Syntaxin 17, an ancient SNARE paralog, plays different and conserved roles in different organisms.

Mammalian syntaxin 17 (Stx17) has several roles in processes other than membrane fusion, including in mitochondrial division, autophagosome formation and lipid droplet expansion. In contrast to conventional syntaxins, Stx17 has a long C-terminal hydrophobic region with a hairpin-like structure flanked by a basic amino acid-enriched C-terminal tail. Although Stx17 is one of the six ancient SNAREs and is present in diverse eukaryotic organisms, it has been lost in multiple lineages during evolution. In the present study, we compared the localization and function of fly and nematode Stx17s expressed in HeLa cells with those of human Stx17. We found that fly Stx17 predominantly localizes to the cytosol and mediates autophagy, but not mitochondrial division. Nematode Stx17, on the other hand, is predominantly present in mitochondria and facilitates mitochondrial division, but is irrelevant to autophagy. These differences are likely due to different structures in the C-terminal tail. Non-participation of fly Stx17 and nematode Stx17 in mitochondrial division and autophagy, respectively, was demonstrated in individual organisms. Our results provide an insight into the evolution of Stx17 in metazoa. This article has an associated First Person interview with the first author of the paper.

Extreme value theory as a framework for understanding mutation frequency distribution in cancer genomes.

Currently, the population dynamics of preclonal cancer cells before clonal expansion of tumors has not been sufficiently addressed thus far. By focusing on preclonal cancer cell population as a Darwinian evolutionary system, we formulated and analyzed the observed mutation frequency among tumors (MFaT) as a proxy for the hypothesized sequence read frequency and beneficial fitness effect of a cancer driver mutation. Analogous to intestinal crypts, we assumed that sample donor patients are separate culture tanks where proliferating cells follow certain population dynamics described by extreme value theory (EVT). To validate this, we analyzed three large-scale cancer genome datasets, each harboring > 10000 tumor samples and in total involving > 177898 observed mutation sites. We clarified the necessary premises for the application of EVT in the strong selection and weak mutation (SSWM) regime in relation to cancer genome sequences at scale. We also confirmed that the stochastic distribution of MFaT is likely of the Fréchet type, which challenges the well-known Gumbel hypothesis of beneficial fitness effects. Based on statistical data analysis, we demonstrated the potential of EVT as a population genetics framework to understand and explain the stochastic behavior of driver-mutation frequency in cancer genomes as well as its applicability in real cancer genome sequence data.

Quantifying local malignant adaptation in tissue-specific evolutionary trajectories by harnessing cancer's repeatability at the genetic level.

Cancer is a potentially lethal disease, in which patients with nearly identical genetic backgrounds can develop a similar pathology through distinct combinations of genetic alterations. We aimed to reconstruct the evolutionary process underlying tumour initiation, using the combination of convergence and discrepancies observed across 2,742 cancer genomes from nine tumour types. We developed a framework using the repeatability of cancer development to score the local malignant adaptation (LMA) of genetic clones, as their potential to malignantly progress and invade their environment of origin. Using this framework, we found that premalignant skin and colorectal lesions appeared specifically adapted to their local environment, yet insufficiently for full cancerous transformation. We found that metastatic clones were more adapted to the site of origin than to the invaded tissue, suggesting that genetics may be more important for local progression than for the invasion of distant organs. In addition, we used network analyses to investigate evolutionary properties at the system-level, highlighting that different dynamics of malignant progression can be modelled by such a framework in tumour-type-specific fashion. We find that occurrence-based methods can be used to specifically recapitulate the process of cancer initiation and progression, as well as to evaluate the adaptation of genetic clones to given environments. The repeatability observed in the evolution of most tumour types could therefore be harnessed to better predict the trajectories likely to be taken by tumours and preneoplastic lesions in the future.