Horizontal Gene Transfer Phylogenetics: A Random Walk Approach.

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.

Detecting horizontal gene transfer: a probabilistic approach.

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 χ2 based approach and provides a lower false positive rate, especially for closely related species and under wide range of genome parameters.

Synteny footprints provide clearer phylogenetic signal than sequence data for prokaryotic classification.

BACKGROUND: Extensive research efforts have been made to reconstruct the Tree of Life, aiming to explain the evolutionary history of life on earth. We expect the advent of next generation sequencing methods to bring us close to solving this challenge. Notwithstanding, with the accumulation of this mass of molecular data, it becomes evident that this solution is more complex and far from reach, especially among prokaryotes. One of the reasons for this is the ability of bacteria to perform horizontal gene transfer (HGT), creating substantial conflicts between different genes histories. Fortunately, evolution has equipped us with several markers with different levels of resolution, among which is synteny - the conservation of gene order along the chromosome. RESULTS: We have performed a comprehensive phylogenomic study via synteny based footprints. We build on the synteny index (SI) concept, defined in a pilot work of ours, and extend it to a systematic phylogenetic method with well defined valid regions of operations. Applying it to the EggNOG repository, divides all species into 39 clusters, agreeing with the conventional taxonomy. We show analytically that the signal of the standard phylogenetic marker, the 16S, is too faint for reliable classification. CONCLUSIONS: This work exhibits three separate yet related contributions. In terms of phylogenetics, it demonstrates quantitatively the advantage of the SI-based approach over the standard sequence based marker. Evolutionarily, the tree we produce is unique both in its specificity and broadness. Methodologically, the U-shape approach we developed, from synthetic realm, to real life and back to simulation, is novel and allow us to simulate the exact realistic conditions.

Personalizing non-small cell lung cancer treatment through patient-derived xenograft models: preclinical and clinical factors for consideration.

PURPOSE: In the pursuit of creating personalized and more effective treatment strategies for lung cancer patients, Patient-Derived Xenografts (PDXs) have been introduced as preclinical platforms that can recapitulate the specific patient's tumor in an in vivo model. We investigated how well PDX models can preserve the tumor's clinical and molecular characteristics across different generations. METHODS: A Non-Small Cell Lung Cancer (NSCLC) PDX model was established in NSG-SGM3 mice and clinical and preclinical factors were assessed throughout subsequent passages. Our cohort consisted of 40 NSCLC patients, which were used to create 20 patient-specific PDX models in NSG-SGM3 mice. Histopathological staining and Whole Exome Sequencing (WES) analysis were preformed to understand tumor heterogeneity throughout serial passages. RESULTS: 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 the 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 aggressiveness, primarily affecting the first passage. Pathology analysis showed preservation of the histological type and grade; however, WES analysis revealed genomic instability in advanced passages, leading to the inconsistencies in clinically relevant alterations between the PDXs and biopsies. CONCLUSIONS: Our study highlights the impact of multiple clinical and preclinical factors on the engraftment success, growth kinetics, and tumor stability of patient-specific NSCLC PDXs, and underscores the importance of considering these factors when guiding and evaluating prolonged personalized treatment studies for NSCLC patients in these models, as well as signaling the imperative for additional investigations to determine the full clinical potential of this technique.

Relation between two evolutionary clocks reveal new insights in bacterial evolution.

New insights in evolution are available thanks to next-generation sequencing technologies in recent years. However, due to the network of complex relations between species, caused by the intensive horizontal gene transfer (HGT) between different bacterial species, it is difficult to discover bacterial evolution. This difficulty leads to new research in the field of phylogeny, including the gene-based phylogeny, in contrast to sequence-based phylogeny. In previous articles, we presented evolutionary insights of Synteny Index (SI) study on a large biological dataset. We showed that the SI approach naturally clusters 1133 species into 39 cliques of closely related species. In addition, we presented a model that enables calculation of the number of translocation events between genomes based on their SI distance. Here, these two studies are combined together and lead to new insights. A principal result is the relation between two evolutionary clocks: the well-known sequence-based clock influenced by point mutations, and SI distance clock influenced by translocation events. A surprising linear relation between these two evolutionary clocks rising for closely related species across all genus. In other words, these two different clocks are ticking at the same rate inside the genus level. Conversely, a phase-transition manner discovered between these two clocks across non-closely related species. This may suggest a new genus definition based on an analytic approach, since the phase-transition occurs where each gene, on average, undergoes one translocation event. In addition, rare cases of HGT among highly conserved genes can be detected as outliers from the phase-transition pattern.