Agriculture first reached the Iberian Peninsula around 5700 BCE. However, little is known about the genetic structure and changes of prehistoric populations in different geographic areas of Iberia. In our study, we focus on the maternal genetic makeup of the Neolithic (~ 5500–3000 BCE), Chalcolithic (~ 3000–2200 BCE) and Early Bronze Age (~ 2200–1500 BCE). We report ancient mitochondrial DNA results of 213 individuals (151 HVS-I sequences) from the northeast, central, southeast and southwest regions and thus on the largest archaeogenetic dataset from the Peninsula to date. Similar to other parts of Europe, we observe a discontinuity between hunter-gatherers and the first farmers of the Neolithic. During the subsequent periods, we detect regional continuity of Early Neolithic lineages across Iberia, however the genetic contribution of hunter-gatherers is generally higher than in other parts of Europe and varies regionally. In contrast to ancient DNA findings from Central Europe, we do not observe a major turnover in the mtDNA record of the Iberian Late Chalcolithic and Early Bronze Age, suggesting that the population history of the Iberian Peninsula is distinct in character. This so-called ‘Neolithic transition’ brought about fundamental changes in economy, social structure, demography and human health, and laid the foundations for agrarian societies and thus for ancient civilizations. Over the course of the 7th and 6th millennia BCE, agriculture spread from the Balkans to Central Europe. Another route of dissemination ran along the Mediterranean coastlines of Greece, Italy and the south of France to the Iberian Peninsula and north to the Paris Basin and Central Europe4. However, the process of Neolithisation was non-linear, with the archaeological record documenting influences of local cultural traditions5. In Iberia, the Neolithic transition, which began around 5700 BCE, appears to have been complex, and Mesolithic and farming communities coexisted and interacted for as long as two millennia6.The changeover from a hunter-gatherer lifestyle to a productive mode of subsistence first emerged around 10,000 BCE in the Near EastLigurian Impressa collections of Italian origin9. Some Early Neolithic sites were also located in the hinterland, suggesting further routes of dissemination through the Pyrenees and/or along major rivers, such as the Ebro16. On the other hand, North African influences and contacts are tangible in the southern Iberian Neolithic13. All in all, Iberia appears a melting pot of influences and groups, combining Neolithic lifeways and with indigenous mechanisms of adaptation14.The artefactual remains, mainly ceramic, attest to the different origins and modes of Neolithisation on the Iberian Peninsula. On one hand stands a Mediterranean maritime colonization by Neolithic pioneers characterized by ceramic with clear parallels to the 15. In the whole territory, for example, there existed sophisticated systems of agriculture and livestock handling, with adaptable crops17 and seasonal strategies in flock management20. The groups of the Franco-Iberian Cardial and the Epicardial pottery styles appeared at this time and recent studies have revealed mutual diachronic influences in the material culture and economies of these cultures21.In the Early Neolithic, we can observe common features shared over large areas, but also some regionally restricted phenomena24. The oldest are individual inhumations, sometimes grouped in cemeteries25. It is also common to find human remains in caves, which were increasingly used for collective burials26. From the late 5th millennium BCE onward, burial monuments appeared, and megalithic tombs became widespread27. This phenomenon links Iberia with other parts of Europe, indicating long-distance networks of communication. Meanwhile, certain parts of northeast Iberia maintained individual inhumations in pits, mostly in small cemeteries28. Besides megalithic tombs, ditched enclosures extending over more than 100 hectares started to dominate the landscapes of southern and central Iberia from 3300 to 3100 BCE, highlighting another widely spread European phenomenon30. During the Iberian Chalcolithic period (3000–2200 BCE), fortified settlements with stone walls and semi-circular bastions appeared in the western and southern parts of the Peninsula, while elsewhere, open settlements were still extant31. The diversity in settlement and burial types suggests the existence of social structures with different levels of complexity32. At the same time, as exchange networks, circulated precious goods such as ivory from Africa and even Asia to Iberia33.An increasing number of burials from that epoch have come to light in the last years34. Iberia’s Bell Beaker assemblages are among the richest and most diverse in western Europe35, both in terms of settlements and burials36. It has thus long been a focus of archaeological research, commencing from migrationist hypotheses and leading up to current social explanations, where the Bell Beaker phenomenon is perceived as a package of prestigious objects exchanged and consumed by elite groups and displayed on special occasions37.From ~2600 BCE onwards, the so-called ‘Bell Beaker phenomenon’ became manifest with its characteristic pottery, copper weapons, gold ornaments, and other prestige goods, an archaeological reflection of important social and economic changes which spread across vast regions of western and central Europe31. El Argar group began to emerge in southeast Iberia, with large and massively fortified urban centers like La Bastida  attested to a rather complex Neolithic transition42. Recent mtDNA and genome-wide analyses have put an emphasis on the genetic affinity and shared Near Eastern ancestry between the early Iberian farmers and the contemporary Central European Linearbandkeramik (LBK) population48. The latest mtDNA and genomic studies have revealed increased subsequent admixture of hunter-gatherer elements during the local middle Neolithic  control region data. Our project completes the latter series by focusing on the archaeological models and hypotheses that have been put forward for the Iberian Peninsula, and where diachronic (i.e. ‘through time’) sampling of ancient DNA allows the detection of demographic changes and discontinuities between 5700 and 1500 BCE. Essential research questions focus on three levels: i) the individual sites, ii) the Iberian Peninsula as a scene of Neolithic transition and iii) a comparison with contemporaneous ancient and modern-day Europeans. A key question of our study of the mtDNA diversity on the Iberian Peninsula through time was to examine to what extent regional and supra-regional cultural groups could be recognized as genetically identifiable entities, as shown in other areas of Europe55. A related question was whether the cultural breaks that can be seen, for instance, at the end of the Neolithic and Chalcolithic periods, were also accompanied by human population turnovers.Despite numerous research projects being carried out over the past years, the Neolithic settlement history of Europe can still only be explained at a broad scaleWe processed ancient DNA samples of 318 human individuals from prehistoric Iberia and generated reproducible mitochondrial hypervariable region haplotypes  from 151 individuals following strict authentication criteria (see Methods). MtDNA haplogroup classification of further 62 samples was based on multiplex typing of single nucleotide polymorphisms (SNPs) . Therefore, we used Holocene period hunter-gatherers from Central, West and North Europe as proxy for the Iberian pre-Neolithic mtDNA substrate in our quantitative analyses . In Iberia, haplogroup U5b was found most frequently in the CI Neolithic dataset , and showed also relative high frequency in the CI Chalcolithic  and NEI Chalcolithic-Early Bronze Age groups .The Iberian pre-Neolithic mtDNA substrate is still poorly understood. Recent NGS studies described hunter-gatherers belonging to haplogroup U5b in north Iberiases Fig. , S15. Th58. Haplogroups K, J, T2, HV, V and X are observed in comparable frequencies in Iberian and Central European groups. However, the proportion of haplogroup H is higher in the Iberian Early Neolithic (EN)  than in Central Europe , while the frequency of N1a is very low . Another difference with regards to Central Europe is the occurrence of haplogroup N* in Neolithic NEI group (Supplementary Tables The haplogroup composition of the Iberian Neolithic population shows similarities to the Early Neolithic data from Anatolia (~6500–6000 BCE), the Carpathian Basin (~5800–4900 BCE), and Central Europe 49. The presence of haplogroup N* in the Pre-Pottery Neolithic in Syria and in NEI can also be interpreted in this light64, but this link needs to be verified by whole mitogenome analyses. Haplogroup compositions of all other Iberian groups are highly similar to each other, resulting in clusters on the PCA plots and Ward clustering tree. Our largest Iberian datasets are from Neolithic and Chalcolithic of CI (n = 62 and 79), which show an elevated frequency of U5b compared to the NEI Neolithic. Interestingly, we also observe probable hunter-gatherer haplogroups U2 and U4, whereas the characteristic EN haplogroups N*, N1a are missing in the CI region. The southeastern Iberian dataset (n = 48) contains eight Late Neolithic individuals with haplogroups K, H and U5b, 28 individuals from the Chalcolithic and 12 from the Early Bronze Age (EBA). Similarly to the CI groups, SEI shows a mixture of haplogroups of the NEI group, but some of them  are missing.We used the haplogroup frequencies of the studied groups for principal component analyses . When compared to published ancient DNA data from the Near East, the western Anatolian Neolithic population is most similar to Central European Early-Middle Neolithic populations and to the Neolithic of northeast Iberia  is too small to estimate the population dynamics in the region reliably (Supplementary Table As Middle Neolithic (~ 4500–3500 BCE) and Late Neolithic (~3500–3000 BCE) periods of most sites are not separable at the current state of archaeological research, we combine these data in our analyses (MLN). Fisher’s exact (p = 0.5473) and population continuity (p = 0.3385–0.3722) tests confirm the connection between the EN and MLN periods of Iberia in general.65, and hints at a connection to the North-West African coasts in prehistoric times.Some of the Iberian Neolithic mtDNA haplogroups U2, N*, N1a) are not observed in the successive Chalcolithic Iberian population (n = 156), whereas others maintain a steady frequency  throughout 3500 years. An interesting exception is haplogroup L1b in the Late Chalcolithic central Iberia at the site Camino de las Yeseras  appears first in the 3000–2500 BCE period of central Iberia. Other typical Central European and Carpathian Basin Neolithic haplogroups such as H5, T1, W and U8, which are observed in later Central European Neolithic periods, are missing from the entire prehistoric Iberian dataset. These qualitative differences account for the separation of most of the Iberian prehistoric groups from the Central European Neolithic and Early Bronze Age populations along the second component of the PCA, shown on Fig. In contrast to U5b, haplogroup U5a , which support population continuity between the two periods. Unfortunately, a finer chronological separation of the Chalcolithic (CHA) dataset cannot be achieved for all samples and therefore we consider only a subset of the CHA dataset (n = 71 for Early and n = 47 for Late CHA) in this analysis  and CHA groups of central Iberia (p = 0.6033–0.6143), and NEI (p = 0.9038–0.7835). This continuous genetic makeup is also supported by non-significant (p = 0.2264 and 0.4484) differences in haplogroup compositions . At the mtDNA haplogroup level the EBA does not show new influences or population changes at the onset of the Iberian Bronze Age according to results from Fisher’s test and test of population continuity, when both the entire Peninsula and southeastern Iberia in particular were considered , and the second largest group is subhaplogroup H3 14%), while 18.6% of the H individuals cannot be assigned to any of the subgroups included in the H-PLEX assay  and the Iberian (entire and late) CHA groups at the haplogroup level, and thus cannot confirm a Late Chalcolithic expansion toward Central Europe as suggested by haplogroup H mitogenome dataThe mtDNA variation in prehistoric Iberia is further explored by HVS-I sequence and haplotype analyses of 238 individuals from the Peninsula. The haplotype diversity of the hunter-gatherer populations of Central Europe (HG_PLEI (Pleistocene): Hd = 0.879; n = 29, HG_HOL (Holocene): Hd = 0.931; n = 35) provides the baseline for this comparative series. The diversity is lower in the Early Neolithic of Iberia  but increases in the following Middle-Late Neolithic  and Chalcolithic periods . The Early Bronze Age shows again a lower diversity , although this could be the result of the small number of investigated individuals (Supplementary Table The haplotype sharing (SHA) between the larger chronological groups detects no hunter-gatherer contribution in the EN group of Iberia, and a low level of hunter-gatherer maternal lineages in the MLN periods (6.8%) Table . During The Iberian early farming groups share generally high number of lineages (38.3–82.4%) with each other , but both CI dataset also share many lineages with all other Iberian groups (Supplementary Table st) are generally low among the Iberian prehistoric groups, and none of them are significant (Supplementary Table Genetic distances (Fst = 0.00173–0.00428), but also high affinities to several European populations . The genetic distances to modern populations increase with the MLN and CHA periods, but show general similarity to modern-day Europe. Recent genomic studies have highlighted the similarity of early farmers to modern South Europeans71, especially to modern day Sardinians49. This picture is not reflected in our mtDNA dataset, where Sardinians rank only the 67th closest to the Iberian EN group out of 133 modern populations, and 46th from the MLN group  of the Iberian Peninsula with 133 modern populations. Genetic distances to modern-day populations are generally low, and restricted to certain region(s) of Europe , which predate the Neolithic expansion, and also based on the high abundances of these lineages in Iberia today79. However, the Franco-Cantabrian glacial refuge theory has not yet been confirmed by ancient mtDNA or genomic analyses, and more Iberian Pleistocene and especially Holocene samples need to be investigated at full mitogenomic resolution.During the Last Glacial Maximum, the Iberian Peninsula, just as Southeast Europe and the Italian Peninsula, formed a classic Glacial Refuge Area for European populations, well documented by archaeological evidence59. The indigenous Iberian late Upper Paleolithic and Mesolithic populations were represented by whole mitogenome haplotypes assigned to haplogroup U5b from Northern Spain59.With the end of the Last Ice Age and the beginning of the Holocene ~12,000 BCE, a common European Mesolithic population emerged, which also included a distinct signal of Near Eastern origin80. Our genetic data support a substantial influx of Neolithic immigrants to northeastern Spain, where farmer lineages are most abundant and diverse, and on to other regions, like the SEI and CI. We found the persistence of ‘hunter-gatherer’ mtDNA haplogroups in the Neolithic to be strongest in Central Iberia. These genetic results match the archaeological data on the mode of Neolithisation described for these areas, so that the fully established Neolithic Iberian communities had distinct hunter-gatherer components82. The further the early farmers advanced into the inner/central and southern parts of the Peninsula, the higher the proportion of indigenous hunter-gatherer lineages. Geography appears to have been a decisive factor in the advance of Neolithic lifeways. Prehistoric central Iberia , however, was never an isolated region surrounded by mountains, but rather an important hub for innovations and impulses coming into Iberia83. On a genetic level, Neolithic central Iberia showed corresponding mitogenomic connections to northeast and southeast Iberian regions  into the hinterland20. Due to the considerable temporal differences between the occupations, the respective sample sets can be considered as genetically independent. The 14C data of the six individuals from the earliest phase cluster closely (Supplementary Information). Genome-wide analyses of five of these EN individuals revealed the group to be early Neolithic immigrants50. Haplogroups N1a, J, T2, K and V appear, while the typical Iberian hunter-gahterer mtDNA haplogroup U5b is missing. Therefore, we suspect the presence of a (still) isolated EN group at Els Trocs. The isolated geographic position in the Pyrenees suggests immigration of this community from north of the Pyrenees  rather than from the west along the Mediterranean coastline. This alternative gateway to Spain has also been proposed based on archaeobotanical evidence84. The first and (to our knowledge) sole Iberian appearance of the mtDNA haplogroup N1a in an adult from Els Trocs, which matches identical HVS-I N1a haplotype in Anatolia and Central Europe, might have arrived on the continental route85. The shared roots between Southwest and Central Europe were especially conspicuous in the Northeast Iberian EN group, as the agreement in the common mtDNA haplogroups such as V, J, K, T2 and X suggests.The Pyrenees are also an area of special interest with regard to the route of immigration of the first farmers. This study includes ten samples from the cave site of Els Trocs, dated to the Early and Middle Neolithic49. The Iberian Middle Neolithic genomic data are still too scarce (concentrated on a single site La Mina) to draw general conclusions about admixture with late hunter-gatherers47. La Mina people had 18.9–22.8% hunter-gatherer ancestry, double than the EN individuals from Els Trocs (6.8–11%)50, and also higher than their contemporaries in Central Europe. These hunter-gatherer proportion further increased in the CHA to 27%50. In our study, we could support the general continuity of maternal lineages between MLN-CHA periods. The increase of hunter-gatherer maternal lineages through time is not seen on large scale SHA (Table st from HG_HOL decreases and the amount of HG_HOL haplotypes increases from the Neolithic to the CHA-EBA periods (0–4.76%). The same trend can be seen in our Fst analyses where the CI_CHA (Fst = 0.17406) appear closer to the HG_HOL group than the CI_Neo group (Fst = 0.21267)  is remarkable, given that ivory adornments of African origin have also been documented at this and other contemporaneous sites36. Our tests of population continuity and Fisher’s tests between Early and Late CHA periods supported population continuity between the two phases. However, our data structure did not allow studying the Early to Late CHA population changes at a regional scale, so that further studies might reveal local variation with regard to individuals with Bell Beaker / non-Bell Beaker cultural affiliations. Interestingly, we did not find evidence for direct genetic links between Chalcolithic Iberia and contemporaneous Central Europe. Links between the two regions had previously been suggested based in particular on Bell Beaker elements present across a wide geographic range91, as well as by the maternal genetic analyses of the El Mirador site40.The Bell Beaker phenomenon was a decisive element in the Iberian Chalcolithic, lasting from the Late CHA to the EBA (~2600–1800 BCE)49. An evaluation of this unexpected observation will need further palaeogenetic studies.Interestingly, we also do not find evidence for influx in the East to West direction, as none of the investigated Chalcolithic individuals show ‘steppe ancestry’, which seen in contemporaneous Central European Corded Ware and Bell Beaker groups, suggesting that eastern influxes did not reach the Iberian Peninsula until later periodsAround 2200 BCE, the emergence of the El Argar group was evidently preceded by a break in Chalcolithic cultural traditions in southeast Iberia. Yet there are no apparent new influences or signals of substantial population change on the mtDNA haplogroup level at this time, so that the observed changes may either be due to an upheaval of existing social structures or an influx of groups that cannot be distinguished from the local population at the present level of genetic resolution, e.g., from Southeastern Europe, as previously proposed for El Argar. Unraveling these apparently contradictory data will certainly require further in-depth analyses both on the archaeological and the archaeogenetic level.92, which profoundly impacted Late Neolithic and EBA groups of Central and North Europe, cannot (yet) be seen in the contemporaneous population substrate of the Iberian Peninsula at the present level of genetic resolution. This highlights the distinct character of the Neolithic transition both in the Iberian Peninsula and elsewhere and emphasizes the need for further in-depth archaeogenetic studies for reconstructing the close reciprocal relationship of genetic and cultural processes on the population level.The present study, based on 213 new and 125 published mtDNA data of prehistoric Iberian individuals suggests a more complex mode of interaction between local hunter-gatherers and incoming early farmers during the Early and Middle Neolithic of the Iberian Peninsula, as compared to Central Europe. A characteristic of Iberian population dynamics is the proportion of autochthonous hunter-gatherer haplogroups, which increased in relation to the distance to the Mediterranean coast. In contrast, the early farmers in Central Europe showed comparatively little admixture of contemporaneous hunter-gatherer groups. Already during the first centuries of Neolithic transition in Iberia, we observe a mix of female DNA lineages of different origins. Earlier hunter-gatherer haplogroups were found together with a variety of new lineages, which ultimately derive from Near Eastern farming groups. On the other hand, some early Neolithic sites in northeast Iberia, especially the early group from the cave site of Els Trocs in the central Pyrenees, seem to exhibit affinities to Central European LBK communities. The diversity of female linages in the Iberian communities continued even during the Chalcolithic, when populations became more homogenous, indicating higher mobility and admixture across different geographic regions. Even though the sample size available for Early Bronze Age populations is still limited, especially with regards to the El Argar group, we observe no significant changes to the mitochondrial DNA pool until the end of our time transect (1500 BCE). The expansion of groups from the eastern steppeThe studied sites are distributed across the Iberian Peninsula, with an emphasis on the archaeologically relevant regions on the Mediterranean coasts, in central and northern Spain and in southern Portugal Fig. . We targ49. Here we also report additional samples from the sites of La Mina and Els Trocs.Altogether, 318 individuals from 57 archaeological sites in Spain, Portugal and Morocco were sampled and analyzed for this study. Whenever possible we preferred samples from recent excavations over those that had been held at museum collections for prolonged periods of time. Teeth and bone samples were taken under clean conditions in Iberian museums, at the Institute of Anthropology at Johannes Gutenberg University in Mainz, or directly on site in the case of La Mina, Arroyal, Els Trocs and in part at La Bastida. Mitochondrial profiles of 37 individuals from our project (Alto de Reinoso (n = 27), La Mina (n = 5) and Els Trocs (n = 5) were previously published by our research teamThe chronological classification of the burials was based on the archaeological data. In order to avoid possible problems due to terminological inconsistencies, we used temporal  and geographic groupings to assess the population mitogenetic data from the Iberian Peninsula. We further distinguished groups based on contextual archaeological evidence, e. g. subsistence strategies, such as hunter-gatherers vs. early farmers. We targeted collecting two to three teeth from each skeleton and sampled bones only when teeth were not available.55. Samples were first UV irradiated for 30 min each side and then, the surface was removed by shot-blasting with aluminium-oxide-abrasive. Samples were then ground to fine powder using a mixer mill (Retsch). The milling process was controlled by hydroxylapatite blank controls, which were treated as samples in the subsequent DNA extraction and amplification steps. Series of samples were tested from each archaeological site, and the amplification success defined any further ancient DNA work. In general, A and B samples from different teeth or bone fragments per individual were analyzed independently. In the sole case is the site of Cova del Cantal, A and B samples, i.e. repeat extractions, derived from the same bone fragment.Standard sample preparation protocols were used during the ancient DNA work in the Institute of Anthropology, Johannes Gutenberg University in Mainz55 and 23 using the silica based DNA extraction protocol68.Of the total 48 extraction batches, 25 were performed using the phenol-chloroform DNA extraction methodet al.44. PCR, amplicon purification and sequencing protocols were the same as reported in Brandt et al.55. 40% of the PCR products were cloned using pUC18 vectors and E.coli cells85, and these clones were re-amplified and re-sequenced.We combined different sets of primer pairs for the amplification of the HVS-I of the mitochondrial genome. Well-preserved samples were amplified in two segments (nucleotide position (np) 16046–16401), average preserved samples in four fragments (np 15997–16409) and samples with very fragmented DNA content were amplified with a combination of six overlapping primer pairs. All HVS I-II primers are listed in Table 56, and typed 22 coding region positions of the mitochondrial genome. Samples assigned to the haplogroup H, were further sub-typed used the H-PLEX17 focusing on 17 subhaplogroup H defining single nucleotide polymorphisms (SNPs)66. Note that recent mitochondrial phylogeny updates have complicated the interpretation of typing results: the H1 defining variant G3010A is now also reported for subhaplogroups H30b, H65a, H79a, H105a (Phylotree Build 17).In order to get precise basal mitochondrial haplogroup determinations, we used the GenoCoRe22 multiplex PCR and ABI PRISM® SNaPshot® typing protocol67. Conditions of the U and T–Plexes are as follows: PCRs were set up in a volume of 25 μI, containing 1 x PCR Gold buffer, 8 mM MgCl2, 0.02 μg BSA, 500 μM dNTPs, 0.02 μM of each primer, 2 U AmpliTaq Gold® DNA polymerase, and 2–3 μI DNA extract. Cycling conditions were 95 °C for 10 minutes, and 35 cycles of 95 °C for 30 seconds, 56 °C for 30 seconds, and 65 °C for 30 seconds, followed by a final adenylation step at 65 °C for 10 minutes for the U-Plex and 32 cycles and an annealing temperature of 59 °C for the H-PLEX17. Amplification success was checked via gel electrophoresis. Purification and SBE-reactions for the U-Plex was set up according to the GenoCoRe22 protocol.Samples belonging to haplogroup U were also tested for six coding region SNPs via multiplex amplification and single base extension protocols, and further categorized into subhaplogroups U2, U3, U4, U5, U5a, U5b, and U8 . PCR products that showed ambiguous nucleotide positions were cloned to monitor possible background contaminations and DNA damage. In addition, for those individuals of the sites Alto de Reinoso and La Mina that showed the same HVS-I haplotype, we also amplified and sequenced the HVS-II via four overlapping primer pairswww.phylotree.org). In all cases where ancient haplotypes differed from the current representative haplotypes on the mtDNA phylotree by private mutations, the latter were double-checked by several PCRs and by sequencing in combination with other SNPs. 5. Characteristic post-mortem DNA damage-derived substitutions  were observed, but these substitutions were neither reproducible nor consistent across sequence assemblies and could easily be distinguished from true SNPs. 6. Altogether 51 of the 2376 (2.15%) blank controls were contaminated during the course of the three-year investigation. No contamination could be detected in any of the multiplex PCRs. Of the 51 contaminated blanks, 0.96% (17 out of 1764) were PCR blanks, 6.4% (20 out of 311) hydroxyapatite milling blanks, and 4.7% (14 out of 301) extraction blanks. All contaminated blanks were sequenced and compared to haplotypes of samples analyzed along with these PCR extraction blanks. Fourteen individuals that were amplified from two extraction events had to be excluded from further analyses due to signs of potential contamination. 7. The HVS-I region was sequenced from all relevant co-workers  and compared with samples and blanks . In all reproducible and reported cases, the HVS-I sequences confirmed the haplogroup assignment by the GenoCoRe22 SNP typing (and vice versa), as did the results of the H-PLEX17, U-Plex and T-Plex assays. 4. All haplotypes could be placed in the mitochondrial phylogeny  from Central and North Europe62. The Iberian hunter-gatherer dataset (n = 3) is too small to form a separate statistical group59. As comparative ancient populations we used Neolithic (5800–4900 BCE) datasets from the Carpathian Basin (CB_EN)62, Early , Middle  and Late Neolithic  series from Central Germany94 and from Central European Early Bronze Age period 92. From France, we included two Neolithic groups from the 4th and 3rd millennium BCE (FRA_GUR and TRE)70. From the Eastern European steppe, we included an mtDNA dataset associated with the Yamnaya culture 95. Published HVS-I and haplogroup data are presented in Supplementary Table We split pre-Neolithic samples of Western and Central Europe into two temporal groups: a Pleistocene group 96, and combined them with our results. The dataset from Cova de Montanissel was only used in haplogroup based tests, due to insufficiently reproduced HVS-I results97. We divided the ancient Iberian dataset into the following geographical groups: southwest Iberia (SWI), southeast Iberia (SEI), central Iberia (CI), northeast Iberia (NEI). For sufficiently large sample numbers, we further sub-divided these into the following chronological groups: Early Neolithic , Middle and Late Neolithic , Early Chalcolithic (~3000–2600 BCE), Late Chalcolithic (~2600–2200 BCE), and Early Bronze Age . Where the chronology could not be defined precisely, we classified the samples into Neolithic (Neo) and Chalcolithic (CHA) periods. Note the differences in chronological terminology between Central Europe and the Iberian prehistory, mostly due to the absence of a distinct ‘Chalcolithic’ period in the archaeological terminology used in Central Europe  to visualize the relationships between relative haplogroup compositions of the Iberian and other prehistoric datasets. PCA has the benefit of reducing the complexity of the entire dataset while maintaining an accurate representation of its variability. PCA was performed with 16 ancient populations comparing haplogroup frequencies of 25 mtDNA haplogroups  p-value, in percentage.Hierarchical clustering was used to construct clusters of the predefined prehistoric datasets, based on similarities (distances) between their haplogroup compositions. This analysis helps interpreting the PCA plots in form of dendrograms. Clustering of haplogroup frequencies was performed using Ward type algorithm and Euclidean distance measurement method. The result was visualized as a dendogram with the 100. The null hypothesis for each Fisher’s exact test was that the Iberian groups belonged to the same population. The resulting p-value thus described the probability of obtaining the observed haplogroup compositions if both groups were part of the same metapopulation. Fisher’s exact test was performed with absolute frequencies of the haplogroups observed in the Iberian dataset. We used all haplogroup results per group in this analyses and tested a series of pairwise comparisons of Iberian chronological and geographic groups, using fisher.test function in R v.3.0.398.Fisher’s exact test is a nonparametric test for statistical significance55. The tests were performed for pairs of chronological groups in specific regions . It was also performed for the entire Iberian time transect, using ‘metapopulations’ from consecutive pairs of chronological periods , EBA). Effective population sizes Ne = 1000, 10,000 were tested, calculating with differences between terminal dates of the chronological periods. Further parameters of the tests are presented in Supplementary Table The test of population continuity explores whether genetic differences between two populations from two or more consecutive time periods can be adequately explained by genetic drift alone or whether a discontinuity between the two populations has to be assumed101. The HVS-I sequence range between np 16048–16410 was used for the calculations , we took the relative chronology of the studied groups into account and tracked first appearance of any given lineage and their transmission (or presence/absence) in subsequent, i.e. later populations. SHA and ancestral SHA were performed as described in Brandt et al.58. Levelplot of the percentage of shared lineages was visualized in R version 3.1.3, using level plot function. First, we considered the same groups that were counted in the Fst analysis. In the next step, we assembled all Iberian regions, and we differentiated chronological groups of the Iberian prehistory  measure the amount of genetic structuring/differentiation in populations. With Fst values we aimed to test whether pairs of populations were genetically distinct, and panmixia could be ruled out as basal assumption. Fst values between pairs of prehistoric populations were calculated using reproduced HVS-I sequences (np 16056–16390) in Arlequin v. 3.5, applying a Tamura and Nei substitution model with a gamma parameter of 0.117102. Fst p values were calculated based on 10,000 permutations, and post hoc adjusted to correct for multiple comparison with the Benjamini and Hochberg method, using the function p. adjust in R. 3.0.3103.Fst values, into spatial (Euclidean) distances and to visualize them in two-dimensional space. MDS was performed in R v. 3.1.3 using metaMDS function in vegan library104. Distance matrix was calculated based on Slatkin Fst values, which Fst values were calculated using HVS-I sequences (np 16056–16390) in Arlequin v. 3.5, applying a Tamura and Nei substitution model with a gamma parameter of 0.117102. We included 16 ancient groups in this analysis  was used to translate the distances/dissimilarities between multidimensional objects, represented by Fct, Fsc and their p values were computed in Arlequin v. 3.5102. Geographic and chronological groups were arranged into different models, consisted of 2–5 clusters that were assumed as plausible from the MDS plot, and AMOVA was conducted for each arrangement , based on HVS-I sequences (np 16056–16390), was performed between a subset of the Iberian and the Central European prehistoric groups used in the MDS (HG groups and YAM were left out). The “among clusters” and “within clusters” variance, Fst values between modern and three prehistoric Iberian groups and mapped the genetic distances by interpolating between the geographic coordinates of modern populations. We calculated genetic distances  randomly choosing a maximum of 140 sequences per population , in order to balance the differences in sample sizes, and calculated Fst values between prehistoric Iberian  and 133 present-day populations. The analysis was performed in Arlequin v. 3.5, using uniform sequence length (np 16068–16365), and Tamura & Nei substitution model with a gamma value of 0.177 (Supplementary Table 102. Fst values were combined with longitudes and latitudes according to context information from the literature. The Fst values and coordinates were interpolated with the Kriging method implemented in Arcmap ArcGIS version 10.3.We calculated FConsensus HVS-I mtDNA sequences were uploaded to NCBI GenBank under the accession numbers KY446554 - KY446703.Supplementary information and figuresSupplementary Tables S1-16


Carotid plaque echogenicity is associated with the risk of cardiovascular events. Gray-scale median (GSM) of the ultrasound image of carotid plaques has been widely used as an objective method for evaluation of plaque echogenicity in patients with atherosclerosis. We proposed a computer-aided method to evaluate plaque echogenicity and compared its efficiency with GSM.One hundred and twenty-five carotid plaques  were collected from 72 patients in this study. The cumulative probability distribution curves were obtained based on statistics of the pixels in the gray-level images of plaques. The area under the cumulative probability distribution curve (AUCPDC) was calculated as its integral value to evaluate plaque echogenicity.κ = 0.673), when the AUCPDC is used for classifier training, whereas GSM is 64.8% (κ = 0.460). The receiver operating characteristic curves were produced to test the effectiveness of AUCPDC and GSM for the identification of echolucent plaques. The area under the curve (AUC) was 0.817 when AUCPDC was used for training the classifier, which is higher than that achieved using GSM (AUC = 0.746). Compared with GSM, the AUCPDC showed a borderline association with coronary heart disease .The classification accuracy for three types of plaques is 78.4% (kappa value, Our experimental results suggest that AUCPDC analysis is a promising method for evaluation of plaque echogenicity and predicting cardiovascular events in patients with plaques. Cardiovascular diseases significantly threaten human health and are the primary cause of death and disability worldwide . Most myIt is well-known that ultrasound imaging is a non-invasive technique for carotid atherosclerosis plaque examination. Ultrasound measurement of plaque echogenicity can provide a risk factor for predicting cardiovascular events –11. VisuThe aim of this study is to examine whether the AUCPDC analysis is a useful method for the evaluation of plaque echogenicity and to further compare its efficiency with GSM.The study protocol was approved by the Institutional Review Board of the third affiliated hospital of Sun Yat-sen University . All participants provided written informed consent.From September 2013 to March 2016, a total of 130 carotid plaques were collected from 74 volunteers, and 5 controversial plaques were excluded after visual classification by two sonographers. The remaining 125 carotid plaques  from 72 volunteers were used in the in the following analysis.1c. The diagnostic criteria for hypertension was defined as systolic blood pressure ≥ 130 mmHg and/or diastolic blood pressure ≥ 80 mmHg or current use of antihypertensive agents. Diabetes was defined as fasting plasma glucose level of ≥ 7.0 mmol/L, and/or 2-hour plasma glucose value of ≥ 11.1 mmol/L, and/or HbA1c level of ≥ 6.5%, and/or treatment with either hypoglycemic agents or insulin [Blood samples were collected after an overnight fast for analysis of total cholesterol, triglyceride, high density lipoprotein cholesterol, low density lipoprotein cholesterol, apolipoprotein A1, apolipoprotein B100, fasting plasma glucose, and HbA insulin ,23.Ultrasound images of carotid plaques were collected by a sonographer that has 5 years of experience in vascular imaging using an Aplio XG (SSA-790A)  equipped with a 5–12 MHz linear-array transducer (PLT-805AT). The carotid artery was examined with the head tilted slightly upward in the mid-line position. The transducer was manipulated so that the near and far walls were parallel to the transducer footprint, and the lumen diameter was maximized in the longitudinal plane.κ) was calculated to evaluate the between-observer agreement.According to the criteria of the European carotid plaque study group, plaques were classified into three different types: echolucent, intermediate and echo-rich plaques . The visThe ultrasound system settings  can impact the brightness and contrast of the B-mode images. In this study, all images were normalized according to the scheme proposed by Sabetai et al . After nIn this study, the plaque was manually segmented by one operator in the gray-level image, and the statistics of the pixels of plaques were obtained. Then, the AUCPDC analysis and GSM analysis were performed for each plaque based on their pixel statistics.x represents the gray scale pixel value, f(x) is the probability density function of x, and f(x) can be calculated as follows:f(x) is defined in Eq .The GSM F(x) of the gray scale distribution, can be expressed as follows,For each plaque, the cumulative distribution function The k-nearest-neighbor (KNN) classification was performed for classifying the three different types of plaques based on AUCPDC or GSM. In order to improve the reliability of classification, a leave-one-out cross validation was implemented in this study. The kappa statistic (κ) was calculated to evaluate the agreement between visual classification and the KNN classification. Furthermore, the receiver operating characteristic (ROC) curves for the KNN classifier were developed to compare the ability of AUCPDC and GSM in identifying echolucent plaques.In order to examine the intra-operator agreement, the AUCPDC analyses were performed at two different times within a 2-month period. Based on the same visual classification, a total of 45 plaques  were randomly selected from the same original images, and the manual plaque segmentation was carried out again by the same operator. The intra-operator agreement was evaluated according to the method proposed by Bland and Altman .J [Youden's index J  is a sinnt model ,28.J.Bootstrap is a useful tool to provide statistical inference to estimate the accuracy and the precision of any statistic through resampling with replacement from the original datasets,30. In tSpearman's rank correlation analysis was also implemented between the GSM, AUCPDC and the status of hypertension, diabetes, coronary heart disease (CHD). Youden's index J was calculated using MedCalc statistical software.All statistical analysis was performed with PASW Statistics 18 and all values were presented as the mean value ± SD, or real number of patients with the percentage in parentheses. The baseline characteristics of the study population are shown in n = 130) into three different types showed a good agreement between two experienced sonographers (κ = 0.942). A total of 5 controversial plaques were excluded, and the 125 consensual plaques were retained in the following analysis.The classification of the carotid plaques (graphers . The betp < 0.001). When type 1 denoted echo-rich, type 2 intermediate type 3 echolucent, the mean AUCPDC had a correlation with the types of plaques .κ = 0.673), which was higher than that obtained by using GSM 64.8% (κ = 0.460). When classification based on GSM, 8 of 35 intermediate plaques were classified correctly, and 21 of 35 intermediate plaques were misclassified as echolucent plaques .tes, CHD . CompareJ, a total of 2000 bootstrapping samples was generated. The Youden’s index J was 0.633 (95% CI: 0.479–0.753) when AUCPDC was used to train the classifier, whereas Youden’s index J was 0.492 (95% CI: 0.326–0.633) when GSM was used.When bootstrap was performed to estimate the Youden's index κ = 0.673), when AUCPDC was used to train the classifier. Previous studies have proven that the echolucent plaque is a high risk indicator of cardiovascular events [r = 0.234, p = 0.050)  . These iκ = 0.79). Mayor et al. analyzed 95 carotid bifurcation plaques with the same five-type classification system, and a between-observer reproducibility of 91% (κ = 0.87) was observed. In this study, the plaques were classified into three different types  according to the scheme of the European carotid plaque study group [κ = 0.942) was achieved.Many studies have shown that visual classification is a feasible and reliable method for classification of ultrasound plaques with different echogenicity ,13,33. Gdy group , and a hBoth GSM and AUCPDC are calculated based on the gray scale distribution. Compared with GSM, the AUCPDC calculation takes into consideration the probability density distribution of gray scale values ranging from 0 to 255. The lower gray scale value with a high probability density gives rise to a higher value of AUCPDC. p = 0.05)  , but theCompared with GSM, the AUCPDC is more effective in classifying three types of plaques and identifying echolucent plaques, suggesting that AUCPDC analysis is a promising method for evaluating plaque echogenicity and predicting cardiovascular risk in patients with plaques. However, this study is limited by the relatively small number of plaques, thus future research is required to further validate our results. Furthermore, a longitudinal, prospective study utilizing carotid ultrasound examination in a large number of patients with atherosclerotic risk is required to assess the value of AUCPDC in predicting the future cardiovascular events.S1 Supporting Information(XLSX)Click here for additional data file.S2 Supporting InformationClassification base on a leave-one-out strategy.(M)Click here for additional data file.


Colony stimulating factor 1 receptor (CSF-1R) regulates the monocyte/macrophage system, which is an essential component of cancer development. Therefore, CSF-1R might be an effective target for anti-cancer therapy. The overexpression of transforming growth factor (TGF)-β stimulated clone-22 (TSC-22) inhibits cancer cell proliferation and induces apoptosis, and TSC-22 is emerging as a key factor in tumorigenesis. In this study, we discovered CSF-1R as a new interacting partner of TSC-22 and identified its elevated expression in cervical cancer cells. In particular, we found that TSC-22 interacted with the intracellular tyrosine kinase insert domain (539–749) of CSF-1R, which activates the AKT and ERK signaling pathways. This binding blocked AKT and ERK signaling, thereby suppressing the transcriptional activity of NF-κB. The overexpression of TSC-22 significantly decreased CSF-1R protein levels, affecting their autocrine loop. TSC-22 injected into a xenograft mouse model of human cervical cancer markedly inhibited tumor growth. The reduction of CSF-1R protein significantly suppresses cervical cancer cell proliferation and motility and induces apoptotic cell death. This association between TSC-22 and CSF-1R could be used as a novel therapeutic target and prognostic marker for cervical cancer. Transforming growth factor-β-stimulated clone-22 (TSC-22) was first isolated from mouse osteoblastic cells as a TGF-β –inducible gene. TSC-22 is a member of the leucine zipper transcription factor family and consists of 144 amino acids. TSC-22 is broadly expressed in almost all human tissues and organs, including the brain, liver, kidney, lung, prostate, testis, and ovary . In cervfms proto-oncogene, is a class III transmembrane tyrosine kinase receptor. CSF-1 is a ligand and a primary growth factor involved in regulating the proliferation, survival, and differentiation of mononuclear phagocytes [CSF-1R expression in various tumor types. Knockdown of CSF-1/CSF-1R increased apoptosis and reduced proliferation and migration of cervical cancer cells [To investigate other roles of TSC-22 in cervical cancer, we performed yeast two hybrid (Y2H) screening, and showed that CSF-1R directly interacts with TSC-22. Colony stimulating factor-1 receptor (CSF-1R), encoded by the c-agocytes . Previouer cells . Overexper cells .CSF-1R is mainly composed of three parts: an extracellular ligand-binding domain, a transmembrane domain, and an intracellular tyrosine domain. CSF-1 activates CSF-1R tyrosine residues, leading to a subsequent phosphorylation cascade . InhibitHere, we demonstrated the anti-proliferative effects of TSC-22 in cervical cancer cells and that TSC-22 induces apoptosis linked to interaction with CSF-1R. The novel molecular mechanism between TSC-22 and CSF-1R suggests a potential treatment for cervical cancer.in vivo . The interaction of TSC-22 with CSF-1R was verified by growth assay and β-galactosidase assay o Figure . To confd Figure . Figure Previous studies reported that TSC-22 is downregulated in cervical cancer tissues , so we cWe examined whether overexpression of TSC-22 inhibits tumor cell growth. We counted the number of viable HeLa and Caski cells transfected with control vector or TSC-22 expression vector every 24 hours. As shown in Figure We also examined colony formation ability. Figure We next investigated how TSC-22 regulates CSF-1R signaling. We made deletion constructs for TSC-22 and CSF-1R to define the specific binding region between TSC-22 and CSF-1R. Their interaction was analyzed using Y2H assays. TSC-22 bound to the region between 539 and 749 amino acids in CSF-1R Figure . On the We hypothesized that TSC-22 affects the CSF-1R signaling pathway because of its interaction with the intracellular tyrosine kinase insert domain (539–749). We analyzed levels of several proteins that mediate CSF-1/CSF-1R signaling in TSC-22-overexpressing cells. We demonstrated that TSC-22 reduces the phosphorylation of ERK and AKT, leading to a decline in their downstream proteins, including GSK-3β, Elk-1, and Egr-1 Figure . CSF-1R Furthermore, TSC-22 decreased the expression of CSF-1R and CSF-1 Figure . When ceWe performed a wound healing assay in HeLa cells. TSC-22 significantly reduced cell migration Figure . These rin vivo, HeLa cells were transfected with TSC-22 expression vector or empty vector. The transfected cells were injected subcutaneously into immune-deficient BALB/c nude mice. The tumor volume was monitored for 33 days. As shown in Figure To assess whether TSC-22 inhibits cervical cancer cell growth Cancer is generally caused by the alteration of regulatory genes. Oncogenes or tumor suppressor genes are expressed abnormally compared to normal genes. Therefore, it is critical in cancer treatment to learn how they control cancer cell proliferation, differentiation, and survival. Previous studies have reported low levels of TSC-22 in various cancers, such as cervical , salivarTo further explore how TSC-22 affects CSF-1R, we investigated the specific binding region between TSC-22 and CSF-1R. CSF-1R consists of 972 amino acids with three main regions. The extracellular segment is a ligand-binding domain (residues 1–512). The membrane spanning region is a transmembrane domain (residues 513–537). The intracellular domain contains a tyrosine kinase domain (residues 538–972), which phosphorylates tyrosine residues . PhosphoOur data revealed that TSC-22 decreased CSF-1R and CSF-1 expression, likely through NF-κB as a transcriptional factor of CSF-1R . HoweverIn conclusion, we propose TSC-22 as a negative regulator of CSF-1R or the CSF-1/CSF-1R signaling pathway. TSC-22 inhibits CSF-1R-dependent signaling by direct interaction, and decreases cervical cancer proliferation and migration. Our findings imply that TSC-22 targeting CSF-1R signaling may have therapeutic effect in cervical cancer.All cell lines were incubated at 37°C in a humidity 5% CO2 incubator, fed every 2-3 days with complete medium and passed when cells are in the confluent state. HEK293a, HeLa, Caski, SiHa, C-33A cell line were cultured in recommended medium supplemented with 10% fetal bovine serum  and 1% Amphotericin B/Streptomycin/Penicillin (Gibco). Cell transfection was performed using the X-tremeGENE HP reagent (Roche Applied Science) according to the manufacturer's instructions.EcoRI restriction site. pOTB7-CSF-1 plasmid was provided from from Korea Human Gene Bank, Medical Genomics Research center, KRIBB, Korea. CSF-1R expression vectors were constructed by cloning full-length CSF-1R into pcDNA4 and pcDNA3- flag vector using EcoRI and BamHI restriction sites. CSF-1 expression vector were provided by Dr. Ghanshyam Swarup, Council of Scientific and Industrial Research, Hyderabad, India.pSM-CSF-1R plasmid was provided by Dr.Martine Roussel, St. Jude Children's Research Hospital and inserted in pcDNA4 vector using EcoRI and Xho I restriction sites and used as baits. Human cDNA library was inserted in pB42AD prey vector for the yeast-two hybrid screening. The wild type and deleted CSF-1R were amplified by PCR and cloned into pGilda vector using EcoRI and BamHI restriction sites and used as baits. Also, the wild type and deleted TSC-22 were amplified by PCR and cloned into pB42AD prey vector between EcoR I and Xho I restriction sites for the growth and ß-galactosidase assay. The primers used for amplication are shown in the Table The EGY48 yeast strain was used in present study and Matchmaker LexA Two-Hybrid system  was used to perform the yeast two-hybrid assay according to the manufacture's instructions. The wild type TSC-22 were amplified by PCR and cloned into pGilda vector using TM.HeLa cells were plated into 6-well plates and transfected with expression vectors. Transfected cells were havested with Detachin solution at different time point and pelleted by centrifugation. Each of the cells was washed once with 1X PBS and resuspended in 100 μl of Annexin V binding buffer. Annexin V-FITC and Hoechst 33342 were then added to cell suspension. After the incubation at 37°C for 15min, cells were washed with Annexin V binding buffer, stained with 2 μl of PI  and immediately analyzed with NucleoCounter® NC-3000Cells were lysed in RIPA buffer  for 30min on ice. The extracts were centrifuged at 13,000rpm for 10min at 4°C, and the protein concentration was determined using the Bradford assay.Protein extracts were separated by SDS-PAGE using 8 to 12% polyacrylamide gel and transferred onto a nitrocellulose membrane . The membranes were blocked for 1hr in 5% skim milk and incubated at 4°C for overnight with specific primary antibody. Antibodies against TSC-22 (sc-101195), CSF-1(sc-1324), CSF-1R(sc-692), pERK (sc-7383), GSK-3β (sc-8257) and GAPDH (sc-25778) were purchased from Santa Cruz Biotechnology. Antibodies against pAKT (#4060) and cleaved PARP (#9541) were purchased from Cell signaling. Antibodies against Akt (1085-1) and flag (F1804) were purchased from EPIT-MICS and Sigma aldrich, respectively. Following primary antibody incubation, the membranes were washed three times for 5min in 1X TBS-T, incubated for 1hr with horseradish peroxidase-conjugated secondary antibody. Immunoreactivity was detected using ECL chemiluminescent solution (advansta) and exposed by Chemidoc (Bio-Rad).Cells were grown on 18mm diameter cover glass (Marienfeld). After 48hr incubation, the cells were rinsed twice with 1X PBS and fixed and permeabilized in methanol-acetone mixture (1:1) for 7min at −20°C. Fixed cells were blocked with 5% BSA/PBS-T  for 1hr at room temperature, and then incubated with CSF-1R antibody (1:250 dilution) and TSC-22 antibody (1:500 dilution) for 16hrs at 4°C. The cells were washed three times with 1X PBS for 5min each and incubated with alexa fluor 488 goat anti-rabbit IgG (Green) and alexa fluor 568 goat anti-mouse IgG (Red) antibody  in darkness for 1hr at room temperature. Finally, the cells were counterstained with 1μg/ml DAPI for 1min at room temperature and mounted on slides. The signals and co-localization were detected using the confocal fluorescence microscopyCells were lysed in NP-40 lysis buffer  for 30min on ice. Extracts were centrifuged at 13,000rpm for 10min at 4°C, and the protein concentration was measured using the Bradford assay. Each cell lysate (1.5mg) was incubated with CSF-R antibody (Santa cruz) or TSC-22 antibody (Santa curz) for overnight at 4°C. Following incubation, protein was immunoprecipitated using protein A/G agarose beads (Santa cruz) for 3hr at 4°C with gently rotation. The immunoprecipitates was washed three times with lysis buffer and boiled in 40 μl of 1X SDS sample buffer for 5min at 95°C. After centrifugation, the supernatant was analyzed by Western blot.5 HeLa cells were seeded on 6-well plates and transfected. At 8hr after transfection, one thousand cells (1×103) were re-plated onto 100mm plate in triplicate and cultured for 14 days to allow colonies to form. Then the media were removed and colonies were stained with 0.5% crystal violet solution (in distilled water) at room temperature for 15min. The plates were rinsed with water and colonies were counted. Values are expressed as mean ±SD from two experiments.1×104 HeLa cells were seeded on 24-well plates and transfected. At 24hr after transfection, The cells were harvested and lysed in lysis buffer provided by manufacturer (promega). Cell extracts were used for the luciferase assay system (promega). Values are the means and standard deviations of results from three independent experiments.1.3×106 HeLa cells were injected s.c. in the middorsal region of BALB/c nude mice 5-6 per group). Tumor size was evaluated using caliper measurements every 3 days. Mice were killed on day 33, and tumors were excised.To establish tumors in mice, TSC-22 expressing pcDNA3 vector-transfected 1 × 10t-test. p < 0.05 was considered statistically significant.Values are presented as the mean ± SD or ± SE. Statistical comparisons between groups were performed using the Student's


Macrophages interact with cells in every organ to facilitate tissue development, function and repair. However, the close interaction between macrophages and parenchymal cells can be subverted in disease, particularly cancer. Motility is an essential capacity for macrophages to be able to carry out their various roles. In cancers, the macrophage’s interstitial migratory ability is frequently co-opted by tumor cells to enable escape from the primary tumor and metastatic spread. Macrophage accumulation within and movement through a tumor is often stimulated by tumor cell production of the mononuclear phagocytic growth factor, colony-stimulating factor-1 (CSF-1). CSF-1 also regulates macrophage survival, proliferation and differentiation, and its many effects are transduced by its receptor, the CSF-1R, via phosphotyrosine motif-activated signals. Mutational analysis of CSF-1R signaling indicates that the major mediators of CSF-1-induced motility are phosphatidyl-inositol-3 kinase (PI3K) and one or more Src family kinase (SFK), which activate signals to adhesion, actin polymerization, polarization and, ultimately, migration and invasion in macrophages. The macrophage transcriptome, including that of the motility machinery, is very complex and highly responsive to the environment, with selective expression of proteins and splice variants rarely found in other cell types. Thus, their unique motility machinery can be specifically targeted to block macrophage migration, and thereby, inhibit tumor invasion and metastasis. Macrophages are found throughout the body where they contribute to tissue development, remodeling and homeostasis . These mDespite their functional complexity, macrophages are frequently categorized in a binary manner as either classically activated, pro-inflammatory (M1) or alternatively activated, anti-inflammatory (M2) macrophages, following exposure to interferon-γ (IFN-γ) and bacterial infections or interleukin (IL)-4 and IL-13, and parasitic infections, respectively  6]..6].As M2 activation can also be caused by other stimuli, M2 macrophages have been further stratified in terms of homeostasis, host defense, wound repair and immune regulation , althougInterstitial migration is central to the macrophage function as they patrol tissues to carry out their many developmental and housekeeping duties . Live imCsf1 in the osteopetrotic mouse (op/opCsf1) produces multiple congenital abnormalities, many of which may result from failure of primitive macrophages to migrate into and populate developing tissues .,46.18,46/− mouse . The sys/− mouse ,81. Two /− mouse ,79.Interestingly, the pathways activated by the two pTyr motifs are quite different and shed light on the mechanisms by which CSF-1R signaling regulates motility in macrophages acutely through rapid stimulation of cell ruffling, membrane protrusion and adhesion and in the longer term through stimulation of gene transcription  10]..10].In macrophages deprived of CSF-1, rapid activation of actin polymerization underpins the dorsal ruffling that becomes visible within a minute of CSF-1 addition . This isClass 1A PI3Ks consist of three p110 catalytic isoforms constitutively associated with the regulatory p85 subunit. Macrophages express all three of them; the ubiquitously expressed p110α and p110β isoforms, and the hematopoietically expressed p110δ, and they have non-redundant biological roles ,89,90. TThe SFKs have many roles in many different cell types. However, a key function of this family of non-receptor tyrosine kinases is the regulation of cell adhesion and motility ,101. MacTAMs contribute to the development and progression of cancer at almost every step in the complex process . MoreoveMacrophages and other mononuclear phagocyte-derived cells, such as osteoclasts and microglia, integrate with parenchymal cells in every tissue and organ system during early embryogenesis. As they move through tissues, they monitor their local environment and respond to perturbations in the steady state. Tumors are aberrant tissues populated with their own population of tissue-adapted macrophages, TAMs. As CSF-1 is the primary regulator of most tissue macrophage populations, including TAMs, and is also a potent macrophage chemokine, CSF-1 or CSF-1R inhibitors have been tested clinically to determine whether they could successfully block TAM recruitment. However, full CSF-1R blockade may have deleterious effects by also inhibiting and depleting anti-tumoral TAM populations. Hence, selective targeting of specific functions of tumor-promoting macrophages, such as their promotion of tumor cell invasion through co-migration of TAMs and tumor cells, might prove to be more therapeutically useful. Using a series of CSF-1-dependent, adherent macrophage cell lines expressing CSF-1R molecules with mutations in individual tyrosine residues, the primary mediators of CSF-1-induced signals regulating macrophage motility and invasive capacity were demonstrated to be a Class 1A PI3K, PI3K p110δ, and a myeloid SFK, Hck. Isoform selective PI3K p110δ and Hck inhibitors are available and may be deployed to prevent the activation of tumor invasion by co-migrating TAMs, which not only provide EGF to stimulate tumor cell motility, but also dig tunnels into the surrounding matrix through which tumor cells can escape.


During dynamic or sustained isometric contractions, bursts of muscle activity appear in the electromyography (EMG) signal. Theoretically, these bursts of activity likely occur because motor units are constrained to fire temporally close to one another and thus the impulses are “clustered” with short delays to elicit bursts of muscle activity. The purpose of this study was to investigate whether a sequence comprised of “clustered” motor unit action potentials (MUAP) can explain spectral and amplitude changes of the EMG during a simulated motor task. This question would be difficult to answer experimentally and thus, required a model to study this type of muscle activation pattern. To this end, we modeled two EMG signals, whereby a single MUAP was either convolved with a randomly distributed impulse train (EMG-rand) or a “clustered” sequence of impulses (EMG-clust). The clustering occurred in windows lasting 5–100 ms. A final mixed signal of EMG-clust and EMG-rand, with ratios (1:1–1:10), was also modeled. A ratio of 1:1 would indicate that 50% of MUAP were randomly distributed, while 50% of “clustered” MUAP occurred in a given time window (5–100 ms). The results of the model showed that clustering MUAP caused a downshift in the mean power frequency  with the largest shift occurring with a cluster window of 10 ms. The mean frequency shift was largest when the ratio of EMG-clust to EMG-rand was high. Further, the clustering of MUAP also caused a substantial increase in the amplitude of the EMG signal. This model potentially explains an activation pattern that changes the EMG spectra during a motor task and thus, a potential activation pattern of muscles observed experimentally. Changes in EMG measurements during fatiguing conditions are typically attributed to slowing of conduction velocity but could, per this model, also result from changes of the clustering of MUAP. From a clinical standpoint, this type of muscle activation pattern might help describe the pathological movement issues in people with Parkinson’s disease or essential tremor. Based on our model, researchers moving forward should consider how MUAP clustering influences EMG spectral and amplitude measurements and how these changes influence movements. During dynamic and fatiguing contractions to volitional failure, bursts of muscle activation appear in the electromyography (EMG) signal. This activation of the muscle has been observed in frequencies anywhere from 20 Hz to 60 Hz and these bursts of activation are observed both in surface and indwelling EMG signals. From a theoretical standpoint, this type of muscle activation of the muscle is likely due to motor unit action potentials (MUAP) firing temporally close to one another. Although there are invasive and non-invasive experimental methods to record a proportion of motor unit activity  an increase in the width and shape of the MUAP due to slowing of conduction velocities; and/or (b) due to synchronous firing of motor units  as motor unit action potential “clustering”. Typically, motor units that fire in close proximity to one another have been referred to as synchronization. This synchronization has been defined as either short-term or long-term synchronization  on EMG power spectra using a simplified model of an EMG signal. The first hypothesis is that the EMG time and frequency domain analyses  observed during fatiguing conditions and certain clinical conditions can be explained by a modeled EMG signal including clustering of motor units during a motor task. The second hypothesis is that, even if there is a larger amount of random superposition of MUAP, a small amount of clustering will still yield distinctive, observable changes in the EMG time and frequency domain. The proposed model can be used to explain the phenomenon of power spectra changes seen in previous work and our sequence of activation during a motor task could provide novel insight to the activation of muscles during dynamic tasks, fatiguing contractions and clinical conditions that to date, has been unaccounted for by previous research.17 points recorded with a sampling rate of 2400 samples, was simulated. The simulation convolved a single MUAP with a set amount of randomly located impulses in time, resulting in a number of randomly distributed MUAP of varying amplitude. The modeled single MUAP was a close replicate of the one published by  are constrained to fire within the 40 ms window. We created EMG signals of different cluster-durations (5–100 ms) in 5 ms increments, yielding 21 different cluster durations. Additionally, it is likely that MUAP clustering is not a phenomenon that is all or nothing and instead, it’s likely that there are different degrees of clustering. Thus, another parameter that was added to the model was the ratio of clustered to randomly distributed MUAP. Specifically, we modeled EMG signals with five different cluster ratios  to determine how the degree of clustering influences the EMG signal. A ratio of 1:1 would indicate that 50% of MUAP are clustered while 50% are randomly distributed, a ratio of 1:2 would indicate that 33% of MUAP were clustered, a ratio of 1:4 would indicate that 20% of MUAP were clustered, and a ratio of 1:10 would indicate that 10% of MUAP were clustered. Figure 10 points and the sequential power spectral densities were averaged, yielding a frequency resolution of 2.34 Hz  with factors CLUSTERRATIO  and CLUSTERDURATION (21 levels: 5–100 ms in 5 ms increments). If there was a significant interaction effect, the location of the significant differences was determined with paired comparisons. If the data did not meet the assumptions of the parametric ANOVA, a Greenhouse-Geisser correction was implemented.To address part of the first hypothesis that EMG frequency domain changes as a result of clustering, we examined the power spectrum from the different modeled EMG signals. To start, the power spectrum was determined for the EMG-rand signal. The nPSD of this EMG-rand signal shows a bell shaped distribution Figure . The powThe nPSD of the EMG-clust showed distinct differences in the shape of the EMG-rand nPSD. Specifically, clustering leads to a bimodal or even multimodal nPSD distribution Figure . The firFigure The bimodal distribution of the EMG-clust nPSD likely causes a substantial shift in the mean and median frequency of the spectrum Figure . As the These effects of MUAP clustering were very prominent as the EMG-clust only contained MUAP arriving temporally close to one another . To answer part of the second hypothesis, we modulated the ratio of clustered to randomly distributed MUAP. The nPSD of the different cluster ratios making up the EMG-mixed signal still reveals how MUAP clustering influences its structure Figure  with effF = 4199.5, p < 0.001). From the significant interaction effect, the important comparisons are the differences in mean frequency between the EMG signals created from different cluster ratios for a given cluster duration  as well as the comparison of the mean frequency of the EMG-rand signal in comparison to an EMG signal containing clustered MUAP  for a given cluster duration . For the comparisons across the different cluster ratios, every comparison was significantly different (p < 0.05), except for the following comparisons: 1:2 vs. 1:4 cluster duration = 85 ms, 1:4 vs. 1:10 cluster duration = 90 ms. For the comparisons of the EMG-rand to the clustered EMG signal, the mean frequency was significantly different for each clustered EMG signal in comparison to the EMG-rand signal when the cluster duration was 5–40 ms.The statistical analysis of the mean EMG power as a function of cluster ratio and cluster duration revealed that there was a significant interaction effect  = 6914.8, p < 0.001). From the significant interaction effect, the important comparisons are the differences in mean EMG power between the EMG signal created from different cluster ratios for a given cluster duration  as well as the comparison of the mean EMG power of the EMG-rand signal in comparison to an EMG signal containing clustered MUAP  for a given cluster duration . For the comparisons across the different cluster ratios, every comparison was significantly different (p < 0.001). For the comparisons of the EMG-rand to the clustered EMG signal, the mean EMG power was significantly different for each clustered EMG signal in comparison to the EMG-rand signal (p < 0.001).The statistical analysis of the mean EMG power as a function of cluster ratio and cluster duration revealed that there was a significant interaction effect  and the ratio of “clustered” MUAP to randomly distributed MUAP was altered. The results of the model indicated that changing these parameters caused substantial changes in the resulting EMG signal. MUAP clustering created a remarkable shift of the power spectrum’s mean frequency and altered the structure and shape of the power spectrum. Further, clustering of MUAP caused a substantial increase in the mean amplitude of the EMG power as a result of a time-frequency analysis. We believe that the modeled sequence of muscle activation during a simulated motor task has implications for the activation of muscles and the corresponding changes in EMG observed during fatiguing and clinical  conditions. In summary, our theoretical model shows that MUAP clustering greatly impacts an EMG and matches experimental work; we suggest that this phenomenon must be considered for future interpretation of EMG research.In support of hypothesis 1, our model indicated that MUAP clustering modified metrics of the power spectrum. Precisely, the mean frequency of an EMG created from “clustered” MUAP shifted to lower frequencies in relation to a model EMG from randomly distributed MUAP. The model indicated that when the cluster-duration was set to 10 ms and the EMG was comprised of only clustered MUAP, there was the largest downward shift in the mean frequency of the EMG from ~82 Hz to ~53 Hz Figure , eccentrIn support of hypothesis 2, the effects of altering the cluster ratio were similar such that less “clustered” MUAP resulted in a less substantial downward shift in the metrics of the power spectrum. The lowering value of the mean frequency of the power spectrum are typically attributed to a widening of the MUAP due to slowing of the conduction velocity  indicated that there was an increase in EMG amplitude characteristics when the clustering windows were in the shorter range  in support of hypothesis 1. Compared to the longer clustering window sizes, this shorter time window resulted in a 100% increase the mean amplitude of the EMG signal. Further, and in support of hypothesis 2, all cluster ratios caused an increase in EMG amplitude. This change in the EMG signal has been observed frequently during muscle fatigue and dynamic tasks. During sustained isometric contractions, there is anywhere between a 50% and 100% increase in EMG amplitude from a baseline recording when a person holds an isometric contraction to volitional exhaustion  and the muscle is only required to be active for this short period of time. Theoretically for this dynamic task, MUAP are constrained to fire together for a precise period or simply “cluster” together. In Maurer et al. , particiPrevious research has examined how the firing rate of the motor units influences power spectrum  recruitment patterns, firing rate, volume conductor effects, and number of motor units found in different muscles to determine how MUAP clustering may be muscle specific. Although the model is simplified, we believe that it is important to use a model such as the one presented here to understand, analyze, and interpret EMG signals, particularly during dynamic tasks.We presented a modified classical model of an EMG using a “clustered” MU firing sequence. MUAP clustering creates substantial changes in the shape and structure of the power spectrum. In dynamic tasks, where there are short intervals of muscle activity, MUAP clustering, theoretically, is likely the most logical method of neuromuscular control. The results of our model indicate that in the interpretation of power spectra during dynamic tasks, the possible modifications of the power spectra due to clustering should be considered. The general measure for fatigue is the lowering of the mean/median frequency and we suggest that the lowering of the mean/median can also be driven by MUAP clustering. Interestingly, the lowering of these metrics, because of clustering, could be a measure of the activation patterns of muscles and therefore, this effect must clearly be separated from peripheral effects in future research. Our model indicates that the rhythmic activation of a muscle is due to clustering of motor units and this clustering dramatically alters EMG spectra and amplitude similar to what is seen in fatiguing and clinical conditions.MJA and VT were responsible for the conception of the idea and design of the model. BMN, MJA and VT were responsible for the interpretation of the work, drafting and revising the manuscript and final approval of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Granulocyte colony-stimulating factor (G-CSF) can induce regulatory T cells (Tregs) as well as myeloid-derived suppressor cells (MDSCs). Despite the immune modulatory effects of G-CSF, results of G-CSF treatment in systemic lupus erythematosus are still controversial. We therefore investigated whether G-CSF can ameliorate lupus nephritis and studied the underlying mechanisms.NZB/W F1 female mice were treated with G-CSF or phosphate-buffered saline for 5 consecutive days every week from 24 weeks of age, and were analyzed at 36 weeks of age.+CD25+Foxp3+ Tregs, with decreased renal infiltration of T cells, B cells, inflammatory granulocytes and monocytes in both kidneys and spleen. G-CSF treatment also decreased expression levels of MCP-1, IL-6, IL-2, and IL-10 in renal tissues as well as serum levels of MCP-1, IL-6, TNF-α, IL-10, and IL-17. When Tregs were depleted by PC61 treatment, G-CSF-mediated protective effects on lupus nephritis were abrogated.G-CSF treatment decreased proteinuria and serum anti-dsDNA, increased serum complement component 3 (C3), and attenuated renal tissue injury including deposition of IgG and C3. G-CSF treatment also decreased serum levels of BUN and creatinine, and ultimately decreased mortality of NZB/W F1 mice. G-CSF treatment induced expansion of CD4+CD25+Foxp3+ Tregs. Therefore, G-CSF has a therapeutic potential for lupus nephritis.G-CSF treatment ameliorated lupus nephritis through the preferential expansion of CD4The online version of this article (doi:10.1186/s12882-016-0380-x) contains supplementary material, which is available to authorized users. Systemic lupus erythematosus (SLE) is an autoimmune disease in which altered T cell function and polyclonal B cell activation followed by autoantibody production including anti-double stranded DNA antibodies (anti-dsDNA), thus, leading to immune complex deposition in multiple organs, particularly in the kidney. These depositions drive local inflammatory responses that can lead to tissue damage and clinical disease , 2.+CD25+Foxp3 regulatory T cells (Tregs) [+Gr-1+ myeloid-derived suppressor cells (MDSC), a subset of innate-like suppressor cells [+Ly6G+Ly6Clow), and monocytic MDCSs (CD11b+Ly6G-Ly6Chigh) [+Ly6G+Ly6Clow and CD11b+Ly6G-Ly6Chigh cells could be inflammatory granulocytes and inflammatory monocytes, respectively, because these populations share surface markers with MDSCs.Granulocyte colony-stimulating factor (G-CSF) is a growth factor for neutrophils, that can accelerate neutrophil reconstitution after bone marrow suppression and activate effector functions of mature neutrophils . G-CSF a (Tregs) , 5. In a (Tregs) , 7. G-CS (Tregs) . Recentlor cells , 10. MDSor cells . MDSCs ale1.Sle2.Sle3 spontaneous mouse model of lupus [Despite the immunomodulatory effects of G-CSF, results of G-CSF treatment in SLE are still controversial. Low-dose G-CSF treatment accelerated lupus nephritis in MRL lymphoproliferation (MRL/lpr) strain mice and increased autoantibody production in B6.Sof lupus , 13. In of lupus , 15. TheNZB/W F1 mice spontaneously develop a disease closely resembling human SLE . Female Spot urine proteinuria was measured using protein reagent strips  once per week for 12 weeks. The measurement was semi-quantitative: 0 = none or trace amount of proteinuria, 1+, 30–100 mg/dL; 2+, 100–300 mg/dL; 3+, 300–1000 mg/dL; 4+, ≥ 1000 mg/dL. Urine albumin concentrations were measured using a mouse albumin ELISA kit  and normalized to urine creatinine concentrations. Serum levels of BUN and creatinine were measured at 32 and 36 weeks using QuantiChrom urea and creatinine assay kits  . Serum cParaffin sections of fixed kidneys were stained with Periodic Acid-Schiff’s (PAS) stain kit, and evaluated according to described protocol , 19. BriKidney cryostat sections were stained with goat anti-mouse IgG (Sigma-Aldrich) or rabbit anti-mouse C3 (Abcam) for 4 h. Then, they were incubated at room temperature with Alexa Fluor 488 donkey anti-goat IgG or Alexa Fluor 568 donkey anti-rabbit IgG  for 1 h. Deposition of IgG and C3 within the peripheral glomerular capillary walls and the mesangium was measured as the mean fluorescence in 10 glomeruli per mouse. Scores were assigned based on the intensity of IgG/C3 deposition (0–3+), where 0 represents no deposition and 3 denotes intense deposition . All hisRenal leukocytes and splenocytes were pretreated with anti-mouse CD16/32 (clone 2.4G2) to block nonspecific Fc binding. Kidney leukocytes were identified by labeling cells with CD45 (30-F11). The fluorochrome-conjugated antibodies (BD Biosciences or eBioscience) were used for flow cytometric analysis: CD45 (30-F11), CD3 (145-2C11), CD4 (RM4-5), CD8 (53-6.7), CD25 (ebioPC61), CD44 (IM7), CD69 (H1.2 F3), Foxp3 (ebio), CD11b (M1/70), Gr-1 , Ly6G (RB6-8c5), Ly6C (AL-21), CD19 (eBio1D3), CD138 (281-2), CXCR5 (SPRCL5), PD-1 (J43), NK1.1 (PK136). Appropriate fluorochrome-conjugated isotype-matched irrelevant monoclonal antibodies were used as negative controls. 7-aminoactinomycin D was also added to distinguish between live and dead cells. The stained cells were analyzed using BD FACSCanto II (BD Biosciences).t-test was used to compare means. Survival rates were analyzed using the Kaplan-Meier method and compared by the log-rank test. Difference in proteinuria during treatment was analyzed using generalized estimating equations. P values of less than 0.05 were considered statistically significant. All data were analyzed using SPSS v.22.0 software .Data were expressed as the mean ± standard error of the mean (SEM). Student’s P < 0.01, Fig. P < 0.01) and 36 weeks (P < 0.05) in the G-CSF group . G-CSF treatment decreased serum levels of anti-dsDNA IgG  cells was also decreased in the G-CSF group  aggravated lupus nephritis in MRL/nd IFN-α , 7, 13. oduction , 24. G-C+Ly6G+Ly6Clow) or monocytes (CD11b+Ly6G-Ly6Chigh) in both kidney and spleen. These results were consistent with previous studies in murine lupus models. In those studies, the percentage of CD11b+Gr-1low monocytes in the kidney increased as the disease progressed in the MRL/lpr mouse model of lupus, and it expanded in B6 mice with established chronic graft-versus-host disease [G-CSF decreased inflammatory granulocytes (CD11b disease , 25. Alt+CD25+ Treg cells were decreased in SLE patients and Treg cell numbers were inversely correlated with disease activity [-Foxp3+ T cells in contrast to CD25+Foxp3+ Tregs. These results demonstrated another benefit of G-CSF treatment for lupus nephritis, because CD25-Foxp3+ T cells were increased in the case of active lupus nephritis and had lower suppressive activity than CD25+Foxp3+ Tregs [We found that Tregs expanded in the G-CSF group and Treg depletion abrogated the G-CSF-mediated beneficial effects on proteinuria in NZB/W F1 mice. These findings were consistent with a previous study, which demonstrated that G-CSF recruited Tregs, and thereby prevented onset of spontaneous diabetes in NOD mice . CD4+CD2activity , 28. Moractivity . Therefoactivity , lack of3+ Tregs , 32.Beneficial effects of G-CSF for lupus nephritis observed in this study suggest G-CSF as potential treatment for lupus nephritis, considering G-CSF is already used in clinical treatment of neutropenia after chemotherapy. Human G-CSF is less potent in mice than murine G-CSF, thus we used a very high dose of human G-CSF in NZB/W F1 mice. We expect to reduce this dose to tolerable levels in clinical trials.Most patients with lupus nephritis take long-term immunosuppressants such as corticosteroids, cyclophosphamide, or mycophenolate mofetil. Therefore, one of the limitations of this study is that we did not evaluate the interacting effects of G-CSF with standard immuosuppressants for lupus nephritis. Further studies are needed to confirm our findings in clinically plausible situations.+CD25+Foxp3+ Tregs instead of MDSC may be the underlying mechanism. Therefore, G-CSF may have a therapeutic potential for lupus nephritis.G-CSF treatment ameliorated lupus nephritis, and preferential expansion of CD4