Punctuational evolution is pervasive in distal site metastatic colonization.

The evolution of metastasis represents a lethal stage of cancer progression. Yet, the evolutionary kinetics of metastatic disease remain unresolved. Here, using single cell CRISPR-Cas9 lineage tracing data, we show that in metastatic disease, gradual molecular evolution is punctuated by episodes of rapid evolutionary change associated with lineage divergence. By measuring punctuational effects across the metastatic cascade, we show that punctuational effects contribute more to the molecular diversity at distal site metastases compared to the paired primary tumor, suggesting qualitatively different modes of evolution may drive primary and metastatic tumor progression. This is the first empirical evidence for distinct patterns of molecular evolution at early and late stages of metastasis and demonstrates the complex interplay of cell intrinsic and extrinsic factors that shape lethal cancer.

Escherichia coli survival in response to ciprofloxacin antibiotic stress correlates with increased nucleoid length and effective misfolded protein management.

The evolution of antibiotic resistance is a fundamental problem in disease management but is rarely quantified on a single-cell level owing to challenges associated with capturing the spatial and temporal variation across a population. To evaluate cell biological phenotypic responses, we tracked the single-cell dynamics of filamentous bacteria through time in response to ciprofloxacin antibiotic stress. We measured the degree of phenotypic variation in nucleoid length and the accumulation of protein damage under ciprofloxacin antibiotic and quantified the impact on bacterial survival. Increased survival was correlated with increased nucleoid length and the variation in this response was inversely correlated with antibiotic concentration. Survival time was also increased through clearance of misfolded proteins, an unexpected mechanism of stress relief deployed by the filamentous bacteria. Our results reveal a diverse range of survival tactics employed by bacteria in response to ciprofloxacin and suggest potential evolutionary routes to resistance.

Fully automated platelet differential interference contrast image analysis via deep learning.

Platelets mediate arterial thrombosis, a leading cause of myocardial infarction and stroke. During injury, platelets adhere and spread over exposed subendothelial matrix substrates of the damaged blood vessel wall. The mechanisms which govern platelet activation and their interaction with a range of substrates are therefore regularly investigated using platelet spreading assays. These assays often use differential interference contrast (DIC) microscopy to assess platelet morphology and analysis performed using manual annotation. Here, a convolutional neural network (CNN) allowed fully automated analysis of platelet spreading assays captured by DIC microscopy. The CNN was trained using 120 generalised training images. Increasing the number of training images increases the mean average precision of the CNN. The CNN performance was compared to six manual annotators. Significant variation was observed between annotators, highlighting bias when manual analysis is performed. The CNN effectively analysed platelet morphology when platelets spread over a range of substrates (CRP-XL, vWF and fibrinogen), in the presence and absence of inhibitors (dasatinib, ibrutinib and PRT-060318) and agonist (thrombin), with results consistent in quantifying spread platelet area which is comparable to published literature. The application of a CNN enables, for the first time, automated analysis of platelet spreading assays captured by DIC microscopy.

A phenotypic switch in the dispersal strategy of breast cancer cells selected for metastatic colonization.

An important question in cancer evolution concerns which traits make a cell likely to successfully metastasize. Cell motility phenotypes, mediated by cell shape change, are strong candidates. We experimentally evolved breast cancer cells in vitro for metastatic capability, using selective regimes designed to simulate stages of metastasis, then quantified their motility behaviours using computer vision. All evolved lines showed changes to motility phenotypes, and we have identified a previously unknown density-dependent motility phenotype only seen in cells selected for colonization of decellularized lung tissue. These cells increase their rate of morphological change with an increase in migration speed when local cell density is high. However, when the local cell density is low, we find the opposite relationship: the rate of morphological change decreases with an increase in migration speed. Neither the ancestral population, nor cells selected for their ability to escape or invade extracellular matrix-like environments, displays this dynamic behavioural switch. Our results suggest that cells capable of distant-site colonization may be characterized by dynamic morphological phenotypes and the capacity to respond to the local social environment.

Cancer cell lines show high heritability for motility but not generation time.

Tumour evolution depends on heritable differences between cells in traits affecting cell survival or replication. It is well established that cancer cells are genetically and phenotypically heterogeneous; however, the extent to which this phenotypic variation is heritable is far less well explored. Here, we estimate the broad-sense heritability (H 2) of two cell traits related to cancer hallmarks--cell motility and generation time--within populations of four cancer cell lines in vitro and find that motility is strongly heritable. This heritability is stable across multiple cell generations, with heritability values at the high end of those measured for a range of traits in natural populations of animals or plants. These findings confirm a central assumption of cancer evolution, provide a first quantification of the evolvability of key traits in cancer cells and indicate that there is ample raw material for experimental evolution in cancer cell lines. Generation time, a trait directly affecting cell fitness, shows substantially lower values of heritability than cell speed, consistent with its having been under directional selection removing heritable variation.

A randomized, placebo-controlled trial using a novel PAP delivery platform to treat patients with OSA and comorbid PTSD.

PURPOSE: Positive airway pressure (PAP) adherence is poor in comorbid OSA/PTSD. SensAwake™ (SA) is a wake-sensing PAP algorithm that lowers pressure when wake is detected. We compared auto-PAP (aPAP) with and without SA for comorbid OSA/PTSD. METHODS: Prospective, randomized crossover study comparing aPAP to aPAP + SA. We enrolled patients with OSA/PTSD who were PAP naïve. Four weeks after randomization, the patients were crossed over to the alternate treatment group, with final follow-up at eight weeks. Sleep questionnaires (ESS, ISI, FSS, and FOSQ-10) were assessed at baseline and follow-up. RESULTS: We enrolled 85 patients with OSA/PTSD. aPAP reduced AHI to < 5/h in both groups. Our primary endpoint, average hours of aPAP adherence (total) after 4 weeks, was significantly increased in the SA group in our intention-to-treat (ITT) analysis (ß = 1.13 (95% CI 0.16-2.1); p = 0.02), after adjustment for ESS differences at baseline. After adjustment for ESS, SA (ITT analysis) also showed significant improvement in percentage of nights used for ≥ 4 h (ß = 14.9 (95% CI 1.02-28.9); p = 0.04). There were trends toward an increase in percentage nights used total (ß = 17.4 (95% CI - 0.1 to 34.9); p = 0.05), average hours of aPAP adherence (nights used) (ß = 1.04 (95% CI - 0.07 to 2.1); p = 0.07), and regular use (OR = 7.5 (95% CI 0.9-64.7); p = 0.07) after adjustment for ESS at baseline. After adjustment for ESS and days to cross over, SA by actual assignment did not show any effect on adherence variables. The ESS, ISI, FSS, and FOSQ-10 all showed significant improvements with PAP, but there were no differences in the magnitude of improvement in any score between groups. CONCLUSIONS: Adherence to aPAP may be improved with the addition of SA and deserves further study. SA is as effective as standard aPAP for normalizing the AHI and improving sleep-related symptoms. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov NCT02549508 https://clinicaltrials.gov/ct2/show/NCT02549508?term=NCT02549508&rank=1 "Comparison Study Using APAP With and Without SensAwake in Patients With OSA and PTSD".

Mathematical modelling of cell migration.

The complexity of biological systems creates challenges for fully understanding their behaviour. This is particularly true for cell migration which requires the co-ordinated activity of hundreds of individual components within cells. Mathematical modelling can help understand these complex systems by breaking the system into discrete steps which can then be interrogated in silico. In this review, we highlight scenarios in cell migration where mathematical modelling can be applied and discuss what types of modelling are most suited. Almost any aspect of cell migration is amenable to mathematical modelling from the modelling of intracellular processes such as chemokine receptor signalling and actin filament branching to larger scale processes such as the movement of individual cells or populations of cells through their environment. Two common ways of approaching this modelling are the use of models based on differential equations or agent-based modelling. The application of both these approaches to cell migration are discussed with specific examples along with common software tools to facilitate the process for non-mathematicians. We also highlight the challenges of modelling cell migration and the need for rigorous experimental work to effectively parameterise a model.

Populus trichocarpa encodes small, effector-like secreted proteins that are highly induced during mutualistic symbiosis.

During symbiosis, organisms use a range of metabolic and protein-based signals to communicate. Of these protein signals, one class is defined as 'effectors', i.e., small secreted proteins (SSPs) that cause phenotypical and physiological changes in another organism. To date, protein-based effectors have been described in aphids, nematodes, fungi and bacteria. Using RNA sequencing of Populus trichocarpa roots in mutualistic symbiosis with the ectomycorrhizal fungus Laccaria bicolor, we sought to determine if host plants also contain genes encoding effector-like proteins. We identified 417 plant-encoded putative SSPs that were significantly regulated during this interaction, including 161 SSPs specific to P. trichocarpa and 15 SSPs exhibiting expansion in Populus and closely related lineages. We demonstrate that a subset of these SSPs can enter L. bicolor hyphae, localize to the nucleus and affect hyphal growth and morphology. We conclude that plants encode proteins that appear to function as effector proteins that may regulate symbiotic associations.

Matrix metalloproteinases in arthritic disease.

The role of matrix metalloproteinases in the degradative events invoked in the cartilage and bone of arthritic joints has long been appreciated and attempts at the development of proteinase inhibitors as potential therapeutic agents have been made. However, the spectrum of these enzymes orchestrating connective tissue turnover and general biology is much larger than anticipated. Biochemical studies of the individual members of the matrix metalloproteinase family are now underway, ultimately leading to a more detailed understanding of the function of their domain structures and to defining their specific role in cellular systems and the way that they are regulated. Coupled with a more comprehensive and detailed study of proteinase expression in different cells of joint tissues during the progress of arthritic diseases, it will be possible for the future development and application of highly specific proteinase inhibitors to be directed at specific key cellular events.