Model-driven characterization of functional diversity of Pseudomonas aeruginosa clinical isolates with broadly representative phenotypes.

Pseudomonas aeruginosa is a leading cause of infections in immunocompromised individuals and in healthcare settings. This study aims to understand the relationships between phenotypic diversity and the functional metabolic landscape of P. aeruginosa clinical isolates. To better understand the metabolic repertoire of P. aeruginosa in infection, we deeply profiled a representative set from a library of 971 clinical P. aeruginosa isolates with corresponding patient metadata and bacterial phenotypes. The genotypic clustering based on whole-genome sequencing of the isolates, multilocus sequence types, and the phenotypic clustering generated from a multi-parametric analysis were compared to each other to assess the genotype-phenotype correlation. Genome-scale metabolic network reconstructions were developed for each isolate through amendments to an existing PA14 network reconstruction. These network reconstructions show diverse metabolic functionalities and enhance the collective P. aeruginosa pangenome metabolic repertoire. Characterizing this rich set of clinical P. aeruginosa isolates allows for a deeper understanding of the genotypic and metabolic diversity of the pathogen in a clinical setting and lays a foundation for further investigation of the metabolic landscape of this pathogen and host-associated metabolic differences during infection.

Comparative metabolic profiling of the mycelium and fermentation broth of Penicillium restrictum from Peucedanum praeruptorum rhizosphere.

Microorganisms in the rhizosphere, particularly arbuscular mycorrhiza, have a broad symbiotic relationship with their host plants. One of the major fungi isolated from the rhizosphere of Peucedanum praeruptorum is Penicillium restrictum. The relationship between the metabolites of P. restrictum and the root exudates of P. praeruptorum is being investigated. The accumulation of metabolites in the mycelium and fermentation broth of P. restrictum was analysed over different fermentation periods. Non-targeted metabolomics was used to compare the differences in intracellular and extracellular metabolites over six periods. There were significant differences in the content and types of mycelial metabolites during the incubation. Marmesin, an important intermediate in the biosynthesis of coumarins, was found in the highest amount on the fourth day of incubation. The differential metabolites were screened to obtain 799 intracellular and 468 extracellular differential metabolites. Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis showed that the highly enriched extracellular metabolic pathways were alanine, aspartate and glutamate metabolism, glyoxylate and dicarboxylate metabolism, and terpenoid backbone biosynthesis. In addition, the enrichment analysis associated with intracellular and extracellular ATP-binding cassette transporter proteins revealed that some ATP-binding cassette transporters may be involved in the transportation of certain amino acids and carbohydrates. Our results provide some theoretical basis for the regulatory mechanisms between the rhizosphere and the host plant and pave the way for the heterologous production of furanocoumarin.

Multi-omics reveals new links between Fructosamine-3-Kinase (FN3K) and core metabolic pathways.

Fructosamine-3-kinases (FN3Ks) are a conserved family of repair enzymes that phosphorylate reactive sugars attached to lysine residues in peptides and proteins. Although FN3Ks are present across the Tree of Life and share detectable sequence similarity to eukaryotic protein kinases, the biological processes regulated by these kinases are largely unknown. To address this knowledge gap, we leveraged the FN3K CRISPR Knock-Out (KO) HepG2 cell line alongside an integrative multi-omics study combining transcriptomics, metabolomics, and interactomics to place these enzymes in a pathway context. The integrative analyses revealed the enrichment of pathways related to oxidative stress response, lipid biosynthesis (cholesterol and fatty acids), and carbon and co-factor metabolism. Moreover, enrichment of nicotinamide adenine dinucleotide (NAD) binding proteins and localization of human FN3K (HsFN3K) to mitochondria suggests potential links between FN3K and NAD-mediated energy metabolism and redox balance. We report specific binding of HsFN3K to NAD compounds in a metal and concentration-dependent manner and provide insight into their binding mode using modeling and experimental site-directed mutagenesis. Our studies provide a framework for targeting these understudied kinases in diabetic complications and metabolic disorders where redox balance and NAD-dependent metabolic processes are altered.

Predicting metabolic modules in incomplete bacterial genomes with MetaPathPredict.

The reconstruction of complete microbial metabolic pathways using 'omics data from environmental samples remains challenging. Computational pipelines for pathway reconstruction that utilize machine learning methods to predict the presence or absence of KEGG modules in incomplete genomes are lacking. Here, we present MetaPathPredict, a software tool that incorporates machine learning models to predict the presence of complete KEGG modules within bacterial genomic datasets. Using gene annotation data and information from the KEGG module database, MetaPathPredict employs deep learning models to predict the presence of KEGG modules in a genome. MetaPathPredict can be used as a command line tool or as a Python module, and both options are designed to be run locally or on a compute cluster. Benchmarks show that MetaPathPredict makes robust predictions of KEGG module presence within highly incomplete genomes.

Dynamic evolution of the mTHF gene family associated with primary metabolism across life.

BACKGROUND: The folate cycle of one-carbon (C1) metabolism, which plays a central role in the biosynthesis of nucleotides and amino acids, demonstrates the significance of metabolic adaptation. We investigated the evolutionary history of the methylenetetrahydrofolate dehydrogenase (mTHF) gene family, one of the main drivers of the folate cycle, across life. RESULTS: Through comparative genomic and phylogenetic analyses, we found that several lineages of Archaea lacked domains vital for folate cycle function such as the mTHF catalytic and NAD(P)-binding domains of FolD. Within eukaryotes, the mTHF gene family diversified rapidly. For example, several duplications have been observed in lineages including the Amoebozoa, Opisthokonta, and Viridiplantae. In a common ancestor of Opisthokonta, FolD and FTHFS underwent fusion giving rise to the gene MTHFD1, possessing the domains of both genes. CONCLUSIONS: Our evolutionary reconstruction of the mTHF gene family associated with a primary metabolic pathway reveals dynamic evolution, including gene birth-and-death, gene fusion, and potential horizontal gene transfer events and/or amino acid convergence.

Comparative analysis of metabolic models of microbial communities reconstructed from automated tools and consensus approaches.

Genome-scale metabolic models (GEMs) of microbial communities offer valuable insights into the functional capabilities of their members and facilitate the exploration of microbial interactions. These models are generated using different automated reconstruction tools, each relying on different biochemical databases that may affect the conclusions drawn from the in silico analysis. One way to address this problem is to employ a consensus reconstruction method that combines the outcomes of different reconstruction tools. Here, we conducted a comparative analysis of community models reconstructed from three automated tools, i.e. CarveMe, gapseq, and KBase, alongside a consensus approach, utilizing metagenomics data from two marine bacterial communities. Our analysis revealed that these reconstruction approaches, while based on the same genomes, resulted in GEMs with varying numbers of genes and reactions as well as metabolic functionalities, attributed to the different databases employed. Further, our results indicated that the set of exchanged metabolites was more influenced by the reconstruction approach rather than the specific bacterial community investigated. This observation suggests a potential bias in predicting metabolite interactions using community GEMs. We also showed that consensus models encompassed a larger number of reactions and metabolites while concurrently reducing the presence of dead-end metabolites. Therefore, the usage of consensus models allows making full and unbiased use from aggregating genes from the different reconstructions in assessing the functional potential of microbial communities.

Ammonium-derived nitrous oxide is a global source in streams.

Global riverine nitrous oxide (N2O) emissions have increased more than 4-fold in the last century. It has been estimated that the hyporheic zones in small streams alone may contribute approximately 85% of these N2O emissions. However, the mechanisms and pathways controlling hyporheic N2O production in stream ecosystems remain unknown. Here, we report that ammonia-derived pathways, rather than the nitrate-derived pathways, are the dominant hyporheic N2O sources (69.6 ± 2.1%) in agricultural streams around the world. The N2O fluxes are mainly in positive correlation with ammonia. The potential N2O metabolic pathways of metagenome-assembled genomes (MAGs) provides evidence that nitrifying bacteria contain greater abundances of N2O production-related genes than denitrifying bacteria. Taken together, this study highlights the importance of mitigating agriculturally derived ammonium in low-order agricultural streams in controlling N2O emissions. Global models of riverine ecosystems need to better represent ammonia-derived pathways for accurately estimating and predicting riverine N2O emissions.

Systems biology of plant metabolic interactions.

Metabolism is the key cellular process of plant physiology. Understanding metabolism and its dynamical behavior under different conditions may help plant biotechnologists to design new cultivars with desired goals. Computational systems biochemistry and incorporation of different omics data unravelled active metabolism and its variations in plants. In this review, we mainly focus on the basics of flux balance analysis (FBA), elementary flux mode analysis (EFMA), and some advanced computational tools. We describe some important results that were obtained using these tools. Limitations and challenges are also discussed.

Fluorescent riboswitch-controlled biosensors for the genome scale analysis of metabolic pathways.

Fluorescent detection in cells has been tremendously developed over the years and now benefits from a large array of reporters that can provide sensitive and specific detection in real time. However, the intracellular monitoring of metabolite levels still poses great challenges due to the often complex nature of detected metabolites. Here, we provide a systematic analysis of thiamin pyrophosphate (TPP) metabolism in Escherichia coli by using a TPP-sensing riboswitch that controls the expression of the fluorescent gfp reporter. By comparing different combinations of reporter fusions and TPP-sensing riboswitches, we determine key elements that are associated with strong TPP-dependent sensing. Furthermore, by using the Keio collection as a proxy for growth conditions differing in TPP levels, we perform a high-throughput screen analysis using high-density solid agar plates. Our study reveals several genes whose deletion leads to increased or decreased TPP levels. The approach developed here could be applicable to other riboswitches and reporter genes, thus representing a framework onto which further development could lead to highly sophisticated detection platforms allowing metabolic screens and identification of orphan riboswitches.

Gene identification, expression analysis, and molecular docking of SAT and OASTL in the metabolic pathway of selenium in Cardamine hupingshanensis.

KEY MESSAGE: Identification of selenium stress-responsive expression and molecular docking of serine acetyltransferase (SAT) and O-acetyl serine (thiol) lyase (OASTL) in Cardamine hupingshanensis. A complex coupled with serine acetyltransferase (SAT) and O-acetyl serine (thiol) lyase (OASTL) is the key enzyme that catalyzes selenocysteine (Sec) synthesis in plants. The functions of SAT and OASTL genes were identified in some plants, but it is still unclear whether SAT and OASTL are involved in the selenium metabolic pathway in Cardamine hupingshanensis. In this study, genome-wide identification and comparative analysis of ChSATs and ChOASTLs were performed. The eight ChSAT genes were divided into three branches, and the thirteen ChOASTL genes were divided into four branches by phylogenetic analysis and sequence alignment, indicating the evolutionary conservation of the gene structure and its association with other plant species. qRT-PCR analysis showed that the ChSAT and ChOASTL genes were differentially expressed in different tissues under various selenium levels, suggesting their important roles in Sec synthesis. The ChSAT1;2 and ChOASTLA1;2 were silenced by the VIGS system to investigate their involvement in selenium metabolites in C. hupingshanensis. The findings contribute to understanding the gene functions of ChSATs and ChOASTLs in the selenium stress and provide a reference for further exploration of the selenium metabolic pathway in plants.

Comprehensive comparative analysis of the periodontal pathogen Porphyromonas gingivalis: exploring the pan-genome, the reconstruction of the gene regulatory network and genome-scale metabolic network.

Porphyromonas gingivalis is a nonmotile, obligate anaerobic, Gram-negative bacterium known for its association with periodontal disease and its involvement in systemic diseases such as atherosclerosis, cardiovascular disease, colon cancer, and Alzheimer's disease. This bacterium produces several virulence factors, including capsules, fimbriae, lipopolysaccharides, proteolytic enzymes, and hemagglutinins. A comparative genomic analysis revealed the open pangenome of P. gingivalis and identified complete type IV secretion systems in strain KCOM2805 and almost complete type VI secretion systems in strains KCOM2798 and ATCC49417, which is a new discovery as previous studies did not find the proteins involved in secretion systems IV and VI. Conservation of some virulence factors between different strains was observed, regardless of their genetic diversity and origin. In addition, we performed for the first time a reconstruction analysis of the gene regulatory network, identifying transcription factors and proteins involved in the regulatory mechanisms of bacterial pathogenesis. In particular, QseB regulates the expression of hemagglutinin and arginine deaminase, while Rex may suppress the release of gingipain through interactions with PorV and the formatum/nitrate transporter. Our study highlights the central role of conserved virulence factors and regulatory pathways, particularly QseB and Rex, in P. gingivalis and provides insights into potential therapeutic targets.

Integrated Analysis of Transcriptome and Metabolome Reveals Differential Responses to Alternaria brassicicola Infection in Cabbage (Brassica oleracea var. capitata).

Black spot, caused by Alternaria brassicicola (Ab), poses a serious threat to crucifer production, and knowledge of how plants respond to Ab infection is essential for black spot management. In the current study, combined transcriptomic and metabolic analysis was employed to investigate the response to Ab infection in two cabbage (Brassica oleracea var. capitata) genotypes, Bo257 (resistant to Ab) and Bo190 (susceptible to Ab). A total of 1100 and 7490 differentially expressed genes were identified in Bo257 (R_mock vs. R_Ab) and Bo190 (S_mock vs. S_Ab), respectively. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed that "metabolic pathways", "biosynthesis of secondary metabolites", and "glucosinolate biosynthesis" were the top three enriched KEGG pathways in Bo257, while "metabolic pathways", "biosynthesis of secondary metabolites", and "carbon metabolism" were the top three enriched KEGG pathways in Bo190. Further analysis showed that genes involved in extracellular reactive oxygen species (ROS) production, jasmonic acid signaling pathway, and indolic glucosinolate biosynthesis pathway were differentially expressed in response to Ab infection. Notably, when infected with Ab, genes involved in extracellular ROS production were largely unchanged in Bo257, whereas most of these genes were upregulated in Bo190. Metabolic profiling revealed 24 and 56 differentially accumulated metabolites in Bo257 and Bo190, respectively, with the majority being primary metabolites. Further analysis revealed that dramatic accumulation of succinate was observed in Bo257 and Bo190, which may provide energy for resistance responses against Ab infection via the tricarboxylic acid cycle pathway. Collectively, this study provides comprehensive insights into the Ab-cabbage interactions and helps uncover targets for breeding Ab-resistant varieties in cabbage.

Prediction of prognosis and immunotherapy efficacy based on metabolic landscape in lung adenocarcinoma by bulk, single-cell RNA sequencing and Mendelian randomization analyses.

Immunotherapy has been a remarkable clinical advancement in cancer treatment, but only a few patients benefit from it. Metabolic reprogramming is tightly associated with immunotherapy efficacy and clinical outcomes. However, comprehensively analyzing their relationship is still lacking in lung adenocarcinoma (LUAD). Herein, we evaluated 84 metabolic pathways in TCGA-LUAD by ssGSEA. A matrix of metabolic pathway pairs was generated and a metabolic pathway-pair score (MPPS) model was established by univariable, LASSO, multivariable Cox regression analyses. The differences of metabolic reprogramming, tumor microenvironment (TME), tumor mutation burden and drug sensitivity in different MPPS groups were further explored. WGCNA and 117 machine learning algorithms were performed to identify MPPS-related genes. Single-cell RNA sequencing and in vitro experiments were used to explore the role of C1QTNF6 on TME. The results showed MPPS model accurately predicted prognosis and immunotherapy efficacy of LUAD patients regardless of sequencing platforms. High-MPPS group had worse prognosis, immunotherapy efficacy and lower immune cells infiltration, immune-related genes expression and cancer-immunity cycle scores than low-MPPS group. Seven MPPS-related genes were identified, of which C1QTNF6 was mainly expressed in fibroblasts. High C1QTNF6 expression in fibroblasts was associated with more infiltration of M2 macrophage, Treg cells and less infiltration of NK cells, memory CD8+ T cells. In vitro experiments validated silencing C1QTNF6 in fibroblasts could inhibit M2 macrophage polarization and migration. The study depicted the metabolic landscape of LUAD and constructed a MPPS model to accurately predict prognosis and immunotherapy efficacy. C1QTNF6 was a promising target to regulate M2 macrophage polarization and migration.

The Fur-like regulatory protein MAP3773c modulates key metabolic pathways in Mycobacterium avium subsp. paratuberculosis under in-vitro iron starvation.

Johne's disease (JD) is a chronic enteric infection of dairy cattle worldwide. Mycobacterium avium subsp. paratuberculosis (MAP), the causative agent of JD, is fastidious often requiring eight to sixteen weeks to produce colonies in culture-a major hurdle in the diagnosis and therefore in implementation of optimal JD control measures. A significant gap in knowledge is the comprehensive understanding of the metabolic networks deployed by MAP to regulate iron both in-vitro and in-vivo. The genome of MAP carries MAP3773c, a putative metal regulator, which is absent in all other mycobacteria. The role of MAP3773c in intracellular iron regulation is poorly understood. In the current study, a field isolate (K-10) and an in-frame MAP3773c deletion mutant (ΔMAP3773c) derived from K-10, were exposed to iron starvation for 5, 30, 60, and 90 min and RNA-Seq was performed. A comparison of transcriptional profiles between K-10 and ΔMAP3773c showed 425 differentially expressed genes (DEGs) at 30 min time post-iron restriction. Functional analysis of DEGs in ΔMAP3773c revealed that pantothenate (Pan) biosynthesis, polysaccharide biosynthesis and sugar metabolism genes were downregulated at 30 min post-iron starvation whereas ATP-binding cassette (ABC) type metal transporters, putative siderophore biosynthesis, PPE and PE family genes were upregulated. Pathway analysis revealed that the MAP3773c knockout has an impairment in Pan and Coenzyme A (CoA) biosynthesis pathways suggesting that the absence of those pathways likely affect overall metabolic processes and cellular functions, which have consequences on MAP survival and pathogenesis.

Development and applications of genome-scale metabolic network models.

The genome-scale metabolic network model is an effective tool for characterizing the gene-protein-response relationship in the entire metabolic pathway of an organism. By combining various algorithms, the genome-scale metabolic network model can effectively simulate the influence of a specific environment on the physiological state of cells, optimize the culture conditions of strains, and predict the targets of genetic modification to achieve targeted modification of strains. In this review, we summarize the whole process of model building, sort out the various tools that may be involved in the model building process, and explain the role of various algorithms in model analysis. In addition, we also summarized the application of GSMM in network characteristics, cell phenotypes, metabolic engineering, etc. Finally, we discuss the current challenges facing GSMM.

Logic programming-based Minimal Cut Sets reveal consortium-level therapeutic targets for chronic wound infections.

Minimal Cut Sets (MCSs) identify sets of reactions which, when removed from a metabolic network, disable certain cellular functions. The traditional search for MCSs within genome-scale metabolic models (GSMMs) targets cellular growth, identifies reaction sets resulting in a lethal phenotype if disrupted, and retrieves a list of corresponding gene, mRNA, or enzyme targets. Using the dual link between MCSs and Elementary Flux Modes (EFMs), our logic programming-based tool aspefm was able to compute MCSs of any size from GSMMs in acceptable run times. The tool demonstrated better performance when computing large-sized MCSs than the mixed-integer linear programming methods. We applied the new MCSs methodology to a medically-relevant consortium model of two cross-feeding bacteria, Staphylococcus aureus and Pseudomonas aeruginosa. aspefm constraints were used to bias the computation of MCSs toward exchanged metabolites that could complement lethal phenotypes in individual species. We found that interspecies metabolite exchanges could play an essential role in rescuing single-species growth, for instance inosine could complement lethal reaction knock-outs in the purine synthesis, glycolysis, and pentose phosphate pathways of both bacteria. Finally, MCSs were used to derive a list of promising enzyme targets for consortium-level therapeutic applications that cannot be circumvented via interspecies metabolite exchange.

Metabolic modeling of Halomonas campaniensis improves polyhydroxybutyrate production under nitrogen limitation.

Poly-hydroxybutyrate (PHB) is an environmentally friendly alternative for conventional fossil fuel-based plastics that is produced by various microorganisms. Large-scale PHB production is challenging due to the comparatively higher biomanufacturing costs. A PHB overproducer is the haloalkaliphilic bacterium Halomonas campaniensis, which has low nutritional requirements and can grow in cultures with high salt concentrations, rendering it resistant to contamination. Despite its virtues, the metabolic capabilities of H. campaniensis as well as the limitations hindering higher PHB production remain poorly studied. To address this limitation, we present HaloGEM, the first high-quality genome-scale metabolic network reconstruction, which encompasses 888 genes, 1528 reactions (1257 gene-associated), and 1274 metabolites. HaloGEM not only displays excellent agreement with previous growth data and experiments from this study, but it also revealed nitrogen as a limiting nutrient when growing aerobically under high salt concentrations using glucose as carbon source. Among different nitrogen source mixtures for optimal growth, HaloGEM predicted glutamate and arginine as a promising mixture producing increases of 54.2% and 153.4% in the biomass yield and PHB titer, respectively. Furthermore, the model was used to predict genetic interventions for increasing PHB yield, which were consistent with the rationale of previously reported strategies. Overall, the presented reconstruction advances our understanding of the metabolic capabilities of H. campaniensis for rationally engineering this next-generation industrial biotechnology platform. KEY POINTS: A comprehensive genome-scale metabolic reconstruction of H. campaniensis was developed. Experiments and simulations predict N limitation in minimal media under aerobiosis. In silico media design increased experimental biomass yield and PHB titer.

Enhancement of acyl-CoA precursor supply for increased avermectin B1a production by engineering meilingmycin polyketide synthase and key primary metabolic pathway genes.

Avermectins (AVEs), a family of macrocyclic polyketides produced by Streptomyces avermitilis, have eight components, among which B1a is noted for its strong insecticidal activity. Biosynthesis of AVE "a" components requires 2-methylbutyryl-CoA (MBCoA) as starter unit, and malonyl-CoA (MalCoA) and methylmalonyl-CoA (MMCoA) as extender units. We describe here a novel strategy for increasing B1a production by enhancing acyl-CoA precursor supply. First, we engineered meilingmycin (MEI) polyketide synthase (PKS) for increasing MBCoA precursor supply. The loading module (using acetyl-CoA as substrate), extension module 7 (using MMCoA as substrate) and TE domain of MEI PKS were assembled to produce 2-methylbutyrate, providing the starter unit for B1a production. Heterologous expression of the newly designed PKS (termed Mei-PKS) in S. avermitilis wild-type (WT) strain increased MBCoA level, leading to B1a titer 262.2 µg/mL - 4.36-fold higher than WT value (48.9 µg/mL). Next, we separately inhibited three key nodes in essential pathways using CRISPRi to increase MalCoA and MMCoA levels in WT. The resulting strains all showed increased B1a titer. Combined inhibition of these key nodes in Mei-PKS expression strain increased B1a titer to 341.9 µg/mL. Overexpression of fatty acid ß-oxidation pathway genes in the strain further increased B1a titer to 452.8 µg/mL - 8.25-fold higher than WT value. Finally, we applied our precursor supply strategies to high-yield industrial strain A229. The strategies, in combination, led to B1a titer 8836.4 µg/mL - 37.8% higher than parental A229 value. These findings provide an effective combination strategy for increasing AVE B1a production in WT and industrial S. avermitilis strains, and our precursor supply strategies can be readily adapted for overproduction of other polyketides.

Loss of function of metabolic traits in typhoidal Salmonella without apparent genome degradation.

Salmonella enterica serovar Typhi and Paratyphi A are the cause of typhoid and paratyphoid fever in humans, which are systemic life-threatening illnesses. Both serovars are exclusively adapted to the human host, where they can cause life-long persistent infection. A distinct feature of these serovars is the presence of a relatively high number of degraded coding sequences coding for metabolic pathways, most likely a consequence of their adaptation to a single host. As a result of convergent evolution, these serovars shared many of the degraded coding sequences although often affecting different genes in the same metabolic pathway. However, there are several coding sequences that appear intact in one serovar while clearly degraded in the other, suggesting differences in their metabolic capabilities. Here, we examined the functionality of metabolic pathways that appear intact in S. Typhi but that show clear signs of degradation in S. Paratyphi A. We found that, in all cases, the existence of single amino acid substitutions in S. Typhi metabolic enzymes, transporters, or transcription regulators resulted in the inactivation of these metabolic pathways. Thus, the inability of S. Typhi to metabolize Glucose-6-Phosphate or 3-phosphoglyceric acid is due to the silencing of the expression of the genes encoding the transporters for these compounds due to point mutations in the transcriptional regulatory proteins. In contrast, its inability to utilize glucarate or galactarate is due to the presence of point mutations in the transporter and enzymes necessary for the metabolism of these sugars. These studies provide additional support for the concept of adaptive convergent evolution of these two human-adapted S. enterica serovars and highlight a limitation of bioinformatic approaches to predict metabolic capabilities. IMPORTANCE: Salmonella enterica serovar Typhi and Paratyphi A are the cause of typhoid and paratyphoid fever in humans, which are systemic life-threatening illnesses. Both serovars can only infect the human host, where they can cause life-long persistent infection. Because of their adaptation to the human host, these bacterial pathogens have changed their metabolism, leading to the loss of their ability to utilize certain nutrients. In this study we examined the functionality of metabolic pathways that appear intact in S. Typhi but that show clear signs of degradation in S. Paratyphi A. We found that, in all cases, the existence of single amino acid substitutions in S. Typhi metabolic enzymes, transporters, or transcription regulators resulted in the inactivation of these metabolic pathways. These studies provide additional support for the concept of adaptive convergent evolution of these two human-adapted S. enterica serovars.

Exploring the regulatory role of non-coding RNAs in fiber development and direct regulation of GhKCR2 in the fatty acid metabolic pathway in upland cotton.

Cotton fiber holds immense importance as the primary raw material for the textile industry. Consequently, comprehending the regulatory mechanisms governing fiber development is pivotal for enhancing fiber quality. Our study aimed to construct a regulatory network of competing endogenous RNAs (ceRNAs) and assess the impact of non-coding RNAs on gene expression throughout fiber development. Through whole transcriptome data analysis, we identified differentially expressed genes (DEGs) regulated by non-coding RNA (ncRNA) that were predominantly enriched in phenylpropanoid biosynthesis and the fatty acid elongation pathway. This analysis involved two contrasting phenotypic materials (J02-508 and ZRI015) at five stages of fiber development. Additionally, we conducted a detailed analysis of genes involved in fatty acid elongation, including KCS, KCR, HACD, ECR, and ACOT, to unveil the factors contributing to the variation in fatty acid elongation between J02-508 and ZRI015. Through the integration of histochemical GUS staining, dual luciferase assay experiments, and correlation analysis of expression levels during fiber development stages for lncRNA MSTRG.44818.23 (MST23) and GhKCR2, we elucidated that MST23 positively regulates GhKCR2 expression in the fatty acid elongation pathway. This identification provides valuable insights into the molecular mechanisms underlying fiber development, emphasizing the intricate interplay between non-coding RNAs and protein-coding genes.