Recent research in plant epigenetics has increased our understanding of how epigenetic variability can contribute to adaptive phenotypic plasticity in natural populations. Studies show that environmental changes induce epigenetic switches either independently or in complementation with the genetic variation. Although most of the induced epigenetic variability gets reset between generations and is short-lived, some variation becomes transgenerational and results in heritable phenotypic traits. The short-term epigenetic responses provide the first tier of transient plasticity required for local adaptations while transgenerational epigenetic changes contribute to stress memory and help the plants respond better to recurring or long-term stresses. These transgenerational epigenetic variations translate into an additional tier of diversity which results in stable epialleles. In recent years, studies have been conducted on epigenetic variation in natural populations related to various biological processes, ecological factors, communities, and habitats. With the advent of advanced NGS-based technologies, epigenetic studies targeting plants in diverse environments have increased manifold to enhance our understanding of epigenetic responses to environmental stimuli in facilitating plant fitness. Taking all points together in a frame, the present review is a compilation of present-day knowledge and understanding of the role of epigenetics and its fitness benefits in diverse ecological systems in natural populations. In recent years, epigenetic modifications of chromatin material have been discovered as a major player in modulating plant fitness under challenging environmental conditions. Epigenetics refers to the study of changes in gene functions that are mitotically and/or meiotically heritable and do not involve a change in DNA sequence but are based on modifications in chromatin organization . EpigeneEpigenetic mechanisms help sessile plants cope with environmental changes either by acclimation or adaptation to the changing environment. Acclimation outcomes include phenotypic responses achieved by altering gene expression and physiological attributes without incurring any genotypic changes in organisms, a phenomenon called phenotypic plasticity . In otheLactuca sativa). In the longer term, only a few plant species could survive due to higher competitive abilities under resource-limiting conditions, whereas plants that control water use by reducing sap flux sensitivity survived under long-term soil moisture changes  among others. These responses of plants could be transient in the short run or may persist over the long term. For example, Jackson and colleagues  found an changes .The most common stress-induced changes include ion toxicity, reactive oxygen species (ROS) production, redox/osmotic imbalances, nutritional disproportionality, and lipid peroxidation that bring changes in the ultrastructure of cellular and organellar membranes, chlorophyll content, photosynthetic capacities, and turgor pressure . Upon peDuring the last few decades, climate change has had an unprecedented impact, which is critically categorized as a global threat to mankind in terms of agricultural and forest productivity . Adding Although epigenetic variation can be induced in the absence of genetic variability, the presence of the latter can influence the former. The genetic makeup of an individual, for instance, can affect the epigenetic marks by altering the number of cytosines available for methylation, small RNA production, availability of transposable elements, genes controlling DNA methylation enzymatic machinery, and histone-mediated chromatin modification . This ofThe growth, reproduction, and survival of natural populations are largely determined by functional attributes like morphological traits, physiology, and phenology. Amidst global climatic changes that have resulted in accentuated biotic and abiotic stresses, plants have made fine adjustments and acquired additional stress mitigation strategies for their survival leading to the adaptive phenotypic divergence that depends upon natural selection, gene flow, and phenotypic plasticity . NaturalLinaria vulgaris  or sudden (such as biotic stresses or climate factors) environmental changes. Epigenetic mechanisms help plants in generating plastic responses to stressful environments . The plaSomatic stress memory involves short-term sustained or pronounced responses that are often accompanied by chromatin modification, histone H3K4 methylation, and reduced nucleosome occupancy . These rArabidopsis under different temperature and dehydration stress conditions. The somatic stress memory in these cases has been shown to vary from a few days to months and is observed to be associated with epigenetic changes in DNA methylation, histone proteins, and small miRNAs (Arabidopsis. It has been observed that the accumulation of memory genes in dehydration stress regulated by H3K4me3 in the first priming stimulus is maintained throughout the memory phase (COLD RESPONSIVE (COR) genes pervaded during cold stress conditions (Most of the reports pertaining to epigenetic regulation of somatic stress memory have been derived from research investigations undertaken in the model plant l miRNAs . For insry phase . Similarnditions . Kim et nditions . This isnditions .FLC has been shown to accumulate H3K27 trimethylation in response to cold temperatures in Arabidopsis. This histone modification at the FLC locus is brought about by a Polycomb group complex recruited by VERNALIZATION INSENSITIVE 3 (VIN3) and VERNALIZATION 5 (VRN5) genes, and long non-coding RNAs (COLDAIR and COOLAIR) activated by low temperatures  has been found to be responsible for the methylation of H3K4me in response to heat stress . Vernalieratures . From theratures .MgMYBML8) was related to the transmission of simulated herbivory (leaf damage) induced trichome production on the underside of leaves in Mimulus guttatus. The epimutation was transgenerational and the unstressed offspring also exhibited increased trichome production, as a response showing preparedness for future herbivory    represenH3K4me2)  and sexuH3K4me2)  and modiH3K4me2) .Arabidopsis while in dme mutants the transmission of the stress memory through the paternal is restored in next generations  in Arabidopsis. The retrotransposon ONSEN which remains suppressed by RdDM (RNA-dependent DNA methylation) mechanism gets activated under heat stress in Arabidopsis. RdDM plays an important role in heat stress defense as mutations in RdDM machinery can limit the heat stress tolerance of plants by activating heat stress-responsive genes  gene. In the mutants of small RNA biogenesis pathway genes such as the rdr2 mutant, the methylation is reduced at CHG and CHH contexts. In a mutant of another gene involved in DNA methylation MET1 (Methyltransferase 1), methylation is reduced in all three contexts , changed methylation (CHG hypomethylation) was observed in most studied DNA sequences. In the progeny, stable inheritance of the modified methylation patterns was observed. These studies showed that the altered epigenetic state induced by heavy metal stress is heritable between plant generations for providing long-term adaptations. Further, Salicylic acid has been observed to induce epigenetic changes in the F2 progeny of dandelions . An alternate approach involves generating epigenetic variability in systems with little genetic diversity and has been adopted in model plants where the availability of detailed biological information, genetic resources, and immense genomic data allows answering complex questions. Many such studies have been conducted on model plants like Zea mays .A. thaliana is ideally suited for studies of natural phenotypic variation and is a good experimental system to explore the mechanistic link between genetic and epigenetic variation, especially with regard to cytosine methylation  with its wild type resulting in lines that were homozygous for ddm1. These epiRILs were genetically nearly identical but differed in the pattern of DNA methylation  in the form of a wide range of novel and stable ecological phenotypes in just a few generations through gene expression regulation . Among aRapid strides have been made in recent years to understand the role of various biological processes and other ecological factors in orchestrating strong epigenetic support to species in natural populations in generating phenotypic plasticity and acquiring evolutionary adaptations . The conArabidopsis thaliana differing in DNA methylation but with little divergence in DNA sequence was compared with standard RILs that had considerable DNA sequence variation (The magnitudes of heritable phenotypic variation in epigenetic recombinant inbred lines (epi-RILs) of ariation . The linhttp://www.issg.org/database), as many as 70% reproduce clonally. Some of the most successful invasive plants are found to be genetically uniform in their introduced ranges. Invasive clonal plant species like knotweeds in the United States, Europe alligator weed (Alternanthera philoxeroides) in China, Spartina anglica in Europe, and water hyacinth (Eichhornia crassipes) outside of South America show almost no detectable genetic diversity even after their establishment into a new environment  in the northeastern United States showed habitat-specific phenotypes when grown in common environments (Alligator weed (Alternanthera philoxeroides), an invasive weed in China that reproduces clonally through stolons. MS-AFLP analysis of natural field-grown plants showed very little genetic but high epigenetic variation. Even after 10 asexual generations, some of the epigenetic differences appeared to be induced by environmental differences  reported the incidence of higher epigenetic diversity compared to genetic diversity in ronments . Furtherronments  implyingferences .Ludwigia grandiflora subsp. hexapetala, is classified as being harmful to European rivers. In French wet meadows, this species has shown a rapid transition from aquatic to terrestrial environments with the emergence of two distinct morphotypes over a period of 5 years. Both genetic and epigenetic mechanisms have been involved in adjustment to the new environment, and are supposed to be possible contributors to this fast terrestrial transition. Artificial hypomethylation was induced on both morphotypes to assess the role of epigenetics. The hypomethylation of the aquatic morphotype mimicked the characteristics of the terrestrial morphotype. This suggests that DNA methylation in a new adaptive genetic capacity is playing a key role in L. grandiflora subsp. hexapetala plasticity during its rapid aquatic to terrestrial transition . The study underlines the potential of long-term epigenetic inheritance in species reproducing without fertilization. Similarly, self-pollinating plants show long-term persistence of epigenetic inheritance compared to cross-pollinated plants  indicated hypermethylation of DNA sequences in the vegetative nucleus during pollen grain maturation. Hypermethylation was essential for the long-term survival of the pollen until it reaches the stigma (Quercus suber (Fagaceae), a wind-pollinated tree, leading to active chromatin and hence a higher potential for transcription, compensating for the chromatin silencing of the generative nuclei. Further, the chromatin compaction level and transcription level of generative nuclei of Q. suber were greater than that of Lilium sps. which can be linked to differences in their pollination or fertilization processes  or unstable. Based on these factors, genetic and epigenetic variability finds a balance for providing a competitive edge to the plant, and finally these two pools of variability either show a trade-off or reinforcement with each other   (OsPCF7)  , delayedctively)  and grai(OsPCF7) . Likewis(OsPCF7) , the hyp in rice . The met in rice . Similar in rice  and histbidopsis . It has bidopsis . Furtherce yield .SET DOMAIN GENE 724 (SDG724) that promotes flowering by methylating the histone H3 lysine 36 (H3K36)  . The use (H3K36) . All theSince it has been established that the epigenetic mechanisms aid plants in stressed conditions , effortsTraditionally, the study of epigenetic changes has been based on DNA methylation profiling, methylation-sensitive amplification polymorphism (MSAP) , and bisHpaII and Msp1, that differ in their sensitivity to the methylation state of cytosine  in procedure but uses two isoschizomers cytosine . The advcytosine . HoweverThe bisulfite treatment is the next most widely used technology after MSAP. It is used for the detection of cytosine methylation both at specific loci and genome-wide levels. Bisulfite treatment of genomic DNA results in the deamination of unmethylated cytosines to uracil but methylated cytosines are not prone to this treatment and still read as C and can be distinguished from unmethylated cytosines . The dowMspI, which targets 5′CCGG3′ sites often found in promoters (The advent of NGS has made it possible to look at DNA methylation changes at the whole genome level using whole genome bisulfite sequencing (WGBS) . Howeverromoters . RRBS alromoters .The methods have also been developed to target particular sequences for DNA methylation analysis such as bisulfite padlock probes (BSPP) . AlthougThe utility of bisulfite sequencing in natural populations became possible after adapting the RADseq and GBS protocols to procure the methylation profile of reduced genome libraries for species where a reference genome is not available. Three RRBS protocols namely epiRAseq , bsRADseAnother technique for accurately studying DNA methylation has been through the deployment of antibodies developed against 5-methylcytidine . This mein vivo cross-linking of proteins to DNA with formaldehyde and then isolation of crosslinked chromatin from nuclei. The isolated chromatin is further sheared into smaller fragments followed by immunoprecipitation with the help of specific antibodies. The isolated DNA with this procedure can be investigated with specific primers or combined with high throughput NGS creating powerful tools like ChIP-Seq  can be used to identify protein modifications and DNA-protein interactions. It involves ChIP-Seq . VariousChIP-Seq  and ChlPhttps://www.pacb.com). Moreover, the expression of small RNAs can be studied through RNA-seq, RT-qPCRs northern blot, and in situ hybridization  is Sequel II/IIe Platform which includes methylation calling in native DNA (MCND) at the same time sequencing is done  species from various ecological communities, 2) model and non-model species, 3) species of contrasting habits, 4) species from extreme habitats, 5) species from varied climatic gradients, 6) species with asexual reproduction, and 7) invasive taxa have empirically pointed out the involvement of epigenetic phenomenon in facilitating adaptative ability in plants to cope with the changing environments. Epigenetic mechanisms, either individually or in accompaniment with genetic variation, represent long-term stress responses as compared to transient metabolic and physiological modifications. While short-term responses ensure survival, long-term genetic and epigenetic changes contribute toward plastic and adaptive responses of evolutionary significance. The incidence of phenotypic plasticity in genetically homogeneous populations strongly supports its epigenetic ontogeny, although the causal relationship between the stresses and plasticity still remains to be deciphered. The ability to modulate gene expression to produce stable and heritable alternate phenotypes that affect reproductive success strongly suggests the role of epigenetics in developing adaptability in natural populations in changing and stressful climates. Furthermore, epiallelic transgenerational diversity resulting in novel phenotypes seems to play a major role in helping plants to adapt to new environments by aiding them in expanding their habitat range.To sum up, evidence indicates that 1) epigenetic marks lead to phenotypic changes in plants and serve as important drivers to rapidly widen the total phenotypic plasticity and phenotypic variance of natural populations, 2) plant epigenomes show high diversity and rapidly generate responses to external stimuli, 3) epigenetic mechanisms help to provide the required variability for selection and expand the plants’ repertoire of adaptive responses 4) epi-variability creates epialleles, that serve as an additional tier of diversity for adaptive evolution 5) epigenetic variability is amenable to selection and may eventually become heritable, 6) epigenetic and genetic fields may complement each other for better execution of phenotypic plasticity and adaptive responses and 7) various biological processes, ecological factors and habitats strongly influence the epigenetic regulatory processes in natural populations . It has


Better predictive biomarkers remain to be discovered to help screen patients who can benefit the most from adjuvant treatment.What can we learn from the DYNAMIC study results? The benefits of adjuvant chemotherapy in stage II CRC remains controversial. Currently, adjuvant therapy is guided by high-risk clinicopathological features. However, the MOSAIC study suggested that high-risk clinicopathological features-guided adjuvant therapy has not been conclusively shown to improve overall survival (OS) in high-risk patients  colon or rectal adenocarcinoma. A total of 455 patients were randomly assigned to two arms in a 2:1 ratio: adjuvant therapy guided by ctDNA results (302 patients) or standard clinicopathological features (153 patients). Patients took ctDNA analysis at 4 or 7 weeks after surgery and those who with a positive result were treated with adjuvant chemotherapy. The primary efficacy endpoint was recurrence-free survival (RFS) at 2 years. Compared to the standard treatment group, patients in the ctDNA-guided group had a lower percentage of adjuvant chemotherapy treatment . The ctDNA-guided strategy was noninferior to standard treatment for 2-year RFS . The RFS rates at 3 years were similar in the ctDNA-guided group and the standard treatment group .3 ctDNA analysis for MRD detection is divided into two methods: tumor-informed analysis and tumor-agnostic analysis. Tumor-informed analysis requires antecedent molecular analysis of the primary tumor which obtained from targeted sequencing or whole-exome sequencing .4 This technique has the advantages of high analytical sensitivity and a low probability of false-positive results, but comes at the cost of longer turnaround times and additional tumor sequencing costs. Conversely, tumor-agnostic analysis, also known as tumor-uninformed analysis, takes advantage of fast turnaround time and conducted to seek genomic alterations and abnormal DNA methylation patterns known to appear in a specific tumor type .4 Jeanne Tie’s team conducted multiple retrospective analyses of ctDNA detection after surgery and adjuvant therapy for CRC, which confirmed a strong correlation between ctDNA positivity and disease recurrence after surgery and adjuvant therapy. In the ASCO GI 2022, the nonrandomized observational GALAXY study in CIRCULATE-Japan, the largest MRD study to date, confirmed that improved DFS rate was related with ctDNA dynamics in MRD-positive patients. ctDNA clearance rate at 12 weeks and 24 weeks as well as DFS rate at 6 months were significantly higher in adjuvant chemotherapy arm compared to non-adjuvant chemotherapy arm in MRD-positive patients. Ongoing randomized VEGA and ALTAIR studies in CIRCULATE-Japan will further optimize the ctDNA-guided adjuvant strategy.In recent years, ctDNA has received extensive attention as a promising biomarker. ctDNAs are DNA fragments derived from tumor cells and released into the blood that theoretically have the same genetic and epigenetic characteristics as the cancer cells from which they originated. Studies in recent years have demonstrated that ctDNA analysis can detect the presence of minimal residual disease (MRD) and can be used to predict postoperative tumor recurrence. The CSCO guidelines for CRC version 2022 mentioned that ctDNA monitoring in postoperative patients could be considered.How long before the ctDNA-guided adjuvant chemotherapy of stage II CRC can reach clinical applications from clinical trials? Two ctDNA tests save nearly half of patients from adjuvant chemotherapy, which is highly encouraging. To answer the question of how long before ctDNA can be routinely used in clinical practice, some pressing issues need to be resolved, including the optimization of detection methods (sensitivity and reproducibility), detection time points and results reporting time, and the ctDNA-guided adjuvant strategies of chemotherapy escalation and de-escalation.5 Therefore, the selection of detection time points and the result reporting time require attention. Before clinical application, standardized ctDNA assay characteristics and the reproducibility of testing results of ctDNA analysis should also be confirmed. Studies have shown that some low-shedding tumors or micrometastases in some specific anatomical sites may cause false-negative ctDNA test results.6 Whether patients with undetected preoperative ctDNA should be excluded from ctDNA-guided adjuvant therapy studies, and the additional cost of routine preoperative ctDNA detection, need to be further explored by researchers. The DYNAMIC study has not addressed which adjuvant chemotherapy regimens should be selected based on ctDNA results, and it is unclear whether modifying the start, duration or intensity of adjuvant therapy based on ctDNA results will positively impact patient outcomes. However, some of the ongoing randomized controlled clinical trials related to ctDNA in stage II CRC may give us some insights . This may have affected the efficacy of chemotherapy. In addition, as cell-free DNA from surgical trauma may still influence the ctDNA assay results up to 4 weeks, it was recommended for the assessment of ctDNA status 4–8 weeks after surgical resection.Increasing the accuracy of recurrence risk prediction after surgery for stage II CRC and the precision of screening patients who can benefit from adjuvant chemotherapy has always been topics that researchers are constantly exploring. Although there are still many questions that need to be verified by more randomized clinical trials, we have to congratulate the DYNAMIC study for its breakthrough and landmark results, which provide high-quality clinical supporting evidence for ctDNA-guided adjuvant chemotherapy strategies. In the future, the results of DYNAMIC study and other ongoing trials may change the level of guidelines recommended for ctDNA testing.


Science by Ritter et al. reported that endosomal sorting complexes required for transport (ESCRT) proteins mediate repair of lesions in the cell membrane caused by the pore-forming toxin perforin at sites of cytotoxic T cell engagement.1 Subsequent entry of granzymes and initiation of apoptosis of cancer cells is thus limited, leading to reduced sensitivity against T cell‑mediated cytotoxicity.1 Along these lines, this could constitute a new critical resistance mechanism for T cell-based treatment approaches.A recent research article published in 2 During granule-mediated cytotoxicity, cell lysis is induced after tumor antigen recognition by secretion of cytotoxic pore-forming perforin. Subsequent osmotic flux through the membrane pores leads to cell swelling and necrotic cell death.2 Perforin enables the uptake of cell death inducing granzymes through diffusion or endocytosis.2 Interestingly, recent studies have suggested that cellular killing mediated by such enzymes is an additive multi-step process, whereby multiple rounds of granule secretion are necessary to induce apoptosis of the target cell.3 This however can be compensated by target cells themselves by repairing such pores in the cellular membrane.1Cellular immunotherapy has gained more relevance in recent years regarding the treatment of various cancers. This therapeutic approach takes advantage of tumor cell killing mediated by cytotoxic lymphocytes including cytotoxic T cells and natural killer (NK) cells. During an immune response, cytotoxic T cells and NK cells can induce apoptosis of target cells through death receptor-mediated cytotoxicity, involving the death receptor ligands, tumor necrosis factor-related apoptosis-inducing ligand and Fas ligand, or by granule-mediated cytotoxicity, involving perforin and granzymes.5 Previous studies have shown that membrane pore formation leads to an increase in calcium influx, as well as a drop in membrane tension, which in return triggers the recruitment of the ESCRT machinery to the site of membrane wounding.5 However the contribution of the ESCRT machinery to tumorigenesis is still controversial, as multiple experimental approaches in different models revealed partially divergent conclusions.6 Besides the defective transmembrane protein homeostasis due to membrane pore formation and the consequent effect on cell signaling, also cytokinetic defects lead to a rather tumorigenic potential.6 It was shown that ESCRT-mutated cells fail to downregulate and degrade cell-surface signaling receptors, leading to a prolonged receptor signaling and hyperproliferation in tissues.7 Contrarily, increased apoptosis and cell-cycle defects might counteract this hyperproliferation.7 Furthermore it has been shown that the ESCRT machinery is involved in exosome generation and autophagy, whereby ESCRT mutated cells show defects in such processes.6 Ultimately, better understanding of these repair mechanisms in the context of cancer development will allow to specifically inhibit these processes and by such prevent tumor cell escape.The ESCRT machinery plays a key role during extracellular budding events necessary for repair of perforin-induced pores.1 Live-cell microscopy of cytotoxic T cells derived from OT-1 mice, recognizing the ovalbumin peptide SIINFEKL on target cells, were used to model this situation. For better visualization, parts of the ESCRT machinery expressed enhanced green fluorescent protein (EGFP) tags. Interaction of cytotoxic T cells with target cells was analyzed in presence of the cell impermeable fluorogenic dye propidium iodide (PI), which, after diffusion through perforin pores, can bind and illuminate nucleic acids in the nucleus and cytosol. The researchers demonstrated that the EGFP-tagged ESCRT proteins were recruited to the site of T cell-target cell contact. To further assess the immunological synapse immediately after secretion of lytic granules, highly sophisticated microscopy techniques were applied. It could be confirmed that the ESCRT machinery is located within the synapse at the site of cytotoxic T cell killing. Therefore, the accumulation of ESCRT proteins near membrane pores is linked to perforin secretion.Here, Ritter et al. investigated the recruitment of the ESCRT machinery to sites of cytotoxic T cell engagement after perforin secretion.In a next step, the authors could show that the cytotoxic capacity of OT-1 T cells was enhanced in ESCRT-inhibited target cells, which were more sensitive to perforin- and granzyme-mediated killing. Delays in pore repair also increased uptake of granzyme B. Thus, ESCRT-inhibited target cells were more sensitive to the cytotoxic effect of perforin and granzyme B. A schematic illustration of the role of membrane repair by the ESCRT machinery for resistance of tumor cells to cytotoxic T cell killing is summarized in Fig. 1 Further application of a human assisted machine learning-computer vision platform allowed for the visualization of T cell-mediated killing, enabling subcellular resolution of the immunological synapse. Through the biological impairment of the ESCRT function, the authors could demonstrate an increased T cell killing capacity of target cells.1 This showcases the importance of the ESCRT machinery for cancer cells to escape T cell-mediated apoptosis, which constitutes a hallmark of cancer. It is important to point out however, that the demonstrated mechanism is restricted to murine cell models and artificial model antigen systems. Further investigations are required to identify if the ESCRT machinery holds a similar pivotal role in human cancer and immune cells, a fundamental question for therapeutic utilization.With this work, the authors have provided detailed spatial and temporal information on membrane repair via the ESCRT machinery by using highly advanced imaging technologies.1 However, a CRISPR knockout or overexpression of a dominant-negative kinase allele impairing the ESCRT function were used for ESCRT inhibition, which are not reasonably feasible for clinical application. To further enhance the translational potential of these findings, the development of specific small-molecule compounds to target membrane-repair pathways would be beneficial. Regarding the fact that membrane-repair mechanisms also exist in non-malignant cells, potential short and long-term side effects of ESCRT-targeted therapy in cancer treatment would need further evaluation. As the multi-subunit ESCRT machinery is composed of numerous proteins, it is unclear whether individual components of the machinery affect tumor cell survival in different manners in comparison to healthy cells.6 Such discriminatory capacity would be pivotal for the application of ESCRT-inhibition.This work illustrated increased target cell killing when inhibiting the ESCRT machinery.The field of immunotherapy is largely focusing on optimization of anti-cancer toxicity mediated by immune cells, such as cytotoxic T cells and NK cells. Despite major advances, immunotherapy does not benefit most patients in the long run, whereby underlying causes are frequently unclear and contrast expectations from preclinical work. The concept of using T cell-mediated cytotoxicity for cancer treatment is indeed supported by the clinical application of immune checkpoint inhibitors, bispecific antibodies, or genetically modified T cells. Along these lines, any resistance mechanism preventing T cell action, will ultimately hamper therapeutic function. Thus, targeting the ESCRT machinery to render cancer cells more susceptible to perforin-mediated cell killing might facilitate a new targeting approach to extend the damage caused by individual cytotoxic T cells. To our knowledge the combination of T cell-based immunotherapy and interference with the ESCRT machinery in such manner is not yet thoroughly investigated.Besides identification of more potential links between cancer and the ESCRT machinery, the complete role of ESCRT inhibition for cancer is still not fully understood. A critical need to understand resistance mechanisms to key immunological approaches remains. The study presented by Ritter et al. provides valuable insights into the membrane repair mechanisms at the immunological synapse, which could be used to enhance efficacy of T cell-based immunotherapy, warranting further investigations and potential clinical translation of the concept.


Sepsis has complex, time-sensitive pathophysiology and important phenotypic subgroups. The objective of this study was to use machine learning analyses of blood and urine biomarker profiles to elucidate the pathophysiologic signatures of subgroups of surgical sepsis patients.This prospective cohort study included 243 surgical sepsis patients admitted to a quaternary care center between January 2015 and June 2017. We applied hierarchical clustering to clinical variables and 42 blood and urine biomarkers to identify phenotypic subgroups in a development cohort. Clinical characteristics and short-term and long-term outcomes were compared between clusters. A naїve Bayes classifier predicted cluster labels in a validation cohort.n = 18) and one cluster characterized by recovery . Cluster I was associated with higher Acute Physiologic Assessment and Chronic Health Evaluation II  and SOFA scores , greater prevalence of chronic cardiovascular and renal disease (P < 0.001) and septic shock . Cluster I had higher mortality within 14 d of sepsis onset  and within 1 y , and higher incidence of chronic critical illness . The Bayes classifier achieved 95% accuracy and identified two clusters that were similar to development cohort clusters.The development cohort contained one cluster characterized by early organ dysfunction (cluster I, Machine learning analyses of clinical and biomarker variables identified an early organ dysfunction sepsis phenotype characterized by inflammation, renal dysfunction, endotheliopathy, and immunosuppression, as well as poor short-term and long-term clinical outcomes. Sepsis is a broad syndrome defined and classified by clinical criteria not necessarily reflective of underlying pathological processes, and most sepsis drugtrials have failed. Given the heterogeneity of sepsis, identifying subgroups of sepsis patients with unique pathophysiological signatures and treatment responses may be necessary to develop successful targeted therapies.6Sepsis, a dysregulated host response to infection leading to life-threatening organ dysfunction, is responsible for more than $20 billion in annual US healthcare expenditures and is associated with 18%–28% mortality.8 Unsupervised machine learning has been used previously to identify subgroups in other complex syndromes, such as acute respiratory distress syndrome (ARDS).9 Given the complex pathophysiology of sepsis and heterogeneity among sepsis patients, a battery of physiologic measurements of organ dysfunction obtained during routine clinical care and blood and urine metabolic and immunologic biomarkers are available to allow early identification of patients at risk for poor short-term and long-term outcomes.6 Illness severity scoring systems such as the Sequential Organ Failure Assessment (SOFA) and Acute Physiologic Assessment and Chronic Health Evaluation II (APACHE II) scores and inflammatory and immunosuppressive biomarkers can forecast mortality within 24 h of sepsis onset and thus may help predict the underlying pathophysiology of various sepsis phenotypes.14 Validating the utility and accuracy of an unsupervised learning model on routinely available clinical variables may provide a basis for future studies differentiating sepsis management efficacy based on phenotype.Grouping routinely collected clinical variables and biomarkers from sepsis patients with clustering, an unsupervised machine learning technique, will allow the identification of entirely novel phenotypes.We used machine learning analyses of clinical variables, as well as blood and urine biomarker profiles to identify phenotypic subgroups in a prospective, longitudinal cohort of surgical sepsis patients. Our objective was to elucidate the pathophysiologic signatures of phenotypic subgroups, with the rationale that a deeper understanding of sepsis phenotypes can inform the development of targeted therapies.NCT02276066) prospective longitudinal study cohort of surgical patients with sepsis. Study protocols were finalized,15 and ethics approval was obtained from the University of Florida Institutional Review Board (IRB201400611) prior to patient enrollment. All study participants or their surrogate decision-maker provided written informed consent. Inclusion criteria for the sepsis cohort were admission to the surgical intensive care unit (ICU), age 18 y or greater, and a clinical diagnosis of sepsis by attending intensivist in surgical ICU with subsequent initiation of the computerized sepsis protocol.16 Patients with pre-existing immunosuppression and those with advanced liver or heart disease were excluded to avoid their potentially confounding effects on the natural history of sepsis and the subsequent development of chronic critical illness.15 The final sepsis diagnosis was clinically adjudicated by investigators during weekly adjudication meetings according to consensus criteria.17 Among 243 patients meeting enrollment criteria with sufficient biomarker data, 157 were included in the development cohort and 86 were included in the validation cohort. Patients were allocated to development or validation cohorts, based on split in time, as illustrated in Clinicaltrials.gov (NCT02276417).Sepsis patients were prospectively recruited between January 2015 and June 2017 from the Persistent Inflammation and Immunosuppression in Sepsis  and expected distances (Dref) is displayed in A resampling approach was used to identify clusters by cutting the dendrogram as previously described.21 in which the cluster analysis was repeated by removing one of the 44 biomarkers at a time, and then hierarchical cluster analysis and selection was performed using the same method as in the primary analysis of two clusters with the significance level of 0.004. Concordance between primary analysis clusters was assessed by Spearman correlations of the cluster labels, summarized in The dependency of clusters on all variables was tested using ‘leave one feature out’ replication ,21 in whA naїve Bayes classifier was trained on the development cohort and used to assign patients to derived clusters in the validation cohort. A naїve Bayes classifier is a model based upon the Bayes theorem obtained by using a set of discriminant functions and estimating relevant probabilities from a training set. In this case, the classifier uses the set of pre-existing cluster prevalence and the presence of each biomarker signature to predict the cluster membership of new patients.23 We compared clinical characteristics and outcomes between the two main clusters using Fisher’s exact test for categorical variables and Student’s t-test or Wilcoxon rank-sum test for continuous variables as appropriate.For each patient, we created a biomarker mosaic using the gene expression dynamics inspector (GEDI) that creates the biomarker mosaics using a self-organizing map algorithm.24 Other outcomes included hospital-free, ICU-free, and mechanical ventilation-free days within 28 d of sepsis onset. Exact dates and times were used to calculate the hospital length of stay, ICU length of stay, and duration of mechanical ventilation. Hospital-free, ICU-free, mechanical ventilation-free, and organ dysfunction free-days within 28 d of sepsis onset were calculated by subtracting the number of days for each outcome from the lesser of 28 d or the number of days between sepsis onset and death. The Social Security Death Index database was used to confirm death dates and obtain death dates for patients who were lost to follow-up.All outcomes were compared among clusters and between cohorts. The primary outcomes were in-hospital and 1-year mortality. Chronic critical illness (CCI) was defined as an ICU length of stay of 14 d or more with evidence of persistent organ dysfunction, determined using components of the SOFA score . Non-CCI patients were those who did not meet the criteria for CCI or early death (death within 14 d of sepsis onset).P < 0.005, Cluster analysis identified two main clusters  and five statistically distinct clusters (I (A), I B), II (A), II (B), II (C)) ,versus 17%, P < 0.001) and higher median APACHE II scores  and SOFA scores . Higher APACHE II scores in cluster I was primarily attributable to higher acute physiology scores  rather than the age or chronic health scores. Two chronic diseases differentiated between clusters: cardiovascular and renal diseases (P < 0.001). Forty-seven percent of patients in cluster I had chronic kidney disease, and 39% had congestive heart failure. In cluster II, 11% patients had chronic kidney disease, and 14% had congestive heart failure. There were no significant differences in age, gender, race, body mass index, or smoking history between clusters.versus 2%, P = 0.001), during admission , and within 1 y . Cluster I had a longer median ICU length of stay  and fewer ICU-free days . The incidence of acute kidney injury (AKI) was nearly two-fold higher in cluster I . Thirty-nine percent of the cluster I patients required renal replacement therapy during admission, while only 5% of cluster II patients required renal replacement therapy (P < 0.001). Overall, 72% of the cluster I patients suffered early death or developed chronic critical illness, compared with 32% of cluster II patients.Associations between cluster biomarker signatures and clinical outcomes are shown in Visual cluster representation using principal component analysis shows that clusters are separated in biomarker space . Biomarkversus 0.96 mg/dL, P < 0.001), cystatin C (2.3 versus 0.8 mg/dL P < 0.001), and blood urea nitrogen . Endotheliopathy may have contributed, as angiopoietin 2 levels were 3–4 times higher in cluster I (27 versus 8 ng/mL P < 0.001), and fms-like tyrosine kinase levels were 2–3 times higher in cluster I . Other renal and acid-base parameters such as anion gap, lactate, Nephrocheck scores, and fluid overload volumes were significantly worse in cluster I .Cluster I demonstrated early renal dysfunction. Cluster I had higher median serum creatinine  and total Acute Physiology Scores  compared with cluster II. Cluster I also had higher SOFA scores at sepsis onset  with higher cardiovascular  and renal  scores at sepsis onset. These were associated with poor short-term and long-term outcomes. Cluster I (n = 29) had higher hospital mortality (P = 0.03) and 1-year mortality (P < 0.001) and had more than twice as many ICU days (P = 0.03). The incidence of early death or chronic critical illness was significantly higher among cluster I patients .To determine whether clusters with similar characteristics could be identified in an independent validation cohort, we trained a naїve Bayes classifier on the development cohort to predict cluster labels in the validation cohort of 86 patients. Some biomarker values  were not available for patients in the validation cohort. As the derivation model was highly sensitive to the absence of singular features from leave-one-out analysis, a naїve Bayes classification model, which is more robust to missing variables, was used to minimize the impact of missing data. The classifier achieved 95% accuracy for leave-one-feature out cross-validation in the development cohort. The classifier was then applied to the validation cohort. The classifier assigned 29 validation cohort patients to cluster I and 57 patients to cluster II. Compared with development clusters, the same patterns of clinical characteristics, outcomes, and biomarker values were observed see , and E5.versus 17%, P < 0.01), different primary sources for sepsis (P = 0.02), and higher maximum renal SOFA scores within 24 h of sepsis onset . The validation cohort had higher in-hospital mortality  and a greater burden of kidney disease and magnitude of AKI. Consistent with these clinical outcomes, the validation cohort had a biomarker profile consistent with kidney disease, including higher serum creatinine and cystatin C. It also featured differences in endothelial dysfunction evident by higher angiopoietin 2 and fms-like tyrosine kinase, a proinflammatory immunosuppressed state manifested as higher tumor necrosis factor-alpha, interferon-gamma, soluble programmed death-ligand 1. Therefore, the identification of early dysfunctional and recovery clusters is reproducible in cohorts that may have different clinical characteristics and biomarker profiles compared to the development cohort.Characteristics of the development and validation cohorts were compared in order to determine if clusters identified in the development cohort were identifiable in an independent validation cohort due to the cohorts being similar , and E8.Using 42 blood and urine biomarkers and routinely collected clinical data, we identified two major clusters of patients with surgical sepsis. Inflammatory, renal, and endothelial biomarkers that differentiated cluster I from cluster II included interleukin 8, tumor necrosis factor-alpha, serum creatinine, cystatin C, blood urea nitrogen, anion gap, fluid overload, lactate, angiopoietin 2, and fms-like tyrosine kinase. These biomarkers contributed significantly to differences in composite biomarker mosaics in both clusters, suggesting that systemic inflammation, renal dysfunction, and endotheliopathy were primary drivers of cluster differentiation. Leave-one-out analysis suggested that all biomarkers contributed significantly to primary cluster assignment because excluding any of these biomarkers would result in different cluster assignments. Although agglomerative hierarchical clustering is dependent on data dimensionality , making it more likely that leave-one-out analysis produces different results as a statistical artifact, we found that a naïve Bayes classifier, which is more robust to dimensional changes, was able to successfully reproduce similar clusters on a validation cohort, suggesting that biomarker profiles reflect underlying septic pathology.et al. (2018),25 in which a similar panel of ten proteins in a prospective cohort of 85 patients predicted sepsis with cardiovascular dysfunction. In addition, cluster I patients had an immunosuppressive phenotype manifest as increased interferon gamma-inducible protein 10 and soluble programmed death-ligand 1. Prior work has not consistently demonstrated concomitant inflammation and immunosuppression, as was observed in our study. In an analysis of peripheral blood leukocyte gene expression among patients with sepsis due to pneumonia, Davenport et al. (2016)26 found that inflammation and immunosuppression occur in separate, distinct sepsis response signatures. However, a meta-analysis of 949 sepsis patients using hierarchical clustering demonstrated significant inflammation and immunosuppression in early sepsis, similar to results from our study.27Consistent with these biomarker profiles, we observed a greater prevalence of chronic renal and cardiovascular disease in cluster I. Furthermore, cluster I had severe early multiorgan failure with a disproportionately high incidence of cardiovascular and renal disease. These results are consistent with a previous study by Garcia-Obregon et al. (2015)7 Cluster I had higher Charlson comorbidity indices, suggesting that they had a greater chronic disease burden prior to the onset of sepsis, but the difference in the acute physiological score between clusters I and II was of greater magnitude. Cluster I had a higher incidence of septic shock, which could be explained by the cardiovascular physiological derangement. Cluster I had worse clinical outcomes with higher early and 1-year mortality rates. The 5% in-hospital mortality and 20% 1-year mortality rates observed in Cluster II are lower than the mortality rates observed in other studies of contemporary populations with sepsis; this may be attributable to the early preserved hemostasis biomarker profile and phenotype observed in Cluster II.The unique biologic signatures of clusters I and II corresponded to different illness severity and clinical outcomes. The acute physiology component of the APACHE score differentiated between clusters, similar to results obtained by Knox et al. (2019)6 demonstrated that there are subgroups of sepsis patients with unique responses to treatments, offering compelling evidence that broadly applied monotherapies for sepsis and septic shock are likely to continue to fail. Our study does not address the hypothesis that different sepsis phenotypes have different treatment responses but supports the hypothesis that clustering analysis can identify hidden patterns and structures within sepsis patient data, identifying phenotypes with distinct short-term and long-term outcomes. Also, clustering analysis may help improve the performance of prediction and risk-stratification for these outcomes by building separate prediction models for each cluster. These observations were made in a prospective study of a relatively small group of patients, suggesting that it is feasible to perform these clustering techniques in clinical settings.High heterogeneity in clinical and biomarker characteristics across phenotypes among sepsis patients may provide insight regarding failed sepsis drug trials. Seymour 28 Calfee et al. (2018)9 performed a secondary, latent class analysis to identify acute respiratory distress syndrome subphenotypes in a multicenter, randomized controlled trial database. This analysis identified distinct hyperinflammatory and hypoinflammatory phenotypes with different biological features and clinical outcomes. Perhaps more importantly, the administration of simvastatin conferred a survival advantage that was specific to the hyperinflammatory group, suggesting that the identification of phenotypes can guide patient-specific treatments. Similarly, Antcliffe et al. (2019)29 performed a secondary analysis of a randomized clinical trial database of sepsis patients to determine whether phenotypes of sepsis patients have unique responses to corticosteroid administration. Patients with an immunocompetent phenotype had increased mortality after corticosteroid administration compared with placebo. These findings suggest that phenotyping techniques not only elucidate underlying pathophysiology but are also associated with unique treatment responses. Machine learning techniques may be ideal for representing complex disease syndromes like sepsis and acute respiratory distress syndrome because their underlying pathophysiology is beyond the reach of additive and linear statistical approaches.30We demonstrate that the application of machine learning analytic methods to a battery of routine clinical, physiologic measurements of organ dysfunction in concert with blood and urine biomarkers of renal function, tissue perfusion, inflammation, and immunosuppression can identify surgical sepsis phenotypes. Although these are done in a small cohort as a proof-of-concept, these findings demonstrate the potential benefit of machine learning-derived clusters of sepsis in treatment. For example, as we utilize routine biomarkers, all procured within hours of ICU admission, machine learning may rapidly identify septic patients at high risk of AKI requiring aggressive resuscitation or renal replacement therapy. Furthermore, as Cluster I is defined by an inflammatory phenotype, these patients may benefit from anti-inflammatory therapy, which can be administered quickly and potentially improve surgical outcomes. Similar approaches have demonstrated efficacy for phenotyping other critical illnesses, suggesting broader implications for understanding the host response to critical illness.32 Third, we use a single time point, rather than longitudinal data. Although we focus on capturing patient profiles shortly after sepsis diagnosis and exclude patients with advanced liver or heart disease, our approach did not account for the evolution of sepsis prior to and after ICU admission. Thus, it is possible that our clusters represent differences in the evolution of sepsis, with Cluster II representing a resolved state. However, given that our study focuses on exploring the ability of machine learning to derive sepsis phenotypes, longitudinal analyses were deemed out of scope. Finally, we did not perform an external validation of our derived clusters using a large dataset, and this should be considered in the interpretation of our findings.Our study has several limitations. First, we used a small data sample of surgical patients from a single institution, limiting the power and generalizability of these findings. In addition, perhaps the greatest value of phenotyping is the ability to assess responses to targeted therapies. Accomplishing this objective would require the application of biomarker signatures to data from randomized controlled trials, which is feasible but beyond the scope of this study. Second, the method we used to evaluate the importance of each biomarker to the clusters  was susceptible to false positives. The merge step of agglomerative clustering cannot be reversed and is dependent on the distance, and subsequently the dimensionality of the data. By reducing the dimensions of the dataset in leave-one-out analysis, reperforming agglomerative clustering is more likely to find different results. Despite the reliance of leave-one-out on statistical methods, we still report these findings (1) to show the magnitude of dependence for each variable and (2) to account for the possibility that some biomarkers may be unimportant even given the clustering method’s limitations. Likewise, we used a naїve Bayes classifier to predict clusters in our validation cohort, which makes an assumption of data independence. Although some collected variables, such as BUN and Cr, do not satisfy the independence assumption, naїve Bayes was chosen for its simplicity and outperformance of other alternatives even in some cases where the independence assumption is not met.Machine learning analyses of clinical and biomarker variables identified an early organ dysfunction sepsis phenotype characterized by inflammation, renal dysfunction, endotheliopathy, and immunosuppression, as well as poor short-term and long-term clinical outcomes. These efforts to elucidate the pathophysiologic signatures of phenotypic subgroups may provide a deeper understanding of sepsis phenotypes that can inform the development of targeted therapies.12345678910figsE4figsE3figsE2figsE1


Myocardial injury as induced by myocardial infarction results in tissue ischemia, which critically incepts cardiomyocyte death. Endothelial cells play a crucial role in restoring oxygen and nutrient supply to the heart. Latest advances in single-cell multi-omics, together with genetic lineage tracing, reveal a transcriptional and phenotypical adaptation to the injured microenvironment, which includes alterations in metabolic, mesenchymal, hematopoietic and pro-inflammatory signatures. The extent of transition in mesenchymal or hematopoietic cell lineages is still debated, but it is clear that several of the adaptive phenotypical changes are transient and endothelial cells revert back to a naïve cell state after resolution of injury responses. This resilience of endothelial cells to acute stress responses is important for preventing chronic dysfunction. Here, we summarize how endothelial cells adjust to injury and how this dynamic response contributes to repair and regeneration. We will highlight intrinsic and microenvironmental factors that contribute to endothelial cell resilience and may be targetable to maintain a functionally active, healthy microcirculation. Fibroblasts are known to orchestra cardiomyocyte function, by providing structural scaffold and electromechanical support , 33. ResThe era of single-cell multi-omics significantly broadens our understanding of how dynamical stress responses change chromatin and transcriptional landscapes and induced a reconsideration of how we define cellular heterogeneity. Interestingly, studies of cardiovascular tissues rarely identified entirely new cell populations but highlight the presence of multiple states of cellular identities within the heart upon injury , 49, 65.The ability of ECs to transiently respond, adapt but also recover maintenance functions strongly impacts long-term cardiac health. Its failure leads to endothelial dysfunction which impairs heart functions . Here, wMyocardial infarction (MI) results in impaired tissue functionality, which in the long term results in chronic heart failure. Mechanistically, incomplete regeneration of infarcted areas leads to accumulation of fibrotic tissue and microvascular dysfunction. While most patients survive the acute hospitalization , 129, 20Restriction of oxygen and nutrient supply after myocardial infarction represents a challenge for all cells in the damaged tissue, leading either to cell death or induction of survival genes. Cell death is induced in cardiomyocytes and interstitial cells within 30 min post infarction. Cells close to the endocardium are more prone to injury, leading to propagation of cell death toward outer regions at the epicardium . MoreoveLoss of oxygen not only induces cell death, but activates and stabilizes the transcription factor HIF-1A, which induces survival genes and controls metabolic adaptation. Induction of HIF-1A augments glycolytic metabolism and leads to higher expression of angiogenic factors, such as ANGPT1 and ANGPT2 or VEGF , 126. ThInflammation starts when cells of the innate immune system recognize danger-associated patterns, which lead to activation of tissue-resident macrophages and neutrophils and production of pro-inflammatory cytokines and chemokines, such as TGF-β, IL-1β, IL-6, TNF-α. Thereby, damage-induced matrix degradation, production of reactive oxygen species (ROS) and complement activation are the three major drivers in the initiation of immune responses , 63. IntThe endothelium responds to the pro-inflammatory environment by supporting immune cell infiltration specifically via degranulation of Weibel–Palade bodies containing P-Selectin  and by iRecently, ECs were discussed to facilitate immune modulation beyond these classical functions in innate immunity. Evidence of a plastic conversion toward “immune cell-like” phenotype (EndICLT) has been shown on single-cell level when comparing static and disturbed flow conditions in a mouse carotid ligation model . This prThe endothelial and hematopoietic fate are tightly linked. In evolution, the emergence of an EC monolayer is hypothesized to occur from specialized circulating blood cells, so called amoebocytes, which epithelized the basal membrane and form a primitive blood tissue barrier . Higher EC may not only partially transition to a hematopoietic fate, but myeloid cells may acquire endothelial cell features. Here, the presence of circulating endothelial cells progenitors (cEPCs) has been debated for long time and hint to interesting links between the endothelial and hematopoietic system , 127. Ore novo formation of mitochondria was associated with an anti-inflammatory phenotype, and suppressed activity of TNF-α and NFκB [The heart requires high amounts of energy and oxygen for ATP production. In fact, no other organ consumes metabolically so much energy as the heart. In homeostasis, the majority of ATP is generated via mitochondrial oxidative phosphorylation in cardiomyocytes. While the heart utilizes nearly all kind of energy sources, fatty acids are by far the most frequent . Cardiacand NFκB . HoweverReactivation of developmental pathways can induce phenotypical changes and promote endothelial-to-mesenchymal transition (EndMT). This process has initially been described during valve formation, when ECs acquire mesenchymal characteristics by following TGF-β gradients in the cardiac jelly , 83. HerHow is this phenotype initiated? Several cytokines in combination with hypoxia initiate EndMA. TGF-β, produced from cells of the innate immune system in response to tissue damage is one of the most potent inducers. Cardiac macrophages selectively express MMP14, which activates latent TGF-β and induce paracrine signaling in ECs post injury . AdditioECs undergoing mesenchymal activation after infarction were more glycolytic than naïve ECs, which maintain oxidative phosphorylation and fatty acid transport . In fact2S are well known to induce an anti-inflammatory phenotype in ECs. This suggests that ECs, which are close to the injury site, are characterized by high level of ROS, glycolysis and mitochondrial dysfunction and share an inflammatory phenotype, which favors activation of TGF-β and hence EndMT. It seems reasonable that prolonged inflammation could lead to continuous transition to a complete mesenchymal phenotype and eventually fibrosis. Likewise, the reversible nature of EndMA after myocardial infarction might be a consequence of high endothelial resilience against inflammation and an active conversion to a naïve state. However, how ECs can switch back to the endothelial phenotype is poorly understood.Another mediator of EndMT is mitochondrial dysfunction and oxidative stress. Elevated levels of ROS promote not only the activation of NFκB, but also suppress KLF4 which deregulates endothelial identity and promotes EndMT , 107. CoSingle cell sequencing studies have consistently reported the expression of cardiomyocyte-specific gene expression signatures in ECs , 124. WhNeovascularization requires either sprouting angiogenesis of existing vessels or de novo formation of new blood vessels in the damaged area. Several cell types and origins have been discussed to infiltrate and expand in the tissue upon injury and contribute to new blood vessel formation.Lineage tracing in early post-natal stages revealed significant contribution of endocardial cells to coronary endothelial cells . Hence, Fibroblasts have also been suggested to acquire endothelial cell phenotypes by undergoing mesenchymal-to-endothelial transition, thereby potentially contributing to de novo vascularization of the infarct zone . HoweverSingle cell sequencing of cardiac endothelial cells described multiple novel subsets of cells, but tissue-resident cardiac microvascular progenitor populations have not been described , 96, 124+ CD200+ ECs were found in large vessels in multiple organs, but not in capillaries. Similarly, rare subpopulations of ECs in the damaged aorta show hyper-proliferative activity associated with increased levels of the bZIP activating transcription factors [CD157 (BST1) was described to mark such a specialized “stem-cell” endothelial subset . Notably factors . ATF3 an factors . WhetherSeveral studies suggest that cardiac microvascular ECs undergo plastic changes upon myocardial infarction and vascularize the infarct area. This includes degradation of the basal membrane, elongation toward a VEGF gradient and stabilization of the new vessel by pericytes. Based on the local environmental cues and the given vascular bed, two ways of sprouting angiogenesis appear to exist: random/stochastic contribution of ECs to the sprouts or the formation of truly new vessels by specialized hyper-proliferative cells which clonally expand. In contrast to post-natal retina angiogenesis, which was shown to occur in a stochastic manner, a clonal expansion of cardiac endothelial cells around the infarct area was noted , 78. Intcxcl12 and cxcr4 [To understand how efficient vessel growth can occur after injury and how EC resilience is controlled, we can take a look at fully regenerative species, such as zebrafish or at mice during post-natal stages. In zebrafish, re-establishment of the vascular network after cryoinjury occurs quickly but superficial, which may promote rapid oxygen and nutrient supply to the damaged tissue and additionally foster cardiomyocyte proliferation by 3D scaffold guidance . Importand cxcr4 . Increasnd cxcr4 , 79. Intnd cxcr4 .+) venous EC populations but higher expression of capillary EC markers were found, which could reflect the immature nature of neonatal blood vessels [While the regenerative capacity is tightly linked to reactivation of the cell cycle in cardiomyocytes, novel studies using single-cell sequencing revealed important differences in the injury response of endothelial cells to infarction in post-natal and adult mice. Compared to adult hearts, less lipocalin-2 (LCN2 vessels . In addi vessels . It is cresilience implies long-term successful adaptations to strong stressors or trauma [Translated on human behavior, the term r trauma . This phIs there a way to increase resistance and resilience? Studies of the last decades have shown that ischemic pre-conditioning, an experimental technique with periodically repeated brief ischemia phases followed by extended reperfusion phases, prevent apoptosis and inflammatory responses , 98. In As shown in different animal models, ischemia in the hind limb also led to beneficial and survival-prolonging effects in subsequent cardiac ischemia . This reLikewise, physical exercise has been described to directly modulate endothelial function by modulating NO bioavailability  and decrIt may be interesting to gain more insights in long-term cardioprotective and the consequences of pre-conditioning mechanisms or exercise on EC plasticity and clonal expansion.Myocardial injury has a major impact on EC phenotypes resulting not only in metabolic adaptation but also in partial fate changes. While it is extensively studied how the injury response is regulated, little is known regarding the resolution of the adaptive responses. Certainly, this is of major importance, since a chronically inflamed endothelium would result in microcirculatory dysfunction, a key pathophysiological component of chronic heart failure. While low levels of ROS act as signaling molecules and may allow temporal adaptation, chronic elevation of ROS, together with reduced NO bioavailability is seen as an early pathological step toward long-term dysfunction . OxidatiHow is the transition back to a “healthy” endothelium regulated? One possibility is that the transition is mediated by the normalization of stimuli, which induced the changes in endothelial identity. Thus, a reduction of inflammatory cytokines and the restoration of oxygen supply resulting in a normoxic environment may be sufficient to de-activate ECs allowing the return to the original maintenance program and identify. This assumption is supported by in vitro findings, showing that the sole withdrawal of TGF-β reverted EndMT . The limHowever, the resolution of inflammation may not occur efficiently during aging or in the presence of risk factors. Here, transient inflammation might turn into chronic inflammation a condition referred to as “inflammaging”. Moreover, it is unclear whether epigenetic changes that may occur during inflammatory activation can be fully be restored. Certainly, future work is needed to understand the transient nature of endothelial plasticity.


Objective: This study aimed to determine the intravitreal concentration of VEGF in eyes with PDR and to evaluate the effects of previous PRP on its level.Methods: It was a cross-sectional study performed at the Clinical Centre University. It included 90 eyes surgically treated with PPV, divided into three groups, group A - patients with PDR with previous PRP, group B - patients with PDR without previous PRP, and group C - PPV performed due to the indication unrelated to diabetes. A vitreous sample was obtained during PPV, and the VEGF concentration was determined using an Enzyme-linked immunosorbent assay test (ELISA). Shapiro-Wilk, nonparametric tests Kruskal-Wallis, Mann-Whithney U test, ANOVA and Spearman’s correlation test were used.Results: The highest vitreous VEGF concentration was in group B - 972.96 (743.33-1149.13) and was higher than in group A - 69.22 (37.33-225.15) and in group C - 19.93 (1.15-32.17) (p<0.001). There was a positive correlation between VEGF vitreous concentration and glucose level in group A patients . Conclusion: As a treatment before PPV surgery, PRP showed to be effective in the reduction of VEGF levels, which also highlighted a decrease in complications during and postoperatively. Abbreviations: DRS = Diabetic Retinopathy Study, PDR = proliferative diabetic retinopathy, VEGF = vascular endothelial growth factor, PRP = pan-retinal photocoagulation, PPV = pars plana vitrectomy, HbA1c = glycosylated hemoglobin, ETDRS = Early treatment diabetic retinopathy study, ESR = erythrocyte sedimentation rate, BCVA = best corrected visual acuity, OCT = optical coherent tomography, ILM = internal limiting membrane, PHACO = phacoemulsification, IOL = intraocular lens, ELISA = Enzyme-linked immunosorbent assay test, AUC = area under the curve, DME = diabetic macular oedema, TDR = tractional retinal detachment, VMT = vitreomacular traction The primary event in PDR is abnormal vasoproliferation caused by an excess production of mitogen, vascular endothelial growth factor (VEGF) [2], with VEGF-A as a key component with the strongest angiogenic stimulation [3]. Retinal hypoxia induces the process of angiogenesis, with VEGF as a main angiogenic factor [4] causing a stimulus for proliferation and migration of endothelial cells along with microvascular permeability and new vessel survival [5]. According to The Diabetic Retinopathy Study (DRS), half of the untreated eyes with proliferative diabetic retinopathy (PDR) result in severe vision-threatening sequelae [6]. Pars plana vitrectomy (PPV) is a surgical treatment of PDR when other treatment options are either insufficient or unsuccessful. Pan-retinal photocoagulation (PRP) was the first and only treatment choice for PDR and is recently supplemented by intravitreal anti-VEGF therapy, increasing the treatment success rate. The main goal of PRP is to decrease retinal ischemic area, neovascularization, and vascular permeability and therefore decrease further VEGF production [7]. Many studies have already reported an increased level of VEGF in eyes with PDR . Although various recent studies are describing VEGF levels before and after anti-VEGF therapy [2], there is a limiting number quantitatively comparing the effect of previously performed PRP on intravitreal VEGF concentration.Diabetic retinopathy can be prevented by systemic control of blood pressure, blood glucose, and glycosylated hemoglobin (HbA1c) levels, which are all responsible for VEGF expression . Therefore, targeting VEGF is the main purpose of a treatment plan in PDR. VEGF has a central role in angiogenic processes and is responsible for endothelial proliferation and migration, increased vascular permeability, and new blood vessel survival . Our study had preoperative PRP performed only in group A. The reasons for not performing PRP pre-treatment in group B included the inability of performance due to decreased transparency of optical media (cataract or haematovitreus), the presence of tractional retinal detachment (TRD) with macular involvement, severe fibrovascular proliferation, and the presence of preretinal or epiretinal membranes or other mechanical components that could only be resolved during PPV. Many other researches also confirm the importance of PRP pre-treatment . Bakhritdinova et al. highly embrace preoperative PRP due to stabilization of proliferative processes, prevention of vitreomacular traction (VMT) formations, thus keeping the anatomical relations and visual acuity more stable [14]. PRP improves retinal function, influences faster and more successful recovery time and has a lower number of complications . Some authors explain the decrease in VEGF levels by a decrease in angiogenic stimuli after PRP, which further prevents and reduces VEGF production, thus causing the regression of neovascularization [15]. Itaya et al. have reported an increased level of VEGF within one week of scatter photocoagulation [16]. Some studies report a significant reduction in aqueous humor VEGF after intravitreal bevacizumab and pre-treatment with PRP, which lasted up for 8 weeks [17]. They claim that the VEGF level increases highly on the first day, has its peak values on the third day, followed by a decrease from the third to the fifth day [16]. It can be concluded that lower VEGF levels increase the recovery effects, which speaks in favor of anti-VEGF therapy as the additional therapy for angiogenesis reduction [18].On-time follow-ups and treatment of PDR using PRP are of high importance and were the golden standard and the only treatment option for PRP before the appearance of anti-VEGF therapy. Many studies have shown that the level of VEGF decreases after PRP [6]. Even though there is an improvement in DME after anti-VEGF injection, ischemic retinal areas still produce VEGF and cause further neovascularization [6]. Takamura et al. showed that PRP prevents the increase in central retinal thickness after intravitreal bevacizumab injection [6]. Their study has also shown that PRP is useful in preventing the recurrence of DME even in wider areas of non-perfused retina [6]. This means that ischaemia and hypoxia are the reason for DME recurrence, which lead to the conclusion that a combination of PRP and anti-VEGF injections is important for successful PDR and DME. Several studies are reporting that anti-VEGF therapy is superior to PRP and causes better improvement in visual acuity [19], but PRP is still frequently used for non-responders and for non-reliable patients who skip follow-ups and monthly injections [20]. Anti-VEGF has also proven to be very successful preoperatively and intraoperatively due to significant decrease of intraoperative and postoperative haemorrhage [21]. It was also reported that VEGF concentration can be used as a predictive factor for certain postoperative complications, such as vitreous haemorrhage or glaucoma .Intravitreal anti-VEGF therapy reduces VEGF levels and presents a successful treatment for DME [13]. Our results confirmed their conclusions as we have shown that PDR patients have significantly higher vitreous VEGF concentration compared to the control group. Our study showed high sensitivity (83.3%) and high specificity (100.0%) of VEGF as a differentiating biomarker in patients with PDR with and without PRP. Therefore, it can be concluded that the VEGF level can be a potential risk factor for PDR development and progression. Wang et al. have reported that progressive forms of PDR have higher concentrations of VEGF compared to both less progressive disease and to control group [22]. The same authors reported a significant positive correlation between vitreous and serum VEGF concentration, hence the conclusion that anti-VEGF therapy would be beneficial in PDR progression reduction. Even though there are many proofs that abnormal glucose levels lead to vascular complications, it is still impossible to state that regulation of blood glucose levels eliminates the risk for complications. Increased VEGF production in diabetic patients can be a result not only of periods of hyperglycaemia and hypoglycaemia, but also of exogenous insulin intake or genetic factors. Studies suggest that blood glucose level fluctuations cause damage known as cell pseudohypoxia, which leads to the synthesis of VEGF [23]. This study results could be improved with increased significance if an additional group of patients preoperatively treated with anti-VEGF was included. Further improvements of this study could have been made by longer follow ups and measuring these long-term results, and comparing them between groups. There was a significant positive correlation between vitreous VEGF concentration and blood glucose levels in patients with previous PRP. This correlates with other studies in which patients with good diabetes control have lower serum and vitreous VEGF levels [The strength of this study is a relatively large sample size of surgically treated patients. The results of the study further prove the benefits of PRP and suggest that it is an important therapeutic tool in the improvement of the outcome of a challenging and severe diagnosis, such as PDR. Increased vitreous VEGF level in PDR patients suggests its role in the pathology of PDR. PRP as a treatment before PPV surgery has shown to be effective in the reduction of VEGF levels that also affects a decrease in complications during and postoperatively. Conflicts of Interest statementThe authors declare that there are no conflicts of interest. Informed Consent and Human and Animal Rights statementInformed consent has been obtained from the patient included in this study. Authorization for the use of human subjectEthical approval: The case report related to human use complies with all the relevant national regulations, institutional policies, is in accordance with the tenets of the Helsinki Declaration, and has been approved by the review board of the Veneto Eye Bank Foundation, Venice, Italy. AcknowledgmentsNone.Sources of FundingNone.Financial Disclosure(s)None.


Intraoperative defibrillation secondary to the usage of electrocautery in a patient with a cardiovascular implantable electronic device is a rare occurrence, and below-the-umbilicus electrocautery use causing inadvertent defibrillation is a near-zero risk. Defibrillation secondary to electrodispersive pad (EDP) radiofrequency dispersion has only ever been theorized. In this report, we describe the case of a 67-year-old male with an automatic implantable cardioverter defibrillator (AICD) undergoing a robotic-assisted left anterior total hip arthroplasty for left hip osteoarthritis who experienced inadvertent intraoperative defibrillation concurrent with electrocautery usage. The defibrillations ceased following contralateral EDP repositioning and application of a donut magnet overlying the patient’s AICD. As medical technology has continued to advance and improve patient welfare, there has been an increase in the overall usage of cardiovascular implantable electronic devices (CIED), including automatic implantable cardioverter defibrillators (AICD) . AdditioThe 2021 European Society of Cardiology (ESC) guidelines on cardiac pacing and resynchronization therapy offer vague advice regarding perioperative alteration of CIEDs for surgeries utilizing below-the-umbilicus electrocautery . The HeaThus, we report a case of an elderly gentleman who had inadvertent intraoperative AICD defibrillation thought to be secondary to electromagnetic stimulation due to the electrodispersive pad (EDP) position while undergoing surgery below the umbilicus.A 67-year-old male presented for robotic-assisted left anterior total hip arthroplasty to correct osteoarthritis of the left anterior hip following the failure of conservative treatment. The patient weighed 107 kg (235 lbs) and was 1.753 m (5’9”) tall. Aside from the patient’s degenerative joint disease, his past medical history consisted of hyperlipidemia, hypothyroidism, obstructive sleep apnea, gastroesophageal reflux disease with a history of antral ulcer, atrial fibrillation, ventricular tachycardia, and non-ischemic cardiomyopathy status post AICD placement. Other procedures include a gastric bypass, tonsillectomy, and ventricular tachycardia ablation. The patient’s AICD was placed 19 years earlier due to cardiac arrest secondary to ventricular tachycardia and non-ischemic cardiomyopathy with a left ventricular ejection fraction of 30-35%. The patient’s most recent transthoracic echocardiogram demonstrated a 40-45% ejection fraction with evidence of moderate mitral and tricuspid regurgitation. Preoperative laboratory analysis demonstrated no abnormalities in serum electrolytes, lipids, or glucose levels. The AICD had been interrogated within six months of the elective surgery; there were no recent defibrillations, the patient was not pacemaker dependent, and an estimated 6.3 years of battery life remained.On the day of surgery, a spinal block with monitored anesthesia care was planned for anesthetic management. The spinal block was completed using 2mL of 0.75% hyperbaric bupivacaine injected intrathecally. The patient was placed in a supine position and the EDP was placed on the patient’s left flank Figure . After iMonopolar electrocautery instruments are also known as “active electrodes” when energized. To carry out the functions of tissue coagulation or fulguration, a second node known as the “dispersive electrode” is required. The dispersive electrode, or EDP, functions to distribute the energy from the active electrode to prevent thermal injury to the patient . This doPlacement of the EDP is perhaps the most important component of ensuring the safe usage of an electrocautery device. Several incidences of intraoperative thermal injuries occurring secondary to improper placement or dysfunction of the EDP have been previously reported -13. MethWhile EDP placement has been shown to induce thermal injury with improper placement or device dysfunction, inadvertent defibrillation secondary to EDP placement has never been reported. In fact, the only time defibrillation secondary to below-the-umbilicus monopolar electrocautery usage has ever been reported to occur was in a patient undergoing total knee arthroplasty . The autNeither the 2011 HRS/ASA expert consensus statement for perioperative CIED management nor the 2021 ESC guidelines on cardiac pacing and cardiac resynchronization therapy offer recommendations for proper placement of the EDP. The 2020 ASA Practice Advisory for perioperative CIED management, which is developed from a survey of expert consultant anesthesiologists and ASA members, has no clear recommendations regarding below-the-umbilicus electrocautery use . In thesThe 2021 ESC guidelines on cardiac pacing and cardiac resynchronization therapy recommend tailoring the peri-operative strategy on alteration of the CIED based on the needs and values of the patient, procedure, and device. However, it is mentioned in the guidelines that most procedures will not require intervention and electromagnetic interference with CIEDs by electrocautery >5cm from the CIED and monopolar electrocautery below the umbilicus is a rare event. In contrast, an editorial published in 2019 called for the preoperative alteration of the AICD, whether by disabling the device or preemptively placing a donut magnet, despite the surgical site of the procedure, due to the potential risk of inadvertent defibrillation and subsequent myocardial injury . A separIn conclusion, we have presented a case that followed the 2020 ASA practice advisory for perioperative CIED management, but the AICD defibrillated twice. Thus, when monopolar electrocautery will be used, we encourage anesthesia personnel to evaluate not only the patient, the device interrogation report, whether the patient is currently pacemaker dependent, and the location of the surgery, but also EDP placement. Most guidelines regarding the perioperative management of CIEDs focus on above the umbilicus procedures, demonstrating the need for more definitive guidance with below the umbilicus operations. Literature regarding EDP placement specifically in patients with CIEDs remains scarce, if not non-existent, given the mere theoretical risk EDP radiofrequency dispersion holds. Further research should determine best practices specifically for perioperative CIED management in surgeries below the umbilicus with limited options for EDP placement.


Public health officials faced with a large number of transmission clusters require a rapid, scalable and unbiased way to prioritize distribution of limited resources to maximize benefits. We hypothesize that transmission cluster prioritization based on phylogenetically derived lineage-level diversification rates will perform as well as or better than commonly used growth-based prioritization measures, without need for historical data or subjective interpretation.9822 HIV pol sequences collected during routine drug resistance genotyping were used alongside simulated sequence data to infer sets of phylogenetic transmission clusters via patristic distance threshold. Prioritized clusters inferred from empirical data were compared to those prioritized by the current public health protocols. Prioritization of simulated clusters was evaluated based on correlation of a given prioritization measure with future cluster growth, as well as the number of direct downstream transmissions from cluster members.Empirical data suggest diversification rate-based measures perform comparably to growth-based measures in recreating public heath prioritization choices. However, unbiased simulated data reveals phylogenetic diversification rate-based measures perform better in predicting future cluster growth relative to growth-based measures, particularly long-term growth. Diversification rate-based measures also display advantages over growth-based measures in highlighting groups with greater future transmission events compared to random groups of the same size. Furthermore, diversification rate measures were notably more robust to effects of decreased sampling proportion.Our findings indicate diversification rate-based measures frequently outperform growth-based measures in predicting future cluster growth and offer several additional advantages beneficial to optimizing the public health prioritization process. Current methods of HIV-1 outbreak management in countries with sufficient resources and government support to sustain continuous monitoring are highly effective . In the Phylogenetic monitoring of HIV-1 transmission is highly amenable because viral populations are known to diverge following transmission due to the rapid rate of evolution of HIV and host-specific selection , 13. VirPrioritized clusters may receive different types of public health attention depending on their characteristics. Linkage or re-linkage to care can help begin or improve adherence to ART, optimize ART regimen choices, and encourage consistent viral load suppression. Contacts of cluster members at risk for transmission can also be offered targeted PrEP and urged to seek diagnostic testing. Both cluster members and their contacts can be encouraged to maintain safe practices regarding their risk behaviours, such as use of condoms or safe injection materials. By employing a combined approach tailored to cluster characteristics, public health teams in BC have previously been able to notably reduce transmission associated with prioritized clusters .Recently, the use of non-phylogenetic measures, either in combination with phylogenetic clustering , 15, nonIn this study, we employ both empirical and simulated data to evaluate the hypothesis that cluster prioritization measures based on phylogenetically derived lineage-level diversification rates will perform at least equally as well as commonly used non-phylogenetic growth-based prioritization measures in stratifying clusters based on transmission activity, while operating independently from supporting meta data and remaining free of need for subjective or historical interpretation. We use BC empirical data to compare phylogenetic diversification rate-based measures to growth-based measures in their ability to separate clusters labelled by BC public health protocol as ‘priority’ from the remaining clusters. We further compare prioritization measure performance through a simulation study to reduce the effects of biases that hinder empirical data, such as incomplete sampling and differential cluster intervention. Thus, simulation facilitates direct comparison of the relationship between each prioritization measure and future cluster growth, as well as the direct number of transmissions resulting from prioritized clusters.In BC, routine clinical care following every new HIV diagnosis includes drug resistance genotype testing performed by the BC Centre for Excellence in HIV/AIDS (BC-CfE). Viral RNA extracted from patient blood samples is used to determine which antiretroviral drugs will be most effective for each individual based on observed viral surveillance drug resistance mutations (SDRMs) . After tet al. [All 35 752 anonymized sequences were aligned with MAFFT v7.310 . Known Set al.  and usedThe diversification rate-based measures assessed include mean (MeanDR), median (MedianDR), maximum (MaxDR) and most recent diversification rate (MostRecentDR), as well as the mean of the top three (Top3MeanDR) and top five diversification rates (Top5MeanDR) in a cluster. Growth-based measures assessed include previous year (PrevYrGrowth), 3-year (Prev3YrGrowth) and 5-year growth (PrevYrGrowth), as well as cluster size (ClustSize), previous year cluster growth squared (ClustGrowthSq) and whether or not more than five cases have joined a cluster in the previous year (PrevYr5CasePlus). ClustGrowthSq and PrevYr5CasePlus were included for the purpose of comparison to previous studies , 24. Cluet al. [Lineage-level diversification rate is a phylogenetically derived measure first described by Jetz et al.  that traU tests.The ability of growth-based measures and diversification rate-based measures to stratify transmission clusters based on transmission activity was evaluated based on the ability of each measure to separate clusters marked as ‘priority’ by the BC public health protocol in a given year from the remainder. Prioritization decisions made by the BC protocol can be a result of rapid cluster growth, rapid symptom onset seen in one or more cluster members, or combinations of other factors recognized by the public health team as concerning. Successful distinction between these two groups of transmission clusters was assessed using Mann–Whitney The need for simulation of the BC epidemic is motivated by two factors. First, intervention decreasing transmission would be expected to reduce lineage-level diversification rate for a transmission cluster, meaning that clusters known to be consistently labelled as priority may display decelerating diversification rates despite having high potential for transmission. Second, evaluating novel methods using the set of labels defined by the BC public health protocol as a benchmark assumes that this method accurately captures the true set of clusters with the greatest potential for transmission, which may not necessarily be the case. Simulated epidemics allow evaluation under circumstances where intervention is not applied differentially between clusters and the true clusters with the greatest potential for propagating future transmissions are known.et al. [Simulation was performed using the FAVITES framework  to createt al.  and six For each replicate of each of the seven parameter sets, simulated sequences spanning the full 10-year simulation period were aligned in MAFFT and phylogenies were inferred with FastTree. After rooting on all branches via LSD2 , each phP < 0.001), thus rationalizing the focus on these groups as primary public health targets. Even within clusters, hundreds of cases occur each year that could be prevented if the accuracy and resolution of prioritization was increased. The right-tailed log distribution of diversification rates highlights the ability of this measure to allow more focused prioritization, as there are very few diversification rates that are very high relative to the majority of the measurements . All tested measures except for the MostRecentDR created two clearly distinct peaks between the two groups of clusters, with the most obvious visual distinction between populations occurring for MeanDR, MaxDR, Top3MeanDR and Top5MeanDR , although none of these four demonstrated clearly defined separation between the two populations of clusters  showed a significant difference between priority and non-priority clusters , suggesting that these measures are limited in the benefit they can confer in heavily under-sampled epidemics , and although some growth-based measures such as PrevYrGrowth again show inverted correlations, other measures such as ClustSize and ClustGrowthSq retain a relatively similar effect size, particularly when the time to start ART or the number of contacts is increased from the base parameter set . However, when the number of direct transmissions was adjusted for current cluster size, the difference in direct transmissions prioritized by diversification rate measures was significantly larger  in the majority of parameter sets, with a notably larger disparity for parameter sets with increased time to start ART or contact network connections (P < 0.001) for clusters prioritized by diversification rate measures (median size = 41) than for clusters prioritized by growth-based measures , unless time to start ART was 0.5 years  consistently performed considerably worse relative to other measures, further demonstrating the limited abilities of measures failing to capture long-term trends. In contrast, MaxDR, MeanDR, Top3MeanDR and Top5MeanDR offered the most consistency, achieving statistically significant separation between the urgent priority and remaining population of clusters in eight of the years studied. Although two growth-based measures (PrevYr5CasePlus and Prev3YrGrowth) meet this standard, they rely entirely on the existence of historical data, a shortcoming that could be eliminated by use of diversification rate-based measures. One such measure, Top5MeanDR, not only achieves statistically significant separation in 8 of the 10 years studied in the BC dataset, but also shows the strongest relationship with cluster growth across all simulated growth periods and parameter sets studied of all tested prioritization measures. This is in concordance with analyses conducted by McLaughlin  in 2019 , which f factors , 34. Fur [et al. , even a Overall, we find that diversification rate-based measures not only frequently outperform growth-based measures in their ability to identify groups with the highest potential for future growth, particularly long-term future growth, but are also less likely to be perturbed by missing data and remain free of need for historical data and subjective interpretation. In contrast, although growth-based measures can sometimes be equally effective as measures based on phylogenetic diversification rates, they have the potential to become misleading in cases of lower sampling proportion. In combination with phylogenetic clustering, phylogenetically derived lineage-level diversification rates can provide a simple, widely applicable and robust solution to focus prioritization of transmission clusters contributing the most to an ongoing epidemic.eoac026_Supplementary_DataClick here for additional data file.