Metabolic Genes Research Articles

Single cell atlas reveals multilayered metabolic heterogeneity across tumour types

Metabolic reprogramming plays a pivotal role in cancer progression, contributing to substantial intratumour heterogeneity and influencing tumour behaviour. However, a systematic characterization of metabolic heterogeneity across multiple cancer types at the single-cell level remains limited. We integrated 296 tumour and normal samples spanning six common cancer types to construct a single-cell compendium of metabolic gene expression profiles and identify cell type-specific metabolic properties and reprogramming patterns. A computational approach based on non-negative matrix factorization (NMF) was utilised to identify metabolic meta-programs (MMPs) showing intratumour heterogeneity. In-vitro cell experiments were conducted to confirm the associations between MMPs and chemotherapy resistance, as well as the function of key metabolic regulators. Survival analyses were performed to assess clinical relevance of cellular metabolic properties. Our analysis revealed shared glycolysis upregulation and divergent regulation of citric acid cycle across different cell types. In malignant cells, we identified a colorectal cancer-specific MMP associated with resistance to the cuproptosis inducer elesclomol, validated through in-vitro cell experiments. Furthermore, our findings enabled the stratification of patients into distinct prognostic subtypes based on metabolic properties of specific cell types, such as myeloid cells. This study presents a nuanced understanding of multilayered metabolic heterogeneity, offering valuable insights into potential personalized therapies targeting tumour metabolism. National Key Research and Development Program of China (2021YFA1300601). National Natural Science Foundation of China (key grants 82030081 and 81874235). The Shenzhen High-level Hospital Construction Fund and Shenzhen Basic Research Key Project (JCYJ20220818102811024). The Lam Chung Nin Foundation for Systems Biomedicine.

Association of genetically predicted human metabolomic gene expression with incident heart failure

Abstract Background Heart failure (HF) represents a significant challenge both for patients and healthcare systems worldwide. Despite ongoing efforts, therapeutic options are limited. The underlying pathophysiology involves intricate changes in cardiac metabolism that strongly influence the clinical phenotype, thus potentially offering novel options for therapeutic targeting. Systematic prioritisation of metabolic genes regarding their phenotypic impact on HF is outstanding. Purpose We investigate the association of genetically predicted metabolic gene expression with the onset of HF within UK Biobank (UKB). We followed up on identified candidate genes using human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) models of cardiomyocyte hypertrophy. Methods Utilising genomic data of 484,372 UKB participants and left ventricle-specific expression quantitative trait loci (eQTL) information from Genotype-Tissue Expression (GTEx) project, we predicted genetically regulated gene expression using established methods (PrediXcan) for all metabolic genes. Subsequently, we employed per-gene Cox proportional hazards (COX-PH) models to assess the association of genetically predicted metabolic gene expression with incident HF. We adjusted models for clinical risk using established risk models (Pooled Cohort Equations to Prevent HF (PCP-HF)). To validate our findings in vitro, we subjected hiPSC-CMs to a sympathomimetic stimulus via treatment with endothelin-1 (ET-1; 10 nM). We evaluate alterations in expression via real-time quantitative polymerase chain reaction (RT-qPCR) and establish methods for assessing hypertrophic remodelling. Results COX-PH models revealed the predicted expression of 104/1515 metabolic genes to be independently associated with the onset of HF. Subsequent refinement with a meta-analysis of commonly dysregulated genes in end-stage HF narrowed our findings to 46 candidate genes, of which several were known for their role in HF pathophysiology. We further traced nine genes for validation in hiPSC-CM models and showed significant dysregulation of several genes following the treatment with ET-1. Flow cytometry measured hiPSC-CM hypertrophy following sympathomimetic stimulation (p=0.036). Extracellular flux analysis revealed canonical changes following treatment with ET-1. Conclusions The study revealed several metabolic genes associated with incident HF, thus confirming previous findings and highlighting novel, putative therapeutic target genes. We confirmed transcriptional changes in an in vitro model. Ongoing work includes further validation via silencing candidate genes in hiPSC-CMs and subjection to the established phenotypic in vitro models.

Transcriptomic data reveals an auxiliary detoxification mechanism that alleviates formaldehyde stress in Methylobacterium sp. XJLW

Methylobacterium sp. XJLW converts formaldehyde into methanol and formic acid via a Cannizzaro reaction in response to environmental formaldehyde stress. Methanol is further assimilated without formaldehyde or formic acid formation, whereas formic acid accumulates without undergoing further metabolism. Synthetic biology-based biotransformation of methanol to generate additional products can potentially achieve carbon neutrality. However, practical applications are hampered by limitations such as formaldehyde tolerance. In this study, we aimed to explore the specific mechanism of strain XJLW in response to formaldehyde stress. Thus, a transcriptomic analysis of XJLW under formaldehyde treatment was performed, revealing changes in the expression of specific genes related to one-carbon metabolism. Central metabolic genes were downregulated, whereas metabolic bypass genes were upregulated to maintain methanol assimilation in XJLW’s response to formaldehyde treatment. In total, 100 genes potentially related to methyl transfer were identified. The function of only one gene, RS27765, was similar to that of glyA, which encodes a methyltransferase involved in one-carbon metabolism. The double-mutant strain, lacking RS27765 and glyA, lost its ability to grow in methanol, whereas the single-mutant strain, lacking only one of these genes, still grew in methanol. Co-expression of RS27765 and RS31205 (YscQ/HrcQ type III secretion apparatus protein) enabled Escherichia coli BL21 (DE3) to effectively degrade methanol. Using protein sequence analysis and molecular docking, we proposed a model wherein RS27765 is necessary for cell growth by using methanol generated via formaldehyde cannizzaro reaction. This process enables direct assimilation of methanol without producing formaldehyde and formic acid as intermediate metabolites. The RS27765 gene cluster, in conjunction with metabolic bypass genes, constitutes a novel auxiliary pathway facilitating formaldehyde stress tolerance in the strain.

Soil viral–host interactions regulate microplastic-dependent carbon storage

Microplastic is globally regarded as an important factor impacting biogeochemical cycles, yet our understanding of such influences is limited by the uncertainties of intricate microbial processes. By multiomics analysis, coupled with soil chemodiversity characterization and microbial carbon use efficiency (CUE), we investigated how microbial responses to microplastics impacted soil carbon cycling in a long-term field experiment. We showed that biodegradable microplastics promoted soil organic carbon accrual by an average of 2.47%, while nondegradable microplastics inhibited it by 17.4%, as a consequence of the virus-bacteria coadaptations to the microplastics disturbance. In the relevant functional pathways, nondegradable microplastics significantly (P < 0.05) enhanced the abundance and transcriptional activity related to complex carbohydrate metabolism, whereas biodegradable microplastics significantly (P < 0.05) promoted functions involved in amino acid metabolism and glycolysis. Accordingly, viral lysis enhanced in nondegradable microplastics treatments to introduce more complex organic compounds to soil dissolved organic matters, thus benefiting the oligotrophs with high carbon metabolic capabilities in exploitation competition. In contrast, biodegradable microplastics enriched viral auxiliary metabolic genes of carbon metabolism through "piggyback-the-winner" strategy, conferring to dominant copiotrophs, enhanced substrate utilization capabilities. These virus-host interactions were also demonstrated in the corresponding soil plastisphere, which would alter microbial resource allocation and metabolism via CUE, affecting carbon storage consequently. Overall, our results underscore the importance of viral-host interactions in understanding the microplastics-dependent carbon storage in the soil ecosystem.

Identification of Key Genes Related to Immune-Lipid Metabolism in Skin Barrier Damage and Analysis of Immune Infiltration.

Several physical and chemical factors regulate skin barrier function. Skin barrier dysfunction causes many inflammatory skin diseases, such as atopic dermatitis and psoriasis. Activation of the immune response may lead to damage to the epidermal barrier. Abnormal lipid metabolism is defined as abnormally high or low values of plasma lipid components such as plasma cholesterol and triglycerides. The mouse skin barrier damage model was used for RNA sequencing. Bioinformatics analysis and validation were performed. Differently expressed genes (DEGs) related to immune and lipid metabolism were screened by differentially expressed gene analysis, and the enriched biological processes and pathways of these genes were identified by GO-KEGG. The interactions between DEGs were confirmed by constructing a PPI network. GSEA, transcription factor regulatory network, and immune infiltration analyses were performed for the 10 genes. Expression validation was performed by public datasets. The expression of key genes in mouse skin tissue was detected by qPCR. The expression of differentially expressed immune cell markers in the skin was detected by immunofluorescence. Based on the trans epidermal water loss (TEWL) score, the expression of key genes was detected by qPCR before skin barrier injury, at 4h and 7d, and at recovery from injury. Il17a, Il6, Tnf, Itgam, and Cxcl1 were immune-related key genes. Pla2g2f, Ptgs2, Plb1, Pla2g3, and Pla2g2d were key genes for lipid metabolism. Database validation and experimental results revealed that the expression trends of these genes were consistent with our analyses. The research value of these genes has been demonstrated through mouse datasets and experimental validation, and future therapeutic approaches may be able to mitigate the disease by targeting these genes to modulate the function of the skin barrier.

Obesity's Unexpected Influence: Reduced Alphavirus Transmission and Altered Immune Activation in the Vector.

Chikungunya virus (CHIKV) and Mayaro virus (MAYV) are emerging/re-emerging alphaviruses transmitted by Aedes spp. mosquitoes and responsible for recent disease outbreaks in the Americas. The capacity of these viruses to cause epidemics is frequently associated with increased mosquito transmission, which in turn is governed by virus-host-vector interactions. Although many studies have explored virus-vector interactions, significant gaps remain in understanding how vertebrate host factors influence alphavirus transmission by mosquitoes. We previously showed that obesity, a ubiquitous vertebrate host biological factor, reduces alphavirus transmission potential in mosquitoes. We hypothesized that alphavirus-infected obese bloodmeals altered immune genes and/or pathways in mosquitoes, thereby inhibiting virus transmission. To test this, we conducted RNA sequencing (RNA-seq) and reverse transcription-quantitative polymerase chain reaction (RT-qPCR) on midgut RNA from mosquitoes fed on alphavirus-infected lean and obese mice. This approach aimed to identify potential antiviral or proviral genes and pathways altered in mosquitoes after consuming infected obese bloodmeals. We found upregulation of the Toll pathway and downregulation of several metabolic and other genes in mosquitoes fed on alphavirus-infected obese bloodmeals. Through gene knockdown studies, we demonstrated the antiviral role of Toll pathway and proviral roles of AAEL009965 and fatty acid synthase (FASN) in the transmission of alphaviruses by mosquitoes. Therefore, this study utilized obesity to identify factors influencing alphavirus transmission by mosquitoes and this research approach may pave the way for designing broadly effective antiviral measures to combat mosquito-borne viruses, such as releasing transgenic mosquitoes deficient in the identified genes.

A new selective force driving metabolic gene clustering.

The formation of clusters of functionally related genes in microbial genomes has puzzled microbiologists since their discovery. Here, we suggest that replication, and the copy number variations due to the replisome passage, might play a role in the process through a perturbation in metabolite homeostasis. We provide theoretical support to this hypothesis, and we found that both simulations and genomic analysis support our hypothesis.

Gene-echocardiography: unmasking the covert role of fatty acid metabolism gene mutations in adult cardiomyopathy

Abstract Background Although the role of fatty acid metabolic deficiencies in cardiomyopathy is established, the precise identity of causative genes and their respective phenotypes in adult cardiomyopathy remains unclear. Methods We implemented a combination of echocardiography and whole exome sequencing in clinically diagnosed cardiomyopathy patients to uncover this relationship. This involved recording cardiac phenotypes, identifying genes associated with disrupted fatty acid metabolism, and subsequently followed up with these patients. Results Out of 802 cardiomyopathy patients, 27 exhibited mutations in genes tied to fatty acid metabolism. Specifically, 16 patients demonstrated a hypertrophic cardiomyopathy (HCM) phenotype, and 11 showed a dilated cardiomyopathy (DCM) phenotype. The implicated genes were SLC22A5 (n=7), ACADVL (n=6), ACADS (n=4), ACADM (n=3), SLC25A20 (n=3), ACADL (n=2), HADH (n=1), and AGK (n=1), with a higher prevalence in males. Among these, the SLC22A5 mutation was predominant and correlated with HCM phenotype. Notably, compound mutations were frequently observed (16/27), leading to earlier disease onset and diminished aortic sinus dimension. Further investigation showed that smaller aortic sinus dimensions resulted from abnormal left ventricular myocardial work. HCM phenotype mutation carriers showed more consistent left ventricular hypertrophy and a higher probability of right ventricular hypertrophy, while DCM phenotype mutation carriers exhibited impaired contractile function across all left ventricle segments. After a 24-month follow-up, the DCM phenotype group demonstrated higher cardiovascular risk, including two fatalities. Conclusion Our investigation highlights the significant prevalence of mutations related to abnormal fatty acid metabolism in adult cardiomyopathy patients, underscoring the utility of a gene-echocardiography approach for diagnosis.

Differential cellular communication in tumor immune microenvironment during early and advanced stages of lung adenocarcinoma.

Heterogeneous behavior of each cell type and their cross-talks in tumor immune microenvironment (TIME) refers to tumor immunological heterogeneity that emerges during tumor progression and represents formidable challenges for effective anti-tumor immune response and promotes drug resistance. To comprehensively elucidate the heterogeneous behavior of individual cell types and their interactions across different stages of tumor development at system level, a computational framework was devised that integrates cell specific data from single-cell RNASeq into networks illustrating interactions among signaling and metabolic response genes within and between cells in TIME. This study identified stage specific novel markers which remodel the cross-talks, thereby facilitating immune stimulation. Particularly, multicellular knockout of metabolic gene APOE (Apolipoprotein E in mast cell, myeloid cell and fibroblast) combined with signaling gene CAV1 (Caveolin1 in endothelial and epithelial cells) resulted in the activation of T-cell mediated signaling pathways. Additionally, this knockout also initiated intervention of cytotoxic gene regulations during tumor immune cell interactions at the early stage of Lung Adenocarcinoma (LUAD). Furthermore, a unique interaction motif from multiple cells emerged significant in regulating the overall immune response at the advanced stage of LUAD. Most significantly, FCER1G (Fc Fragment of IgE Receptor Ig) was identified as the common regulator in activating the anti-tumor immune response at both stages. Predicted markers exhibited significant association with patient overall survival in patient specific dataset. This study uncovers the significance of signaling and metabolic interplay within TIME and discovers important targets to enhance anti-tumor immune response at each stage of tumor development.

Identification and Functional Analysis of Key Genes Regulating Organic Acid Metabolism in Jujube Fruit

Organic acids are crucial indicators of fruit flavor quality, but the metabolic characteristics and regulatory genes of organic acids during jujube fruit development remain largely unexplored. In this study, the cultivar ‘Heigeda’ with a high organic acid content was used as the experimental material. The organic acid content was quantified, and key candidate genes were identified through transcriptome analysis. The results indicated that malic acid and citric acid were the main organic acid content in jujube fruit and increased gradually with fruit development. Transcriptome analysis identified nine genes associated with malic acid and seven with citric acid, with four genes co-regulating malic acid and citric acid. Functional assays by transient overexpression and silencing of these four genes in the jujube fruits revealed that overexpression significantly upregulated the malic and citric acid content. However, only the silencing of aconitase1 (ZjACO1) and aconitase3 (ZjACO3) significantly downregulated the content of malic and citric acids. Therefore, aconitase1 (ZjACO1) and aconitase3 (ZjACO3) are considered the key genes that regulate the metabolism of citric acid and malic acid in jujube fruits. Our study can enrich the regulation mechanism of the organic acid metabolism of jujube fruit and provide theoretical support for the efficient cultivation of jujube fruit.

Transcriptional and physiological plasticity of the green peach aphid (Hemiptera: Aphididae) to cabbage and pepper plants.

Defensive metabolites and nutrient restriction of host plants are 2 major obstacles to the colonization of insect herbivores. The green peach aphid (GPA) Myzus persicae (Sulzer) broadly colonizes plants with diverse nutritional and defensive traits. However, how GPA adapts to nutritional and defensive traits within different plants remains largely unknown. To elucidate this, we first investigated the performances and transcriptomes of GPA feeding on cabbage Brassica oleracea and pepper Capsicum annuum. The green peach aphid had lower weight and fecundity when feeding on cabbage than on pepper. The transcriptomic analysis found 824 differentially expressed genes (DEGs), and 13 of the top 20 Kyoto Encyclopedia of Genes and Genomes pathways are related to nutrient metabolism, energy metabolism, and detoxification. Specifically, we found 160 DEGs associated with the metabolism of protein and amino acids, sugar and lipids, and xenobiotic substances, 86 upregulated in cabbage-fed GPA. Fourteen cathepsin B genes were strongly upregulated in cabbage-fed GPA, and were enriched in lysosome pathway and 2 dominated gene ontology terms peptidase activity and proteolysis. In addition, cabbage-fed GPA upregulated sugar and lipid digestion, while downregulated lipid biosynthesis processes. Furthermore, 55 metabolic detoxification enzyme genes were differentially expressed between GPA on 2hosts, and detoxification enzyme activities of GPA indeed changed accordingly to the host. Then, we found that cabbage has lower amino acids nutrition quality for GPA compared to pepper. Our results suggested that adjustment of nitrogen nutrient metabolism, sugar and lipid metabolism, and metabolic detoxification in a host-specific manner play crucial roles in the adaptations of GPA to different host plants.

Versatile MXenzymes Scavenging ROS for Promotion of Seed Germination under Salt Stress.

Salinization is recognized as a global problem, restricting agricultural production and sustainability. Targeting the salinity-induced oxidative stress, antioxidant treatment represents a protective strategy to improve plant salt tolerance. Herein, we report a V4C3 MXene nanozyme (MXenzyme), which exhibits good biocompatibility and excellent reactive oxygen species scavenging activity to ameliorate the salt stress-induced inhibition of seed germination. V4C3 MXenzyme treatment can significantly relieve salinity-induced oxidative stress and restore the antioxidant system in pea seeds, thus improving the phenotypic traits during germination. The molecular mechanism by which antioxidant V4C3 MXenzymes augment salt tolerances is revealed through transcriptomics and metabolomics. V4C3 MXenzymes significantly regulate the gene expression of antioxidant enzymes and molecule biosynthesis that correlate closely with hormone signal transduction genes and energy metabolism genes. With correlation and the combined analysis, redox homeostasis targeted by antioxidant V4C3 MXenzymes plays a critical role in promoting plant growth under salt stress.

Chronic exposure to polycyclic aromatic hydrocarbons alters skin virome composition and virus-host interactions.

Exposure to polycyclic aromatic hydrocarbons (PAHs) in polluted air influences the composition of the skin microbiome, which in turn is associated with altered skin phenotypes. However, the interactions between PAH exposure and viromes are unclear. This study aims to elucidate how PAH exposure affects the composition and function of skin viruses, their role in shaping the metabolism of bacterial hosts, and the subsequent effects on skin phenotype. We analyzed metagenomes from cheek skin swabs collected from 124 Chinese women in our previous study and found that the viruses associated with the two microbiome cutotypes had distinct diversities, compositions, functions, and lifestyles following PAH exposure. Moreover, exposure to high concentrations of PAHs substantially increased interactions between viruses and certain biodegrading bacteria. Under high-PAH exposure, the viruses were enriched in xenobiotic degradation functions, and there was evidence suggesting that the insertion of bacteriophage-encoded auxiliary metabolic genes into hosts aids biodegradation. Under low-PAH exposure conditions, the interactions followed the "Piggyback-the-Winner" model, with Cutibacterium acnes being "winners," whereas under high-PAH exposure, they followed the "Piggyback-the-Persistent" model, with biodegradation bacteria being "persistent." These findings highlight the impact of air pollutants on skin bacteria and viruses, their interactions, and their modulation of skin health. Understanding these intricate relationships could provide insights for developing targeted strategies to maintain skin health in polluted environments, emphasizing the importance of mitigating pollutant exposure and harnessing the potential of viruses to help counteract the adverse effects.

Apolipoprotein E Induces Lipid Accumulation Through Dgat2 That Is Prevented with Time-Restricted Feeding in Drosophila

Background: Apolipoprotein E (ApoE) is the leading genetic risk factor for late-onset Alzheimer’s disease (AD), which is the leading cause of dementia worldwide. Most people have two ApoE-ε3 (ApoE3) alleles, while ApoE-ε2 (ApoE2) is protective from AD, and ApoE-ε4 (ApoE4) confers AD risk. How these alleles modulate AD risk is not clearly defined, and ApoE’s role in lipid metabolism is also not fully known. Lipid droplets increase in AD. However, how ApoE contributes to lipid accumulation in the brain remains unknown. Methods: Here, we use Drosophila to study the effects of ApoE alleles on lipid accumulation in the brain and muscle in a cell-autonomous and non-cell-autonomous manner. Results: We report that pan-neuronal expression of each ApoE allele induces lipid accumulation specifically in the brain, but not in the muscle. However, this was not the case when expressed with muscle-specific drivers. ApoE2- and ApoE3-induced lipid accumulation is dependent on the expression of Dgat2, a key regulator of triacylglycerol production, while ApoE4 still induces lipid accumulation even with knock-down of Dgat2. Additionally, we find that implementation of time-restricted feeding (TRF), a dietary intervention in which food access only occurs in the active period (day), prevents ApoE-induced lipid accumulation in the brain of flies and modulates lipid metabolism genes. Conclusions: Altogether, our results demonstrate that ApoE induces lipid accumulation in the brain, that ApoE4 is unique in causing lipid accumulation independent of Dgat2, and that TRF prevents ApoE-induced lipid accumulation. These results support the idea that lipid metabolism is critical in AD, and that TRF could be a promising therapeutic approach to prevent ApoE-associated dysfunction in lipid metabolism.

A benchmark of RNA-seq data normalization methods for transcriptome mapping on human genome-scale metabolic networks

Genome-scale metabolic models (GEMs) cover the entire list of metabolic genes in an organism and associated reactions, in a tissue/condition non-specific manner. RNA-seq provides crucial information to make the GEMs condition-specific. Integrative Metabolic Analysis Tool (iMAT) and Integrative Network Inference for Tissues (INIT) are the two most popular algorithms to create condition-specific GEMs from human transcriptome data. The normalization method of choice for raw RNA-seq count data affects the model content produced by these algorithms and their predictive accuracy. However, a benchmark of the RNA-seq normalization methods on the performance of iMAT and INIT algorithms is missing in the literature. Another important phenomenon is covariates such as age and gender in a dataset, and they can affect the predictivity of analysis. In this study, we aimed to compare five different RNA-seq data normalization methods (TPM, FPKM, TMM, GeTMM, and RLE) and covariate adjusted versions of the normalized data by mapping them on a human GEM using the iMAT and INIT algorithms to generate personalized metabolic models. We used RNA-seq data for Alzheimer’s disease (AD) and lung adenocarcinoma (LUAD) patients. The results demonstrated that RNA-seq data normalized by the RLE, TMM, or GeTMM methods enabled the production of condition-specific metabolic models with considerably low variability in terms of the number of active reactions compared to the within-sample normalization methods (FPKM, TPM). Using these models, we could more accurately capture the disease-associated genes (average accuracy of ~0.80 for AD and ~0.67 for LUAD) for the RLE, TMM, and GeTMM normalization methods. An increase in the accuracies was observed for all the methods when covariate adjustment was applied. We found a similar accuracy trend when we compared the metabolites of perturbed reactions to metabolome data for AD. Together, our benchmark study shows that the between-sample RNA-seq normalization methods reduce false positive predictions at the expense of missing some true positive genes when mapped on GEMs.

Surface microlayer-mediated virome dissemination in the Central Arctic

BackgroundAquatic viruses act as key players in shaping microbial communities. In polar environments, they face significant challenges such as limited host availability and harsh conditions. However, due to the restricted accessibility of these ecosystems, our understanding of viral diversity, abundance, adaptations, and host interactions remains limited.ResultsTo fill this knowledge gap, we studied viruses from atmosphere-close aquatic ecosystems in the Central Arctic and Northern Greenland. Aquatic samples for virus-host analysis were collected from ~60 cm depth and the submillimeter surface microlayer (SML) during the Synoptic Arctic Survey 2021 on icebreaker Oden in the Arctic summer. Water was sampled from a melt pond and open water before undergoing size-fractioned filtration, followed by genome-resolved metagenomic and cultivation investigations. The prokaryotic diversity in the melt pond was considerably lower compared to that of open water. The melt pond was dominated by a Flavobacterium sp. and Aquiluna sp., the latter having a relatively small genome size of 1.2 Mb and the metabolic potential to generate ATP using the phosphate acetyltransferase-acetate kinase pathway. Viral diversity on the host fraction (0.2–5 µm) of the melt pond was strikingly limited compared to that of open water. From the 1154 viral operational taxonomic units (vOTUs), of which two-thirds were predicted bacteriophages, 17.2% encoded for auxiliary metabolic genes (AMGs) with metabolic functions. Some AMGs like glycerol-3-phosphate cytidylyltransferase and ice-binding like proteins might serve to provide cryoprotection for the host. Prophages were often associated with SML genomes, and two active prophages of new viral genera from the Arctic SML strain Leeuwenhoekiella aequorea Arc30 were induced. We found evidence that vOTU abundance in the SML compared to that of ~60 cm depth was more positively correlated with the distribution of a vOTU across five different Arctic stations.ConclusionsThe results indicate that viruses employ elaborate strategies to endure in extreme, host-limited environments. Moreover, our observations suggest that the immediate air-sea interface serves as a platform for viral distribution in the Central Arctic.Dvb_jZZ1H7pg55rxrDSVb2Video

The transcription factor TaFDL2-1A functions in auxin metabolism mediated by abscisic acid to regulate shoot growth in wheat.

Genetic strategies can be effective in improving wheat (Triticum aestivum L.) drought stress tolerance, but accumulating evidence suggests that overexpressing drought-resistance genes, especially genes related to the abscisic acid (ABA) signaling pathway, can retard plant growth. We previously characterized the positive roles of the wheat bZIP transcription factor TaFD-Like2-1A (TaFDL2-1A) in drought stress tolerance and ABA biosynthesis and response, whereas a dwarfing shoot exhibited under normal conditions. This study determined the underlying mechanisms that allow TaFDL2-1A to affect shoot growth. Overexpressing TaFDL2-1A decreased cell length, cell width, leaf size, shoot length, and biomass in wheat. The results of RNA-seq showed that multiple differently expressed transcripts are enriched in the auxin signaling pathway. Further analysis indicated higher expression levels of Gretchen Hagen3 (GH3) genes and lower indole-3-acetic acid (IAA) concentrations in the TaFDL2-1A overexpression lines. Exogenous IAA treatment restored the phenotypes of the TaFDL2-1A overexpression lines to wild-type levels. Transcriptional regulation analysis suggested that TaFDL2-1A enhances the expression of auxin metabolism genes, such as TaGH3.2-3A, TaGH3.2-3B, TaGH3.8-2A, and TaGH3.8-2D, by directly binding to ACGT core cis-elements. Furthermore, tafdl2 knock-out plants had lower expression levels of these GH3 genes and higher IAA levels than Fielder wheat. These GH3 gene expression and IAA levels were induced and reduced in Fielder wheat and tafdl2 knock-out plants treated with exogenous ABA. Our findings elucidate mechanisms underlying the functional redundancy of TaFDL2-1A in the crosstalk between ABA and IAA to affect shoot growth and provide insights into the balance between drought resistance and yield in wheat.

Use of transcriptomics and genomics to assess the effect of disinfectant exposure on the survival and resistance of Escherichia coli O157:H7, a human pathogen

Disinfectants are essential for biosecurity, preventing the persistence and spread of zoonotic pathogens on farms and subsequent human infections. In this study, transcriptomics and genomics were utilised to assess the effect of disinfectant exposure on pathogenic Escherichia coli. The exposure of E. coli O157:H7 to sub-optimal concentrations of commonly used farm disinfectants elicited changes in both the transcriptome and genome. The transcriptomics identified upregulation of &amp;gt;300 genes and downregulation of &amp;gt;100 genes with functions, which included stress response, metabolism, transcription, transportation, membrane-associated and virulence genes. The phage shock protein (psp) operon was highly upregulated in response to a quaternary ammonium compound (QAC)-containing disinfectant, which has not previously been associated with a response to chemical stress. Disinfectant-adapted isolates generated by exposure to sub-lethal disinfectants levels demonstrated resistance to several common antibiotics and decreased sensitivity to biocides. Whole genome sequencing of the mutant strains indicated that they had acquired mutations in the genes associated with the upregulation of the multiple antibiotic resistance (MAR) efflux system (lon protease and marR) and topoisomerase genes (gyrA and gyrB). The disinfectant-adapted isolates also exhibited increased expression of transcription, respiration and several pH stress response genes localised in the “acid fitness island.” This study demonstrated that sub-optimal disinfectant concentrations allow E. coli O157:H7 to adapt and survive disinfection and develop antibiotic resistance. These changes could have implications for disease treatment and elimination on farms. Although E. coli O157:H7 and farm disinfectants were the focus of this study, we believe these findings are also applicable to other settings, including hospitals.

Activation of estrogen-related receptor γ by calcium and cadmium

ObjectiveEstrogen-related receptor γ (ERRγ) is a metabolic regulator with no identified physiological ligands. This study investigates whether calcium is an ERRγ ligand that mediates the effects of glucagon and whether cadmium, which mimics the effects of calcium, disrupts metabolism through ERRγ.MethodHepG2, MCF-7, and HEK293T transfected with ERRγ were treated with glucagon, calcium, cadmium, ERRγ agonist, or ERRγ inhibitor. Cells were then collected for in vitro assays including real-time qPCR, Western blot, ChIP, immunofluorescence, mutational analysis, or gene set enrichment analysis. Molecular dynamics simulations were performed to study mutation sites.ResultsIn HepG2 cells, treatment with glucagon, calcium, or cadmium re-localized ERRγ to the cell nucleus, recruited ERRγ to estrogen-related response elements, induced the expression of ERRγ-regulated genes, and increased extracellular glucose that was blocked by an ERRγ antagonist. In MCF-7 cells and HEK293T cells transfected with ERRγ, similar treatments induced the expression of metabolic genes. Mutational analysis identified S303, T429, and E452 in the ligand-binding domain as potential interaction sites. Molecular dynamics simulations showed that calcium induced changes in ERRγ similar to ERRγ agonist.ConclusionThe results suggest that calcium is a potential ligand of ERRγ that mediates the effects of glucagon and cadmium disrupts metabolism through ERRγ.

Single-cell omics and machine learning integration to develop a polyamine metabolism-based risk score model in breast cancer patients

BackgroundBreast cancer remains the leading malignant neoplasm among women globally, posing significant challenges in terms of treatment and prognostic evaluation. The metabolic pathway of polyamines is crucial in breast cancer progression, with a strong association to the increased capabilities of tumor cells for proliferation, invasion, and metastasis.MethodsWe used a multi-omics approach combining bulk RNA sequencing and single-cell RNA sequencing (scRNA-seq) to study polyamine metabolism. Data from The Cancer Genome Atlas, Gene Expression Omnibus, and Genotype-Tissue Expression identified 286 differentially expressed genes linked to polyamine pathways in breast cancer. These genes were analyzed using univariate COX and machine learning algorithms to develop a prognostic scoring algorithm. Single-cell RNA sequencing validated the model by examining gene expression heterogeneity at the cellular level.ResultsOur single-cell analyses revealed distinct subpopulations with different expressions of genes related to polyamine metabolism, highlighting the heterogeneity of the tumor microenvironment. The SuperPC model (a constructed risk score) demonstrated high accuracy when predicting patient outcomes. The immune profiling and functional enrichment analyses revealed that the genes identified play a crucial role in cell cycle control and immune modulation. Single-cell validation confirmed that polyamine metabolism genes were present in specific cell clusters. This highlights their potential as therapeutic targets.ConclusionsThis study integrates single cell omics with machine-learning to develop a robust scoring model for breast cancer based on polyamine metabolic pathways. Our findings offer new insights into tumor heterogeneity, and a novel framework to personalize prognosis. Single-cell technologies are being used in this context to enhance our understanding of the complex molecular terrain of breast cancer and support more effective clinical management.