeutic agent for respiratory diseases.

Functional cooperation of the glycine synthase-reductase and Wood-Ljungdahl pathways for autotrophic growth of Clostridium drakei.

Among CO2-fixing metabolic pathways in nature, the linear Wood-Ljungdahl pathway (WLP) in phylogenetically diverse acetate-forming acetogens comprises the most energetically efficient pathway, requires the least number of reactions, and converts CO2 to formate and then into acetyl-CoA. Despite two genes encoding glycine synthase being well-conserved in WLP gene clusters, the functional role of glycine synthase under autotrophic growth conditions has remained uncertain. Here, using the reconstructed genome-scale metabolic model iSL771 based on the completed genome sequence, transcriptomics, 13C isotope-based metabolite-tracing experiments, biochemical assays, and heterologous expression of the pathway in another acetogen, we discovered that the WLP and the glycine synthase pathway are functionally interconnected to fix CO2, subsequently converting CO2 into acetyl-CoA, acetyl-phosphate, and serine. Moreover, the functional cooperation of the pathways enhances CO2 consumption and cellular growth rates via bypassing reducing power required reactions for cellular metabolism during autotrophic growth of acetogens.

Novel Activity of ODZ10117, a STAT3 Inhibitor, for Regulation of NLRP3 Inflammasome Activation.

The NLRP3 inflammasome serves as a host defense mechanism against various pathogens, but there is growing evidence linking its activation in sterile condition to diverse inflammatory diseases. Therefore, the identification of specific inhibitors that target NLRP3 inflammasome activation is meaningful and important for novel therapies for NLRP3 inflammasome-associated diseases. In this study, we identified a chemical compound, namely ODZ10117 (ODZ), that showed NLRP3 inflammasome-targeting anti-inflammatory effects during the screening of a chemical library for anti-inflammatory activity. Although ODZ was initially discovered as a STAT3 inhibitor, here we found it also has inhibitory activity on NLRP3 inflammasome activation. ODZ inhibited the cleavage of caspase-1 and IL-1ß-induced canonical NLRP3 inflammasome triggers, but had no effect on those induced by AIM2 or NLRC4 triggers. Mechanistically, ODZ impairs NLRP3 inflammasome activation through the inhibition of NLRP3-NEK7 interaction that is required for inflammasome formation. Moreover, the results obtained from the in silico docking experiment suggested that ODZ targets NLRP3 protein, which provides evidence for the specificity of ODZ to the NLRP3 inflammasome. Furthermore, ODZ administration significantly reduced MSU-induced IL-1ß release and the mortality rate of mice with LPS-induced sepsis. Collectively, these results demonstrate a novel effect of ODZ10117 in regulating NLRP3 inflammasome activation both in vitro and in vivo, making it a promising candidate for the treatment of NLRP3-inflammasome-associated immune disorders and cancer.

Delineating transcriptional crosstalk between Mycobacterium avium subsp. paratuberculosis and human THP-1 cells at the early stage of infection via dual RNA-seq analysis.

Mycobacterium avium subsp. paratuberculosis (MAP) is the causative agent of Johne's disease, a chronic debilitating disease in ruminants. To control this disease, it is crucial to understand immune evasion and the mechanism of persistence by analyzing the early phase interplays of the intracellular pathogens and their hosts. In the present study, host-pathogen interactions at the transcriptomic level were investigated in an in vitro macrophage infection model. When differentiated human THP-1 cells were infected with MAP, the expression of various genes associated with stress responses and metabolism was altered in both host and MAP at 3 h post-infection. MAP upregulates stress-responsive global gene regulators, such as two-component systems and sigma factors, in response to oxidative and cell wall stress. Downstream genes involved in type VII secretion systems, cell wall synthesis (polyketide biosynthesis proteins), and iron uptake were changed in response to the intracellular environment of macrophages. On the host side, upregulation of inflammatory cytokine genes was observed along with pattern recognition receptor genes. Notably, alterations in gene sets involved in arginine metabolism were observed in both the host and MAP, along with significant downregulation of NOS2 expression. These observations suggest that the utilization of metabolites such as arginine by intracellular MAP might affect host NO production. Our dual RNA-seq data can provide novel insights by capturing the global transcriptome with higher resolution, especially in MAP, thus enabling a more systematic understanding of host-pathogen interactions.

Cellular responses to reactive oxygen species are predicted from molecular mechanisms.

Catalysis using iron-sulfur clusters and transition metals can be traced back to the last universal common ancestor. The damage to metalloproteins caused by reactive oxygen species (ROS) can prevent cell growth and survival when unmanaged, thus eliciting an essential stress response that is universal and fundamental in biology. Here we develop a computable multiscale description of the ROS stress response in Escherichia coli, called OxidizeME. We use OxidizeME to explain four key responses to oxidative stress: 1) ROS-induced auxotrophy for branched-chain, aromatic, and sulfurous amino acids; 2) nutrient-dependent sensitivity of growth rate to ROS; 3) ROS-specific differential gene expression separate from global growth-associated differential expression; and 4) coordinated expression of iron-sulfur cluster (ISC) and sulfur assimilation (SUF) systems for iron-sulfur cluster biosynthesis. These results show that we can now develop fundamental and quantitative genotype-phenotype relationships for stress responses on a genome-wide basis.

Numerical Study on Death of Squamous Cell Carcinoma Based on Various Shapes of Gold Nanoparticles Using Photothermal Therapy.

Due to increased exposure to ultraviolet radiation caused by increased outdoor activities, the incidence of skin cancer is increasing. Incision is the most typical method for treating skin cancer, and various treatments that can minimize the risks of incision surgery are being investigated. Among them, photothermal therapy is garnering attention because it does not cause bleeding and affords rapid recovery. In photothermal therapy, tumor death is induced via temperature increase. In this study, a numerical study based on heat transfer theory was conducted to investigate the death of squamous cell carcinoma located in the skin layer based on various shapes of gold nanoparticles (AuNPs) used in photothermal therapy. The quantitative correlation between the conditions of various AuNPs and the laser intensity that yields the optimal photothermal treatment effect was derived using the effective apoptosis ratio. It was confirmed that optimal conditions exist for maximizing apoptosis within a tumor tissue and minimizing the thermal damage to surrounding normal tissues when using AuNPs under various conditions. Furthermore, it is envisioned that research result will be utilized as a standard for photothermal treatment in the future.

Optimization of Photothermal Therapy Treatment Effect under Various Laser Irradiation Conditions.

The photothermal effect refers to a phenomenon in which light energy is converted into heat energy, and in the medical field, therapeutics based on this phenomenon are used for anticancer treatment. A new treatment technique called photothermal therapy kills tumor tissue through a temperature increase and has the advantages of no bleeding and fast recovery. In this study, the results of photothermal therapy for squamous cell carcinoma in the skin layer were analyzed numerically for different laser profiles, intensities, and radii and various concentrations of gold nanoparticles (AuNPs). According to the heat-transfer theory, the temperature distribution in the tissue was calculated for the conditions under which photothermal therapy was performed, and the therapeutic effect was quantitatively confirmed through three apoptotic variables. In addition, the laser intensity and the volume fraction of AuNPs were optimized, and the results provide useful criteria for optimizing the treatment effects in photothermal therapy.

Study on the Optimal Treatment Condition Control of Photothermal Therapy under Various Cooling Time Ratios of Lasers.

Photothermal therapy is a treatment technique that has attracted attention as an alternative to conventional surgical techniques. It is based on the photothermal effect, wherein light energy is converted into thermal energy, and facilitates rapid recovery after treatment. This study employed various laser irradiation conditions and presented conditions with the optimal treatment effects through a numerical analysis based on heat transfer. A skin layer comprising four stages containing squamous cell carcinoma was targeted, and the treatment effect was confirmed by varying the heating conditions of the laser and volume fraction of gold nanoparticles. The therapeutic effect was confirmed through both the apoptosis retention ratio, which quantitatively estimated the degree of maintenance of the apoptosis temperature range within the tumor, and the thermal hazard retention value, which quantitatively calculates the amount of thermal damage to the surrounding normal tissues. Finally, the optimal treatment conditions were determined based on the laser intensity, cooling time ratio, and volume fraction of injected gold nanoparticles through numerical analysis.

Genomic diversity of Mycobacterium avium subsp. paratuberculosis: pangenomic approach for highlighting unique genomic features with newly constructed complete genomes.

Mycobacterium avium subsp. paratuberculosis (MAP) is a causative agent of Johne's disease, which is a chronic granulomatous enteropathy in ruminants. Determining the genetic diversity of MAP is necessary to understand the epidemiology and biology of MAP, as well as establishing disease control strategies. In the present study, whole genome-based alignment and comparative analysis were performed using 40 publicly available MAP genomes, including newly sequenced Korean isolates. First, whole genome-based alignment was employed to identify new genomic structures in MAP genomes. Second, the genomic diversity of the MAP population was described by pangenome analysis. A phylogenetic tree based on the core genome and pangenome showed that the MAP was differentiated into two major types (C- and S-type), which was in keeping with the findings of previous studies. However, B-type strains were discriminated from C-type strains. Finally, functional analysis of the pangenome was performed using three virulence factor databases (i.e., PATRIC, VFDB, and Victors) to predict the phenotypic diversity of MAP in terms of pathogenicity. Based on the results of the pangenome analysis, we developed a real-time PCR technique to distinguish among S-, B- and C-type strains. In conclusion, the results of our study suggest that the phenotypic differences between MAP strains can be explained by their genetic polymorphisms. These results may help to elucidate the diversity of MAP, extending from genomic features to phenotypic traits.

Characterizing posttranslational modifications in prokaryotic metabolism using a multiscale workflow.

Understanding the complex interactions of protein posttranslational modifications (PTMs) represents a major challenge in metabolic engineering, synthetic biology, and the biomedical sciences. Here, we present a workflow that integrates multiplex automated genome editing (MAGE), genome-scale metabolic modeling, and atomistic molecular dynamics to study the effects of PTMs on metabolic enzymes and microbial fitness. This workflow incorporates complementary approaches across scientific disciplines; provides molecular insight into how PTMs influence cellular fitness during nutrient shifts; and demonstrates how mechanistic details of PTMs can be explored at different biological scales. As a proof of concept, we present a global analysis of PTMs on enzymes in the metabolic network of Escherichia coli Based on our workflow results, we conduct a more detailed, mechanistic analysis of the PTMs in three proteins: enolase, serine hydroxymethyltransferase, and transaldolase. Application of this workflow identified the roles of specific PTMs in observed experimental phenomena and demonstrated how individual PTMs regulate enzymes, pathways, and, ultimately, cell phenotypes.

Induction of Apoptotic Temperature in Photothermal Therapy under Various Heating Conditions in Multi-Layered Skin Structure.

Recently, photothermal therapy has attracted attention as an alternative treatment to conventional surgical techniques because it does not lead to bleeding and patients quickly recover after treatment compared to incisional surgery. Photothermal therapy induces tumor cell death through an increase in the temperature using the photothermal effect, which converts light energy into thermal energy. This study was conducted to perform numerical analysis based on heat transfer to induce apoptosis of tumor tissue under various heating conditions in photothermal therapy. The Monte Carlo method was applied to evaluate a multi-layered skin structure containing squamous cell carcinoma. Tissue-equivalent phantom experiments verified the numerical model. Based on the effective apoptosis retention ratio, the numerical analysis results showed the quantitative correlation for the laser intensity, volume fraction of gold nanorods injected into the tumor, and cooling time. This study reveals optimal conditions for maximizing apoptosis within tumor tissue while minimizing thermal damage to surrounding tissues under various heating conditions. This approach may be useful as a standard treatment when performing photothermal therapy.

Unlocking the biosynthesis of sesquiterpenoids from methane via the methylerythritol phosphate pathway in methanotrophic bacteria, using α-humulene as a model compound.

Isoprenoids are an abundant and diverse class of natural products with various applications in the pharmaceutical, cosmetics and biofuel industries. A methanotroph-based biorefinery is an attractive scenario for the production of a variety of value-added compounds from methane, because methane is a promising alternative feedstock for industrial biomanufacturing. In this study, we metabolically engineered Methylotuvimicrobium alcaliphilum 20Z for de novo synthesis of a sesquiterpenoid from methane, using α-humulene as a model compound, via optimization of the native methylerythritol phosphate (MEP) pathway. Expression of codon-optimized α-humulene synthase from Zingiber zerumbet in M. alcaliphilum 20Z resulted in an initial yield of 0.04 mg/g dry cell weight. Overexpressing key enzymes (IspA, IspG, and Dxs) for debottlenecking of the MEP pathway increased α-humulene production 5.2-fold compared with the initial strain. Subsequently, redirecting the carbon flux through the Embden-Meyerhof-Parnas pathway resulted in an additional 3-fold increase in α-humulene production. Additionally, a genome-scale model using flux scanning based on enforced objective flux method was used to identify potential overexpression targets to increase flux towards isoprenoid production. Several target reactions from cofactor synthesis pathways were probed and evaluated for their effects on α-humulene synthesis, resulting in α-humulene yield up to 0.75 mg/g DCW with 18.8-fold enhancement from initial yield. This study first demonstrates production of a sesquiterpenoid from methane using methanotrophs as the biocatalyst and proposes potential strategies to enhance production of sesquiterpenoid and related isoprenoid products in engineered methanotrophic bacteria.

Genome-scale evaluation of core one-carbon metabolism in gammaproteobacterial methanotrophs grown on methane and methanol.

Methylotuvimicrobium alcaliphilum 20Z is a promising platform strain for bioconversion of one-carbon (C1) substrates into value-added products. To carry out robust metabolic engineering with methylotrophic bacteria and to implement C1 conversion machinery in non-native hosts, systems-level evaluation and understanding of central C1 metabolism in methanotrophs under various conditions is pivotal but yet elusive. In this study, a genome-scale integrated approach was used to provide in-depth knowledge on the metabolic pathways of M. alcaliphilum 20Z grown on methane and methanol. Systems assessment of core carbon metabolism indicated the methanol assimilation pathway is mostly coupled with the efficient Embden-Meyerhof-Parnas (EMP) pathway along with the serine cycle. In addition, an incomplete TCA cycle operated in M. alcaliphilum 20Z on methanol, which might only supply precursors for de novo synthesis but not reducing powers. Instead, it appears that the direct formaldehyde oxidation pathway supply energy for the whole metabolic system. Additionally, a comparative transcriptomic analysis in multiple gammaproteobacterial methanotrophs also revealed the transcriptional responses of central metabolism on carbon substrate change. These findings provided a systems-level understanding of carbon metabolism and new opportunities for strain design to produce relevant products from different C1-feedstocks.

Adaptive laboratory evolution of Escherichia coli lacking cellular byproduct formation for enhanced acetate utilization through compensatory ATP consumption.

Acetate has attracted great attention as a carbon source to develop economically feasible bioprocesses for sustainable bioproducts. Acetate is a less-preferred carbon source and a well-known growth inhibitor of Escherichia coli. In this study, we carried out adaptive laboratory evolution of an E. coli strain lacking four genes (adhE, pta, ldhA, and frdA) involved in acetyl-CoA consumption, allowing the efficient utilization of acetate as its sole carbon and energy source. Four genomic mutations were found in the evolved strain through whole-genome sequencing, and two major mutations (in cspC and patZ) mainly contributed to efficient utilization of acetate and tolerance to acetate. Transcriptomic reprogramming was examined by analyzing the genome-wide transcriptome with different carbon sources. The evolved strain showed high levels of intracellular ATP by upregulation of genes involved in NADH and ATP biosynthesis, which facilitated the production of enhanced green fluorescent protein, mevalonate, and n-butanol using acetate alone. This new strain, given its high acetate tolerance and high ATP levels, has potential as a starting host for cell factories targeting the production of acetyl-CoA-derived products from acetate or of products requiring high ATP levels.

Prevalence of extended-spectrum ß-lactamases in the local farm environment and livestock: challenges to mitigate antimicrobial resistance.

The effectiveness of antibiotics has been challenged by the increasing frequency of antimicrobial resistance (AR), which has emerged as a major threat to global health. Despite the negative impact of AR on health, there are few effective strategies for reducing AR in food-producing animals. Of the antimicrobial resistant microorganisms (ARMs), extended-spectrum ß-lactamases (ESBLs)-producing Enterobacteriaceae are an emerging global threat due to their increasing prevalence in livestock, even in animals raised without antibiotics. Many reviews are available for the positive selection of AR associated with antibiotic use in livestock, but less attention has been given to how other factors including soil, water, manure, wildlife, and farm workers, are associated with the emergence of ESBL-producing bacteria. Understanding of antibiotic resistance genes and bacteria transfer at the interfaces of livestock and other potential reservoirs will provide insights for the development of mitigation strategies for AR.