Cardiovascular complications have emerged as a major concern for cancer patients. Many chemotherapy agents are cardiotoxic and some appear to also alter lipid profiles, although the mechanism for this is unknown. We studied plasma lipid levels in 12 breast cancer patients throughout their chemotherapy. Patients received either four cycles of doxorubicin and cyclophosphamide followed by weekly paclitaxel or three cycles of epirubicin, cyclophosphamide and 5’-fluorouracil followed by three cycles of docetaxel. Patients demonstrated a significant reduction (0.32 mmol/L) in high density lipoprotein cholesterol (HDL-C) and apolipoprotein A1 (apoA1) levels (0.18 g/L) and an elevation in apolipoprotein B (apoB) levels (0.15 g/L) after treatment. Investigation of the individual chemotherapy agents for their effect on genes involved in lipoprotein metabolism in liver cells showed that doxorubicin decreased ATP binding cassette transporter A1 (ABCA1) via a downregulation of the peroxisomal proliferator activated receptor γ (PPARγ) and liver X receptor α (LXRα) transcription factors. In contrast, ABCA1 levels were not affected by cyclophosphamide or paclitaxel. Likewise, apoA1 levels were reduced by doxorubicin and remained unaffected by cyclophosphamide and paclitaxel. Doxorubicin and paclitaxel both increased apoB protein levels and paclitaxel also decreased low density lipoprotein receptor (LDLR) protein levels. These findings correlate with the observed reduction in HDL-C and apoA1 and increase in apoB levels seen in these patients. The unfavourable lipid profiles produced by some chemotherapy agents may be detrimental in the longer term to cancer patients, especially those already at risk of cardiovascular disease (CVD). This knowledge may be useful in tailoring effective follow-up care plans for cancer survivors. Cancer is a common cause of death worldwide . Much prA common chemotherapy regimen for breast cancer patients includes a combination of doxorubicin and cyclophosphamide followed by paclitaxel . DoxorubAlthough cardiotoxicity plays a large role in the increased risk of CVD in breast cancer patients through significant alterations in heart function , it is aHere we aimed to investigate the longitudinal effect of chemotherapy on lipids in the same group of patients by monitoring the serum lipid profiles of 12 breast cancer patients throughout their multi-agent chemotherapy treatments. We show that the chemotherapy treatment produced significant alterations in plasma lipids and apolipoprotein levels creating an unfavourable profile with respect to CVD. Furthermore, we show that individual chemotherapy agents alter the expression levels of key molecules involved in LDL and HDL metabolism in an agent dependent manner, uncovering mechanisms for the alteration in lipid profiles.Ethical approval for this study was obtained from the Lower South Regional Ethics Committee (LRS/10/03/009) and the Southern District Health Board Ethics Committee (Project ID 00626); all participants gave written consent.Twelve women (aged between 25 and 65) with newly diagnosed primary breast cancer undergoing multi-agent chemotherapy were recruited over a twelve month period from the Oncology Department at Dunedin Hospital. Participants were excluded from the study if they had recurrent breast cancer, distant metastases, known thyroid problems or diabetes. The majority of patients were hormone receptor positive, both estrogen receptor (ER) and progesterone receptor (PR). Only two were human epidermal growth factor receptor 2 (HER2) positive. None were on antiestrogen therapy. The 12 patients were in various stages of the disease  classification system). The chemotherapy regimens used in seven women consisted of four, three-weekly treatments with doxorubicin and cyclophosphamide followed by 12 weekly treatments of paclitaxel. Five women received three, three-weekly cycles of epirubicin, cyclophosphamide and 5’-fluorouracil, followed by three, three-weekly cycles of docetaxel. Baseline blood samples were taken 1–3 days before the first chemotherapy treatment was administered, midpoint samples were taken within 1–10 days of the last multi-agent treatment and the final blood sample was taken within 1–10 days of the last single-agent treatment, after an overnight fast.Plasma lipid levels were measured in baseline, midpoint and final samples. Total cholesterol, HDL cholesterol and triglyceride concentrations were determined using enzymatic reagents . LDL cholesterol concentrations were calculated using the Friedewald equation . Plasma 2. HepG2 cells were treated with doxorubicin (from 2.5 nM to 25 nM), epirubicin (from 2.5 nM to 25 nM), paclitaxel (from 2.5 nM to 25 nM) and cyclophosphamide (from 1 μg/ml to 100 μg/ml) individually for 24 hours in DMEM. Cell viability was checked using the Trypan blue exclusion method -cholesterol for 48 hours. Cells were treated with doxorubicin (from 2.5 nM to 25 nM), paclitaxel (from 2.5 nM to 25 nM) and cyclophosphamide (from 1 μg/ml to 100 μg/ml) for 24 hours in serum free DMEM then treated with 20 μg/ml purified lipid-free apoA1 for 2 hours. Tritium decay over 5 minutes in the medium and cell lysates with Optiphase Hisafe II scintillation fluid (Perkin Elmer) were measured as disintegrations per minute (dpm) using a liquid scintillator analyzer . Cholesterol efflux was calculated using the following equation:Cholesterol efflux assays on HepG2 cells were performed as described previously . Cells wEach parameter in the lipid data was expressed as mean ± SEM. As one midpoint sample was missing for one participant and a final point sample was missing for another, we carried forward the last data point for each of these participants. We used a paired student t-test to compare the effect of anthracycline-containing multi-agent regimens at baseline to midpoint measures. The added effect of the taxane was analysed with a paired student t-test comparing baseline to final measures. In both cases p<0.05 was deemed significant. mRNA, protein and cholesterol efflux data are expressed as mean ± SEM and significance was tested by unpaired student t-test using GraphPad Prism software.The plasma lipid levels of each participant before chemotherapy are given in Since the expression of ABCA1 in liver cells is a major contributor to HDL levels via its role in cholesterol efflux , we inveSince the nuclear receptor PPARγ regulates ABCA1 expression by LXRα , we checAs the breast cancer patients were on combined doxorubicin (or epirubicin) and cyclophosphamide therapy for 12 weeks followed by a further 12 weeks on paclitaxel (or docetaxel), we also investigated the effect of physiological levels of cyclophosphamide and paclitaxel on ABCA1. Cyclophosphamide and paclitaxel did not appear to significantly affect ABCA1 protein levels  or apoA1As chemotherapy treatment significantly increased apoB levels in breast cancer patients, we investigated the effect of doxorubicin, cyclophosphamide and paclitaxel on apoB protein levels in HepG2 cells. Doxorubicin and paclitaxel increased the apoB protein level by 1.6 fold and by 1.9 fold at 25 nM, respectively . WhereasWe investigated the effects of doxorubicin, cyclophosphamide and paclitaxel on 3-Hydroxy-3-Methylglutaryl-CoA Reductase (HMGCR), the rate controlling enzyme required for cholesterol synthesis (25). Doxorubicin significantly reduced HMGCR protein levels to 0.45 fold at 25 nM doxorubicin . In contin vitro study to look at the effect of each chemotherapy agent in isolation. We hypothesised that different agents would have different effects on lipid metabolism genes in the liver, which may relate to the changes seen in plasma lipid profiles. We show in an in vitro setting that the different agents used in combination chemotherapy do have differing effects on genes involved in lipid metabolism. As far as we are aware, our study is the first to dissect the effect of various chemotherapy agents on lipid metabolism at a cellular level. Interestingly, these effects were reflected in the observed changes in the lipid profiles of the patients.Our results show that chemotherapy significantly alters plasma lipid and apolipoprotein levels in breast cancer patients. Specifically, HDL-C and apoA1 levels were significantly decreased and apoB levels significantly increased. Lipid modulations did not appear to be affected by the disease severity. Reduced HDL-C and apoA1 levels and increased apoB levels are well established risk factors for the development of CVD . SeveralWe showed that doxorubicin reduced the expression of the ABCA1 gene in liver cells through downregulation of the LXRα and PPARγ nuclear receptors that regulate its expression. Moreover, doxorubicin decreased apoA1 protein levels as well. This effect appeared to be specific to doxorubicin as no effect on ABCA1 and apoA1 levels was apparent from cyclophosphamide or paclitaxel. As ABCA1 and apoA1 are crucial for HDL production from the liver , this efDoxorubicin (and epirubicin) are anthracyclines that intercalate between DNA to inhibit both DNA and RNA synthesis. One would therefore expect a global reduction in gene expression with doxorubicin treatment. While this seems to be the case for genes in the HDL production pathway, it is not always so. Doxorubicin treatment is known to increase the expression of some inflammatory genes  and hereA significant increase in apoB levels was seen in patients at the final time point after single agent paclitaxel treatment. Interestingly, our cellular studies showed that the taxane, paclitaxel, promoted an increase in apoB expression (more so than doxorubicin) and in addition induced a significant reduction in LDLR expression. Overexpression of apoB in the liver would be expected to increase the secretion of apoB-associated lipoproteins into blood and a reduction in LDLR, the main removal route for LDL, would also be expected to increase apoB levels. These combined effects likely explain why the apoB increase was only significant after paclitaxel treatment. The length of effect of paclitaxel on apoB levels after completion of treatment remains to be seen.Chemotherapy drugs also showed a differential effect on HMGCR, the rate determining enzyme required for cholesterol synthesis . DoxorubIn conclusion, our study shows that lipid alterations occur with chemotherapy and are specific to the chemotherapy agent used. Doxorubicin promotes an HDL-C lowering effect while paclitaxel promotes an increase in apoB, the combination of which would be expected to have negative consequences on CVD risk. In contrast, cyclophosphamide does not seem to perturb HDL or apoB metabolism. Although the longevity of the lipid alterations needs to be established, our study suggests that lipid alterations should be considered in the longer term management plan for cancer patients in order to reduce their risk of CVD.The lack of follow up lipid data is the major limitation to the study and would be required to establish if the lipid changes were maintained and therefore could have clinical implications. The study also only investigated a limited number of lipid metabolism targets in response to chemotherapy drug treatment. It would be useful to investigate a broader range of metabolic targets. It would also be useful to compare other classes of chemotherapeutic drugs such as the antiestrogenic and immunomodulating drugs for their effect on lipid levels. Furthermore, the exact biological actions of chemotherapy agents need to be better characterised.S1 Fig(DOCX)Click here for additional data file.S2 Fig(DOCX)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.


Mycobacterium tuberculosis infection generates pulmonary granulomas that consist of a caseous, necrotic core surrounded by an ordered arrangement of macrophages, neutrophils and T cells. This inflammatory pathology is essential for disease transmission and M. tuberculosis has evolved to stimulate inflammatory granuloma development while simultaneously avoiding destruction by the attracted phagocytes. The most abundant phagocyte in active necrotic granulomas is the neutrophil. Here we show that the ESAT-6 protein secreted by the ESX-1 type VII secretion system causes necrosis of the neutrophils. ESAT-6 induced an intracellular Ca2+ overload followed by necrosis of phosphatidylserine externalised neutrophils. This necrosis was dependent upon the Ca2+ activated protease calpain, as pharmacologic inhibition prevented this secondary necrosis. We also observed that the ESAT-6 induced increase in intracellular Ca2+, stimulated the production of neutrophil extracellular traps characterised by extruded DNA and myeloperoxidase. Thus we conclude that ESAT-6 has a leukocidin function, which may facilitate bacterial avoidance of the antimicrobial action of the neutrophil while contributing to the maintenance of inflammation and necrotic pathology necessary for granuloma formation and TB transmission. Mycobacterium tuberculosis remains a leading source of mortality by infectious disease, with one-third of the world's population infected, 8.6 million new cases of TB and 1.3 million deaths annually.1 The fundamental feature of TB transmission is the generation of a pulmonary tubercle lesion that contains a cuff of immune cells surrounding a necrotic core laden with extracellular bacteria. This lesion may ‘cavitate' into the airways of the lung releasing the bacteria to allow transmission via the respiratory route. The essential contribution of macrophage and neutrophil cell death to the generation of this pathology has been recognised in many studies; however, notably in a comprehensive study of TB lesion development, Medlar2 observed that the polymorphonuclear cells were attracted to lesions following the death of mononuclear cells and that the bulk of necrotic tissue in human caseating tubercle lesions represented dead polymorphonuclear cells.Tuberculosis (TB) caused by 7 After initial infection into the lungs, it is supposed that the bacterium is phagocytosed by alveolar macrophages which migrate into the interstitium of the lung.8 The bacterium is able to replicate intracellularly in the macrophage, inhibiting apoptosis until at a certain bacterial load it induces necrosis of the macrophage.9 The ensuing inflammation attracts monocytes and neutrophils from post-capillary venules that engulf the released bacteria, and thus sequential rounds of replication and inflammation enable the generation of the tubercle lesion. However, although we are beginning to understand the mechanisms of macrophage cell death control,6 we know very little about how M. tuberculosis modulates neutrophil death.We now know that the bacterium has evolved mechanisms to regulate the mode and timing of macrophage cell death.11 which may be mediated by the direct generation of reactive oxygen species (ROS) or by apoptosis of the infected neutrophils and subsequent efferocytosis of the apoptotic body combined with ROS-dependent killing.13 Additionally, neutrophil apoptosis has been linked to effective generation of adaptive immunity in M. tuberculosis infection.14 However, to counter this, M. tuberculosis has been recently shown to inhibit neutrophil apoptosis14 and furthermore, has been observed to induce necrosis.12 Interestingly, neutrophil necrosis only occurs on exposure to virulent strains which express the region of difference 1 (RD1) which encodes a type VII secretion system (ESX) that secretes proteins including the abundant early secretory antigen-6 (ESAT-6).17 Thus the bacterial induction of pro-inflammatory neutrophil necrosis may have dual benefit to the pathogen by removing the antimicrobial threat of the neutrophil while simultaneously facilitating the generation of the necrotic cavitating lesions that drive TB transmission.It is also clear that in some circumstances neutrophils have an antimycobacterial capacity,19 whereby death of the neutrophil results in formation of a structure made of DNA with a histone backbone which contains neutrophil elastase, myeloperoxidase (MPO) and metalloproteinases. These ‘traps' are known to be produced in vivo and associate with bacteria in some infections.22 Importantly they have been shown to be produced by M. tuberculosis infected neutrophils in vitro23 although with no bactericidal activity. As well as ‘NETosis' there are also other described mechanisms of neutrophil death, one of which is ‘secondary necrosis'. In vitro aging, without any stimuli, results in the necrosis of neutrophils that have undergone apoptosis (termed secondary necrosis). Previous investigations have shown that neutrophils that have externalised phosphatidylserine, and are therefore ‘apoptotic', can undergo secondary necrosis caused by a Ca2+ influx leading to the activation of a subtype of Ca2+ activated protease, calpain.24 This we termed Ca2+ Induced Necrosis (CAIN). In the present study, we elucidate the molecular events that link the RD1 encoded ESX-1 type VII secretion system with neutrophil necrosis. We chose to investigate the ESAT-6 protein, which is secreted by ESX-1, because it interacts with lipid membranes and is thought to be pore-forming,28 thus it has the potential to influence intracellular Ca2+. Furthermore, calpain has been shown previously to be active in M.bovis infections that were dependent on the RD1 locus.29 ESAT-6 has also been shown to have cytotoxic effects to pneumocytes30 and T lymphocytes.31 Therefore, this study aimed to determine if ESAT-6 had a leukocidin action, and if so, whether this was dependent on intracellular flux of Ca2+, activation of calpain, and further, if this resulted in the formation of neutrophil extracellular traps (NETs).The mechanism of necrosis in neutrophils can be varied and controlled. The most recent to be described is ‘NETosis',2+ as a mechanism of secondary necrosis in human neutrophils.24 To examine the effect of ESAT-6 on neutrophil Ca2+ we loaded 24- h aged cells with fluo4-AM, a fluorescent Ca2+ indicator that stays within intact cells following uptake and conversion from its AM ester to the hydrophilic fluo4 molecule.32 Using confocal microscopy we observed that the addition of ESAT-6 caused an increase in intracellular Ca2+ levels in a proportion of neutrophils within 20 min of exposure. The sequential micrographs in 2+ influx as increasing Fluo4 fluorescence (green) in a phosphatidylserine externalised  cell that ultimately undergoes necrosis  at 60 min post-ESAT-6 addition. A movie of this response is available  did not induce neutrophil necrosis. Further analysis by flow cytometry confirmed that necrosis was occurring preferentially in the annexin V positive neutrophils  is a binding/interaction partner for ESAT-6 and thus membrane perturbation occurs only in PS externalised cells. Alternatively, the membrane repair mechanisms that confer resistance to bacterial and host derived membrane pores, such as endocytosis and lysosomal degradation of pore forming proteins48 may be less effective in PS externalised neutrophils. This is because effector proteins of these repair mechanisms including annexin A1 and synaptotagmin VII require PS to be available in the cytosolic leaflet of the cell membrane to catalyse their localisation to the cell membrane via their C2 domains. A similar mechanism may also explain the calpain dependence of necrosis that we observed, because calpain normally translocates to the cell surface upon activation in a process involving PS and elevated Ca2+.49 However, in cells with externalised PS there may be insufficient PS available in the cytosolic leaflet, thus the protease remains free to roam the cytosol in an uncontrolled but active proteolytic state resulting in necrosis.It was striking that ESAT-6 specifically raised cytosolic CaStaphylococcus aureus50 and leukotoxin from Mannheimia haemolytica caused NET formation.51 The mechanism of NETosis by bacterial toxins was previously unknown, but we provide evidence that, at least in the case of ESAT-6, it is Ca2+ dependent. This is supported by work from others that have showed calcium ionophore A2318752 and thapsigargin,53 which increases intracellular Ca2+ by inhibition of sarcoplasmic/endoplasmic ATPase (SERCA), can induce NET formation in a mechanism suggested to be through peptidylarginine deiminase (PAD) 4 hypercitrullination of histones.54 Indeed, we show that ESAT-6 and ionomycin treated neutrophils exhibited similar external localisation of MPO indicative of NET formation. NETosis has not been previously described as requiring PS externalisation.19 However, we have identified that, similar to secondary necrosis, ESAT-6 specifically induced NETosis in PS externalised cells.We also used flow cytometry and confocal laser scanning microscopy to observe NET formation, demonstrating ESAT-6 treated necrotic neutrophils produced NETs. Induction of NETosis by pathogen derived membrane pores is a mechanism that has been identified recently, for example purified Panton–Valentine leukocidin (PVL) and leukotoxin GH from 2+ dependent secondary necrosis or CAIN and NETosis provide a mechanistic basis for previous observations that virulent strains of M. tuberculosis carrying the ESX-1 secretion system can kill neutrophils when in co-culture.12 This leukocidin virulence mechanism may remove the antimycobacterial threat of the neutrophil while simultaneously generating nutrient rich necrotic tissue that fuels extracellular growth of the mycobacterium. This is consistent with observations of human tubercle lesions packed with dying neutrophils2 and more recent observations that the development of caseous, necrotic granulomas in a mouse model of progressive TB were associated with the presence of extracellular bacteria, neutrophil necrosis and NET-like structures.55 Thus ESAT-6 induction of NETs may be an important mechanism for the generation of lung pathology associated with active transmissible tuberculosis. There remains the possibility that other M. tuberculosis molecules can also induce neutrophil death because RD1 deleted mutants retain a residual cytotoxicity.12 Future work will aim to identify further mycobacterial effectors of neutrophil necrosis and establish if pharmacologic intervention of neutrophil necrosis alters pathology during infection.Our observations that ESAT-6 causes CaNeutrophils were isolated from whole blood samples from healthy volunteers (BCG vaccinated) obtained through favourable ethical opinion by the University of Surrey Ethical Board.Lactococcus lactis  was used for experiments. Recombinant M. tuberculosis protein Rv0435c was used as a non-ESX-1 related protein control. Ionomycin was added to cells at 5 μM. Viability of neutrophils was measured using propidium iodide at 500 nM (Sigma-Aldrich). For flow cytometry, phosphatidylserine externalisation was detected using annexin V-FITC (2.5 μg/ml) and for confocal laser scanning microscopy annexin V-alexa fluor 647 (2.5 μg/ml) was used . Fluo4-AM (10 μM) was used to measure Ca2+ concentration . FITC conjugated antibodies to MPO and isotype control (347201—BioLegend), were used for flow cytometric analysis of neutrophil death.Dextran and ficoll  were used for neutrophil purification from whole blood. Recombinant ESAT-6 expressed in 54 For further purification, the resulting white blood cells were separated using a ficoll density gradient.56 The neutrophils were suspended in RPMI with 10% FCS, stable l-glutamine (0.29 mg/ml), penicillin (0.1 mg/ml), streptomycin (0.1 mg/ml), and FCS (10%) and maintained at 37 °C, 5% CO2 overnight, in cell culture flasks before treatments and further analysis. Before manipulation, neutrophil numbers were counted using a haemocytometer and purity determined (>95%) .Buffy coats were prepared by dextran sedimentation and haemolysis as previously described.μg/ml as stated), PBS vehicle or control protein was then added at the concentration stated in text. Proteins were added from a 2x stock solution to reduce locality artefacts. The increase in necrosis was then measured in each population as an increase in the percentage of propidium iodide positive neutrophils (as determined by gating). Where stated, the data was normalised to the increase in necrosis in vehicle only controls to enable comparisons between donors and expressed as a percentage increase.At the beginning of each experiment, the viability and phosphatidylserine externalisation of the aged neutrophils was measured using propidium iodide and annexin V-FITC, respectively, by flow cytometry using a FACS Canto . ESAT-6 (20 or 40 0), or the closest recording to that point. To determine whether the neutrophils had externalised phosphatidylserine, they were also stained prior to stimulation with annexinV-fluor647. To confirm necrosis, we stained for extracellular DNA with propidium iodide. Images were analysed uniformly between controls and treatment using NIS Elements (Nikon) or Zen 2009 (Zeiss), and images produced using ImageJ (NIH).Images were produced on either a LSM 510  or A1M  CLSM with the neutrophils kept in glass-bottomed petri dishes . Measurements were obtained of the fluo4-AM dyed neutrophils by same sized region of interest tracking through the time-course of the experiment with the mean fluorescence intensity (MFI) measured. This was divided by the MFI of time 0 . The FITC-conjugated antibodies for MPO or the isotype control were added in PBS with block at 1 : 50 for 30 min. The neutrophils were centrifuged and resuspended in PBS ready for flow cytometric analysis or CLSM imaging.We examined the extruded MPO content of NETs stimulated by the addition of ESAT-6 at 40 t test or ANOVA with Dunnett's post test as stated.The results are presented as the mean+S.E.M. All statistics are Student's


Danio rerio has been established as a robust model to investigate the underlying mechanism of cardiac regeneration. This review discusses the current understanding of the endogenous mechanisms behind cardiac regeneration in zebrafish, with a particular focus on cardiomyocyte dedifferentiation, transdifferentiation, and proliferation.The adult mammalian heart is increasingly recognized as a regenerative organ with a measurable capacity to replenish cardiomyocytes throughout its lifetime, illuminating the possibility of stimulating endogenous regenerative capacity to treat heart diseases. Unlike mammals, certain vertebrates possess robust capacity for regenerating a damaged heart, providing a model to understand how regeneration could be augmented in injured human hearts. Facilitated by its rich history in the study of heart development, the teleost zebrafish  Myocardial infarction is a leading cause of death worldwide. Urgently needed are the therapies that facilitate replacement of damaged cardiac tissue with new functional cardiomyocytes. In principle, the damaged heart could be regenerated by transplanting cardiomyocytes differentiated in vitro from a variety of cellular sources such as endogenous cardiac stem/progenitor cells , 2, embrAnother approach would be to identify successful examples of cardiac regeneration in nature, elucidate their underlying mechanisms, and then attempt to apply the insights gained to humans through the provision of the appropriate regenerative stimuli. Urodele amphibians and teleosts are well-known examples of animals that possess remarkable regenerative capacity in a variety of structures and organs as adults , 14. Amocmlc2/myl7) gene drives the myocardial expression of tamoxifen-inducible Cre recombinase (CreER) and the other is an indicator line in which enhanced green fluorescent protein (EGFP) will be expressed in CreER+ cells, only when CreER excises a loxP-flanked stop cassette after tamoxifen treatments. In the double transgenic context of these lines, almost all Cmlc2+ cardiomyocytes were labeled with EGFP in the uninjured heart by tamoxifen treatments, enabling the examination of the extent of contribution of existing cardiomyocytes to the regenerated cardiac tissue. After the labeling, the hearts were injured at the ventricular apex with resection surgery, removing approximately 20 % of the ventricular muscle, and histologically examined at 30 days post-injury, a timepoint at which regeneration is normally completed. The result clearly showed that the majority of the regenerated myocardium retains EGFP expression, with no significant difference detected in the proportion of the EGFP+ muscle in the regenerated tissue compared with that in the uninjured tissue. Thus, existing Cmlc2+ cardiomyocytes, but not Cmlc2− non-myocytes, are the major source for new cardiac muscle during zebrafish heart regeneration , a ligand for a G protein-coupled receptor [Acute inflammatory response is another immediate event induced by injury. Although inflammation appears to be intimately associated with fibrotic responses and regeneration failure observed in most adult mammalian tissues , a recenreceptor , as a careceptor .2O2) has been identified as one of the earliest signals released from damaged tissues to induce inflammatory responses [2O2 level can be monitored and found that H2O2 production is markedly upregulated in the epicardium near the wound area [2O2 production as well as transgenic overexpression of catalase, a scavenger enzyme for H2O2, impaired cardiac muscle regeneration with collagenous scar formation, suggesting that the injury-induced H2O2 signal is required for regenerative proliferation of cardiomyocytes. Biochemical analyses using cell lines and zebrafish embryos revealed that elevated H2O2 promotes ubiquitination-dependent protein degradation of Dusp6, a redox-sensitive phosphatase that negatively regulates MAP kinase signaling. Together, these results suggested that H2O2 plays a critical role in priming cardiomyocyte proliferation after injury by unlocking the suppression of MAP kinase mediated by Dusp6 [The reactive oxygen species hydrogen peroxide (Hesponses . Xiong aund area . Pharmacby Dusp6 .Regenerative proliferation needs to be downregulated as the damaged tissue is restored. Although knowledge remains limited, several studies have reported mechanisms underlying this regulation as described below. Consistent with the results obtained from mouse studies, a recent zebrafish study showed that phosphorylated-p38 Mapk is present in the nuclei of non-proliferating cardiomyocytes but disappears when cardiomyocytes enter the mitotic cycle in vitro and in vivo . The myoA mechanism underlying negative regulation of cardiomyocyte proliferation has also been attributed to miRNAs. As described earlier, miR-99/100 represses regenerative proliferation of cardiomyocytes by downregulating the expression of Fntβ and Smarca5 . UnbiaseZebrafish hearts regenerate mainly through cardiomyocyte dedifferentiation and proliferation ••, 22••,


The cost of whole-genome bisulfite sequencing (WGBS) remains a bottleneck for many studies and it is therefore imperative to extract as much information as possible from a given dataset. This is particularly important because even at the recommend 30X coverage for reference methylomes, up to 50% of high-resolution features such as differentially methylated positions (DMPs) cannot be called with current methods as determined by saturation analysis. To address this limitation, we have developed a tool that dynamically segments WGBS methylomes into blocks of comethylation (COMETs) from which lost information can be recovered in the form of differentially methylated COMETs (DMCs). Using this tool, we demonstrate recovery of ∼30% of the lost DMP information content as DMCs even at very low (5X) coverage. This constitutes twice the amount that can be recovered using an existing method based on differentially methylated regions (DMRs). In addition, we explored the relationship between COMETs and haplotypes in lymphoblastoid cell lines of African and European origin. Using best fit analysis, we show COMETs to be correlated in a population-specific manner, suggesting that this type of dynamic segmentation may be useful for integrated (epi)genome-wide association studies in the future. Here, Libertini and colleagues devise a computation tool that can analyze whole-genome bisulfite sequencing (WGBS) data to recover of ∼30% of the lost differential methylation position information. They use COMETgazer and COMETvintage to analyze 13 diffferent methylome data to demonstrate their performance. Whole-genome bisulfite sequencing (WGBS) is the method of choice for the generation of reference methylomes1256methylSeekRAnother area of recent advancement includes analyses based on patterns of comethylation which were first observed over short (∼1 kb) distances by chromosome-wide profiling162 measureMethylSeekR which was developed to identify active regulatory regions by segmenting methylomes into umethylated regions (UMRs), low-methylated regions (LMRs) and partially methylated domains (PMDs). MethylSeekR. Compared to fixed regional thresholds used by MethylSeekR, COMETgazer uses dynamic segmentation parameters for CpG-wise processing of methylation values along chromosomes, allowing for higher resolution analysis of comethylation. Comparative analysis of M1, for example, reveals an average block size of ∼1,000 bp for COMETgazer compared to ∼25,000 bp for PMDs defined by MethylSeekR as regions of extended variable methylation, irrespective of methylation level. First, we assessed the performance of COMETgazer and COMETvintage on 13 WGBS methylomes which are summarized in Next, we compared COMET/DMC and DMR analyses to assess the possible recovery of DMP information lost in dependence of coverage after downsampling IHEC replicate methylomes M7–10 against deep replicates M1–2 as part of a separate saturation analysishttps://github.com/rifathamoudi/COMETgazer. Semi-quantitative DMP content recovery was measured by overlaps of significant features through smoothing as implemented in BSmoothAs our separate saturation analysis revealed that DMP calling at ∼30X coverage only captures ∼50% of DMPs in a replicate analysisP-value=0.00112) for r2=0.9 and OMg=0.1  OMg=0.1  which de OMg=0.1 . Taken tA recent saturation analysis of WGBS data revealed a major limitation for calling DMPs in methylomes generated at the recommended reference coverage of 30X ref. . Using a56In addition to DMP recovery, we show COMET analysis to complement low-resolution functional methylome studies using PMD analysis. The COMETgazer algorithm provides a fine-grained segmentation of the methylome which breaks down variable regions (and detects regions of transitions) with an average block size of ∼1,000 bp for COMETs compared to ∼25,000 bp for PMDs, facilitating the identification of novel regulatory elements such as promoters and enhancers within PMDs through differential comethylation using a negative binomial model. We propose that the oscillatory patterns of DNA methylation and the number of COMETs (the fragmentation) may be used as an additional metric to characterize epigenomes and are currently pursuing an integrative analysis with other epigenomic datasets including additional modalities  from the International Human Epigenome Consortium (IHEC). A more speculative application of COMET analysis may be to harness it in the future for epigenome-wide association studiesThe key metrics of the methylomes used here are summarized in et al.The M1 and M2 datasets have been deposited together into EGA under accession number EGAD00001001261. The M3 dataset was downloaded from GEO using accession numbers: GSM1112840 (M7), GSM1112841 (M8) GSM916051 (M9), GSM1112848 (M10), belonging to superseries GSE46644. The M4 dataset was downloaded from GEO series GSE17917 described in Lister Read mapping. Two reference sequences were prepared based on the hg19 human reference; reference_C2T had the C residues replaced by Ts, and reference_G2A had the Gs replaced by As. The sample preparation protocol followed ensures that reads from end 1 are from either of the original DNA strands, and are therefore generally C deficient (as unmethylated C residues are converted to T), and reads from end 2 are from the complement to the original strands and are therefore generally G deficient. The read data were fully converted prior to alignment, converting the remaining C's to T's in end 1, and converting G's to A's in end 2. The WGBS data was aligned using the GEM aligner et al.)Genotype and methylation status were estimated simultaneously using software developed at the Centre Nacional d'Anàlisi Genòmica. A Bayesian model, taking into account the probability of under and over conversion and sequencing error, was used to estimate the joint posterior probability of genotype and methylation at each genomic site covered by at least 2 reads. The marginal posterior genotype probability was estimated by numerical integration of the joint posterior (using Gaussian quadrature). For sites where a single genotype presented >99% of the posterior distribution, the maximum likelihood estimate of the methylation and the standard error of the estimate conditional the most probable genotype were calculated. CpG calls for downstream analysis were produced from pairs of sites called as homozygous C followed by homozygous G with high confidence (posterior probability >0.99).P<0.05 after FDR adjustment), 10% methylation difference and computed with RADmeth (Dolzhenko and Smith)P<0.05) with minimum 10% methylation difference and computed with custom software. Differentially methylated regions (DMRs) were defined as iCGs with 10% minimum methylation difference and at least 3 DMPs per region and computed using BSmooth Workflow and feature definitions. Here we describe the definition of COMETs, and their relative count distributions as follows:The feature and saturation analysis was conducted at University College London. All methylomes were analysed on autosomes only. Features of increasing complexity were defined and computed for the subsequent saturation analysis as follows: Informative CpG sites (iCGs) were defined as canonical CpGs of at least 8X read coverage. Coverage was calculated on median counts across all iCGs and shown as iCG saturation curve. Counts were independently and randomly downsampled for every CpG. Differentially methylated positions (DMPs) in replicate analysis were defined as iCGs of genome-wide significance (oscillator of methylation (OM) (We define the stochastic ion (OM)  as the oet al. (2013)   CpG beta value. The harmonics define the segmentation of the COMETs in a sequential manner across chromosomes treating the data as if it were a time series. This definition is inspired by Ulrich 13) ref.  and Ryan14) ref. , applicaDefine the CpG data points: by definition only canonical iCG are taken into considerationSmooth CpG methylation (beta) scoresCalculate OM (Single CpG delta) globallyn and OMn+1 oscillate around 0, with the arbitrary parameter (threshold) of dynamic oscillation termed OMg (oscillator of methylation grade) set to be +/− 0.1 OM over smoothed beta scores (at least +/− 10% methylation difference), roughly representing 8% of the delta (OM) distribution. This step is illustrated in Define COMETs as regions of contiguous iCG where OMCOMETs are thus calculated using the following COMETgazer algorithm:Observed Oscillatory Rhythm Transition of COMET Longitudinally Obtained Undulation Domains (OORTcloud) was calculated by binning COMETs over 100,00 bp windows at each methylation level  as shown in Bioconductor package edgeR P<0.05. An example of DMP, DMR and COMET comparison is shown in For the DMC analysis, OORTcloud distributions were built in a count matrix in R. Differential methylome structure as defined by sample COMET counts was assessed with a negative binomial model using replicate values for the two samples  using the https://github.com/rifathamoudi/COMETgazer) is shown in The workflow for COMET analysis (Step 1 (tool: COMETgazer)Key feature: COMET, i.e., region of co-methylationProcess: For each methylome, individual CpG methylation level (beta) distributions were used to compute OM scores and segment samples into COMETs.Result: profiling the structure of the methylomeStep 2 (tool: COMETvintage)Key feature: OORTcloud distributions, i.e., distributions of COMET countsProcess: For each methylome, COMETs were binned into count distributions reflecting the COMET domains (OORTcloud).Result: profiling the distribution of COMET counts across the methylomeStep 3 (tool: COMETvintage)Key feature: Differentially methylated COMETs (DMC)Process: For differential methylation analysis, COMET domains (OORTcloud) were assembled into a count matrix to call regions of differential methylated COMET (DMC) counts.Result: assessing DMCs between methylomes.2 threshold of 0.9 and calculated by binning the data into 100 bp windows and estimating the coverage counts of these over 100,000 bp windows for three distributions (depending on size) using two quantile thresholds . In this manner, we obtained values for large (laHAB), medium (meHAB) and small haplotype blocks (smHAB) for each of the windows. The same procedure was applied to COMETs, obtaining counts for large (laCOMET), medium (meCOMET) and small (smCOMET) COMET by size. For each window, the normalized COMET score (NC) and normalized haplotype block score (NH) was obtained as follows:Data were normalized to a 0–1 scale in order to compare COMETs with linkage disequilibrium (LD). Haplotype blocks for the sample Coriell NA18507 (HapMap GM18507) representing an African (YRU) haplotype and HapMap data for an European haplotype (CEU) were defined using a rThe scores for haplotype blocks and COMETs obtained were rescaled to a 0–1 distribution for a direct comparison. The genome was segmented according to haplotype block size into 10 quantile regions. For each segment, Pearson correlation analysis was carried out in order to estimate the significance of the relationship between median haplotype block size and median COMET size  represen2 were used. An example region for OMg=0.1 and r2>0.9 is illustrated in P<0.0001, hypergeometric test).Multiple combinations of OMg and rhttps://github.com/rifathamoudi/COMETgazer.COMETgazer is available for download at How to cite this article: Libertini, E. et al. Information recovery from low coverage whole-genome bisulfite sequencing. Nat. Commun. 7:11306 doi: 10.1038/ncomms11306 (2016).Supplementary Figures 1-6, Supplementary Tables 1-4, Supplementary Notes 1-2 and Supplementary References


Despite major advances in the generation of genome-wide binding maps, the mechanisms by which transcription factors (TFs) regulate cell type identity have remained largely obscure. Through comparative analysis of 10 key haematopoietic TFs in both mast cells and blood progenitors, we demonstrate that the largely cell type-specific binding profiles are not opportunistic, but instead contribute to cell type-specific transcriptional control, because (i) mathematical modelling of differential binding of shared TFs can explain differential gene expression, (ii) consensus binding sites are important for cell type-specific binding and (iii) knock-down of blood stem cell regulators in mast cells reveals mast cell-specific genes as direct targets. Finally, we show that the known mast cell regulators Mitf and c-fos likely contribute to the global reorganisation of TF binding profiles. Taken together therefore, our study elucidates how key regulatory TFs contribute to transcriptional programmes in several distinct mammalian cell types. EH Davidson (June 2014)See also: Transcription factor (TF) proteins decode the gene regulatory instructions encoded within our genomes and unsurprisingly therefore have emerged as key regulators of cellular identity. Individual TFs bind to short sequence motifs within gene regulatory regions, where the regulatory code is translated into gene expression changes through co-operative interactions between individual TFs as well as the recruitment of accessory proteins such as histone-modifying enzymes and the various components of the RNA polymerase holocomplex. TF binding to DNA therefore represents the first major information processing event during the regulation of gene expression and has therefore been a major focus of modern biomedical research ever since the first description of the lac operon -dependent HPC7 cell line  and mast cells (y-axis). Differential expression analysis revealed approximately 9K genes expressed at comparable levels in both cell types (category I), approximately 4K genes that are HPC7 or mast cell type-specific  and approximately 10K genes that are not expressed in either of the two cell types (category IV). Using independent microarray expression data for primary common myeloid progenitors and mast cells  and the left (HPC7) panels showed some overlap of binding peaks, but also substantial differences in binding locations for the same TF with several regions showing consistent binding by multiple TFs in either one or the other cell type. This observation suggested that even though the Kit locus is bound by all 10 factors in both cell types, the 10 TFs interact with the Kit gene locus in a cell type-specific manner.Visual inspection of ChIP-Seq results demonstrated very good signal-to-noise ratios for all 20 datasets and also suggested that binding profiles for most TFs diverged substantially between HPC7 and mast cells. This observation is illustrated in Fig To gauge the extent of cell type-specific binding at the level of the entire genome, we mapped binding peaks for all 10 TFs in both cell types and determined the extent of cell type-specific and shared peaks. This analysis demonstrated that with the exception of CTCF, all TFs showed largely non-overlapping binding sites Fig C. These Having identified predominantly cell type-specific binding patterns for key regulatory TFs raised the question as to whether TFs are passively recruited to cell type-specific regions of open chromatin with no major regulatory impact, or whether they actively participate in two different transcriptional programmes. To evaluate the extent to which cell type-specific binding of shared TFs might be associated with gene expression, we developed multivariate linear regression models to correlate TF binding information in the two cell types as the predictor variables with gene expression data as the response variable Fig A. SpecifR2 ˜ 0.227, Fig R2 values of 0.414 when genes bound by five or more TFs simultaneously were included  and predicted values are much better correlated with observed values (correlation coefficient ˜0.649 for 5 or more TFs). A downside of this approach, however, is that predictions are only possible for a subset of genes if stringent cut-offs are implemented about the minimum numbers of TFs that must be bound to a given locus (Supplementary Tables S3 and S4).Simple linear regression models including those genes bound by at least one TF  showed some correlation between differential binding of shared TFs and gene expression in the two cell types  than GAM without interaction terms  (Supplementary Table S5). We were also able to identify interesting TF pairs that co-operate to affect cell type-specific gene expression. For example, pairwise interaction between the ETS factors Erg and Fli1 with several other TFs was significantly correlated with gene expression changes (Supplementary Table S6).We therefore sought to develop an alternative approach that would allow us to include co-operativity in our modelling without having to focus analysis on subsets of genes. In addition to the 10 predictor variables for 10 shared TFs, we examined the predictive power of 45 pairwise TF combination variables in a generalised additive model (GAM)  even in the absence of interaction terms. Importantly, our observation that differential binding of shared factors is predictive of differential gene expression is consistent with the notion that cell type-specific binding of shared TFs makes meaningful contributions to differential gene expression.Predictions obtained using GAM were more accurate than the corresponding results obtained from genes simultaneously bound by 2 or more TFs (8261 genes) with the linear regression model  but on a larger set of genes.In order to investigate the effect that the addition of MITF and c-FOS would have to the predictive power of GAM, we then recalculated our model including these two factors . We may not have seen a significant improvement in et al, P-value ˜0) between those genes with high H3AcK27 in at least 1 cell type and genes that are well predicted  by the GAM (≥ 2 TFs with interaction) . To exclude the possibility that this strong association was a contribution of non-differentially expressed genes, we performed the chi-square test on differentially expressed genes only and showed that the association is still significant . Taken together, our model captures biologically relevant links between differential TF binding and differential gene expression, which shows a significant overlap with other known features of active gene regulatory elements (such as H3AcK27) and with likely other features.We wondered what known features of regulatory regions might correlate with the ability of our model to make good predictions. It has been suggested that acetylation of lysine 27 of histone H3 (H3AcK27) is a good mark to identify active regions of the genome .While results in the previous section underscored the importance of cell type-specific factors such as MITF for cell type-specific expression, our mathematical modelling and DNA sequence motif content analysis also suggested that shared TFs play an active role in mast cell-specific gene expression. To evaluate the contribution of shared TFs to mast cell-specific expression experimentally, we performed knock-down experiments for several shared TFs in primary mast cells, followed by microarray gene expression profiling. TFs were knocked down following transfection of retroviral shRNA vectors with shRNA against luciferase serving as a negative control . The fraction of differentially expressed genes following TF knock-down with binding peaks for the respective TF in their loci ranged from 10.8 to 70.3% . We next overlapped differentially expressed genes with binding peaks with our initial expression scatterplot . The Cx3cr1 promoter was bound by ERG, FLI1, PU.1 and GATA2  and also contained relevant binding sites conserved between mouse and human  largely non-overlapping binding profiles of shared TFs which are predictive of differential gene expression, (ii) cell type-specific TFs are likely drivers of global TF binding patterns of shared TFs and (iii) cell type-specific binding of shared TFs actively contributes to cell type-specific gene expression. Taken together therefore, our results argue against the currently prevalent model that a large proportion of TF binding events are opportunistic with no immediate transcriptional function. Of note, repeated cycles of moving between experimental and computational analysis were required to make a compelling case for this important point.in silico experimentation to select the most promising hypotheses for subsequent experimental validation. Previous attempts to model gene expression from TF binding data have largely focused on multi-TF ChIP-Seq data from a single cell type . We would argue that GAMs are more representative of current transcription factor knowledge because they take into account non-linear and combinatorial transcription factor effects on gene expression. Moreover, our recursive strategy of moving between experimentation and computation prompted us to perform additional ChIP-Seq experiments for Mitf and c-fos, which in turn allowed us to revisit the GAMs in conjunction with the Mitf and c-fos datasets. This analysis showed that genes more strongly upregulated in mast cells than predicted by differential binding of the shared factors commonly contained several binding peaks for the mast cell-specific TFs. What might have been perceived as a deficiency in our model was therefore utilised to make new discoveries about transcriptional control mechanisms that may distinguish two closely related cell types.Contemporary biological research increasingly employs mathematical modelling approaches, not only because the size of datasets is growing exponentially, but also because the development of a mathematical framework consistent with biological observations offers potential insights into underlying mechanisms and permits et al, High-throughput sequencing has transformed the analysis of transcription factor function allowing for the first time the production of genome-wide binding maps in complex mammalian genomes. The generation of new insights from these largely descriptive genome-wide maps has, however, proved much harder than might have been anticipated. One particular puzzling result was the observation that unlike with genes, where genome sequencing showed mammalian genomes to have far fewer genes than had been expected , 1% penicillin/streptomycin (Sigma), 150 micromolar MTG (Sigma), 10% stem cell factor conditional media from BHK/MKL cells and 10 ng/ml of recombinant mIL-3 (Peprotech). Cells were frequently transferred to new flasks to remove adherent cells and experiments were performed after 3 weeks, when cultures were homogenous, as confirmed by the presence of FcERI by FACS and toluidine blue staining of cytospins .et al, et al, et al, et al, ChIP assays were performed as previously described  and cov(mast), for all the merged coordinates. Coverage scores were counted using the et al, et al, fpkm (fragments per kilobase of exon per million fragments) values. Differential expression was calculated using Cuffdiff , while significant transcripts with absolute fold change ≥ 4 were classified as cell type-specific (categories II and III). Transcripts with fpkm values ≤ 2 were considered not expressed (category IV). Mapped reads were converted to density plots and displayed as UCSC genome browser custom tracks.Total RNA was isolated using Tri-Reagent (Sigma-Aldrich) and digested with DNase I using TurboDNA (Applied Biosystems/Ambion). DNaseI-digested total RNA was processed and sequenced by BGI-Hong Kong. RNA-seq data were aligned to the mm9 transcriptome using TopHat , cloned into pGL2basic (Promega) and confirmed by sequencing. Cells were transfected and assayed as previously described . Samples were amplified using TotalPrep 96-RNA amplification kit (Applied Biosystems/Ambion) and hybridised to MouseWG-6 v2 microarrays (Illumina). Raw data were processed in e Smyth, . Probes gcrma algorithm, probes with “absent” call in all samples were removed, and remaining data were quantile-normalised in R using the affy package . CEL files were processed by t, while differential TF scores (ΔTF) for a given TF were calculated for each peak region r in the coverage score matrix and then averaged for each gene. Peaks were assigned to genes using annotation from UCSC where peaks within ±1 kb of the transcription start site or within intragenic regions were assigned to the respective gene. The remaining intergenic peaks were assigned to both the nearest 5′ and 3′ genes. If no gene was found within 50 kb of an intergenic peak, it was not assigned to any genes. There were 151,925 TF-bound regions in total. 110,667 regions mapped to 19,163 genes and 41,258 regions did not map to any genes. Of 110,667 regions mapped to genes, 46,536 regions were bound by 2 or more TFs.Differential scores were calculated for each of the 10 shared TFs and the RNA-seq data  instead of the linear forms ∑βnXn to allow non-linear relationships between the predictor variables  and the response variable. GAMs with and without interaction terms were analysed using the mgcv package in R  between the 10 factors. Only genes with at least 2 TFs bound were used for this analysis since genes bound by 1 TF are not suitable for testing interactions. Differential gene expression (ΔGE) is assumed to have a Gaussian distribution with an identity link function. Restricted maximum-likelihood (REML) . This type of model uses the sum of smooth functions ∑s Wood,  estimatiDe novo motif analysis on the central 100-bp peak regions was carried out using the Homer software  were reported. To carry out the motif content analysis, the same procedure as above was carried out on cell type-specific and common peak regions. Peak regions for each of the 10 shared TFs were divided into three categories: HPC7-specific (HPC7-bound only), mast cell-specific (mast-bound only) and common (HPC7/mast peaks overlapping ≥ 1 bp) regions. Results from de novo motif analysis were combined in a matrix where columns are datasets, rows are Jaspar/UniProbe motifs and each row/column value is x or 0, where x corresponds to the fraction of peaks with significant  match to a particular motif, while 0 is a non-significant match. The matrix was used to generate a heatmap that indicates fraction of peaks containing motif in cell type-specific and common regions.ggplot2  and assigned the identifier GSE48086.The raw and processed data from the ChIP-Seq, RNA-Seq and microarray experiments reported in this publication have been submitted to the NCBI Gene Expression Omnibus (


Invadopodia are actin-rich cell membrane projections used by invasive cells to penetrate the basement membrane. Control of invadopodia stability is critical for efficient degradation of the extracellular matrix (ECM); however, the underlying molecular mechanisms remain poorly understood. Here, we uncover a new role for podoplanin, a transmembrane glycoprotein closely associated with malignant progression of squamous cell carcinomas (SCCs), in the regulation of invadopodia-mediated matrix degradation. Podoplanin downregulation in SCC cells impairs invadopodia stability, thereby reducing the efficiency of ECM degradation. We report podoplanin as a novel component of invadopodia-associated adhesion rings, where it clusters prior to matrix degradation. Early podoplanin recruitment to invadopodia is dependent on lipid rafts, whereas ezrin/moesin proteins mediate podoplanin ring assembly. Finally, we demonstrate that podoplanin regulates invadopodia maturation by acting upstream of the ROCK-LIMK-Cofilin pathway through the control of RhoC GTPase activity. Thus, podoplanin has a key role in the regulation of invadopodia function in SCC cells, controlling the initial steps of cancer cell invasion. Despite current efforts to understand cancer cell invasion, the mechanisms underlying BM transmigration by tumour cells remain elusive. Accumulating evidence demonstrates that cancer cells overcome the BM barrier by forming specialised F-actin-rich projections termed invadopodia, which serve as a localised source of matrix proteases and mediate the degradation of the extracellular matrix (ECM).3 A four-step model for invadopodia assembly has been defined revealing that stabilisation and complete maturation of invadopodia are required for efficient ECM degradation.6 Although several studies have described their structural components,9 there is a great interest in understanding the regulatory events controlling invadopodia stability and maturation.12Approximately 90% of human tumours arise from epithelial tissues, which are separated from the supporting stroma by the basement membrane (BM).13 Podoplanin is a type I transmembrane mucin widely known as a marker for lymphatic endothelial cells that has a critical role during development of the heart, lungs and lymphatic endothelial system.16 The expression of podoplanin is significantly enhanced during tissue-remodelling processes and in several types of human cancer, including SCCs,19 where podoplanin upregulation is often found at the leading invasive edge of tumour nests and is generally associated with poor clinical outcome.24In vitro, podoplanin expression in tumour cells is linked to enhanced migratory and invasive features.25 Among the best defined partners for podoplanin are the C-type adhesion receptor 2 (Clec-2),25 the adhesion molecule CD44 and the ERM  protein family, which link the underlying actin cytoskeleton to podoplanin cytoplasmic (CT) tail. Podoplanin binding to the standard isoform of CD44 in carcinoma cells seems crucial for podoplanin-dependent stimulation of directional migration26 as well as for tethering tumour cells to hyaluronan-rich ECM.27 In addition, the binding of podoplanin to ERM proteins triggers the activation of the small GTPase RhoA, regulating actin dynamics.28 Podoplanin-mediated regulation of the actin cytoskeleton is often associated with the acquisition of a mesenchymal phenotype , although podoplanin can also stimulate tumour progression in the absence of a complete EMT.23In vitro, podoplanin-mediated invasion depends on the activity of matrix metalloproteinases;23 however, the underlying molecular mechanisms remain poorly understood.Squamous cell carcinoma (SCC) is a malignant tumour of stratified epithelia characterised by invasive growth into the connective tissue. Interestingly, primary cancer cells obtained from patients with invasive SCCs spontaneously assemble active invadopodia when cultured in two-dimensional substrates.In this study, we sought to investigate whether podoplanin pro-invasive properties could be related to invadopodia activity in SCC cells. We describe podoplanin as a novel component of invadopodia adhesion rings, where it controls invadopodia stability and promotes efficient degradation of the ECM. We also characterise the molecular mechanisms underlying this novel function of podoplanin. These results demonstrate a direct involvement of podoplanin in ECM-remodelling processes.in vitro.21 As podoplanin is commonly upregulated in human SCCs, we analysed its expression in a panel of human SCC cell lines. Overall, SCC cells showed high levels of podoplanin compared with non-transformed HaCaT keratinocytes  at invadopodia was confirmed by immunofluorescence co-staining with the invadopodium markers actin and cortactin, and colocalisation with areas of matrix degradation. Confocal microscopy analysis revealed that podoplanin was mostly absent from the invadopodia actin core but accumulated to podosome-like adhesion rings of actively degrading invadopodia in HN5 and SCC13 cells . The prele talin . The locle talin . These r4 led to a further increase in the number of active invadopodia  and plated onto unlabelled gelatin , which disassembles lipid rafts, inhibiting invadopodia formation and function.ol cells . Finallyol cells . Togethe32 HN5 cells expressing Lifeact-Ruby were transiently transfected with wild-type podoplanin or podoplanin mutant constructs lacking the CT tail (PDPNΔCT), the extracellular domain (PDPNΔEC) or the ERM-binding motif (PDPNQN.N). In addition, a podoplanin mutant that prevents podoplanin self-assembly and association to lipid rafts (PDPNG137L) was also used /LIMK2(D451A) mutants significantly reduced the ability of control cells to degrade the matrix, whereas no significant effect was observed upon expression in podoplanin knockdown cells . Treatment of control cells with a specific inhibitor of ROCK (H-1152) reduced pCofilinS3 and invadopodia-mediated matrix degradation to levels seen with podoplanin knockdown . Howeverwn cells . Convers37 Growing evidence suggest that carcinoma cells do not entirely digest the BM, but rather make small perforations that are the result of efficient invadopodial activity.39 Podoplanin is highly upregulated at the early stages of SCC progression24 and in physiopathological situations involving tissue remodelling.18 In all these situations, podoplanin expression is induced in the basal layer of keratinocytes that are in close contact with the BM. Although the mechanisms controlling this upregulation are completely unknown, podoplanin expression can be modulated by a number of factors, including those with the potential to induce invadopodia formation/function such as EGF and TGFβ.41 Our present study suggests that podoplanin mediates ECM remodelling in all these contexts by controlling the stability of invadopodia in keratinocytes, pointing to a direct role for this glycoprotein in cancer cell adhesion and invasion. Our data are consistent with a recent report showing the association of podoplanin with invadopodia, although its specific localisation and functional role in these structures was not addressed.42 On the basis of the data presented here, we propose a model in which podoplanin is dispensable for the initial formation of invadopodia but promotes invadopodia stabilisation and maturation, leading to efficient ECM degradation , have also been found in the invadopodia actin core in different cell types,48 which may account for the ECM degradation efficiency. Therefore, it is essential to understand how this differential localisation affects the invasive potential of tumour cells and how the specific segregation of invadopodial components into the adhesion ring/actin core is controlled. Our results showing that ERM proteins mediate podoplanin ring assembly suggest an important function for ERM proteins in the spatial organisation of essential invadopodia components in order to regulate their functionality, a hypothesis that warrants further investigation.It is worth noticing that podoplanin is specifically located at invadopodia but it is not a component of focal adhesions or podosomes of differentiated THP-1 cells -E, point31 It has been proposed that lipid rafts act as platforms for localising and activating the molecular machinery required for invadopodia formation and function.49 Our results support this hypothesis, and are in agreement with our previous observations, evidencing the importance of podoplanin recruitment to lipid microdomains for its functionality.32 Interestingly, ezrin has also been shown to facilitate the formation of a lipid raft localised signalling complex promoting NHE1-dependent invadopodia formation and proteolytic activity.47Podoplanin association to lipid rafts is essential for efficient podopolanin recruitment to invadopodia and associated ECM degradation. Invadopodia are lipid rafts-enriched domains whose assembly and function are dependent on these microdomains.32 this domain is essential for podoplanin-induced invadopodia stability through the activation of the RhoC-cofilin pathway. Regulation of cofilin activity is a critical event for the maturation of invadopodia precursors into fully active invadopodia.51 Although cofilin activation is required for actin polymerisation after invadopodia precursor formation,6 the actin severing activity of cofilin has to be finely regulated in order to prevent from excessive severing and actin depolymerisation, allowing the stabilisation of the invadopodium. Interestingly, in metastatic mammary adenocarcinoma cells RhoC activity is spatially confined to areas surrounding the actin core of invadopodia, which in turn increase pCofilinS3 and therefore inactivation, focusing cofilin activity to the actin core.10 Thus, podoplanin might be controlling RhoC activity specifically at adhesion rings, allowing the inactivation of cofilin in this specific compartment of invadopodia.Besides the importance of podoplanin CT motif for lipid raft association,In summary, the results presented here show the functional role of podoplanin in modulating matrix remodelling through invadopodia. Together with previous data, these results demonstrate the involvement of podoplanin in two key features of aggressive tumours: increased directed cell motility and efficient remodelling of the ECM. Our results also demonstrate that invadopodia adhesion rings regulate the efficiency of ECM degradation, highlighting the interest of molecules that regulate the invadopodium adhesion ring assembly such as ERM proteins. Understanding the key signalling molecules that regulate the crucial stages in the invadopodium life cycle will enable the development of targeted therapies designed at preventing cancer cell invasion.52). All cell lines were maintained at 37 °C in a 5% CO2 humidified atmosphere. HaCaT PDPN-GFP and HN5 PDPNshRNA cell transfectants have been described before.26 For the experiments described below, cells were cultured on 0.5% glutaraldehyde-crosslinked gelatin or TRITC-gelatin where indicated.Characteristics and cell culture conditions of SCC cell lines are described in 13 To assess the ability of cells to form invadopodia and degrade matrix, 3–4 × 105 cells were plated on the coated coverslips in six-well plates containing three coverslips/well and incubated at 37 °C for the indicated time (6–18 h). Cells and images were then processed as indicated below.The matrix-degradation assay has been performed as described previously.26 Fixed cell images were taken using a NIKON A1R confocal laser-scanning microscope equipped with a Plan Apo VC x60 Oil 1.4NA or Plan Fluor x40 Oil 1.3NA objective lenses or Zeiss LSM510 confocal microscope using a × 63 oil objective. Images were captured using NIS Elements software  and analysed using FIJI (http://fiji.sc/Fiji). Invadopodia were identified as morphologically characteristic actin-cortactin dots (⩾1 μm) found at the bottom of the cell. Foci of degraded matrix were visible as dark dot-like areas that lack fluorescence and appear as ‘holes' in the bright fluorescent gelatin matrix. For the analysis of invadopodia-mediated ECM degradation, only those invadopodia associated with ECM degradation were considered as active. The number of cells forming active invadopodia was manually counted. A cell containing at least one active invadopodia under the cell body or near the cell edge was counted as a cell-forming active invadopodia. The area of gelatin degradation was measured using an ImageJ plugin previously described.53Cells plated on labelled or unlabelled crosslinked gelatin were fixed after the indicated time points in 3.7% formaldehyde and processed as previously described.For podoplanin-actin and podoplanin-gelatin imaging, cells stably expressing PDPN-GFP were transiently transfected with Lifeact-Ruby when indicated. After 24 h, the basal cell surface was imaged every 30 s for 3 h by the NIKON A1 confocal system previously described using the × 60 objective. Image acquisition was performed using NIS Elements software. Playback rates of movies shown are 10 frames/s. Kymography analysis was performed using FIJI by selecting areas covering one single invadopodium.For live-cell invadopodium analysis, raw intensity data were first corrected for background fading during imaging. The actin-containing invadopodia were thresholded, traced at their largest area and fluorescence intensity was then determined at each time point. With this data invadopodium lifetime and total number of invadopodia/cell were determined as described in the figure legend. These analyses were performed using FIJI software.M MβCD was added to the medium at the times indicated for a maximum of 20 min. Cells were fixed and processed as described above and co-stained with phalloidin and cortactin to visualise invadopodia. For reassembly experiments, cells were treated with 10 mM MβCD for 20 min, washed with normal medium and allowed to resume invadopodia formation at the indicated times for a maximum of 6 h.Control and PDPN knockdown cells were seeded on glass coverslips coated with unlabelled crosslinked gelatin and allowed to spread for 3 h in normal medium. To evaluate the rate of invadopodia disassembly 10 mM G6001 to prevent invadopodia formation and activity. Invadopodia formation and function was resumed after G6001 washout and addition of fresh medium supplemented with 10% fetal bovine serum at the indicated time points. Under these conditions, HN5 cells form inactive invadopodia during the first 30 min after washout and actively degrading invadopodia after 1 h. Cells were then processed for ECM degradation analysis or western blot as in the supplemental methods.For synchronisation experiments, cells were seeded on glass coverslips coated with TRITC-crosslinked gelatin or plastic petri dishes coated with unlabelled crosslinked gelatin and serum-starved overnight in the presence of 50 μP<0.05 was reached. Significance values and N numbers for each data set are shown in each figure legend. All statistical analyses were performed using GraphPad Prism 5.0 software.Three to four independent experiments were carried out for each experimental condition. Data are presented as mean±s.e.m. Significance was determined using one-way analysis of variance followed by Bonferroni's multiple comparison test. Data were considered as significant if


Optimal athletic performance is often linked to body composition, therefore depending on sport and position, a decrease in body fat would lean toward an increase in athletic fitness. A recent trend in decreasing body fat while maintaining muscle has emerged, called intermittent fasting. Some research has suggested that ingesting calories only in a certain time frame during the day  would put the body into fat metabolism while avoiding muscle catabolism during the remaining fasting hours. The purpose of this study is to investigate the effects of intermittent fasting and resistance training on body composition, mood state, and resting energy expenditure.Twenty-four participants  volunteered to participate in this study and were randomly assigned to one of three groups: Resistance training only (RT), where they performed 4 upper and lower body split workouts a week, Intermittent Fasting only (IMF), this group consumed calories in an 8 hour window while maintaining normal free living activity, and Intermittent Fasting plus Resistance Training (IMFRT), this group participated in 4 workouts per week as well as only ingesting calories within the 8 hour window. Participants completed baseline testing consisting of Resting Energy Expenditure on a TrueOne ParvoMedics metabolic cart to determine caloric need, as well as measuring body composition using dual energy x-ray absorptiometry scan (DEXA). For the next 30 days the participants followed their group protocol, while coming in at day 15 for a weigh-in as well as a completion of a mood state questionnaire. The final testing session occurred at the end of the 30-day workout and/or dietary intervention. Data analysis was performed using a MANCOVA [3(group)x 2(time) gender as a covariate], as well as Independent Samples T-Test to determine individual group differences, post hoc test were set at p<0.05. Consent to publish the results was obtained from all participants.There was a significant linear interaction shown for lean mass from Day 1 to Day 30 (p=0.002). A significant difference in Resting Energy Expenditure was also found for the groups (p=.010). Independent samples t-tests found a significant difference in weight (p=0.037) as well as fat mass (p=0.036) between the Intermittent Fasting plus Resistance Training group compared to the Resistance training only group. There were no significant differences shown between the Intermittent Fasting plus Resistance Training and Intermittent Fasting only. Also when compared, the Intermittent Fasting and Resistance Training only group had no significant differences. A Nonparametric tests reviled no significant differences between mood states for the groups.An 8-hour eating and 16-hour fasting day resulted in a decrease in fat mass as well as weight for the Intermittent Fasting plus Resistance Training group when compared to the Resistance Training only group. On the other hand no differences were found between the Resistance Training only group and Intermittent Fasting group, hinting to that intermittent fasting alone may not be affective in decreasing body fat percent. However, when paired with resistance training, lean mass can be retained and/or enhanced while decreasing body fat, thus enhancing body composition.


Blood was collected and stored in RNAlater® at -80°C until analysis of selected transcripts by real-time RT-PCR. Comparable changes with fasting were detected in the expression of lipid metabolism-related genes when RNA from either PBMC or whole blood of rats or mice was used. HFD-induced excess body weight and fat mass associated with expected changes in the expression of metabolism-related genes in whole blood of mice. Changes in gene expression in whole blood of RA-treated mice reproduced known transcriptional actions of RA in hepatocytes and adipocytes. Reduced expression of Fasn, Lrp1, Rxrb and Sorl1 could be validated as early biomarkers of metabolic health in young Rb+/- mice using whole blood RNA. Altogether, these results support the use of whole blood RNA in studies aimed at identifying blood transcript-based biomarkers of nutritional/metabolic status or metabolic health. Results also support reduced expression of Fasn, Lrp1, Rxrb and Sorl1 in blood cells at young age as potential biomarkers of metabolic robustness.Blood cells are receiving an increasing attention as an easily accessible source of transcript-based biomarkers. We studied the feasibility of using mouse whole blood RNA in this context. Several paradigms were studied: (i) metabolism-related transcripts known to be affected in rat tissues and peripheral blood mononuclear cells (PBMC) by fasting and upon the development of high fat diet (HFD)-induced overweight were assessed in whole blood RNA of fasted rats and mice and of HFD-fed mice; (ii) retinoic acid (RA)-responsive genes in tissues were assessed in whole blood RNA of control and RA-treated mice; (iii) lipid metabolism-related transcripts previously identified in PBMC as potential biomarkers of metabolic health in a rat model were assessed in whole blood in an independent model, namely retinoblastoma haploinsufficient (Rb Peripheral blood mononuclear cells (PBMC) constitute an easily obtainable fraction of blood cells  consisting basically in lymphocytes and monocytes , 3. PBMCMouse is the most used animal model and there is extensive literature on the use of mouse models for drug testing and biomarker discovery . SurprisTranscriptional analysis of mouse whole blood RNA, rather than PBMC RNA, could therefore be a better choice for the identification of transcript-based biomarkers in mice as part of the preclinical validation of human biomarkers. Here, we aimed to establish the usefulness of whole blood transcriptional analysis in mice as a tool in studies dealing with metabolic and physiological adaptations in front of nutritional and pharmacological interventions and to validate previously reported early blood cells transcript-based biomarkers of metabolic health. To these ends, several experimental paradigms were studied. First, transcript levels of metabolism-related genes known to be affected in rat PBMC by fasting , 22 and All animal protocols were in accordance with the national and European ethical guidelines for the use and care of laboratory animals and were approved by the Bioethical Committee of the University of the Balearic Islands. In all the experiments, animals were housed in plastic cages at 22°C with a period of light/dark of 12 h (lights on from 08:00 to 20:00 hours).ad libitum access to chow diet, and a fasted group, animals deprived of food for 12 h (from 20:00 to 08:00h). Coprophagy was prevented by changing the cage immediately prior to food deprivation. At the beginning of the light cycle, animals under ad libitum or fasting conditions were weighed and blood was collected from the saphena vein. Part of the blood (~70 μL) was collected in heparinized capillary tubes and used to obtain plasma, another part (~2mL) was mixed with EDTA and used to obtain the PBMC fraction (see below), and a third part (~200 μL) was mixed with RNAlater®  to quickly stabilize the RNA and stored at -80°C for later whole blood RNA isolation and gene expression analysis. In parallel, an experiment with 2.5-month-old NMRI male mice (Charles River Laboratories) was conducted with the difference that mice were deprived of food for just 8h (from 24:00 to 08:00h) and blood was collected from the submandibular vein. One part of the blood (~70 μL) was collected with heparinized capillary tubes to obtain plasma, and another part (~200 μL) was mixed with RNAlater®  and stored at -80°C for later RNA isolation and gene expression analysis. The experiments using male Wistar rats and NMRI mice were performed twice.Two and a half-month-old male Wistar rats  housed individually and fed with a standard chow diet  were used. The animals were distributed into two groups : a control-fed group, animals provided with RNAlater®  and stored at -80°C for later RNA isolation and gene expression analysis.A separate fasting experiment was conducted with 3-month-old CD-1 female mice (own production) housed 2–3 mice per cage under the standard conditions indicated above and distributed into a control-fed group and a fasted group deprived of food for 12 h (from 20:00 to 08:00h) . At the beginning of the light cycle, both groups of animals were anaesthetized by intraperitoneal injection of ketamine (100 mg/kg body weight) and xylazine (10 mg/kg body weight) and blood was collected by cardiac puncture (open approach) using a 23 G needle attached to a 1 mL syringe with EDTA. Part of the blood (~70 μL) was used to obtain plasma, another part (~750 μL) was used to obtain the PBMC fraction (see below), and a third part (~200 μL) was mixed with ad libitum with a defined low fat diet  or a defined high fat diet . Body weight and body composition using EchoMRI-700™ analyzer  were assessed the day before blood extraction. At the beginning of the light cycle, whole blood was collected from the submandibular vein (~150 μL), mixed with RNAlater®  and stored at -80°C for later RNA isolation and gene expression analysis.One and a half-month-old C57BL/6J male mice (Charles River Laboratories) housed 2–3 mice per cage were used. Mice  were fed trans RA  at a dose of 50 mg/kg body weight or the vehicle (100 μL olive oil) during the 4 days before euthanization . Body weight and energy intake were followed daily during the treatment period. Energy intake was estimated on a per-cage basis from the actual amount of food consumed by the animals. The animals were killed with CO2 and decapitated at the start of the light cycle. Blood was collected from the neck and serum prepared by centrifugation and frozen at -80°C until biochemical analysis. A part of the blood (~200 μL) was mixed with RNAlater®  and stored at -80°C for later RNA isolation and gene expression analysis. Epididymal WAT (eWAT), liver and gastrocnemius skeletal muscle were excised in their integrity and weighted. The weight of the eWAT depot expressed as percentage of body weight was previously shown to largely correlate with total body fat content ).We have previously demonstrated that PBMC express key genes involved in energy homeostasis and can reflect transcriptional adaptations to acute changes in feeding conditions (fasting and refeeding) that occur in key tissues such as liver or adipose tissue –7, 22. HPBMC can reflect adaptations and deregulations related to increased adiposity induced by the intake of hyperlipidic diets , 12, 23 Studies by independent groups including ours have shown that, in rodents, treatment with ATRA at different dosages and routes of administration reduces body weight and adiposity and enhances glucose tolerance and insulin sensitivity see , 25). AT. AT25]).As shown in Findings in this work indicate that gene expression in whole blood reflects known ATRA effects on FA and energy metabolism in adult mice, and suggest that gene expression in whole blood would mainly reflect or mimic the metabolic scenario found in densely vascularized tissues, like liver or BAT. Moreover, transcriptional markers (such as the Mest gene) of WAT expansion, a process tightly associated with angiogenesis , have bebona fide RAR target genes, such as the ATRA hydroxylase Cyp26a1 [A direct effect of ATRA modulating gene expression in blood cells through their nuclear receptors (and/or other mechanisms) cannot be discarded. In fact, retinoic acid receptor (Rar) and retinoid X receptor (Rxr) isoforms were expressed in whole blood of mice , where t Cyp26a1  and Ucp1 Cyp26a1 , found i Cyp26a1  than in  Cyp26a1 . Blood c Cyp26a1 .ad libitum fed dams [versus control young rats at weaning.Recently, Konieczna and collaborators  identififed dams . The pro+/- mice are resistant to high fat diet-induced obesity [+/–mice were leaner than their WT littermates, displaying 26% reduced body fat content. Of the potential early biomarkers of metabolic health identified from PBMC transcriptome in rats, reduced expression of Fasn, Lrp1, Rxrb and Sorl1 were validated in 2-month-old Rb+/–mice using real-time RT-PCR on whole blood RNA  and control (now WT littermates) groups at older ages, suggesting their potential usefulness as biomarkers at early ages only. Fasn, Lrp1, Rxrb and Sorl1 are involved in lipogenesis/adipogenesis and fat lipoprotein uptake  and the+/- mice. Because a prominent feature of the Rb+/- mouse model is WAT-to-BAT remodeling (WAT browning) [+/–mice and WT littermates (data not shown), suggesting a poor potential of gene expression profiles of whole blood to reflect WAT browning in this model. To be noted, our recently published results indicate that bone morphogenetic protein (BMP) signaling pathways might be key to WAT browning in the Rb+/- model [® platform (https://genevestigator.com/gv/index.jsp), unlike that of retinoid receptors (Rars and Rxrs) (GENEVESTIGATOR® and this work). This fact could explain results obtained in this work evidencing that gene expression in whole blood of mice reflects WAT browning linked to ATRA treatment but not to Rb haploinsufficiency.We wanted to ascertain to what extent gene expression changes in whole blood could reflect the metabolic environment and transcriptional changes in key tissues previously described in Rbrowning) , 27, exp/- model  and mRNAUsing several independent experimental paradigms, this study reveals the usefulness of whole blood transcriptional analysis as a tool in studies related to metabolic and physiological adaptations in front of acute metabolic stressors and nutritional interventions, as well as in the validation and assessment of early transcript-based biomarkers of metabolic health. This is of particular interest in studies using mouse models, where the amount of blood available is limited. Findings in this work also support reduced expression of Fasn, Lrp1, Rxrb and Sorl1 in blood cells at a young age as putative biomarkers of metabolic robustness.


Escherichia coli gene deletion strains, we find that the width of the DFE varies dramatically between antibiotics and, contrary to conventional wisdom, for some drugs the DFE width is lower than in the absence of stress. We show that this previously underappreciated divergence in DFE width among antibiotics is largely caused by their distinct drug-specific dose-response characteristics. Unlike the DFE, the magnitude of the changes in tolerated drug concentration resulting from genome-wide mutations is similar for most drugs but exceptionally small for the antibiotic nitrofurantoin, i.e., mutations generally have considerably smaller resistance effects for nitrofurantoin than for other drugs. A population genetics model predicts that resistance evolution for drugs with this property is severely limited and confined to reproducible mutational paths. We tested this prediction in laboratory evolution experiments using the “morbidostat”, a device for evolving bacteria in well-controlled drug environments. Nitrofurantoin resistance indeed evolved extremely slowly via reproducible mutations—an almost paradoxical behavior since this drug causes DNA damage and increases the mutation rate. Overall, we identified novel quantitative characteristics of the evolutionary landscape that provide the conceptual foundation for predicting the dynamics of drug resistance evolution.The emergence of drug resistant pathogens is a serious public health problem. It is a long-standing goal to predict rates of resistance evolution and design optimal treatment strategies accordingly. To this end, it is crucial to reveal the underlying causes of drug-specific differences in the evolutionary dynamics leading to resistance. However, it remains largely unknown why the rates of resistance evolution via spontaneous mutations and the diversity of mutational paths vary substantially between drugs. Here we comprehensively quantify the distribution of fitness effects (DFE) of mutations, a key determinant of evolutionary dynamics, in the presence of eight antibiotics representing the main modes of action. Using precise high-throughput fitness measurements for genome-wide  Escherichia coli shows that the fitness effects of genome-wide mutations vary drastically with different antibiotics; this is explained by the specific dose-response characteristics of each drug and has implications for predicting the evolution of resistance.A study of the bacterium  Escherichia coli, when exposed to eight different antibiotics that represent the main modes of drug action. We find that the overall magnitude of these fitness effects varies enormously among antibiotics and show that this phenomenon is explained by the drug-specific response of bacteria to small changes in drug dose. We further show that, unlike the fitness effects—which vary greatly—the changes in tolerated drug concentration generated by resistance-conferring mutations are similar for most drugs, with the notable exception of an outlier drug called nitrofurantoin . Using a combination of mathematical modeling and evolution experiments, we propose new quantitative measures that enable partial predictions about how quickly resistance to a given drug will evolve and whether it will reproducibly occur by exploiting the same mutations.When bacteria acquire drug resistance through mutation of their genomes, it renders once-powerful treatments useless. For some antibiotics, bacteria develop resistance rapidly, while for other antibiotics this process can be very slow. The causes of such differences in evolutionary dynamics between drugs remain poorly understood. Determining how these mutations that confer drug resistance affect bacterial fitness is a crucial requirement for developing predictions about evolution. In this study, we systematically measured the fitness effects of thousands of mutations that mimic all viable spontaneous gene disruptions in the bacterium  The recent concurrence of a diminishing discovery rate of novel antibiotics with rapidly emerging drug resistant pathogens is an alarming concern for global public health –3. For sEscherichia coli gene deletion strains. The width of the DFE varies considerably among antibiotics—an effect we found to be explained by the drugs’ distinct dose-response characteristics. The underlying variations in inhibitory concentrations resulting from genome-wide genetic perturbations have similar magnitude for most antibiotics but are extremely small for the prodrug nitrofurantoin. We provide evidence supporting that this small resistance variability strongly affects evolutionary dynamics for nitrofurantoin, limits resistance development, and confines evolution to reproducible mutational paths.The distribution of fitness effects (DFE) of mutations is a key determinant of evolutionary dynamics . SpecifiE. coli gene deletion strains , where pj is the empirical probability that locus j is mutated in a randomly chosen evolution replicate, and the summation is over all loci. This entropy H measures the diversity of mutated loci in the six evolution replicates: loci that are reproducibly mutated in all (or none) of the six replicates contribute zero to this measure; loci that are mutated in some, but not all, of the replicates contribute a value that corresponds to the Shannon information (in bits) about the identity of the clone gained from identifying that the locus is mutated; e.g., a locus that is mutated in half of the replicates has pj = 1/2 and contributes exactly one bit of information to H. Error bars in jackknife). The two-sample t test in ttest2).Mutation diversity in p-values were obtained using a custom implementation of Sherlock and Weng’s GO:Termfinder software [We performed gene ontology enrichment analysis  on the 2software , and BonlexA, recA, polB, recN and quantified as described [SOS response induction  was measescribed .S1 Data(XLSX)Click here for additional data file.S1 Fign and IC50 for mutants (colored circles) and WT replicates (black stars); marginal cumulative distributions (“cdf”) are shown along axes. (D–F) As A–C for ciprofloxacin. (G–M) As C for nitrofurantoin (G), tetracycline (H), chloramphenicol (J), mecillinam (K), cefoxitin (L), and ampicillin (M). Numerical data is in  As (TIFF)Click here for additional data file.S2 Figcf. (A) Pearson correlation coefficients of effective drug concentrations of genome-wide gene deletion mutants cf.  for all (TIFF)Click here for additional data file.S3 Fig50 increase over time for a drug with narrow distribution of relative IC50 changes ; sample runs are light gray; mean from 200 runs is black. (B) As A but for wider distribution of relative IC50 changes (σ = 0.3). (C) Average fold-change in IC50 after 250 generations . (D) Relative fold-change in IC50 per fixed mutation. Increasing the width of the distribution of relative IC50 changes accelerates resistance evolution; the width of this distribution is directly reflected in the relative fold-change in IC50 per fixed mutation. (E–H) As A–D but for varying dose-sensitivity n. Reducing dose-sensitivity decelerates resistance evolution. (J–M) As A–D but for varying mutation rate μ. Increasing mutation rate accelerates resistance evolution and slightly increases the resistance increase per fixed mutation. Unless stated otherwise, the dose-sensitivity is n = 3, the width of the distribution of relative IC50 changes is σ = 0.3, and the mutation rate is μ = 10−7.(A) Simulation results from the population genetics model as in (TIFF)Click here for additional data file.S4 Fig(A) Schematic: the width of the DEC determines the increase in drug resistance  resulting from a typical beneficial mutation; for a narrow DEC this increase is small (blue arrow), whereas for a wide DEC it is large (magenta arrow). (B) This increase in drug resistance due to beneficial mutations  translates into their selection coefficients  via the dose-response curve. A smaller resistance increase implies a lower selective advantage (blue arrows), reducing the probability and rate of fixation of the corresponding mutation. (C) Schematic illustrating how DEC width affects the dynamics of resistance evolution in a morbidostat.(TIFF)Click here for additional data file.S5 FiglexA, recA, polB, and recN in response to nitrofurantoin (magenta), ciprofloxacin (gray), and tetracycline (black) as a function of growth rate at different drug concentrations . SOS induction in response to nitrofurantoin is similar to that of ciprofloxacin (used as positive control) at the same growth rate; in contrast, no induction occurs for tetracycline (used as a negative control). Numerical data is in Transcriptional regulation of SOS response promoters (TIFF)Click here for additional data file.S6 Fign = 1) as observed for trimethoprim (n = 7) as observed for mecillinam and cefoxitin  Simulation results as in ethoprim  and avaiefoxitin . For thi(TIFF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.