Longitudinal multi-omics of host-microbe dynamics in prediabetes.

Type 2 diabetes mellitus (T2D) is a growing health problem, but little is known about its early disease stages, its effects on biological processes or the transition to clinical T2D. To understand the earliest stages of T2D better, we obtained samples from 106 healthy individuals and individuals with prediabetes over approximately four years and performed deep profiling of transcriptomes, metabolomes, cytokines, and proteomes, as well as changes in the microbiome. This rich longitudinal data set revealed many insights: first, healthy profiles are distinct among individuals while displaying diverse patterns of intra- and/or inter-personal variability. Second, extensive host and microbial changes occur during respiratory viral infections and immunization, and immunization triggers potentially protective responses that are distinct from responses to respiratory viral infections. Moreover, during respiratory viral infections, insulin-resistant participants respond differently than insulin-sensitive participants. Third, global co-association analyses among the thousands of profiled molecules reveal specific host-microbe interactions that differ between insulin-resistant and insulin-sensitive individuals. Last, we identified early personal molecular signatures in one individual that preceded the onset of T2D, including the inflammation markers interleukin-1 receptor agonist (IL-1RA) and high-sensitivity C-reactive protein (CRP) paired with xenobiotic-induced immune signalling. Our study reveals insights into pathways and responses that differ between glucose-dysregulated and healthy individuals during health and disease and provides an open-access data resource to enable further research into healthy, prediabetic and T2D states.

A serum-induced gene signature in hepatocytes is associated with pediatric nonalcoholic fatty liver disease.

OBJECTIVE: Pediatric nonalcoholic fatty liver disease (NAFLD) is a growing problem, but its underlying mechanisms are poorly understood. We used transcriptomic reporter cell assays to investigate differences in transcriptional signatures induced in hepatocyte reporter cells by the sera of children with and without NAFLD. METHODS: We studied serum samples from 45 children with NAFLD and 28 children without NAFLD. The sera were used to induce gene expression in cultured HepaRG cells and RNA-sequencing was used to determine gene expression. Computational techniques were used to compare gene expression patterns. RESULTS: Sera from children with NAFLD induced the expression of 195 genes that were significantly differentially expressed in hepatocytes compared to controls with obesity. NAFLD was associated with increased expression of genes promoting inflammation, collagen synthesis, and extracellular matrix remodeling. Additionally, there was lower expression of genes involved in endobiotic and xenobiotic metabolism, and downregulation of peroxisome function, oxidative phosphorylation, and xenobiotic, bile acid, and fatty acid metabolism. A 13-gene signature, including upregulation of TREM1 and MMP1 and downregulation of CYP2C9, was consistently associated with all diagnostic categories of pediatric NAFLD. CONCLUSION: The extracellular milieu of sera from children with NAFLD induced specific gene profiles distinguishable by a hepatocyte reporter system. Circulating factors may contribute to inflammation and extracellular matrix remodeling and impair xenobiotic and endobiotic metabolism in pediatric NAFLD.

Metagenomics: a path to understanding the gut microbiome.

The gut microbiome is a major determinant of host health, yet it is only in the last 2 decades that the advent of next-generation sequencing has enabled it to be studied at a genomic level. Shotgun sequencing is beginning to provide insight into the prokaryotic as well as eukaryotic and viral components of the gut community, revealing not just their taxonomy, but also the functions encoded by their collective metagenome. This revolution in understanding is being driven by continued development of sequencing technologies and in consequence necessitates reciprocal development of computational approaches that can adapt to the evolving nature of sequence datasets. In this review, we provide an overview of current bioinformatic strategies for handling metagenomic sequence data and discuss their strengths and limitations. We then go on to discuss key technological developments that have the potential to once again revolutionise the way we are able to view and hence understand the microbiome.

Microbiome Signatures Associated With Steatohepatitis and Moderate to Severe Fibrosis in Children With Nonalcoholic Fatty Liver Disease.

BACKGROUND & AIMS: The intestinal microbiome might affect the development and severity of nonalcoholic fatty liver disease (NAFLD). We analyzed microbiomes of children with and without NAFLD. METHODS: We performed a prospective, observational, cross-sectional study of 87 children (age range, 8-17 years) with biopsy-proven NAFLD and 37 children with obesity without NAFLD (controls). Fecal samples were collected and microbiome composition and functions were assessed using 16S ribosomal RNA amplicon sequencing and metagenomic shotgun sequencing. Microbial taxa were identified using zero-inflated negative binomial modeling. Genes contributing to bacterial pathways were identified using gene set enrichment analysis. RESULTS: Fecal microbiomes of children with NAFLD had lower α-diversity than those of control children (3.32 vs 3.52, P = .016). Fecal microbiomes from children with nonalcoholic steatohepatitis (NASH) had the lowest α-diversity (control, 3.52; NAFLD, 3.36; borderline NASH, 3.37; NASH, 2.97; P = .001). High abundance of Prevotella copri was associated with more severe fibrosis (P = .036). Genes for lipopolysaccharide biosynthesis were enriched in microbiomes from children with NASH (P < .001). Classification and regression tree model with level of alanine aminotransferase and relative abundance of the lipopolysaccharide pathway gene encoding 3-deoxy-d-manno-octulosonate 8-phosphate-phosphatase identified patients with NASH with an area under the receiver operating characteristic curve value of 0.92. Genes involved in flagellar assembly were enriched in the fecal microbiomes of patients with moderate to severe fibrosis (P < .001). Classification and regression tree models based on level of alanine aminotransferase and abundance of genes encoding flagellar biosynthesis protein had good accuracy for identifying case children with moderate to severe fibrosis (area under the receiver operating characteristic curve, 0.87). CONCLUSIONS: In an analysis of fecal microbiomes of children with NAFLD, we associated NAFLD and NASH with intestinal dysbiosis. NAFLD and its severity were associated with greater abundance of genes encoding inflammatory bacterial products. Alterations to the intestinal microbiome might contribute to the pathogenesis of NAFLD and be used as markers of disease or severity.

MSC: a metagenomic sequence classification algorithm.

MOTIVATION: Metagenomics is the study of genetic materials directly sampled from natural habitats. It has the potential to reveal previously hidden diversity of microscopic life largely due to the existence of highly parallel and low-cost next-generation sequencing technology. Conventional approaches align metagenomic reads onto known reference genomes to identify microbes in the sample. Since such a collection of reference genomes is very large, the approach often needs high-end computing machines with large memory which is not often available to researchers. Alternative approaches follow an alignment-free methodology where the presence of a microbe is predicted using the information about the unique k-mers present in the microbial genomes. However, such approaches suffer from high false positives due to trading off the value of k with the computational resources. In this article, we propose a highly efficient metagenomic sequence classification (MSC) algorithm that is a hybrid of both approaches. Instead of aligning reads to the full genomes, MSC aligns reads onto a set of carefully chosen, shorter and highly discriminating model sequences built from the unique k-mers of each of the reference sequences. RESULTS: Microbiome researchers are generally interested in two objectives of a taxonomic classifier: (i) to detect prevalence, i.e. the taxa present in a sample, and (ii) to estimate their relative abundances. MSC is primarily designed to detect prevalence and experimental results show that MSC is indeed a more effective and efficient algorithm compared to the other state-of-the-art algorithms in terms of accuracy, memory and runtime. Moreover, MSC outputs an approximate estimate of the abundances. AVAILABILITY AND IMPLEMENTATION: The implementations are freely available for non-commercial purposes. They can be downloaded from https://drive.google.com/open?id=1XirkAamkQ3ltWvI1W1igYQFusp9DHtVl.

Long noncoding RNAs in B-cell development and activation.

Long noncoding RNAs (lncRNAs) are potentially important regulators of cell differentiation and development, but little is known about their roles in B lymphocytes. Using RNA-seq and de novo transcript assembly, we identified 4516 lncRNAs expressed in 11 stages of B-cell development and activation. Most of these lncRNAs have not been previously detected, even in the closely related T-cell lineage. Comparison with lncRNAs previously described in human B cells identified 185 mouse lncRNAs that have human orthologs. Using chromatin immunoprecipitation-seq, we classified 20% of the lncRNAs as either enhancer-associated (eRNA) or promoter-associated RNAs. We identified 126 eRNAs whose expression closely correlated with the nearest coding gene, thereby indicating the likely location of numerous enhancers active in the B-cell lineage. Furthermore, using this catalog of newly discovered lncRNAs, we show that PAX5, a transcription factor required to specify the B-cell lineage, bound to and regulated the expression of 109 lncRNAs in pro-B and mature B cells and 184 lncRNAs in acute lymphoblastic leukemia.

CGAT: computational genomics analysis toolkit.

Computational genomics seeks to draw biological inferences from genomic datasets, often by integrating and contextualizing next-generation sequencing data. CGAT provides an extensive suite of tools designed to assist in the analysis of genome scale data from a range of standard file formats. The toolkit enables filtering, comparison, conversion, summarization and annotation of genomic intervals, gene sets and sequences. The tools can both be run from the Unix command line and installed into visual workflow builders, such as Galaxy.

Dynamic Human Gut Microbiome and Immune Shifts During an Immersive Psychosocial Therapeutic Program.

Background: Depression is a leading cause of disability worldwide yet its underlying factors, particularly microbial associations, are poorly understood. Methods: We examined the longitudinal interplay between the microbiome and immune system in the context of depression during an immersive psychosocial intervention. 142 multi-omics samples were collected from 52 well-characterized participants before, during, and three months after a nine-day inquiry-based stress reduction program. Results: We found that depression was associated with both an increased presence of putatively pathogenic bacteria and reduced microbial beta-diversity. Following the intervention, we observed reductions in neuroinflammatory cytokines and improvements in several mental health indicators. Interestingly, participants with a Prevotella-dominant microbiome showed milder symptoms when depressed, along with a more resilient microbiome and more favorable inflammatory cytokine profile, including reduced levels of CXCL-1. Conclusions: Our findings reveal a protective link between the Prevotella-dominant microbiome and depression, associated with a less inflammatory environment and moderated symptoms. These insights, coupled with observed improvements in neuroinflammatory markers and mental health from the intervention, highlight potential avenues for microbiome-targeted therapies in depression management.

Changes in intestinal permeability and gut microbiota following diet-induced weight loss in patients with metabolic dysfunction-associated steatohepatitis and liver fibrosis.

Weight loss improves metabolic dysfunction-associated steatohepatitis (MASH). We investigated whether there were associated changes in intestinal permeability, short-chain fatty acids (SCFAs), and gut microbiota, which are implicated in the pathophysiology of MASH. Sixteen adults with MASH, moderate fibrosis, and obesity received a low-energy total diet replacement program for 12 weeks and stepped food re-introduction over the following 12 weeks (ISRCTN12900952). Intestinal permeability, fecal SCFAs, and fecal microbiota were assessed at 0, 12, and 24 weeks. Data were analyzed using mixed-effects linear regression and sparse partial least-squares regression. Fourteen participants completed the trial, lost 15% (95% CI: 11.2-18.6%) of their weight, and 93% had clinically relevant reductions in liver disease severity markers. Serum zonulin concentrations were reduced at both 12 and 24 weeks (152.0 ng/ml, 95% CI: 88.0-217.4, p < 0.001). Each percentage point of weight loss was associated with a 13.2 ng/mL (95% CI: 3.8-22.5, p < 0.001) reduction in zonulin. For every 10 ng/mL reduction in zonulin, there was a 6.8% (95% CI: 3.5%-10.2, p < 0.001) reduction in liver fat. There were reductions in SCFA and alpha diversity evenness as well as increases in beta diversity of the gut microbiota at 12 weeks, but the changes did not persist at 24 weeks. In conclusion, substantial dietary energy restriction is associated with significant improvement in MASH markers alongside reduction in intestinal permeability. Changes in gut microbiota and SCFA were not maintained with sustained reductions in weight and liver fat, suggesting that microbiome modulation may not explain the relationship between weight loss and improvements in MASH.

A pro-inflammatory gut mucosal cytokine response is associated with mild COVID-19 disease and superior induction of serum antibodies.

The relationship between gastrointestinal tract infection, the host immune response, and the clinical outcome of disease is not well understood in COVID-19. We sought to understand the effect of intestinal immune responses to SARS-CoV-2 on patient outcomes including the magnitude of systemic antibody induction. Combining two prospective cohort studies, International Severe Acute Respiratory and emerging Infections Consortium Comprehensive Clinical Characterisations Collaboration (ISARIC4C) and Integrated Network for Surveillance, Trials and Investigations into COVID-19 Transmission (INSTINCT), we acquired samples from 88 COVID-19 cases representing the full spectrum of disease severity and analysed viral RNA and host gut cytokine responses in the context of clinical and virological outcome measures. There was no correlation between the upper respiratory tract and faecal viral loads. Using hierarchical clustering, we identified a group of fecal cytokines including Interleukin-17A, Granulocyte macrophage colony-stimulating factor, Tumor necrosis factorα, Interleukin-23, and S100A8, that were transiently elevated in mild cases and also correlated with the magnitude of systemic anti-Spike-receptor-binding domain antibody induction. Receiver operating characteristic curve analysis showed that expression of these gut cytokines at study enrolment in hospitalised COVID-19 cases was associated negatively with overall clinical severity implicating a protective role in COVID-19. This suggests that a productive intestinal immune response may be beneficial in the response to a respiratory pathogen and a biomarker of a successful barrier response.

Longitudinal profiling of the microbiome at four body sites reveals core stability and individualized dynamics during health and disease.

To understand dynamic interplay between the human microbiome and host during health and disease, we analyzed the microbial composition, temporal dynamics, and associations with host multi-omics, immune and clinical markers of microbiomes from four body sites in 86 participants over six years. We found that microbiome stability and individuality are body-site-specific and heavily influenced by the host. The stool and oral microbiome were more stable than the skin and nasal microbiomes, possibly due to their interaction with the host and environment. Also, we identified individual-specific and commonly shared bacterial taxa, with individualized taxa showing greater stability. Interestingly, microbiome dynamics correlated across body sites, suggesting systemic coordination influenced by host-microbial-environment interactions. Notably, insulin-resistant individuals showed altered microbial stability and associations between microbiome, molecular markers, and clinical features, suggesting their disrupted interaction in metabolic disease. Our study offers comprehensive views of multi-site microbial dynamics and their relationship with host health and disease. Study Highlights: The stability of the human microbiome varies among individuals and body sites.Highly individualized microbial genera are more stable over time.At each of the four body sites, systematic interactions between the environment, the host and bacteria can be detected.Individuals with insulin resistance have lower microbiome stability, a more diversified skin microbiome, and significantly altered host-microbiome interactions.

Longitudinal profiling of the microbiome at four body sites reveals core stability and individualized dynamics during health and disease.

To understand the dynamic interplay between the human microbiome and host during health and disease, we analyzed the microbial composition, temporal dynamics, and associations with host multi-omics, immune, and clinical markers of microbiomes from four body sites in 86 participants over 6 years. We found that microbiome stability and individuality are body-site specific and heavily influenced by the host. The stool and oral microbiome are more stable than the skin and nasal microbiomes, possibly due to their interaction with the host and environment. We identify individual-specific and commonly shared bacterial taxa, with individualized taxa showing greater stability. Interestingly, microbiome dynamics correlate across body sites, suggesting systemic dynamics influenced by host-microbial-environment interactions. Notably, insulin-resistant individuals show altered microbial stability and associations among microbiome, molecular markers, and clinical features, suggesting their disrupted interaction in metabolic disease. Our study offers comprehensive views of multi-site microbial dynamics and their relationship with host health and disease.

Role of Oxylipins in the Inflammatory-Related Diseases NAFLD, Obesity, and Type 2 Diabetes.

Oxygenated polyunsaturated fatty acids (oxylipins) are bioactive molecules established as important mediators during inflammation. Different classes of oxylipins have been found to have opposite effects, e.g., pro-inflammatory prostaglandins and anti-inflammatory resolvins. Production of the different classes of oxylipins occurs during distinct stages of development and resolution of inflammation. Chronic inflammation is involved in the progression of many pathophysiological conditions and diseases such as non-alcoholic fatty liver disease, insulin resistance, diabetes, and obesity. Determining oxylipin profiles before, during, and after inflammatory-related diseases could provide clues to the onset, development, and prevention of detrimental conditions. This review focusses on recent developments in our understanding of the role of oxylipins in inflammatory disease, and outlines novel technological advancements and approaches to study their action.

Skin Microbiome Variation with Cancer Progression in Human Cutaneous Squamous Cell Carcinoma.

The skin microbiome plays a critical role in skin homeostasis and disorders. UVR is the major cause of nonmelanoma skin cancer, but other risk factors, including immune suppression, chronic inflammation, and antibiotic usage, suggest the microbiome as an additional, unexplored risk factor and potential disease biomarker. The overarching goal was to study the skin microbiome in squamous cell carcinoma (SCC) and premalignant actinic keratosis compared with that in healthy skin to identify skin cancer‒associated changes in the skin microbiome. We performed a high-resolution analysis of shotgun metagenomes of actinic keratosis and SCC in healthy skin, revealing the microbial community shifts specific to actinic keratosis and SCC. Most prominently, the relative abundance of pathobiont Staphylococcus aureus was increased at the expense of commensal Cutibacterium acnes in SCC compared with that in healthy skin, and enrichment of functional pathways in SCC reflected this shift. Notably, C. acnes associated with lesional versus healthy skin differed at the strain level, suggesting the specific functional changes associated with its depletion in SCC. Our study revealed a transitional microbial dysbiosis from healthy skin to actinic keratosis to SCC, supporting further investigation of the skin microbiome for use as a biomarker and providing hypotheses for studies investigating how these microbes might influence skin cancer progression.

Exploratory studies of oral and fecal microbiome in healthy human aging.

Growing evidence has linked an altered host fecal microbiome composition with health status, common chronic diseases, and institutionalization in vulnerable older adults. However, fewer studies have described microbiome changes in healthy older adults without major confounding diseases or conditions, and the impact of aging on the microbiome across different body sites remains unknown. Using 16S ribosomal RNA gene sequencing, we reconstructed the composition of oral and fecal microbiomes in young (23-32; mean = 25 years old) and older (69-94; mean = 77 years old) healthy community-dwelling research subjects. In both body sites, we identified changes in minor bacterial operational taxonomic units (OTUs) between young and older subjects. However, the composition of the predominant bacterial species of the healthy older group in both microbiomes was not significantly different from that of the young cohort, which suggests that dominant bacterial species are relatively stable with healthy aging. In addition, the relative abundance of potentially pathogenic genera, such as Rothia and Mycoplasma, was enriched in the oral microbiome of the healthy older group relative to the young cohort. We also identified several OTUs with a prevalence above 40% and some were more common in young and others in healthy older adults. Differences with aging varied for oral and fecal samples, which suggests that members of the microbiome may be differentially affected by aging in a tissue-specific fashion. This is the first study to investigate both oral and fecal microbiomes in the context of human aging, and provides new insights into interactions between aging and the microbiome within two different clinically relevant sites.

Murine Model for Measuring Effects of Humanized-Dosing of Antibiotics on the Gut Microbiome.

There is a current need for enhancing our insight in the effects of antimicrobial treatment on the composition of human microbiota. Also, the spontaneous restoration of the microbiota after antimicrobial treatment requires better understanding. This is best addressed in well-defined animal models. We here present a model in which immune-competent or neutropenic mice were administered piperacillin-tazobactam (TZP) according to human treatment schedules. Before, during and after the TZP treatment, fecal specimens were longitudinally collected at established intervals over several weeks. Gut microbial taxonomic distribution and abundance were assessed through culture and molecular means during all periods. Non-targeted metabolomics analyses of stool samples using Quadrupole Time of Flight mass spectrometry (QTOF MS) were also applied to determine if a metabolic fingerprint correlated with antibiotic use, immune status, and microbial abundance. TZP treatment led to a 5-10-fold decrease in bacterial fecal viability counts which were not fully restored during post-antibiotic follow up. Two distinct, relatively uniform and reproducible restoration scenarios of microbiota changes were seen in post TZP-treatment mice. Post-antibiotic flora could consist of predominantly Firmicutes or, alternatively, a more diverse mix of taxa. In general, the pre-treatment microbial communities were not fully restored within the screening periods applied. A new species, closely related to Eubacterium siraeum, Mageeibacillus indolicus, and Saccharofermentans acetigenes, became predominant post-treatment in a significant proportion of mice, identified by 16S rRNA gene sequencing. Principal component analysis of QTOF MS of mouse feces successfully distinguished treated from non-treated mice as well as immunocompetent from neutropenic mice. We observe dynamic but distinct and reproducible responses in the mouse gut microbiota during and after TZP treatment and propose the current murine model as a useful tool for defining the more general post-antibiotic effects in the gastro-intestinal ecosystem where humanized antibiotic dosing may ultimately facilitate extrapolation to humans.

Longitudinal Analysis of Serum Cytokine Levels and Gut Microbial Abundance Links IL-17/IL-22 With Clostridia and Insulin Sensitivity in Humans.

Recent studies using mouse models suggest that interaction between the gut microbiome and IL-17/IL-22-producing cells plays a role in the development of metabolic diseases. We investigated this relationship in humans using data from the prediabetes study of the Integrated Human Microbiome Project (iHMP). Specifically, we addressed the hypothesis that early in the onset of metabolic diseases there is a decline in serum levels of IL-17/IL-22, with concomitant changes in the gut microbiome. Clustering iHMP study participants on the basis of longitudinal IL-17/IL-22 profiles identified discrete groups. Individuals distinguished by low levels of IL-17/IL-22 were linked to established markers of metabolic disease, including insulin sensitivity. These individuals also displayed gut microbiome dysbiosis, characterized by decreased diversity, and IL-17/IL-22-related declines in the phylum Firmicutes, class Clostridia, and order Clostridiales This ancillary analysis of the iHMP data therefore supports a link between the gut microbiome, IL-17/IL-22, and the onset of metabolic diseases. This raises the possibility for novel, microbiome-related therapeutic targets that may effectively alleviate metabolic diseases in humans as they do in animal models.

New statistical method identifies cytokines that distinguish stool microbiomes.

Regressing an outcome or dependent variable onto a set of input or independent variables allows the analyst to measure associations between the two so that changes in the outcome can be described by and predicted by changes in the inputs. While there are many ways of doing this in classical statistics, where the dependent variable has certain properties (e.g., a scalar, survival time, count), little progress on regression where the dependent variable are microbiome taxa counts has been made that do not impose extremely strict conditions on the data. In this paper, we propose and apply a new regression model combining the Dirichlet-multinomial distribution with recursive partitioning providing a fully non-parametric regression model. This model, called DM-RPart, is applied to cytokine data and microbiome taxa count data and is applicable to any microbiome taxa count/metadata, is automatically fit, and intuitively interpretable. This is a model which can be applied to any microbiome or other compositional data and software (R package HMP) available through the R CRAN website.

Evaluation of 16S rRNA gene sequencing for species and strain-level microbiome analysis.

The 16S rRNA gene has been a mainstay of sequence-based bacterial analysis for decades. However, high-throughput sequencing of the full gene has only recently become a realistic prospect. Here, we use in silico and sequence-based experiments to critically re-evaluate the potential of the 16S gene to provide taxonomic resolution at species and strain level. We demonstrate that targeting of 16S variable regions with short-read sequencing platforms cannot achieve the taxonomic resolution afforded by sequencing the entire (~1500 bp) gene. We further demonstrate that full-length sequencing platforms are sufficiently accurate to resolve subtle nucleotide substitutions (but not insertions/deletions) that exist between intragenomic copies of the 16S gene. In consequence, we argue that modern analysis approaches must necessarily account for intragenomic variation between 16S gene copies. In particular, we demonstrate that appropriate treatment of full-length 16S intragenomic copy variants has the potential to provide taxonomic resolution of bacterial communities at species and strain level.

Disease dynamics over very different time-scales: foot-and-mouth disease and scrapie on the network of livestock movements in the UK.

We analyse the relationship between the network of livestock movements in the UK and the dynamics of two diseases: foot-and-mouth disease (FMD), which has an incubation period of days, and scrapie, which incubates over years. For FMD, the time-scale of expected epidemics is similar to the time-scale of the evolution of the network. We argue that, under appropriate conditions, a static network analysis can be an appropriate tool for gaining insights into disease dynamics even when the relevant time-scales are similar, as with FMD. We show that a subclass of 'linkage moves' maintains the network structure, and so removing these links has a dramatic effect on the number of potentially infected farms, an effect corroborated by simulations. In contrast, because scrapie has a low probability of transmission per contact and a long incubation period, a static network representation is probably appropriate; however, the signature of the network in the pattern of transmission is likely to be faint. Scrapie-notifying farms were more likely to be associated with each other via trading at markets than were control farms; however, network community structure proves to be less representative of prevalence patterns than geographical region. These contradictory indicators emphasize that appropriate observation time frames and good discrimination among types of potentially infectious contacts are vital in order for network analysis to be a valuable epidemiological tool.