A faecal microbiota signature with high specificity for pancreatic cancer.

BACKGROUND: Recent evidence suggests a role for the microbiome in pancreatic ductal adenocarcinoma (PDAC) aetiology and progression. OBJECTIVE: To explore the faecal and salivary microbiota as potential diagnostic biomarkers. METHODS: We applied shotgun metagenomic and 16S rRNA amplicon sequencing to samples from a Spanish case-control study (n=136), including 57 cases, 50 controls, and 29 patients with chronic pancreatitis in the discovery phase, and from a German case-control study (n=76), in the validation phase. RESULTS: Faecal metagenomic classifiers performed much better than saliva-based classifiers and identified patients with PDAC with an accuracy of up to 0.84 area under the receiver operating characteristic curve (AUROC) based on a set of 27 microbial species, with consistent accuracy across early and late disease stages. Performance further improved to up to 0.94 AUROC when we combined our microbiome-based predictions with serum levels of carbohydrate antigen (CA) 19-9, the only current non-invasive, Food and Drug Administration approved, low specificity PDAC diagnostic biomarker. Furthermore, a microbiota-based classification model confined to PDAC-enriched species was highly disease-specific when validated against 25 publicly available metagenomic study populations for various health conditions (n=5792). Both microbiome-based models had a high prediction accuracy on a German validation population (n=76). Several faecal PDAC marker species were detectable in pancreatic tumour and non-tumour tissue using 16S rRNA sequencing and fluorescence in situ hybridisation. CONCLUSION: Taken together, our results indicate that non-invasive, robust and specific faecal microbiota-based screening for the early detection of PDAC is feasible.

Implementing and testing Bayesian and maximum-likelihood supertree methods in phylogenetics.

Since their advent, supertrees have been increasingly used in large-scale evolutionary studies requiring a phylogenetic framework and substantial efforts have been devoted to developing a wide variety of supertree methods (SMs). Recent advances in supertree theory have allowed the implementation of maximum likelihood (ML) and Bayesian SMs, based on using an exponential distribution to model incongruence between input trees and the supertree. Such approaches are expected to have advantages over commonly used non-parametric SMs, e.g. matrix representation with parsimony (MRP). We investigated new implementations of ML and Bayesian SMs and compared these with some currently available alternative approaches. Comparisons include hypothetical examples previously used to investigate biases of SMs with respect to input tree shape and size, and empirical studies based either on trees harvested from the literature or on trees inferred from phylogenomic scale data. Our results provide no evidence of size or shape biases and demonstrate that the Bayesian method is a viable alternative to MRP and other non-parametric methods. Computation of input tree likelihoods allows the adoption of standard tests of tree topologies (e.g. the approximately unbiased test). The Bayesian approach is particularly useful in providing support values for supertree clades in the form of posterior probabilities.

Horizontal gene flow from Eubacteria to Archaebacteria and what it means for our understanding of eukaryogenesis.

The origin of the eukaryotic cell is considered one of the major evolutionary transitions in the history of life. Current evidence strongly supports a scenario of eukaryotic origin in which two prokaryotes, an archaebacterial host and an α-proteobacterium (the free-living ancestor of the mitochondrion), entered a stable symbiotic relationship. The establishment of this relationship was associated with a process of chimerization, whereby a large number of genes from the α-proteobacterial symbiont were transferred to the host nucleus. A general framework allowing the conceptualization of eukaryogenesis from a genomic perspective has long been lacking. Recent studies suggest that the origins of several archaebacterial phyla were coincident with massive imports of eubacterial genes. Although this does not indicate that these phyla originated through the same process that led to the origin of Eukaryota, it suggests that Archaebacteria might have had a general propensity to integrate into their genomes large amounts of eubacterial DNA. We suggest that this propensity provides a framework in which eukaryogenesis can be understood and studied in the light of archaebacterial ecology. We applied a recently developed supertree method to a genomic dataset composed of 392 eubacterial and 51 archaebacterial genera to test whether large numbers of genes flowing from Eubacteria are indeed coincident with the origin of major archaebacterial clades. In addition, we identified two potential large-scale transfers of uncertain directionality at the base of the archaebacterial tree. Our results are consistent with previous findings and seem to indicate that eubacterial gene imports (particularly from δ-Proteobacteria, Clostridia and Actinobacteria) were an important factor in archaebacterial history. Archaebacteria seem to have long relied on Eubacteria as a source of genetic diversity, and while the precise mechanism that allowed these imports is unknown, we suggest that our results support the view that processes comparable to those through which eukaryotes emerged might have been common in archaebacterial history.

L.U.St: a tool for approximated maximum likelihood supertree reconstruction.

BACKGROUND: Supertrees combine disparate, partially overlapping trees to generate a synthesis that provides a high level perspective that cannot be attained from the inspection of individual phylogenies. Supertrees can be seen as meta-analytical tools that can be used to make inferences based on results of previous scientific studies. Their meta-analytical application has increased in popularity since it was realised that the power of statistical tests for the study of evolutionary trends critically depends on the use of taxon-dense phylogenies. Further to that, supertrees have found applications in phylogenomics where they are used to combine gene trees and recover species phylogenies based on genome-scale data sets. RESULTS: Here, we present the L.U.St package, a python tool for approximate maximum likelihood supertree inference and illustrate its application using a genomic data set for the placental mammals. L.U.St allows the calculation of the approximate likelihood of a supertree, given a set of input trees, performs heuristic searches to look for the supertree of highest likelihood, and performs statistical tests of two or more supertrees. To this end, L.U.St implements a winning sites test allowing ranking of a collection of a-priori selected hypotheses, given as a collection of input supertree topologies. It also outputs a file of input-tree-wise likelihood scores that can be used as input to CONSEL for calculation of standard tests of two trees (e.g. Kishino-Hasegawa, Shimidoara-Hasegawa and Approximately Unbiased tests). CONCLUSION: This is the first fully parametric implementation of a supertree method, it has clearly understood properties, and provides several advantages over currently available supertree approaches. It is easy to implement and works on any platform that has python installed. AVAILABILITY: bitBucket page - https://afro-juju@bitbucket.org/afro-juju/l.u.st.git. CONTACT: Davide.Pisani@bristol.ac.uk.