The dramatic decrease in time and cost for generating genetic sequence data has opened up vast opportunities in molecular systematics, one of which is the ability to decipher the evolutionary history of strains of a species. Under this fine systematic resolution, the standard markers are too crude to provide a phylogenetic signal. Nevertheless, among prokaryotes, genome dynamics in the form of horizontal gene transfer (HGT) between organisms and gene loss seem to provide far richer information by affecting both gene order and gene content. The “synteny index” (SI) between a pair of genomes combines these latter two factors, allowing comparison of genomes with unequal gene content, together with order considerations of their common genes. Although this approach is useful for classifying close relatives, no rigorous statistical modeling for it has been suggested. Such modeling is valuable, as it allows observed measures to be transformed into estimates of time periods during evolution, yielding the “additivity” of the measure. To the best of our knowledge, there is no other additivity proof for other gene order/content measures under HGT. Here, we provide a first statistical model and analysis for the SI measure. We model the “gene neighborhood” as a “birth–death–immigration” process affected by the HGT activity over the genome, and analytically relate the HGT rate and time to the expected SI. This model is asymptotic and thus provides accurate results, assuming infinite size genomes. Therefore, we also developed a heuristic model following an “exponential decay” function, accounting for biologically realistic values, which performed well in simulations. Applying this model to 1,133 prokaryotes partitioned to 39 clusters by the rank of genus yields that the average number of genome dynamics events per gene in the phylogenetic depth of genus is around half with significant variability between genera. This result extends and confirms similar results obtained for individual genera in different manners.
Background: Horizontal gene transfer (HGT) is the event of a DNA sequence being transferred between species not by inheritance. HGT is a crucial factor in prokaryotic evolution and is a significant source for genomic novelty resulting in antibiotic resistance or the outbreak of virulent strains. Detection of HGT and the mechanisms responsible and enabling it, is hence of prime importance. Existing algorithms rely on a strong phylogenetic signal distinguishing the transferred sequence from its recipient genome. Closely related species pose an even greater challenge as most genes are very similar and therefore, the phylogenetic signal is weak anyhow. Notwithstanding, the importance of detecting HGT between such organisms is extremely high for the role of HGT in the emergence of new highly virulent strains. Results: In a recent work we devised a novel technique that relies on loss of synteny around a gene as a witness for HGT. We used a novel heuristic for synteny measurement, SI (Syntent Index), and the technique was tested on both simulated and real data and was found to provide a greater sensitivity than other HGT techniques. This synteny-based approach suffers low specificity, in particular more closely related species. Here we devise an adaptive approach to cope with this by varying the criteria according to species distance. The new approach is doubly adaptive as it also considers the lengths of the genes being transferred. In particular, we use Chernoff bound to decree HGT both in simulations and real bacterial genomes taken from EggNog database. Conclusions: Here we show empirically that this approach is more conservative than the previous χ 2 based approach and provides a lower false positive rate, especially for closely related species and under wide range of genome parameters.
Summary The rapid increase of molecular, as well as other types, of available classification data has created the need to combine this data into a unified hypothesis. Supertree methods are essential when amalgamating phylogenetic information from various, possibly conflicting, sources into a single tree. The goal of a supertree algorithm is to satisfy maximally each such source of information in the output tree. Triplets, rooted trees over three leaves, are the minimal piece of such information when dealing with rooted trees. Due to its fundamental role in phylogenetics, extensive effort has been dedicated to several aspects regarding triplets’ research. We have devised a new tool, Triplet MaxCut (TMC), performing various operations in rooted supertree, principally amalgamating rooted trees based on amalgamating rooted triplets. The utility and efficiency of the algorithm is demonstrated by both simulation study and four real data supertree inputs.
Despite the widespread utilization of Patient-Derived Xenografts (PDXs) as preclinical platforms in lung cancer research, there are concerns regarding their capability to accurately represent the tumor's clinical and molecular features across sequential passages. In this study, we established a Non-Small Cell Lung Cancer (NSCLC) PDX model in NSG-SGM3 mice and assessed clinical and preclinical factors throughout subsequent passages. Our cohort consisted of 40 NSCLC patients, which were used to successfully create 20 patient-specific PDX models in NSG-SGM3 mice. We found that the main factors that contributed to the growth of the engrafted PDX in mice were a higher grade or stage of disease, in contrast to a long duration of chemotherapy treatment which was negatively correlated with PDX propagation. Successful PDX growth was also linked to poorer prognosis and overall survival, while growth pattern variability was affected by the tumor's aggressiveness, primarily affecting the first passage. Pathology analysis showed preservation of histological type and grade; however, Whole Exome Sequence (WES) analysis revealed genomic instability in advanced passages, leading to the inconsistency of clinically relevant alterations between PDXs and biopsies. Multiple clinical and preclinical factors affect the engraftment success, growth kinetics, and tumor stability of patient-specific NSCLC PDXs; thus, their use for prolonged treatment evaluation studies remains questionable.
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