Deletions and duplications of the multigenic 16p11.2 and 22q11.2 copy number variant (CNV) regions are associated with brain-related disorders including schizophrenia, intellectual disability, obesity, bipolar disorder, and autism spectrum disorder (ASD). The contribution of individual CNV genes to each of these identified phenotypes is unknown, as well as the contribution of these CNV genes to other potentially subtler health implications for carriers. Hypothesizing that DNA copy number exerts most effects via impacts on RNA expression, we attempted a novel in silico fine-mapping approach in non-CNV carriers using both GWAS and biobank data.We first asked whether gene expression level in any individual gene in the CNV region alters risk for a known CNV-associated behavioral phenotype(s). Using transcriptomic imputation, we performed association testing for CNV genes within large genotyped cohorts for schizophrenia, IQ, BMI, bipolar disorder, and ASD. Second, we used a biobank containing electronic health data to compare the medical phenome of CNV carriers to controls within 700,000 individuals in order to investigate the full spectrum of health effects of the CNVs. Third, we used genotypes for over 48,000 individuals within the biobank to perform phenome-wide association studies between imputed expressions of individual 16p11.2 and 22q11.2 genes and over 1500 health traits.TMEM219, INO80E, YPEL3), BMI , and IQ (SPN), using conditional analysis to identify upregulation of INO80E as the driver of schizophrenia, and downregulation of SPN and INO80E as increasing BMI. We identified both novel and previously observed over-represented traits within the electronic health records of 16p11.2 and 22q11.2 CNV carriers. In the phenome-wide association study, we found seventeen significant gene-trait pairs, including psychosis  and mood disorders (SCARF2), and overall enrichment of mental traits.Using large genotyped cohorts, we found individual genes within 16p11.2 associated with schizophrenia (Our results demonstrate how integration of genetic and clinical data aids in understanding CNV gene function and implicates pleiotropy and multigenicity in CNV biology.The online version contains supplementary material available at 10.1186/s13073-021-00972-1. Multi-gene copy number variants (CNVs), including a 600-kb region at 16p11.2 and a 3-Mb region at 22q11.2, are known causes of multiple brain-related disorders. The 16p11.2 CNV, originally identified as a risk factor for autism spectrum disorder (ASD), has also been associated with schizophrenia, bipolar disorder, intellectual disability, and obesity . The 22qRAI1 gene due to point mutation  associated with schizophrenia. In order to condition on INO80E, for example, the predicted expression of INO80E was regressed out of the predicted expressions of YPEL3 and TMEM219. Conditioning was only done in tissues where the predicted expressions of the genes were correlated (Spearman correlation P < 0.05). Another set of conditioned predicted expressions was generated by adjusting the predicted expression of INO80E by the predicted expressions of . Separately, these per-tissue conditioned predicted expressions were used as inputs for a MultiXcan analysis and METAL meta-analysis on schizophrenia as described earlier. All three individually associated genes were tested in this manner. The same analysis was later used to test for independence of association between BMI in the UK Biobank as well as psychosis and morbid obesity traits in the PheWAS. The Pcond reported in the text is the p value of a gene-trait pair when adjusting for all other genes considered for conditioning for this trait, unless otherwise stated. To validate that our approach explained all GWAS signal at the loci, we also conditioned the MultiXcan analysis on lead GWAS SNP(s) that were also eQTLs. The GWAS conditioning was performed in PLINK using the --condition function, with principal components (and age for BMI) as covariates. Linkage disequilibrium patterns in the region were visualized using LocusZoom  × pi where pi is the probability of a randomly drawn (without replacement) code belongs to category i. pi can be estimated by the number of codes belonging to category i divided by all codes tested (n = 1531). The significance threshold was 0.05/[17 categories] = 0.0029.The top 1% (top 15 traits) of every gene’s association results were kept for analysis. A binomial test was used to compare whether the number of traits in any clinical category  were over-represented in the top 1% of results compared to the proportion of each category among all 1531 traits tested. The expected number of each clinical category as determined by [15 traits × p values for these gene-trait pairs in our PheWAS were selected and shown in Additional file To analyze the overlap between PheWAS results and known Mendelian phenotypes associated with these genes, we used OMIM . “16p11.n = 48, median age 11), “16p11.2 deletion” , “22q11.2 duplication” . Additional individuals in the 22q11.2 deletion case group were identified by querying the medical records for alternate terms including: “velocardiofacial”, “DiGeorge”, “conotruncal anomaly face,” “Cayler,” “Opitz G/BBB,” “Shprintzen,” and “CATCH22” . Individuals were excluded from case groups if they were included in the genotyped sample used for the gene-by-gene analysis, or if their records included a mention of additional CNVs. Individuals within the 16p11.2 case groups were also excluded if the size of the reported CNV was 200–250 kb. Individuals within the 22q11.2 case group were excluded if the size of the CNV was smaller than 500 kb or if there was a mention of “distal” when referring to the deletion or duplication. PheWAS was carried out, with each of the four carrier categories as cases and over 700,000 medical home individuals as controls, using age, sex, and self-reported race as covariates. The medical home individuals are patients seen at a Vanderbilt affiliated clinic on five different occasions over the course of 3 years. Because the sample size for this analysis was larger , and we used traits that were present in 20 or more individuals, there were more traits available for analysis here, n = 1795. After calculating PheWAS, we excluded over-represented traits that were present in < 5% of carriers from further analyses.3.1 million electronic medical records from the SD at VUMC were queried for keywords corresponding to copy number variants at 16p11.2 and 22q11.2  using a one-tailed Wilcoxon rank sum test.For the second comparison, individuals carrying “extreme” predicted expression across a CNV region were identified using a sequence of rankings. Each expression measurement  was classified as “extreme” if it ranked above the top 2nd percentile or below the bottom 2nd percentile of the BioVU cohort, “normal” if the measurement was between the 25th and 75th percentile, or “neither.” For a gene expressed in only one tissue, the gene’s “extreme” expression label is simply the same as the tissue’s “extreme” label. For a gene with multiple tissue expressions, we counted the number of tissues with “extreme” expression and assigned a gene-level “extreme” label to individuals with the most tissues consistently expressing “extremes” for the gene. A gene-level “normal” label was assigned to half of the cohort who had no extreme expression in any tissues and had the most tissues with “normal” expressions. The remaining individuals received a “neither” label for the gene. After obtaining the gene-level labels , we then ranked the individuals by the number of “extreme” expression genes, and labeled a subset of individuals  as extreme expression carriers. Note that we consider extreme high and extreme low predictions together due to prior data showing that eQTL direction can be specific to cell types or tissues, which our cross-tissue approach cannot distinguish . These wP < 7.9 × 10−5. For replication studies, the significance threshold was set at 0.05 in order to test a single gene. The exception was in the BMI UK Biobank dataset. We first tried a phenotype-swapping approach to generate an expected distribution for the 16p11.2 genes. The distributions were null and did not yield meaningful comparisons. Instead, 100 random subsets of adjacent genes of approximately the same length and gene count as the CNV were tested for association with BMI. The 95th percentile of the MultiXcan p values for these genes was used as a permutation-based significance threshold.The significance threshold used for each discovery MultiXcan or S-MultiXcan association study and conditional analysis was 0.05/(number of traits × number of CNV genes tested). In practice, this usually meant 5 traits and 127 CNV genes, for a threshold of −5. For genes having no phenome-wide significant results, their top 15 associations, corresponding to the top 1% of the 1,531 phecodes, were used. In the carrier vs. non-carrier PheWAS, there were 1795 phecodes tested overall, corresponding to a Bonferroni-corrected phenome-wide significance threshold of 2.79 × 10−5. Additional traits meeting a false discovery rate threshold of 0.05 were considered in identifying traits both over-represented in carriers and represented in individual gene PheWAS.In the gene-based PheWAS study, there were 1531 phecodes (each with at least 20 cases) tested overall, corresponding to a Bonferroni-corrected phenome-wide significance threshold of 3.3 × 10chordDiagram method in the circlize package was used to generate the circle summary plots [p value of association used as the weighting to determine the edge width. For the 22q11.2 circle plot, only associations with P < 5 × 10−3 were used in order to create a legible plot. Descriptions were cut off at 55 characters; to read the entire descriptions, see Tables The ry plots . The genIn order to find genes at copy number variant loci driving brain-related disorders, we performed an association analysis between imputed gene expression levels and five traits: schizophrenia, IQ, BMI, bipolar disorder, and ASD. It has been observed that copy number variants (including 16p11.2 and 22q11.2) affect expression of nearby genes , 45, 82.P < 7.9 × 10−5) with schizophrenia  and INO80E (P = 5.3 × 10−10). This positive direction of effect is consistent with the association between 16p11.2 duplications and schizophrenia [YPEL3, was significantly associated with schizophrenia in the negative direction (P = 4.9 × 10−6). For IQ, there was one strong positive association at the 16p11.2 locus . Intellectual disability is observed in both deletions and duplications of 16p11.2, so there was no expected direction of effect [SPN (P = 6.2 × 10−18), TMEM219 (P = 2.2 × 10−5), TAOK2 (P = 8.5 × 10−11), and INO80E (P = 1.0 × 10−7). We focused on genes with negative associations with BMI because, in humans, obesity is associated with deletions at 16p11.2 [KCTD13 (P = 9.5 × 10−6) and MVP (P = 2.1 × 10−5), were significantly associated with BMI in the positive direction. No gene at 16p11.2 was significantly associated with bipolar disorder or ASD  with having an ICD10 diagnosis of schizophrenia . Using a permutation-based approach within individual-level data, we adjusted the significance threshold to 8.8 × 10−11. All genes from the discovery analysis replicated , KCTD13 (P = 1.2 × 10−30), TMEM219 (P = 7.1 × 10−37), MVP (P = 5.1 × 10−11), and INO80E (P = 1.9 × 10−27). We were not able to replicate the IQ result in the UK Biobank, because the UK Biobank sample overlapped with our discovery GWAS.The UK Biobank GWAS of BMI is highly inflated, including in the 16p11.2 region. Nearly every 16p11.2 gene showed association at the previously used threshold P < .9 × 10−5INO80E, YPEL3, and TMEM219 were moderately correlated , consistent with the relationships between the observed expressions of these genes (measured expression of INO80E is correlated with measured expression of the other genes in the range − 0.36 to 0.31). In order to pick out the gene(s) driving the association signal, we used a conditional analysis approach . The same pattern was not observed for TMEM219 or YPEL3, suggesting INO80E explains the entire 16p11.2 signal for schizophrenia.We performed an additional fine-mapping study on the three genes associated with schizophrenia. Linkage disequilibrium between the eQTL SNPs in predictive models may lead to correlation among predicted expressions for nearby genes, so it is possible that not all three detected association signals are independent. The predicted expressions of SPN, INO80E, TMEM219, TAOK2 in the negative direction, as well as KCTD13 and MVP in the positive direction. Due to the inflation in the UK Biobank data, all these genes had very low p values even after conditioning; however, we see that some genes’ association results stayed in the same range, while others increased in p value by five orders of magnitude or more after adjusting by the other five genes. Based on these observations, it is likely that SPN , INO80E , and KCTD13  were independently associated with BMI, while TMEM219 , TAOK2 , and MVP  were significant in the discovery analysis primarily due to correlation with one of the independent genes.While we did not have individual-level data for the GIANT consortium, we obtained individual-level BMI data from the UK Biobank . We perfINO80E is our proposed sole driver gene), conditioning on one GWAS SNP  was sufficient to explain the GWAS peak in the region , conditioning on four GWAS/eQTL SNPs was sufficient to explain both the GWAS and MultiXcan signal , rs9936474 (GWAS P = 5.1 × 10−31), rs2008514 (GWAS P = 3.3 × 10−29), and rs8046707 (GWAS P = 3.2 × 10−19). The first two SNPs explain the GWAS signal within the region, and the latter two come from more distal GWAS peaks that are nevertheless involved in the expression prediction of 16p11.2 genes; as a result, four SNPs are needed to fully nullify the MultiXcan signal. The schizophrenia variant rs4788200 is not a strong eQTL for any gene-tissue pair, but it appears in the models for INO80E in 22/37 tissues where INO80E has models. Similarly, one of the BMI SNPs, rs4787491 is an expression-decreasing eQTL for INO80E in 35/37 tissues and is generally strong: the distribution of weights of this SNP was significantly different from the distribution of all INO80E-predicting SNPs, . We conclude that our approach is sufficient for explaining GWAS signal and that the multi-SNP predictive models involving both nearby and more distal SNPs are advantageous.To validate that our approach explained all GWAS signal at the locus, we took two phenotypes in which we had both GWAS signal and individual-level data available—PGC Schizophrenia and UK Biobank BMI—and conditioned the MultiXcan analysis on lead GWAS SNP(s) in those datasets that were also eQTLs. In schizophrenia (where P < 2.8 × 10−5) fell into three major categories: (1) known primary CNV clinical features, including possible reasons for the referral of the patient for genetic testing , (2) secondary CNV features known to be present in carriers but unlikely to be a primary reason for referral for genetic testing, (3) novel diagnoses not previously reported , developmental delays and disorders (P = 6.3 × 10−10), delayed milestones (P = 1.4 × 10−11) [. In addition, 16p11.2 deletion carrier status was associated with autism (P = 1.3 × 10−10) and mental retardation (P = 7.9 × 10−13) [GERD (P = 1.1 × 10−5) has been previously observed in carriers but was unlikely to be a primary reason for genetic testing [GERD was accompanied by other digestive diagnoses such as dysphagia (P = 1.3 × 10−7) and diseases of esophagus (P = 4.3 × 10−7). Muscle weakness (P = 2.8 × 10−6) and abnormal movements (P = 3.9 × 10−6) are consistent with neurological traits reported in 16p11.2 deletion carriers such as hypotonia and motor impairments [Sleep apnea (P = 8.9 × 10−6) was a novel phenotype, potentially related to increased BMI in deletion carriers.16p11.2 deletion carrier status was associated with developmental diagnoses Fig. : lack ofrs P = 6. × 10−10,× 10−13) . The dig testing . GERD waairments . Sleep alack of normal physiological development (P = 5.6 × 10−15), developmental delays and disorders (P = 2.5 × 10−13), delayed milestones (P = 9.0 × 10−13), autism (P = 1.3 × 10−12), and mental retardation (P = 1.6 × 10−7) [valvular heart disease/heart chambers (P = 4.6 × 10−10) and cardiac shunt/heart septal defect (P = 3.2 × 10−8), both of which have been reported previously [convulsions (P = 2.9 × 10−8) in the biobank [Infantile cerebral palsy (P = 4.9 × 10−6), while a potential reason for genetic testing, has not previously been associated with 16p11.2 duplications. While the 16p11.2 CNV contains genes such as SPN and MVP that are active in the immune system, there is no prior evidence of the susceptibility of duplication carriers to infection, making the diagnosis Bacterial infection NOS (P = 5.5 × 10−7) a novel finding.16p11.2 duplication carrier status was similarly associated with developmental diagnoses Fig. : lack ofeviously . 16p11.2 biobank . Infanticardiomegaly (P = 3.5 × 10−258) and cardiac shunt/heart septal defects (P = 4.7 × 10−285) , developmental delays and disorders (P = 6.3 × 10−29), delayed milestones (P = 6.0 × 10−11) [cleft palate (P = 9.4 × 10−80) were also over-represented. The secondary known traits for 22q11.2 deletion carriers included immunity deficiency (P < 10−285), and disorders involving the immune mechanism (P < 10−285). Previously, it has been reported that 50% of 22q11.2 deletion carriers have T cell dysfunction and 17% have humoral dysfunction [hyperpotassemia (P = 1.4 × 10−10).For 22q11.2 deletion carriers, the canonical associated features were cardiac defects such as ts P = 4. × 10−285ts P = 4. × 10−285ts P = 4. × 10−285delayed milestones (P = 1.1 × 10−13), lack of normal physiological development (P = 9.7 × 10−13), pervasive developmental disorders (P = 1.2 × 10−6) [. 22q11.2 duplication status was associated with cardiac phenotypes such as cardiac shunt/ heart septal defect (P = 2.3 × 10−5). Cardiac features have not as often been reported in 22q11.2 duplication carriers compared to 22q11.2 deletion carriers [abnormality of gait (P = 3.1 × 10−12) and hearing loss (P = 2.1 × 10−7) have also been seen in 22q11.2, including as indications for genetic testing [22q11.2 duplication carrier status was also associated with developmental diagnoses Fig. : delayed × 10−6) . 22q11.2carriers . Remaini testing .n = 20 cases) in this subset. There were six phenome-wide significant (P < 3.3 × 10−5) gene-trait associations at 16p11.2 including the following: INO80E with skull and face fracture and other intercranial injury (P = 1.9 × 10−15), NPIPB11 with psychosis (P = 1.0 × 10−5), and SLX1B with psychosis (P = 3.0 × 10−5). There were eleven phenome-wide significant gene-trait associations at 22q11.2 including as follows: AIFM3 with renal failure (P = 2.3 × 10−5), LZTR1 with malignant neoplasm, other (P = 1.4 × 10−5), SCARF2 with mood disorders (P = 1.3 × 10−5), PI4KA with disorders of iris and ciliary body (P = 1.1 × 10−7), and disorders resulting from impaired renal function (P = 2.2 × 10−5). These include two renal traits, consistent with the 22q11.2 deletion carrier status association with renal failure. The associations of LZTR1 and PI4KA with neoplasms and eye disorders correspond to similar traits associated with these genes in prior literature [As our study of the impact of the entire CNV on phenotype confirmed our ability to detect important CNV-associated traits within the BioVU biobank, our next goal was to catalog how each individual CNV gene might affect the medical phenome. We generated predicted expression for CNV and flanking genes, as in the initial GWAS analyses, for the 48,630 non-CNV carrier individuals genotyped in BioVU. We tested 1531 medical phenotypic codes meeting frequency criteria , endocarditis (P = 0.0046), cardiomyopathy (P = 0.0055), and coronary atherosclerosis (P = 0.0076) among its top 1% phenome associations [TBX6 at 16p11.2, which has a role in bone development and scoliosis, has pathologic fracture of vertebrae in its top 1% phenome associations (P = 0.0028) [TANGO2 mutations at 22q11.2 have been associated with metabolic abnormalities such as hypoglycemia, as well as epilepsy, and our PheWAS for TANGO2 showed abnormal glucose (P = 0.0013) and epilepsy, recurrent seizures, and convulsions (P = 0.0049) as top phenotypes [P < 0.05, and 12 at P < 0.1. In 6 of the 13 genes, the relevant clinical traits are within the top 1% of PheWAS associations for the gene , although not phenome-wide significant, associations in the BioVU cohort. ciations –31. TBX6 0.0028) –94. TANGenotypes , 96. We enotypes , and broP = 5.2 × 10−5). Of all 17 clinical categories tested, “mental disorders” was the only category with enrichment p value meeting multiple testing thresholds , IQ-related traits , BMI-related traits , and ASD-related traits  , BMI-related traits , ASD-related traits , and bipolar-related traits (mood disorders) (Table Speech and language disorder vs. Autism). Instead, we compared the top association statistics within our GWAS discovery and replication datasets for the genes identified to be associated with brain-related traits in PheWAS as an extension of this study , AIFM3 (mood disorders—in UK Biobank bipolar F31 P = 0.04), SCARF2 (mood disorders—in UK Biobank bipolar F31 P = 0.003), HIC2 , ZNF48 (bariatric surgery—in UK Biobank BMI 3.7 × 10−6). Of these, the association between SCARF2 and mood disorders reached phenome-wide significance in the PheWAS.Some of the top 1% PheWAS traits for CNV genes overlapped with the original five traits we studied: schizophrenia, IQ, BMI, bipolar disorder, and ASD. At 16p11.2, there were genes whose top PheWAS results included schizophrenia-related traits (s) Table . At 22q1s) Table . We coulpsychosis: NPIPB11, BOLA2, MAPK3, SEPT1, SLX1B, TBC1D10B. By comparing whether the p value of association stayed constant vs. increased after conditioning, we found that NPIPB11, SEPT1, SLX1B, and TBC1D10B were likely independent associations, whereas BOLA2 and MAPK3 may be associated with psychosis at least partly by correlation with the other four. We also performed the same analysis for 22q11.2 genes whose top phenome associations included morbid obesity: SNAP29, P2RX6, P2RX6P. Of these genes, the only one with a p value increase was P2RX6P, suggesting that its association with morbid obesity may be explained at least in part by another gene. From conditional analysis, we see evidence of a multigenic contribution to both traits from CNV genes.Predicted expression may be correlated between nearby genes, thus multiple genes can share a PheWAS trait association due to correlation alone. We are underpowered for independence testing for the majority of our GWAS traits, but we selected several notable traits that appeared in multiple genes to test for independence, in the same way as in our GWAS analysis  whose top PheWAS associations included the 16p11.2 deletion-associated trait of upper gastrointestinal congenital anomalies, though we are underpowered to know whether all these signals are independent. Of the genes that we found as drivers in the first analysis of GWAS datasets, we note that INO80E’s top PheWAS results overlap the 16p11.2 deletion-associated trait other specified cardiac dysrhythmias and SPN’s top PheWAS results overlap the 16p11.2 duplication-associated trait of failure to thrive (childhood).Traits that were found both in 16p11.2 carriers and in individual genes’ PheWAS results included primary CNV traits such as ns Table . There wtachycardia  and primary brain-related traits such as autism . We also found genes with top PheWAS results overlapping secondary traits from the carrier screen, such as diseases of the larynx and vocal cords .Over 30 genes at 22q11.2 had a top PheWAS trait overlapping a trait over-represented in 22q11.2 duplication or deletion carriers Table . Top Phep value) lists of traits in CNV carriers. We found that in all four cases , the top traits in the “extreme expression non-carrier” group were more likely to rank near the top of the CNV carrier traits than would be expected by chance; the distribution was significantly shifted for 22q11.2 genes , IQ (SPN), and BMI  in the expected direction based on known 16p11.2 biology. We then used EHR data to detect (known and novel) traits over-represented in 16p11.2 and 22q11.2 carriers for comparison with individual gene results. Third, we used the same EHR system biobank containing over 1500 medical traits to explore the consequences of expression variation of 16p11.2 and 22q11.2 CNV genes in non-carriers, and we identified enrichment of brain-related traits as well as individual genes potentially driving carrier-associated traits. The results from the GWAS-derived and PheWAS analyses can be considered as independent ways to probe the function of CNV genes using expression imputation.In this study, we sought to identify individual genes in the 16p11.2 and 22q11.2 regions driving brain-related disorders, as well as the impact of both the entire CNV and specific CNV genes on the medical phenome. In a novel in silico approach to CNV fine-mapping, we tested whether genetically driven predicted expression variation of the individual genes in each CNV was associated with ascertained brain-related disorders ascertained in GWAS data. We identified individual genes at 16p11.2 whose expression was associated with schizophrenia , many of which overlapped with highly associated GWAS SNPs for both IQ and BMI, rather than contributing to noise.isorders , 104. Reormality . We noteINO80E was associated with both schizophrenia and BMI, and SPN was associated with both BMI and IQ. Genetic correlations of at least − 0.05 and as much as − 0.5 have been estimated for the BMI/IQ and SCZ/BMI pairs, suggesting that pleiotropy may play a general role in these disorders [SPN drove the associations with both IQ and BMI.Our results give evidence that pleiotropy is involved in the pathogenicity of 16p11.2, as opposed to a strictly “one gene, one trait” model. Specifically, isorders –109. ConMVP and KCTD13 were associated with BMI in the opposite (positive) direction, and YPEL3 with schizophrenia in the negative direction. We resolved the schizophrenia result by conditional analysis, where we found that YPEL3 was associated with schizophrenia simply due to correlation with INO80E. For BMI, we were able to use UK Biobank data to determine that MVP was not an independent association with BMI, while KCTD13 remained. For an example like KCTD13, we offer three explanations: these results may be false-positives due to correlation-based “hitchhiking,” they may demonstrate a limitation of our approach, or they may have a true BMI-increasing effect. First, we cannot rule out that it “hitchhikes” to statistical significance with other negatively associated genes due to correlation but does not contribute to BMI itself. Second, this result might represent a limitation of our eQTL-based method. KCTD13 is a highly brain-expressed gene, but had no high-quality brain prediction models [KCTD13 expression in the brain may be brain-specific, and brain may be the only relevant tissue for the effect of KCTD13 on BMI. That is, KCTD13 may have a strong negative correlation with BMI, but falsely appears positive due to the specific eQTLs used for expression prediction. Such tissue-specific eQTL directions of effect have been observed for at least 2000 genes [KCTD13 could have a true BMI-increasing effect. If so, the 16p11.2 region contains both BMI-increasing and BMI-decreasing genes, and the effect of the BMI-decreasing genes is stronger. Such a model is a potential explanation for the observation that duplications at 16p11.2 in mice, unlike humans, are associated with obesity [While most associations we detected were in the expected direction given previous knowledge, n models . The dir00 genes . Improve obesity . One setdelayed milestones, mental retardation, and pervasive developmental disorders, were among the top over-represented traits in both 16p11.2 and 22q11.2 CNV carriers. 22q11.2 deletion carriers were strongly associated with cardiac congenital anomalies and cleft palate, two of the hallmark features of the CNV. Even though the total number of CNV carriers within the biobank was relatively small, the strong known clinical associations were observed. At the same time, we identified novel traits that may be confirmed in larger samples of CNV carriers such as sleep apnea in 16p11.2 deletions and hyperpotassemia in 22q11.2 deletions.Our PheWAS of traits over-represented in 16p11.2 and 22q11.2 carriers served as a validation of our biobank EHR approach via detection of previously identified CNV-associated traits. Brain-related traits, such as AIFM3 at 22q11.2 was associated with renal failure. AIFM3 is a gene in a proposed critical region for 22q11.2-associated kidney defects and led to kidney defects in zebrafish [SNAP29, another gene associated with kidney defects in the same study, had renal failure, NOS in its top 1% phenome associations. LZTR1 was significantly associated with malignant neoplasm, other. This gene is a cause of schwannomatosis, a disease involving neoplasms  [PI4KA, associated with disorders of iris and ciliary body in our study, showed eye-related phenotypes [TBX1 on the circulatory system, of TANGO2 on glucose and epilepsy, and of TBX6 on the musculoskeletal system at this threshold [TBX6 contributing to scoliosis , and one was identified as an eQTL in our approach; perhaps an even stronger signal could have been observed if all three were included [mental disorders category were over-represented in the top 1% of associations among all genes tested, and mental disorders was the only category significantly enriched. Some mental disorders, such as psychosis, were top PheWAS hits for multiple genes, but we were underpowered for rigorous independence testing. Moreover, three novel brain-related gene-trait pairs reached phenome-wide significance: NPIPB11 and SLX1B near the CNV breakpoint at 16p11.2 with psychosis, as well as SCARF2 at 22q11.2 with mood disorders. The expression of SLX1B is modified in 16p11.2 carriers; NPIPB11 expression differences have not been detected in transcriptomic studies of 16p11.2 [SCARF2 has recently been proposed as a driver of schizophrenia within a fine-mapping study within CNV carriers [mood disorders in the clinical data allowed us to find a new candidate, SCARF2, at 22q11.2 that we were unable or underpowered to detect in the ascertained bipolar data alone.Our PheWAS between the predicted expressions of 16p11.2 and 22q11.2 genes and 1500 medical phenotypic codes resulted in 17 phenome-wide significant gene-trait pairs. Some of these genes have been shown to drive similar traits in prior literature. The gene ebrafish . SNAP29, benign) . Model oenotypes , 91. Bechreshold , 92–94. included . Notably 16p11.2 , 45. SCAcarriers . IntegraWe find that our results support the underlying hypothesis in which small changes in CNV gene expression affect risk for CNV-associated traits. In the three best-powered traits we had available—schizophrenia, BMI, and IQ—we were clearly able to prioritize individual gene(s) at 16p11.2. Similarly, we were able to detect PheWAS traits driven by small expression differences in CNV genes that were overlapping with traits in CNV carriers in the same biobank. Strikingly, we found that our gene-based PheWAS overlapped well with the carrier screen PheWAS for 22q11.2 when we found the most “CNV-like” extreme expression non-carriers. This observation validates our underlying model in which non-carriers with genetically predicted expression differences are more likely to show carrier-like traits.cis-heritability of gene expression, they may not capture the entire variability of the expression of a gene . For example, the expression predictions of these genes are calculated solely using cis-eQTLs within 1 MB of the gene [trans-eQTLs to explore the genetic effect of expression variation accurately. Similarly, we have not considered trans-effects due to chromosome contacts, such as those that exist between the 16p11.2 region described here and another smaller CNV region elsewhere at 16p11.2 [The 16p11.2 and 22q11.2 CNVs are significant risk factors for ASD and schizophrenia, respectively, and yet no individual genes in either CNV were associated with case-control status for the associated trait in the best-powered datasets available to us. Assuming the true causal gene(s) for these disorders do exist within the CNV, limitations in our approach may preclude us from discovering them. As our predicted expressions are based on GWAS data, we end up underpowered to detect gene-based association signal where we are underpowered to detect SNP-based association signal. This is particularly true for ASD, in which the sample size is over 4 times less than that of schizophrenia. At the same time, predictive models for gene expression are imperfect; while they capture some of the the gene . It may  16p11.2 , 114. Mo 16p11.2 , 116. IfRather than focusing on any specific tissue(s), we chose to perform a cross-tissue analysis, an approach that improves power to detect gene-trait associations and detected 16p11.2 genes associated with schizophrenia, IQ, and BMI . While wA further limitation is that the variation in expression that can be modeled using eQTLs may be considerably smaller for some genes than the effect of deletions and duplications. For example, there may be a gene at 22q11.2 for which decreases in expression contribute to schizophrenia, but only when expression levels are reduced beyond a threshold, e.g., to nearly 50% of the expression levels of non-carriers. We saw an improvement in the overlap between the gene-by-gene and carrier/non-carrier PheWAS traits when we restricted our analyses to the individuals with the most extreme CNV gene expression across the region, supporting this threshold hypothesis which could be pursued in further study.Drosophila model for 16p11.2 genes has shown evidence of epistasis between genes within a CNV as a modifier of phenotype [Alternatively, the overlap with carrier phenotypes observed when considering predictions across the CNV region could support a multi-gene hypothesis. So far, we have considered the effect of each CNV gene independently, when the genes may not be acting independently. A henotype . If therhenotype , 119.dementia with cerebral degenerations in 22q11.2 deletion carriers, anterior horn cell disease in 16p11.2 deletion carriers, and cerebral degenerations, unspecified in 16p11.2 duplication carriers. These findings show a need for longitudinal studies of carrier cohorts and studies of carriers in older age. Such additional data may point to additional clinical features of 16p11.2 and 22q11.2 CNV carriers.Because the CNV carrier individuals in our biobank are young (median age < 18), we do not yet know what traits might commonly occur once individuals reach older age. There were traits in our analysis that were over-represented in older CNV carriers, but difficult to interpret as they did not meet our frequency threshold, including the following: In developing our approach, we hypothesized that naturally occurring variation in gene expression of CNV genes in non-carriers would convey risk for traits seen in CNV carriers. We found that this was true for at least three 16p11.2-associated traits: BMI, schizophrenia, and IQ. Promisingly, the direction of association was generally consistent with whether the trait was found in duplication or deletion carriers. Our approach is computationally efficient, extendable to other CNV-trait pairs, and overcomes one limitation of animal models by testing the effect of CNV genes specifically in humans.In this study, we synthesized information from both large GWAS studies and EHR-linked biobanks, benefiting from the strengths of both approaches. Psychiatric brain-related disorders such as autism, schizophrenia, and bipolar disorder have a population frequency below 5%, so large datasets specifically ascertained for brain-related disorders are better at providing sufficient statistical power for association analysis, especially when the effect of each gene is small. On the other hand, the presence of many diagnostic codes in a biobank helps identify brain-related traits that may be relevant to CNVs but not the primary reported symptoms, such as speech and language disorder. We were also able to carry out two distinct and complementary analyses using the same dataset. The presence of CNV carrier status in the EHR-linked biobank allowed us to probe the phenotypic consequences of the entire deletion or duplication. Then, we were able to test each CNV gene for association with the same diagnostic descriptions.Our novel approach provided insights into how individual genes in the 16p11.2 and 22q11.2 CNVs may drive health and behavior in a human population. Expression imputation methods allowed us to study the predicted effects of individual CNV genes in large human populations. The incorporation of medical records into biobanks provided a way to determine clinical symptoms and diagnoses to which expression differences in the genes may contribute. We expect our ability to detect genes with this type of approach to increase in the coming years, as more individuals in biobanks are genotyped, the number of individuals contributing to large cohorts grows, and the methods to more finely and accurately predict gene expression improve. Additional experiments on our newly prioritized genes are necessary to determine their specific functional impact on brain-related disorders and to evaluate their value as putative therapeutic targets.Additional file 1: Table S1. List of genotyped discovery and replication cohorts used in the study. List of datasets used for discovery and replication of association results with sample sizes. The specific cohorts from the Psychiatric Genomics Consortium that were used for this analysis are listed. All variables from the UK Biobank that were used for replication are shown.Additional file 2: Table S2. List of genes at or near 16p11.2 and 22q11.2. List of coding and non-coding genes in the CNV region, as well as flanking genes 200 kb on either side. Genes for which PrediXcan models based on GTEx v7 were available and the range of model qualities (R2) are noted, along with the number of tissues in which prediction models were available. Genes are annotated with their type , whether they are in the CNV or flanking, and any other names by which they may be referred in the literature.Additional file 3: Fig. S1-S6. Supplementary figures.Additional file 4: Table S3. Mendelian phenotypes annotated to 16p11.2 and 22q11.2 genes in PheWAS results. We compare Mendelian phenotypes annotated to 16p11.2 and 22q11.2 genes  with our imputed gene expression PheWAS results. For each of the Mendelian traits, we list one or more related traits that were tested in PheWAS along with the p-value, selecting the trait(s) with the best p-value to represent. Traits that are in the top 1% of associations for individual genes are marked. This table is a proof-of-concept that our PheWAS approach can pick up known gene-phenotype associations but has not been quantified for enrichment due to the subjective nature of identifying related traits.Additional file 5: Table S4: Identifying 16p11.2 and 22q11.2 cases from electronic health records (EHR). Keyword searches across all documents within the Vanderbilt EHR were performed to identify individuals carrying 16p11.2 or 22q11.2 CNVs. Individuals with documents containing matching keywords were reviewed manually to confirm the presence of 16p11.2 or 22q11.2 CNV. Individuals were excluded from case groups if their records included a mention of additional CNVs. Individuals within the 16p11.2 case groups were also excluded if the size of the reported CNV was 200-250 kb. Individuals within the 22q11.2 case group were excluded if the size of the CNV was smaller than 500 kb or if there was a mention of “distal” when referring to the deletion or duplication. Confirmed case numbers are listed, with the non-genotyped counts in parentheses. Non-genotyped individuals were used for downstream phenome-wide analyses.Additional file 6: Table S5. Results of MultiXcan and S-MultiXcan associations between CNV genes and autism, schizophrenia, bipolar disorder, BMI, and IQ. For autism, bipolar disorder, and schizophrenia, z-scores and p-values come from a METAL meta-analysis across PGC cohorts. For BMI and IQ, mean z-scores and p-values come directly from S-MultiXcan output. Genes in each CNV are sorted by chromosomal position.Additional file 7: Table S6. Comparison of association results to independent data. For each gene-trait pair, we list the original p-value, the GWAS trait(s) that we classified as most similar to a PheWAS trait, its best p-value in an independent dataset, the number of GWAS datasets that were used for this trait, and the rank of this gene within that dataset. For UK Biobank summary statistics, we have genome-wide data; for datasets with individual-level data, only 16p11.2 and 22q11.2 genes were calculated. See Table Additional file 8: Table S7. Conditional analysis for independence of associations. Conditional analysis was performed on the PGC schizophrenia data, the UK Biobank BMI data, as well as two BioVU clinical trait associations . For each trait, we performed MultiXcan first adjusting for a specific gene, then by leaving a gene in and adjusting all the other genes associated with that trait out. The Pcond reported in the text is the p-value of this gene-trait pair when adjusting for all other genes considered for conditioning for this trait, unless otherwise stated.Additional file 9: Table S8. Traits over-represented in CNV carriers. The four categories of CNV carrier – 16p11.2 duplication, 16p11.2 deletion, 22q11.2 duplication, 22q11.2 deletion – were tested separately. The results for all clinical traits tested are provided. The number of cases and controls for each trait is given, as well as whether the p-value meets either Bonferroni or FDR correction. Traits in bold were represented in over 5% of carriers.Additional file 10: Table S9. Top PheWAS associations of 16p11.2 and 22q11.2 genes. The top 15 associated traits for each gene, regardless of p-value, are shown. These represent the top 1% of associations among all traits tested. Genes are listed in alphabetical order, with each trait’s sample size and phecode noted [de noted .Additional file 11: Table S10. Enrichment of clinical categories among the top PheWAS associations. The top 15 traits (codes) for each gene analyzed (n = 1470 gene-trait pairs) were divided into 17 clinical categories (observed counts column). The values in the expected counts column are calculated as 1470 * {the proportion of traits of that category tested}. For example, 159 out of 1531 codes tested were from the “circulatory system” category, so the expected counts for “circulatory system” are calculated as 1470*159/1531. The last column contains the p-value from a binomial test comparing whether the observed proportion of clinical categories is more extreme than expected.Additional file 12. Supplemental Acknowledgements. Members of the Psychiatric Genomics Consortium who contributed to this work.


Streptococcus thermophilus to obtain two different productions . Plate counts demonstrated the ability of both starter formulations to drive the fermentation process, since S. thermophilus counts reached 109 CFU/g in both productions. Generally, the two starter formulations did not affect the chemical composition of QdO cheeses that contained, on average, 64.08% dry matter of which approximately 54.99% were fats and 36.39% proteins. Among chemical parameters, significant differences were registered for secondary lipid oxidation state (significantly lower for QdO-P2), fatty acids and volatile organic compounds (VOCs). However, the differences registered among cheese VOCs from were not perceived by the panelists who recognized both cheese productions highly similar, although QdO-P2 cheeses were mostly appreciated by the judges. This study allowed to produce a novel fresh ovine cheese with specific chemical and sensorial characteristics well appreciated by consumers.This work was performed to produce a new soft ewe’s milk cheese, namely “Quadrello di ovino” (QdO) cheese, to enlarge ewe’s dairy product portfolio of South Italy, barely limited to Pecorino cheese typology. Cheese making was performed applying the technology for “Crescenza” cheese typology with some modifications. In particular, pasteurized ewes’ milk was inoculated with two commercial starter formulations (SF1 and SF2) of  The great diversity of cheeses registered worldwide depends on several factors, mainly type of milk, technology applied, starter and secondary cultures inoculated and conditions of ripening . StrictlItaly and France are probably the countries with the highest world’s cheese diversity. In particular, Italy is the world’s country with the highest number of food products that enjoy a recognition of quality status. Among these, 56 different cheeses are indicated as protected designation of origin (PDO), protected geographical indication (PGI) or traditional specialty guaranteed (TSG) products . These rBesides traditional cheeses, new types of cheeses are being produced for several reasons such as improving the functional properties, to face allergies, to encounter the request of vegetarians, to respond to new lifestyles, to increase economic revenues and also for religious motivations ,8,9. In Regarding the economic implications of developing new cheeses, in southern Italy there is a strong need to valorize ewe’s milk productions. The two main Italian islands, Sicily and Sardinia, represent the regions where sheep milk is produced at consistent levels . In ordeThe main objective of this work was to produce a new soft ewe’s milk cheese applying the technology of Crescenza cheese typology by means of commercial thermophilic LAB. The final cheeses were characterized for their microbiological and physicochemical traits, volatile organic compounds (VOCs) profile as well as sensory features.Raw ewe’s whole milk was obtained from several farms and transported daily by a temperature-controlled road tanker to the dairy factory “Il Cacio Siciliano” located in Belmonte Mezzagno . Bulk milk (500 L) was kept refrigerated (4–6 °C) under stirring until cheese making. Pasteurization occurred at 71 °C for 10 s in a PS15351 COMAT pasteurizer . After heat treatment, the milk was cooled at 38 °C to perform starter inoculation. The characteristics of pasteurized milk (average data of the bulks used in this study) were: pH 6.60 ± 0.03; somatic cell count 3.12 ± 0.13 Log, fat 5.86 ± 0.25%; protein 4.99 ± 0.15%; casein 3.87± 0.31%; and lactose 3.81 ± 0.35%.S. thermophilus . Freeze-dried cells (5 Units) were re-activated for 10 min in 2 L of pasteurized ewe’s milk, kept under manual agitation, to prepare starter inoculums. Two different freeze-dried starter formulations CR/A (SF1) and LYOBAC-D CRM (SF2) were purchased from microMilk s.r.l.  and Alce International s.r.l. , respectively. Both commercial starter cultures were prepared from defined strains of The novel cheese object of study, “Quadrello di ovino” (QdO) cheese, owes its name to the little square shape given to this sheep milk cheese. QdO production  was perfBulk milk was transferred to a stainless-steel vat and, after starter preparation inoculation, subjected to a gentle agitation (20 rpm) for 5 min, then left to rest for 40 min. The milk was re-heated by direct steam injection to bring the temperature back to 38 °C and added with 60 mL of Naturen Premium 225 liquid rennet  characterized by a strength of 1:22,500. After about 20 min, the coagulum was first subjected to a coarse cut performed by a stainless-steel rod and, after additional 50 min, the cut was concluded with a cheese harp containing vertical and horizontal wires to obtain cubes of about 3 cm × 3 cm × 3 cm. The curd was transferred into cuboid moulds (10 cm × 10 cm × 7 cm) and stewed under steam at 45 °C for 30 min, turned upside down stewed for other 30 min and then left acidifying until pH 5.4. The cheeses were transferred into a cold chamber  for 24 h before being immersed into a saturated brine for 15 min and then stored at 6 °C, RH > 90% covered by plastic sheets for 4 d. Finally, the cheeses were packed into plastic boxes and sealed with a transparent film. The day after, cheese production was performed with the second starter inoculum and both productions were repeated three times in three consecutive months. Samples of pasteurized milk, inoculated milk, curd and cheese, were collected from each cheese making and subjected to several analyses. w/v) sodium citrate solution (135 mL) by means of the stomacher Bag-Mixer 400  for 2 min at the maximum speed (blending power—4). Curd and cheese homogenates were then subjected to the decimal serial dilution as reported above in Ringer’s solution.Milk samples (1 mL) were serially diluted (1:10) in Ringer’s solution . Curd and cheese samples (15 g) were homogenized with 2% , members of Enterobacteriaceae family, Escherichia coli, Listeria monocytogenes and Salmonella spp. TMM were grown on Skim milk agar ISO 6610 incubated aerobically at 30 °C for 72 h. de Man–Rogosa–Sharpe (MRS) agar and Medium 17 (M17) were used for the growth of rod- and coccus-shaped LAB, respectively, after 48 h incubation at 30 °C for mesophilic groups and at 44 °C for thermophilic groups. Pseudomonas species were investigated on Pseudomonas agar base (PAB) supplemented with Cephaloridine–Fucidin–Cetrimide (CFC), incubated aerobically at 25 °C for 48 h. Members of Enterobacteriaceae family were detected on violet red bile glucose agar (VRBGA), after incubation at 37 °C for 24 h. L. monocytogenes were cultivated on Listeria selective agar base (LSAB) added with SR0140E supplement, incubated at 37 °C for 48 h. Hektoen enteric agar (HEA), incubated at 37 °C for 24 h, was specifically used for enteric Gram-negative bacteria , while Baird Parker (BP) agar with rabbit plasma fibrinogen supplement, incubated in the same conditions, for CPS.Cell suspensions of pasteurized milk samples were analysed by plate count for the enumeration of the total mesophilic microorganisms (TMM), LAB, Cell suspensions of inoculated milk, curds and cheeses were analysed for TMM and thermophilic coccus-shaped LAB on M17 agar as reported above. All media and chemicals were purchased from Oxoid . Plates counts were performed in triplicate.Color of internal surfaces of the cheeses was assessed by a Minolta Chroma Meter CR300  using the illuminant C; measurements of lightness , redness  and yellowness  were performed according to the CIE L* a* b* system .2) of samples (2 cm × 2 cm × 2 cm) kept at room temperature (22 °C). This parameter was measured as index of cheese hardness with an Instron 5564 tester . Cheese hardness was evaluated by the maximum resistance to compression  and analysed for content of basic ingredients as reported by Bonanno et al. . Briefly2/kg fat) as an index of primary lipid oxidation [v/v) trichloroacetic acid aqueous solution (2 mL) was then added and the sample was mixed in a vortex mixer for a few seconds, then filtered through Whatman No. 1 filter paper. A 0.02 M aqueous solution (5 mL) of thiobarbituric acid was added to 5 mL filtrate. The solution was placed in a hot water bath (90 °C) for 20 min then refrigerated. The absorbance of the supernatant was read at 530 nm using a Hach DR3900 spectrophotometer (Hach Company), after centrifugation at 4500 rpm for 5 min. TBARS were quantitatively determined, using 1,1,3,3-tetramethoxypropane solutions at concentrations ranging from 0.016 to 0.165 μg/mL for the calibration curve (R2 = 0.99). Physicochemical determinations were performed in triplicate.The oxidation status of fat was assessed in the freeze-dried cheese samples by determining the peroxide value  were directly methylated in screw-cap Pyrex culture tube on lyophilized samples (500 mg) with the addition of 0.7 mL of 10 N KOH in water, 5.3 mL of MeOH and 0.58 mL of 24 N Hin water . Methyl in water : the indin water : . The samples were equilibrated at 40 °C for 30 min. The SPME fiber was exposed to the cheeses for 30 min in the headspace of the sample kept at 40 °C. The flavour compounds were desorbed for 10 min from the fiber to the column through a splitless injector at 250 °C. Before use, fiber was conditioned and cleaned at 270 °C for 30 min, following instructions from Supelco®. All samples were prepared in triplicates in standard 20 mL-volume headspace vials. The method proposed by Tunick and co-workers  was follQuantification of volatile compounds was performed using an Agilent 7000C GC system, fitted with a fused silica DB-5MS capillary column   coupled to an Agilent triple quadrupole Mass Selective Detector MSD 5973; ionization voltage 70 eV; electron multiplier energy 2000 V; transfer line temperature, 270 °C. Solvent delay: 0 min. Helium was the carrier gas (1 mL/min). The oven temperature was initially kept at 40 °C for 5 min. Then gradually increased to 250 °C at 2 °C/min rate. Held for 15 min and finally raised to 270 °C at 10 °C/min. Volatile compounds were injected at 250 °C automatically with the splitless mode. The individual peaks were analysed using the GC-MSolution package, Version 2.72. Identification of compounds was carried out using Adams , NIST 11Cheese samples were also evaluated for their sensory characteristics by a panel of 15 assessor members . All panelists were trained at the Department of Agricultural, Food and Forest Sciences—University of Palermo following the ISO 8589  indicatip values of p < 0.05 and are marked with different letters.Microbiological, physicochemical and sensory evaluation data were subjected to one-way variance analysis (ANOVA) using XLStat software version 7.5.2 for Excel . The Duncan procedure and Tukey’s test was applied for pairwise comparison. Statistical significance was attributed to p > 0.05) were observed among both pasteurized milks for the levels of TMM, mesophilic and thermophilic rod- and coccus-shaped LAB. Thermophilic coccus and mesophilic rod LAB were found at the same level (103 CFU/mL) of TMM, while thermophilic rod and mesophilic coccus LAB were one log cycle lower for both matrices. The results of Pseudomonas, members of Enterobacteriaceae family, L. monocytogenes, Salmonella spp., E. coli and CPS are not reported in The levels of the different microbial groups investigated in pasteurized milk used for QdO-P1 and QdO-P2 are reported in S. thermophilus starter cultures and the levels of fermenting microorganisms in inoculated milks, curds and cheeses are reported in The bulk milks were then inoculated with the commercial p > 0.05) were found for the levels of TMM and S. thermophilus in all samples analysed. In particular, the level of these microorganisms for the P1 and P2 productions were almost perfectly superimposable. After inoculation with each commercial starter culture, P1 and P2 milks showed about 107 CFU/mL of TMM and S. thermophilus. After coagulation, these microorganisms were counted at about 108 CFU/g in curds for both productions showing an increase of about 1 Log cycle. Both QdO-P1and QdO-P2 cheeses reached LAB values of about 9 Log CFU/g.According to Duncan test, no statistically significant differences (p < 0.05) by the two starter formulations. Physical measurements (colorimetric parameters and hardness) of the final cheeses are reported in p > 0.05) were observed between QdO-P1 and QdO-P2 cheeses regarding the basic compositional analysis, TBARS values registered for QdO-P1 cheese were higher than those recorded for QdO-P2 cheese. The final cheeses were characterized  by an avFAs composition of the experimental cheeses is shown in c9) was the most representative as fatty acid . All cheeses were characterized by a high content of trans-vaccenic acid  while the cis-vaccenic acid (C18:1Δc11) showed a much lower concentration, barely 0.64 and 0.59 for QdO-P1 and QdO-P2, respectively. Regarding PUFA, linoleic acid  was the most representative FAs . The use of different S. thermophilus strains determined significant differences in FAs composition of QdO cheeses. Considering the short-chain  and medium-chain  FAs, QdO-P1 cheeses showed significantly higher concentrations than QdO-P2 cheeses, except for myristoleic acid (C14:1Δc9) which showed no significant differences. Significant differences were also found between long-chain FAs . In particular, palmitic, stearic, oleic, trans-vaccenic, γ-linolenic, eicosenoic acid and the other C18:1 isomers, were significantly higher in QdO-P2 cheeses than in QdO-P1 cheeses. An opposite trend was registered for heptadecenoic (C17:1), cis-vaccenic (C18:1Δc11), linoleic, α-linolenic , eicosadienoic acid , and the other C18:2 isomers, that were significantly higher in P1. The other identified LCFAs did not show significant differences among cheese productions.Among MUFA, oleic acid , PUFA (p = 0.005), n6 PUFA (p = 0.013) and n3 PUFA (p = 0.002) which were higher in QdO-P1 rather than in QdO-P2 cheeses, while MUFA (p < 0.001) were at the highest concentrations in QdO-P2 cheeses. The health indices investigated were also influenced by the type of inoculum used: both n6/n3 ratio and HPI were higher in QdO-P2 cheeses . Individual fatty acid determined differences in SFA  were identified among cheeses. Ketones represented the major VOCs class of QdO-P1 cheeses (41.21 ± 0.96 ppm), followed by esters (19.32 ± 0.56 ppm) and ethereal compounds (9.03 ± 0.21 ppm). Among ketones, acetoin was the most abundant compound (31.33 ± 0.73 ppm). In descending order of quantity, the second is phenethyl hexanoate (17.25 ± 0.40 ppm). QdO-P2 cheeses showed a higher presence of short-chain carboxylic acids (acetic acid and butanoic acid) (21.25 ± 0.49 ppm), straight-chain esters  (25.52 ± 0.60 ppm), and a lower concentration of ketones (1.49 ± 0.04 ppm) than QdO-P1 cheeses.Aldehydes were negligible in QdO-P2 cheeses, but were detected at very low concentrations  in QdO-P1 cheeses. Significant differences were found among QdO-P1 and QdO-P2 cheeses for the production of ethyl ether  and ethyl benzene, 1,3-dimethyl benzene and 1-butenyl benzene. Among terpenes sylvestrene was found in QdO-P2 cheeses. Among amines only dimethylamine (4.94 ± 0.39 ppm) was detected in QdO-P1 cheeses.p > 0.05) were found for uniformity, odour of milk, odour of butter, salty, acid, bitter and unpleased odour and aroma, while different levels of significance emerged for all other attributes evaluated.The sensory characteristics of QdO cheeses were evaluated by judges of different age and gender . In partQdO-P1 and QdO-P2 cheeses were scored at similar level by the panelists for colour, intensity of odour, sweet, spicy and taste persistency. All these attributes reached the highest in QdO. Solubility of CCCh cheese was comparable to that of QdO-P2 cheese. CCCh and QdO-P2 cheeses were not statistically different also for overall assessment, intended as the degree of overall satisfaction.Sicily is a southern Italian region where cheese making is a very ancient daily activity . Up to dStreptococcus thermophilus, typical thermophilic dairy LAB [The main hypothesis of this work was that a new typology of cheese could be produced from ewe’s milk and that this fresh cheese could be well appreciated by consumers. With this in mind, QdO cheese was processed with commercial culture preparations of airy LAB , used asairy LAB ,35,36. Aairy LAB . It is aairy LAB . Streptococcus strains are particularly flexible and diverse [Regarding the inoculums for curd acidification, two different commercial preparations were used in order to evaluate the influence of different strains on the final characteristics of QdO cheese. The metabolic activities of  diverse  and meta diverse . Thus, tPseudomonas, members of Enterobacteriaceae family, L. monocytogenes, Salmonella spp., E. coli and CPS) were undetectable. Barbaccia et al. [8 CFU/g in curds as a consequence of whey draining [The pasteurised bulk milks were microbiologically investigated, and the densities of LAB were almost at the same level of TMM. These results might be ascribable to the ability of the indigenous milk bacteria to survive during the pasteurization process ,42, but a et al.  reporteddraining . Both Qddraining .2 on average. This value is extremely lower than those estimated for “Primosale” cheese for which hardness was in the range 0.98–1.10 N/mm2 [The cheeses were analyzed for several quality parameters, first of all for their physical characteristics (color and hardness). Color represents an important quality index of soft cheeses, including Crescenza type, that strongly affects the acceptability by consumers . The thr10 N/mm2 . S, than QdO-P2 cheese. The lower values registered for the latter cheese are presumably due to the higher content in polyphenols of the bulk milk [The basic chemical composition of QdO cheese showed an average content of fats and proteins of 35.24 and 23.32%, respectively, which are much higher than the levels reported for Crescenza cheese generally characterized by 24% of fats and 14% of proteins ,36. Thesulk milk  or to thulk milk ,57.Regarding the composition of FAs, both experimental cheeses showed profiles comparable to those detected for other Sicilian ewe’s milk cheeses ,58. The VOCs emitted from food products are decisive for the perception of aroma and taste . RecentlQdO cheese was characterized by alcohols, ethers, aldehydes, ketones, carboxylic acids, esters, amines, and monoterpenes. The class of carboxylic acids is mostly present in cheeses . In partBefore commercialization of novel food products, the evaluation of sensory traits plays an important role in the assessment of overall acceptability . For thiS. thermophilus starters. The microbiological analysis demonstrated the suitability of S. thermophilus to be used as fermenting agent in QdO cheese production. Lowest secondary lipid oxidation was observed in QdO-P2 cheese and, although the differences of VOCs emitted from the two QdO cheeses were not perceived by the panelists, they showed a higher appreciation for QdO-P2 cheese that, in terms of judges’ preference was comparable to commercial Crescenza cheese. These results clearly highlighted the huge marketability perspectives of QdO cheese and provided evidence on the possibility to enlarge the dairy product portfolio of fresh ewe’s cheeses produced in Sicily.This study provided an extended analysis on the microbiological, physicochemical, VOCs profile and sensory characteristics of a novel soft ewe’s milk cheese produced at industrial scale level with two defined commercial


Guhong injection (GHI) is a drug for ischemic stroke created by combining safflower, a traditional Chinese medicine, and aceglutamide, a Western medicine. In this study, we investigated the curative effect of GHI on cerebral ischemia–reperfusion (I/R) injury via the PKC/HIF-1α pathway in rats. Adult male Sprague Dawley rats were randomly divided into seven groups: sham-operated, middle cerebral artery occlusion (MCAO), GHI, nimodipine injection (NMDP), MCAO + LY317615 (PKC inhibitor), GHI + LY317615, and NMDP + LY317615. After establishing an MCAO rat model, we performed neurological deficit testing, 2,3,5-triphenyltetrazolium chloride staining, hematoxylin and eosin (HE) staining, enzyme-linked immunosorbent assay, Western blotting, and q-PCR to detect the brain damage in rats. Compared with the MCAO group, the GHI and GHI + LY317615 group showed neurological damage amelioration as well as decreases in serum hypoxia-inducible factor-1α (HIF-1α), protein kinase C (PKC), and erythropoietin levels; brain HIF-1α and inducible nitric oxide synthase protein expression; and brain HIF-1α and NOX-4 mRNA expression. These effects were similar to those in the positive control groups NMDP and NMDP + LY317615. Thus, our results confirmed GHI can ameliorate cerebral I/R injury in MCAO rats possibly via the PKC/HIF-1α pathway. Ischemic stroke, a serious cerebrovascular disorder, is a leading cause of death and disability globally . Its patCarthamus tinctorius Linné  extract  and aceglutamide (a Western medicine) (Carthamus tinctorius Linné is commonly known as Safflower (honghua in Chinese), which is used in a dried form for treating various cardiovascular and cerebrovascular diseases clinically because its effects include blood stasis removal and meridian dredging (Guhong injection (GHI) is a typical sterile solution created by combining aqueous edicine) . In otheedicine) . GHI hasedicine) . Acetylgedicine)  is an acedicine) . Aceglutedicine) . Carthamdredging . Hydroxydredging , a main dredging . HoweverNicotinamide adenine dinucleotide phosphate oxidase (NOX) 4 is a major reactive oxygen species source, and it plays a key role in cerebral I/R injury treatment . It is iIn this study, we investigated the pharmacological effects of GHI on cerebral I/R injury in rats and elucidated the underlying mechanism. Our results may lay an experimental foundation for clinical research on GHI in cerebrovascular disease treatment.GHI was procured from Tonghua Guhong Pharmaceutical , nimodipine injection (NMDP) from Bayer Schering AG , and 2,3,5-triphenyltetrazolium chloride (TTC) from Hangzhou Dacheng Biotechnology . Moreover, enzyme-linked immunosorbent assay (ELISA) kits for HIF-1α, EPO, and PKC were purchased from Nanjing Jiancheng Bioengineering Institute . iNOS, PHD2, and HIF-1α antibodies were purchased from abcam Biotechnology Co., Ltd. .According to the Chinese national standard [No. WS-10001-(HD-1506)-2004] promulgated by Chinese Pharmacopoeia Commission and the process of GHI: Each 1,000 ml of GHI contains safflower equivalent to 500 g and aceglutamide equivalent to 30 g . AccordiAdult male Sprague Dawley rats  were purchased from the Animal Center of Zhejiang Chinese Medicine University . The rats were housed at room temperature with humidity 60 ± 5% under a consistent 12-h light–dark cycle with ad libitum access to food and water. All the experiment procedures were approved by the Animal Subjects Review Board of Zhejiang Chinese Medical University.Middle cerebral artery occlusion (MCAO) method was performed according to a previously described method . In brien = 18 per group): sham-operated (Sham), MCAO, GHI, NMDP, MCAO + LY317615, GHI + LY317615, and NMDP + LY317615. The rats were injected with 10 ml/kg twice per day of GHI or NMDP intraperitoneally , symmetry of movements (0–3 points), symmetry of forelimbs (0–3 points), climbing wall of wire cage (1–3 points), reaction to touch on either side of trunk (1–3 points), and response to vibrissal touch (1–3 points) were evaluated. The maximum and minimum scores were 18 and 3, respectively.In the Longa scoring system, the scores were as follows: 0 for no deficits, 1 for contralateral forepaw inflection when tail is lifted, 2 for spontaneous circling to the contralateral side, 3 for hemiplegia to the contralateral side, and 4 for inability to move spontaneously without consciousness.The infarct volume was determined using 2,3,5-triphenyltetrazolium chloride (TTC) staining. Rat brain samples were collected 7 days after treatment administration and immediately frozen at −20°C for 15 min. The frozen samples were then cut into 2-mm-thick coronal slices and then stained with 2% TTC solution at 37°C for 20 min in the dark. These slices were photographed, and the infarct volume was calculated using ImageJ.The hippocampus samples of brain tissues were collected in a cryotube with 4% paraformaldehyde after reperfusion. The samples were dehydrated and embedded in paraffin, after which coronal sections were prepared. These coronal slices were stained with hematoxylin and eosin (HE) and then used for studying histopathology. Finally, the hippocampal tissues were analyzed by acquiring images on a microscope .Rat blood samples were collected from the heart under deep anesthesia, and the serum samples were prepared by centrifugation at 4,500 rpm for 12 min at 4°C. The supernatant (serum) was than extracted and stored at −20°C before ELISA. Next, by using specific ELISA kits according to the manufacturer's instruction, we determined serum HIF-1α, PKC, and EPO concentrations.The protein samples of rat brain were lysed using a lysis buffer, followed by centrifugation at 12,000 rpm for 5 min at 4°C. Then, the samples were electrophoresed via the sodium dodecyl sulfate polyacrylamide gel electrophoresis and transferred onto a polyvinylidene difluoride membrane. After we added a blocking solution containing 5% skimmed milk powder with shaking for 2 h, the membrane was incubated in a primary antibody dilution buffer with shaking at 4°C overnight. This was followed by the addition of secondary antibodies and shaking for 1 h. Finally, electrochemiluminescence was observed using a chemi-capture software program.Total RNA of brain samples were extracted using TRIzol solution, followed by centrifugation at 12,000 rpm and 4°C for 10 min after chloroform addition. Next, we aspirated the supernatant. To this, we added isopropyl alcohol and centrifuged the solution at 12,000 rpm for 15 min at 4°C. The precipitate was washed with absolute ethanol to obtain RNA.This total RNA was reverse transcribed using the HiFiScript cDNA Synthesis Kit (Kangwei Century Biotechnology). This was followed by the real-time quantitative polymerase chain reaction (q-PCR) with the following cycling parameters: 40 cycles of 95°C for 15 s and 60°C for 60 s. The relative mRNA expression levels were standardized using β-actin (the housekeeping gene). The primer sequences are listed in p < 0.05 considered to denote statistically significant.All statistical analyses were performed on GraphPad Prism . Here, one-way analysis of variance was used to compare the differences between multiple groups. All data are presented as means ± standard deviations, with p < 0.05 or p < 0.01).The neurological scores were evaluated 1, 3, and 7 days after MCAO. As shown in P < 0.01), indicating that GHI could alleviate neurological deficits.Based on the Longa scores (which were consistent with the Garcia scores), the neurologic deficits were the most serious in the MCAO group. Compared with the MCAO group, the GHI, NMDP, MCAO + LY317615, GHI + LY317615, and NMDP + LY317615 groups showed significant decreased in the Longa scores on days 1, 3, and 7 . In addition, compared with those in the MCAO group, EPO, HIF-1α, and PKC levels in the GHI, GHI + LY317615, MCAO + LY317615, NMDP, and NMDP + LY317615 groups were significant lower. These levels were similar to those in the Sham group.Rat serum EPO, HIF-1α, and PKC expression was measured using specific ELISA kits. As shown in p < 0.01). Moreover, compared with the MCAO group, the GHI, NMDP, MCAO + LY317615, GHI + LY317615, and NMDP + LY317615 groups had significantly lower HIF-1 and iNOS expression and significantly higher PHD2 expression (p < 0.05 or p < 0.01). Thus, GHI alleviated cerebral I/R injury possibly via the PKC/HIF-1α pathway.Representative Western blots are shown in Nox4 and Hif1α mRNA expression (p < 0.01). By contrast, compared with the MCAO group, the GHI, NMDP, MCAO + LY317615, GHI + LY317615, and NMDP + LY317615 groups had lower Nox4 and Hif1α mRNA expression (p < 0.05 or p < 0.01).As shown in Nowadays, ethnomedicine or ethnopharmacy is playing a more and more important role in the treatment of tumor, immunity, and inflammation , especiaIn the recent years, ischemic stroke has caused high mortality and morbidity worldwide . CerebraGHI, a formulation made by combining traditional Chinese and Western medicine components, is used for treating cerebral ischemia and coronary microvascular disease . We haveIn the current study, the rat model of MCAO was established to explore the effects of GHI on cerebral I/R injury and understand their underlying mechanisms. First, the Garcia and Longa scoring systems were used to detected neurological deficit, with the former affording more specific results than the latter. Moreover, the Garcia score included the tests for wire cage wall climbing and tactile reflexes. Based on the results of the neurological deficit scores and TTC staining, the rats in the GHI and GHI + LY317615 groups were found to have less neurological damage and lower infarct volume—indicating that GHI had protective effects on cerebral I/R injury. HE staining showed similar results in that the GHI and GHI + LY317615 groups demonstrated significant improvements in the neuron number and morphology compared with the MCAO group. In general, GHI’s effects were similar to those of the positive control drug, NMDP.Hif1α mRNA and HIF-1α expression. These results indicated that GHI could protect against cerebral I/R injury by inhibiting the PKC/HIF-1α signaling pathway. NOX4 is a major prooxidant enzyme in cerebral ischemia pathogenesis. PKC is a protein family with containing >10 isozymes, and it plays a main role in the NADPH oxidase signaling pathway, MAPK signaling pathway, and NF-κB signaling pathway (Moreover, hypoxia is a key factor determining cell death or survival in ischemic injury. The mechanism underlying the effects of GHI on cerebral I/R injury was investigated using ELISA, Western blotting, and qPCR. The results revealed that compared with the MCAO group, the GHI and GHI + LY317615 groups had significantly lower serum HIF-1α, PKC, and EPO concentrations and brain  pathway . During  pathway . Moreovein vitro experiments, GHI needs further follow-up verification.Taken together, the current results demonstrated that GHI has protective effects against cerebral ischemia via the PKC/HIF-1α signal pathway. As a formulation combining traditional Chinese medicine with Western medicine, GHI can have more multitarget pharmacological effects. Therefore, other mechanisms underlying GHI’s protective effects against cerebral ischemia warrant further investigation. Especially The PKC/HIF-1α pathway in the condition of hypoxia was shown in


The levels of glucocorticoid, adrenocorticotropic hormone (p < 0.01); lactic acid, IL-4, IL-10 (p < 0.05) significantly increase after HLJDD intervention. In addition, we adopt multi-omics analysis approach to investigate the potential biomarkers. Nontargeted metabolomics demonstrate that the levels of 7 differential metabolites approach that in the healthy control group after HLJDD intervention, which are correlated with histidine metabolism, beta-alanine metabolism and sphingolipid metabolism through metabolic pathway analysis. Targeted lipidomics results and receiver operating characteristic curve analysis show that 13 differential lipids are identified in the three groups mainly focuse on lysophosphatidylcholines, lysophosphatidylethanolamines. Finally, the network associations of those differential biomarkers reveal the regulation of adenosine triphosphate and tricarboxylic acid cycle play essential role in the therapeutic effect mechanism of HLJDD in “Shanghuo”. The study has laid the foundation for further revealing the mechanism and finding clinical biomarkers related to “Shanghuo”.“Shanghuo”  is caused by exuberant endogenous fire, which does not have a comprehensive and systematic traditional Chinese medicine theory. In previous study, we had evaluated the therapeutic effect of Huanglian Jiedu Decoction (HLJDD) (granule) on patients with “Shanghuo”, however, the specific mechanism was not clear, which need further exploration. To explain its intervention mechanism, we select 57 patients with oral diseases caused by “Shanghuo” and 20 health volunteers to divide into oral disease group, HLJDD intervention group and healthy control group. Firstly, biochemical indicators before and after HLJDD intervention are detected, such as inflammatory factors, oxidative stress factors and energy metabolism factors. The results exhibit that HLJDD significantly decreases indicators succinic acid ( The terRhizoma Coptidis, Radix Scutellariae, Cortex Phellodendri, and Fructus Gardeniae at a ratio of 3:2:2:3. It is a classic Chinese herbal prescription that has attracted substantial attention from researchers. HLJDD is a typical syndrome drug widely used in heat clearing and detoxifying. The main therapeutic effects are purging fire for removing toxins and it is used for treating all kinds of heat toxins and San Jiao heat syndromes. Modern research studies have confirmed that the potential applications of HLJDD have increased, including anti-inflammatory, antioxidant, antithrombotic, hypoglycemic, hypolipidemic, hypotensive, and antitumoral effects.  clearance . Huanglieffects. . The “Foeffects. . Pharmaceffects. . Howeverin vivo effects exerted by HLJDD and provided basic chemical information for our study. Recently, scholars evaluated the effects of HLJDD by simulating rat “Shanghuo” gingivitis, and the results showed that HLJDD inhibited energy metabolism and oxidative stress through tuberous sclerosis complex signaling pathway and glycolysis-related molecules  analytical techniques were applied for qualitative and quantitative analysis of the chemical profiles of HLJDD. Sixty-nine compounds were identified, including iridoids, alkaloids, flavonoids, triterpenoids, monoterpenes and phenolic acids, and 17 major characteristic constituents were selected as quality control markers in HLJDD . Combineolecules . The actolecules . Througholecules . Combineolecules . As a pholecules . The intolecules . Some scolecules . The AMPolecules . In summIn this study, we investigated the intervention mechanism and potential biomarkers of HLJDD (granule) in the treatment of “Shanghuo”. First, we decided to detect the changes in related biochemical indicators before and after HLJDD intervention, and multiplex cytokine assay technology was used to detect inflammatory factors interleukin-4 (IL-4), IL-1β, IL-8, IL-10, IL-6, IL-12p70, IL-13, monocyte chemotactic protein-1 (MCP-1), macrophage inflammatory protein-1alpha (MIP-1α), and tumor necrosis factor-alpha (TNF-α). Then we analyzed oxidative stress factors extracellular superoxide dismutase (SOD3), 4-hydroxynonenal (4-HNE) and adrenocorticotropic hormone (ACTH) and energy metabolism factors citric acid (CA), succinic acid (SA), glucocorticoid (GC), adenosine triphosphate (ATP), lactate dehydrogenase (LDH), lactic acid (LA), pyruvic acid (PA), thyroid-stimulating hormone (TSH) using ELISA technology. Additionally, metabolomics helped clarify the course of the disease and provided useful information for preventing and diagnosing disease, discussing pathogenesis, and researching drug action mechanisms . In receCases were collected from January 2018 to August 2018 in Beijing University of Chinese Medicine Third Affiliated Hospital, Dongfang Hospital (Beijing University of Chinese Medicine), and the Department of Stomatology of Affiliated Hospital of Chengdu University of TCM. According to the inclusion and exclusion criteria of TCM syndromes and clinical manifestations in combination with western medicine diagnosis of “Shanghuo”, a total of 57 “Shanghuo” cases were included as the oral disease group. The detailed information of the diagnostic criteria is listed in the supplement materials. The cases we selected were diagnosed with recurrent oral ulcers, pericyxitis of wisdom teeth, and recurrent herpes stomatitis caused by exuberance of stomach fire. The same 57 patients were also treated with HLJDD (granule) as the intervention group. The average age of the patients was 36.22 ± 13.26 years, and the group included 21 males and 36 females aged 21–60 years. At the same time, we recruited 20 healthy volunteers as the healthy control group, which consisted of 6 males and 14 females. Their ages ranged from 22 to 58 years, with an average age of 33.30 ± 13.33 years.Rhizoma Coptidis 900 g, Radix Scutellariae 600 g, Cortex Phellodendri 600 g, and Fructus Gardeniae 900 g. These four drugs were identified by taxonomy, namely Coptis chinensis Franch., Scutellaria baicalensis Georgi, Phellodendron chinense Schneid. and Gardenia jasminoides Ellis. The above four Chinese medicines were soaked in water for 1 h and decocted twice, then filtered, and the filtrate was combined and concentrated into a thick paste, dried under reduced pressure, and pulverized into powder. Mixed evenly according to the ratio of 60 g of microcrystalline cellulose per 100 g of powder, and made into HLJDD formula granule (3.2 g/bag). They were provided by Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences. The qualitative analysis of the granules and the quantitative analysis of its 9 main active ingredients  are shown in the supplementary materials.This formula granule was composed of Patients with oral diseases were given HLJDD formula granules within 0.5–1 h after breakfast and dinner, mixed with hot water above 80 °C and taken 1 bag at a time, twice daily. The course of treatment was 3–5 days until the main symptoms disappeared.HLJDD granules (3.2 g/bag) were provided by Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences (Batch number: 1801001Y). Methanol, formic acid, water and acetonitrile were purchased from Thermo Fisher Scientific (China) Co., Ltd . Ammonium acetate was obtained from Sigma-Aldrich. All other chemicals and solvents were of analytical grade.To explore the changes in biochemical indicators of inflammation before and after HLJDD intervention, the levels of 10 inflammatory factors in plasma were measured. The factors included interleukin-4 (IL-4), IL-1β, IL-8, IL-10, IL-6,IL-12p70, IL-13, monocyte chemotactic protein-1 (MCP-1), macrophage inflammatory protein-1 alpha (MIP-1α) and tumor necrosis factor-α (TNF-α). The concentrations (pg/ml) of cytokines in plasma were analyzed by Luminex Multiplex Cytokine Kits  according to the manufacturer’s recommendation.To analyze the effects of HLJDD on energy metabolism and oxidative stress in the “Shanghuo” group, we measured citric acid (CA), succinic acid (SA), glucocorticoid (GC), adenosine triphosphate (ATP), lactate dehydrogenase (LDH), lactic acid (LA), pyruvic acid (PA), thyroid-stimulating hormone (TSH), extracellular superoxide dismutase (SOD3), 4-hydroxynonenal (4-HNE) and adrenocorticotropic hormone (ACTH) in plasma by ELISA method. All operations were performed strictly in accordance with the corresponding manufacturer’s instructions for ELISA .Plasma samples were thawed on ice at 4°C for 30–60 min. One hundred microliters of each plasma sample was transferred to a 1.5 ml centrifuge tube containing 300 µL acetonitrile. Then the sample mixture was vortexed for 15 s and sonicated for 3 min. After centrifugation , 100 µL of the supernatant was transferred into standard autosampler vials with 250 µL microinlets.Analysis was performed on a Thermo Scientific™ Dionex™ UltiMate™ 3,000 Rapid Separation LC system coupled to a Q Exactive™ hybrid quadrupole Orbitrap mass spectrometer equipped with a HESI-II probe. The column was a BEH amide column  operated at 40°C. For HILIC separation, mobile phase A was acetonitrile, mobile phase B was water, and both A and B contained 0.1% formic acid and 10 mmol/L ammonium acetate. The sample manager temperature was set at 4°C, the flow rate was 300 μL/min and the injection volume was 1 µL. Then the samples were eluted using the following gradient conditions: 0–1 min, 5% B; 1–7 min, 5–50% B; 7–9 min, 50% B; 9–9.1 min, 95% B, and the equilibration time was 3.9 min with 95% B.6 ions. The LC-MS system was controlled using Xcalibur 2.2 SP1.48 software (Thermo Fisher Scientific).The positive HESI-II spray voltage was 3.7 kV, the heated capillary temperature was 320°C, the sheath gas pressure was 30 psi, the auxiliary gas setting was 10 psi, and the heated vaporizer temperature was 300°C. A full scan range from 50 to 1,500 (m/z) was used. The resolution was set at 70,000. The maximum isolation time was 50 ms. Automated gain control was targeted at 1 × 10Plasma samples were thawed on ice at 4°C for 30–60 min. Then, 600 µL of methanol/chloroform (1:3) organic solvent was added to a labeled 1.5 ml centrifuge tube containing 100 µL of plasma sample with 10 µL of lipid internal standard solution. The samples were then sonicated for 1 h and mixed well. A volume of 100 µL of water was added to each sample, followed by centrifugation at 12,000 rpm at 4 °C for 10 min. A volume of 300 µL of the chloroform layer was collected, concentrated and dried, after which 200 µL of isopropanol/acetonitrile (1/1) was added for reconstitution. The samples were then sonicated and centrifuged at 12,000 rpm at 4°C for 10 min.The supernatant was then analyzed by LC-20AXR Ultra-Fast Liquid Chromatography coupled to a Qtrap 5,500 tandem quadrupole mass spectrometer. A Waters UPLC BEH C8  was used for the separation of lipids; acetonitrile/water (60/40) was used for mobile phase A and isopropanol/acetonitrile (90/10) was used for mobile phase B. Both the A and B phases contained 5 mmol/L ammonium acetate. The samples were eluted using the following gradient conditions: 0–2 min, 0–30% B; 2–12 min, 30–70% B; 12–12.5 min, 70–95% B; 12.5–13 min, 95–100% B; 13–13.1 min, 100–0% B, and the equilibration time was 1.9 min with 0% B. The column was operated at 40°C, the flow rate was 260 μl/min and the sample manager temperature was set at 4°C.The electrospray ionization source was operated in positive ionization mode with the following settings: spray voltage, 5.5 kV; heated capillary temperature, 500°C; sheath gas pressure, 30 psi; auxiliary gas, 10 psi; and heated vaporizer temperature, 300°C. The analytes were determined by monitoring the precursor–product transition in MRM mode.To monitor the system’s stability and the reproducibility of the samples, quality control (QC) samples were prepared by pooling equal volumes of each plasma sample. The pretreatment of the QC samples was in accordance with that of the plasma samples. The QC samples had the same composition and should have clustered together. Relatively clustered QC samples demonstrate that the system has good repeatability and the collected data are worthy of further study. In our study, three QC samples were continuously injected at the beginning of the run and then injected at regular intervals of six or eight samples throughout the analytical run to provide data from which repeatability could be assessed.R) and m/z data pairs. Targeted lipidomics data were processed using Skyline software for the relative quantification of the lipid species according to the retention time (tR) and accurate mass in the constructed lipid database. Then, the adduct ions of each “feature”  were deconvoluted, and these features were identified in the human metabolome database and lipidmaps. Preprocessed data were imported to SIMCA 14.1 software (Umetrics) for multivariate analysis. Unsupervized separation was assessed by principal component analysis (PCA) using Parteo standardization to distinguish the differences in samples among all variables. The data were further processed by orthogonal partial least squares discrimination analysis (OPLS-DA) to identify the potential biomarkers based on the variable importance in the projection (VIP) value  and t-test (p < 0.05).For nontargeted metabolomics, all data obtained were processed using Progenesis QI software for imputing raw data, aligning and picking peaks and normalization to produce peak intensities for retention time  and IL-8 (p < 0.05). The levels of IL-4 and IL-10 were significantly increased after HLJDD intervention (p < 0.05). In addition, the levels of IL-6, IL-1β, IL-12p70, and MCP-1 showed downward trends, and the levels of IL-13 and MIP-1 showed upward trends, but these differences were not significant , ATP, CA (p < 0.01), LDH and PA (p < 0.05) after HLJDD intervention. The levels of GC (p < 0.01) and LA (p < 0.05) significantly increased and the level of TSH showed a downward trend after HLJDD intervention . The level of ACTH was significantly increased (p < 0.01) and the level of SOD3 showed a nonsignificant upward trend after HLJDD intervention , LPC 15:0–1, LPC 16:0, LPC 16:1, LPC 17:0, LPC 17:1, LPC 18:0, LPC 18:1, LPC 18:3, LPC 19:0, LPC 20:1, LPC 20:2, LPC 20:4, lysophosphatidylethanolamine 16:0 (LPE 16:0), LPE 18:0, LPE 18:1, LPE 18:2, phosphatidylcholine 26:0 (PC 26:0), PC 28:0, PC 32:2, PC 32:3, PC 34:2, PC 34:4, PC 36:2-2, PC 36:4-1, PC 36:6, PC 37:2-1, PC 37:3-1, PC 38:2-1, PC 39:4-1, PC 42:4, PC 42:5-2, phosphatidylethanolamine 34:1 (PE 34:1), PE 34:2, PE 34:3-2, PE 35:2, PE 36:1-2, PE 36:2-1, PE 36:4, PE 38:3-1, sphingomyelin 39:1 (SM 39:1), SM 40:3, SM 41:1, SM 42:4, SM 43:1, and SM 43:2. Furthermore, 26 differential lipids were discovered in the oral disease group and HLJDD intervention group. Moreover, 22 differential lipids were common among the three groups and had a certain improvement trend after HLJDD intervention. Among them, the concentrations of the following 19 lipids increased after HLJDD intervention: LPC 14:0, LPC 15:0–1, LPC 16:0, LPC 16:1, LPC 17:0, LPC 17:1, LPC 18:0, LPC 18:1, LPC 19:0, LPC 20:1, LPC 20:2, LPC 20:4, LPE 16:0, LPE 18:0, LPE 18:1, LPE 18:2, PC 39:4-1, PE 36:4 and SM 42:4. The concentrations of the following three lipids decreased after HLJDD intervention: PC 34:2, PC 36:2-2 and PC 42:5–2. The results indicated that the differential lipids were mainly concentrated in lysophosphatidylcholines (LPCs), lysophosphatidylethanolamines (LPEs), phosphatidylcholines (PCs). The concentrations (μmol/L) of the differential lipids in each group under positive ion mode are shown in p-values (p < 0.01)  and multi-omics analysis  were explored through MetaboAnalyst and Cytoscape v3.4.0 software from three perspectives, including the KEGG global metabolic network, metabolite-metabolite interaction network and protein-enzyme/transporter-metabolite network. The potential pathways of differentially expressed biomarkers based on KEGG global metabolic network analysis are listed according to their  < 0.01) . The res < 0.01) . Further < 0.01) . The difRhizoma Coptidis, Radix Scutellariae, Cortex Phellodendri and Fructus Gardeniae at a ratio of 3:2:2:3. The components in TCM prescription are complex, but not all of them has pharmacological activity. Therefore, the separation and identification of these pharmacodynamic components are of great significance. So we conduct the qualitative analysis of the HLJDD granules and the quantitative analysis of its 9 main active ingredients. A total of 87 major compounds are tentatively screened and characterized. From the results, we can see that it mainly contains three types of compounds, namely alkaloids , flavonoids  and iridoids . Many studies have manifested that alkaloids from Rhizoma Coptidis and Cortex Phellodendri, favonoids from Radix Scutellariae and iridoids from Fructus Gardeniae are three major active components in HLJDD, therefore they are regarded as markers for quality control of HLJDD  is a classical TCM formula with heat-dissipating and detoxifying effects, which is comprised of of HLJDD . Althoug effects . HLJDD h effects . Our resIn TCM, it is believed that endogenous and exogenous heat and toxins are the pathogenic mechanism of infammation. To some extent, infammatory and allergic mediators, as well as infammatory factors generated by infammations and allergies are considered as toxins leading to the heat syndromes to appear in the context of infammatory and allergic responses. HLJDD inhibits the inflammatory process by reducing the accumulation of reactive oxygen free radicals, interfering with lipid metabolism pathways, and regulating mitochondrial membrane potential. Analysis of potential molecular mechanisms and core targets showed that the NF-κB, STAT and PPAR pathways reduced the expression of inflammatory factors to achieve inflammatory immunity . In partClinical and experimental studies have confirmed that when oral ulcers occur, abnormally activated macrophages in the oral mucosa secrete a large number of proinflammatory factors, and the activated NF-κB inhibitory protein (IκB) kinase complex promotes NF-κB translocation to the nucleus, indirectly initiating the expression of genes such as the proinflammatory factors TNF-α and IL-1β, and inhibiting the expression of anti-inflammatory factors. The balance between anti-inflammatory factors and proinflammatory factors is disrupted, thereby exacerbating oral mucosal damage . TNF-α hMonocyte chemotactic protein-1  is a monocyte chemokine with strong specificity that can promote the chemotaxis and adhesion of monocytes to damaged endothelial cells. Recent studies have shown that MCP-1 plays an important role in the healing process by quickly binding to CC chemokine receptor 2 and activating the inflammatory signaling pathway, resulting in irreversible damage and necrosis of mucosal cells and tissue . Other lIn summary, by exploring changes in inflammation indicators, we found that HLJDD had a good therapeutic effect on “Shanghuo”. The results showed that HLJDD mainly regulated the synthesis and secretion of TNF-α, IL-10, IL-8 and IL-4.TCM corrects the rise and fall of Yin and Yang, and the change in cold and heat in the human body by regulating the metabolism of the body. “Shanghuo” belongs to the category of “heat syndrome” in TCM. Modern research has shown that heat syndromes are closely related to energy metabolism, especially thyroid function and sodium pump activity .Radix Scutellariae inhibit substance and energy metabolism. The effects of aglycones and glycosides are similar to those of the whole components of the herb and are more obvious in the heat-syndrome rats (p < 0.001 and p < 0.01). Combined with the above literature research, we inferred that HLJDD might regulate energy metabolism, thereby mediating inflammatory factors to treat “Shanghuo”.In the “Shanghuo” state, the accumulation of ATP in the human body cause the AMP/ATP ratio to decrease; then, the function of AMPK is inhibited, and the catabolic pathway is suppressed. On the other hand, the anabolic pathway that consumes ATP is activated to promote energy conversion and utilization . In ratsome rats . The rolome rats . In the ome rats . In our + or NADH). Under hypoxic conditions, PA is converted to LA by the action of LDH and NADH  is a glycoprotein hormone synthesized and secreted by the pituitary gland that directly regulates thyroid function and controls the process leading to increased thyroid hormone production and secretion. It is a sensitive indicator of hypothalamic-pituitary-thyroid (HPT) axis function. Currently, research on TSH promoting the secretion of proinflammatory factors by adipocytes has mainly focused on subclinical hypothyroidism and cardiovascular diseases. Studies have found that the risk of hypercholesterolemia and cardiovascular disease increased among subclinical hypothyroidism patients, whose TSH levels were increased . The expIn conclusion, the effects of HLJDD intervention on energy metabolism in “Shanghuo” might mainly be related to the TCA cycle and glycolytic pathway.Under normal circumstances, the production and elimination of ROS in the body maintain an oxidation-antioxidation balance. Once the balance is disrupted, the body develops oxidative stress, which causes damage to the mitochondrial respiratory chain. When “Shanghuo” occurs, the body is in a state of conflict between peroxidation and antioxidants. The increase in lipid peroxides indicates that a peroxidation reaction has occurred, and some symptoms of “Shanghuo”  follow. Simultaneously, the antioxidant defense system is activated, and the total antioxidant level of T-AOC is increased. A large amount of glutathione and superoxide dismutases (SODs) are generated; therefore, the adverse stimulus is eliminated in time, which is one of the reasons for the low level of ROS in the body . The antSODs are enzymes that catalyze the removal of superoxide free radicals. In mammals, SOD1 (Cu/ZnSOD), SOD2 (MnSOD) and SOD3 (ecSOD) are three distinct members of this metalloenzyme family. SODs have been increasingly recognized for their regulatory functions in growth, metabolism and oxidative stress responses . As an eα (PPARα) specifically binds to the peroxisome proliferator response element (PPRE) on the promoter of the gene encoding the key 4-HNE metabolic enzyme fatty aldehyde dehydrogenase (FALDH) gene, thereby upregulating FALDH expression, which accelerates the clearance of 4-HNE . Under normal circumstances, 4-HNE is maintained at a very low physiological concentration in cells or tissues. When the body is stimulated and oxidative stress occurs, 4-HNE significantly increases, which has been confirmed in tissues such as hepatocytes, renal tubular cells, heart and small intestine . In addiof 4-HNE . Tan IIAof 4-HNE . When 4-of 4-HNE . In our l-lysine increased and l-carnitine decreased in the HLJDD intervention group compared with the oral disease group. l-lysine regulates cell proliferation, differentiation and excitability as well as intercellular interactions through the neurotransmitter system , oxidative stress factors (4-HNE and ACTH), energy metabolism factors , and differential lipids and metabolites related to energy metabolism. Additionally, the network associations of those differentially expressed biomarkers were explored. All these differential biomarkers indicated that the mechanism of the therapeutic effect of HLJDD on “Shanghuo” mainly involved in the regulation of ATP and the TCA cycle. These findings are important for providing an investigational basis for understanding the pathophysiology of “Shanghuo” onset and the treatment mechanism of HLJDD. However, this study still has some limitations, multi-factor detection and multi-omics analysis only provide us with a direction, and in-depth research needs to continue. In the following research, we will focus on energy metabolism and the TCA cycle, study the changes in the content of key intermediates of the cycle in body fluids, and establish the cell model to detect this change process to further identify biomarkers and verify the intervention of HLJDD on the syndrome of “Shanghuo”.The Trial Registration Number: NCT03469232. Written informed consent was obtained from all patients. The study was conducted in accordance with the ethical principles of the Declaration of Helsinki, Good Clinical Practice implemented by the China Food and Drug Administration of the People's Republic of China, and International Ethical Guidelines Biomedical Research Involving Human Subjects promulgated by the Council for International Organizations of Medical Sciences. The clinical study protocol and informed consent forms were reviewed and approved by the Ethics Committee of Institute of Basic Research in Clinical Medicine . All subjects were informed in oral and written forms of the objectives, procedures, and risks of participating in the study.