Role of Gut Microbiota in Statin-Associated New-Onset Diabetes-A Cross-Sectional and Prospective Analysis of the FINRISK 2002 Cohort.

BACKGROUND: Dyslipidemia is treated effectively with statins, but treatment has the potential to induce new-onset type-2 diabetes. Gut microbiota may contribute to this outcome variability. We assessed the associations of gut microbiota diversity and composition with statins. Bacterial associations with statin-associated new-onset type-2 diabetes (T2D) risk were also prospectively evaluated. METHODS: We examined shallow-shotgun-sequenced fecal samples from 5755 individuals in the FINRISK-2002 population cohort with a 17+-year-long register-based follow-up. Alpha-diversity was quantified using Shannon index and beta-diversity with Aitchison distance. Species-specific differential abundances were analyzed using general multivariate regression. Prospective associations were assessed with Cox regression. Applicable results were validated using gradient boosting. RESULTS: Statin use associated with differing taxonomic composition (R2, 0.02%; q=0.02) and 13 differentially abundant species in fully adjusted models (MaAsLin; q<0.05). The strongest positive association was with Clostridium sartagoforme (ß=0.37; SE=0.13; q=0.02) and the strongest negative association with Bacteroides cellulosilyticus (ß=-0.31; SE=0.11; q=0.02). Twenty-five microbial features had significant associations with incident T2D in statin users, of which only Bacteroides vulgatus (HR, 1.286 [1.136-1.457]; q=0.03) was consistent regardless of model adjustment. Finally, higher statin-associated T2D risk was seen with [Ruminococcus] torques (ΔHRstatins, +0.11; q=0.03), Blautia obeum (ΔHRstatins, +0.06; q=0.01), Blautia sp. KLE 1732 (ΔHRstatins, +0.05; q=0.01), and beta-diversity principal component 1 (ΔHRstatin, +0.07; q=0.03) but only when adjusting for demographic covariates. CONCLUSIONS: Statin users have compositionally differing microbiotas from nonusers. The human gut microbiota is associated with incident T2D risk in statin users and possibly has additive effects on statin-associated new-onset T2D risk.

Efficient computation of Faith's phylogenetic diversity with applications in characterizing microbiomes.

The number of publicly available microbiome samples is continually growing. As data set size increases, bottlenecks arise in standard analytical pipelines. Faith's phylogenetic diversity (Faith's PD) is a highly utilized phylogenetic alpha diversity metric that has thus far failed to effectively scale to trees with millions of vertices. Stacked Faith's phylogenetic diversity (SFPhD) enables calculation of this widely adopted diversity metric at a much larger scale by implementing a computationally efficient algorithm. The algorithm reduces the amount of computational resources required, resulting in more accessible software with a reduced carbon footprint, as compared to previous approaches. The new algorithm produces identical results to the previous method. We further demonstrate that the phylogenetic aspect of Faith's PD provides increased power in detecting diversity differences between younger and older populations in the FINRISK study's metagenomic data.

Dealing with dimensionality: the application of machine learning to multi-omics data.

MOTIVATION: Machine learning (ML) methods are motivated by the need to automate information extraction from large datasets in order to support human users in data-driven tasks. This is an attractive approach for integrative joint analysis of vast amounts of omics data produced in next generation sequencing and other -omics assays. A systematic assessment of the current literature can help to identify key trends and potential gaps in methodology and applications. We surveyed the literature on ML multi-omic data integration and quantitatively explored the goals, techniques and data involved in this field. We were particularly interested in examining how researchers use ML to deal with the volume and complexity of these datasets. RESULTS: Our main finding is that the methods used are those that address the challenges of datasets with few samples and many features. Dimensionality reduction methods are used to reduce the feature count alongside models that can also appropriately handle relatively few samples. Popular techniques include autoencoders, random forests and support vector machines. We also found that the field is heavily influenced by the use of The Cancer Genome Atlas dataset, which is accessible and contains many diverse experiments. AVAILABILITY AND IMPLEMENTATION: All data and processing scripts are available at this GitLab repository: https://gitlab.com/polavieja_lab/ml_multi-omics_review/ or in Zenodo: https://doi.org/10.5281/zenodo.7361807. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Association of Long-Term Habitual Dietary Fiber Intake since Infancy with Gut Microbiota Composition in Young Adulthood.

BACKGROUND: Dietary fiber is an important health-promoting component of the diet, which is fermented by the gut microbes that produce metabolites beneficial for the host's health. OBJECTIVES: We studied the associations of habitual long-term fiber intake from infancy with gut microbiota composition in young adulthood by leveraging data from the Special Turku Coronary Risk Factor Intervention Project, an infancy-onset 20-y dietary counseling study. METHODS: Fiber intake was assessed annually using food diaries from infancy ≤ age 20 y. At age 26 y, the first postintervention follow-up study was conducted including food diaries and fecal sample collection (N = 357). Cumulative dietary fiber intake was assessed as the area under the curve for energy-adjusted fiber intake throughout the study (age 0-26 y). Gut microbiota was profiled using 16S ribosomal ribonucleic acid amplicon sequencing. The primary outcomes were 1) α diversity expressed as the observed richness and Shannon index, 2) ß diversity using Bray-Curtis dissimilarity scores, and 3) differential abundance of each microbial taxa with respect to the cumulative energy-adjusted dietary fiber intake. RESULTS: Higher cumulative dietary fiber intake was associated with decreased Shannon index (ß = -0.019 per unit change in cumulative fiber intake, P = 0.008). Overall microbial community composition was related to the amount of fiber consumed (permutational analysis of variation R2 = 0.005, P = 0.024). The only genus that was increased with higher cumulative fiber intake was butyrate-producing Butyrivibrio (log2 fold-change per unit change in cumulative fiber intake 0.40, adjusted P = 0.023), whereas some other known butyrate producers such as Faecalibacterium and Subdoligranulum were decreased with higher cumulative fiber intake. CONCLUSIONS: As early-life nutritional exposures may affect the lifetime microbiota composition and disease risk, this study adds novel information on the associations of long-term dietary fiber intake with the gut microbiota. This trial was registered at clinicaltrials.gov as NCT00223600.

Bone marrow metabolism is affected by body weight and response to exercise training varies according to anatomical location.

AIM: High body weight is a protective factor against osteoporosis, but obesity also suppresses bone metabolism and whole-body insulin sensitivity. However, the impact of body weight and regular training on bone marrow (BM) glucose metabolism is unclear. We studied the effects of regular exercise training on bone and BM metabolism in monozygotic twin pairs discordant for body weight. METHODS: We recruited 12 monozygotic twin pairs (mean ± SD age 40.4 ± 4.5 years; body mass index 32.9 ± 7.6, mean difference between co-twins 7.6 kg/m2 ; eight female pairs). Ten pairs completed the 6-month long training intervention. We measured lumbar vertebral and femoral BM insulin-stimulated glucose uptake (GU) using 18 F-FDG positron emission tomography, lumbar spine bone mineral density and bone turnover markers. RESULTS: At baseline, heavier co-twins had higher lumbar vertebral BM GU (p < .001) and lower bone turnover markers (all p < .01) compared with leaner co-twins but there was no significant difference in femoral BM GU, or bone mineral density. Training improved whole-body insulin sensitivity, aerobic capacity (both p < .05) and femoral BM GU (p = .008). The training response in lumbar vertebral BM GU was different between the groups (time × group, p = .02), as GU tended to decrease in heavier co-twins (p = .06) while there was no change in leaner co-twins. CONCLUSIONS: In this study, regular exercise training increases femoral BM GU regardless of weight and genetics. Interestingly, lumbar vertebral BM GU is higher in participants with higher body weight, and training counteracts this effect in heavier co-twins even without reduction in weight. These data suggest that BM metabolism is altered by physical activity.

Impacts of maternal microbiota and microbial metabolites on fetal intestine, brain, and placenta.

BACKGROUND: The maternal microbiota modulates fetal development, but the mechanisms of these earliest host-microbe interactions are unclear. To investigate the developmental impacts of maternal microbial metabolites, we compared full-term fetuses from germ-free and specific pathogen-free mouse dams by gene expression profiling and non-targeted metabolomics. RESULTS: In the fetal intestine, critical genes mediating host-microbe interactions, innate immunity, and epithelial barrier were differentially expressed. Interferon and inflammatory signaling genes were downregulated in the intestines and brains of the fetuses from germ-free dams. The expression of genes related to neural system development and function, translation and RNA metabolism, and regulation of energy metabolism were significantly affected. The gene coding for the insulin-degrading enzyme (Ide) was most significantly downregulated in all tissues. In the placenta, genes coding for prolactin and other essential regulators of pregnancy were downregulated in germ-free dams. These impacts on gene expression were strongly associated with microbially modulated metabolite concentrations in the fetal tissues. Aryl sulfates and other aryl hydrocarbon receptor ligands, the trimethylated compounds TMAO and 5-AVAB, Glu-Trp and other dipeptides, fatty acid derivatives, and the tRNA nucleobase queuine were among the compounds strongly associated with gene expression differences. A sex difference was observed in the fetal responses to maternal microbial status: more genes were differentially regulated in male fetuses than in females. CONCLUSIONS: The maternal microbiota has a major impact on the developing fetus, with male fetuses potentially more susceptible to microbial modulation. The expression of genes important for the immune system, neurophysiology, translation, and energy metabolism are strongly affected by the maternal microbial status already before birth. These impacts are associated with microbially modulated metabolites. We identified several microbial metabolites which have not been previously observed in this context. Many of the potentially important metabolites remain to be identified.

The gut microbiome is a significant risk factor for future chronic lung disease.

BACKGROUND: The gut-lung axis is generally recognized, but there are few large studies of the gut microbiome and incident respiratory disease in adults. OBJECTIVE: We sought to investigate the association and predictive capacity of the gut microbiome for incident asthma and chronic obstructive pulmonary disease (COPD). METHODS: Shallow metagenomic sequencing was performed for stool samples from a prospective, population-based cohort (FINRISK02; N = 7115 adults) with linked national administrative health register-derived classifications for incident asthma and COPD up to 15 years after baseline. Generalized linear models and Cox regressions were used to assess associations of microbial taxa and diversity with disease occurrence. Predictive models were constructed using machine learning with extreme gradient boosting. Models considered taxa abundances individually and in combination with other risk factors, including sex, age, body mass index, and smoking status. RESULTS: A total of 695 and 392 statistically significant associations were found between baseline taxonomic groups and incident asthma and COPD, respectively. Gradient boosting decision trees of baseline gut microbiome abundance predicted incident asthma and COPD in the validation data sets with mean area under the curves of 0.608 and 0.780, respectively. Cox analysis showed that the baseline gut microbiome achieved higher predictive performance than individual conventional risk factors, with C-indices of 0.623 for asthma and 0.817 for COPD. The integration of the gut microbiome and conventional risk factors further improved prediction capacities. CONCLUSIONS: The gut microbiome is a significant risk factor for incident asthma and incident COPD and is largely independent of conventional risk factors.

Aberrations in the early pregnancy serum metabolic profile in women with prediabetes at two years postpartum.

INTRODUCTION: Aberrations in circulating metabolites have been associated with diabetes and cardiovascular risk. OBJECTIVES: To investigate if early and late pregnancy serum metabolomic profiles differ in women who develop prediabetes by two years postpartum compared to those who remain normoglycemic. METHODS: An NMR metabolomics platform was used to measure 228 serum metabolite variables from women with pre-pregnancy overweight in early and late pregnancy. Co-abundant groups of metabolites were compared between the women who were (n = 40) or were not (n = 138) prediabetic at two years postpartum. Random Forests classifiers, based on the metabolic profiles, were used to predict the prediabetes status, and correlations of the metabolites to glycemic traits (fasting glucose and insulin, HOMA2-IR and HbA1c) and hsCRP at postpartum were evaluated. RESULTS: Women with prediabetes had higher concentrations of small HDL particles, total lipids in small HDL, phospholipids in small HDL and free cholesterol in small HDL in early pregnancy (p = 0.029; adj with pre-pregnancy BMI p = 0.094). The small HDL related metabolites also correlated positively with markers of insulin resistance at postpartum. Similar associations were not detected for metabolites in late pregnancy. A Random Forests classifier based on serum metabolites and clinical variables in early pregnancy displayed an acceptable predictive power for the prediabetes status at postpartum (AUROC 0.668). CONCLUSION: Elevated serum concentrations of small HDL particles in early pregnancy associate with prediabetes and insulin resistance at two years postpartum. The serum metabolic profile during pregnancy might be used to identify women at increased risk for type 2 diabetes.

The preterm gut microbiota and administration routes of different probiotics: a randomized controlled trial.

BACKGROUND: Preterm children with their aberrant gut microbiota and susceptibility to infections and inflammation constitute a considerable target group for probiotic therapy to generate the age-appropriate healthy microbiota. METHODS: 68 preterm neonates were randomized into five intervention groups: Beginning from the median age of 3 days, 13 children received Lactobacillus rhamnosus GG (LGG) directly orally, and 17 via the lactating mother. 14 children received LGG with Bifidobacterium lactis Bb-12 (Bb12) orally, and 10 via the lactating mother. 14 children received placebo. The children's faecal microbiota was assessed at the age of 7 days by 16S rRNA gene sequencing. RESULTS: The gut microbiota compositions of the children directly receiving the probiotic combination (LGG + Bb12) were significantly different from those of the children receiving the other intervention modes or placebo (p = 0.0012; PERMANOVA), the distinction being due to an increase in the relative abundance of Bifidobacterium animalis (P < 0.00010; ANCOM-BC), and the order Lactobacillales (P = 0.020; ANCOM-BC). CONCLUSION: The connection between aberrant primary gut microbiota and a heightened risk of infectious and non-communicable diseases invites effective microbiota modulation. We show that the direct, early, and brief probiotic intervention of LGG + Bb12 109 CFU each, is sufficient to modulate the gut microbiota of the preterm neonate. IMPACT: Preterm children have a higher risk of several health problems partly due to their aberrant gut microbiota. More research is needed to find a safe probiotic intervention to modify the gut microbiota of preterm children. The maternal administration route via breast milk might be safer for the newborn. In our study, the early and direct administration of the probiotic combination Lactobacillus rhamnosus GG with Bifidobacterium lactis Bb-12 increased the proportion of bifidobacteria in the preterm children's gut at the age of 7 days, but the maternal administration route was not as effective.

Quantifying the impact of ecological memory on the dynamics of interacting communities.

Ecological memory refers to the influence of past events on the response of an ecosystem to exogenous or endogenous changes. Memory has been widely recognized as a key contributor to the dynamics of ecosystems and other complex systems, yet quantitative community models often ignore memory and its implications. Recent modeling studies have shown how interactions between community members can lead to the emergence of resilience and multistability under environmental perturbations. We demonstrate how memory can be introduced in such models using the framework of fractional calculus. We study how the dynamics of a well-characterized interaction model is affected by gradual increases in ecological memory under varying initial conditions, perturbations, and stochasticity. Our results highlight the implications of memory on several key aspects of community dynamics. In general, memory introduces inertia into the dynamics. This favors species coexistence under perturbation, enhances system resistance to state shifts, mitigates hysteresis, and can affect system resilience both ways depending on the time scale considered. Memory also promotes long transient dynamics, such as long-standing oscillations and delayed regime shifts, and contributes to the emergence and persistence of alternative stable states. Our study highlights the fundamental role of memory in communities, and provides quantitative tools to introduce it in ecological models and analyse its impact under varying conditions.

Infant gut microbiota and negative and fear reactivity.

BACKGROUND: Studies indicate that gut microbiota is related to neurodevelopmental and behavioral outcomes. Accordingly, early gut microbiota composition (GMC) has been linked to child temperament, but research is still scarce. The aim of this study was to examine how early GMC at 2.5 months is associated with child negative and fear reactivity at 8 and 12 months since they are potentially important intermediate phenotypes of later child psychiatric disorders. METHODS: Our study population was 330 infants enrolled in the longitudinal FinnBrain Birth Cohort Study. Gut microbiota composition was analyzed using stool sample 16s rRNA sequencing. Negative and fear reactivity were assessed using the Laboratory Temperament Assessment Battery (Lab-TAB) at child's age of 8 months (n =150) and the Infant Behavior Questionnaire-Revised Short Form (IBQ-R SF) at child's age of 12 months (n = 276). CONCLUSIONS: We found a positive association between alpha diversity and reported fear reactivity and differing microbial community composition based on negative reactivity for boys. Isobutyric acid correlated with observed negative reactivity, however, this association attenuated in the linear model. Several genera were associated with the selected infant temperament traits. This study adds to the growing literature on links between infant gut microbiota and temperament informing future mechanistic studies.

Towards standardized and reproducible research in skin microbiomes.

Skin is a complex organ serving a critical role as a barrier and mediator of interactions between the human body and its environment. Recent studies have uncovered how resident microbial communities play a significant role in maintaining the normal healthy function of the skin and the immune system. In turn, numerous host-associated and environmental factors influence these communities' composition and diversity across the cutaneous surface. In addition, specific compositional changes in skin microbiota have also been connected to the development of several chronic diseases. The current era of microbiome research is characterized by its reliance on large data sets of nucleotide sequences produced with high-throughput sequencing of sample-extracted DNA. These approaches have yielded new insights into many previously uncharacterized microbial communities. Application of standardized practices in the study of skin microbial communities could help us understand their complex structures, functional capacities, and health associations and increase the reproducibility of the research. Here, we overview the current research in human skin microbiomes and outline challenges specific to their study. Furthermore, we provide perspectives on recent advances in methods, analytical tools and applications of skin microbiomes in medicine and forensics.

Can gut microbiota throughout the first 10 years of life predict executive functioning in childhood?

Animal models suggest that the gut microbiota can influence cognitive development and functioning via various pathways. In line with that, a first human study found associations between infant fecal microbiota composition and cognition at 2 years of age. This longitudinal study investigated whether fecal microbiota composition in infancy and childhood is associated with executive functioning in childhood. We followed healthy individuals from birth to their 10th year of life. Executive functioning was assessed using the Digit Span working memory test at 10 years of age and the ecologically valid Behavior Rating Inventory for executive functioning at 8 and 10 years. Stool samples were collected at month 1, 3 and 4 as well as at 6 and 10 years. The V4 region of the 16S ribosomal RNA was analyzed to determine microbial composition at the genus level. Using established statistical techniques for microbiota analysis, we did not find associations between fecal microbiota composition and executive functioning after accounting for breastfeeding, maternal education, child sex and age. Our study results are most compatible with the absence or only a weak relationship between infant and childhood fecal microbiota composition and executive functioning in childhood in healthy community children.

Modelling spatial patterns in host-associated microbial communities.

Microbial communities exhibit spatial structure at different scales, due to constant interactions with their environment and dispersal limitation. While this spatial structure is often considered in studies focusing on free-living environmental communities, it has received less attention in the context of host-associated microbial communities or microbiota. The wider adoption of methods accounting for spatial variation in these communities will help to address open questions in basic microbial ecology as well as realize the full potential of microbiome-aided medicine. Here, we first overview known factors affecting the composition of microbiota across diverse host types and at different scales, with a focus on the human gut as one of the most actively studied microbiota. We outline a number of topical open questions in the field related to spatial variation and patterns. We then review the existing methodology for the spatial modelling of microbiota. We suggest that methodology from related fields, such as systems biology and macro-organismal ecology, could be adapted to obtain more accurate models of spatial structure. We further posit that methodological developments in the spatial modelling and analysis of microbiota could in turn broadly benefit theoretical and applied ecology and contribute to the development of novel industrial and clinical applications.

Gut microbiota diversity but not composition is related to saliva cortisol stress response at the age of 2.5 months.

Human brain and intestinal microbes reportedly maintain a constant bidirectional connection through diverse neural, endocrine, immune, and metabolic pathways. Increasing evidence indicates that this communication system, referred to as microbiota-gut-brain axis, enables the gut microbes to influence several aspects of brain function and behavior, including hypothalamic-pituitary-adrenal (HPA) axis stress responses, and on the other hand, stress can affect gut microbiota. However, the role of gut microbiota in the HPA axis functioning in humans remains to be specified especially in early life. This study aimed at identifying the potential link between the cortisol stress response and the gut microbiota at the age of 2.5 months. Fecal microbiota profiles were acquired by 16S rRNA gene sequencing, while salivary cortisol responses after an exposure to a mild acute stressor represented the HPA axis reactivity. We observed that a blunted cortisol stress response was weakly associated with a diverse gut microbiota diversity at the age of 2.5 months. Gut microbiota composition was not associated with cortisol stress responsiveness, but rather with covariates, i.e. factors that influence gut microbiota composition and colonization.LAY SUMMARYThis exploratory study aimed at identifying possible links between cortisol stress responses and fecal microbiota composition in early infancy. In a well-characterized study population of 2.5-month-old infants, we observed that an attenuated cortisol stress responsiveness after a mild stressor was weakly associated with a diverse fecal microbiota. Our results suggest that the gut microbiota composition is associated with environmental factors, such as delivery mode and number of siblings, rather than with cortisol stress responsiveness, in this age group.

Gut microbiota composition is associated with temperament traits in infants.

BACKGROUND: One of the key behavioral phenotypes in infancy are different temperament traits, and certain early life temperament traits have been shown to precede later mental health problems. Differences in the gut microbiota composition (GMC) have been suggested to link with neurodevelopment. For example, toddler temperament traits have been found to associate with differences in GMC; however, studies in infants are lacking although infancy is a rapid period of neurodevelopment as well as GM development. Thus, we aimed to investigate association between infant GMC and temperament. METHODS: The study population (n = 301, 53% boys) was drawn from the FinnBrain Birth Cohort Study. Stool samples were collected from the 2.5-month-old infants and sequenced with 16S Illumina MiSeq platform. GMC taxonomic composition (at Genus and OTU level), observed sample clusters, diversity and richness were investigated in relation to the maternal reports of Infant Behavior Questionnaire -Revised (IBQ-R) at the age of 6 months. RESULTS: Three sample clusters (Bifidobacterium/Enterobacteriaceae, Bacteroides, V. Dispar) based on GMC were identified, of which Bifidobacterium/Enterobacteriaceae-cluster presented with higher scores on the IBQ-R main dimension regulation and its subscale duration of orienting compared to Bacteroides-cluster. The clusters associated with temperament in a sex-dependent manner. The IBQ-R main dimension surgency (positive emotionality) was associated positively both with genus Bifidobacterium and Streptococcus. Alpha diversity had a negative association with negative emotionality and fear reactivity. CONCLUSION: This is the first study demonstrating associations, but not causal connections, between GMC and temperament in young infants in a prospective design.

Stool Microbiota Composition Differs in Patients with Stomach, Colon, and Rectal Neoplasms.

BACKGROUND: Microbial ecosystems that inhabit the human gut form central component of our physiology and metabolism, regulating and modulating both health and disease. Changes or disturbances in the composition and activity of this gut microbiota can result in altered immunity, inflammation, and even cancer. AIM: To compare the composition and diversity of gut microbiota in stool samples from patient groups based on the site of neoplasm in the gastrointestinal tract (GIT) and to assess the possible contribution of the bacterial composition to tumorigenesis. METHODS: We studied gut microbiota by16S RNA gene sequencing from stool DNA of 83 patients, who were diagnosed with different GIT neoplasms, and 13 healthy individuals. RESULTS: As compared to healthy individuals, stools of patients with stomach neoplasms had elevated levels of Enterobacteriaceae, and those with rectal neoplasms had lower levels of Bifidobacteriaceae. Lower abundance of Lactobacillaceae was seen in patients with colon neoplasms. Abundance of Lactobacillaceae was higher in stools of GIT patients sampled after cancer treatment compared to samples collected before start of any treatment. In addition to site-specific differences, higher abundances of Ruminococcus, Subdoligranulum and lower abundances of Lachnoclostridium and Oscillibacter were observed in overall GIT neoplasms as compared to healthy controls CONCLUSION: Our study demonstrates that the alterations in gut microbiota vary according to the site of GIT neoplasm. The observed lower abundance of two common families, Lactobacillaceae and Bifidobacteriaceae, and the increased abundance of Enterobacteriaceae could provide indicators of compromised gut health and potentially facilitate GIT disease monitoring.

Binning metagenomic contigs by coverage and composition.

Shotgun sequencing enables the reconstruction of genomes from complex microbial communities, but because assembly does not reconstruct entire genomes, it is necessary to bin genome fragments. Here we present CONCOCT, a new algorithm that combines sequence composition and coverage across multiple samples, to automatically cluster contigs into genomes. We demonstrate high recall and precision on artificial as well as real human gut metagenome data sets.

Improved taxonomic assignment of human intestinal 16S rRNA sequences by a dedicated reference database.

BACKGROUND: Current sequencing technology enables taxonomic profiling of microbial ecosystems at high resolution and depth by using the 16S rRNA gene as a phylogenetic marker. Taxonomic assignation of newly acquired data is based on sequence comparisons with comprehensive reference databases to find consensus taxonomy for representative sequences. Nevertheless, even with well-characterised ecosystems like the human intestinal microbiota it is challenging to assign genus and species level taxonomy to 16S rRNA amplicon reads. A part of the explanation may lie in the sheer size of the search space where competition from a multitude of highly similar sequences may not allow reliable assignation at low taxonomic levels. However, when studying a particular environment such as the human intestine, it can be argued that a reference database comprising only sequences that are native to the environment would be sufficient, effectively reducing the search space. RESULTS: We constructed a 16S rRNA gene database based on high-quality sequences specific for human intestinal microbiota, resulting in curated data set consisting of 2473 unique prokaryotic species-like groups and their taxonomic lineages, and compared its performance against the Greengenes and Silva databases. The results showed that regardless of used assignment algorithm, our database improved taxonomic assignation of 16S rRNA sequencing data by enabling significantly higher species and genus level assignation rate while preserving taxonomic diversity and demanding less computational resources. CONCLUSION: The curated human intestinal 16S rRNA gene taxonomic database of about 2500 species-like groups described here provides a practical solution for significantly improved taxonomic assignment for phylogenetic studies of the human intestinal microbiota.

Cancer gene prioritization by integrative analysis of mRNA expression and DNA copy number data: a comparative review.

A variety of genome-wide profiling techniques are available to investigate complementary aspects of genome structure and function. Integrative analysis of heterogeneous data sources can reveal higher level interactions that cannot be detected based on individual observations. A standard integration task in cancer studies is to identify altered genomic regions that induce changes in the expression of the associated genes based on joint analysis of genome-wide gene expression and copy number profiling measurements. In this review, we highlight common approaches to genomic data integration and provide a transparent benchmarking procedure to quantitatively compare method performances in cancer gene prioritization. Algorithms, data sets and benchmarking results are available at http://intcomp.r-forge.r-project.org.