Transcriptomic meta-analysis to identify potential antifungal targets in Candida albicans.

BACKGROUND: Candida albicans is a fungal pathogen causing human infections. Here we investigated differential gene expression patterns and functional enrichment in C. albicans strains grown under different conditions. METHODS: A systematic GEO database search identified 239 "Candida albicans" datasets, of which 14 were selected after rigorous criteria application. Retrieval of raw sequencing data from the ENA database was accompanied by essential metadata extraction from dataset descriptions and original articles. Pre-processing via the tailored nf-core pipeline for C. albicans involved alignment, gene/transcript quantification, and diverse quality control measures. Quality assessment via PCA and DESeq2 identified significant genes (FDR < = 0.05, log2-fold change > = 1 or <= -1), while topGO conducted GO term enrichment analysis. Exclusions were made based on data quality and strain relevance, resulting in the selection of seven datasets from the SC5314 strain background for in-depth investigation. RESULTS: The meta-analysis of seven selected studies unveiled a substantial number of genes exhibiting significant up-regulation (24,689) and down-regulation (18,074). These differentially expressed genes were further categorized into 2,497 significantly up-regulated and 2,573 significantly down-regulated Gene Ontology (GO) IDs. GO term enrichment analysis clustered these terms into distinct groups, providing insights into the functional implications. Three target gene lists were compiled based on previous studies, focusing on central metabolism, ion homeostasis, and pathogenicity. Frequency analysis revealed genes with higher occurrence within the identified GO clusters, suggesting their potential as antifungal targets. Notably, the genes TPS2, TPS1, RIM21, PRA1, SAP4, and SAP6 exhibited higher frequencies within the clusters. Through frequency analysis within the GO clusters, several key genes emerged as potential targets for antifungal therapies. These include RSP5, GLC7, SOD2, SOD5, SOD1, SOD6, SOD4, SOD3, and RIM101 which exhibited higher occurrence within the identified clusters. CONCLUSION: This comprehensive study significantly advances our understanding of the dynamic nature of gene expression in C. albicans. The identification of genes with enhanced potential as antifungal drug targets underpins their value for future interventions. The highlighted genes, including TPS2, TPS1, RIM21, PRA1, SAP4, SAP6, RSP5, GLC7, SOD2, SOD5, SOD1, SOD6, SOD4, SOD3, and RIM101, hold promise for the development of targeted antifungal therapies.

The health effects of artificial sweeteners: Towards personalized quantification and prediction through gut microbiome.

Image 1.

Leveraging high-resolution omics data for predicting responses and adverse events to immune checkpoint inhibitors.

A long-standing goal of personalized and precision medicine is to enable accurate prediction of the outcomes of a given treatment regimen for patients harboring a disease. Currently, many clinical trials fail to meet their endpoints due to underlying factors in the patient population that contribute to either poor responses to the drug of interest or to treatment-related adverse events. Identifying these factors beforehand and correcting for them can lead to an increased success of clinical trials. Comprehensive and large-scale data gathering efforts in biomedicine by omics profiling of the healthy and diseased individuals has led to a treasure-trove of host, disease and environmental factors that contribute to the effectiveness of drugs aiming to treat disease. With increasing omics data, artificial intelligence allows an in-depth analysis of big data and offers a wide range of applications for real-world clinical use, including improved patient selection and identification of actionable targets for companion therapeutics for improved translatability across more patients. As a blueprint for complex drug-disease-host interactions, we here discuss the challenges of utilizing omics data for predicting responses and adverse events in cancer immunotherapy with immune checkpoint inhibitors (ICIs). The omics-based methodologies for improving patient outcomes as in the ICI case have also been applied across a wide-range of complex disease settings, exemplifying the use of omics for in-depth disease profiling and clinical use.

Metabolic engineering of human gut microbiome: Recent developments and future perspectives.

Many studies have demonstrated that the gut microbiota is associated with human health and disease. Manipulation of the gut microbiota, e.g. supplementation of probiotics, has been suggested to be feasible, but subject to limited therapeutic efficacy. To develop efficient microbiota-targeted diagnostic and therapeutic strategies, metabolic engineering has been applied to construct genetically modified probiotics and synthetic microbial consortia. This review mainly discusses commonly adopted strategies for metabolic engineering in the human gut microbiome, including the use of in silico, in vitro, or in vivo approaches for iterative design and construction of engineered probiotics or microbial consortia. Especially, we highlight how genome-scale metabolic models can be applied to advance our understanding of the gut microbiota. Also, we review the recent applications of metabolic engineering in gut microbiome studies as well as discuss important challenges and opportunities.

Whole-genome sequencing reveals high-risk clones of Pseudomonas aeruginosa in Guangdong, China.

The ever-increasing prevalence of infections produced by multidrug-resistant or extensively drug-resistant Pseudomonas aeruginosa is commonly linked to a limited number of aptly-named epidemical 'high-risk clones' that are widespread among and within hospitals worldwide. The emergence of new potential high-risk clone strains in hospitals highlights the need to better and further understand the underlying genetic mechanisms for their emergence and success. P. aeruginosa related high-risk clones have been sporadically found in China, their genome sequences have rarely been described. Therefore, the large-scale sequencing of multidrug-resistance high-risk clone strains will help us to understand the emergence and transmission of antibiotic resistances in P. aeruginosa high-risk clones. In this study, 212 P. aeruginosa strains were isolated from 2 tertiary hospitals within 3 years (2018-2020) in Guangdong Province, China. Whole-genome sequencing, multi-locus sequence typing (MLST) and antimicrobial susceptibility testing were applied to analyze the genomic epidemiology of P. aeruginosa in this region. We found that up to 130 (61.32%) of the isolates were shown to be multidrug resistant, and 196 (92.45%) isolates were Carbapenem-Resistant Pseudomonas aeruginosa. MLST analysis demonstrated high diversity of sequence types, and 18 reported international high-risk clones were identified. Furthermore, we discovered the co-presence of exoU and exoS genes in 5 collected strains. This study enhances insight into the regional research of molecular epidemiology and antimicrobial resistance of P. aeruginosa in China. The high diversity of clone types and regional genome characteristics can serve as a theoretical reference for public health policies and help guide measures for the prevention and control of P. aeruginosa resistance.

Systems Metabolic Engineering of Industrial Microorganisms.

The green and sustainable production of chemicals, materials, fuels, food, and pharmaceuticals has become a key solution to the global energy and environmental crisis [...].

Self-organized metabotyping of obese individuals identifies clusters responding differently to bariatric surgery.

Weight loss through bariatric surgery is efficient for treatment or prevention of obesity related diseases such as type 2 diabetes and cardiovascular disease. Long term weight loss response does, however, vary among patients undergoing surgery. Thus, it is difficult to identify predictive markers while most obese individuals have one or more comorbidities. To overcome such challenges, an in-depth multiple omics analyses including fasting peripheral plasma metabolome, fecal metagenome as well as liver, jejunum, and adipose tissue transcriptome were performed for 106 individuals undergoing bariatric surgery. Machine leaning was applied to explore the metabolic differences in individuals and evaluate if metabolism-based patients' stratification is related to their weight loss responses to bariatric surgery. Using Self-Organizing Maps (SOMs) to analyze the plasma metabolome, we identified five distinct metabotypes, which were differentially enriched for KEGG pathways related to immune functions, fatty acid metabolism, protein-signaling, and obesity pathogenesis. The gut metagenome of the most heavily medicated metabotypes, treated simultaneously for multiple cardiometabolic comorbidities, was significantly enriched in Prevotella and Lactobacillus species. This unbiased stratification into SOM-defined metabotypes identified signatures for each metabolic phenotype and we found that the different metabotypes respond differently to bariatric surgery in terms of weight loss after 12 months. An integrative framework that utilizes SOMs and omics integration was developed for stratifying a heterogeneous bariatric surgery cohort. The multiple omics datasets described in this study reveal that the metabotypes are characterized by a concrete metabolic status and different responses in weight loss and adipose tissue reduction over time. Our study thus opens a path to enable patient stratification and hereby allow for improved clinical treatments.

Cell-free fetal DNA for genetic evaluation in Copenhagen Pregnancy Loss Study (COPL): a prospective cohort study.

BACKGROUND: One in four pregnancies end in a pregnancy loss. Although the effect on couples is well documented, evidence-based treatments and prediction models are absent. Fetal aneuploidy is associated with a higher chance of a next successful pregnancy compared with euploid pregnancy loss in which underlying maternal conditions might be causal. Ploidy diagnostics are therefore advantageous but challenging as they require collection of the pregnancy tissue. Cell-free fetal DNA (cffDNA) from maternal blood has the potential for evaluation of fetal ploidy status, but no large-scale validation of the method has been done. METHODS: In this prospective cohort study, women with a pregnancy loss were recruited as a part of the Copenhagen Pregnancy Loss (COPL) study from three gynaecological clinics at public hospitals in Denmark. Women were eligible for inclusion if older than 18 years with a pregnancy loss before gestational age 22 weeks (ie, 154 days) and with an intrauterine pregnancy confirmed by ultrasound (including anembryonic sac), and women with pregnancies of unknown location or molar pregnancies were excluded. Maternal blood was collected while pregnancy tissue was still in situ or within 24 h after pregnancy tissue had passed and was analysed by genome-wide sequencing of cffDNA. Direct sequencing of the pregnancy tissue was done as reference. FINDINGS: We included 1000 consecutive women, at the time of a pregnancy loss diagnosis, between Nov 12, 2020, and May 1, 2022. Results from the first 333 women with a pregnancy loss (recruited between Nov 12, 2020, and Aug 14, 2021) were used to evaluate the validity of cffDNA-based testing. Results from the other 667 women were included to evaluate cffDNA performance and result distribution in a larger cohort of 1000 women in total. Gestational age of fetus ranged from 35-149 days (mean of 70·5 days [SD 16·5], or 10 weeks plus 1 day). The cffDNA-based test had a sensitivity for aneuploidy detection of 85% (95% CI 79-90) and a specificity of 93% (95% CI 88-96) compared with direct sequencing of the pregnancy tissue. Among 1000 cffDNA-based test results, 446 (45%) were euploid, 405 (41%) aneuploid, 37 (4%) had multiple aneuploidies, and 112 (11%) were inconclusive. 105 (32%) of 333 women either did not manage to collect the pregnancy tissue or collected a sample classified as unknown tissue giving a high risk of being maternal. INTERPRETATION: This validation of cffDNA-based testing in pregnancy loss shows the potential and feasibility of the method to distinguish euploid and aneuploid pregnancy loss for improved clinical management and benefit of future reproductive medicine and women's health research. FUNDING: Ole Kirks Foundation, BioInnovation Institute Foundation, and the Novo Nordisk Foundation.

Modeling the metabolic dynamics at the genome-scale by optimized yield analysis.

The hybrid cybernetic model (HCM) approach is a dynamic modeling framework that integrates enzyme synthesis and activity regulation. It has been widely applied in bioreaction engineering, particularly in the simulation of microbial growth in different mixtures of carbon sources. In a HCM, the metabolic network is decomposed into elementary flux modes (EFMs), whereby the network can be reduced into a few pathways by yield analysis. However, applying the HCM approach on conventional genome-scale metabolic models (GEMs) is still a challenge due to the high computational demands. Here, we present a HCM strategy that introduced an optimized yield analysis algorithm (opt-yield-FBA) to simulate metabolic dynamics at the genome-scale without the need for EFMs calculation. The opt-yield-FBA is a flux-balance analysis (FBA) based method that can calculate optimal yield solutions and yield space for GEM. With the opt-yield-FBA algorithm, the HCM strategy can be applied to get the yield spaces and avoid the computational burden of EFMs, and it can therefore be applied for developing dynamic models for genome-scale metabolic networks. Here, we illustrate the strategy by applying the concept to simulate the dynamics of microbial communities.

Machine learning for data integration in human gut microbiome.

Recent studies have demonstrated that gut microbiota plays critical roles in various human diseases. High-throughput technology has been widely applied to characterize the microbial ecosystems, which led to an explosion of different types of molecular profiling data, such as metagenomics, metatranscriptomics and metabolomics. For analysis of such data, machine learning algorithms have shown to be useful for identifying key molecular signatures, discovering potential patient stratifications, and particularly for generating models that can accurately predict phenotypes. In this review, we first discuss how dysbiosis of the intestinal microbiota is linked to human disease development and how potential modulation strategies of the gut microbial ecosystem can be used for disease treatment. In addition, we introduce categories and workflows of different machine learning approaches, and how they can be used to perform integrative analysis of multi-omics data. Finally, we review advances of machine learning in gut microbiome applications and discuss related challenges. Based on this we conclude that machine learning is very well suited for analysis of gut microbiome and that these approaches can be useful for development of gut microbe-targeted therapies, which ultimately can help in achieving personalized and precision medicine.

One-year supplementation with Lactobacillus reuteri ATCC PTA 6475 counteracts a degradation of gut microbiota in older women with low bone mineral density.

Recent studies have shown that probiotic supplementation has beneficial effects on bone metabolism. In a randomized controlled trial (RCT) we demonstrated that supplementation of Lactobacillus reuteri ATCC PTA 6475 reduced bone loss in older women with low bone mineral density. To investigate the mechanisms underlying the effect of L. reuteri ATCC PTA 6475 on bone metabolism, 20 women with the highest changes (good responders) and the lowest changes (poor responders) in tibia total volumetric BMD after one-year supplementation were selected from our previous RCT. In the current study we characterized the gut microbiome composition and function as well as serum metabolome in good responders and poor responders to the probiotic treatment as a secondary analysis. Although there were no significant differences in the microbial composition at high taxonomic levels, gene richness of the gut microbiota was significantly higher (P < 0.01 by the Wilcoxon rank-sum test) and inflammatory state was improved (P < 0.05 by the Wilcoxon signed-rank test) in the good responders at the end of the 12-month daily supplementation. Moreover, detrimental changes including the enrichment of E. coli (adjusted P < 0.05 by DESeq2) and its biofilm formation (P < 0.05 by GSA) observed in the poor responders were alleviated in the good responders by the treatment. Our results indicate that L. reuteri ATCC PTA 6475 supplementation has the potential to prevent a deterioration of the gut microbiota and inflammatory status in elderly women with low bone mineral density, which might have beneficial effects on bone metabolism.

Dataset for suppressors of amyloid-ß toxicity and their functions in recombinant protein production in yeast.

The production of recombinant proteins at high levels often induces stress-related phenotypes by protein misfolding or aggregation. These are similar to those of the yeast Alzheimer's disease (AD) model in which amyloid-ß peptides (Aß42) were accumulated [1], [2]. We have previously identified suppressors of Aß42 cytotoxicity via the genome-wide synthetic genetic array (SGA) [3] and here we use them as metabolic engineering targets to evaluate their potentiality on recombinant protein production in yeast Saccharomyces cerevisiae. In order to investigate the mechanisms linking the genetic modifications to the improved recombinant protein production, we perform systems biology approaches (transcriptomics and proteomics) on the resulting strain and intermediate strains. The RNAseq data are preprocessed by the nf-core/RNAseq pipeline and analyzed using the Platform for Integrative Analysis of Omics (PIANO) package [4]. The quantitative proteome is analyzed on an Orbitrap Fusion Lumos mass spectrometer interfaced with an Easy-nLC1200 liquid chromatography (LC) system. LC-MS data files are processed by Proteome Discoverer version 2.4 with Mascot 2.5.1 as a database search engine. The original data presented in this work can be found in the research paper titled "Suppressors of Amyloid-ß Toxicity Improve Recombinant Protein Production in yeast by Reducing Oxidative Stress and Tuning Cellular Metabolism", by Chen et al. [5].

Suppressors of amyloid-ß toxicity improve recombinant protein production in yeast by reducing oxidative stress and tuning cellular metabolism.

High-level production of recombinant proteins in industrial microorganisms is often limited by the formation of misfolded proteins or protein aggregates, which consequently induce cellular stress responses. We hypothesized that in a yeast Alzheimer's disease (AD) model overexpression of amyloid-ß peptides (Aß42), one of the main peptides relevant for AD pathologies, induces similar phenotypes of cellular stress. Using this humanized AD model, we previously identified suppressors of Aß42 cytotoxicity. Here we hypothesize that these suppressors could be used as metabolic engineering targets to alleviate cellular stress and improve recombinant protein production in the yeast Saccharomyces cerevisiae. Forty-six candidate genes were individually deleted and twenty were individually overexpressed. The positive targets that increased recombinant α-amylase production were further combined leading to an 18.7-fold increased recombinant protein production. These target genes are involved in multiple cellular networks including RNA processing, transcription, ER-mitochondrial complex, and protein unfolding. By using transcriptomics and proteomics analyses, combined with reverse metabolic engineering, we showed that reduced oxidative stress, increased membrane lipid biosynthesis and repressed arginine and sulfur amino acid biosynthesis are significant pathways for increased recombinant protein production. Our findings provide new insights towards developing synthetic yeast cell factories for biosynthesis of valuable proteins.

Microbiome and metabolome features of the cardiometabolic disease spectrum.

Previous microbiome and metabolome analyses exploring non-communicable diseases have paid scant attention to major confounders of study outcomes, such as common, pre-morbid and co-morbid conditions, or polypharmacy. Here, in the context of ischemic heart disease (IHD), we used a study design that recapitulates disease initiation, escalation and response to treatment over time, mirroring a longitudinal study that would otherwise be difficult to perform given the protracted nature of IHD pathogenesis. We recruited 1,241 middle-aged Europeans, including healthy individuals, individuals with dysmetabolic morbidities (obesity and type 2 diabetes) but lacking overt IHD diagnosis and individuals with IHD at three distinct clinical stages-acute coronary syndrome, chronic IHD and IHD with heart failure-and characterized their phenome, gut metagenome and serum and urine metabolome. We found that about 75% of microbiome and metabolome features that distinguish individuals with IHD from healthy individuals after adjustment for effects of medication and lifestyle are present in individuals exhibiting dysmetabolism, suggesting that major alterations of the gut microbiome and metabolome might begin long before clinical onset of IHD. We further categorized microbiome and metabolome signatures related to prodromal dysmetabolism, specific to IHD in general or to each of its three subtypes or related to escalation or de-escalation of IHD. Discriminant analysis based on specific IHD microbiome and metabolome features could better differentiate individuals with IHD from healthy individuals or metabolically matched individuals as compared to the conventional risk markers, pointing to a pathophysiological relevance of these features.

Impairment of gut microbial biotin metabolism and host biotin status in severe obesity: effect of biotin and prebiotic supplementation on improved metabolism.

OBJECTIVES: Gut microbiota is a key component in obesity and type 2 diabetes, yet mechanisms and metabolites central to this interaction remain unclear. We examined the human gut microbiome's functional composition in healthy metabolic state and the most severe states of obesity and type 2 diabetes within the MetaCardis cohort. We focused on the role of B vitamins and B7/B8 biotin for regulation of host metabolic state, as these vitamins influence both microbial function and host metabolism and inflammation. DESIGN: We performed metagenomic analyses in 1545 subjects from the MetaCardis cohorts and different murine experiments, including germ-free and antibiotic treated animals, faecal microbiota transfer, bariatric surgery and supplementation with biotin and prebiotics in mice. RESULTS: Severe obesity is associated with an absolute deficiency in bacterial biotin producers and transporters, whose abundances correlate with host metabolic and inflammatory phenotypes. We found suboptimal circulating biotin levels in severe obesity and altered expression of biotin-associated genes in human adipose tissue. In mice, the absence or depletion of gut microbiota by antibiotics confirmed the microbial contribution to host biotin levels. Bariatric surgery, which improves metabolism and inflammation, associates with increased bacterial biotin producers and improved host systemic biotin in humans and mice. Finally, supplementing high-fat diet-fed mice with fructo-oligosaccharides and biotin improves not only the microbiome diversity, but also the potential of bacterial production of biotin and B vitamins, while limiting weight gain and glycaemic deterioration. CONCLUSION: Strategies combining biotin and prebiotic supplementation could help prevent the deterioration of metabolic states in severe obesity. TRIAL REGISTRATION NUMBER: NCT02059538.

Mapping of Nonhomologous End Joining-Mediated Integration Facilitates Genome-Scale Trackable Mutagenesis in Yarrowia lipolytica.

Genome-scale mutagenesis, phenotypic screening, and tracking the causal mutations is a powerful approach for genetic analysis. However, classic mutagenesis approaches require extensive effort to identify causal mutations. It is desirable to demonstrate a powerful approach for rapid trackable mutagenesis. Here, we mapped the distribution of nonhomologous end joining (NHEJ)-mediated integration for the first time and demonstrated that it can be used for constructing the genome-scale trackable mutagenesis library in Yarrowia lipolytica. The sequencing of 9.15 × 105 insertions showed that NHEJ-mediated integration inserted DNA randomly across the chromosomes, and the transcriptional regulatory regions exhibited integration preference. The insertions were located in both nucleosome-occupancy regions and nucleosome-free regions. Using NHEJ-mediated integration to construct the genome-scale mutagenesis library, the new targets that improved ß-carotene biosynthesis and acetic acid tolerance were identified rapidly. This mutagenesis approach is readily applicable to other organisms with strong NHEJ preference and will contribute to cell factory construction.

Developments in Fatty Acid-Derived Insect Pheromone Production Using Engineered Yeasts.

The use of traditional chemical insecticides for pest control often leads to environmental pollution and a decrease in biodiversity. Recently, insect sex pheromones were applied for sustainable biocontrol of pests in fields, due to their limited adverse impacts on biodiversity and food safety compared to that of other conventional insecticides. However, the structures of insect pheromones are complex, and their chemical synthesis is not commercially feasible. As yeasts have been widely used for fatty acid-derived pheromone production in the past few years, using engineered yeasts may be promising and sustainable for the low-cost production of fatty acid-derived pheromones. The primary fatty acids produced by Saccharomyces cerevisiae and other yeasts are C16 and C18, and it is also possible to rewire/reprogram the metabolic flux for other fatty acids or fatty acid derivatives. This review summarizes the fatty acid biosynthetic pathway in S. cerevisiae and recent progress in yeast engineering in terms of metabolic engineering and synthetic biology strategies to produce insect pheromones. In the future, insect pheromones produced by yeasts might provide an eco-friendly pest control method in agricultural fields.

Combinatorial, additive and dose-dependent drug-microbiome associations.

During the transition from a healthy state to cardiometabolic disease, patients become heavily medicated, which leads to an increasingly aberrant gut microbiome and serum metabolome, and complicates biomarker discovery1-5. Here, through integrated multi-omics analyses of 2,173 European residents from the MetaCardis cohort, we show that the explanatory power of drugs for the variability in both host and gut microbiome features exceeds that of disease. We quantify inferred effects of single medications, their combinations as well as additive effects, and show that the latter shift the metabolome and microbiome towards a healthier state, exemplified in synergistic reduction in serum atherogenic lipoproteins by statins combined with aspirin, or enrichment of intestinal Roseburia by diuretic agents combined with beta-blockers. Several antibiotics exhibit a quantitative relationship between the number of courses prescribed and progression towards a microbiome state that is associated with the severity of cardiometabolic disease. We also report a relationship between cardiometabolic drug dosage, improvement in clinical markers and microbiome composition, supporting direct drug effects. Taken together, our computational framework and resulting resources enable the disentanglement of the effects of drugs and disease on host and microbiome features in multimedicated individuals. Furthermore, the robust signatures identified using our framework provide new hypotheses for drug-host-microbiome interactions in cardiometabolic disease.