The relationships between LAMS and CBF were < 30% and the HI was more pronounced in M1 occlusions  and proximal M2 occlusions . The LAMS also correlated with a Tmax > 6 s in M1 occlusions , and negatively correlated with the CBV index in M2 occlusions . There were no significant correlations between the LAMS and intracranial ICA occlusions. Conclusions: The results of our preliminary study indicate that the LAMS is positively correlated with the estimated ischemic core, perfusion deficit, and HI, and negatively correlated with the CBV index in patients with anterior circulation LVO, with stronger relationships in the M1 and M2 occlusions. This is the first study showing that the LAMS may be correlated with the collateral status and estimated ischemic core in patients with LVO.Background: The Los Angeles Motor Scale (LAMS) is a rapid pre-hospital scale used to predict stroke severity which has also been shown to accurately predict large vessel occlusions (LVOs). However, to date there is no study exploring whether LAMS correlates with the computed tomography perfusion (CTP) parameters in LVOs. Methods: Patients with LVO between September 2019 and October 2021 were retrospectively reviewed and included if the CTP data and admission neurologic exams were available. The LAMS was documented based on emergency personnel exams or scored retrospectively using an admission neurologic exam. The CTP data was processed by RAPID  with an ischemic core volume , time-to-maximum (Tmax) volume (Tmax > 6 s delay), hypoperfusion index (HI), and cerebral blood volume (CBV) index. Spearman’s correlations were performed between the LAMS and CTP parameters. Results: A total of 85 patients were included, of which there were 9 intracranial internal carotid artery (ICA), 53 proximal M1 branch middle cerebral artery M1, and 23 proximal M2 branch occlusions. Overall, 26 patients had LAMS 0–3, and 59 had LAMS 4–5. In total, LAMS positively correlated with CBF < 30% (Correlation Coefficient (CC): 0.32,  Stroke is one of the leading causes of death and disability, with ischemic stroke being the most common . Of thosThe Los Angeles Motor Scale (LAMS) is one such rapid pre-hospital scale that was developed to predict stroke severity  and is uA comprehensive baseline computed tomography (CT) imaging–comprised of non-contrast CT (NCCT), CT angiography (CTA), and CT perfusion (CTP)–is widely utilized for triaging patients presenting with acute ischemic stroke (AIS) secondary to anterior circulation LVO for MT. The addition of CTP to comprehensive baseline imaging increases the sensitivity for detecting ischemic core compared to NCCT only  while ofOur preliminary study aimed to explore the relationship between the LAMS score and baseline CTP parameters. Such a relationship may suggest a biological correlation between the LAMS and collateral status and ischemic core.The data for the study population was collected through the Johns Hopkins Hospital Comprehensive Stroke Center Database. This study was approved through the Johns Hopkins School of Medicine institutional review board (JHU-IRB00269637). In this retrospective multicenter analysis, we identified consecutive patients from 1 September 2019 to 1 April 2021 who underwent NCCT, CTA, and CTP with confirmed anterior circulation LVO on CTA , M1, and proximal M2 segments of the middle cerebral artery (MCA). Patients were excluded from the analysis if CTP was obtained >24 h after admission or after a mechanical thrombectomy procedure or if patients were incorrectly classified as having LVO . Patients were included in the study if they had an LVO, CTP with available post-processed parameters, and a documented neurologic exam before the time of imaging. The baseline and clinical data collected for each patient included demographics, risk factors for AIS , a baseline LAMS score, and a site of occlusion. When the LAMS score or admission NIHSS data was unavailable, the scores were estimated based on admission neurologic exam and history . The LAMThe NCCT was acquired on the Siemens Flash and/or Drive  with the following parameters: The helical mode at a 5 mm slice thickness (ST) .The CT ASPECTS score was obtained from documented experienced neuroimaging interpreters. If ASPECTS was unavailable, CT imaging was reviewed independently by a board-certified neuroradiologist to determine the ASPECTS score .The CTP was performed on a Siemens Flash and/or Drive  with the following parameters: The injection of 50 mL non-ionic iodinated contrast with 30 mL saline flush at 5–6 mL/s with a coverage of 70–100 mm at 5 mm slice thickness, 70 kVP, 200 effective mAs, rotation time 0.25 s, average acquisition time 60 s, collimation 48 × 1.2 mm, pitch value 0.7, 4D range 114 mm × 1.5 s. The CTP images are then post-processed using RAPID commercial software  for generating Tmax maps. HI is then automatically calculated as a ratio of the calculated Tmax > 10 s delay volume divided by a Tmax > 6 s delay volume in mL (Tmax > 6 s) in the affected hemisphere. The ischemic core on the CTP is quantitatively estimated by the relative cerebral blood volume (rCBF) < 30% volume in mL (rCBF < 30%). Tissue at risk is defined as a Tmax > 6 s. Mismatch volume is defined as the difference between the Tmax > 6 s and the rCBF < 30%. The mismatch ratio is calculated as the Tmax > 6 s divided by the rCBF < 30%. The CBV index is automatically calculated by RAPID as the ratio of mean rCBV values within the Tmax >6 s region with the mean CBV in normal brain regions. The target mismatch profile for CTP is defined based on the prior study by Lansberg et al. . p-value ≤ 0.05, otherwise was non-significant.The collected data were coded, tabulated, and statistically analyzed using IBM SPSS statistics  software version 28.0, IBM Corp., Chicago, IL, USA, 2021. The quantitative data was described as mean ± SD (standard deviation) and then compared using an independent t-test after being tested for normality using the Shapiro-Wilk test. The qualitative data were described as numbers and percentages and compared using the Chi-squared test and Fisher’s Exact test for variables with small, expected numbers. The correlation coefficients (CC) of the LAMS with the CTP parameters were calculated using the Spearman correlation test. The level of significance was taken at Eighty-five patients were included. Twenty-eight (32.9%) were scored with a LAMS 0–3 with 57 (67.1%) in the LAMS 4–5 category. p = 0.02). Please refer to Eighty-five patients had LAMS, ASPECTS, and CTP parameters available for analysis . PatientThe 85 patients included in the study were comprised of nine distal intracranial ICA ; 53 M1 (62.3%); and 23 proximal M2  occlusions. p < 0.01), Tmax > 6 s , and HI , and small negative correlation with the CBV index  with the CBV index based on a post-processed analysis from a widely used commercial software platform  , Tmax > 6 s , and HI , and negatively correlated with ASPECTS   .p < 0.01; 0.48, p < 0.03 respectively). The LAMS score was strongly negatively correlated with the CBV index   . There wIn summary, in patients with M1 or proximal M2 occlusions, the LAMS score is positively correlated with a CBF < 30% and HI. In patients with M2 occlusions, the LAMS score is further positively correlated with a Tmax > 6 s, and in patients with M1 occlusions, the LAMS is further negatively correlated with the ASPECTS score. In patients with intracranial ICA occlusions, no significant correlation between the LAMS score and the perfusion imaging parameters were demonstrated.We demonstrate that the LAMS score showed weak to moderate significant positive correlations with an rCBF < 30, a Tmax > 6 s, and HI in all patients with LVOs, though when affected vessels were analyzed separately, there were no correlations in patients with ICA occlusions. The results indicate that a rapid pre-hospital stroke scale may help determine which patients within the anterior circulation LVO subset also have a larger ischemic core and poor CS, an important biological correlation. After stratifying by the LVO segment , the results show that these relationships are most pronounced in MCA occlusions. There is no significant correlation between the LAMS score and the CTP imaging parameters in distal intracranial ICA occlusions, however, the sample size of patients with intracranial ICA occlusions was small. Future research should include a larger sample of patients with intracranial ICA occlusions to assess for a possible relationship. The positive correlation between the LAMS and HI index and the negative correlation of the CBV index specifically support the potential relationship between the LAMS and CS. Poor CS has been shown to be associated with a larger core infarct size on admission  and largThe moderate to strong correlations between the LAMS and a CBF < 30% indicate that the LAMS may also predict an ischemic core. In addition, the admission NIHSS has been associated with an ischemic core volume, with a lower NIHSS correlating with a smaller ischemic core . Our resThere are several limitations to this preliminary study. First, the LAMS scores, when unavailable from EMS personnel, were obtained retrospectively based on admission exams. These exams were usually performed by the consulting neurologist as opposed to EMS personnel. However, the entry LAMS score has been shown to have good convergent validity with the admission NIHSS and admission physical exam when retroactively scored . PatientIn summary, this is the first study exploring the relationship between a rapid pre-hospital stroke scale and cerebral perfusion parameters. The results show that the LAMS may be correlated with CS and the ischemic core in patients with LVO. More research needs to be performed to validate these results. Nevertheless, these results suggest that a simple pre-hospital scale such as the LAMS may have value beyond LVO detection by providing information on the ischemic core and CS at the time of presentation. Poor CS specifically is associated with higher rates of infarct growth , and pat


Entodinium (55.44%), Ophryoscolex (10.51%) and Polyplastron (10.19%), while the fungal genera were dominanted by Mucor (15.39%), Pichia (9.88%), Aspergillu (8.24%), Malassezia (7.73%) and unidentified_Neocallimastigaceae (7.72%). The relative abundance of eukaryotic genera Ophryoscolex, Enoploplastron and fungal genus Mucor were found to differ significantly among the four treatment groups (P < 0.05). Moreover, Spearman correlation analysis revealed that the ciliate protozoa and fungi were negatively correlated with each other. The results of this study provided newer information about the effects of Se on rumen eukaryotic diversity patterns using 18s rRNA high-throughput sequencing technology.Selenium (Se) is an important microelement for animal health. However, the knowledge about the effects of Se supplementation on rumen eukaryotic community remains less explored. In this study, the ruminal eukaryotic diversity in three months old Shaanbei white cashmere wether goats, with body weight (26.18 ± 2.71) kg, fed a basal diet  were compared to those animals given basal diet supplemented with different levels of organic Se in the form of Selenohomolanthionine (SeHLan), namely low Se group , medium Se group  and high Se group  using 18S rRNA amplicon sequencing. Illumina sequencing generated 2,623,541 reads corresponding to 3123 operational taxonomic units (OTUs). Taxonomic analysis revealed that Eukaryota (77.95%) and Fungi (14.10%) were the dominant eukaryotic kingdom in all samples. The predominant rumen eukaryotic phylum was found to be Ciliophora (92.14%), while fungal phyla were dominated by Ascomycota (40.77%), Basidiomycota (23.77%), Mucoromycota (18.32%) and unidentified_Fungi (13.89%). The dominant eukaryotic genera were found to be  These rumen microorganisms act synergistically to help ruminants to efficiently degrade complex plant biomass such as cellulose, hemicellulose, lignin and pectin into absorbable compounds such as proteins, simple sugars and volatile fatty acids (VFAs), ultimately producing meat and milk for human needs2. Diets and additives directly affect rumen microbial population3. Numerous studies confirmed that trace elements affect gut health by modulating microbial community structure6. Essential trace element selenium (Se) as a component of glutathione peroxidase (GSH-Px) can protect cell membranes from peroxide damage7. Dietary Se supplementation has been confirmed to improve the antioxidant status of rumen microorganisms, thereby promoting rumen microbial growth and rumen fermentation, improve the apparent digestibility of nutrients, and ultimately promote the animal growth and production11. According to a report, sodium selenate-supplemented diet not only increased the abundance of amylolytic bacteria, sunch as Ruminococcus and Fibrobacter, but also promoted the activity of cellobiase, carboxymethyl cellulase, xylanase and protease12. Despite numerous studies have been conducted to understand the effect of dietary Se addition on the structure and diversity of rumen bacterial flora, there is a distinct lack of information about the effects of Se supplementation in diets on rumen eukaryotes. Mihaliková et al.13 demonstrated that supplementation of Se (disodium selenite or selenized yeast) in diet induced significant growth of the ruminal Ophryoscolex and Diploplastron population in sheep based on morphological observation and counting. To the best of our knowledge, limited study has focused on identifying and understanding the diversity of ruminal eukaryotes of receiving Se supplementation in ruminants using high-throughput sequencing technology, and this requires further research and investigation.Ruminants have an extremely diverse microbiota consisting of protozoa, fungi, archaea, bacteria and bacteriophage15. Rumen ciliated protozoa contribute to intra-ruminal circulation of microbial proteins16 and to degrade complex carbohydrate and produce volatile fatty acids, lactic acid, carbon dioxide and hydrogen enhancing methanogenesis in the rumen17. As a unicellular organisms, ciliated protozoa utilize the products from phagocytosis of bacteria to meet their nutritional needs18. Furthermore, ruminal protozoa (Litostomatea) is widely recognized for the role of cellulose degradation in the rumen19. Ruminal anaerobic fungi are capable of producing highly active fibrolytic enzymes that disrupt plant cell walls. A previous study showed that anaerobic fungi are widespread in the digestive tract of herbivores20. Fungal zoospores preferentially colonize lignin-rich regions of plant cell walls and dissolve these regions. These degradation products serve as substrates for fiber-degrading rumen bacteria and play an important role in further feed digestion and utilization21. However, due to its unique anatomical features and important functions of the rumen, in-depth studies of ruminant eukaryotic populations in ruminants are very scarce.Protozoa and fungi are the main eukaryotic organisms in the rumen, account for up to 50% and 20% of the total rumen microbial biomass, respectively22. Our previous study has confirmed that dietary addition of Selenohomolanthionine (SeHLan) changed the rumen bacterial community structure and exerted a positive effect on the rumen bacterial metabolic function of Shaanbei white cashmere wether goats using 16s rRNA gene sequencing11. In the present study, we further evaluated the effects of dietary SeHLan supplementation on eukaryotic diversity of Shaanbei white cashmere wether goats using 18S rRNA amplicon sequencing. The results of our study will broaden our understanding and provide newer information of the effect of Se on rumen eukaryotic diversity in Shaanbei white cashmere goats.Previous studies on the effects of dietary Se supplementation for ruminants mostly focused on its deficiency, rumen bacterial community and animal growth. However, few studies reported the effects of dietary Se supplementation on the structure of ruminant eukaryotes. For the study of morphologically indistinguishable eukaryotic species, the previous studies showed that the eukaryotic 18S rRNA gene can be used as a molecular marker to identify different eukaryotesP > 0.05). Furthermore, the Good’s coverage index value in LSE significantly higher than that in the MSE group (P < 0.05). The PcoA based on Bray–Curtis dissimilarity matrices showed a clear differences of the eukaryotic communities between control group and SeHLan supplemented groups , n = 32] per sample. A total of 3,123 operational taxonomic units (OTUs) were obtained based on the 97% similarity threshold in the present study. The number of unique OTU in CG, LSE, MSE and HSE groups were 209 (6.69%), 255 (8.17%), 590 (18.89%), 391 (12.52%), respectively  and Fungi  were the most dominant eukaryotic kingdom in all samples and rumen eukaryotes are dominated by ciliated protozoa (belonging to kingdom of Eukaryota) and fungi. The results suggest that with the dietary SeHLan addition level increasing, the relative abundance of rumen Eukaryota gradually decreases, while the relative abundance of fungi gradually increases in general. The taxonomical composition of ruminal eukaryotes was investigated by sequencing the V4 region of the 18S rRNA gene on the rumen samples with the results uploaded to the NCBI’s sequence read archive (PRJNA846111).Entodinium , Ophryoscolex  and Polyplastron   Fig. . At the ly) Fig. .Figure 3Ophryoscolex and Enoploplastron in four groups were statistically significant (P < 0.05) ; Colpoda, Isotricha and Ophryoscolex were correlated positively with the MSE group (medium SeHLan addition) and the HSE group (high SeHLan addition) correlated positively with the relative abundance of Polyplastron, Codonellopsis, Pithites, Pseudocohnilembus, Arcuospathidium, Oxytricha, Dileptus, Litonotus, unidentified_Litostomatea, Tetrahymena, Rimostrombidium, Urostyla, Trochilia, Cryptocaryon, Parastrombidinopsis, Didinium, Favella, and Stenosemella.The differences in relative abundance of 5) Table . To prov5) Table . Based oPichia , Flammulina , Malassezia , Mucor , unidentified_Neocallimastigaceae , and Aspergillus , followed by Saccharomyces , Cyllamyces , Arthrinium , and Tausonia  , unidentified_Fungi , Basidiomycota  and Mucoromycota  Fig. . At the ly) Fig. .Figure 5Mucor in differnent groups were statistically significant (P < 0.05) ; the abundance of Mortierella, Hodophilus, Sarocladium, Kurtzmanomyces, Archaeorhizomyces, Filobasidium, Arthrinium, Talaromyces, and Meyerozyma-Candida_clade were correlated positively with the MSE group (medium SeHLan addition) and the HSE group (high SeHLan addition) correlated positively with the relative abundance of Rhizomucor, Melampsora, unidentified_Diversisporales, Nakaseomyces-Candida_clade, Dioszegia, Tausonia and Bipolaris.The differences in relative abundance of family Mucoraceae and genus 5) Table . To prov5) Table . Based oEntodinium, under phylum Ciliophora were negatively correlated with Fungi, such as Cladosporium , Aspergillus  and Rhizopus . Cladosporium, Aspergillus and Malassezia which all belong to fungi exhibited a high positive correlation with each other  , and Schizophyllum was positively correlated with Flammulina  , while genera Entodinium of Ciliophora demonstrated a negative correlation with most fungal genera like Aspergillus  and Geminibasidium  , genera of ciliate protozoa Enoploplastron with Polyplastron  and Isotricha , respectively  with igraph package Fig. . For CG 01) Fig. A. In the23. Studies on the rumen eukaryotic ecology and taxonomy have also been hampered by the difficulties in culture and their polymorphism. Additionally, scarce data is available on the effects of dietary selenium supplementation on rumen eukaryotic diversity compared to rumen bacteria or other natural environments. In recent years, due to the rapid development of molecular biology technology and high-throughput sequencing technology, 18S rRNA gene has been used as an important eukaryotic marker gene to detect uncultured species for better analysis and study of the diversity and evolutionary relationships of ruminal eukaryotes24. Among the nine interspersed hypervariable regions of 18S rRNA gene for eukaryotic diversity investigation, V4 and V9 regions are mainly used as molecular markers for the analysis of eukaryotic diversity25. In the present study, we conducted 70d feed experiment with different SeHLan supplementation  in diets and the rumen eukaryotic diversity was analyzed using sequence the V4 hypervariable region of 18S rRNA gene. The obtained eukaryotic diversity and richness indices, including Observed species, Chao1 and ACE, PD_whole_tree, of MSE group were all higher than those of other three groups. However, the α-diversity indices  were not significantly different among four treatment groups, indicating that SeHLan supplementation did not affect the diversity and abundance of rumen eukaryotic communities of Shaanbei white cashmere wether goats. Studies of growing puppies26, Tibetan Sheep10 and goats11 who reported that the alpha-diversity index of the gut and rumen bacterial community were also not affected by the selenite- or SeY-supplemented diets, those results are consistent with ours.Previously, most studies on rumen eukaryotes, especially for protozoa, were morphologically observed with the aid of microscopes22, cattle27, goat and sheep28 and other ruminants29, those studies are consistent with our results. Further, overwhelming majority of the sequence variants of Ciliophora were assigned to genera Entodinium, Ophryoscolex and Polyplastron in the present study. This is consistent with the results reported by Shin et al.30 who found that Entodinium as the dominant protozoan genus in the rumen of cows. However, a study by Mishra et al.22 who reported that most of sequence of Ciliophora were taxonomically unassigned genera, and a small number of sequences were belonged to Entodinium in the rumen of camel. The reasons for those inconsistent results may be related to different experimental animal breeds or diet. Several early studies have confirmed that ruminal ciliate protozoa play an important role in promoting or protecting ruminal strictly anaerobic fermentation, especially for cellulolytic bacteria, by scavenging dissolved oxygen that enters the rumen together with the ingested feed, drinking water or saliva32. Ciliate protozoa are also responsible for acetogenesis and nitrogen fixation or methanogenesis33, defense against predators34 or as nutrients for host cells35. In addition, rumen ciliates also play an important role in maintaining prokaryotic population structure and diversity in the rumen and promoting efficient bacterial nitrogen use for the host by digesting and fermenting feed and shaping rumen microbiome17.In the present study, the dominant abundance of eukaryotic phyla are Eukaryota of Ciliophora, followed by Fungi of Ascomycota, Basidiomycota, Mucoromycota and unidentified_Fungi, which accounted for more than 86% of the total eukaryotic populations. Amongst them, Ciliophora accounted for more than 75% of total eukaryotes. Several previous studies have reported that Ciliophora as the rumen dominant eukaryotes in Camel14. In this study, we found that with the increase of dietary SeHLan addition, the relative abundance of Ciliophora was decreased, the relative abundance of fungi of Ascomycota showed a trend of first increasing and then decreasing, while the relative abundance of fungi of Basidiomycota first decreased and eventually increased. However, the proportion of phyla, including Ciliophora, fungi of Ascomycota and Basidiomycota, in the SeHLan added groups  were all higher than that in the control group. The relative abundance of fungi of Mucoromycota increased with the SeHLan supplementation level, while the relative abundance of fungi of unidentified_Fungi showed a increasing to decreasing trend with an increase in dietary SeHLan supplementation. However, the proportion of phyla, including fungi of Mucoromycota and unidentified_Fungi, in the control group were all higher than that in the Se-supplemented diets . Those results indicated that the diet supplemented with a low or medium level of SeHLan may be more suitable for the survival of the phyla Ciliophora, fungi of Basidiomycota, and fungi of Ascomycota. However, SeHLan supplemented diets may inhibit the growth of rumen fungi of Mucoromycota and unidentified_Fungi. Previous research has also confirmed that rumen microbial population is capable of incorporating selenium into their cells and ruminal protozoa can influence animal selenium status36. Our previous study indicated that dietary supplementation of low (LSE) and medium (MSE) SeHLan could effectively increase the body weight, average daily gain, improve the nutrients utilization and food conversion ratio of Shaanbei white cashmere wether goats, while high SeHLan (HSE) is not beneficial for cashmere goat growth11. Therefore, we can speculate that addition of SeHLan to the diet changed the structure of the rumen eukaryotic population of Shaanbei white cashmere goats, thereby improving the feed utilization efficiency and promoting production, and Ciliophora, Ascomycota and Mucoromycota are mainly responsible for selenium utilization and have beneficial effects on the rumen eukaryotic organisms. However, this assumption requires further investigation.Fungi have a low rumen biomass and they are thought to be a minor contribution for plant degradation, although they also produce specific enzymesEntodinium was found gradually decreased with SeHLan supplemented level. The relative abundance of genus Ophryoscolex first increased and eventually decreased with dietary SeHLan addition, while the relative abundance of genus Polyplastron was found gradually increased with SeHLan-supplemented level. In general, compared with the control group, a higher proportion of genera Entodinium, Ophryoscolex and Polyplastron were found in the rumen of three SeHLan supplemented group’s goats. Our results are consistent with previous reports by Mihaliková et al.13 and Naziroglu et al.37 who also found that supplementation of feed with inorganic (sodium Se) or organic Se (yeast Se) induced significantly growth of genera Entodinium, Polyplastron and Ophryoscolex in the rumen of lambs. Previous studies also confirmed that Se has a stimulating effects on the growth of some rumen ciliate genera. Akbar et al.38 investigated that lactating Murrah buffaloes fed Se enriched diets had the significantly higher protozoan count. Fujihara et al.36 also reported that when a diet supplemented with 0.2 mg/kg Se in the form of selenite, rumen protozoa could capture 0.52 mg Se/kg of protozoan dry matter in sheep. As we all known, Se as a component of glutathione peroxidase (GSH-Px) can protect cell membranes from peroxide damage. Therefore, we can speculate that the explanation for higher abundance of Entodinium, Ophryoscolex and Polyplastron in the rumen of three SeHLan supplemented group’s goats may be due to the effects of Se on the antioxidant status of the rumen Entodinium, Ophryoscolex and Polyplastron. It means that the appropriate amount of SeHLan added to experimental goats might induced higher activity of selenoenzymes in the rumen protozoa. However, this assumption requires further investigation for the activity of rumen protozoal selenoenzymes.Taxonomic classification in our study showed that the relative abundance of genus 39 reported that the growth of O. caudatus f. tricoronatus and Entodunium caudatum were inhibited at dietary Se supplementation level equal to and/or higher than 0.1 and 0.5 mg/L. Our results also suggest that dietary supplementation with high level of SeHLan decreased the Entodinium abundance, whereas supplementing the diets with low or medium level of SeHLan may be beneficial for promoting growth of genus Entodinium, which is the predominant rumen eukaryotic genus belong to Phylum Ciliophora in the present study. As the smallest, simplest and common rumen ciliates, Entodinium is present in the rumen of almost all ruminants, and the main function of Entodinium is to degrade starch which is essential for maintaining its growth40. The species belonging to the genus Entodinium was found engulfed starch and further fermented into acetic acid and butyric acid, which together account for 80–90% of total volatile fatty acids, while producing smaller amounts of formic acid, propionic acid, carbon dioxide and hydrogen41. In addition, Entodinium has a great potential to engulf and degrade bacteria42 contributing to the bacterial nitrogen turnover17 and lactic acid metabolism40. A previous study confirmed that the abundance of genus Entodinium was higher in low-RFI  steers than that in high-RFI steers43. Yuste et al.44 reported that the genus Entodinium account for 93% of the total rumen protozoal community when beef cattle fed a high concentrate based diet. Therefore, high abundance of genus Entodinium in the rumen may play an important role in regulating rumen lactic acid metabolism, stabilizing rumen fermentation and improving animal feed utilization efficiency. In the present study, the relative abundance of Entodinium decreased with the dietary SeHLan-supplemented level, and low or medium level dietary Se supplementation stimulates the growth of Entodinium in the rumen of Shaanbei white cashmere wether goats. The Ophryoscolex and Polyplastron population were all higher in the SeHLan-supplemented groups than control group which is also supposed to dietary Se addition improved the antioxidant properties and ultimately promoted ruminal protozoa growth45.According to a previous study, Kisidayova et al.46, nitrogen fixation47, methanogenesis48, acetogenesis33, provide a defense against predators49, and provide nutrients to host cells46. Therefore, other scientific methods such as metagenomics and metatranscriptomics analysis are required to further characterization of the prokaryotic communities associated with the eukaryotic population and their composition and roles in the rumen receiving Se supplemented in the diet.Se affects rumen bacterial abundance and ultimately affects rumen microbial fermentation, while few studies have addressed the effects of Se on rumen eukaryotic populations or the composition and function of the eukaryotic population  associated with rumen prokaryotic community  and the extent of this association. In this study, we found that the major ciliate protozoa and fungi were negatively correlated with each other, suggesting that Se affects the interrelationship between rumen protozoa and fungi. However, this assumption needs further more investigation. Previous research confirmed that protozoa associated prokaryotes displayed a variety of functions, such as the adaptability of host cellsOphryoscolex, Dasytricha and Enoploplastron, fungal family Mucoraceae and genus Mucor were found to differ significantly among the four treatment groups. Moreover, Spearman correlation analysis revealed that the ciliate protozoa and fungi were negatively correlated with each other. However, further studies are needed to determine Se affect on the rumen eukaryotic population  associated with rumen prokaryotic community  and the extent of this association.The results of this study confirmed that Se has a stimulating effects on the growth of some rumen ciliate and fungal genera. Dietary supplementation SeHLan increased the relative abundance of Eukaryota, while decreased the relative abundance of Fungi. The relative abundance of protozoal genera https://arriveguidelines.org).Our animal experiment was approved by the Institutional Animal Care and Use Committee of the Yulin University  under permit number YLU2021-2 and was performed in the Laboratory Animal Center of Cashmere goat research institution located in the suburb of Yuyang District, Yulin, China. All experiments were performed in accordance with the university’s guidelines and regulations for animal research (file no: YLU2021-2), and all methods are reported in the present study in accordance with ARRIVE guidelines . Three treatment group’s animals with different levels SeHLan addition to make the Se contents as follows: low Se group (LSE: 0.3 mg/kg DM), medium Se group (MSE: 0.6 mg/kg DM), and high Se group (HSE: 1.2 mg/kg DM).Thirty-two three months old Shaanbei white cashmere wether goats with similar body [(26.18 ± 2.71) kg] were selected and randomly assigned into 4 treatment groups. The basal diet was formulated according to Chinese Feeding Standard of Meat-Producing Sheep and Goats (NY/T 816-2004) as described previously11. Each rumen content sample was immediately sealed in a centrifuge tube and frozen in liquid nitrogen and then stored at -80℃ for eukaryotic diversity analysis.The feeding experiment period lasted for 70 days including 10 days adaptation and 60 days of formal feeding trial period. All animals were fed at 09:00 a.m. and 17:00 p.m., with free access to water. On the last day of feeding trial, rumen samples from each goat were collected after 2 h morning feeding using an oral stomach tube. The first 50 mL of rumen samples in each sampling were discarded to prevent the potential saliva contamination, and final 20 mL of rumen sample were collected from each goat for further analysis51. PCR reaction system contained 15 µL Phusion® High-Fidelity PCR Master Mix (New England Biolabs), 0.2 µM each primer and 10 ng DNA, and cycling program started at 98 ℃ (1 min) for the step of denaturation, followed by 30 cycles at 98 ℃ (10 s), 50 ℃ (30 s) and 72 ℃ (30 s) and a final extension at 72 ℃ for 5 min. PCR products were mixed with an equal volume of 1X loading buffer and detected on a 2% agarose gel. PCR products were purified using Qiagen Gel Extraction Kit  according to the manufacturer's instructions. Sequencing libraries were constructed with NEBNext® Ultra™ II DNA Library Prep Kit (Cat No. E7645) according to the manufacturer's instructions. The Qubit@ 2.0 Fluorometer (Thermo Scientific) and Agilent Bioanalyzer 2100 system were employed to evaluate the quality of the library. Finally, the library was sequenced on an Illumina NovaSeq platform.The total genome DNA of all rumen samples were extracted using the CTAB/SDS method and diluted to 1 ng/µL with sterile water. The V4 hypervariable region of the 18S rRNA gene was amplified with the following primers 528F (5′-GCGGTAATTCCAGCTCCAA-3′) and 706R (5′-AATCCRAGAATTTCACCTCT-3′) as previous studieshttp://ccb.jhu.edu/software/FLASH/)52, which was designed to merge paired-end reads when at least some of the reads overlap the read generated from the opposite end of the same DNA fragment, and the splicing sequences were called raw tags. Raw tags were quality filtered under specific filtering conditions to get the high-quality clean tag53 in accordance with the QIIME 54 quality controlled process. The tags were compared with the Silva database (https://www.arb-silva.de/)55 using UCHIME algorithm 56 to detect chimera sequences. Removal of the chimera sequences57 to finally got the effective Tags. Clustering of sequence analyses into OTUs by UPARSE software  based on 97% similarity58. Using the mothur algorithm and the SILVA database (http://www.arb-silva.de/) to annotate the taxonomic assignment with a 0.80 confidence threshold and to identify the dominant species within each sample55. Using MUSCLE 3.8.31 to perform the multiple sequence alignment59. Taxonomical data was amalgamated for each group and relative proportions of the eukaryotes at the genus level in each group were correlated by calculated the Spearman correlation coefficient method through R software (Version 2.15.3) with igraph package.Based on the unique barcodes, the generated 250 bp paired-end reads were assigned to samples and truncated by cutting off the barcodes and primer sequences, and merged using FLASH  were calculated with QIIME (Version 1.9.1) and displayed in R software (Version 2.15.3) for analyze the diversity, richness and uniformity. Principal coordinate analysis (PCoA) was performed using ade4 package and ggplot2 package in R software (Version 2.15.3) to obtain principal coordinates and visualize differences of samples in complex multi-dimensional data based on Bray–Curtis dissimilarity matrices. A heatmap of the top 35 most abundant eukaryotic genera was constructed using R software (Version 2.15.3) with heatmap package. Comparison of the alpha-diversity indices by one-way ANOVA (IBM SPSS Statistics 20) using a completely randomized design. The comparison for the eukaryotes relative abundances among the four treatments using IBM SPSS Statistics 20 by the Kruskal–Wallis rank sum test. Significant difference value was set at The authors confirm that this study was performed in accordance with the Guidelines of Good Experimental Practices adopted by the Institute of Shaanxi Provincial Engineering and Technology Research Center of Cashmere Goats, Yulin University, Yulin, China. All experimental protocols involving animals were approved by the Animal Care Committee of Yulin University  and were under the university’s guidelines for animal research (file no: YLU2021-2).Supplementary Tables.


In women, placental corticotropin releasing hormone (CRH) can be detected in maternal blood throughout pregnancy and is important in the regulation of the timing of parturition. However, its role in other mammalian species is unclear. In fact, very little is known about the presence and localization of CRH in placentas other than human. In this study we report for the first time the presence of CRH in feline placenta and maternal serum.via immunohistochemistry. Finally, presence of CRH in maternal blood from early (¾21 days) and mid (25-35 days) stages of pregnancy was investigated by ELISA.Presence of CRH mRNA and protein was assessed using RT-PCR and Western blot, respectively, in at term domestic cat placentas opportunistically obtained at a local animal shelter and spay clinic. In addition, CRH localization within the placenta was demonstrated CRH mRNA and protein were detected in feline placentas, and localized to larger decidual cells and fetal trophoblast cells, including the binucleate cells. CRH was detectable in maternal blood collected from early-stage pregnancies, and amounts significantly increased in mid-gestation samples.This is the first report on the presence and localization of CRH in the feline placenta, and its increase in maternal serum during the first half of pregnancy. These data lay the foundation for future studies to determine if CRH can be used as potential novel marker for early pregnancy diagnosis, determination, and monitoring in felids, and could greatly increase efficiency and success in zoo breeding programs utilizing artificial reproductive technologies for endangered feline species. Corticotropin releasing factor, from here on will be referred to as corticotropin releasing hormone (CRH), is a peptide hormone released by hypothalamic neurons in the paraventricular nucleus that activates the hypothalamic-pituitary-adrenal (HPA) axis. Circadian rhythm is the main regulator of CRH release and is necessary for maintaining normal homeostasis for example, in response to many exogenous and endogenous stimuli including stress. CRH leads to the release of adrenocorticotropic hormone (ACTH) from the anterior pituitary gland, which in turn acts on the adrenal cortex to stimulate the release of cortisol. Furthermore, during pregnancy, cortisol derived from the maternal adrenal gland plays a central role in fetal growth and organ maturation. Because of its immunomodulatory function, cortisol also plays an important role in establishment and maintenance of pregnancy by inhibiting immune rejection responses around the time of implantation. During pregnancy cortisol together with progestins play an important role in the regulation of maternal immune tolerance. A fine balance between cortisol and progesterone signaling is necessary for the maintenance of pregnancy in humans through the regulation of endometrial receptivity, uterine quiescence, and placental vascularization  but with higher quality ultrasound equipment and operator skill, gestational sacs can be detected in the domestic cat on day 10 after mating and the embryo can be detected as a hyperechogenic spot in the gestational sac from day 14 after mating . HoweverAlthough the queen is an induced ovulator, progesterone is not a useful marker for pregnancy as ovulation in the queen can also occur spontaneously with a reported incidence in domestic cats ranging from 30 to 87% –15. A psThe overall goals of this study were to determine if CRH; i) is present and produced by the feline placenta, ii) can be detected in serum of pregnant felines/queens, and if CRH is present, iii) can be detected early in pregnancy and increases in maternal serum during pregnancy. Little is known about the presence and localization of CRH in species other than human placentas. Not only would this expand our current understanding on the possible role of CRH in different placental types as it relates to pregnancy and/or parturition, but in feline species this could provide for future opportunities to identify a novel marker for early pregnancy diagnosis, determination, and monitoring.Post-partum domestic cat placental tissues were obtained opportunistically from a local animal shelter and spay clinic. Sections of the zone of attachment containing the labyrinth layer of the placenta were removed and snap frozen or fixed in 4% paraformaldehyde for RNA and immunohistochemical analysis, respectively. Blood samples also were opportunistically obtained from remaining blood taken for pre-anaesthetic profiles prior to ovariohysterectomy of pregnant and non -pregnant queens from the same local animal shelter and spay clinic. Blood samples from queens in early (¾21 days) and mid (25-35 days) stages of pregnancy Figure 1Total RNA was isolated from postpartum placental tissue samples (the zonary region) using Qiagen RNeasy mini kit (74104) according to the manufacturer’s instructions. Briefly, approximately 30 mg of frozen tissue was homogenized in RLT Lysis buffer, processed for RNA isolation, and eluted in 30μl of DNAse/RNAse free water. RNA purity and concentration was analyzed using a NanoDrop ND-1000 UV-Vis spectrophotometer. All samples used for subsequent analysis had a 260/280 absorbance ratio between 1.8 and 2.2.Total RNA was reverse transcribed into cDNA using the Bio-Rad iScript cDNA synthesis kit (1708891) according to the manufacturer’s instructions. Briefly, 4μl iScript reaction mix, 1μl reverse transcriptase, and 1μl (500-1000ng) cDNA were combined. The placental tissue samples were placed in a thermocycler with an initial step at 25°C for 5 minutes for priming, followed by 46°C for 20 minutes for reverse transcription, and 95°C for 1 minute for reverse transcriptase inactivation. The samples were stored in the -20°C freezer overnight.https://uswest.ensembl.org/index.html), and Primer3 (https://bioinfo.ut.ee/primer3-0.4.0/) was used to design primers. Primer sequences are provided in To confirm the presence of CRH mRNA in postpartum placental tissue, forward and reverse primers were designed. Briefly, the feline CRH sequence was obtained in Ensembl  was collected and combined with 300 μL RIPA lysis buffer , and 10 μl of protease and phosphatase inhibitor and EDTA cocktail  were added to each sample. After homogenization samples were sonicated and centrifuged at 10,000 rpm at 4°C for 10 minutes. The pellet was discarded and the supernatant was added to a new tube with 3 μL PMSF . A BCA assay was conducted, and protein concentrations were determined using a Pierce™ Protein Assay Kit  and a Fluostar Omega (BMG Labtech) microplate reader equipped with Omega software.Placental protein samples from post -partum placentas were combined with 6x loading buffer and vortexed and spun down for 15 seconds each. Samples were boiled for 10 minutes, and 50μl containing 30μg of total protein sample was resolved on a 10% Mini-PROTEAN TGX™ Precast Protein Gel , which included a Precision Plus Protein WesternC™ ladder , at 130V and run for 35 minutes at 4°C. Protein samples were transferred onto a PVDF membrane  at 100V for 2 hours at 4°C. Following 3 washes (5 minutes each) in 1X TBST, the membrane was blocked using 5% powdered milk for 1 hour at room temperature. After blocking non-specific binding sites, membranes were incubated with anti-human CRF rabbit polyclonal antibody  at a 1:2000 dilution on a shaker overnight at 4°C. As a negative control, 10% rabbit serum was used without CRH antibody. and Precision Protein StrepTactin-HRP conjugate  diluted in 5% milk for 1 hour at room temperature. Following 3 washes in 1x TBST for 5 minutes each, membranes were incubated in SuperSignal™ West Dura Extended Duration Substrate  for 5 minutes. The membrane was then visualized and imaged using a BioRAD ChemiDoc MP Image System.The following day, PVDF membranes were washed with 1x TBST , and incubated with goat anti-rabbit IgG HRP conjugated antibody Immunohistochemistry was carried out on post -partum and early/mid pregnancy placental tissue (zone of attachment) using paraffin-embedded, 5μm term placental feline tissue sections. Tissue de-waxing and rehydration was performed by two five-minute washes in CitriSolv , two five-minute washes in 100% ethanol and four five-minute washes in 90%, 70%, 50% and 30% ethanol and finally deionized water for two minutes. After rehydration, the tissue sections were microwaved for 20 minutes in citric acid-based Antigen Unmasking Solution . After washing in 1x PBS for five minutes, the samples were incubated in a humidity chamber with 3% hydrogen peroxide each for twenty minutes. To prevent nonspecific binding, tissue sections were incubated for thirty minutes with Blocking Serum  from the Vectastain ABC Kit Peroxidase . After blocking, tissue sections were incubated with anti-human CRF rabbit polyclonal antibody  at 1:20 diluted in Blocking Serum overnight in a humidified chamber at 4°C. The following day, sections were washed in 1x PBS for five minutes, and incubated for thirty minutes with biotinylated secondary antibody solution provided in the Vectastain ABC kit. After a subsequent thirty minute incubation with Vectastain ABC Reagent, the slides were washed in 1x PBS for five minutes, and incubated with DAB Substrate  for 3 minutes, and sections were mounted in Vectashield Antifade Mounting Medium . The slides were then viewed and photographed using a brightfield microscope to identify CRH localization in term placental feline tissue.via Microsoft Excel 365 and Prism 9 using One-Way ANOVA test followed by Tukey’s post- test. Data is presented as mean ± SEM and p ≤ 0.05 was considered statistically significant.Serum was collected from two non-pregnant, two early pregnant and two mid-pregnant cats  have been used to detect CRH. The ELISA test  has a detection range between 12.35-1,000 pg/ml and a sensitivity of 5.19 pg/ml and was done in duplicates (n=2) for all samples according to the manufacture’s instruction. Thereafter, data was analyzed To determine if CRH is present in the feline placenta, total RNA was isolated from 3 different post-partum placental tissues, reversed transcribed and PCR amplified using primers designed against a fragment of the feline CRH transcript. In order to determine the cellular localization of CRH within the zone of attachment of the placenta, immunohistochemistry was performed. As indicated in Demonstrating the presence of CRH transcript and protein in the placenta, we next wanted to determine if CRH is secreted into and detectable in maternal serum during pregnancy. To this end, an ELISA specific for human CRH was used. Results in The goal of this study was to determine if CRH is present in the feline placenta and maternal circulation, and if maternal systemic blood levels of CRH increase during early gestation. We opportunistically were able to obtain domestic feline placentas and maternal serum from a local cat shelter and spay clinic. Using primers designed to amplify feline CRH we demonstrated for the first time that CRH mRNA in a species other than human or rodent, and also is present in feline placental tissue and maternal blood. This primer set was designed to amplify CRH in cats and dogs (data not shown). Using a polyclonal antibody we detected an ~22 kDa size protein which corresponds to the predicted size for CRH preprohormone . Additionally, immunohistochemistry revealed CRH positive cells in the fetal trophoblast cells, including binucleate cells. Finally, ELISA analysis on maternal feline serum indicated that CRH levels nearly doubled from 108.5 pg/ml non-pregnant to 213.4 pg/ml mid pregnancy serum levels. CRH in serum from non-pregnant felines likely originate from peripheral tissues, including ovaries, endometrial glands, liver, stomach, intestine, pancreas, lungs, adrenal glands, and inflammatory sites . In our study, CRH was detected in postpartum placentas of queens, suggesting CRH likely would have been detected in maternal serum throughout pregnancy. However, collection of late gestation and at term blood samples was not possible, therefore it is unclear if CRH levels in the cat would continue to increase similar to humans, higher primates and sheep. Future studies are required to confirm increasing levels of maternal CRH in pregnant queens. The early detection of CRH in maternal serum and its increase during the first half of pregnancy suggests a potential role for CRH in implantation and fetal development similar to humans and higher primates.via qPCR and Western blot, respectively . Colorado State University Institutional Animal Care & Use Committee did not require the study to be reviewed or approved by an ethics committee because these were samples opportunistically obtained after a routine surgical procedure was performed. These samples were disposed of after the surgery had taken place.MW performed the PCR and Western blot experiments and wrote the first draft of this manuscript. PM and EP conducted the ELISA and IHC experiments, respectively. GB and FH developed the idea and wrote the manuscript. GB provided direction and guidance on the molecular experiments, and FH coordinated and collected the tissues. All authors contributed to the article and approved the submitted version.


Ms5.0 earthquake and Menyuan Ms6.9 earthquake are the two Ms ≥ 5.0 earthquakes in recent years. The spatio-temporal evolution of Rn across the fault before the two Ms ≥ 5.0 earthquakes were explored by combining a solid seismogenic model and numerical simulation results in this study. The results demonstrates the spatial distribution of Rn concentration intensity varies over time, indicating the evolving characteristics of fracture zone activity. The time-series variation characteristics are closely related the Zhangye Ms5.0 earthquake and Menyuan Ms6.9 earthquake. Overall, in the seismic source area and surrounding medium area of Zhangye Ms5.0 earthquake, the soil gas Rn anomaly across faults characterized by a turning upward trend after continuous decline. The closer to the source area, the more obvious the upward trend. For Menyuan Ms6.9 earthquake, the survey line (HT1) located in the main fracture zone of the earthquake and the survey line  closer to the epicenter also showed a similar trend, while the other measurement lines in far-field exhibit declining trend before the Menyuan Ms6.9 earthquake. Therefore, the continuous decline trend of soil gas may be crucial information for medium-term earthquake preparation in the seismogenic zone, and the trend of turning upward after continuous decline is a significant signal of short-term seismogenic event in far-field. This research could improve the understanding of the anomalous features of soil gas precursors and tracking the active sections of the fault. According to the model, the earthquake area canseismic source area, the surrounding medium area be divided into three sections: the seismic source area, the surrounding medium area, and the fracture fragmentation area.The mid-eastern segment of the Qilianshan fault zone (QLF) on the northeastern margin of the Qinghai–Tibet Plateau is considered one of the key seismic hazard areas. The Zhangye  Soil gas geochemical properties of deep gases discharged in geologically active areas has been extensively employed for precursors of tectonic 15. The preparation and occurrence of earthquakes encompass a complex physical and chemical process, involving the energy transfer of deep materials, changes in medium conditions, and interactions between underground fluids, such as water and gas, and rocks under stress17. This process necessitates the migration, differentiation, and evolution of fluids18. Active fault zones serve as channels for underground fluid escape and are crucial locations for earthquake preparation and occurrence24. The gas geochemical properties of active fracture zones are often linked to the region's seismic activity27. In fact, these properties have been successfully utilized to elucidate key earthquake mechanisms along major active fault zones in Taiwan, Japan, and China33. Soil gases  in fracture zones frequently exhibit anomalies before and after earthquakes34, with the magnitude of these anomalies often fluctuating alongside seismic activity35. Research on the relationship between released CO2 fluxes and seismic activity in the L'Aquila region and the Apennines in Italy has demonstrated that CO2 gas released through rupture zones plays a crucial role in earthquake nucleation, occurrence, and aftershock activity36. Continuous monitoring of soil gas Rn concentrations on the Muzaffarabad fault in Pakistan abnormal Rn concentrations approximately 30 days prior to seven earthquakes, with magnitudes ranging from 0.8 to 4.9, impacting the rift37. Subsequent to the Wenchuan Ms8.0 earthquake, anomalies in soil gas He, CH4, Rn, H2, and Hg concentrations were found to be closely associated with surrounding aftershocks during drilling in the seismogenic rupture zone by scientific drilling holes No.1 (WFSD-1) and No. 2 (WFSD-2)39. Emission of CO2 was observed in association with earthquakes at the Lassen Peak volcano   and the Eger Rift (Czech Republic), indicating close connections between the Earth's surface and its crust by fluid transport. 575 earthquakes occurred at Cava dei Selci  from 2009 to 2021 showed that increase of CO2 flux seemed related to extensional deep earthquakes40. Additionally, the soil gas He and H2 anomalies in the Longmenshan rupture zone were observed to decrease as aftershock intensity diminished.Subsurface fluids constitute essential components of the Earth system, playing a critical role in Earth’s evolution as an effective medium for interconnecting the planet's various layersMs6.9 earthquake  occurred in Menyuan County, Haibei Prefecture, Qinghai Province, China, with a focal depth of approximately 10 km. Additionally, the Zhangye Ms5.0 earthquake took place in the middle western section of the Qilian Mountain seismic belt on September 16, 2019. This study aims to investigate the spatial and temporal evolution of cross-fault Rn concentrations prior to two Ms ≥ 5.0 earthquakes in order to explore effective information on earthquake precursors. The research could enhance our understanding of the anomalous features of soil gas precursors and facilitate tracking of active fault sections.It is evident that underground fluids have gained increasing prominence in earthquake prediction and related studies in recent years. However, due to difficulty in natural earthquake prediction, research on combining earthquake monitoring results with physical prediction mechanisms is still very scarce. Most observations on seismic geochemical effects lack physical mechanism support for identifying anomalies. This is the key to break through the extraction of fault Soil gas earthquake anomaly precursors. The mid-eastern region of the Qilian Mountains Fault (QLF) is considered a key hazard area in Mainland China. Since 2016, six periods of Rn concentration data have been gathered from nine measurement lines deployed across the Middle East section of the QLF. On January 8, 2022, an 42. This region is characterized by tectonic deformation and intersected by numerous active faults, rendering it one of the most seismically active and intense areas in mainland China, with substantial crustal movements46. The primary active fracture zones in this study area include the Sunan-Qilian fault, the Yumushan fault, the Minle-Damaying fault, the Huangcheng-Shuangta fault, and the Lenglongling fault, all of which are crucial components of the Qilian Mountains' northern margin  measurements were conducted at nine survey sites  situated on various NWW-oriented faults running parallel to each other and in oblique rows, covering the mid-eastern region of the Qilian fault Table .Table 1L61. No extreme climatic variations were observed during the monitoring period, and the meteorological conditions at each measuring point remained relatively stable.To minimize the impact of meteorology on soil gas concentrations, surveys were conducted under stable meteorological conditions in June of each year from 2016 to 2021. Rn concentrations, temperature, humidity, and atmospheric pressure were simultaneously measured using an AlphaGUARD detector . According to the detector's requirements, 15 to 20 values were measured during each assessment. All samples were collected using a stainless-steel probe inserted into the ground at a depth of 80–100 cm, depending on soil consistency and thickness, in order to minimize the effects of meteorological variablesIn this paper,the concentration strength of each survey site was calculated.Concentration intensity refers to the measurement of the change degree of the of fault gas concentration near the fault plane relative to the background concentration. It can intuitively reflect several characteristics of fault media such as porosity, fragmentation, and fracture development, as well as the mechanics and motion state of fault activity It is the main characteristic parameter for analyzing the spatial characteristics of fault gas.The calculation formula is as follows:Concentration strength (S) = Anomaly threshold/background value.S_max). This approach utilizes the maximum abnormal concentration values to calculate concentration intensity, better explaining the peak value of the concentration intensity for each profile. Additionally, this method minimizes differences in site-effect factors that influence gas release concentrations, such as soil and rock properties63. Details of the nine survey lines are listed in Table The background value is obtained by calculating the mean value of the remaining data after removing jump values, reflecting the normal accumulation level of specific elements or components in a given fault zone. The anomaly threshold can be expressed as the background value plus ‘n’ standard deviations n = 1, 2, 3,…); in this case, n = 2 was used. The purpose of this is to eliminate the measured value instability caused by environment. Calculations and evaluations led to the decision to use the maximum value method for determining the concentration strengthof soil gas Rn mm/a, and the left-sliding rate was (0.95 ± 0.11) mm/a25. The linear fitting results of the vertical sliding rate of the Minle-Damaying fault were (0.91 ± 0.09) mm/a41. The Huangcheng–Shuangta fault experienced significant activity in the Holocene, most notably the Gulang Ms8.0 earthquake in 192743. The Quaternary slip rate of the Lenglongling fault was determined to be (4.3 ± 0.7) mm/a by radiocarbon dating methods, while the slip rate since the late Holocene was (4.3 ± 0.36) mm/a49. The Huangcheng–Shuangta and Lenglongling fracture zones exhibit more activity than other fracture zones, indicating a relatively open fault. This corresponds well with the spatial distribution pattern of soil gas Rn in the QLF.The spatial distribution of seismic activity is illustrated in Fig. Ms6.9 earthquake on January 8, 2022, and the Zhangye Ms5.0 earthquake on September 16, 2019 have occurred. The seismogenic fault for the Menyuan Ms6.9 earthquake is the western segment of the Lenglongling fault, which ruptured to the Tolashan fault on the west side of its northwest end69, while the Zhangye Ms5.0 seismogenic fault is the northern margin fault of the Qilian Mountain (Sunan Qilian section).The Rn trend changes before and after 2019 have captured our particular interest, as they may be related to the Zhangye Ms5.0 earthquake, it has been observed that strong earthquakes typically do not occur in the high seismicity zones of fault systems, but rather in seismic gaps72. Subsequently, the concept of seismic gaps was introduced, and a solid seismogenic model was established to elucidate the conditions, processes, and associated precursors of strong earthquakes, as well as their spatial–temporal evolution74. This model is supported by a wealth of evidence from deep structures, mechanical analyses, rock fracture experiments, numerical simulation results, and observed facts75. According to the model, the earthquake area can be divided into three sections: the seismic source area, the surrounding medium area, and the fracture fragmentation area  and Dilatancy Hypothesis model (DD) have been proposed. However, these models mainly address the issue of epicenter precursors, and they cannot explain the complex spatiotemporal evolution process of precursorskes Ms ≥ .0, it hakes Ms ≥ .0, it haMs5.0 earthquake, the Rn concentration intensity of HT2 (27km away from the epicenter) decreased significantly from 2016 to 2017 and increased significantly from 2017 to 2019. In HT3 (2 km away from the epicenter), the Rn concentration intensity decreased from 2016 to 2018 and increased significantly from 2018 to 2019. Other survey lines also demonstrated some trend anomalies, but the short-term anomalies (turning upward trend after continuous decline) were not more pronounced than in HT2 and HT3. For the Menyuan Ms6.9 earthquake, HT1, HT4, and HT7 also displayed turning upward trends after continuous decline before the earthquake. HT1 is located on the the northern edge of the Qilian Mountains, the southern section of Sunan, 200 km from the epicenter. Despite this distance, it is situated along the main fault at the northern margin of the Qilian fault. The Lenglongling fault zone, where the Menyuan Ms6.9 earthquake occurred, is also an integral component of the northern Qilian fold belt in terms of geotectonics. Several aftershocks with a magnitude of Ms ≥ 3.0 were recorded in the region following the Menyuan Ms6.9 earthquake. Furthermore, except for three lines, all other measurement lines exhibited a continuing decline trend before the Menyuan Ms6.9 earthquake. The Menyuan Ms6.9 seismic activity and tectonic deformation directly expose the adjustment and transmission of tectonic deformation throughout the Qilian Mountain fault zone. As the survey lines did not directly cross the seismic fault of the Menyuan Ms6.9 earthquake, the abnormal features were not as pronounced as those observed in the Zhangye Ms5.0 earthquake.This model effectively explains the relationship between two Ms ≥ 5.0 earthquakes in the QLF. For the Zhangye The response of soil gas Rn to seismic events is not only related to the magnitude and distance from the epicenter but also controlled by the tectonic stress of deep major faults. Thus, the present observation results and the analysis of seismic cases support the body seismogenic model and numerical simulation computation results. This model elucidates the relationship between tectonic geochemical characteristics and rupture blockage, offering theoretical support for determining the time, space, and intensity of seismogenesis. The robust body seismogenic model can clearly explain the relationship between tectonic geochemical features and fracture locking, providing theoretical support for estimating the time, space, and intensity of earthquake nucleation.From a spatial distribution perspective, in 2016, the highest concentration intensity was observed in the middle (HT5) and west section (HT1). From 2017 to 2020, the spatial distribution characteristics of concentration intensity were stronger in the east than in the west.In 2021, the spatial distribution was less distinct, with values across the entire fault zone uniformly. The spatial distribution of Rn concentration intensity varies over time, indicating the evolving characteristics of fracture zone activity.Ms5.0 and Menyuan Ms6.9 earthquakes. For the Zhangye Ms5.0 earthquake, the Rn concentration intensity in major survey lines (except HT8 and HT9) showed a noticeable "V" temporal evolution trend, with an continue decline and then turn upward. For the Menyuan Ms6.9 earthquake, HT1 (located on the the northern edge of the Qilian Mountains) and HT4, and HT7 also displayed turning upward trends after continuous decline before the earthquake.While the other measurement lines exhibit a continued declining trend before the Menyuan Ms6.9 earthquake.From a temporal evolution trend before earthquake,the soil gas Rn concentration intensity across mid-eastern of the Qilian fault zone(QLF) exhibited a significant response to the Zhangye These observational facts were supported by the sturdy body seismogenic model and numerical simulation results. Consequently, we hypothesized that the continuous decline trend of fault gas may serve as a reliable indicator of the interlocked section of fault tectonic activity, and the trend of turning upward after continuous decline may represent a significant signal of a medium and short-term seismogenic event in the source area. Furthermore, the sturdy body seismogenic model could potentially provide theoretical support for determining the time, space, and intensity of seismogenesis based on the relationship between the tectonic geochemical characteristics and rupture locking.In this study, we explored the spatial–temporal evolution characteristics of soil gas Rn from 2016 to 2021 in the mid-eastern of the Qilian fault zone (QLF) before Zhangye Ms5.0 earthquake and Menyuan Ms6.9 earthquake by combining a solid seismogenic model and numerical simulation results. By analyzing the spatial–temporal evolution characteristics of soil gas Rn before two Ms ≥ 5.0 earthquakes in the Qilian fault zone, the following conclusions can be drawn:In summary, seismic prediction remains a difficult global scientific problem, and fluid geochemistry is one of the potential tools for earthquake prediction. The extraction of earthquake precursor information from soil gas must be based on a specific physical mechanism of seismogenesis. The results of long-term research have shown that, by studying the physical mechanisms of forecasting, the sensitivity and convenience of fault gas can be used to trace locked segments directly on dangerous faults for the identification and determination of precursor information, which can compensate for the limitations of springs and well-exposure locations used by subsurface fluid.


Helicobacter pylori, a dominant member of the gastric microbiota, shares co-evolutionary history with humans. This has led to the development of genetically distinct H. pylori subpopulations associated with the geographic origin of the host and with differential gastric disease risk. Here, we provide insights into H. pylori population structure as a part of the Helicobacter pylori Genome Project (HpGP), a multi-disciplinary initiative aimed at elucidating H. pylori pathogenesis and identifying new therapeutic targets. We collected 1011 well-characterized clinical strains from 50 countries and generated high-quality genome sequences. We analysed core genome diversity and population structure of the HpGP dataset and 255 worldwide reference genomes to outline the ancestral contribution to Eurasian, African, and American populations. We found evidence of substantial contribution of population hpNorthAsia and subpopulation hspUral in Northern European H. pylori. The genomes of H. pylori isolated from northern and southern Indigenous Americans differed in that bacteria isolated in northern Indigenous communities were more similar to North Asian H. pylori while the southern had higher relatedness to hpEastAsia. Notably, we also found a highly clonal yet geographically dispersed North American subpopulation, which is negative for the cag pathogenicity island, and present in 7% of sequenced US genomes. We expect the HpGP dataset and the corresponding strains to become a major asset for H. pylori genomics. Helicobacter pylori, often found in the human stomach, can be classified into distinct subpopulations associated with the geographic origin of the host. Here, the authors provide insights into H. pylori population structure by collecting over 1,000 clinical strains from 50 countries and generating and analyzing high-quality bacterial genome sequences.The bacterium  Helicobacter pylori has co-existed with humans for more than 100,000 years. It is the primary etiologic agent associated with gastric diseases such as ulcers and gastric cancer. Still, while over half the world population is colonized with H. pylori, less than 2% will end up with gastric cancer2. The intimate symbiotic relationship with humans, together with predominantly vertical transmission, has led H. pylori to evolve into multiple distinct geographic populations5. The phylogeographic structure of H. pylori is classified into major populations (“hp”) and subpopulations (“hsp”) that correlate with ancient human migrations6. However, most worldwide efforts in this regard have been based on the analysis of only a handful of genes rather than whole genomes4. The risk of developing disease from H. pylori infection varies greatly by geography7 and genomic studies of both humans and H. pylori are required to identify the factors that modify this risk.Helicobacter pylori Genome Project (HpGP) is an international and multidisciplinary initiative to sequence and map H. pylori population structure by collecting strains worldwide. Here we analyze 1011 H. pylori genomes, sequenced with PacBio Single Molecule, Real-Time long-read technology, which made it possible to acquire complete assemblies. By relating the HpGP dataset to a reference set of known population assignment, we were able to quantify, with great resolution, the different inferred ancestral sources of H. pylori subpopulations and the recent and ongoing admixture among subpopulations.The HpGP has assembled clinical strains from 50 countries, including 12 countries from which no H. pylori genome sequences have previously been published , chromosome painting, and network analyses of shared core genome features as described8, and discriminant analysis of principal components (DAPC)9. To anchor the dataset, we used 255 H. pylori reference genomes with known Hp/hsp population assignments, representing 17 global subpopulations  Southwest Europe, including Latin America and Northeast Africa, (ii) Northern and Central Europe, Middle East, and Central Asia, (iii) Western and Southern Africa, including Africa2 and North, South and Central America, and (iv) North, Central and East Asia, and Indigenous populations in America. In total, these formed 17 main subpopulations  and the reference genomes from Australia/New Guinea  differentiated extensively from the others. A further DAPC analysis not considering these two groups showed a clear separation of two of the remaining clusters from the others: one composed of isolates of African and American origin  and one that includes isolates from Central/East Asia and Indigenous Americans . The remaining (groups 1 and 3) were more similar and intertwined, representing Southern Europe/Northeast Africa and Eurasia/Central Asia and Americas, respectively 10. We confirmed a gradient in inferred ancestry along both the north-south axis with increasing Asian ancestry and decreasing African ancestry in the hspEurasia1 and hspNEurope populations and the east-west axis with hspSWEurope having a higher proportion of hspAfrica1WAfrica ancestry and with the similar contribution of hpNEAfrica as the Eurasia2 population  and its subpopulation hspSiberia1 were represented by genomes from central and eastern Siberia. In our analyses, these two populations did not segregate, and HpGP genomes from Kazakhstan and Kyrgyzstan were also associated with this cluster https://hpgp.shinyapps.io/Interactive_figures, Fig. HpGP “hspUral” clade not to have pronounced contribution by the hspUral references but relatively high hpAsia2, hpNorthAsia and hspEAsia painting proportions , Ghana, and Nigeria, adding to previous knowledge from the Gambia and South Africa. The fineSTRUCTURE analysis confirms earlier observations of the presence of four African populations in this continent, hpAfrica2, in the HpGP dataset represented in South Africa; hspAfrica1SAfrica, which reaches as far north as DRC; hspAfrica1WAfrica represented in the Gambia, as previously reported, and hpNEAfrica. However, the Ghanaian and Nigerian genomes grouped with the more admixed hspAfrica1NorthAmerica and hspAfrica1MiscAmericas populations, interspersed with, and by chromosome painting indistinguishable from genomes from the US, Puerto Rico (US territory), Dominican Republic, Colombia, and Brazil, likely a result of the trans-Atlantic slave trade from West Africa into the Americas.The HpGP genomes clearly formed two segregated groups except for one Malaysian and one Swiss genome that grouped with the references .The H. pylori are unexpected as unrelated strains differ in their DNA sequence at almost all genes. To further investigate the novel US subpopulation, we performed core genome (cg) MLST of the entire dataset  of these pairs is found between a group of 12 US strains, showing allele distances between 0.83 and 0.94 . Thus, this group represents older clonal relationships, a putative “deep clone”; a set of strains that share a recent common ancestor but have diverged via homologous recombination at a large fraction of their genome. Three strains are somewhat less related to these 12, sharing between 1% and 7% of genes, and were conservatively excluded from this clonal group. Other pairs involving more than two samples from the same population also showed deep clonal relationships . However, the amount and pattern of alleles shared between these samples could be better explained by genetic drift and further analysis within this population is needed to define the boundaries of a putative clone.High levels of sequence homogeneity within set Fig. . Within HpGP strains from the deep clonal group were sampled from California, Wisconsin, Tennessee, Arkansas, Georgia, and Texas and, in total, represented a fifth of the HpGP US genomes. Kmer-based clustering analysis showed an additional five public genomes from two other geographical sources, Ohio and Louisiana, associating closely with the proposed deep clonal group. We used ClonalFrameML to estimate the relationships between the genomes. Assuming a previously estimated 1.38 × 10–5 mutation rate per site per year16, the common ancestor lived an estimated 175 years before the strains were collected , while the majority of internal nodes are estimated to be less than 50 years old  and 18% (proportion in HpGP), while it has not yet been found outside the US. The US population in the year 1830 was less than 13 million individuals and has increased to over 330 million through natural population growth and immigration. Assuming the lineage was introduced into the US by a single individual around 1830 and infected 10-fold or more people in each human generation, it would be present in around 3 million individuals today, or about 4% of sampled individuals. These calculations ignore factors such as mixed infection and are subject to many uncertainties but demonstrate that a high level of non-vertical transmission and a significant fitness advantage over other H. pylori is necessary to explain the current frequency of Hp_Clone_US-1 in US individuals.The frequency of the deep clone Hp_Clone_US-1 in the US population is likely somewhere between 3%  generated by the HpGP, including shared resources, codes, and interactive visualizations, together with our main results, will be widely used and will facilitate secondary analyses with the ultimate goal of reducing the burden of the pathologies associated with this bacterial carcinogen.This HpGP samples represent a convenient set. Contributors of samples were identified through advertisements at international scientific meetings, direct invitations to known colleagues and investigators with published sets of H. pylori strains, as well as referrals. A limited number of H. pylori genomes was publicly available from Spain, one of the main countries responsible for colonial activities in the Americas. Thus, in collaboration of members of the Spanish Association of Gastroenterology, we oversampled this country to better understand the admixed genomes from individuals from Latin America and the Caribbean.The n = 351) and cultures  of H. pylori from patients with non-atrophic gastritis (n = 606), advanced intestinal metaplasia , and gastric cancer (n = 233). Samples were collected between 1995 and 2020. Biospecimens were shipped to the Division of Gastroenterology, Hepatology, and Nutrition at Vanderbilt University for processing. Before shipment, clinical information and sample descriptions were submitted to the coordinating center at the US National Cancer Institute to confirm eligibility. Biospecimens from the 72 collaborating centers were shipped frozen on dry ice. All individuals provided informed consent, and local Institutional Review Boards approved sample collection. The HpGP was exempted from institutional review board evaluation by the National Institutes of Health Office of Human Subjects Research Protection. The summary statistics of 1011 included strains are presented in Table We obtained gastric tissues . The single colony was also expanded and used for DNA extraction using Qiagen, QIAamp DNA Mini kit , following the protocol and using the EB buffer to elute the DNA. Original cultures (pooled or single colonies) were processed using the same protocol.Gastric tissues (biopsies or fragments from resections) were homogenized under sterile conditions in 100 μL of sterile phosphate-buffered saline  using a homogenizer . Then, 300 μL of sterile PBS was added to each sample, mixed, and plated onto two selective Trypticase soy agar (TSA) plates with 5% sheep blood containing vancomycin (20 mg/L), bacitracin (200 mg/L), nalidixic acid (10 mg/L) and amphotericin B (2 mg/L) . In addition, a 1:10 dilution was plated on a no-antibiotic TSA plate . Agar plates were incubated under microaerobic conditions  at 37 °C for 4–6 days until small gray translucent colonies appeared. Gram stains and assays for oxidase and urease were performed. Colony morphology was consistent with the characteristic shape of DNA samples were sequenced at the Cancer Genomics Research Laboratory at the US National Cancer Institute. The manufacturer’s protocol was performed for constructing whole-genome libraries from microbial DNA using the SMRTbell Template Prep Kit. Briefly, 1000 ng of genomic DNA, as determined by Quant-iT™ PicoGreen dsDNA Reagent , was sheared using the g-TUBE  to an average fragment size of 10 kb. Following fragmentation and purification, DNA damage, and end repair, hairpin adapters were ligated to the fragment ends to generate SMRTbell libraries. For sequencing on the PacBio RSII instrument, standard hairpin adapters were used. For sequencing on the PacBio Sequel and Sequel II instruments, barcoded hairpin adapters were used. For the Sequel and Sequel II, barcoded SMRTbell libraries were pooled (up to 8 for the Sequel and up to 48 for the Sequel II), and stringent purification was performed using AMPure PB beads to remove small fragments. Following purification, sequencing primer annealing and DNA polymerase binding of the pooled SMRTbell libraries was performed according to the manufacturer’s protocols. SMRT sequencing of the libraries proceeded on the PacBio instrument using 1 SMRT Cell per isolate (RSII) or pool of libraries . Genome coverage ranged from 111 to 5678× .Since samples were sequenced from multiple generations of PacBio instruments , raw data from the RSII in h5 format were converted to Sequel’s subreads XML format so that the same analytical pipeline could be applied to data from all three instruments sequencer. Thus, RSII data were reanalyzed by the same analysis pipeline as Sequel and Sequel II after initial assembly by HGAP3. The original assemblies (from HGAP3) and the new assemblies from the newer SMRTlink assembly tools  were compared and were highly consistent. Newly reassembled RSII data that generated a single contiguous chromosomal contig were kept.24 on the HiFi  reads. Prior to Hifiasm, raw subreads were converted to circular consensus reads, filtering for CCS reads with minimum predicted read quality higher than 0.99. Chromosomes and plasmids were assembled, which proved to be the most accurate and efficient to achieve complete assembly of all contigs in silico. Circularization with circlator v1.5.325 was performed on every contig in each strain. Bacterial chromosomal contig start points were all shifted to NusB gene with an additional 12 nt at the 3’-end. MUMmer v3.2326 was used to perform a self-alignment to screen for assembly issues or artifactual contigs, and prokka v1.14.627 annotation of conserved H. pylori genes was run on both raw subread and HiFi read assemblies. The assemblies generated from raw subreads were generally used for methylation calling and downstream analysis unless they failed to be circularized, or prokka annotation suggested a pseudogene percentage higher than 5%, or they contained an unexpected number of tRNA/rRNAs. In those samples, the Hifiasm assembly was used. Candidate chromosomal contigs were also aligned to a published 26695 H. pylori strain (NC_000915.1) as a sanity check to verify the contig shared high homology with known H. pylori. The detailed analyses of HpGP plasmid sequences and their geographic and chromosomal contexts will be reported in full elsewhere.Whole-genome assembly using raw subreads was performed using SMRTLink’s HGAP4/Microbial Assembly, as well as Hifiasm v0.13-r308https://www.pacb.com/blog/beyond-contiguity/). First, we assessed sequence contiguity and determined that the HpGP de novo assemblies all have a contig N50 over 1 Mb. As expected, single chromosomal contigs range from ~1.5 to 1.7 Mb. Second, we measured the completeness of our assemblies using BUSCO  scores28 v5.1.3. BUSCO checks the presence or absence of highly conserved genes, and a score >95% is considered a good assembly. For the assemblies that did not achieve a BUSCO score as high as 95%, we either discarded that sequence, or a second attempt was carried out either in silico or in the laboratory. All 1011 HpGP assemblies have BUSCO scores above 95%. To further measure correctness, we checked the ratio of pseudogenes, including frameshifted, incomplete, internal stop, ambiguous residues, and multiple problems against the total number of genes. Any assembly with a ratio of pseudogene of more than 5% of the total was discarded. Although the HpGP set includes assemblies from three different PacBio sequencing instruments , the measures of assembly quality  were similar for the 1011 assemblies from these instruments.To address the assembly quality, we applied the 3Cs protocol suggested by PacBio 31. For sequences that could not be circularized (n = 7), 100 Ns were added to mark breakpoint locations in the genomic sequences. The individual accession numbers and genome statistics are presented in Supplementary Data The HpGP dataset to previous knowledge about H. pylori population structure, we used a reference dataset representing the 17 global H. pylori subpopulations  and the 255 representative genomes, a total of 1267 genomes. The analysis was performed using the panaroo pipeline v1.2.1032 using 90% protein sequence identity and 75% gene length coverage cut-off.All population structure analyses  method to cluster individuals based on the coancestry matrix as described36. The results were visualized as a heat map with each cell indicating the proportion of DNA “chunks” a recipient receives from each donor. Furthermore, the posterior distribution of the clusters was visualized using fineSTRUCTURE’s tree-building algorithm to define the populations and subpopulations produced. With the previously obtained coancestry matrix, multiple principal component analysis (PCA) was calculated to analyze the population structure in detail. Principal components (PCs) 1 to 11 were calculated and visualized using R.The genome-wide haplotype data was calculated as described previously9. DAPC is a multivariate approach, not model based; hence, it makes no assumptions about Hardy-Weinberg or linkage equilibrium on genetic loci. Before running the DAPC, we assessed the number of clusters most supported for our H. pylori dataset by employing the find.clusters function in adegenet R package, comparing the results of 100 independent runs using a custom-made R script and selecting the optimal number of clusters according to Bayesian information criteria (BIC). In order to assess the uncertainty of the group assignments of each individual, we visualized posterior group membership probabilities based on the DAPC analysis using the function compoplot.We employed discriminant analysis of principal components (DAPC) to further investigate the genetic structure of our data. DAPC describes clusters in genetic data by creating synthetic variables (discriminant functions) that maximize variance among groups while minimizing variance within groups37, we extracted 601,000 SNPs from the 1267 genome panaroo core gene alignment. We employed the function optim.a.score of the adegenet R package to identify the optimal number of principal components to consider for the analyses, as too many could lead to overfitting, while a low number of components could decrease discriminatory power between groups.Using SNP-sites v2.5.1find.cluster analysis: K = 6. Initially, we ran the DAPC considering all the sequences in our alignment so as to visualize the entire genetic variability of our data. Given the outlier position of the two groups, we subsequently ran another DAPC analysis excluding these outliers to better emphasize the differences among the other clusters. Finally, we computed posterior group membership probability for each individual. This parameter is based on the retained discriminant functions of the DAPC analysis and represents the probability of each sample to be assigned to a group, which can be interpreted in order to assess how clear-cut or admixed the clusters are. We also ran the DAPC procedure considering K = 17, the same number of clusters identified by the fineSTRUCTURE analysis.We first ran a DAPC employing the most supported number of clusters/groups as estimated by the 38 v4.0a166, using maximum likelihood criteria. Each distance was normalized between 0 and 1 as previously described8. With this normalization, 0 means the highest genetic similarity, and 1 signifies the highest dissimilarity between two strains. Next, a complete network is created, where all pairs of strains have a measure of genetic distance based on this previous normalization. Strains are represented as vertices, and their distances are represented as edges. In the beginning, this network is fully connected and has no perceptible structure. A process of edge and node pruning is carried out to reveal the underlying structure of the genetic similarity between strains. This process consists of ranking the values of the edges and removing them subsequently, starting with the most dissimilar . This process is continued until the network is subdivided into a determined number of Connected Components (CC). We consider a CC as a set of more than two nodes connected between them but isolated from other groups of nodes. If a single node is stripped of all its edges (singleton), we discard this node from the set of nodes of the resulting network. Figure The core gene alignment obtained with panaroo was used to estimate distances with PAUPH. pylori isolates, we conducted chromosome painting using ChromoPainterV235, designating all genomes as recipients (1011 HpGP genomes), and randomly selected ~20 isolates per population as donors 40. We first used Prodigal41 v2.6.3, including the option -t to create a training file from the assembled version of the 26695 H. pylori reference strain resequenced as part of the HpGP dataset. Then, the “CreateSchema” module of chewBBACA was applied to the 1011 HpGP genomes and the Prodigal training file to estimate a whole-genome MLST (wgMLST) scheme. The 3943 wgMLST genes were then compared with the “AlleleCall” module, using the default BSR threshold of 0.6. A total of 867 genes identified as paralogs were removed from the wgMLST using the “RemoveGenes” module, reducing the scheme to 3076 loci. We then used the “ExtractCgMLST” module to create a cgMLST with all loci present in more than 95 percent of strains (--t 0.95), obtaining a total of 981,110 alleles for 1040 loci, an average of 943 different alleles per locus.To investigate the existence of clonal relationships in 42 v1.5.0, running it in “--wgMLST” mode with the “distance” method (-method distance) while ignoring missing data (--missing 0). We analyzed the distribution of cgMLST distances between pairs of strains in categories such as “US clone”, “US clone boundary”, “US non-clone”, “Chile”, “Chilean hspSWEuropeChile”, “non-Chilean hspSWEuropeChile”, “within the same country”, and “between different countries”, as depicted in Fig. We last used the cgMLST allelic profile to calculate pairwise distances with GrapeTreeH. pylori database (https://enterobase.warwick.ac.uk/species/index/helicobacter) with the US as the country of isolation as of September 18, 2022 (n = 226). Sixty-seven sequences were either isolated from non-human hosts, results of experimental infections, repeated samplings from the same individual or overlapping the HpGP set, thus were excluded. The remaining 151 genomes (Supplementary Data HpGP US genomes and the 255 references in a kmer-based genomic distance analysis using mash v2.343. The five genomes clustering with the US deep clone were added to the dataset used for in-depth analysis.We downloaded all whole-genome sequences publicly available in the EnteroBase HpGP-USA-401, HpGP-USA-404, and HpGP-USA-414 had diverged from the clone both by phylogeny and chromosome painting profile and were excluded from further analysis. A phylogenetic tree was computed using PhyML v3.144 and input to ClonalFrameML v1.11-3-g4f13f2345, executed using default parameters. Node ages were determined using the R BactDating package46, using 10,000 Markov chain Monte Carlo iterations and a mutation rate of 1.38 × 10−5 per site per year, as has previously been estimated16.A core gene alignment of the highly clonal US genomes, including the five public ones, was generated with panaroo using the settings described above. Three genomes, 47 and ggmaps48 package in R. The painting profiles were summarized as described above, and plotting and statistical analysis was performed in R using the ggplot2 and plotly49 packages.The map figures of the dataset’s geographical distribution, including the gray background map, were plotted using the ggplot2HpGP set of H. pylori strains is available from the US National Cancer Institute for scientific purposes upon a reasonable request. However, restrictions apply to its availability as some samples require authorization from contributing centers to be distributed to third parties.The Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Dataset 1Supplementary Dataset 2Supplementary Dataset 3Supplementary Dataset 4Reporting SummarySource Data