extfieldsofstudy: Geography Medicine

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Ancient DNA from South-East Europe Reveals Different Events during Early and Middle Neolithic Influencing the European Genetic Heritage

The importance of the process of Neolithization for the genetic make-up of European populations has been hotly debated, with shifting hypotheses from a demic diffusion (DD) to a cultural diffusion (CD) model. In this regard, ancient DNA data from the Balkan Peninsula, which is an important source of information to assess the process of Neolithization in Europe, is however missing. In the present study we show genetic information on ancient populations of the South-East of Europe. We assessed mtDNA from ten sites from the current territory of Romania, spanning a time-period from the Early Neolithic to the Late Bronze Age. mtDNA data from Early Neolithic farmers of the Starčevo Criş culture in Romania (Cârcea, Gura Baciului and Negrileşti sites), confirm their genetic relationship with those of the LBK culture (Linienbandkeramik Kultur) in Central Europe, and they show little genetic continuity with modern European populations. On the other hand, populations of the Middle-Late Neolithic (Boian, Zau and Gumelniţa cultures), supposedly a second wave of Neolithic migration from Anatolia, had a much stronger effect on the genetic heritage of the European populations. In contrast, we find a smaller contribution of Late Bronze Age migrations to the genetic composition of Europeans. Based on these findings, we propose that permeation of mtDNA lineages from a second wave of Middle-Late Neolithic migration from North-West Anatolia into the Balkan Peninsula and Central Europe represent an important contribution to the genetic shift between Early and Late Neolithic populations in Europe, and consequently to the genetic make-up of modern European populations.

Introduction
from Anatolia was not a single event but was represented by several waves of migrants [24]. In this respect the Proto-Sesklo culture in Greece, from which directly Starčevo-Criş in the North Balkans and indirectly LBK in Central Europe originate [25][26] represents only the first great wave of Neolithisation of Europe [27]. A later great wave of migration from North-West Anatolia led to important cultures of South-Eastern Europe such as Vinča and Boian cultures [28]. Secondly, there is a total absence of aDNA data from South-East Europe in the current models.
In the present study we have assessed the mtDNA variability from 63 individuals recovered from 10 archaeological sites in Romania spanning a period of five and a half millennia (c. 6300-1100 cal BC) between the Early Neolithic to the Late Bronze Age in Romania (Table 1, Fig 1). This is a strategic area of South-East Europe, from which different prehistoric human groups have passed and later spread throughout Europe. These sites encompass several major cultural events: i. the first Neolithic complex of the Gura Baciului-Cârcea group (also called Precriş culture) of Starčevo-Criş culture, which has the same origin in the Proto-Sesklo culture and it is partially contemporary with LBK culture in Central Europe; ii. the Boian, Zau and Gumelniţa cultures, that represent a continuum of a second migration in the Middle/Late Neolithic and Eneolithic, which has its origin in North-West Anatolia (Demircihoyuk) through East Bulgaria [28][29]; iii. the Eneolithic complex of Decea Mureşului, that represents a possible eastern migration [30][31][32]; and iv. the Early and Late Bronze Age complex of Floreşti-Polus, that represents new migratory movements most likely originating in the North steppes of the Black Sea [29]. The aim of the study is to shed light on the genetics of the different waves of migration of Neolithic and Bronze Age populations penetrating Europe from Anatolia and the steppes north of the Black Sea. We also assess the genetic impact of prehistoric events in the genetic composition of the present-day European populations.

Results
The mtDNA variability of prehistoric groups from Romania Ancient DNA analysis was performed from 80 teeth remains belonging to 63 individuals recovered from ten prehistoric sites (Table 1, Fig 1 and S1 Table). We have performed 14 C dating for eleven human remains from six Romanian sites (S8 Fig). One of the samples was discarded Table 1. Prehistoric samples from Romania analysed in the present study: Chronology, Cultural stages (also in Supporting Information S1 Table), Archaeological sites and Sample size (I.D.: Identification name; N analysed: Number of individuals analysed; N rep: number of individuals with reproducibility results). because it provided inconsistent dating, while the others were consistent with the archeological dating. Fifty nine informative mtDNA sequences were obtained from a total of 63 individuals, accounting for an overall efficiency of 93% (4 individuals were discarded due to inconsistent results) (S2 Table, Supporting Information). A number of individuals (17%) has been replicated independently, which consisted in performing the extraction, amplification and sequencing of two samples from the same individual by different researchers at different periods of time. The number of molecular targets was quantified for each extract by means of RT-qPCR. The results showed that the number of molecules/μl in the extracts ranged between 200-66000 (S2 Table), values falling within the limits proposed for reliable aDNA studies [33]. In order to identify any possible contamination that might have occurred in the different stages of the laboratory work, at least two extraction controls and several PCR negative controls were included in each amplification reaction. The rate of contamination for this analysis was 1.3%.
In addition, a total of 192 PCR products from 26 individuals were cloned, of which a minimum of ten clones per PCR product were selected and sequenced (S6 Table). The results were used to determine the degree of coincidence between the consensus sequence of the clones and the sequence obtained by direct sequencing. A mean of 8.20 mutations per fragment cloned (~100 pb) were rejected as these mutations were found uniquely in different clones. These mutations have been considered as artefacts resulting from post-mortem damage to aDNA.

Early Neolithic: Starčevo Criş culture
The mtDNA variability observed in the samples from Early Neolithic in Romania (E_NEO) (n = 5, Gura Baciului, Negrileşti, and Cârcea sites), showed five haplotypes (haplotype diversity = 0.99±0.0395) that were assorted into four European haplogroups (H, HV, J and T1a) ( Table 2). The haplogroup H is the most frequent in the present-day European populations and the haplogroups J and T1 are suggested to be as markers of the Neolithic diffusion from Near East [5].   [28]. Gumelniţa and Boian are two related cultures, having the same area, same type of settlements, economy and burials, being only different in their chronology. Most archeologists believe that these two cultures represent a continuum [28,34]. The samples from the Iclod site belong to the Zau culture (who is contemporary with both Boian and Gumelniţa). Therefore, we decided to analyse the samples belonging to Boian, Gumelniţa and Zau cultures together: for the sake of simplicity we will call them M_NEO during the population genetic analysis. In addition, no statitically significance differences were found between these sites, supporting the decision to analyse them together. The analysis of their mtDNA variability showed 29 mitochondrial haplotypes (haplotype diversity = 0.8095±0.0052), which were assorted into eight different haplogroups (H, HV, R, J, K, T, U, W) ( Table 2). The most frequent is haplogroup H (58.5%), which showed a high diversity including 13 different haplotypes, while the next most frequent haplogroups were U (12.2%) and J (12.2%). Within haplogroup U five different haplotypes can be seen, with four of them corresponding to the subhaplogroups that were frequent in the European hunter-gatherers (U5 and U4). The haplogroups J and T (T1), which have been proposed as genetic markers of the Neolithic demic diffusion from the Near East [5], showed a frequency of 12.2% and 4.8% respectively. These values are similar to those found in modern European populations, and the same was true for the rest of the haplogroups (K, W, HV, R).

Eneolithic: Decea Mureşului non-indigenous culture
The samples of Eneolithic in Romania were obtained from two individuals recovered from the Decea Mureşului site (samples identified as Eneol) and belonging to a cultural phenomenon known under the same name. Generally, archaeologists consider that the Decea Mureşului culture is the result of a migration of non-indigenous populations coming from the North Pontic steppes [31]. The material culture of these intrusive communities differs fundamentally from that of the local Eneolithic cultures (e.g. Boian, Gumelniţa, Petresti, Cucuteni, Tiazapolgar, etc.) [28]. This is the reason why the samples of the Decea Mureşului culture were analysed separately of other cultures from the same chronological sequence (e.g. the local Gumelniţa culture). The two different mitochondrial haplotypes obtained in two individuals recovered from the Decea Mureşului cemetery correspond to haplogroup K. These haplotypes are unique, not found in any prehistoric sample, either Romanian or European (Table 2). These mitochondrial DNA haplotypes have only been found, albeit with a low frequency, in the present-day Middle East populations (1%).

Early and Late Bronze Age: Copăceni and Noua cultures
The two individuals from the Copăceni group, an Early Bronze Age site (E_BA), showed two different haplotypes, which are included in haplogroup K. These haplotypes are common in present-day and ancient European populations. On the other hand, the mtDNA data obtained from Noua Culture, a Late Bronze Age site (L_BA) in Romania, correspond to eight different haplotypes (haplotype diversity = 0.8889±0.0074), assorted into four European haplogroups (H, HV, U5 and W) (haplogroup diversity = 0.8214±0.1007). It should be highlighted that the haplotypes ht12 and ht13 in the L_BA site, belonging to subhaplogroup U5 (one of the most ancient in Europe) were also found in the Middle-Late Neolithic (M_NEO) groups from Romania ( Table 2). One of the haplotypes (ht8 corresponding to haplogroup W) was found in two different individuals in the L_BA site (P22 and P23) ( Table 2). As archaeological and anthropological context suggested a possible kinship relation between these two individuals, the analysis of five autosomic STRs in the samples was performed (AMG, D13S317, D2S1338, D18S51, D16S5399 AmpFlSTR MiniFiler PCR amplication Kit, Life Technologies); this genetic analysis confirmed that they likely were sister and brother (initially called "Romeo and Juliet" as they were thought to be a young couple of lovers [35]) (S2 Table). For this reason, only one of these two individuals has been included in the diversity and statistical analysis.

Comparison of ancient and present-day populations from Romania
A pairwise Fst test based on the mitochondrial haplotype variability showed significant differences between ancient (present study) and modern Romanian populations [36] (S3 Table). No conclusions can be drawn for Eneol and E_BA populations due to the small sample size of those groups (n = 2). When the analysis was performed on the mitochondrial haplogroup variability, the M_NEO and present-day Romania (ROM) populations did not show statistical differences. Analysis of Median Joining Network within prehistoric Romanian populations (presented in the S1 Fig), showed that the most frequent haplotype was rCRS (the central node in the Network, ht16 in Table 2), that was shared by individuals from the Early Neolithic (E_NEO), Middle/Late Neolithic and Eneolithic (M_NEO) and present-day (ROM) groups. Two other shared haplotypes in this network were the 16270 (ht13, U5 in Table 2) and 16192-16270 (ht12, U5 in Table 2), polymorphisms that appeared in M_NEO and L_BA groups. The rest of the haplotypes are specific to each archeological/cultural group.
As it can be seen in the network (S1 Fig

Comparison of ancient populations from Romania with other ancient populations from Europe
Early Neolithic from Romania. The first Neolithic inhabitants of Europe are described archeologically as belonging to the Aegean Early Neolithic cultures [27], from which the bearers of both the Starčevo-Criş-Körös complex in Serbia, Romania and Hungary [28,37] and the Linear Pottery culture in Central Europe (LBK) [21] emerged. No statistical significant differences were found between mtDNA frequency distribution of these two cultures which is in line with the archaeological evidence of a common origin in the Sesklo cultural complex. It is noteworthy to observe that the haplogroup N1a found in the individuals of LBK culture and which is considered a hallmark of the Early Neolithic populations in Central Europe was absent in the Starčevo-Criş culture groups; however, a bias due to the low number of Early Neolithic samples from Romania cannot be excluded as a cause for this difference (S3 Fig).
Middle/Late Neolithic and Eneolithic from Romania. The population corresponding to the Boian, Zau and Gumelniţa cultures from Romania studied here (n = 41) was compared with populations of Central Europe represented by the Baalberge, Salzmünde and Bernburg cultures [21], because of their chronological proximity. The S4 Fig shows that both groups share similar frequencies for haplogroups J, R, U and W, whereas important differences were found for haplogroups H (58.5% in Romania and 22% in Central Europe), K (4.8% and 17% respectively) and T (4.8% and 14.8% respectively). Haplogroups N and X were absent in the Middle/Late Neolithic and Eneolithic (M_NEO) Romanian population. This led to statistically significant differences between Romanian and Central European Neolithic populations for both mtDNA haplogroups and haplotypes (p = 0.00000±0.0000). Median Joining Network analysis of the mtDNA haplotypes of M_NEO groups from Romania and Central Europe displayed differences in their haplotypes distributions (S5 Fig). The only shared polymorphisms are those corresponding to the rCRS (central node of the Network) and polymorphisms 16069 (haplogroup J) and 16298 (haplogroup HV).
The mitochondrial haplotypes obtained in two individuals recovered from the Decea Mureşului site belonged to haplogroup K (Table 2). Therefore, we have performed a Median Joining Network for this haplogroup (S6 Fig), which includes all haplotypes corresponding to the ancient populations of Romania (present study), Czech Republic (Vedrovice) [38], Near Eastern [22], as well as present-day populations (Romania, Near Eastern and Eastern Europe). The network showed that the only shared polymorphisms between Decea Mureşului samples and the rest are those of the central node and two other polymorphisms shared with ancient and modern Near Eastern populations.
Early and Late Bronze Age from Romania. Important population shifts due to migratory events coming especially from the East occurred in the Bronze Age on the present territory of Romania. The Early Bronze Age II phase of Floreşti-Polus site is represented by a novel culture (Copăceni group) characterized by the presence of tumuli and megaliths, and associated with the Yamnaya culture from the Crimea/Volga basin [29,35]. From this stage, only two individuals were available, who showed the same haplogroup K. In contrast, the late phase of Floreşti-Polus site represents a new migration event related to the Noua-Sabatinovka culture [29,35]. Therefore we compared the mtDNA haplogroup frequency of L_BA individuals from Polus with a Bronze Age group from Ukraine [39] (S7 Fig). These two Bronze Age populations shared haplogroups H, U and W, with the largest differences referred to the frequency of haplogroup W. The Bronze Age Ukraine population presented the highest mtDNA haplogroup diversity, due most likely to its large sample size. Significant statistical differences between these groups have not been detected.

Multivariate analysis: Ancient populations from Romania in the context of past and present-day populations
We have analyzed the variability of mtDNA haplogroups of ancient Romania groups in the context of other ancient and present-day populations from Europe and Middle East (S4 Table) through two different multivariate analyses: PCA and MDS, Figs 2 and 3. Eneolithic (Eneol) and Early Bronze Age (E_BA) samples from Romania were excluded due to their small sample size. In Figs 2 and 3, the Principal Component Analysis (PCA) and Multidimensional Scaling Analysis (MDS) are shown.
The two first components of the PCA explained 47% of the variance. PC1, representing 30% of the total variance, was related to the haplogroups D, C, M and N (0.962, 0.952, 0.942 and 0.717 respectively). Present-day European populations lay at one end of this axis, the opposite end being associated to the Middle East populations. Prehistoric populations are distributed following a heterogeneous pattern between these two extremes (Fig 2). Early Neolithic (E_NEO) populations from Romania and Central Europe clustered together, while the Middle/ Late Neolithic and Eneolithic (M_NEO) population from Romania is not clustered with the Middle Neolithic from Central Europe, but with the modern European populations instead. Overall, a similar conclusion can be inferred from PC2 (17% of the total variance). In this case, the variation is explained by haplogroup H, which had the highest correlation value with this component (0.691). The M_NEO group from Romania showed a high frequency for haplogroup H (58.5%), basically similar to modern Europeans, but different from the Early Neolithic groups from Romania.
Finally, a MDS providing a two-dimensional view of a F ST distances matrix was performed. F ST values were calculated according to the frequency of the mitochondrial haplogroups. The results of this analysis are shown in Fig 3, with a reliable graphic representation of the genetic distances (RSQ of 0.99071 and Stress of 0.07553). As previously shown, hunter-gatherer populations in Scandinavia [8][9][10] and Central Europe [11,21] (HG_SCA and HG_CE) are clearly different from all other populations in the analysis. The Early Neolithic groups from Romania and Central Europe [14][15]21] (E_NEO_Romania and E_NEO_CE) are close despite differences in haplogroup distribution (S3 Fig). In contrast, the Middle Neolithic groups from Romania and Central Europe [21] (M_NEO_Romania and M_NEO_CE) are separated. In the case of Romania, the M_NEO group had a higher genetic distance from the Early Neolithic (E_NEO_Romania) than with the present-day Romanian population. On the contrary, Early and Middle Neolithic populations in Central Europe [21] lay closer to each other than any of them with the present population of the same area. Lastly, the Late Bronze Age Romanian group is closer to Bronze Age from Ukraine than to the M_NEO_Romania (Fig 3).

Discussion
In the present study we analysed mtDNA from 59 Neolithic, Eneolithic and Bronze Age individuals recovered from ten archaeological sites in Romania (Table 1), in order to evaluate the potential genetic impact of the different ancient populations in South-East Europe spanning from Early Neolithic to the Late Bronze Age (6300 BC to 1100 BC) on the genetic composition of present-day European populations.

The Early Neolithic farmers in Europe
One of the most hotly debated aspects concerning the origin of Europeans is represented by the relative contribution of Palaeolithic/Mesolithic hunter-gatherers versus the Neolithic farmers for the genetic heritage of modern populations. Two major models for the role of Neolithic farmers and the spread of agriculture have been proposed: a demic diffusion (DD) model and a cultural diffusion (CD) model. In the DD model the Neolithic farmers have a much bigger genetic impact on the make-up of modern Europeans than in de CD model. Although early analyses considered only two models, a number of mtDNA studies in Neolithic populations have indicated a more complex pattern for Neolithic transition. Thus, the random dispersion model proposes that Neolithic farmers had a different impact on the various geographic regions (central Europe, Mediterranean Europe and Cantabrian fringe), at different periods of time [12,17,[20][21][22][23].
Studies from Central and West Europe, especially the analysis of mitochondrial diversity of LBK culture groups, showed no continuity between the first farmers of Europe and the modern Europeans, thus proposing that these Neolithic pioneers had little genetic impact on the current European population [11,[14][15]21]. This hypothesis is supported by our data, which show a close genetic proximity of Early Neolithic group from Romania (Starčevo-Criş culture) with Early Neolithic populations such as LBK but no genetic continuity with modern Romanian populations (Figs 2 and 3, S3 Table). These data are in line with the idea of a common origin of the LBK and Starčevo-Criş cultures from the Aegean Neolithic cultures of Northern Greece/Thessaly, the first Neolithic complex in Europe [24]. The differential distribution of the mtDNA haplogroup in both Early Neolithic groups (S3 Fig)-highlighting the absence of N1a lineage in E_NEO_Romania, a Neolithic marker in Central Europe-may reflect a differential genetic impact of the Neolithic pioneers in these areas.

The genetic shift between Early and Middle Neolithic in Europe
A comprehensive study of mtDNA spanning a period from the Early Neolithic to the Bronze Age in Central European populations has been recently completed [21]. In this study, by comparing different Neolithic populations of Central Europe with a Central European metapopulation, the authors proposed four major demographic events. Their analysis supported a model of continuity between Late Neolithic and modern European populations, while Early and Middle Neolithic populations showed a limited genetic impact in this region. A similar genetic shift has been identified by an exhaustive analysis based on haplogroup H [40], showing a minimal genetic continuity between Early Neolithic and Middle/Late Neolithic groups in Central Europe, which the authors consider 'a previously unrecognised major genetic transition' [40].
Several scenarios have been proposed to account for this genetic shift between Early/Middle and Late Neolithic in Central Europe, suggesting an influence of the CWC (Corded Ware culture) from the East and of the BBC (Bell Beaker culture) from the West in the Late Neolithic. The impact of people of the CWC culture, in turn massively influenced by a possible influx of populations from the East from the Yamnaya culture, has been proposed to be especially important [41]. While this idea is certainly possible, none of the models studied to date have taken into consideration another possible and obvious explanation, namely a new wave of Neolithic migration into Europe through the 'traditional route' of the Balkan Peninsula. This new wave of Neolithic migrations are represented by Vinča and Dudeşti cultures (5500-5000 BC), that trace their origin in North-West Anatolia on the basis of ceramics features [28]. The Boian, Zau and Gumelniţa cultures from Middle-Late Neolithic (M_NEO) from Romania are the direct continuation of this cultural complex; the M_NEO group from Romania displayed differences in haplotype (S5 Fig) and haplogroup distributions (S4 Fig) with the Middle Neolithic from Central Europe.
Interestingly, the genetic analysis of a relatively large number of samples of Boian, Zau and Gumelniţa cultures in Romania (n = 41) (M_NEO) identified a close genetic proximity between this Neolithic group and the Eastern and Central extant European populations. This was shown in the multivariate analysis, where M_NEO and modern populations from Romania are very close, in contrast with Middle Neolithic and modern populations from Central Europe (Figs 2 and 3). Whereas the genetic analysis of modern populations from Central Europe showed a limited genetic impact of the E_NEO_CE and M_NEO_CE groups in this region [21], the mtDNA data of the M_NEO groups from Romania suggest a high genetic impact on modern population in this region (see S2 Fig for shared polymorphisms). The above mentioned data allow us to suggest that the populations of this putative second wave of Neolithic migration from Anatolia caused a much stronger impact on the genetic make-up of the European populations than the earlier farmers of the Starčevo-Criş and LBK cultures. This hypothesis is supported by the larger number of archaeological sites for the Middle/ Late Neolithic and Eneolithic cultures compared with Early Neolithic cultures in South-East Europe, which indicates higher population numbers [28][29]. It is reasonable to hypothesize an interaction of the Vinča-Dudeşti and Zau-Boian-Gumelniţa cultures with the Late Neolithic cultures of Central Europe. This would have led to gene flow and permeation in Central Europe cultures of mtDNA lineages from the second great Neolithic migrations of South-East Europe, and may have had an important contribution to the genetic shift between Early and Late Neolithic populations in Europe. The hypothesized contribution of Middle Neolithic migrations from North-West Anatolia into the Balkan Peninsula and Central Europe may explain the position of the BBC (Late Neolithic in Central Europe), close to the M_NEO groups from Romania in the multivariate analysis (Figs 2 and 3).
One last aspect concerns the presence of U haplogroups in four individuals from two of the Middle/Late Neolithic sites: Curatesti and Sultana-Valea Orbului. While it could be argued that these individuals share a genetic background with European hunter-gatherers [42] that interacted with and adopted farmer lifestyles, more genetic studies to include local hunter-gatherer populations and nuclear DNA are needed to discern such a possibility. On the other hand, it should be pointed out that no statistical differences of mtDNA between the Curatesti and Sultana-Valea Orbului sites and the other Middle/Late Neolithic populations from Romania were detected.
Two Eneolithic (Eneol) individuals from Romania have been analyzed, showing the same mitochondrial haplotype (haplogroup K) ( Table 2). These haplotypes are unique, not found in any mtDNA database of ancient populations. The network performed with the haplotypes corresponding to haplogroup K (S6 Fig) showed that the two individuals from the Decea Mureşului site shared polymorphisms with the ancient and present-day populations from the Near East. Although the two individuals from Decea Mureşului are associated to the Suvorovo culture from the North-Pontic steppes [29][30][31][32], and this has been suggested to represent the first contact between Transylvania and North-Pontic steppes, we have not found genetic evidence in the present study to support this hypothesis.

Bronze Age and the influence of migrations from the East
The archeological data from the Bronze Age in the central Transylvanian plateau of Romania describe at least three major cultures, two of them probably originating and being related to cultures from the East: 1) the Early Bronze Age represented by Copăceni group in the Floreşti-Polus site, which is related to the Yamnaya culture [29]; and 2) the Late Bronze Age complex from Floreşti-Polus site which is related to the Noua-Sabatinovka culture from the North of Black Sea [29]. The most representative number of samples (n = 9) corresponded to the Late Bronze Age (L_BA_Romania). This sample showed a closer genetic similarity with the Bronze Age population from Ukraine than to any other ancient population from Romania. Both F ST distance (S3 Table) and multidimensional scaling analysis (Figs 2 and 3) showed significant differences between Late Bronze Age and Middle Neolithic from Romania, although both populations shared two haplotypes corresponding to haplogroup U5 (ht12 and 13) ( Table 2). These results could reflect the influence of migrations from the East into the Bronze Age population of Romania. On the other hand, the unusual mtDNA haplogroup distribution [(H (37.5%), U (25%), HV (25%), W (12.5%)], described in the L_BA_Romania group and the genetic distance to the modern Romania population (Fig 3), suggest that the contribution of L_BA_Romania to the present-day Romanians was relatively limited. Nevertheless, studies on more individuals are necessary to draw definitive conclusions. Also, the impact of the early Bronze Age migrations on the modern South-East Europeans cannot be assessed in our study, due to the low number of samples.
Finally, in this study we report genetic information on the Neolithic and Bronze Age populations of the Balkan Peninsula, a crucial piece of the puzzle integrating the major demographic and cultural changes that took place from the Neolitic period onwards in South-East Europe. Based on aDNA studies from sites of the Starčevo-Criş culture (Cârcea/Gura Baciului/Negrileşti sites), we confirm their genetic relationship with the LBK culture, both originating in the Proto-Sesklo cultures of Northern Greece. In addition, our data support the strong genetic differences between these first European farmers and the later Neolithic farmers. In addition, we provide for the first time a glimpse to the genetic make-up of the farmers from a later Neolithic migration from Anatolia Vinča and Dudeşti cultures that later evolved in the Boian, Zau and Gumelniţa cultures in South-East Europe. The strong genetic resemblance of individuals from these cultures with the modern populations leads us to propose the hypothesis that they had an important contribution to the genetic heritage of Eastern and Central Europeans. In contrast, no such influence could be demonstrated for Late Bronze Age migrations.
All in all, these data leads to the hypothesis that the Early to Middle/Late Neolithic genetic transition in South-East Europe was strongly influenced by a second migration of farmers from Anatolia during the Middle Neolithic. This scenario may thus lead to a model in which a cultural diffusion process initially brought into Central Europe by small numbers of farmers of the Starčevo-Criş and LBK cultures was later accompanied by a demic expansion of larger numbers of immigrant farmers of the Vinča-Dudeşti and Boian-Gumelniţa cultures. Additional studies are needed in order to define in detail the Neolithic processes of migration in South-East Europe, including an assessment of the local Mesolithic populations, and a more extensive study assessment of Neolithic and Bronze Age Balkan cultures.

Populations
A mtDNA analysis of a total of 63 individuals recovered from ten sites located in Northern and Southern Romania was carried out; the chronology of these sites ranges from Early Neolithic to the Late Bronze Age. The Early Neolithic (6500-5500 cal BC) sites of Cârcea (Dolj county), Negrileşti (Galaţi county) and Gura Baciului (Cluj county) are associated to the Starčevo-Criş culture (VI millennium BC). Another five sites correspond to Middle/Late Neolithic and Eneolithic period (5500-3800 cal BC): Iclod (Cluj county), Vărăşti (Călăraşi county), Curăteşti (Călăraşi county), Sultana-Malu Roşu (Călăraşi county) and Sultana-Valea Orbului (Călăraşi county). The Late Eneolithic period (4500-3800 BC) is represented by Decea Mureşului site (Alba county), and finally the Bronze Age period by the site of Floresti-Polus (Cluj county). These ten sites were put into several cultural and chronological groups, in order to characterize changes in the mtDNA variability from Early Neolithic (E_NEO) to Late Bronze Age (L_BA) (Fig 1 and Table 1, S7 Fig). Early Neolithic (E_NEO_ROMANIA) (6500-5500 cal BC) Five individuals from the Early Neolithic Romania period come from three sites: 1) Cârcea site is located on the banks of the Cârcea River. Most of the human bones were found in the settlement's "defense ditch" and they were among ceramic fragments and animal bones [43]. 2) Negrileşti is a grave found at 2.90 m depth. The skeleton lying on the right side with bent legs carried on the abdomen and the chest a deposit of snails and a stone [44]. 3) Gura Baciului burials consisted of an inhumation and incineration pit where seven skeletons were inhumated in a bent position [45][46].

Middle/Late Neolithic and Eneolithic (M_NEO_ROMANIA) (5500-3800 cal BC)
Forty-five samples from individual graves have been recovered from five Middle and Late Neolithic sites. From geographical point of view most of these sites are placed in southeaster area of Romania, near Danube River (Vărăşti) or on the high terrace of Mostiştea Lake (Curăteşti, Sultana-Malu Roşu, Sultana-Valea Orbului). The only exception is the Iclod cemetery that is located in Transylvania, on the banks of the Someşul Mic River. In terms of cultural framework, Iclod cemetery belongs to Zau culture [47][48]; Curăteşti and Sultana-Valea Orbului to Boian culture [49], and Vărăşti and Sultana-Malu Roşu are settlements belonging to Boian and Gumelniţa communities using the same cemetery [49].

Eneolithic (Eneol_ROMANIA) (4500-3800 cal BC)
This period is represented by Decea Mureşului site (Alba county), dated in the end of the 5th millennium BC. Samples for mtDNA analysis were taken from two of the discovered graves. Exceptional grave goods and the use of ocher and stone mace-head, represent the first contact (migration) between Transylvania and North-Pontic steppes [50].
Early Bronze Age (E_BA ROMANIA) (2600-2100 cal BC) Two samples were taken from the great barrow/tumulus from Floresti-Polus (Cluj county) [51][52]. This funerary complex belongs to Copaceni group, dating from the period II of the Early Bronze Age in Transylvania. The Yamnaya culture (Pit-Grave culture) [53][54], that influence this group, appears at the end of 4th millennium BC in the north steppes of the Black Sea [55] and, later it cover a large area to the west, including Transylvania.
Late Bronze Age (L_BA ROMANIA) (1500-1050 cal BC) Nine samples for mtDNA analysis come from eight graves from Floresti-Polus (the largest necropolis of Noua culture from Transylvania) [51]. The local populations contributed to cultural genesis of this archaeological complex (Monteoru and Komarov cultures from Moldavia and some eastern contribution-most often attributed to the Iranian people (ancestors cimirienilor, scythians) who, in the second millennium BC dominated a Ponto-Caspian steppes) [56].

DNA isolation and genetic studies
The processing of the ancient samples in the laboratory involved the application of a series of strict criteria for the authentication of results, detailed in [57][58][59][60]. In our case, the extraction and preparation of the PCR was undertaken in a specific lab for aDNA, which consist in a positive-pressure sterile chamber, located in a physically separated space from the laboratory where post-PCR processes are carried out. All the work surfaces were cleaned regularly with sodium hypochlorite and irradiated with UV light. Suitable disposable clothing was worn (lab coat, mask, gloves and cap). Contamination controls were applied in both the extraction and amplification processes.
Selection of samples for performing the present study was made from teeth without caries or deep fissures that might extend into the pulp. Whenever possible, more than one tooth was taken from each individual for duplicate analysis, with the duplicates being analysed in various sessions by different researchers at the University of the Basque Country (UPV/EHU).
In order to eliminate surface contamination, the teeth were subjected to a process of depurination using acids, and the entire surface was irradiated with ultraviolet light [61]. The extraction process followed the protocol described by [62]: the tissue (root of the tooth or powdered bone) was incubated with stirring for 2 hours at 56°C in a lysis buffer (5 ml) (0.5 M EDTA pH 8.0-8.5; 0.5% SDS; 50 mM Tris HCl pH 8.0; 0.01 mg/ml proteinase K). The DNA was recovered using phenol and chloroform and then concentrated and purified (Centricon-30, Amicon). Each extraction session involved two contamination controls that were applied to the entire process, except no dental or bone tissue was added.

Analysis of mtDNA variability
Sequencing of HVR-I [nucleotide positions (nps) 15,998-16,400] and HVR-II (nps 16504-429) as per [63], was undertaken in six overlapping fragments, each with a length of approximately 100 bp (base pair). Besides, the fragment between primers 8F and 8R [12] was amplified in all samples to determine position 73 of HVR-II of the mtDNA. The amplification of each fragment was undertaken in independent PCRs. In the case of positive amplification and the absence of contamination, the amplifications were purified by ExoSAP-IT (USB Corporation), with subsequent sequencing in an ABI310 automatic sequencer using chemistry based on Big-Dye 1.1 (Life Technology).
The results obtained were edited with BioEdit software (http://www.mbio.ncsu.edu/ BioEdit/bioedit.html) and the sequences were aligned manually. The sequences obtained in the present study are deposited in Genbank under accession numbers KR149064-KR149120.
In order to classify the mitochondrial variability of the individuals analyzed in this study, we proceeded to amplify 11 markers, which are required for defining the 10 Caucasian haplogroups described [64]. The protocol and primers are described in [65]. The digestion patterns were verified using a fragment analyzer (Bioanalyzer, Agilent Technologies).

Authentication methods
In addition to the precautions taken to avoid contamination, other authentication criteria such as duplication, quantification, cloning and sequencing were applied.
Duplication: A duplicate analysis was performed for 10.3% of individuals at different times and by different researchers at the University of the Basque Country (UPV/EHU).
Quantification of target DNA: Amplifiable DNA was quantified by means of the quantitative PCR (qPCR) of a fragment of 113 bp length of HVR-I, using Taqman probe [66].
Cloning: In order to detect possible heterogeneities in the PCR products that may correspond to either post-mortem damage and/or mixed contamination, a fragment of HVR-I was cloned by means of the TOPO TA Cloning Kit (Invitrogen). Linkage to the vector pCR2.1-TOPO and chemical transformation of the cells TOP10F' (One Shot E. coli) were performed following the supplier's instructions [12] (S6 Table).
We have determined the HVR-I and HVR-II sequence of the mtDNA of the researchers and archaeologists who handled the samples in order to discard possible contamination (S5 Table).
Confirmation of the haplogroups obtained by sequencing and cloning of the HVR I of the mtDNA was verified by identifying the SNPs of the coding region by PCR-RFLPs.
In order to identify any possible contamination that might occur in the various stages of the genetic analysis, at least two blanks were included in each extraction round with a control of the PCR in each amplification reaction. If any contamination was detected, the results obtained were discarded.

Statistical Analysis
Genetic diversity [67] and genetic distances (Fst analysis) were calculated using the statistical package Arlequin 3.11 [68].
Principal Component Analyses (PCAs) was conducted using as variables the frequencies of the mitochondrial haplogroups obtained in this study together with the data from present-day and prehistoric populations taken in the literature (S4 Table) (SPSS 17 Software). In addition, a distance matrix was calculated between the populations studied and those existing in the literature by means of the Arlequin 3.11 program [68]. This distance matrix has been depicted in two dimensions by means of a Multidimensional Scaling (MDS) analysis (SPSS 17 Software).
Furthermore, a Median-Joining Network (MJN) has been constructed using the sequences of ancient and present-day groups from Romania and some ancient groups from Europe that have so far been published, using the Network 4.6.0.0 program (http://www.fluxusengineering.com). Given the high mutation rate of HVR-I from mtDNA, we applied the substitution rates obtained by Meyer et al. [69][70] to establish varying mutational weights ranging from 0 to10, for this reason some mutation remove in the networks and the reticulations are reduced.
This manuscript involves field studies of anthropological specimens. All necessary permits were obtained for the described study, which complied with all relevant regulations. The National History Museum of Transylvania and "Francisc I. Rainer" Institute of Anthropology, Romanian Academy, gave us the permission to use these samples. We have not conducted field work on site.  Table) and present-day Romania population (28). Middle-Late Neolithic group (pink) and present-day Romania (yellow).  [22] and from Czech Republic (orange) [39]. Present-day populations from: Romania (black), East of Europe (yellow), Near Eastern (green) (S4 Table).  Table. A general chronology of sites analysed in the present study.


extfieldsofstudy: Chemistry Medicine

text:
Recombinant α-NAcetylgalactosaminidase from Marine Bacterium-Modifying A Erythrocyte Antigens.

A plasmid based on pET-40b was constructed to synthesize recombinant α-N-acetylgalactosaminidase of the marine bacterium Arenibacter latericius KMM 426T (α-AlNaGal) in Escherichia coli cells. The yield of α-Al- NaGal attains 10 mg/ml with activity of 49.7 ± 1.3 U at 16°C, concentration of inductor 2 mM, and cultivation for 12 h. Techniques such as anion exchange, metal affinity and gel filtration chromatography to purify α-AlNaGal were applied. α-AlNaGal is a homodimer with a molecular weight of 164 kDa. This enzyme is stable at up to 50°C with a temperature range optimum activity of 20–37°C. Furthermore, its activity is independent of the presence of metal ions in the incubation medium. 1H NMR spectroscopy revealed that α-AlNaGal catalyzes the hydrolysis of the O-glycosidic bond with retention of anomeric stereochemistry and possesses a mechanism of action identical to that of other glycoside hydrolases of the 109 family. α-AlNaGal reduces the serological activity of A erythrocytes at pH 7.3. This property of α-AlNaGal can potentially be used for enzymatic conversion of A and AB erythrocytes to blood group O erythrocytes.

INTRODUCTION α-N-Acetylgalactosaminidases (EC 3.2.1.49) catalyze the removal of 2-acetamido-2-deoxy-D-glucopyranosyl residues bound via the α-O-glycosidic bond (Gal-NAcα) from the non-reducing ends of oligosaccharides and glycoconjugates: in particular agglutinogens of the human blood groups A and AB. α-N-Acetylgalactosaminidases can be used to study the structure of natural compounds and synthesize new oligosaccharides [1]. The study of α-N-acetylgalactosaminidases is also associated with their involvement in the catabolism of complex oligosaccharides in the human body [2]. The practical interest in the enzyme has stemmed from the fact that it can potentially be used for enzymatic conversion of the blood groups A and AB to the universal blood group O via deglycosylation of antigenic determinants [3]. For this purpose, glycoside hydrolases of family 27 (GH27) from chicken liver and family 36 (GH36) from Clostridium perfringens bacterium were isolated [4,5]. These enzymes have a number of disadvantages for biotechnological application, such as an unphysiological pH optimum and inefficiency in converting erythrocytes of subtype A 1 .
α-N-Acetylgalactosaminidase of Arenibacter latericius KMM 426 T , which effectively inactivates the serological activity of the A 1 and A 2 antigens of erythrocytes at neutral pH, was discovered by screening 3,000 strains of marine bacteria [6,7]. Based on the classification of structural homology, α-N-acetylgalactosaminidase of Arenibacter latericius KMM 426 T is classified as belonging to the glycoside hydrolase family 109 (GH109) [8,9].
A method for synthesizing recombinant α-N-acetylgalactosaminidase (α-AlNaGal) to study its enzymatic properties is suggested in this work.
The nucleotide sequence of the α-AlNaGal gene was amplified from the genomic DNA of marine bacterium A. latericius type strain KMM 426 T using primers: Nac40_NcoF (5'-TTAACCATGGAAAATCTTTAT-T T T C A G G G T G G G G C T A A G T A C A T G G G C G -GTTTTTCTGCT-3') and Nac40_SalIR (5'-TTAA-GTCGACACCCTGAAAATAAAGATTTTCGCTTA-CAATATCTAATGGTGCAGTGGT-3') (Eurogene). PCR amplification was performed in an Eppendorf amplifier using the following program: 95°C for 2 min and 35 cycles of 95°C for 15 s, 72°C for 1 min. The α-AlNaGal gene was cloned into vector pET-40b(+) (Novagen) at the NcoI-SalI restriction sites after the DsbC sequence and His-tag. Recombinant plasmids were obtained in Escherichia coli DH5α cells. The α-AlNaGal-producing strain was obtained by transformation of plasmid into E. coli Rosetta(DE3). An overnight culture of the producing strain was grown in a 1-l flask with a liquid LB medium (pH 7.7) containing 25 mg/ml of kanamycin at 37°C and shaking at 200 rpm. When the culture reached the OD 600 of 0.6-0.8, it was induced with 0.2 mM isopropyl-β-D-1-thiogalactopyranoside (IPTG) and incubated at 16°C for 12 h.
Activity of α-AlNaGal was determined according to the cleavage of p-nitrophenyl-α-N-acetylgalactosaminide. The reaction mixture (400 µl) contained 10 mM NaH 2 PO 4 , рН 7.2, 3 mM substrate, and the enzyme. After 20 min of incubation at 20°C, the reaction was terminated by adding 0.6 ml of 1-M Na 2 CO 3 . Absorbance at 400 nm was used to calculate the amount of the released product. One unit of activity (U) was defined as the amount of enzyme catalyzing the formation of 1 µM of p-nitrophenol per minute. Specific activity was estimated as units of enzyme activity per milligram of the protein. Protein concentration was determined according to the Bradford method. The yield of the total enzyme activity was 49.7 ± 1.3 U per 1 l of culture broth.
Purification of α-AlNaGal was carried out at +6°C. E. coli cells were centrifuged at 5,000 rpm for 10 min, re-suspended in 200 µl of buffer A (0.01 M NaH 2 PO 4 , рН 7.8, 0.01% NaN 3 ), and sonicated using a UZDN 2-T ultrasonic disperser (USSR). The solution was centrifuged (25 min, 11,000 rpm) and added to the column (2.5 × 37 cm) containing a DEAE-MacroPrep ion exchange resin (Bio-Rad) equilibrated with buffer A. Elution was performed with a linear gradient of 0-0.25 M NaCl in buffer A. The active fractions were collected and loaded onto a column (1 × 2 cm) with Ni-agarose (Qiagen). The protein was eluted using 50 mM EDTA. The eluate was loaded onto a Sephacryl S-200HR (Sigma) gel filtration column equilibrated with buffer A. Homogeneity of α-AlNaGal was confirmed using a 12% polyacrylamide gel in the presence of sodium dodecyl sulfate (SDS-PAGE) (Fig. 1). The results of gel filtration revealed that α-AlNaGal is a homodimer with a molecular weight of 164 kDa (96 kDa after DsbC plasmid sequence at the site of enterokinase (Novagen) had been removed). The enzyme is stable at up to 50°C with a temperature range of optimum activity of 20-37°C, while its activity is independent of the presence of metal ions in the incubation medium. The additional amino acid residues have no influence on the enzymatic properties; therefore, their removal can be neglected. The optimum pH was determined in 20 mM Na + -phosphate and glycine-NaOH buffers at intervals of pH 5.4-8.2 and 8.0-10.0 ( Fig. 2A). The study of the α-AlNaGal properties revealed a possibility of usage to deglycosylate blood group A erythrocyte determinants (blood transfusion station, Vladivostok) at neutral pH. Blood group A erythrocytes were washed with a normal saline solution and then diluted with a Na + -phosphate isotonic buffer to a final concentration of 20%. 0.02 ml of the obtained suspension was mixed with 0.08 ml of the α-AlNaGal solution (0.004 U) in the same buffer. After 24 h of incubation at 26°C, erythrocytes were washed three times using the same buffer (pH 7.3) with gentle shaking. A 1% suspension was prepared and then mixed with an anti-A serum (Mediclon, Russia) in a series of double-dilution steps in 96well plates (Costar). After 1 h of incubation at room temperature, agglutination titer was measured (Fig.  2B). The results of an immunological analysis showed that the serological activity of A antigens of erythrocytes treated with α-AlNaGal decreases as a result of their enzymatic transformation to H antigens, because no agglutination was observed up to a titer of 1/16. α-AlNaGal causes neither nonspecific aggregation of erythrocytes nor their hemolysis.
The enzyme of marine bacterium Arenibacter latericius type strain KMM 426 T can fully inactivate the se- rological activity of A erythrocytes at neutral pH and compares favorably with α-N-acetylgalactosaminidases from chicken liver and C. perfringens, which affect only the A 2 subgroup of erythrocytes [5,6]. Being classical hydrolases, the GH27 and GH36 enzymes catalyze the hydrolysis of the O-glycosidic bond of their substrate via the double displacement mechanism with retention of the stereochemistry of the anomeric center of the substrate [10]. More recently, an enzyme of a new GH109 family has been isolated from pathogenic bacterium Elizabethkingia meningoseptica. This enzyme had properties similar to those of α-N-acetylgalactosaminidase of the A. latericius type strain KMM 426 T and a different mechanism of hydrolysis of the classical hydrolases [8]. The mechanism includes stages of elimination of the O-glycosidic bond and proton exchange at C2 of N-acetylgalactosamine with retention of anomeric stereochemistry.
The configuration of the anomeric center of the hydrolysis products of α-AlNaGal was directly examined using 1 H NMR spectroscopy. The experiment was carried out at 20°C using a DRX-500 NMR spectrometer (Bruker). 1 H NMR spectra were acquired using a spectral width of 5,000 Hz over 32,000 data points. Prior to the analysis, 0.6 ml of a 50 mM Na + -phosphate solution (pH 7.5) containing 6.0 mM p-nitrophenyl-α-N-acetylgalactosaminide substrate was evaporated and dissolved in 0.6 ml of D 2 O. The deuterium-exchanged α-AlNaGal was obtained using Vivaspin turbo 10 k MWCO columns (Sartorius). Chemical shifts in spectra were referenced to acetone (δ = 2.22 ppm) in D 2 O used as an external standard. After measuring the initial spectra of the substrate at t = 0 min, 0.1 ml of the deuterium-exchanged α-AlNaGal, containing 0.98 U, was added to 6.0 mM of the deuterium-exchanged p-nitrophenyl-α-N-acetylgalactosaminide in 0.6 ml D 2 O to initiate the reaction. The 1 H NMR spectra were automatically recorded at 10 min intervals for 180 min after the onset of the reaction. Figure 3 shows the resonance regions ∆δ=5.30-5.20 ppm and ∆δ=4.75-4.10 ppm of the 1 H NMR spectrum of the reaction products. The product, with a resonance signal at 5.22 ppm, is formed during the first minutes after enzyme addition (Fig. 3A). This signal corresponds to the proton of the anomeric center of unbound N-acetylgalactosamine (GalNAcα). Signal intensity increases during the following 10 min of the reaction. The signal of the β-anomer of GalNAcα with the chemical shift at 4.64 ppm as a result of mutarotation appears only after 20 min of the reaction's onset (Fig. 3B). The spectra of αand β-anomers of unbound GalNAcα show that the signals are observed as doublets with SSCC of 3.8 and 7.8 Hz, and a singlet. These observations indicate that proton-deuterium substitution takes place at C2. Such a catalytic mechanism is typical of glycoside hydrolases GH109 [8,11].

CONCLUSIONS
The recombinant protein α-AlNaGal with a molecular weight of 164 kDa, with the properties of α-N-acetylgalactosaminidase of marine bacterium A. latericius type strain KMM 426 T , was synthesized. α-AlNaGal catalyzes the hydrolysis of the α-O-glycosidic bond with retention of the stereochemistry of the anomeric center of the substrate and proton exchange to deuterium of the solvent at C2 via a mechanism typical of glycoside hydrolases of the GH109 family. α-AlNaGal deglycosylates A antigens of the blood at pH 7.5. This property demonstrates that α-AlNaGal can be used to obtain blood group O erythrocytes.


extfieldsofstudy: Biology Medicine

s2fieldsofstudy: Biology Medicine

text:
Inhibition of integrin β1 decreases the malignancy of ovarian cancer cells and potentiates anticancer therapy via the FAK/STAT1 signaling pathway

Integrin β1 (ITGB1) is frequently upregulated in ovarian cancer, and promotes ovarian tumorigenesis and cancer progression. However, the effects of ITGB1 inhibition on ovarian cancer progression and anticancer therapy remain to be elucidated. The results of the present study indicated that ITGB1 was upregulated in HO-8910 and HO-8910PM ovarian cancer cell lines, and knockdown of ITGB1 using short hairpin RNA markedly increased tumor cell apoptosis, decreased tumor cell adhesion and migration, and reduced tumor cell invasion by suppressing matrix metalloproteinase (MMP)-2 and MMP-9 expression. Furthermore, the results of the present study provided evidence regarding the role of ITGB1 inhibition in bevacizumab anticancer therapy. The activation of signal transducer and activator of transcription 1 (STAT1) by focal adhesion kinase (FAK) is involved in integrin-mediated cell migration and adhesion. In the present study, the expression levels of FAK were markedly upregulated in ovarian cancer. The adherence and migratory potentials of ovarian cancer cells were significantly reduced when the FAK/STAT1 signaling pathway was inhibited by fludarabine. The results of the present study demonstrated that ITGB1 inhibition effectively reduced tumorigenesis and disease exacerbation, and contributed to bevacizumab anticancer therapy via the FAK/STAT1 signaling pathway, suggesting that inhibition of ITGB1 is a potential novel therapeutic strategy for ovarian carcinogenesis.

Introduction
Ovarian cancer is the most lethal gynecological malignancy, and one of the leading causes of morbidity and mortality worldwide (1). In ovarian carcinoma, cancer cells frequently metastasize by implanting onto the peritoneal mesothelial surface of the abdominal cavity. The resulting peritoneal implants are characterized by the adhesion, migration, and invasion of the tumor cells into the peritoneum and underlying organs (2).
Integrins are a large family of cell surface adhesion proteins that are involved in epithelial cell-matrix interactions. Upregulation of integrins has been associated with malignancy (3,4), particularly during invasion, metastasis, and angiogenesis (5,6). Increasing evidence suggests that aberrantly expressed integrins have a role in ovarian cancer pathophysiology (7)(8)(9). Integrin β1 (ITGB1), regulates broader functional activities such as cellular proliferation, adhesion, and invasion, and previous studies have suggested its implication in therapeutic resistance in numerous solid cancer models (10)(11)(12) and hematopoietic malignancies (13,14). Focal adhesion kinase (FAK) is upregulated in various malignancies, such as breast, colon, prostate, neck, and ovarian cancer (15,16). FAK is an important regulator of survival, proliferation, migration, and invasion, processes that are all involved in tumorigenesis and metastasis (17,18). Therefore, FAK signaling may be regarded as a potential target in the development of anticancer drugs. A previous study demonstrated that FAK, identified as a downstream kinase of ITGB1, has an important role in integrin-stimulated signaling events. Integrin clustering induces autophosphorylation of FAK and activates signaling pathway networks (19). However, the mechanisms underlying ITGB1 inhibition-induced suppression of ovarian cancer progression, and the contribution of ITGB1 inhibition in anticancer therapy remains to be elucidated. The present study investigated the effects of ITGB1 inhibition on tumor apoptosis, invasion and migration, and bevacizumab anticancer therapy in ovarian cancer cells. Furthermore, the present study investigated the signaling pathways that may mediate the anti-invasive and anti-migratory effects that resulted from ITGB1 inhibition. Western blot analysis. Whole cell extracts were prepared using radioimmunoprecipitation assay lysis reagent (Sigma-Aldrich), according to the manufacturer's instructions. The proteins were then quantified using a bicinchoninic acid assay (Pierce Biotechnology, Inc., Rockford, IL, USA), prior to being separated by 10% SDS-PAGE, and detected by western blotting on a polyvinylidene difluoride membrane (EMD Millipore, Billerica, MA, USA). The membranes were blocked with 3 ml Tris-buffered saline with Tween 20 (TBST; Auragene Bioscience Co., Changsha, China) with 5% bovine serum albumin (Auragene Bioscience Co.) for 2 h at room temperature. The membranes were incubated overnight at 4˚C with the following primary antibodies: Rabbit polyclonal anti-ITGB1 (sc-13590; 1:200), anti-STAT1 (sc-345; 1:500), anti-phosphorylated STAT1 (sc-135648; 1:500), anti-FAK (sc-558; 1:500), anti-matrix metalloproteinase (MMP)-2 (sc-13594; 1:500) and MMP-9 (sc-21733; 1:200) primary antibodies (Santa Cruz Biotechnology, Inc., Dallas, TX, USA). The membranes were incubated for 1 h at 37˚C with the anti-β-actin antibody (sc-8979; 1:6,000; Santa Cruz Biotechnology, Inc.). The membranes were washed 3 times with TBST, then incubated with the horseradish peroxidase-conjugated goat anti-rabbit immunoglobulin G secondary antibody (Pierce Biotechnology, Inc.), followed by 3 additional washes with TBST. A SuperSignal West Femto Enhanced Chemiluminescence Detection system (Pierce Biotechnology, Inc.) was used for detection. The blots were analyzed with the CanoScan LiDE 110 scanner (Canon, Inc., Tokyo, Japan) and Image Pro-Plus software, version 6.0 (Media Cybernetics, Inc., Rockville, MD, USA).

Inhibition of integrin β1 decreases the malignancy of ovarian cancer cells and potentiates anticancer therapy via the FAK/STAT1 signaling pathway
In vitro adhesion assay. The cells were pretreated with or without bevacizumab (10 µg/ml) for 24 h. The cells were then suspended in serum-free Dulbecco's modified Eagle's medium (GE Healthcare Life Sciences) to form a single-cell suspension, prior to being seeded at a density of 10 5 cells/well into 96-well plates precoated with Matrigel™ (BD Biosciences, Franklin Lakes, NJ, USA). The wells were incubated at 37˚C for 50 min, and washed three times with phosphate-buffered saline (PBS) in order to remove non-adherent cells. Cell viability was determined using an MTT assay.
Cell survival assay. An MTT assay was used to estimate cell viability (23). Briefly, the cells were plated at a density of 1x10 4  Migration assay. For the wound healing assay, cells were seeded in 12-well plates and grown to 90% confluence. Monolayers in the center of the wells were scraped with pipette tips and washed with PBS. Cell movement into the wound area was monitored and the images were captured at 0 and 24 h using the AE31 light microscope. The migration distance between the leading edge of the migrating cells and the edge of the wound was compared, as previously described (24).
Statistical analysis. Each experiment was repeated in triplicate. Data are presented as the mean ± standard deviation, and analyzed using SPSS 18.0 (SPSS, Inc., Chicago, IL, USA). Statistical comparisons between the groups were analyzed using two-tailed Student's t-tests. P<0.05 was considered to indicate a statistical significant difference.

Expression levels of ITGB1 and FAK in ovarian cancer cells.
To assess whether ITGB1 and FAK were dysregulated in ovarian cancer cells, the expression levels of ITGB1 and FAK in normal ovarian cells (HOSEpic) and ovarian cancer cells (HO-8910 and HO-8910PM) were analyzed. As shown in Fig. 1A, the mRNA expression levels of ITGB1 and FAK were significantly increased in metastatic ovarian cancer cell lines, as compared with the normal ovarian cells, with the expression levels of ITGB1 and FAK being the highest in the highly metastatic HO-8910PM cell line. Similar results were obtained from the western blot analysis (Fig. 1B). These data suggest that suppression of ITGB1 may be associated with carcinogenesis of ovarian cancer.

ITGB1 inhibition suppresses cell invasion and MMP expression in ovarian cancer cells.
Previous studies have demonstrated that ITGB1 may mediate tumor growth, apoptosis, adhesion, and signaling in various cancer cells (7,22).

A B
Due to the fact that invasion is an important factor for cancer expansion and metastasis, the present study examined whether ITGBI inhibition altered the rate of invasion of cancer cells through transwell chambers coated with Matrigel™. Anti-ITGB1 lentiviral vectors were transfected into HO-8910 and HO-8910PM cells to inhibit the expression of ITGB1. Concordant with the previous results, significant attenuation of ovarian cancer cell invasion was observed in the lentivirus (LV)-ITGB1-RNA interference (RNAi) group, as compared with the control and LV-NC groups ( Fig. 2A).    ITGB1 inhibition enhances the effect of bevacizumab treatment. Bevacizumab offers promise as an antiangiogenic in the treatment of ovarian cancer (27); however, acquired resistance limits the use of bevacizumab as a therapeutic agent. The present study demonstrated the effects of the combination of ITGB1 inhibition and bevacizumab treatment on apoptosis, adhesion and migration of ovarian cancer cells. Ovarian cancer cells with knocked down ITGB1 were treated with 10 µg/ml bevacizumab for 24 h, and cell apoptosis, adhesion, and invasion were detected.
As shown in Fig. 3, MTT assay and flow cytometry were used to detect HO-8910 and HO-8910PM cell apoptosis. Cellular proliferation markedly decreased following ITGB1 inhibition and treatment with 10 µg/ml bevacizumab (Fig. 3A  and B). Furthermore, ITGB1 inhibition further reduced cell proliferation in bevacizumab-treated ovarian cancer cells. Concordant with the results of the MTT assay, Annexin V/PI staining demonstrated that the percentage of apoptotic cells was significantly increased in both the LV-ITGB1 and bevacizumab (Beva) groups, as compared with the control group. In addition, ITGB1 inhibition further increased cell apoptosis in bevacizumab-treated ovarian cancer cells. These results suggest that ITGB1 inhibition regulates bevacizumab therapy by decreasing cell proliferation and increasing cell apoptosis.
The adhesion of cancer cells to endothelial cells or the ECM has an important role in establishing metastasis (20). Integrin regulates adhesion and signaling in ovarian cancer (7). The present study evaluated the effects of ITGB1 silencing on ovarian cancer cell adhesion to ECM proteins. The number of adherent cells markedly decreased following ITGB1 inhibition (Fig. 4). The inhibitory effects of cell adhesion induced by ITGB1 silencing may contribute to the inhibition of cell metastasis. Concordant with the results of the apoptosis analysis, ITGB1 inhibition enhanced the effects of bevacizumab treatment against cell adhesion in ovarian cancer. As shown in Fig. 4, significant attenuation of ovarian cancer cell adhesion was observed in the LV-ITGB1 + Beva group, as compared with the Beva group.
The inhibitory effects of ITGB1 suppression on ovarian cancer cell migration were determined by wound healing assay. HO-8910 and HO-8910PM cells were transfected with A B C ITGB1-RNAi and treated with bevacizumab, and wound closure was monitored and the images of the wound closure were captured. The results of the wound healing assay indicated that ITGB1 inhibition significantly attenuated HO-8910 and HO-8910PM cell migration after 48 h (Fig. 5). These data indicate that ITGB1 silencing may effectively inhibit HO-8910 and HO-8910PM cell motility. Furthermore, cell migration was significantly decreased in the LV-ITGB1 + Beva group, as compared with the Beva group. These data suggest that the the inhibitory effects of combination treatment of bevacizumab and ITGB1 have an improved effect compared with bevacizumab simple treatment alone on HO-8910 and HO-8910PM cell migration.

ITGB1/FAK/STAT1 pathway in ovarian cancer cells.
The subsequent experiments of the present study focused on the signaling pathways involved in ITGB1-mediated suppression of metastatic potential in ovarian cancer cells. To determine whether the STAT1 signaling pathway was involved in cell adhesion and migration in ovarian cancer, HO-8910 and HO-8910PM cells were treated with 1.54 µg/ml fludarabine for 24 h. ITGB1 and FAK expression were not significantly affected by fludarabine. However, STAT1 and p-STAT1 expression were markedly downregulated by fludarabine (Fig. 6A). As shown in Fig. 6B, the adhesion ratio was significantly decreased in the fludarabine group, as compared with the control group. Concordant with the adhesion assay results, fludarabine significantly inhibited ovarian cancer cell migration, as determined by the wound healing assay (Fig. 6C). FAK and c-Src form a dual kinase complex, which functions to promote cell motility, cell cycle progression, and cell survival. Previous studies have demonstrated that ITGB1-mediated FAK signaling is closely associated with tumor growth and metastasis (19,28). In a previous study, co-immunoprecipitation and in vitro binding assays demonstrated that STAT1 was transiently and directly associated with FAK during cell adhesion (29), and its activity was induced by the integrin signaling pathway. These results indicate that the ITGB1/FAK/STAT1 pathway is involved in cell adhesion and migration in ovarian cancer.

Discussion
Integrin-mediated cell adhesion and migration have essential roles in cell growth and development. Previous studies have demonstrated that ITGB1 is able to mediate ovarian carcinoma cell adhesion, invasion, and migration (8,30). In the present study, the anti-metastatic effects of ITGB1 inhibition on the HO-8910 and HO-8910PM ovarian cancer cell lines, as well as its molecular mechanism of action, were investigated. ITGB1 inhibition induced cell apoptosis, which was determined by the inhibition of cell adhesion, migration, and invasion, as well as by the suppression of MMP-2 and MMP-9 expression. The results of the present study also demonstrated that ITGB1 inhibition enhanced bevacizumab treatment in ovarian cancer. Furthermore, the inhibition of STAT1 signaling by fludarabine revealed that the ITGB1/FAK/STAT1 pathway may be associated with the molecular mechanisms that underlie the anti-invasive effects of ITGB1 inhibition.
Metastasis is closely associated with cancer therapeutic efficacy and patient prognosis. Metastasis is a multistep process involving numerous factors. Cellular migration, the attachment of cancer cells to the ECM components, and invasion into surrounding tissues are critical to metastasis. Therefore, decreased migration, cell-matrix adhesion, and invasive potential may contribute to the prevention of metastasis. In the present study, the effects of ITGB1 inhibition on apoptosis, migration, invasion, and adhesion to ECM proteins were determined. The results indicated that ITGB1 inhibition significantly increased cell apoptosis, as determined by flow cytometry, and suppressed the migration and invasion of ovarian cancer cells, as determined by wound healing and transwell invasion assays. The cell adhesion assay revealed that inhibition of ITGB1 attenuated the adhesion of ovarian cancer cells to Matrigel™. These results indicated that anti-migration, anti-invasion, and anti-adhesion functions may be important contributors to the anti-metastatic activity of ITGB1 inhibition. MMPs are a well-known family of zinc-binding enzymes that have been reported to be upregulated in cancer, and numerous studies have demonstrated that overexpression of MMPs facilitates cancer cell progression, suggesting that MMPs are also involved in metastasis (31,32). In the present study, the inhibition of ITGB1 suppressed MMP-2 and MMP-9 protein expression. These results suggested that ITGB1 inhibition has the potential to inhibit ovarian cancer metastasis by suppression of MMP-2 and MMP-9 expression. In conclusion, inhibition of ITGB1 resulted in tumor cell apoptosis and disrupted tumor mass formation.
Previous studies demonstrated that ITGB1 may be associated with therapeutic resistance to various agents and ionizing radiation in the treatment of cancer (33)(34)(35)(36). Notably, ITGB1-mediated resistance is thought to occur at the level of the tumor cells themselves. A recent study demonstrated that ITGB1 inhibition combined with bevacizumab treatment reduced the risk of resistance in glioblastoma (34). In the present study, ITGB1 inhibition enhanced the effects of bevacizumab on apoptosis, adhesion, and migration of ovarian cancer cells. The results lead to the hypothesis that ITGB1 inhibition combined with bevacizumab treatment may reduce the required dose of the bevacizumab anticancer agent, thus potentially reducing drug-related morbidity in ovarian cancer.
It has been suggested that integrin/FAK has an important role in regulating various cellular functions, including adhesion, migration, invasion, survival, growth, and differentiation (37). FAK activates STAT1 in integrin-mediated cell migration and adhesion (29). A previous study demonstrated that FAK/STAT1 increased the malignant potential of ovarian epithelium (28). Therefore, ITGB1/FAK/STAT1 signaling is a promising therapeutic target for ovarian cancer. In the present study the adherence and migratory potentials of ovarian cancer cells were significantly reduced following the inhibition of the ITGB1/FAK/STAT1 signaling pathway by fludarabine. These results revealed that inhibiting FAK/STAT1 signaling exerts anti-metastatic effects on ovarian cancer cells.
These data suggested that ITGB1 inhibition effectively reduced tumorigenesis and disease exacerbation, and contributed to bevacizumab-induced anticancer therapy via the FAK/STAT1 signaling pathway. This further elucidated the anti-tumor molecular mechanisms underlying ITGB1 inhibition, suggesting that targeting ITGB1 is a potential strategy for the prevention and treatment of ovarian cancer metastasis.