The paper presents a literature review on the development of microvoids in metals, leading to ductile fracture associated with plastic deformation, without taking into account the cleavage mechanism. Particular emphasis was placed on the results of observations and experimental studies of the characteristics of the phenomenon itself, without in-depth analysis in the field of widely used FEM modelling. The mechanism of void development as a fracture mechanism is presented. Observations of the nucleation of voids in metals from the turn of the 1950s and 1960s to the present day were described. The nucleation mechanisms related to the defects of the crystal lattice as well as those resulting from the presence of second-phase particles were characterised. Observations of the growth and coalescence of voids were presented, along with the basic models of both phenomena. The modern research methods used to analyse changes in the microstructure of the material during plastic deformation are discussed. In summary, it was indicated that understanding the microstructural phenomena occurring in deformed material enables the engineering of the modelling of plastic fracture in metals. Ductile fracture criteria can be classified based on the physical sense of the quantities defining them. If the criterion includes values derived by macroscopic analysis based on mechanics of deformable bodies, without precisely defined areas of fracture initiation, a global criterion is formulated. The most known and commonly used global ductile fracture criteria  were determined on the basis of such quantities as J integral and crack tip opening displacement δT.Another approach involves analysis of the stress and strain (or other parameter) fields in the most stressed areas of the material (process zone). The stress and strain state in the process zone determines the strength of the whole element. Such an approach and arising criteria are local. The criterion values of local models depend only on the material properties.An undoubted advantage of using a local approach to the analysis of the fracture is the independence of the obtained results on the geometry of the samples used. The local approach is based on actual physical phenomena (degradation of the microstructure) that occur in the material subjected to load. In general, understanding and describing these phenomena allow for their inclusion in the cracking description of any element type, and thus the development of a universal, comprehensive method of predicting the durability of structural elements and assessing the safety of their work.Conducting an analysis according to a local approach requires the use of hybrid methods: obtaining data from experimental research  and supplementing them with numerical, metallographic and fractographic analyses.Among the physical phenomena taken into account in the local approach to fracture, the nucleation and growth of microvoids in the structure of the material subjected to plastic deformation is of particular practical importance. Observation of this phenomenon is the basis for formulating theoretical models of nucleation, growth and coalescence of voids, which in turn enables the establishment of models of plastic materials with microdamages. These issues are therefore crucial for assessing the safety of structural elements. This article is a literature review on the microstructure observations and void growth in deformed metals. Particular emphasis was placed on ductile fracture associated with substantial plastic deformation, without taking into account cleavage cracking. The results of pioneering works from the second half of the twentieth century, as well as the latest results obtained with advanced research tools (such as microtomography), are discussed. The structure of the article is divided into sections according to the different mechanisms in ductile fracture: Void initiators are primarily defects in the crystalline structure  and second-phase particles, which can be introduced into the material microstructure intentionally  or constitute an undesirable contamination of the material. All these discontinuities, being local stress concentrators, under plastic deformation create voids with sizes in the order of tenths of a micrometre. As the plastic strain increases, the voids created in this way increase their size many times and then coalesce, forming a larger defect and leading to failure. An example of a sample structure in which a fracture developed as a result of the growth and coalescence of voids is presented in A separate issue is the macroscopic observation of fracture surfaces at void-induced failure. Ductile fracture of a round tensile bar has been relatively well-documented in the literature . InitialAn in-depth analysis of microstructures with the use of different methods allowed for the development of the systematics of the mechanisms of the formation of voids. This applies to both the nucleation of the voids and their subsequent development.The initial experimental observations of the development of voids in plastically deformed metals took place at the turn of the 1950s and 1960s . Since tIn general, voids are nucleated in the vicinity of stress concentrators on a microscopic scale . In the In the absence of material discontinuities, the formation of voids is observed as planar slip band decohesion or grain boundary decohesion (homogeneous nucleation). One of the first widely known studies in which the participation of dislocations in the formation of voids was experimentally confirmed was developed by Gardner et al. , who notThe observations of the development of voids in the Nb-Cr-Ti alloy  showed tThe concept of void nucleation as a result of vacancy condensation and the presence of dislocation boundaries has been known for a long time ,18, but In the next stage, only a small quantity of the nanovoids grew to the microscale, contributing to the initiation of cracking. It was noted that all microscale voids were associated with a dislocation boundary. By carefully analysing the microstructure of the material in the neck area, the authors distinguished three basic groups of microvoids, depending on their location and the mechanism of formation, namely: (i) voids related to intragranular, inclusion-free dislocation boundaries; (ii) voids associated with the inclusion-free intersection between one or more dislocation boundaries and one or more grain boundaries; (iii) voids associated with an inclusion intersected by a dislocation boundary.As mentioned before, voids can be initiated by grain separation. This mechanism is favoured by the following conditions: high share of the hydrostatic stress; small value of the void spacing to void diameter ratio; and high value of the precipitate-free zone (PFZ) thickness to void spacing ratio . In technical alloys, voids are often initiated by the failure of second-phase particles, randomly distributed over the material matrix (heterogeneous nucleation). The presence of a hard particle locally limits elastic and plastic deformation of the matrix, which in turn causes local stress concentration. With the increase in plastic strain, the stress value also increases, which ultimately leads to the particle cracking or its separation from the matrix . As the The occurrence of one of the two mentioned mechanisms depends largely on the mechanical properties of the matrix and the particle  and the strength of the matrix–particle interface. In general, the phenomenon of particle separation is primarily observed in relatively soft, ductile matrices. However, high yield stress and hardening exponent of the matrix as well as the high particle stiffness promote particle cracking. Moreover, the nucleation mechanism depends on the particle geometry  and its orientation with respect to the direction of the principle tensile stresses. Larger particles usually break, as do elongated particles parallel to the principal stress direction. In practice, engineering materials have different void populations; therefore, both mechanisms occur simultaneously. The local stress state is also of great importance, namely the predominance of normal stress over shear stress, which most often forces the particle fracture ,25.Due to the heterogeneous stress state changing with time, the nucleation of voids is continuous across the entire range of plastic strain. In other words, the development of microdamage includes the simultaneous growth and coalescence of existing voids as well as the nucleation of new ones .Among the experimental studies, the process of nucleation of voids and microcrack formation by fracture of silicon particles in Al-Si alloys is relatively well-documented. The most important results described in the literature are summarised in . As showAs noted in , the pheTo understand the phenomenon of void nucleation by fracture of the second-phase particles, it is important to estimate their mechanical properties, especially strength. The first and best-known attempts were described in ,34. In  of ROI was determined as a function of true strain. From the very beginning, a constant increase in the number of voids is visible. Just before failure, one can see a flattening of the curve, which seemingly means a reduction in the intensity of void nucleation at this stage. In fact, as noted by the authors of :  and the strain-hardening capacity of the material . For engineering materials in the occurrence of ductile failure, the first three mechanisms identified by Noell et al.  will be There are many analytical models in the literature describing ductile failure according to the void mechanism. An important aspect is the possibility of their experimental validation. Particularly difficult to validate experimentally is the phenomenon of void coalescence ,113,114.The authors of  proposedWhen analysing the growth process and the coalescence of voids in the material, an important aspect is to determine the volume contribution of the voids and their initial shape ,116,117.The influence of the shape of the void on the nature of its growth can be determined using other advanced research methods. In , a reseaThe Rice–Tracey void growth model with Huang  correctiResearchers are also using advanced computed tomography techniques to verify growth models and in situ void fusion: SRCT and SRCL . The ideAnalysis using numerical calculations  helps understand the growth process and the coalescence of voids in detail. Very often, an elementary cell model (representative RVE volume model) is used, which in its structure contains a void or particle with specific material characteristics. Classical studies consider the analysis of cylindrical voids, spherical voids and spherical particles. Parameters that can be taken into account in the RVE loading process are the stress triaxiality parameter (T), the Lode parameter (L) and the shear coefficient (S). There has been much work involving studies of the effect of stress triaxiality on the development on ductile failure, including a description of the void growth and coalescence process. However, it has become an important task to determine the simultaneous influence of the three aforementioned parameters of stress triaxiality, Lode parameter and shear rate when analysed using RVE. A solution to the task posed was proposed in . The RVEThe contribution of the critical level of plastic strain, the stress triaxiality factor and the Lode parameter to the realisation of the ductile fracture process has been highlighted in a number of works by Wierzbicki and coworkers ,137,138.The influence of the Lode parameter value on the void growth and coalescence process has formed the basis of a number of papers ,143,144.Due to its great practical importance, the phenomenon of ductile failure associated with the development of voids in metals has been the subject of interest of researchers since the turn of the 1950s and 1960s. Over about 6 decades, many research techniques have been developed that have contributed to a better understanding of the nature of this phenomenon.It was indicated that void formation in metals is induced by material discontinuities on the nano- and microscopic scale. The void initiators mainly include (i) intersection of the slip bands, (ii) grain boundaries, (iii) twin boundaries, (iv) vacancy clusters and (v) second-phase particles (inclusions and precipitates).The void formation mechanism depends primarily on the material purity. In the absence of second-phase particles, voids are formed mainly in the vicinity of defects of the crystal structure . Materials that are commonly used in engineering  most often show the presence of inclusions, which, as stress concentrators, are void initiators. Depending on the geometrical and strength parameters of particles and matrix, voids are formed by particles’ separation or fracture.As has been demonstrated, the local values of the accompanying stresses, measured on a microscopic scale, are much higher than those measured macroscopically, and range from several hundred to several thousand MPa.After the nucleation process, voids grow with increasing strain and change their shape and position in the material. The moment of localisation of strain in the immediate vicinity of voids is identified with the initiation of the coalescence process. Three ways of realising the coalescence of voids in the material are characterised: internal necking, shear coalescence and necklace coalescence. Selected analytical models relating to the growth process and the coalescence of voids under ductile failure mechanism are cited in this paper. An important element is the possibility of experimental verification of the classical models. This verification is possible by using advanced measurement methods  for in situ testing. Despite enormous progress in this subject, there are still many unresolved issues. The course and intensity of the void development are significantly influenced by the load conditions, most importantly the stress state. However, the literature rarely mentions other factors that undoubtedly determine the development of voids, e.g., dynamic phenomena, temperature, etc.It has not been unequivocally determined whether the stress or strain criteria must be satisfied for the void nucleation. Moreover, the literature lacks unambiguous sets of parameters describing the development of voids in materials with practical engineering application. Little is known yet about the development of voids under low-triaxiality conditions.It should be strongly emphasised here that the observations of the development of voids are not only of cognitive importance, but above all they become the basis for the formulation of theoretical models, which further allows for the description of defective materials. It is an issue of great practical importance, thanks to which it becomes possible to perform an engineering safety assessment of structural elements containing defects, operating in pre-failure conditions. In the future, an even more accurate understanding of the ductile failure process according to the void formation mechanism will be possible with the development of advanced testing and measurement methods. These can be used for in situ research. An important aspect of supplementing the information obtained about the void mechanism will be the inclusion of the results of advanced numerical calculations.


Fifty years ago, Richard Lewontin found that the vast majority of human genetic variation falls within (~85%) rather than between (~15%) racial groups. This result has been replicated numerous times since and is widely taken to support the notion that genetic differences between racial groups are trivial and thus irrelevant for clinical decision-making. The aim of this study was to consider how the apportionment of pharmacogenomic variation within and between racial and ethnic groups relates to risk disparities for adverse drug reactions. We confirmed that the majority of pharmacogenomic variation falls within (97.3%) rather than between (2.78%) the three largest racial and ethnic groups in the United States: Black, Hispanic, and White. Nevertheless, pharmacogenomic variants showing far greater within than between-group variation can have high predictive value for adverse drug reactions, particularly for minority racial and ethnic groups. We predicted excess adverse drug reactions for minority Black and Hispanic groups, compared to the majority White group, and considered these results in light of the apportionment of genetic variation within and between groups. For 85% within and 15% between group variation, there are 700 excess adverse drug reactions per 1,000 patients predicted for a recessive effect model and 300 for a dominant model. We found high numbers of predicted Black and Hispanic excess adverse drug reactions for widely prescribed platinum chemotherapy compounds, such as cisplatin and oxaliplatin, as well as controlled narcotics, including fentanyl and tramadol. Our results indicate that race and ethnicity, while imprecise proxies for genetic diversity, correlate with patterns of pharmacogenomic variation in a way that is clearly relevant to medical treatment decisions. The effects of this variation is particularly pronounced for Black and Hispanic minority groups, owing to genetic differences from the majority White group. Treatment decisions that are made based on (assumed) White pharmacogenomic variant frequencies can be harmful for minority groups. Ignoring clinically relevant genetic differences among racial and ethnic groups, however well-intentioned, will exacerbate rather than ameliorate health disparities. Critics hold that race and ethnicity are socially defined, rather than biological or anthropological categories, and emphasize the potential stigmatizing effects of race-based medicine7. There is, however, broad agreement that the use of race and ethnicity as markers of biological difference in the clinical setting can only be justified if the benefits outweigh the harm.The question of whether race and ethnicity can serve as proxies for biological differences with relevance for clinical decision-making has received much attention as of late. Advocates for the continued use of race and ethnicity emphasize the importance of these categories for biological, social, and environmental determinants of health8. Lewontin’s fundamental result has been confirmed numerous times since and is widely taken to support the irrelevance of genetic differences between racial and ethnic groups10. Here, we consider differences in the frequencies of pharmacogenomic variants11, which affect how individuals respond to medication and are thus particularly relevant to clinical decision-making, among the three largest racial and ethnic groups in the United States: Black, Hispanic, and White12. The aims of this study were (1) to characterize the apportionment of pharmacogenomic variation within and between groups and (2) to relate the apportionment of variation to disparities in the risk of adverse drug reactions (ADRs). There are more than two million serious ADRs among hospitalized patients in the US every year, leading to more than 100,000 deaths13. ADRs are a major public health problem and one that is largely preventable14; moreover, racial and ethnic minorities in the US bear a disproportionate burden of ADRs16. There are numerous pharmacogenomic variants associated with drug toxicity, which can be used to predict and avoid ADRs17.The extent of genetic differences that exist among racial and ethnic groups is central to this debate. Richard Lewontin first studied the apportionment of genetic variation among racial groups fifty years ago. His seminal paper showed that ~85% of human genetic variation was found within racial groups, compared to only ~15% between groups, and he concluded that race is “of no social value … is positively destructive of social and human relations … [and is] of virtually no genetic or taxonomic significance”18. In other words, according to the logic of Lewontin and his intellectual heirs, race and ethnicity are poor proxies for genetic diversity and thus irrelevant for pharmacogenomic informed therapeutic decision making. We tested these expectations via analysis of pharmacogenomic variation for a cohort of US study participants who self-identified as Black, Hispanic, or White, and via simulation of allele frequencies within and between groups for pharmacogenomic variants that are associated with drug toxicity.Given what is known about the apportionment of human genetic variation, the majority of pharmacogenomic variation is expected to fall within rather than between racial and ethnic groups. If this proves to be the case, then race and ethnicity are expected to hold low predictive value for the genetic risk of ADRs19. HRS participants self-identified their race and their ethnicity (SIRE) according to US Office of Management and Budget (OMB) standards20 and provided DNA samples for genome-wide genotype analysis. HRS participant genome-wide genotypes were characterized using the Illumina Omni2.5 BeadChip. Participant genotypes were filtered to remove variants with > 1% missingness and < 1% minor allele frequency among samples using PLINK v221. The final genome-wide genotype dataset consists of 2,252,836 biallelic genetic variants.Study participants were taken from the Michigan Health and Retirement Study (HRS), a national representative longitudinal panel study of Americans over the age of 5021.Genomic relationships among study participants were inferred via principal components analysis (PCA) of the participant genome-wide genotype data using the FastPCA program implemented in PLINK v2FST) was used to partition human genetic variation within (1−FST) and between (FST) SIRE group pairs22. FST values were calculated for individual genetic variants as follows:The fixation index  is calculated by combining expected variant heterozygosity values within groups HT):The fixation index  and dominant  modes of action.The numbers of excess predicted adverse drug reactions for Black and Hispanic minority groups, compared to the White majority reference group, were calculated for (1) simulated group-specific allele frequencies and (2) HRS group-specific allele frequencies for known pharmacogenomic variants. SIRE group-specific allele frequencies were considered together with SIRE group population fractions, taken from 2021 US Census datap, and p.Recessive model pmin ∗ (1 − pmin) is the heterozygous genotype fraction for the minority SIRE group toxicity associated allele p, and 2 ∗ pmaj ∗ (1 − pmaj) is the heterozygous genotype fraction for the majority SIRE group toxicity associated allele p.Dominant model 17. Pharmacogenomic variant chromosomal locations, clinical annotations, effect allele identity, mode of effect (recessive or dominant), and evidence levels were taken from PharmGKB. Evidence levels correspond to pharmacogenomic variant-drug response association confidence: 1) high, 2) moderate, 3) low, 4) unsupported.Pharmacogenomic variants with empirically supported toxic drug reaction associations were taken from the Pharmacogenomic Knowledge Database (PharmGKB), which provides manually curated pharmacogenomic variant annotations along with details on their drug response phenotypesWe characterized the relationship between race, ethnicity, and genomic variation using a cohort of 8,912 participants from the University of Michigan Health and Retirement Study (HRS). The three demographic categories studied here correspond to participants who self-identified their race and ethnicity (SIRE) as non-Hispanic Black , Hispanic of any race , and non-Hispanic White . Group percentages correspond roughly to current US Census estimates, with a slight overrepresentation of Black (13.6% expected) and White (59.3% expected) participants compared to an underrepresentation of Hispanic (18.9% expected) participants. Additional non-Hispanic racial categories – American Indian or Alaska Native, Asian, Native Hawaiian or Other Pacific Islander – did not yield sufficient numbers of participants  for stratified analysis.The genomic relationships among study participants were characterized using principal components analysis (PCA) of genome-wide genotype data and visualized in light of participants SIRE . The firST; ST scales from 0–1 and measures the amount of genetic variation apportioned between population groups; 1− FST is taken as the amount of within group variation. FST values are calculated as shown in the Methods section, considering both SIRE group-specific allele frequencies and group population numbers. The vast majority of human genomic diversity is found within rather than between groups for all three pairwise SIRE group comparisons: Black-Hispanic average within group percent genetic variation=94.8% and between=5.2%, Black-White average within=96.3% and between=3.7%, Hispanic-White average within=99.5% between=0.5%. These results are consistent with the average values of 85% within and 15% between group variation found by Lewontin in 1972, with even more variation found within rather than between SIRE groups for the HRS participants.We next compared the overall genomic diversity captured by PCA to the percent of genetic variation apportioned within and between pairs of SIRE groups, as measured the fixation index FST; . FST scaFST) and between (FST) group percent variation values were calculated for all possible pairs of group-specific allele frequencies. Results of this simulation are shown for comparisons of Black and White SIRE groups  protein coding gene. The G effect allele is found at high frequency in Black (0.88) and Hispanic (0.64) study participants compared to White (0.37) participants, leading to predicted excesses of 629 (recessive) and 379 (dominant) adverse drug reactions per 1,000 Black patients and predicted excesses of 629 (recessive) and 379 (dominant) adverse drug reactions per 1,000 Hispanic patients, compared to the White majority group. This variant is associated with adverse reactions to 9 different drugs, including the widely prescribed cisplatin and oxaliplatin chemotherapy drugs. Several other variants with large numbers of predicted excess adverse reactions are associated with platinum chemotherapy compounds as well as other widely prescribed drugs, such as the controlled narcotics fentanyl, haloperidol, and tramadol. A full list of predicted excess adverse drug reactions for toxicity associated variants can be found in The toxicity-associated pharmacogenomic variant rs11615 is a synonymous A/G variant in the 26.Our analysis of the HRS participant cohort illuminates the relationship between race, ethnicity, and genetics for the three largest racial and ethnic groups in the US: Black, Hispanic, and White. While we do find obvious genetic differences between these three demographic groups, there are no discrete boundaries between groups. Rather, our cohort shows a continuum of genetic diversity and admixture, with the vast majority of variation falling within rather than between groups. Our results are consistent with previous large-scale studies of the US population, all of which show characteristic group-specific patterns of diversity coupled with a continuum of admixture within and between groups10. Lewontin concluded that race is both a destructive force in human relations and a construct that has virtually no genetic significance. His admonition has been widely adopted as an argument against any consideration of whatsoever of race and ethnicity in genetics research and clinical decision making10. We show here that clinically relevant genetic differences between racial and ethnic groups can exist even when there is far more variation within rather than between groups. In particular, if Black and Hispanic minority patients in the US are treated based on the assumption of no meaningful genetic differences with the majority White group, they are likely to suffer numerous, and largely avoidable, adverse drug reactions.Fifty years ago, in the pre-DNA sequencing era, Richard Lewontin marshalled data on protein polymorphisms to show that the vast majority of human genetic diversity falls within rather than between racial groups. This seminal result has held up remarkably well over the years, through the invention of DNA sequencing and well into the genomic era27, and they are known to cause a wide array of adverse effects28. We found a number of platin-associated pharmacogenomic variants with toxicity risk alleles at elevated frequencies in minority Black and Hispanic populations  as an example of how patient race and ethnicity can be used to inform treatment decisions. Platins, such as cisplatin, oxaliplatin, and carboplatin, are used to treat almost half of cancer patients receiving chemotherapyulations . For pla33. Indeed, race and ethnicity are widely recognized as social rather than biological or genetic categories38. Nevertheless, ignoring race and ethnicity in treatment decisions has the potential to cause tangible harm to patients. The harm to patients caused by the race-blind approach is exemplified by the case of the blood thinner Plavix in Hawaii39. In 2021, the pharmaceutical companies Bristol-Myers Squibb and Sanofi were ordered to pay $834 million to the state based on their failure to disclose the drug’s diminished effects in Asian patients compared to Black or White patients. In 2015, adverse reactions to the government prescribed HIV medication efavirenz occurred among ~20% of patients in Zimbabwe, despite previous warnings of a genetic variant found at high frequency in the population that is associated with slow metabolism and accumulation of the drug in the bloodstream41. As we have shown here, there are scores of drugs that present similar, and perhaps even more extreme, dangers of adverse drug reactions to Black and Hispanic patients in the US.The use of race and ethnicity as a proxy for genetic differences with clinical relevance can only be justified if (1) benefits outweigh harm and (2) there are no alternative methods for patient stratification with equal or superior accuracy. Objections to the use of race and ethnicity in genetics research and clinical decision making are grounded in entirely reasonable concerns about the potential stigmatizing effects of highlighting biological and genetic differences between racial and ethnic groups42. The Hispanic ethnic category was added in 1980, and race and ethnicity are now treated separately in the US census. Other cosmopolitan countries have their own systems of racial and ethnic classification, which may or may not be similar to that of US. The United Kingdom uses the term ethnicity in a way that is analogous to race in the US, but their ethnic categories can differ from the racial categories in the US owing to the pattern on immigration from Commonwealth countries. For example, the Asian census category in the UK refers primarily to individuals of Indian, Pakistani, and Bangladeshi origin. At the global level, genetic diversity is largely continuously distributed, based on reproductive isolation by distance, with discontinuities introduced by major geographic barriers  and social barriers (e.g. assortative mating). Accordingly, the extent to which race and ethnicity can serve as proxies for genetic diversity will depend on historical patterns of immigration and current demography for any given country. The three largest racial and ethnic groups in the US – Black, Hispanic, and White – include individuals with ancestry from geographically diverse regions – Africa, the Americas, and Europe – home to populations that evolved separately for many thousands of years before coming together in the post-Columbian era. Thus, US racial and ethnic groups can be readily genetically distinguished, since genetic diversity closely tracks continental ancestry44. This fact is underscored by a recent study of >200,000 US military veterans, which showed 99.47% correspondence between genome-wide patterns of genetic diversity and participant SIRE25. Nevertheless, as the US and other cosmopolitan nations become increasingly diverse, owing to ongoing immigration and increasing rates of intermarriage, the correspondence between race, ethnicity, and genetic diversity is expected to break down.The social definition of race and ethnicity is supported by the fact that racial and ethnic group delineations change over time and space. In the US, racial and ethnic classification have changed 20 times since the first US Census in 1790, which contained only three race categories: White, All Other Free Persons, and Slaves6, and pharmacogenomic genotyping is an even more direct way to assess the presence of toxicity associated variants45. If genomic and genetic technologies of these kinds were widely available for patients, then race and ethnicity would indeed be rendered irrelevant for therapeutic decision making. However, minority populations continue to be vastly underrepresented in clinical genetics cohorts49 and are less likely to have access to genomic medicine technologies51. Until these disparities in research and health care access are rectified, race and ethnicity should continue to serve as a tool for pharmacogenomic patient stratification.Finally, it is worth reiterating that race and ethnicity remain imprecise proxies for genetic diversity, particularly for individual genetic loci, and it must be stressed that there are alternative methods that provide greater accuracy for patient stratification. Genetic ancestry is a far more precise proxy for genetic diversity, and a superior means for stratifying patient populationsWhen it comes to life and death clinical treatment decisions, race and ethnicity clearly matter. Our results demonstrate that the therapeutic relevance of patient race and ethnicity hold despite the demonstrably true facts that (1) race and ethnicity are imprecise proxies for genetic diversity and (2) the vast majority of human genetic variation falls within rather than between demographic groups. Genetic differences between patients that self-identify as belonging to different racial and ethnic groups are nonetheless highly predictive of adverse drug reactions, and this is especially true for minority populations with genetic profiles that differ from the majority group. Disregarding patient race and ethnicity, however well intentioned, will exacerbate rather than alleviate health disparities.Supplementary Table 1Supplementary Table 2Supplementary Figures 1 and 2


Mangifera indica accessions to explore whether the genetic structure of the Australian mango gene pool explained variation in trunk circumference, fruit blush colour and intensity. Multiple population genetic analyses indicate the presence of four genetic clusters and show that the most genetically differentiated cluster contains accessions imported from Southeast Asia (mainly those from Thailand). We find that genetic structure was strongly associated with three traits: trunk circumference, fruit blush colour and intensity in M. indica. This suggests that the history of these accessions could drive spurious associations between loci and key mango phenotypes in the Australian mango gene pool. Incorporating such genetic structure in associations between genotype and phenotype can improve the accuracy of genomic selection, which can assist the future development of new cultivars.Genomic selection is a promising breeding technique for tree crops to accelerate the development of new cultivars. However, factors such as genetic structure can create spurious associations between genotype and phenotype due to the shared history between populations with different trait values. Genetic structure can therefore reduce the accuracy of the genotype to phenotype map, a fundamental requirement of genomic selection models. Here, we employed 272 single nucleotide polymorphisms from 208  Tree crops can be more sustainably cultivated over time than annual field crops, thus helping to manage food supply for an increasing world population2. To create new tree fruit cultivars with improved productivity and quality, we must develop breeding technologies that overcome biological limitations to their production. Tropical species, such as mango, are often large and vigorous3, leading to canopies that rapidly outgrow their orchard space. This generates shade, providing a breeding ground for disease4. To avoid the adverse effects of tree size, trees are traditionally planted at low density and heavily pruned each year4, leading to a reduction in overall production per hectare and an increased cost per unit output. Consequently, a quest to breed smaller, less vigorous trees while maintaining high yields of quality fruit is underway6. Such efforts will produce mango that can be grown in intensive, high-density orchards that produce more fruit per hectare7.Horticultural tree crops are vital for sustainable food production4), can result in a selection process of up to or longer than 10 years from field planting8, making the rapid development of new cultivars unfeasible. The time for cultivar development could be reduced by predicting future phenotypic performance in young individuals using genomic selection, as demonstrated in apples9, sweet cherry10 and strawberry11. Genomic selection uses genotype to phenotype maps from a training population to predict phenotypic variation in untested populations using marker data13. Thus, once a genomic selection model has been created, the length and expense of phenotyping key traits may be reduced. Genomic selection for tree size and vigour of progeny could therefore improve the breeding process and reduce the cost of mango breeding compared to traditional breeding approaches.Traditional tree breeding is slow, as evaluations require an assessment of phenotypic performance in mature trees over many years to account for the effects of variable spatial and temporal environments on phenotypic diversity. These evaluations, in combination with a long juvenile phase , fruit blush colour and intensity – in the Australian mango gene pool. We assessed 272 SNP markers genotyped in 208 M. indica accessions imported worldwide and revealed statistical associations between genetic markers and traits arising from genetic structure. These results will help guide future studies incorporating genetic structure into their genomic selection models.Several studies have evaluated the genetic structure of domesticated mangoM. indica accessions  and a principal component analysis (PCA) across all 208 ons Fig. . Consistons Fig. a, indicaons Fig. . In the ons Fig. a and conons Fig. ; Fig. 2.ons Fig. b, genetiM. indica accessions , Southeast Asia formed one group, while all other accessions were in a second group  and observed heterozygosity (HO) were high across the world, with the Americas having the highest levels of observed heterozygosity (HO = 0.49) and Southeast Asia having the lowest (HO = 0.39). Accessions from the Americas contain an excess of heterozygote individuals . On the other hand, accessions from Southeast Asia are mildly inbred . Private alleles were absent in all regions, indicating either a large intermixing population or the presence of ancestral polymorphisms that have not been sorted across geography.Genetic diversity analyses revealed high levels of heterozygosity and variable patterns of inbreeding across regions Table . Levels ST between genetic clusters and between regions of import. Comparisons between regions have low levels of FST, which range from − 0.016 to 0.112 . In contrast, FST between clusters ranged from 0.051 to 0.286, with cluster 1 comparisons having the highest values . However, given that 39% of mango accessions lacked fruit blush colour and therefore lacked fruit blush intensity, we removed ‘no blush’ and retested the association. It led to a significant yet weaker association between the fruit traits , indicating the importance of no blush in our understanding of the genetics of blush in mango. We found no correlation between trunk circumference and fruit blush colour  and trunk circumference and fruit blush intensity , suggesting trunk circumference is likely to be genetically independent of these fruit traits.Phenotypic correlation analyses revealed associations between fruit blush colour and intensity but not between them and trunk circumference. Trunk circumference, a continuous trait, was highly variable at 9 years, ranging from 27 to 70 cm, while categorical fruit traits were less variable  and fruit blush intensity , but not trunk circumference . Of the regions that had more than ten samples, trunk circumference ranged from a mean of 48.1 ± 1.8 (n = 38) in South Asia to a mean of 52.5 ± 1.3 (n = 46) in the Americas  and the presence of blush , where 18% of individuals in cluster 1 had blush, whereas 70% and 69% of individuals from clusters 2 and 3 had blush, respectively. Cluster 1 is more likely to have lower blush intensity than the other clusters when the ‘no blush’ category is excluded . Finally, cluster had a significant effect on trunk circumference , where cluster 1  and cluster 2  had the largest trunk circumference and cluster 4 had the smallest . Overall, we expect that genetic diversity and factors specific to the region of import will likely influence the genotype to phenotype map of these key mango traits.Fruit blush colour, intensity and trunk circumference were all associated with the four clusters assigned in the HCA. Cluster assignment had a significant effect on fruit blush colour  to Southeast Asia, such as those found in Warschefsky and von Wettberg31 . It is unclear what is driving this difference in the number of private alleles between the two studies. However, some of the factors that could be contributing to the variation in sampled loci include different cultivars, contrasting sequencing techniques (SNP chip vs. Restriction site associated DNA markers), different approaches for calling variants, and for filtering of the minor allele frequency43. The genetic differentiation observed between Southeast Asia and the rest of the world might have been driven by regional cultural differences. For example, in Southeast Asia, mangoes are incorporated into savoury dishes, which might have led to the selection of immature mangoes that stay green while ripening and therefore lack blush31. On the other hand, red blush is favoured around the world44, likely accentuating genetic differentiation between accessions from Southeast Asia and the rest of the world.In our study, cluster 1 (containing only Southeast Asian countries) comprises the most genetically differentiated accessions from across the world. Previous studies support this observation; Warschefsky and von Wettberg46. The genetic architecture of selected traits will largely determine the extent of this correlated evolution. In this study, we show that fruit blush colour and intensity are highly correlated, which might imply a shared genetic architecture. Therefore, selection for either of these traits could partially drive the evolution of the other. For example, the evolution of low blush intensity, but not trunk circumference, might have arisen from selection of low levels of blush colour in Southeast Asia. Selection of polygenic traits and recruitment of pleiotropic genes can also affect levels of genetic differentiation across the genome. Selection for trunk circumference, which is a polygenic trait47, might therefore drive changes in allelic frequencies across many loci. In contrast, fruit colour pigments and their levels, are often controlled by fewer loci in simpler biochemical pathways50. In general, we expect genes controlling plant growth and development53 to be important drivers of genetic differentiation between accessions and merit further attention considering the influence of the genetic architecture of selected traits on population structure.Artificial selection for these cultural preferences may have driven some of the genetic differentiation identified in the Australian mango gene pool. It is well accepted that selecting one trait can incidentally lead to the evolution of other traits through genetic linkage55. A high level of polyembryony can freeze the genetic diversity in a population, as instead of allowing hybridisation and creating unique individuals through recombination, it propagates genetically identical individuals56. Polyembryony can therefore create genetic bottlenecks if only a fraction of the original genetic diversity is propagated, consistent with the signature of inbreeding in Southeast Asian accessions we found in this study. Furthermore, previous studies have found genetic clustering of mango accessions according to their ability to produce polyembryonic seed58. However, embryo type is conflated with geographic region in these studies, where Southeast Asian accessions dominate the polyembryony types. Therefore, without future work teasing apart the contribution of polyembryony and geographic region than we lack an understanding of the various causes of polyembryonic selection and inbreeding on the genetic diversity of tree crops.Polyembryony could have contributed to the origin of genetic differences between Southeast Asia and other accessions. Southeast Asian accessions are typically polyembryonic, where all but one (the zygotic embryo) of the multiple somatic embryos are genetically identical to the maternal parent. Polyembryony is likely to easily be maintained under moderate to strong selection as it is thought to be inherited through a single dominant gene61. For instance, increasing genetic diversity by using a variety of races or genetic clusters in the training and validation sets produced higher prediction accuracies in rice, sorghum16 and wheat61. But genetic diversity is known to reduce prediction accuracy when estimation error is high, which occurs in small populations or when there is low marker density62. By definition, using markers close to the causative variants will augment prediction accuracy during breeding; however, this is hard to achieve with low-density genotyping techniques such as SNP chips and Genotyping by Sequencing. With sequencing that covers the entire genome , factors influenced by linkage disequilibrium can be better controlled, such as finding markers in tight linkage with causative loci. As such, population size, marker density, the genetic structure of the population, and the genetic architecture of the chosen traits will play a significant role in the accuracy of genomic selection models.Genetic diversity and partitioning of genetic structure influence prediction accuracy in genomic selection models across horticultural crops61. The first approach includes principal components from genetic structure analyses as covariates in the model66. However, this method can double-count genetic structure because some elements are included in the model through the genomic relationship matrix67. Another common approach for accounting for genetic structure in genomic selection models is ensuring an equal contribution across genetic clusters in training and validation sets. This stratified sampling approach has been shown to increase prediction accuracy in sorghum16 and maize, and could be an effective method in the Australian mango gene pool. In general, choosing the most accurate genomic selection model will largely depend on the breeding population's genetic structure and the number of samples.To ameliorate the adverse effects of genetic structure in genomic selection models, there are two major approaches used across horticultural cropsThe results of this study reveal that a horticultural species spread across the world has a genetic structure that can create statistical associations between three key traits and genetic markers. To remove the effects of spurious markers, breeders should fully characterise the genetic structure of their breeding population. This will allow them to incorporate sample stratification to improve the performance of genomic selection models. Together with best practices of genomic selection , these considerations can improve the genotype to phenotype map to assist in choosing individuals with accurate breeding values and help advance future parental selection. We hope our study encourages other horticultural breeding programs to follow similar methods.All plant material used in this research was sourced and collected from the Walkamin Research Station, Queensland , where trees are held as a living collection. The Department of Agriculture and Fisheries granted permission as stated in the National Tree Genomics Program – Phenotype Prediction project (AS17000) for use and collection of materials from mango trees from their government station. This study complies with relevant institutional, national, and international guidelines and legislation.M. indica accessions were used from the gene pool collection of the Australian Mango Breeding Program at Walkamin Research Station. These accessions were imported from 21 countries across six geographical regions and were grafted onto the uniform polyembryonic rootstock, Kensington Pride. See Table A total of 208 68. DNA isolation for these genotypes was described in Kuhn et al.69. Briefly, young leaf samples were collected from Walkamin Research Station and the glasshouse at Mareeba Research Facility, Queensland . DNA was extracted using 20 mg of fresh sample with the Qiagen Plant DNeasy kit. SNP genotyping was performed on these DNA samples using the Fluidigm EP-1 platform with 384 biallelic SNP markers. Finally, 272 SNP markers were selected for further analyses, where 236 markers belong to one of 20 linkage groups (7–20 markers per linkage group), and the location of the remaining 36 markers in the genome is unknown68. Genotypically identical individuals across the 272 SNPs were consolidated, leaving 208 mango accessions for the analyses. On average, 98% of the 272 SNPs used in this study were successfully genotyped in every accession.To identify some of the genotypic diversity in the Australian mango gene pool, we used the genotypes from Kuhn et al.M. indica accessions. First, pairwise genetic distances between all accessions were calculated using the percentage method by the “ape” v5.3 R-package70. The HCA was conducted by “stats” v3.6.2 R-package with complete linkage clustering. This computes all pairwise dissimilarities between the accessions in a cluster and accessions in another cluster and considers the largest value of these dissimilarities as a distance between the two clusters. To assess the optimal number of clusters, we used the elbow method71, which plots the total within-cluster sum of squares (WSS) against the number of clusters to show the ‘elbow’ where the WSS rate of decrease slows and indicates diminishing returns with more clusters72.To examine the genotypic clustering of the mango accessions due to genotypic similarity, we performed a hierarchical cluster analysis (HCA) of the 208 73. The PCA was performed using the “stats” v3.6.2 R-package74. Ellipses were constructed for each of the four clusters in the HCA to identify the position of every individual in a cluster in multivariate space with 95% probability.We assessed the major patterns of genetic similarity among the 208 mango accessions in multivariate space using a principal components analysis (PCA) with 272 SNPs. Missing SNP data were imputed using the regularised iterative PCA algorithm with the “missMDA” v1.17 R-packageM. indica accessions with STRUCTURE v2.3.439. STRUCTURE is a Bayesian Markov chain Monte Carlo (MCMC) program that assigns individuals into genetic clusters (K) based on their genotypes by assuming Hardy Weinberg equilibrium within a cluster. It gives each accession an admixture coefficient to depict the proportion of the genome originating from a particular K cluster. We ran the admixture model and the correlated allele frequency model75 with ten independent runs of 100,000 burn-in and 100,000 MCMC iterations for K = 1 to K = 7. We visually inspected summary statistics of MCMC runs to ensure convergence of model parameters. Results were summarised and plotted in the “pophelper” v2.2.7 R-package76. The optimal K value (which represents the most likely number of sub-populations) was estimated by the Evanno method40, which uses the second-order rate of change in the log probability of data between successive K values in the R-package StructureSelector77. The optimal K value was also estimated using LnP(K), the mean log probability of the data. We also followed suggestions by Pritchard et al.78 and Lawson, et al.79 and plotted the lowest K values that capture the primary structure in the data.We determined levels of admixture between all 208 ST and 95% confidence intervals were estimated by “hierfstat” v0.4.22 R-package80. Each accession was assigned to a cluster based on the HCA, and each country of import was grouped into six geographic regions. We calculated 95% confidence intervals for each pairwise comparison using 1000 bootstrap replicates. Significance was determined by whether the confidence interval overlapped with 0.To examine the level of differentiation between the clusters and geographical regions, Weir and Cockerham’s pairwise FM. indica accessions for each of the six geographic regions. A genind object was created in “adegenet” v2.1.2 R-package82 for input into “hierfstat” v0.4.22 R-package80 to calculate observed heterozygosity (Ho), expected heterozygosity (HE) and the inbreeding coefficient (FIS). To determine whether FIS was significantly different from 0, we calculated 95% confidence intervals for each pairwise comparison using 1,000 bootstrap replicates. The number of private alleles (Pr) was calculated with the “poppr” v2.8.6 R-package84.Measures of genetic diversity were calculated for all 208 87. Trunk circumference was measured 10 cm above the graft when the trees were 9 years old at Walkamin Research Station. After maturity (> 5 years old), fruit blush colour and intensity were assessed once a year using ten ripe fruits from each mango accession for at least 2 years. Fruits were taken from the outside of the tree, where they are exposed to full sun and have well developed blush. Fruit blush included five categories: no blush, orange, pink, red and burgundy  and intensity  against the region of import and a linear model for trunk circumference. Region of import was the explanatory variable in each model and included the regions shown in Table We then tested for an effect of genetic structure on the three phenotypes using the optimal cluster assignment of K = 4 from the HCA. Likelihood-ratio chi-square tests were performed for whether cluster explained (1) fruit blush colour, and (2) the presence (n = 127) vs absence of blush (n = 81), irrespective of the intensity of blush. We then removed the individuals with no blush from the dataset to test whether there was a significant difference in fruit blush intensity between clusters for just the individuals with fruit blush using a likelihood-ratio chi-square test with an odds ratio. Finally, we performed a mixed linear model to test the effect of cluster on trunk circumference. JMP v15.2.0 (SAS 2015) produced all statistical results reported here.Supplementary Information 1.Supplementary Information 2.


Salmonella Typhi through DNA and IgM-antibody detection methods as a prelude to extended surveillance activities at sites in Ghana, Madagascar, and Ethiopia.We aimed to assess the prevalence of We performed species-specific real-time polymerase reaction (RT-PCR) to identify bacterial nucleic acid, and enzyme-linked immunosorbent assay (ELISA) for detecting HlyE/STY1498-, CdtB/STY1886-, pilL/STY4539- and Vi-antigens in blood and biopsy specimens of febrile and non-febrile subjects. We generated antigen-specific ELISA proxy cut-offs by change-point analyses, and utilized cumulative sum as detection method coupled with 1000 repetitive bootstrap analyses. We computed prevalence rates in addition to odds ratios to assess correlations between ELISA outcomes and participant characteristics.p = 0.034) in Adama Zuria/Ethiopia, STY1498 , STY1886  and STY4539  in Diego/Madagascar, and STY1498  in Kintampo/Ghana. We found statistical significance in non-febrile male versus female subjects for STY1498  in Adama Zuria/Ethiopia, Vi  in Diego/Madagascar, and STY4539  in Kintampo/Ghana.Definitive positive RT-PCR results were obtained from samples of febrile subjects originating from Adama Zuria/Ethiopia , Wolayita Sodo/Ethiopia , Diego/Madagascar , and Kintampo/Ghana , and from samples of non-febrile subjects from Wolayita Sodo/Ethiopia . While IgM antibodies against all antigens were identified across all sites, prevalence rates were highest at all Ethiopian sites, albeit in non-febrile populations. Significant correlations in febrile subjects aged < 15 years versus ≥ 15 years were detected for Vi (Odds Ratio (OR): 8.00, S. Typhi burden was likely missed. Change-point analyses for generating ELISA proxy cut-offs appeared robust, though misclassification is possible. Our findings provided important information that may be useful to assess sites prior to implementing surveillance for febrile illness including Salmonella disease.Findings indicate non-discriminatory stages of acute infections, though with site-specific differences. Immune responses among non-febrile, presumably healthy participants may mask recall and/or reporting bias leading to misclassification, or asymptomatic, subclinical infection signs induced by suppression of inflammatory responses. As most Ethiopian participants were ≥ 15 years of age and not at high-risk, the true The online version contains supplementary material available at 10.1186/s12879-022-07726-3. Salmonella disease is reported less frequently from Africa compared to Asia largely because its prevalence is assessed via cross-sectional disease investigations at settings scattered across the continent than long-term disease monitoring , Ghana [1B] and Madagascar [1C]. Notes: The location of each site is indicated as a black dot and the site’s name in red font; Ethiopia : nine regional states  and two chartered cities  are shown;SNNPR=Southern Nations, Nationalities and People’s Region; Ghana : ten regions  are shown; Madagascar : six provinces  are shown.Additional file 2. Distribution of ELISA OD values by antigen.Additional file 3. Mean ELISA ODs by antigen. Notes: OD=optical density; red dot=mean of original data; blue dot=mean of each 1,000 bootstrap random samples; light red dashed line=upper and lower 95% confidence interval (CI) of original data; dark red dashed line=upper and lower 99% confidence interval (CI) of original data


Gene flow governs the contemporary spatial structure and dynamic of populations as well as their long-term evolution. For species that disperse using atmospheric or oceanic flows, biophysical models allow predicting the migratory component of gene flow, which facilitates the interpretation of broad-scale spatial structure inferred from observed allele frequencies among populations. However, frequent mismatches between dispersal estimates and observed genetic diversity prevent an operational synthesis for eco-evolutionary projections. Here we use an extensive compilation of 58 population genetic studies of 47 phylogenetically divergent marine sedentary species over the Mediterranean basin to assess how genetic differentiation is predicted by Isolation-By-Distance, single-generation dispersal and multi-generation dispersal models. Unlike previous approaches, the latter unveil explicit parents-to-offspring links  and implicit links among siblings from a common ancestor . We find that almost 70 % of observed variance in genetic differentiation is explained by coalescent connectivity over multiple generations, significantly outperforming other models. Our results offer great promises to untangle the eco-evolutionary forces that shape sedentary population structure and to anticipate climate-driven redistributions, altogether improving spatial conservation planning. This study uses a compilation of 58 population genetic studies of 47 phylogenetically divergent marine sedentary species over the Mediterranean basin to assess how genetic differentiation is predicted by different dispersal models. Multi-generation dispersal models reveal implicit links among siblings from a common ancestor  that could improve spatial conservation planning. By introducing foreign alleles to local populations, gene flow spreads adaptative changes and tends to alleviate the effect of inbreeding depression3. Simultaneously, gene flow homogenizes allele frequency among populations, which counteracts the effects of local adaptation, reducing the mean fitness of populations 2. Fundamentally, gene flow is ensured when dispersal is followed by reproduction and subsequent offspring survival6. A common confusion prevails between demographic connectivity , which is usually assessed by direct detection of individuals , and genetic connectivity , which is indirectly estimated thanks to population genetics analyses10. In this way, demographic and genetic connectivity seem to interact on specific -yet poorly appreciated- temporal and spatial scales14. This may explain the numerous mismatches reported between demographic connectivity and gene flow estimates9.Gene flow counterbalances natural selection and genetic drift, reshuffles mutations among spatial locations and contributes to shaping the contemporary spatial patterns of biodiversity16. Dispersal results from movements of individuals themselves or movements induced by third parties categorized as biotic  or abiotic, that is driven by winds and ocean currents19. This study focuses on species that, as adults, have no or little displacement abilities  so that their connectivity is mostly ensured by the abiotic dispersal of propagules, like numerous marine and terrestrial taxa. This alleviates the difficulties in appraising the movements of wild populations and biotic third parties. In marine sedentary populations, early-life non-motile stages  are regularly released in the water column and are then passively transported across the seascape by anisotropic currents over various spatial scales21, ensuring the replenishment of both local and distant populations23. As such, the proper evaluation of current-driven dispersal should help us disentangling the evolutionary forces  shaping marine biodiversity and its climate-driven redistributions24. The inherent spatial scales of genetic structures are generally a few orders of magnitude higher than potential dispersal distances over a single generation25, even for species exhibiting rare long-distance dispersal27. Likewise, a single-generation dispersal event does not allow to evaluate the evolutionary timescale over which gene flow shapes genetic diversity6. Theory predicts instead that consecutive dispersal events of numerous propagules, acting in synergy with other evolutionary forces, shape together the genetic diversity observed at broad-scale28. Consequently, modelling genetic diversity from the unique perspective of water-borne dispersal should enlighten the typical scales and relative importance of evolutionary forces that shape the spatial structure of marine sedentary populations.This paradox could stem from the fact that dispersal is a complex and multi-aspect process involving interlocked ecological and evolutionary mechanisms29. First, it requires to jointly account for the spatio-temporal variability of currents, the species-specific early-life traits, the habitat patchiness30, as well as to consider all possible connectivity pathways31. Second, it must simulate consecutive dispersal events by considering multiple generations of migrants where each intermediary connected population acts as a stepping-stone. Third, it must simulate all possibly existing populations, not just known or sampled ones. Biophysical models, which simulate explicitly the dispersal of propagules by oceanic chaotic flows, have been widely used in the last few decades to derive physical connectivity metrics such as dispersal kernels33. Simulations of single-generation dispersal commonly provide quantitative estimates of how distant populations are connected with each other. However, they rarely match observed gene flows8, possibly due to intrinsic flaws such as disregarding the multi-generational character of successive dispersal events36, while overlooking intermediate stepping-stone connections.Modelling water-borne dispersal is a multidisciplinary challenge sharing tight commonalities with air-borne dispersal38. As shortest and most-probable paths of such networks differ39, these methods neglect all other possible pathways that may drastically change the resulting connectivity40. Another approach uses Markov chains and matrix multiplications to estimate the probability of connection between locality-pairs over a given number of generations43. Studies using this method did not consider all the inherent dispersal pathways as they only assessed the connection probabilities occurring at a given number of generations  while neglecting all intermediate connections associated to any number of generations lower or equal to the prescribed number of dispersal events44. Moreover, and to our knowledge, all present modelling approaches simulate stepping-stone dispersal of single lineages; in other words, they estimate the connectivity resulting from explicit parents-to-offspring connections, i.e., filial connectivity. However, it is conspicuous that two fully disconnected localities, which are both replenished by migrants originating from the same source locality, should share common alleles and thus display similar allele frequencies.In the marine realm, current models considering multi-generational dispersal are seldom used, concern only a few specific species or taxa, and still inadequately explain genetic differentiation measures. They rely on the computation of connectivity matrices, which are mathematical objects describing dispersal of propagules exchanged between discrete populations, hereafter called localities. Such matrices can be interpreted as adjacency matrices of directed and weighted networks (or graphs). Thus, an approach consists in considering network theory tools such as shortest paths analysis to estimate the strength of connections among two distinct localities over multiple dispersal events44, which highlights implicit links among siblings through common ancestors has been overlooked to-date, although it could largely alter gene flow predictions and contribute to the aforementioned discrepancies between predicted dispersal and realized gene flow assessments1. Note that here the term “coalescent” refers to the dispersal model, not to the genetic model. Modelling such implicit connections would permit going beyond the concept of pairwise interactions addressing higher-order dynamics, a perspective that is lately attracting lots of interests, both in Network Theory45 and Theoretical Ecology47.The consideration in dispersal models of this conceptual view of coalescent connectivity44. Our models provide not only a precise estimation of the explicit links  but also allow computing, for the first time, the implicit links existing between any locality-pair having common source localities through multi-generational dispersal . After parameterizing our model with the main dispersal traits of various taxa encompassing seagrasses, algae and metazoans, we test modelled gene flow predictions against an extensive compilation of observed genetic structures  over the whole Mediterranean Sea. We find that coalescent connectivity through multi-generation dispersal explains almost 70% of the observed variance of genetic structures, substantially improving gene flow predictions with respect to previous approaches. Furthermore, the optimal number of generations to best predict gene flow significantly correlates with the sampling coverage scaled by the species-specific dispersal abilities, enlightening the typical scales of eco-evolutionary processes. It suggests that our model could be used to infer population genetic structures, a key pre-requisite for management and protection.In this work, we present an exhaustive comparison between demographic and genetic basin-scale connectivity based on classical and novel dispersal metrics across a meta-analysis of several marine taxa. While our results mainly apply to marine sedentary populations whose dispersal is mediated by ocean currents, our models and conclusions have the potential to transform how water- as well as air-borne dispersal of sedentary terrestrial populations are evaluated. Here, we introduce state-of-the-art multi-generation dispersal models that evaluate all connections among locality-pairs for a fixed number of generations while simultaneously accounting for those accumulated by previous generations44, simulating filial and coalescent connectivity respectively, against an extensive compilation of 58 genetic structures observed in the Mediterranean Sea. The dataset contains 3821 observed 52, fed by the horizontal multi-year velocity field from an operational data-assimilative ocean model53. Each species is characterized by three main dispersal traits: Pelagic Larval Duration , followed by both sea-least cost (0.58) and Euclidian (0.59) IBD models. The highest mean R2 stem from both multi-generation models, with 0.68 for explicit dispersal and 0.69 for implicit dispersal models.Among the 58 compiled basin-scale population genetic studies Fig. , we testels Fig.  considerNext, we evaluate the quality  of each model relative to each of the other models, and then to all models together, thanks to the AIC estimator. To do so, the same expected value,R2 values range from 0.11 for the colonial ascidian Botryllus schlosseri55 to 1 for the ascidian Halocynthia papillosa56, the sea cucumber Holothuria mammata57 and the sea snail Phorcus turbinatus56 . Similar results are obtained for instance for a seagrass species, Cymodocea nodosa62 , the genetic structure (high R2 = 0.77***).When inspecting the study-specific accuracy of the best multi-generation implicit dispersal model, s56 Fig. . For stuobs Fig. . \documea62 Fig. . Althougbtw) has a significant effect on model fit results  as well as with the range of R2 = 0.18***, Supplementary Table R2, i.e., successful gene flow predictions, as a function of categories of number of sampled localities is well predicted by a logit model using a binomial distribution  could be interpreted as the average velocity of genetic connectivity in km.generations−1. Altogether and assuming that the species-specific dispersal traits have been accurately parametrized, it suggests that the non-significant gene flow predictions  could be attributed to too scarcely and spatially-restricted sampling rather than to the models abilities themselves.We then test the robustness of the multi-generation implicit dispersal model with respect to the species and studies attributes. None of these factors  as reflected in observed genetic differentiation and as predicted by theoretical gene flow magnitudes7. Consequently, the reduced gene flow measured across these barriers is probably due to the large environmental gradients  rather than to intrinsic transport barriers (as propagules effectively disperse through them44). Since IBD is an analytical model with little biological meaning as it tentatively explains genetic differentiation by only accounting for geographical distances, it does not allow disentangling the relative importance of evolutionary processes that control gene flow. In line with the distinction between IBD and Isolation-by-Environment71, and since our mechanistic multi-generation dispersal models realistically simulate stepping-stone dispersal, one can tease apart the respective role of evolutionary forces in driving gene flow. In fact, the non-significant predictions of observed genetic differentiation suggests that strong evolutionary forces  not considered in our approach are at play. Moreover, our analysis indicates that addressing implicit connections and thus going beyond simple pairwise perspectives 45, can significantly improve the understanding of biogeographical and genetic patterns47. Altogether, our results suggest that the supposed physical barriers often underlined in seascape analyses are more permeable to dispersal than previously thought70, and that genetic isolation in the marine realm could be rather due to environmental selection acting on drifting propagules and settled adults as well as intrinsic reproductive isolation72. Since ocean currents73, transport and mixing processes74, as well as ocean temperatures75, are already changing fast, the structure of marine populations is expected to fluctuate accordingly, consistently with the recent evidence of spatial reorganization of marine biodiversity24.Our results also show that IBD models (Euclidian and sea-least-cost distance) better explain observed genetic differentiation than the single-generation dispersal model, in accord with previous studies37 or set arbitrarily the number of generations from dozens (as it was considered sufficient to span the studied domain35) to thousands  of generations43. It illustrates that the typical time scales over which demographic connectivity interplays with genetic connectivity are difficult to infer3. The relationship between the main dispersal trait (PLD) and the spatial extent of the sampling divided by the optimal number of generations . Moreover, island model theory assumes that the metapopulation has reached an equilibrium between gene flow and genetic drift76, suggesting that the implicit model should predict best 43 . The relatively low median optimal number of generations disclosed here  with 25 or less intermediate steps83, they can also foster abrupt transitions45. Investigating such aspects in marine ecosystems would provide crucial information about how they could react to future perturbations and open the way to novel approaches to spatial ecology. In the context of biodiversity loss84 and spatial reorganization24, it is indeed urgent to better understand the eco-evolutionary dynamics that continuously shape population structures to improve protection strategies85 and management of natural resources such as forestry and fisheries86.Last but not least, our multi-generation mechanistic dispersal models, which allow assessing both filial and coalescent connectivity and are applicable to other taxonomic groups and ecosystems, could serve as future guidelines to optimize sampling design for population genetic studies and anticipate the structure of wild sedentary populations. Moreover, while higher-order interactions can stabilize competitive ecosystemsOphioderma longicauda87 and Hexaplex trunculus88.We screen the published literature over the last two decades to gather population genetic studies focussing on marine species at basin-scale in the Mediterranean Sea. While our meta-analysis intends to be the most comprehensive possible in terms of collected data and taxa covered, pre-selected studies are filtered out based on two criteria: the biological traits of the species and the sampling design. In this way, we exclude datasets that are not appropriate to address our research question while optimizing statistical discriminatory power. More specifically, we select studies (i) that target poorly mobile species, i.e., characterized by a biphasic life cycle with early-life free-swimming dispersing propagules  and sessile to sedentary adult behaviour Fig.  and (ii)Dbtw, quantitatively evaluates the spatial coverage of the sampling strategy carried out in each study.To gauge the sampling strategy of each selected study, we compute the mean straight-line geographical distance (in km) between sampled localities. This geometrical metric, that we called Based on literature review . It combines a particle-tracking model with graph theory tools to generate and analyse connectivity matrices  variability of ocean currents is negligible as compared to their inter- and intra-annual variations, our approximation using 10 recent years allows comprehending the full variability of both contemporary and past oceanic flows. By computing billions of Lagrangian trajectories and recording their initial and final positions, the LFN constructs 402 connectivity matrices for each of the 10 habitat/PLD combinations, resulting in 4020 matrices in total. The elements mij of each raw n × n connectivity matrix encode the number of propagules advected between all locality-pairs; they are converted into backward-in-time dispersal probabilities thanks to a column-normalization:The Mediterranean Sea surface characterized by favourable habitats is subdivided into several ¼° sub-areas that are considered in our analysis as theoretically isolated marine localities, resulting in tat Fig.  while thies Fig. . For eacM are aggregated  according to their starting dates to match each species-specific spawning phenology. We average 402 matrices for year-round release and about 100 matrices for seasonal release  that fits best its dispersal traits, averaging ten years of realistic current-driven dispersal in the Mediterranean Sea. Single-generation dispersal estimates are directly extracted from one of these composite matrices. As explained next, multi-generational dispersal estimates are finally obtained by applying additional computations on these composite matrices.Then connectivity matrices 44 evaluate filial connectivity from parents to children, assuming unique lineage  the adequate consideration of putative intermediate connections or steps between any of the non-sampled localities of both habitats G = 2 generations based on the composite matrix P is:The general expression of explicit dispersal probabilities over L is the all-ones matrix minus the identity matrix. When applying Eq.  and the sea-least-cost distance, which corresponds to the length of the shortest path considering only maritime areas. Sea-least-cost distances are calculated thanks to the Marmap package90 (version 1.0.4) on R (version 4.0.2). For the IBD analyses, we consider a two-dimensional dispersal model and thus test for predictions of e(distance)54.We test for the predictions of R2 is computed with the function ‘r.squaredGLMM’ of the MuMIn package (version 1.43.17). All these tests are computed using the R software (version 4.0.5). For each study, we compare MLPE model predictions to determine the best model ability to predict gene flow. More specifically, we compute for each study the relative likelihood exp((AICmin− AICi)/2) to determine the probability of each model i  to minimize the information loss of being the best one. We cross-compare model predictions across the entire meta-analysis by computing (i) the relative likelihood for each model against the four others  and (ii) the non-symmetrical pairwise relative likelihood difference . For example, for models A and B whose relative likelihood are 1 and 0.7 respectively, the relative likelihood difference between A and B is 0.3 and the relative likelihood difference between B and A is −0.3.To estimate the quality (predictive power) of each model relative to each of the other models, we also test for predictions of R2 and p-values) against species-specific  and studies-specific (marker, number of sampled localities, Dbtw and We test the sensitivity of MLPE model results (Throughout the entire manuscript, asterisks inform about the significance of all statistical tests, as follows: *≤ 0.05; **≤ 0.005; ***≤ 0.0005 and “ns” stands for statistically non-significant.Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Reporting Summary


Despite advances in molecular characterization, glioblastoma (GBM) remains the most common and lethal brain tumour with high mortality rates in both paediatric and adult patients. The signal transducer and activator of transcription 3 (STAT3) is an important oncogenic driver of GBM. Although STAT3 reportedly plays a role in autophagy of some cells, its role in cancer cell autophagy remains unclear. In this study, we found Serine‐727 and Tyrosine‐705 phosphorylation of STAT3 was constitutive in GBM cell lines. Tyrosine phosphorylation of STAT3 in GBM cells suppresses autophagy, whereas knockout (KO) of STAT3 increases ULK1 gene expression, increases TSC2‐AMPKα‐ULK1 signalling, and increases lysosomal Cathepsin D processing, leading to the stimulation of autophagy. Rescue of STAT3‐KO cells by the enforced expression of wild‐type (WT) STAT3 reverses these pathways and inhibits autophagy. Conversely, expression of Y705F‐ and S727A‐STAT3 phosphorylation deficient mutants in STAT3‐KO cells did not suppress autophagy. Inhibition of ULK1 activity (by treatment with MRT68921) or its expression (by siRNA knockdown) in STAT3‐KO cells inhibits autophagy and sensitizes cells to apoptosis. Taken together, our findings suggest that serine and tyrosine phosphorylation of STAT3 play critical roles in STAT3‐dependent autophagy in GBM, and thus are potential targets to treat GBM. Autophagy has a complex and context‐dependent role in tumour development and cancer therapy.Although these studies underscore the relevance of autophagy in GBM, little is known about the function of STAT3 signalling in regulating autophagy in GBM. Pharmacologic inhibition of either JAK2 (using SAR317461)22.1Materials purchased include the following: Foetal bovine serum ; Enhanced chemiluminescence (ECL) Western blot detection system ; Protease/Phosphatase Inhibitor Cocktail, cleaved active caspase‐3 (Asp 175); LC3‐I/LC3‐II; SQSTM1/p62, p‐Akt Ser473, HDAC‐6, phospho‐S6 Ribosomal protein Ser235/236, p‐STAT3 Y705, p‐STAT3 Ser727, Acetyl‐STAT3 Lys685, total‐STAT3, p‐AMPKα Thr172, Total‐ΑΜPΚα, p‐ULK1 Ser555, p‐ULK1 Ser638, Total‐ULK1, p‐TSC2 Ser1387, p‐TSC2 Ser1462, Total‐Tuberin/TSC2, Beclin‐1, BNIP3 and Cathepsin‐D antibodies ; Alexa‐Fluor 488 conjugated, Alexa‐Fluor 647, and Cy3 conjugated secondary antibodies (Molecular Probes); Anti‐trimethyl STAT3 Lys180, and Bafilomycin A1, (EMD Biosciences/Millipore Corp.); ULK1 Inhibitor (MRT68921) (MedChemExpress). The ULK1 siRNA and transfection reagent were obtained from Santa Cruz Biotechnology. The cell death detection ELISA kit was purchased from Roche (Millipore Sigma). All chemicals were of the highest purity commercially available.2.22, as described previously.MT330  and LN229 (ATCC CRL‐2611) were grown in DMEM containing high glucose, containing 10% foetal bovine serum, and supplemented with 1X antibiotic‐antimycotic solution  at 37°C with 5% COSTAT3 was knocked out in both LN229 and MT330 cells by CRISPR/Cas9‐mediated gene editing, and the constructs for wild‐type (WT), Y705‐STAT3 and S727A‐STAT3 mutants were expressed in STAT3‐KO cells by lentiviral transduction, as described previously.2.3Cell lysates were prepared using mammalian protein extraction buffer  and a protease inhibitor cocktail followed by SDS‐PAGE. Proteins were transferred to Immobilon‐P membranes (Millipore Bedford) and probed with primary antibodies overnight at 4°C in TBS buffer containing 0.1% Tween‐20 and 5% nonfat dry milk (Bio‐Rad). Membranes were subsequently incubated with horseradish peroxidase‐conjugated secondary antibodies at room temperature for 1 h, and the immunocomplexes were visualized by the ECL detection system (Perkin Elmer) quantified on the Azure Biosystems C500. Membranes were stripped and re‐probed for actin or GAPDH as loading controls. Representative Western blots from three experiments are shown. Densitometric analysis of all Western blots was performed using Image J software.2.4g for 15 min at 4°C. The cell extracts containing equal amounts of protein were incubated with STAT3 antibody overnight at 4°C followed by addition of protein A/G agarose beads (Santa Cruz Biotechnology) with gentle rocking for 2 h. The beads were washed 3 times with lysis buffer and once with PBS, and the immunocomplexes were released by heating in Laemmli sample buffer and analysed by Western blotting using trimethyl‐STAT3 antibody (EMD Biosciences/Millipore Corp.).Glioblastoma cells were rinsed with ice cold PBS and lysed using a cell lysis buffer  containing protease and phosphatase inhibitors (Thermo Fisher Scientific). The lysates were clarified by centrifugation at 14,300 2.5Cells were cultured in chamber slides (Millipore) to ~70% confluence and washed with PBS. Cells were fixed in 4% paraformaldehyde and methanol, and permeabilized with 1% Triton X‐100. After blocking with 5% goat serum, cells were incubated with anti‐rabbit LC3 and anti‐mouse p62 antibodies and subsequently stained with Alexa Fluor 488 (goat anti‐rabbit) and Alexa Fluor 633 (goat anti‐mouse) secondary antibodies, as described previously.2.62 incubator. The following day complete growth medium containing 2 times the normal serum and antibiotics was added without removing the transfection mixture. After an additional incubation for 18–24 h, the medium was aspirated and replaced with fresh 1X growth medium. After another 24 h of incubation, cells were treated with or without 100 nM Baf and assayed for autophagy and apoptosis markers. Efficiency of transfection was monitored using FITC‐conjugated control siRNA.MT330 cells were grown to 60%–70% confluency in 6‐well tissue culture plates, and siRNA transfection was performed using a protocol available from Santa Cruz Biotechnology. For ULK1 siRNA transfection, the cell monolayer was washed with siRNA transfection medium (Santa Cruz) and the siRNA/transfection reagent mixture was added dropwise on to the cell monolayer and incubated overnight at 37°C in a CO2.7TRIzol reagent (Thermo Fisher Scientific) was used to extract total‐RNA. Total RNA concentrations were quantified by measuring A260 and A280 using NanoDrop spectrophotometry. Total‐RNA (1 mg) was reverse‐transcribed to cDNA using a kit from Promega and following manufacturer's instructions. The cDNA was diluted 1:5 with DNase‐free water. Real‐time qPCR was performed using an Ariamx Real‐Time PCR system (Agilent Technologies) with 2.5 ml of the cDNA product in a 25 ml reaction mixture containing 1X SYBR® Green Master Mix (Applied Biosystems) and 120 nM forward and reverse primers. The primers used for human ULK1 forward (5′‐ GGGCAAGTTCGAGTTCTCC‐ 3′), reverse (5′‐ GCCATTTCCTGGAAGTCGTA‐ 3′); BNIP3 forward (5′‐ CGCAGCTGAAGCACATCC‐ 3′), reverse (5′ – CTTGGAGCTACTCCGTCCAG– 3′); p62 forward (5′‐GCCTCTGGTTCTGACACTTT‐3′), reverse (5′‐GGTGAGGTGGAAGGCATTTA‐3); beta actin forward (5′‐ ACCTTCTACAATGAGCTGCG‐ 3′) and reverse (5′‐ CCTGGATAGCAACGTACATGG‐ 3′) sequences were used. The qPCR conditions were 50°C for 2 min, 95°C for 10 min, followed by 40 cycles of 95°C for 15 s and 60°C for 1 min, as described previously. Samples were analysed using the comparative ΔCT method, and gene expression was normalized to beta‐actin expression. Data were presented as fold changes with empty‐vector as control set to 1.0.2.8The quantitative DNA fragmentation assay was performed using a cell death ELISA kit as described earlier.2.9t‐test was used to assess statistical significance. Data are expressed as mean ± SE. Experiments were repeated three times, with triplicate samples for each. Unless otherwise stated, values of *p < 0.05, **p < 0.01, ***p < 0.001 and ****p < 0.0001 were considered significant.All data were analysed by GraphPad Prism 9 program (GraphPad Software Inc.), and an unpaired 2‐tailed Student's 33.1We previously established STAT3‐KO MT330 GBM cells by CRISPR/Cas9 gene editing; as a control, cells were transduced with empty vector (EV). Absence of STAT3 protein in MT330 cells was validated in whole‐cell extracts by immunoblotting with antibodies to STAT3 Figure . STAT3 eTo characterize whether STAT3‐deficiency may regulate autophagy in MT330 GBM cells, we examined AMPKα and Unc‐51‐like kinase 1 (ULK1) phosphorylation status in STAT3‐KO MT330 cells. AMPKα is activated in response to cellular stressSince AMPKα inhibits mTORC1 by phosphorylating and activating TSC2,3.2We also examined the role of STAT3 in GBM autophagy regulation in another GBM cell line, LN229 cells, in which STAT3 was also knocked out or restored with WT‐STAT3 and STAT3 phosphodeficient mutants (Y705F and S727A). Consistent with the results in MT330 cells, STAT3 was phosphorylated on both Y705 and S727 sites in EV‐transduced LN229 cells Figure . Rescue To validate our findings on the role of STAT3 in autophagy, LN229 cells were treated with Baf and LC3‐II levels determined. Baf is a specific inhibitor of vacuolar‐type ATPase that is known to prevent the fusion of autophagosomes with lysosomes resulting in LC3‐II accumulation and blockade of the autophagic flux.Deletion of STAT3 in LN229 cells increased AMPKα activity ,We also examined the role of STAT3 in mTOR phosphorylation in LN229 cells. The mTOR protein and its basal phosphorylation status at S2481 were unaltered in the KO and lines expressing STAT3 phosphorylation‐inactive mutants Figure , demonstWe examined whether STAT3‐KO activates ULK1 signalling in LN229 cells. Our data show that ULK1 protein was detectable in LN229 cells. Although STAT3‐KO decreased ULK1 protein levels Figure , it sign3.3To further assess the impact of STAT3 deletion and rescue of KO cells with STAT3 mutants, MT330 cells were treated with Baf. Under basal conditions, LC3‐II levels were undetectable in control MT330 EV cells and Baf treatment had no effect on LC3‐II levels, showing minimal basal autophagy Figure . In contThe p62 protein has often been used as an inverse marker for autophagy flux, and high expression of p62 is found in GBM patient tumours.Additional evidence for the induction of autophagy was seen by an increase in the levels of mature heavy chain Cathepsin‐D in STAT3‐KO cells, which was blocked when KO cells were rescued with WT‐STAT3 Figure . In cellWe used a catalytic mTOR inhibitor, Everolimus (RAD001), to further characterize the role of STAT3 in regulating autophagy by mTORC1. Treatment of EV MT330 cells with Everolimus inhibited both mTOR S2448 phosphorylation Figure  and pS6KSince Prom1 is a pro‐autophagy protein in normal cells,3.4BNIP3 has been shown to play a role in regulating the autophagy pathway.Consistent with data in Figure We next examined p62 and ULK1 transcript levels in LN229 cells. Our data demonstrate that STAT3‐KO and expression of STAT3 mutants significantly reduced p62 protein levels Figure . To demo3.5To further validate the role of STAT3 in autophagy, we performed immunolocalization studies to detect LC3 and p62 puncta formation. Under basal conditions, we observed diffuse LC3 (green) and p62 (red) staining (green) predominantly in the cytoplasm of control (EV) MT330 cells with some nuclear staining Figure . Treatme3.6Because STAT3‐KO in MT330 cells induces autophagy and concomitantly activates AMPKα/ULK1 signalling, we tested the effects of pharmacologic inhibitors of ULK1. Previous studies showed that MRT68921 (inhibitor of ULK1 and ULK2) blocks autophagy flux,MRT68921 also significantly increased LC3‐II levels in STAT3‐KO cells and in STAT3‐KO cells transduced with the phosphorylation‐defective Y705F or S727A mutants Figure . Given tTo further elucidate the role of ULK1 inhibition in MT330 cells, we determined the effect of MRT68921 treatment on apoptosis as determined by caspase‐3 cleavage. MRT68921 induced apoptosis in STAT3‐KO and STAT3‐mutant expressing lines but not in control EV and cells reconstituted with WT‐STAT3 Figure . InteresTo further confirm the role of ULK1 in autophagy regulation, we knocked down ULK1 with siRNA in STAT3‐deleted MT330 cells and STAT3‐mutant expressing lines that contained high levels of ULK1 Figure . ControlTo investigate whether ULK1 knockdown induces apoptosis, we measured caspase‐3 activation. Consistent with our observations involving inhibiting ULK1 activity by MRT68921 Figure . HoweverBased on our data, we propose a conceptual model Figure , left bySTAT3 deletion also increases expression of the pro‐autophagy protein, Prom1,4A key finding of these studies is that constitutive STAT3 phosphorylation suppresses autophagy in GBM cells. KO of STAT3 and the expression of STAT3 phosphorylation‐defective mutants in GBM cells increase autophagy via upregulation of p62/SQSTM1 degradation, LC3 conversion and lysosomal activity. Loss of STAT3 functional activity increases phosphorylation of AMPKα, TSC2 and ULK1 on multiple sites, which were reversed by expression of ectopic WT‐STAT3 in STAT3‐KO GBM cells. Expression of the Y705F and S727A mutants in STAT3‐KO LN229 and MT330 cells robustly increases autophagy, demonstrating that STAT3 function is indispensable for suppressing autophagy in GBM cells.The role of autophagy in cancer is controversial because it has been reported to both promote and inhibit tumorigenesis.STAT3 undergoes serine and tyrosine phosphorylation that are molecular switches governing STAT3 activation and localization. Besides its well‐known Y705 phosphorylation, STAT3 is phosphorylated on S727, which regulates its mitochondrial localization.STAT3 has been shown to regulate autophagy through several mechanisms.Since enhanced autophagy results in p62 degradation and upregulation of Prom1 expression, we expected that induction of autophagy flux by STAT3 deletion would lead to enhanced degradation of p62 and increase Prom1 protein levels. Supporting this, STAT3‐KO and Y705 mutant expressing cells showed reduced p62 protein levels by activation of autophagy flux. Control and WT‐STAT3 expressing STAT3‐KO cells had reduced autophagy flux and impaired autolysosomal p62 degradation in GBM cells. STAT3‐KO and mutant expressing cells show upregulation of Prom1 protein levels, confirming autophagy induction. In addition, our results showed for the first time that p62 gene expression was significantly downregulated in these lines demonstrating that p62 is transcriptionally regulated by STAT3. Reconstitution of STAT3‐KO cells with WT‐STAT3 restored p62 gene expression confirming that STAT3 regulates p62 at the transcriptional level in MT330 cells. In contrast, p62 gene expression was unaltered in STAT3‐KO and phosphorylation‐defective mutant expressing LN229 lines. However, p62 protein levels were still reduced in these cells, suggesting transcription‐independent but autophagy‐dependent regulation of p62 in LN229 cells.AMPKα maintains energy homeostasis and plays an important role in autophagy induction. AMPKα negatively regulates mTORC1 through TSC2, activates ULK1 Ser555 and Beclin1 Thr388 phosphorylation, all of which initiate autophagy.AMPKα activation inhibits mTORC1, which leads to autophagy. In addition to regulating mTORC1, AMPKα activation inhibits tumour cell growth by phosphorylating TSC2 on S1387, which in turn inhibits mTORC1 leading to autophagy activation.Consistent with STAT3 suppressing autophagy through inhibition of AMPKα/ULKI signalling, we found that inhibiting ULK1 activity by MRT68921 or ULK1 protein knockdown by siRNA decreased autophagy, and sensitized STAT3‐KO and STAT3‐phosphodeficient mutant expressing lines to caspase‐3‐dependent apoptosis. These results suggest that autophagy is cytoprotective in STAT3‐KO cells. Thus, approaches directed at inhibiting ULK1 may inhibit autophagy through downstream blockade of ULK1‐Atg14‐Beclin1 signalling and consequently lead to GBM cell death. To our knowledge, this is the first example of the involvement of ULK1 signalling in the regulation of STAT3‐dependent autophagy/apoptosis and highlight the targeting of both STAT3 and ULK1 as a potential novel therapeutic approach for GBM treatment.Sujoy Bhattacharya: Conceptualization (lead); data curation (lead); formal analysis (lead); investigation (lead); methodology (lead); project administration (lead); validation ; writing – original draft (lead); writing – review and editing (supporting). Jinggang Yin: Data curation (supporting); formal analysis (supporting); investigation (supporting); methodology (supporting). Chuanhe Yang: Data curation (supporting); formal analysis (supporting); investigation (supporting). Yinan Wang: Data curation (supporting); investigation (supporting); methodology (supporting). Michelle Sims: Data curation (supporting); methodology (supporting). Lawrence M. Pfeffer: Conceptualization (supporting); data curation (supporting); investigation (lead); project administration (supporting); resources (supporting); supervision (supporting); validation (supporting); writing – original draft (supporting); writing – review and editing . Edward Chaum: Conceptualization (supporting); formal analysis (supporting); funding acquisition (lead); investigation (supporting); project administration (lead); resources (lead); supervision (lead); writing – original draft (supporting); writing – review and editing (supporting).The authors confirm that there are no conflicts of interest.Figure S1Click here for additional data file.