Antiphospholipid syndrome (APS) is a chronic autoimmune disease with a high prevalence in females. Published data have identified pregnant women with APS may suffer from recurrent miscarriage, fetal death. However, the association between antiphospholipid antibody (aPL) and fetal growth restriction (FGR) remains controversial. This study aims to systematically review the literature on population-based studies investigating an association between aPL and FGR.  The literature was searched on 1 November, 2021, using Ovid MEDLINE, Embase, and Cochrane Central Register of Controlled Trials (CENTRAL), following the MOOSE checklist. Study inclusion criteria focused on peer-reviewed published articles that reported an association between aPL and FGR. Quality assessment was performed based on the Newcastle-Ottawa scale. The between-study heterogeneity was assessed by the Q test. Publication bias was assessed by funnel plots. β2GP1), and FGR was 1.26 , 2.25 , and 1.31 , respectively. Lupus anticoagulant (LA) did not increase the chance of FGR .  Twenty-two studies (with 11745 pregnant women) were included in the final analysis. Pooled odds ratio for association of aPL, anticardiolipin antibodies (ACA), anti-beta2 glycoprotein 1 antibodies (β2GP1 are strongly associated with FGR. There are currently insufficient data to support a significant relationship between LA and FGR. Our meta-analysis showed that aPL increased the risk of FGR. The risk of FGR varies with the aPL types. ACA and  The current diagnosis of APS requires at least one laboratory and clinical criterion each . Laboratβ2GP1) . Furtherβ2GP1) . APS is β2GP1) –8. Childβ2GP1) . HoweverWe followed the methods of Xu et al. . A systeTwo reviewers (JFX and YT) independently evaluated the titles and abstracts. Duplications were removed using EndNote online software. Disagreements were resolved by the third reviewer (BP). Only case-control study, cohort study, and cross-sectional study were evaluated. We excluded the studies with the following criteria: (1) presence of other autoimmune diseases; (2) patients undergoing serious internal and surgical diseases; and (3) multiple pregnancies.We used a standard form to extract data, including the journal, first author's name, publication year, country, study design, time of study conduct, the age of patients, ascertainment method of diseases and control or comparison group selection, types of aPL, the definition of FGR, risk factors for FGR, number of population analyzed, odd ratios (ORs) or relative risks (RRs) and 95% confidence intervals (95%CIs), and statistical methods. When effect estimates for association between aPL and FGR were not listed in the original article but enough information was available, their effect estimates were calculated by STATA 12.0 statistical software. Authors were contacted if important information was lost. Data extraction was conducted independently by two of the reviewers (JFX and YT).The outcome indicator was the occurrence of FGR. The FGR positivity is defined as ultrasound estimated fetal weight less than 10th percentile  or birthTwo researchers (JFX and DJC) independently assessed the methodological quality of studies, using the Newcastle-Ottawa quality assessment scale (NOS) for cohort and case-control study . A scoreI2 statistic. A random effects model was used because we assumed the presence of heterogeneity in these clinical studies with different clinical settings [P values <0.05 were considered statistically significant. We conducted a sensitivity analysis to exclude these studies, which varied dramatically from all other included studies in methodology or findings. Subgroup analysis by study type was also conducted to assess the potential sources of heterogeneity. Publication bias was assessed by funnel plots and Begg's test.We used the MOOSE checklist and PRISMA guidelines for this systematic review , 15. We settings . P valuen = 358), Embase (n = 265), and Cochrane Central Register of Controlled Trials (CENTRAL) database (n = 104). After removing 173 duplicates using EndNote software (EndNote X7) and 12 duplicates manually, the titles and abstracts of 542 papers were scrutinized. The reference lists of relevant reviews were hand searched. A total of 119 articles were assessed for eligibility. Of them, 97 were excluded for the reasons, such as presence of other autoimmune diseases (n = 56), patients undergoing serious internal and surgical diseases (n = 39), and multiple pregnancies (n = 2). Of the 119 citations identified, 22 articles with 11745 cases  was observed in our analysis  –29, 31. analysis .I2 = 0%, P = 0.540) was observed in analysis of ACA.Results derived from ten included studies , 32–34 wβ2GP1 and FGR  among four cohort studies [I2 = 0%, P = 0.780) was observed in analysis of β2GP1. studies , 18, 22 I2 = 50.3%, P = 0.05).P = 1.000, P = 0.721, P = 0.734, and P = 1.000, respectively.Funnel plots were used to explore the publication bias of the included studies Figures –S1C. FunA sensitivity analysis for each meta-analysis with at least three studies was conducted. Within each meta-analysis, each study was taken out, respectively, to assess for its influence on the overall risk estimates.The association of FGR with APS has been reported by some studies , 37–39. β2GP1. In the present study, we demonstrated that women with aPL positivity have a higher risk of FGR. According to our analysis, the presence of aPL increases the odds of FGR 1.26 times . This is concordant with recently published data by Högdén et al. showing that the persistent presence of aPL is associated with FGR [In this systematic review and meta-analysis, we aimed to provide a comprehensive overview of available evidence on the association between aPL and FGR. The aPL refers to three tests including ACA, LA, and with FGR , 37. Apawith FGR . Some rewith FGR , while mwith FGR , emphasiwith FGR .Screening for ACA is most commonly used in clinical practice as one of the criteria for classification of APS. Some studies reported that the incidence of FGR was higher in positive ACA than in negative ACA cases , 48. Theβ2GP1 are rarely the sole antibodies detected in patients with clinical features of APS. However, it is a main target of antiphospholipid antibodies, playing a role in pathogenesis of adverse obstetric outcomes [β2GP1 are the ones associated with the lowest live birth rate and highest incidences of FGR, very preterm FGR, and stillbirth compared with ACA or with LA alone [β2GP1 and FGR  among four cohort studies [β2GP1 may be beneficial to APS patients who have a history of FGR.LA alone . We foun studies , 18, 22.β2GP1 positivity [Data from a case-control study of 2257 pregnancies reported that significantly more women positive for LA had pregnancies complicated by FGR compared with women who were LA negative . Furthersitivity . TherefoCurrently, the treatment strategies to prevent APS-related obstetric complications include antiplatelet (low-dose aspirin) , anticoaOur study is the most comprehensive review evaluating the association between aPL and FGR by the type of aPL. Also, we conducted separate meta-analyses by the types of aPL. Furthermore, we evaluated risk factors for FGR of studies included. Finally, half of the included studies were published after 2010, suggesting a minor effect of publication years. Furthermore, we evaluated risk factors for FGR of studies included. However, our study also has some limitations. Firstly, some included studies had limited number of FGR cases, which might generate spurious associations. Additionally, most patients with FGR were treated with drugs like aspirin and heparin, which might lead to the underestimation of risk. Finally, different definitions of FGR can cause more variations. Therefore, caution is needed when interpreting the results.Recently, a systematic review and meta-analysis evaluated pregnancy outcomes in patients with APS . HoweverOur meta-analysis showed that aPL positivity increased the risk of FGR. Furthermore, the risk of FGR varies with the aPL antibody type. Therefore, it is necessary to measure aPL when FGR occurs. Women who have experienced adverse pregnancy outcomes, particularly severe FGR, should be screened for the presence of aPL. In order to generate the best evidence, we recommend future population-based studies to further investigate the association between aPL and FGR by adopting a prospective cohort study approach with large study populations, carefully selected disease-free controls, good adjustment for potential confounding factors, and longer duration of follow-up through multiple pregnancies.


Three methylation loci  were consistently hypomethylated in the minor allele carriers, being cross-dependent on several SNPs. We further investigated whether DNA methylation at these CpGs is associated with depressive psychiatric phenotypes in independent cohorts. The first site (cg02825527) was hypomethylated in blood (exp(β) = 84.521, p value ~ 0.003) in participants with severe suicide attempts (n = 88). The same locus showed increased methylation in glial cells (exp(β) = 0.041, p value ~ 0.004) in the validation cohort, involving 29 depressed patients and 29 controls, and showed a trend for association with suicide  and trend for association with depression treatment . The second CpG (cg18302629) was significantly hypomethylated in depressed participants (exp(β) = 56.374, p value ~ 0.023) in glial cells, but did not show associations in the discovery cohorts. The last methylation site (cg19624444) was hypomethylated in the whole blood of severe suicide attempters; however, this association was at the borderline for statistical significance . This locus, however, showed a strong association with depression treatment in the validation cohort (exp(β) = 2.237, p value ~ 0.003) with 377 participants. The direction of associations between psychiatric phenotypes appeared to be different in the whole blood in comparison with brain samples for cg02825527 and cg19624444. The association analysis between methylation at cg18302629 and cg19624444 and MAD1L1 transcript levels in CD14+ cells shows a potential link between methylation at these CpGs and MAD1L1 expression. This study suggests evidence that methylation at MAD1L1 is important for psychiatric health as supported by several independent cohorts.Depression is a multifactorial disorder representing a significant public health burden. Previous studies have linked multiple single nucleotide polymorphisms with depressive phenotypes and suicidal behavior. MAD1L1 is a mitosis metaphase checkpoint protein that has been linked to depression in GWAS. Using a longitudinal EWAS approach in an adolescent cohort at two time points (The online version contains supplementary material available at 10.1186/s13148-022-01394-5. Major depressive disorder (MDD) is a multifactorial disease affecting about 6% of the adult population worldwide . The undFKBP5, also known as FKBP51), a gene encoding enzyme related to the functioning of steroid hormone receptors [Suicide, in turn, is another considerable public health problem. World Mental Health (WMH) Survey Initiative reported that a global lifetime prevalence of suicide attempts could reach up to 2.7% based on the data from 17 countries . Suicidaeceptors , has beeeceptors .In addition to genetic factors, DNA methylation is one of the most intensively studied areas of epigenetic alterations in depression or increased suicide risk . DNA metSKA2), a gene involved in the inhibition of the mitotic spindle assembly checkpoint complex [Mitotic arrest deficient 1 like 1 (MAD1L1) is a component of the mitotic spindle assembly checkpoint localized on kinetochores. This protein functions to prevent the transition to anaphase of mitosis by inhibiting the anaphase-promoting complex until chromosomes are properly aligned and all kinetochores are attached to microtubules during the metaphase , 16. Sev complex , is asso complex –21. Ther complex , MAD1L1  complex –29, bipo complex , and dep complex –38. Seve complex , 40. TheMAD1L1 could mediate their effect through local epigenome-wide significant alterations in DNA methylation. In our analysis using two cohorts of adolescents, we applied a longitudinal epigenome-wide approach to identify blood methylation changes that are consistently associated with depressive SNPs. Furthermore, we investigated how these dependent methylation changes might be associated with the psychiatric health of individuals, including depression and suicidal behavior in several independent cohorts. Here, we report a novel methylation locus (cg02825527) that showed an association with severe suicide attempt in the blood samples. We validated this site in the analysis of postmortem brain samples of MDD individuals compared with controls. Furthermore, in two other open-access cohorts, this locus demonstrated a clear statistical tendency toward an association with similar phenotypes. We also confirm the association between cg19624444 and depression that has been identified previously [In the context of this work, we investigated how previously discovered depression-related single nucleotide polymorphisms (SNPs) at eviously . Additiocases and controls, respectively (see below). Eventually, part of the recall cohort (78 adolescents) was analyzed at both screening and recall, while the other 91 individuals had DNA methylation measurement at recall only. Methylation analysis at the recall was also performed in two batches, and the batch covariable was included in the analysis.To discover SNP–CpG associations, we used two adolescent cohorts: “screening” and “recall.” The study initially consisted of 786 adolescents (more than 900 as of 2022) aged 14–16 years that were randomly selected from public schools in Uppsala County, Sweden, starting from 2012. The aim of the study was to identify genetic factors and epigenetic shifts in the whole blood related to risk for psychiatric disorders among otherwise healthy adolescents. The DNA methylation analysis at the screening was conducted in two time batches: batch one—129 and batch two—92 . The recall cohort is composed of 169 individuals recruited approximately 1 year after participating in screening. Participants subject to methylation analysis in the recall were analyzed based on the depression-related scores in the computer-administered Development and Well-Being Assessment (DAWBA) questionnaire, where we selected participants with the highest and lowest scores as The phenotypical assessment of individuals was undertaken during screening and recall. Height, age, and gender were self-reported, and the body weight was measured to calculate the body mass index (BMI). Participants answered a series of psychiatric health questionnaires. The computer-administered Development and Well-Being Assessment (DAWBA) questionnaire was used for two adolescent cohorts . The risParticipants included in this study were invited to Suicide Prevention Clinic at the Karolinska University Hospital for the assessment of psychiatric health related to suicidal behavior and clinical follow-up. Individuals with mental retardation, schizophrenia, intravenous drug abuse, and dementia were excluded. This cohort is composed of 88 patients who at least once tried to commit suicide prior to visiting the clinic (between the years 2000–2005). A more detailed description of the cohort creation has been published previously . Particiwww.genotyping.se). The facility is a part of the National Genomics Infrastructure supported by the Swedish Research Council for Infrastructures and Science for Life Laboratory, Sweden. The Illumina Infinium array includes 700,078 genetic variants. The data for other remaining SNPs were imputed using IMPUTE2 software, and prior to imputation, several quality control (QC) steps had been performed as described in our previous publication [Blood samples have been collected in K2EDTA blood tubes . Genomic DNA has been extracted using E.Z.N.A. Blood DNA Kit . Then, extracted DNA was used for genotyping or methylation profiling. The genotyping procedure for the initial 786 participants in the adolescent cohorts was performed using the Illumina Infinium array at the SNP&SEQ Technology Platform in Uppsala . All procedures have been performed according to manufacturer protocol. DNA methylation profiling was performed for the adolescent cohort at screening, using Illumina Infinium HumanMethylation450 Array. This bead chip is designed to capture ~ 450,000 methylation sites. Methylation profiling for the adolescent cohort at recall and SKI was undertaken, utilizing Illumina Infinium MethylationEPIC Array, which is designed to target ~ 850,000 CpG sites. All procedures related to sample preparations, array processing, and scanning were performed at the SNP&SEQ Technology Platform in Uppsala. For each sample, 250 ng of bisulfite-converted DNA was used.n = 40 and BA25, n = 35) were obtained from the Douglas Bell Canada Brain Bank. BA11 samples were available from all individuals, whereas three MDD and two controls for BA25 were missing. Further information could be found in the corresponding article [The first open-access cohort that was used for analysis is deposited on ArrayExpress (E-GEOD-41826) and Gene Expression Omnibus (GEO)—GSE41826. The cohort includes 29 postmortem MDD samples and 29 matched controls. The prefrontal cortical samples (BA was not specified) were obtained from the NICHD Brain Bank of Developmental Disorders. Then, nuclei were extracted from cells and separated into neuronal and non-neuronal pools, using fluorescence-activated sorting, based on the expression of NeuN. A detailed description of the process is provided in the original publication . The sec article . The thi article , Childho article , and sev article . Methyla article .Based on the information regarding treatment for depression, we stratified the cohort into depressed and non-depressed individuals. The initial cohort includes 422 individuals, but some of them had missing information regarding analysis-related variables: gender, age, depression treatment, BMI, and ethnicity. These individuals  were removed from the analysis, and the resulting cohort included 377 participants.wateRmelon package [sva” package to adjust for the possible bias [minfi-based implementation of the Houseman algorithm to adjust the methylation data for white peripheral blood cell heterogeneity  [M values were applied for further analyses. Methylation data for E-GEOD-41826 were obtained with Illumina Infinium HumanMethylation450 array from DNA isolated from neuronal and non-neuronal nuclei. Quantile normalized beta values were obtained from ArrayExpress without additional normalization and adjustment procedures. Further information on the data processing is available in the initial publication [minfi-based Houseman algorithm [DNA methylation data for screening, recall, and SKI cohorts were processed starting from raw IDAT files. We used a minfi-based framework  for data package . Since dble bias , 60. Addlocytes) . Blood clocytes) , and thelication . Data folication . Brain clication . This palication . We perflgorithm . Cell prp value less than 0.00005 and probes where more than 75% of samples have a detection p value less than 0.01. Probes from sex chromosomes, non-CpGs, and probes with missing beta values were removed. We removed all probes that have an SNP with a minor allele frequency (MAF) higher than 5% within the probe sequence. All probes that have an SNP at the CpG site and a single-base extension were excluded. For the HumanMethylation450 arrays, we did additional filtering, removing cross-reactive probes identified by Chen et al. [Prior to the analysis, we performed filtering and removal of the non-reliable probes from the methylation data. First, we kept the samples where more than 75% of samples have a detection n et al.  and Bentn et al. , leadingn et al. , resultiMAD1L1 gene were identified. Two of the SNPs were excluded: rs12668848 since it demonstrated no confidence in association with depression [p = 9*10−7). SNP information, such as genomic coordinates and MAF, was collected from the National Center for Biotechnology Information (NCBI) dbSNP portal (https://www.ncbi.nlm.nih.gov/snp/) [https://genome.ucsc.edu/). Linkage disequilibrium for SNPs was calculated using an online tool SNIPA with default settings [Depression-related SNPs were identified using the last updated version of the databases GWAS Catalog  and DisGpression  and rs11pression . We keptov/snp/)  and fromsettings .gviz. Data for gene sequences, CpG islands, histone modifications, and transcription factor binding were obtained from the UCSC genome browser. Only depression-related transcription factors that were identified by GWAS Catalog were selected. Identification of depression-related proteins was based on the key search term “depress” applied to disease/trait description for GWAS Catalog SNPs dataset. Histone modification data deposited in UCSC were initially derived from ENCODE project [http://www.roadmapepigenomics.org/) [Genomic context exploration was performed using the R package “gviz” . Ideogra project  for normcs.org/) . We obtacs.org/) . For claSpearman correlations were used to assess the correspondence between DNA methylation in blood and DNA methylation in three brain regions: BA10 , BA20 , and BA7 . We used an online BECon portal to obtain the correlation data . To idenlimma package in R) applying an empirical Bayes method [limma-based linear models:To investigate SNP–CpG associations, statistical analyses using R (version 4.1.1) were applied. The analysis was conducted both for screening and recall cohorts. A linear-model-based approach  method. Since we tested several SNP sites for every CpG, the resulting sets of FDR-adjusted p values were further corrected by the number of SNP sites tested. An adjusted p value of < 0.05 was considered significant. It has been concluded that EWAS analyses could be prone to genomic inflation and biases [t-statistics from limma for potential bias and inflation [t-statistics and adjusted p values were calculated along with standard estimations from limma.d biases . We usednflation . Bias- aMAD1L1 gene to investigate SNP–CpG interaction architecture using cohorts of adolescents. First, we extracted all variants and CpGs from the MAD1L1 gene coordinates ± 10 000 bp (Chr7:1845430–2282580) that passed QC steps before imputation [MatrixEQTL package. The analyses were adjusted for covariates that were the same as for the limma models above for adolescent screening and recall cohorts. Models were adjusted with the false discovery rate. To investigate linkage disequilibrium for the Top 10 impacting variants, SNIPA with default setting was applied. The R package dplyr was used to calculate the relative placement of CpGs and SNPs, and the ggplot2 to plot the mQTL heatmap.We additionally performed local methylation quantitative trait loci (mQTL) analysis around the putation . Second,p values for coefficients were calculated with the Wald test (implemented in R in the “summary” function). All models were adjusted for confounders depending on the particular cohort (see below). The statistical models used in the discovery cohorts were adjusted for sex, BMI, age, and batch:To investigate associations between methylation and psychiatric phenotype, we used a binary logistic regression model, the psychiatric phenotype was treated as a dependent binary outcome variable, i.e., individuals at low-risk vs. high-risk or individuals with severe suicidal attempt vs. non-severe suicidal attempt, and methylation M value was an independent variable. Native R-based binary logistic regression models were applied for analysis. Two-tailed The ethnical background was not reported in the study.In the SKI cohort, covariates were selected based on the biological relevance, considering the following: sex, age, batch, the presence of other personality disorders (yes/no), and the status of alcohol addiction (yes/no). These models were not adjusted for BMI since these data were missing for 10 participants. The ethnical background was not included since this factor contained eight different levels, and the presence of certain groups only in cases or controls would create issues fitting a model. The final binary logistic model included:In the three open-access cohorts, we used similar methods and covariables were based on biological relevance and availability of the data. We specified the model formulas for the cohorts E-GEOD-41826, GSE88890, and GSE72680, respectively:In the equations above, diagnosis stands for depression or control, group—suicide or control, and Depr.Treatment—treatment or no treatment, respectively. The “Cell.prop.” coefficient depicts blood cell proportion estimations for GSE72680 that were included as covariables. Given that substance misuse data were missing for many participants in GSE72680, the authors decided not to include these covariables in the analysis.p value) were considered. We included only those CpG sites that passed QC steps for methylation data preprocessing . The first cohort contained transcriptome and methylation data obtained from cultured peripheral blood mononuclear cells from 10 healthy male donors. The initial study investigated associations between DNA methylation and aging in the context of exposure to peroxisome proliferator WY14,643 . In the 14+ samples, collected from 1202 individuals [The second cohort (GSE56047) contained a large transcriptome and methylome data from CDividuals . The metn = 34) and low risk (n = 135). High-risk groups had a relatively higher proportion of women, other characteristics appeared to be similar in both screening and recall. The characteristics of the adolescent cohorts are given in Table The adolescent cohort was stratified into two depression risk groups: high depression risk and low depression risk. In screening, the cohort included 24 individuals in the high-risk group and 197 individuals in the low-risk group. In the recall, these numbers were as follows: high risk  are shown in Additional file MAD1L1 gene based on the previous GWAS. For this purpose, we used the GWAS Catalog and DisGeNet databases, and eight unique SNPs  were identified as candidates for analysis. We inspected whether our adolescent cohorts at screening and recall have sufficient representation of genotypes at the specified SNPs and also whether these SNPs are indeed associated with depression or its manifestation. Two SNPs (rs12668848 and rs1107592) did not pass the control as described in the methods, and thus were excluded from the subsequent analysis. Information regarding the six remaining SNPs is available in Table limma models included two steps, i.e., the discovery of SNP–CpGs pairs using the adolescent cohort at screening and the separate discovery of SNP–CpGs pairs at recall. Only CpGs with DNA methylation levels associated with more than one SNP were considered since the associations may provide more biological relevance. We added this restriction since we assume that the depression contribution of SNPs and their effect on methylation is likely to be shared given their close positions and association with the same gene.The flowchart of the analysis is presented in Fig. MAD1L1 gene and were associated with either of the four depression-related MAD1L1 SNPs  showed associations with more than one SNP ; however, the overall results were not affected, yielding only cg02825527, cg18302629, and cg19624444 as both screening and recall cross-dependent CpGs. Identified CpGs demonstrated weak correlations at both screening and recall, with Spearman's correlation coefficients ranging from 0.21 to 0.38 . Only cg02825527 and cg18302629 were not correlated at screening.The epigenome-wide analysis at screening identified 17 unique CpG sites that reached epigenome-wide significance, 10 of which were located within or nearby the Visualizations for SNP–CpG pairs for identified lead CpGs are available in Additional file At screening, genotypes at all six investigated SNPs were associated with DNA methylation levels at cg19624444, while genotypes at four SNPs were associated with DNA methylation at cg02825527, and two SNPs were associated with DNA methylation at cg18302629. Using SNIPA, linkage disequilibrium was observed between several investigated SNPs  = 84.521, p value ~ 0.003) in the SKI cohort  = 109.18, p value ~ 0.004) and cg19624444 . DNA methylation levels at none of the three CpGs were associated with the DAWBA depression band in the adolescent cohorts.We investigated if the candidate CpG sites demonstrate associations with psychiatric phenotypes and severity of the suicidal attempts in the discovery and SKI cohorts. Lower methylation profiles at cg02825527 were associated with the severity of the suicide attempt (exp(β) = 0.041, p value ~ 0.004, Additional file β) = 56.374, p value ~ 0.023, Additional file T test , which confirmed the results from binary logistic regression models for cg02825527 and cg18302629 . Methylation of cg19624444 did not demonstrate an association with depressive phenotypes in any cell type.We sought to replicate the results in open-access cohorts. In the cohort E-GEOD-41826, we investigated our target CpGs, cg02825527, cg18302629, and cg19624444, separately in neural and glial cells. Methylation levels at cg02825527 were significantly associated with depression diagnosis (yes/no) in glial cells (exp(β) = 0.197, p value = 0.089, Additional file β) = 29.755, p value = 0.098; exp(β) = 0.009, p value = 0.06; Additional file We performed an additional investigation of identified CpG sites in the second open-access cohort—GSE88890. The methylation data were available for two brain cortical regions (BA11 and BA25), and we investigated them separately. The methylation of cg02825527 in the BA11 region tended to have an association with a suicide death; however, the association failed to become statistically significant (exp(β) = 1.119, p value ~ 0.075) and the association failed to become statistically significant. However, both groups appear to have many outliers based on box plot visualization  = 2.237, p value ~ 0.003, Additional file β) = 2.517, p value ~ 0.01); however, there was no association for two other CpGs.Lastly, we studied cg02825527, cg18302629, and cg19624444 in the Grady Trauma Project cohort (E-GEOD-72680). A binary logistic regression identified a statistical tendency toward an association between lower blood methylation at cg02825527 and treatment for depression. It should be noted that the direction of the association matches the findings from blood in the SKI cohort, although the effect size is relatively small  = 0.01), and a trend for association between methylation at cg19624444 and intensity of the same probe  = 0.058). The models assume the opposite direction for both CpG sites . In the first cohort, we regressed the M values of cg02825527, cg18302629, and cg19624444 against one available probe of MAD1L1  and found no association for any of the CpGs . In total, we identified eight nominally significant CpG pairs such as cg18302629–cg10782349 and others that had similar directions for associations  [We did not find any published articles on cg02825527 and cg18302629 loci in relation to depression nor for other diseases or biological processes. However, a recent study by Shen et al. identified cg19624444 as a depression-related methylation site that was associated with a depression polygenic risk score. This CpG has also shown casual associations based on Mendelian randomization analysis conducted in the same study . Our SNP2 pairs) . This pr2 pairs) .MAD1L1. There is a general assumption that methylation within a promoter region of a gene tends to have a negative impact on a gene expression, leading to its silencing [MAD1L1 or other genes. Also, we do not observe any overlap with depression-related transcription factor binding sites. However, the expression of MAD1L1 may be regulated by other transcription factors. We were able to observe a potential overlap of cg19624444 with acetylation marks; however, they are relatively distant. Interestingly, the transcriptome-methylation association analysis, in turn, indicates a potential relationship between methylation at cg18302629 and cg19624444 with expression levels of MAD1L1. However, we are not able to investigate if these associations hold in the brain tissue, and in glial cells in particular. Additionally, the opposite directions for both CpGs and the absence of associations for cg02825527 provide fairly little clarity in the interpretation given that both MAD1L1 Illumina probes in the analysis correspond to the same transcript. Additionally, MAD1L1 could be expressed in form of alternative variants, which could be affected by methylation at investigated CpGs and left undetected by the transcriptome array. However, the three candidate CpGs predominantly do not overlap with transcription start sites/promoter regions of alternative MAD1L1 variants  to promote cell cycle progression [FKBP5 gene that was previously reported to be associated with suicide and depression [ZNF701) and was reported to be associated with stress-related suicidal ideation [The metaphase checkpoint protein MAD1L1 is primarily responsible for the cell cycle regulation, and thus it might be important for neural function only in the early stage of the lifespan since neurons are not capable of cell division, and adult neurogenesis occurs only in a few areas of the brain , 94. Thiideation –21. Thisgression . SKA2 wagression . Additiogression . We werepression . Also, fideation . Additioideation . Taken t14+ cells may not be reflected in the brain. We could not draw conclusions regarding the causality of identified methylation changes given the overall cross-sectional design of the study. Additionally, participants in the adolescent cohorts in the present study were not evaluated by a psychiatrist, and thus it limits the clinical validity of associations (even though we did not observe them in these cohorts). Also, the analyses may be affected by hidden confounding factors, such as antidepressant intake. This is particularly important for the cohorts investigating suicide, given that these individuals are likely to have some kind of antidepressant treatment. The strength of our study, however, is a longitudinal approach to identify consistently differentially methylated loci coupled with the use of several independent cohorts for phenotypical associations and transcriptome analysis. Such an approach should increase the likelihood to identify biologically relevant methylation changes and could avoid potential hidden biases in comparison with a single cohort.It should be noted that our study has limitations. First, individual cohorts have a relatively small number of participants, and thus results should be interpreted with caution. Additionally, the cohorts at screening and recall do not fully overlap, so conclusions regarding longitudinal changes in methylation need to be studied separately in a larger cohort. Even though the SNP–CpG interactions appear similar at screening and recall based on mQTL analysis, the differences in genetic architectures may affect SNP–CpG pairs. Also, there are potential genetic differences in other cohorts used in the study. Given that investigated CpGs show either no or inverse correlation with methylation in the brain and phenotypical associations were also inverse, the biological impact of differential methylation is not clear. The relatively weak association between methylation at investigated CpGs and MAD1L1 transcriptome in CDMAD1L1 gene in relation to suicide severity and depression. There is weak evidence of an association between methylation at cg18302629 and cg19624444 with an expression of MAD1L1 in CD14+ cells. However, there are no available data if the expression levels of MAD1L1 in glia are related to depressive phenotypes or whether the investigated CpGs affect MAD1L1 expression in the brain. The logical step forward would be an investigation of genetic, epigenetic, and proteomic profiles simultaneously. Ideally, this should be studied both in the blood and in the brain samples, so a potential explanation of brain changes would also offer a path for a clinical diagnostic/screening application.Our results suggest an association between methylation changes in the Additional file 1: Fig S1. This Manhattan plot shows identified SNP-related CpG sites in the recall adolescent cohort. This figure comprises results for all investigated SNP sites. All four studied SNPs were tested independently. Analyses were conducted with the R package “limma,” the dominant model was used. The smallest p value for every SNP–CpG association is included in the image. Raw p values from limma are visualized. The dashed red line shows a threshold for significance after correction for multiple testing  and the number of SNP sites. Only probes that passed the initial QC were included in the analysis. CpG sites located at the MAD1L1 gene are highlighted in yellow. All statistically significant results are annotated by name. Abbreviations: SNP, single nucleotide polymorphism; QC, quality control.Additional file 2: Fig S2. This figure shows the DNA methylation  of identified lead CpGs in relation to alleles of all related investigated SNPs based on data at screening. All depicted associations were found to be statistically significant after correction for multiple testing and the number of SNP sites using limma-based models. For rs2056477, there were 2 alternative alleles available, and all discovered different genotypes are depicted.Additional file 3: Fig S3. This figure shows the DNA methylation  of identified lead CpGs in relation to alleles of all related investigated SNPs based on data at recall. All depicted associations were found to be statistically significant after correction for multiple testing and the number of SNP sites. For rs2056477, there were 2 alternative alleles available, and all discovered different genotypes are depicted.Additional file 4: Fig S4. This figure shows QQ plots of the p-values obtained in the mQTL analysis in the adolescent screening and recall samples. The figure A corresponds to screening, whereas the figure B corresponds to recall.Additional file 5: Fig S5. This figure shows heatmaps of the SNP–CpG interactions in the mQTL analysis. The X-axis shows coordinates for SNPs, the Y-axis represents coordinates for CpGs. Both coordinates correspond to the genome assembly hg19. The first row with plots shows heatmaps for -log10 p values (raw) and beta coefficients in the linear models  for the adolescent screening sample. The second row depicts similar maps for the adolescent recall sample. The linear models were generated with the R package MatrixEQTL and assumed additive genetic effects and the heatmaps were created with ggplot2 (see methods).Additional file 6: Fig S6. A. Methylation of cg02825527 in BA11 brain region in the GSE88890 cohort. This figure shows methylation of cg02825527  for suicide and control individuals in GSE88890. B. Methylation of cg19624444 in BA25 brain region in the GSE88890 cohort. This figure shows methylation of cg02825527  for suicide and control individuals in GSE88890. C. Methylation of cg18302629 in BA25 brain region in the GSE88890 cohort. This figure shows methylation of cg02825527  for suicide and control individuals in GSE88890. D. Methylation of cg02825527 in E-GEOD-72680. This graph shows methylation of cg02825527  for individuals on antidepressant treatment and without it. E. Methylation of cg19624444 in E-GEOD-72680. This figure depicts methylation of cg19624444  for individuals on antidepressant treatment and without it.Additional file 7: Fig S7. This figure shows associations between candidate CpGs and stress-related CpGs in the samples where we observed associations (or trends) between MAD1L1 methylation and depression/suicide phenotype. The figure includes all pairs (without accounting for direction) that were nominally significant based on the linear model adjusted for the cohort-specific covariates. The color of the nodes indicates the following: green—sample, blue—CpG–CpG pairs that were observed in one cohort, orange—CpG–CpG pairs that were observed in two cohorts, red—CpG–CpG pairs that were observed in more than two cohorts.Additional file 8: Fig S8. A. This figure shows alternative transcripts of MAD1L1 obtained from the AceView portal (https://www.ncbi.nlm.nih.gov/IEB/Research/Acembly/av.cgi?db=human&term=MAD1L1&submit=Go) and the relative positions of candidate CpG sites. B. This figure shows NCBI RefSeq tracks of the MAD1L1 gene obtained from the UCSC genome browser. The red MAD1L1 transcript is associated with Illumina HumanHT-12 V4.0 MAD1L1 probes. The orange MAD1L1 transcript is associated with the Affymetrix Human Gene 1.1 ST MAD1L1 probe.Additional file 9: Table S1. Demographical characteristics of the GSE88890 cohort for two brain regions BA11. Participants were grouped based on the death cause: suicide or non-psychiatric sudden death. For the age variable, sample mean ± standard deviation is shown. Min stands for a minimal value, and max shows the maximal value.Additional file 10: Table S2. Demographical characteristics of the E-GEOD-72680 cohort. Participants were grouped based on the depression treatment status. For numerical variables, sample mean ± standard deviation is shown. Min stands for a minimal value, and max shows the maximal value. Abbreviations: BMI, body mass index; BD, bipolar disorder; CTQ, Childhood Trauma Questionnaire; PTSD, post-traumatic stress disorder; BDI, Beck's Depression Inventory.Additional file 11: Table S3. Results for SNP–CpG association analysis based on R limma models. Tables for screening and recall are provided separately. Limma models were run independently for every SNP site for all available CpG sites; only statistically significant results are shown. Adjusted p values were calculated using the false discovery rate method for every SNP separately. Adjusted p values were further adjusted by the number of SNP sites tested. Additionally, moderated t values from limma were adjusted for genomic inflation and bias with the R package bacon. Similar statistics as for limma t values were calculated for bacon-adjusted t values. Cumulated positions for CpGs were calculated based on the data from (https://www.ncbi.nlm.nih.gov/grc/human/data?asm=GRCh37). Associated genes and coordinates for CpGs were obtained from Illumina annotation files for a corresponding array. Column names state the following: Gene, a gene associated with an SNP; SNP, investigated SNP; SNP coords, coordinates of SNP; CpG; associated CpG site; CpG chrom., a chromosome of a CpG; CpG pos., coordinates of a CpG (for the current chromosome); CpG cumul. Pos., the cumulative position of a CpG; CpG gene, a gene associated with a CpG; logFC, estimate of the log2-fold-change corresponding to the effect; AveExpr, average log2 value for the probe over all arrays and channels; t, moderated t-statistic; P.Value, raw p value; adj.P.Value, FDR-adjusted p value with no correction for the number of SNPs and inflation/bias; B, log-odds that the site is differentially methylated; adj.P.Val.SNP, limma FDR-adjusted p value corrected for the number of SNPs; Inflation.Bacon, inflation factor calculated in bacon; Bias.Bacon, bias of estimations; T.Bacon, corrected moderated T statistics; P.val.Bacon, p value after correction for inflation and bias; Adj.P.Val.Bacon, FDR-adjusted p value after correction for inflation and bias; adj.P.Val.SNP.Bacon, FDR-adjusted p value corrected for the number of SNPs, inflation, and bias. Abbreviations: SNP, single nucleotide polymorphism; FDR, false discovery rate.Additional file 12: Table S4. Linkage disequilibrium for investigated SNPs based on the output from (https://snipa.helmholtz-muenchen.de/snipa3/) with default settings. Only associations between investigated SNPs are shown. Column description provided by SNIPA: QRSID, Query SNP rsID; RSID, Proxy SNP rsID; RSALIAS, Proxy SNP alias rsID(s); CHR, Chromosome; POS1, Sentinel SNP Position; POS2, Proxy SNP Position; DIST, Distance; R2, LD r^2; D, LD D; DPRIME, LD D'; MAJOR, Proxy Allele A; MINOR, Proxy Allele B; MAF, Allele B Frequency; CMMB, Recombination Rate (CM/Mb); CM, Genetic distance (CM); COMPEFFECTS, Compressed functional annotation of Proxy SNP; TRAIT, Associated with trait (yes/no); CISEQTL, Associated with eQTL in cis (yes/no); TRANSEQTL, Associated with eQTL in trans (yes/no); GENES, Genes hit or close-by (distance 5 KB max); REGGENES, Potentially regulated genes (linked via promoter or enhancer); EQTLGENES, Genes linked via eQTL associations. Abbreviations: SNP, single nucleotide polymorphism.Additional file 13: Table S5. 5.1 Genetic variants used for mQTL analysis. The column headers correspond to the IMPUTE2 output. 5.2 The mQTL analysis in the adolescent screening sample. The statistics were calculated in the package MatrixEQTL. P values were adjusted with false discovery rate method. 5.3 Statistics of the candidate CpG sites in screening. Depr. SNP placement column indicates a relative placement of a MAD1L1 depression-related SNP in the CpG-specific mQTL table (sorted by FDR). Depr. SNP name column indicates the name of the MAD1L1 depression-related SNP in the column with placement. Top 10 Variants column shows the Top 10 variants associated with a CpG in the CpG-specific mQTL table (sorted by FDR). LD with depr.SNPs column shows if the SNP in the Top 10 list is in LD with at least one MAD1L1 depression-related SNP. 5.4 Statistics of the MAD1L1 depression-related SNPs in screening. The Top CpG placement column shows a relative placement of a candidate CpG site in the SNP-specific mQTL table (sorted by FDR). The CpG name column shows the name of the CpG associated with its placement in the previous column. 5.5 The mQTL analysis in the adolescent recall sample. The statistics were calculated in the package MatrixEQTL. P values were adjusted with the false discovery rate method. 5.6 Statistics of the candidate CpG sites in the recall. Depr. SNP placement column indicates a relative placement of a MAD1L1 depression-related SNP in the CpG-specific mQTL table (sorted by FDR). Depr. SNP name column indicates the name of the MAD1L1 depression-related SNP in the column with placement. Top 10 Variants column shows the Top 10 variants associated with a CpG in the CpG-specific mQTL table (sorted by FDR). LD with depr.SNPs column shows if the SNP in the Top 10 list is in LD with at least one MAD1L1 depression-related SNP. 5.7 Statistics of the MAD1L1 depression-related SNPs in the recall. The Top CpG placement column shows a relative placement of a candidate CpG site in the SNP-specific mQTL table (sorted by FDR). The CpG name column shows the name of the CpG associated with its placement in the previous column.Additional file 14: Table S6. Coefficients for binary logistic regression models, where depression diagnosis is a dependent outcome variable depending on methylation at cg02825527  or cg18302629  in glial cells adjusted for confounders. Statistics for the models were calculated using a native implementation of the binary logistic regression model in the R programming language.Additional file 15: Table S7. Results for gene ontology analysis for MAD1L interacting partners based on the STRING database data. For further information, please refer to the STRING database. Analysis was conducted with default settings. Abbreviations: TFAMP1, transcription factor A, mitochondrial pseudogene 1; ELFN1, extracellular leucine-rich repeat and fibronectin type III domain containing 1; ELFN1-AS1, ELFN1 antisense RNA 1; MAD1L1, mitotic arrest deficient 1 like 1; SNORA114, small nucleolar RNA, H/ACA Box 114; LOC105375303, homo sapiens uncharacterized LOC105375303; MIR4655, microRNA 4655; MRM2, mitochondrial RRNA methyltransferase 2; NUDT1, nudix hydrolase 1; MIR6836, microRNA 6836; SNX8, sorting nexin 8; EIF3B, eukaryotic translation initiation factor 3 subunit B; CHST12, carbohydrate sulfotransferase 12; GRIFIN, galectin-related inter-fiber protein; PAX5, paired box 5; EP300, E1A binding protein p300; SP4, sp4 transcription factor; BRAG, brain angular gyrus; BRAC, brain anterior caudate; BRCG, brain cingulate gyrus; BRGM, brain germinal matrix; BRHM, brain hippocampus middle; BITL, brain inferior temporal lobe; BDPC, brain dorsolateral prefrontal cortex; BRSN, brain substantia nigra.Additional file 16: Table S8. Coefficients for candidate CpGs in the transcriptome–methylation association analysis in GSE49065. The expression of MAD1L1 was regressed against a candidate CpG site and age. Each row shows coefficients and estimates with statistics obtained from individual linear models. The models were implemented in R, using the standard implementation of linear regression. Confidence intervals were calculated with the R package stats.Additional file 17: Table S9. 9.1 The source publications of stress-related CpG sites. 9.2 CpG–CpG associations in the SKI. The models were calculated, using the standard implementation of linear regression. The covariates are specific for every cohort (see methods). The p values were adjusted with the false discovery rate method. The Stress CpG genes were obtained from Illumina annotation files for HumanMethylation450 and MethylationEPIC arrays. 9.3 CpG–CpG associations in the Grady sample. 9.4 CpG–CpG associations in the E-GEOD-41826 (glia) sample. 9.5 CpG–CpG associations in the GSE88890 (BA11) sample. 9.6 CpG–CpG associations in the GSE88890 (BA25) sample. 9.7 Pooled CpG–CpG associations that were nominally significant and detected more than once. 9.8 Pooled CpG–CpG associations that were nominally significant, were detected more than once, and had matching directions for beta coefficients.Additional file 18: Table S10.Color codes for chromatin states tracks in Fig.


P = 9.21 × 10−8), CD , and UC . The results of the sensitivity analyses were robust and no evidence of pleiotropy was observed. Bidirectional MR analyses showed no causal effects of IBD, CD, or UC on SCZ. This study suggests that SCZ has causal effects on IBD and its subtypes, whereas IBD has no effect on SCZ. Brain-gut axis interactions may help clarify the causal relationship between SCZ and IBD. However, further studies are needed to elucidate the biological mechanisms behind the brain-gut interactions.It has been reported that schizophrenia (SCZ) and inflammatory bowel disease (IBD) are related. However, whether there is a bidirectional interaction between them remains unclear. The aim of this study was to conduct a bidirectional Mendelian randomization (MR) analysis to elucidate the causal relationship between SCZ and IBD and its subtypes, including Crohn’s disease (CD) and ulcerative colitis (UC). Single-nucleotide polymorphisms (SNPs) extracted from the summary data of genome-wide association studies were used as genetic instruments. MR was performed using the inverse-variance-weighted method. The MR-Egger and weighted median methods were used for sensitivity analyses. Analysis using 70 SNPs as genetic instruments showed that SCZ was associated with an increased risk of IBD (OR = 1.14, 95% CI: 1.09–1.20,  SCZ is a chronic psychiatric disorder that mainly manifests as cognitive and behavioral abnormalities2, whereas IBD is characterized by immune dysregulation and inflammation of the gut3. Observational epidemiological investigations have indicated that patients with SCZ have an increased risk of developing IBD4 and vice versa6. Emerging genetic evidence also shows that there is a genetic association between the two diseases8. However, whether there is a bidirectional interaction between SCZ and IBD remains unclear.Schizophrenia (SCZ) and inflammatory bowel disease (IBD) are responsible for a substantial proportion of cases of disability in the general population worldwide9. The fundamental framework of the MR study design is that if genetic variants can predict the level or biological effect of a modifiable exposure to some extent, then they should also be causally associated with the exposure-related disease outcome to the same extent that they act on the exposure10. Utilizing the fact that genetic variants are randomly assigned before birth and fixed at conception, the MR design can prevent confounding, reverse causation, and various biases that are common in observational investigations11. In addition, the considerable increase in the number of publicly available genome-wide association studies (GWASs) has provided abundant data sources and increased the statistical power of MR. This makes the MR very popular for elucidating causality.Mendelian randomization (MR) is an alternative tool for identifying causal associations between modifiable exposure and disease outcomes using genetic variants as instrumental variablesIn this study, we conducted a bidirectional MR analysis to elucidate the causal relationship between SCZ and IBD and its subtypes, including ulcerative colitis (UC) and Crohn’s disease (CD). We hypothesized that there is a bidirectional causal interaction between SCZ and IBD.R2 = 3.5%; F = 42.8; Supplementary Table P = 9.21 × 10−8; Table P = 0.025), the weighted median method  and the MR-PRESSO method  was robust. The results of the MR-Egger intercept test suggested no evidence of horizontal pleiotropy (P = 0.350). Significant causal effects of SCZ on CD  , and 50  SNPs were extracted for IBD, CD, and UC, respectively. However, based on these SNPs, no causal effects of IBD -III or DSM-IV, after an interview by a psychiatrist and review of medical records. Genotypes were gathered from each cohort and were processed by the PGC using unified quality control procedures. Association meta-analysis was conducted using an inverse-weighted fixed-effects model, after adjusting for the first ten principal components.Information on the genetic associations with SCZ were obtained from the GWAS by the Psychiatric Genomics Consortium (PGC)33. The study participants comprised 25,042 cases and 34,915 controls for IBD, 12,366 cases and 33,609 controls for UC, and 12,194 cases and 28,072 controls for CD. All the study participants were of European ancestry, and all cases were diagnosed using accepted endoscopic, histopathological, and radiological criteria. Association tests were performed using an additive frequentist model conditioned on the first ten principal components for each cohort, followed by a meta-analysis using the weighted standard error method.Summary data for IBD, UC, and CD were derived from the study by the International Inflammatory Bowel Disease Genetics ConsortiumP < 5 × 10−8) threshold from each GWAS, and pruned for independence using a clumping procedure in PLINK v1.9 (http://www.cog-genomics.org/plink/1.9/), setting a linkage disequilibrium threshold of r2 < 0.001 in a 10-Mb window. Datasets for exposure and outcome were then harmonized, and palindromic SNPs with intermediate allele frequencies were excluded. For SNPs absent in the outcome dataset, a proxy SNP (r2 > 0.8) was used or discarded if no proxy was available. Two parameters, the proportion of variance explained by the SNPs (R2) and F statistics, were used to evaluate the strength of the selected instrument34. Typically, an F statistic >10 is considered sufficiently informative for MR analyses35.Genetic instruments for SCZ and IBD were extracted using the same criteria. We selected all relevant SNPs at the genome-wide significance (http://cnsgenomics.com/shiny/mRnd/). There were 87% power to detect a relative 10% difference (an OR of at least 1.10 or 0.90) in risk of SCZ on IBD, and 97% power to detect a 10% difference in risk of IBD on SCZ.Power calculations were performed using the online tool mRnd . Statistical significance was set at P < 0.05.The MR analysis was conducted in two directions. The first was the causal effect of genetically predicted SCZ on IBD, whereas the second was the reverse effect of genetically predicted IBD on SCZ. The IVW method was used for the standard MR analysis, in which all genetic variants were assumed to be valid instruments. Therefore, the MR provided a true slope of SNP-outcome association on SNP-exposure association with the intercept constrained to zeroSupplemental Material


Worldwide 85% of cervical cancer (CC) related deaths occur in low- and middle-income countries. Sub-Saharan Africa is burdend by an overlapping high incidence of CC as well as HIV infection, a risk factor for HPV associated disease progression. Recent upscaling of CC screening activities increased the number of CC diagnoses in a previous unscreened population. The aim of the 2H study was to follow up on women with CC in the context of available health care services in Tanzania in relation to their HIV infection status.This longitudinal observational cohort study included women with histological confirmed CC from Mbeya, Tanzania, between 2013–2019. All women were referred for CC staging and cancer-directed therapies (CDT), including surgery and/or radio-chemotherapy, or palliative care. Annual follow-up focused on successful linkage to CDT, interventions and survival. We assessed factors on compliance, used Kaplan–Meier-Survivor functions to evaluate survival time and poisson regression models to calculate incidence rate ratios on mortality (IRR) two years after diagnosis.p < 0.001). Median follow up period was 11.9 months (range 0.2–67.2). Survival information, available in 231 cases, demonstrated for women diagnosed in early-stage disease a median survival time of 38.3 months, in advanced-stage 16.0 months and late-stage disease 6.5 months after diagnosis. Of all women, 42% received CDT or palliative support. HIV co-infection and education were associated with higher health care compliance. CDT was significantly associated with lower 2-year mortality rates . HIV coinfection did not impact mortality rates after diagnosis.Overall, 270 women with CC (123 HIV infected) were included. Staging information, available in 185 cases, showed 84.9% presented with advanced stage disease (FIGO ≥ IIB), no difference was seen in respect to HIV status. HIV-infected women were 12 years younger at the time of cancer diagnosis (median age 44.8 versus 56.4 years, High numbers of advanced and late staged CC were diagnosed, compliance to CDT was low. A beneficial impact of CDT on CC mortality could be demonstrated for local health care services. This study indicates challenges for successful linkage and supports an effective scale up of cancer care and treatment facilities. One in twenty-four women from low-income countries develops cervical cancer (CC) until the age of 79 years . WorldwiStudies from 2009–2019 have suggested a higher risk of death during the first year following cancer diagnosis for HIV infected patients in Botswana, Uganda and northern Tanzania –13. The Cervical screening programmes to detect and treat precancerous lesions, and HPV vaccination programmes are the corner stones to reduce CC mortality in developing countries , 15. MajFrom 2013 until end 2020 we conducted the 2H (HIV and HPV) Study in Mbeya, Tanzania, evaluating the impact of HIV on HPV-associated disease, and specifically in this study to follow up on woman with CC diagnosis in the context of available health care services in Tanzania.https://mzrh.go.tz/index.html) is one of four tertiary hospitals within the country and provides health care services to the south-west of Tanzania, serving a mainly rural or semi-rural population. Ethical approval was obtained from the Mbeya Medical Research and Ethics review Committee, the Tanzanian National Health Research Ethics Committee, and the Ethics Committee of the University Munich (LMU).The 2H study was a prospective, longitudinal cohort study that included HIV infected and non-infected women, who underwent cervical screening procedures in Mbeya, Tanzania. In this study we focused on women who were diagnosed with CC with the aim to analyze survival in the context of HIV co-infection and other risk-factors and to evaluate the uptake and impact of cancer care and treatment interventions, including compliance to offered health care services. The study was conducted at the National Institute for Medical Research-Mbeya Medical Research Center (NIMR-MMRC) in collaboration with the HIV Care and Treatment Clinic (CTC) and the META Gynecological Hospital of the Mbeya Zonal Referral Hospital (MZRH) in Mbeya. The MZRH  on to the cervix uteri to identify aceto-white lesions suspicious for cervical intraepithelial dysplasia. In addition, samples for cytology and HPV-genotyping were taken from the ecto- and endocervix using an ayre’s spatula and a cytobrush, the results have been recently published . In caseFor all women with unknown HIV status, HIV testing was offered at baseline and during annual follow-up visits according to the national Voluntary Counselling and Testing (VCT) HIV test algorithm: a screening rapid test , and if reactive, followed by a second confirmatory rapid test . In the case of discordant results, an enzyme-linked immunosorbent assay (ELISA)  was performed. All tests were performed at the College of American Pathologists (CAP) accredited NIMR-MMRC laboratories. For HIV infected women HIV history and antiretroviral therapy (ART) information were collected through interviews and patient’s hospital charts. CD4 T-cell counts were analyzed from peripheral blood samples (Becton Dickinson FACSCount™ system).Annual follow-up visits were targeted, which included for CC diagnosed women updated information on their general health status, quality of life, access to health care services, therapeutic interventions, and palliative care information. Participants who missed their follow-up date were reminded via telephone call or text message. Following three unsuccessful attempts, physical home tracing was conducted. Reasons and information related to early termination such as death, study withdrawal or relocation were recorded, women who could not be traced were classified as lost to follow-up (LTFU).Clinical and laboratory data were recorded in study specific case report forms and entered twice into the study tailored database which was programmed in Structured Query Language (SQL). Descriptive analyses reported the median and range for continuous variables, as well as the number and percentage of participants for binary and categorical variables, using Fisher’s exact test for binary and Mann–Whitney-U-Test in groups without standard distribution for continuous variables. To estimate relationships of different factors with health care compliance, we used a robust binary regression model to calculate risk ratios (RR) and performed a Wald test for hypothesis testing. For survival time analysis we used the Kaplan–Meier survivor function and a log-rank-test for assessment of significance. For binary regression analysis in mortality data, we used a robust Poisson regression model to calculate incidence rate ratios (IRR) two years after diagnosis. To test for dispersion, a chi-square goodness of fit test was performed. All reported p-values were two-sided and for all statistical tests an alpha level of < 0.05 was used to define significance. All statistical analyses were performed using Stata statistics software , graphs were additionally drawn in MS Excel and GraphPad Prism (version 9.0.0)..Between June 2013 and September 2019, 270 out of 2078 screened women (44.9% HIV-positive) were diagnosed with CC and recruited into the study. Of those 123 (45.6%) were HIV infected and 147 (54.4%) HIV non-infected. Until September 2020 231 women (85.6%) had at least one follow-up visit or available outcome tracing information and could be therefore analyzed for survival assessments . The youngest age at cancer diagnosis in an HIV infected women was 21 years. Age group distribution at diagnosis is further shown in Fig. Early-stage disease (FIGO I to IIA) was diagnosed in 10.4%, advanced stage (FIGO IIB to IIIB) in 56.3% and later stage (IV) in 1.9% of women, for the remaining 31.5% no staging was available. The distribution of FIGO staging for 185 women with available information was largely similar in HIV infected and non-infected women Fig. A. The meAmong HIV infected women, 88 (71.5%) were on ART at the time of cancer diagnosis, the median CD4-cell count was 365 cells/µl, and 20.3% had severe immune suppression < 200 cells/µL.When asked for any symptoms possibly related to CC, abnormal vaginal discharge (82.6%) and lower abdominal pain (65.5%), and for sexually active women pain during sex (82.4%) and post coital bleeding (70.6%) were reported.p = 0.012), or had higher level of education (RR 1.44 (1.10–1.89), p = 0.008). For other factors such as age (RR 1.00 (0.99–1.01), p = 0.573) or disease stage (RR 0.80 (0.60–1.08), p = 0.150) we did not find significant association with health care compliance. During home tracing visits, reasons for not sustained of rejected health care services attendance was asked from some of those women, however not systematically. Frequently provided answers included lack of trust in the medical care, the institutions and recommended interventions, fear of surgery, preferred options for spiritual healing, financial difficulties, lack of time with upcoming harvest seasons and overcrowded hospitals.A referral letter that included clinical and pathological diagnostic results were provided to all women by the study clinic. Following linkage to the MZRH Gynecology and further ORCI in Dar es Salaam, 113 (42%) received medical treatment and care including surgery in 22 (8%) cases, radio-chemotherapy in 75 (28%) cases, or palliative care only in 16 cases (6%) mostly in the form of pain relieve. During tracing we obtained information about 81 women (30%) not receiving or requesting further care or treatment services, 44 of these women (54.3%) have had attended health care services after diagnosis for further treatment evaluation and counselling. In 76 cases (28%) no information on further treatment and care was available, but it can be assumed that most of these women also did not receive or request any further services, as many of those were in advanced stage disease (61% stage ≥ IIB) or died at home . An overview of intervention by disease severity stage is provided in Fig. Survival or mortality information was analyzed from 231 women for whom outcome information or follow-up visits were available. High mortality rates were characteristic in our study population with an overall median survival time of 15.2 months (IQR 7.3-not estimable) after diagnosis, and one- and three-year survival rates of 56.3% and 13.1%, respectively.p < 0.001). Higher mortality rates were significantly associated with increasing FIGO stages .Survival rates were significantly associated with FIGO stages as demonstrated by Kaplan Meier Fig.  indicatip = 0.004). This was significantly associated with advanced stage disease, but not significant with early-stage disease mortality, likely due to low sample size and consequently large confidence intervals .Receiving CDT  was overall significantly associated with lower 2 years mortality rates , with a median survival time of 17.7 months (IQR 7.8-not estimable) in HIV infected versus 13.7 months (IQR 6.5–38.3 months) in non-infected women (p = 0.108). In FIGO stage adjusted regression analysis a significant lower mortality rate was observed when comparing HIV-infected women on ART to women without current ART . HIV infected women not on ART also had a significant higher mortality rate compared to women with no HIV coinfection , p = 0.033), and when comparing HIV-infected women on ART versus not HIV infected women no difference in mortality was observed  or the lowest ever measured CD4 count using a cut-off of 200 cells/µl (IRR 1.07 (95% CI 0.80–1.44), p = 0.629).The HIV status did not significantly impact mortality rates  we had information about successful linkage for further care and treatment from the gynaecology department, however, only 42% received cancer-directed therapy (CDT) or palliative support. This again is in line with a recently published register study including nine population-based cancer registries from eight SAA countries reporting that only 15.8% of CC patients received CDT with curative potential and in addition 22% without curative potential . There aAnother barrier to cancer screening, prevention and CDT is lack of knowledge, attitude and practice towards offered or available health services, including fear, stigma and misinformation surrounding CC, which was recently reviewed for Tanzania . In our An important objective of our study was to investigate the impact of HIV co-infection on CC. We did not observe an impact of HIV co-infection on overall mortality rates or a trend towards disease severity as compared to not HIV infected patients. However, we found a significant impact of antiretroviral treatment resulting in comparable mortality rates for HIV-infected women on ART and HIV non-infected women. For HIV-infected women without ART higher mortality rates could be demonstrated in comparison to HIV-infected women on ART and HIV non-infected women. This might explain inconclusive results also reported in other studies. In a smaller study from Uganda the median survival for HIV infected women was significantly shorter than those of non-infected women, but when adjusted for FIGO stages Hazard ratios were not significantly different anymore . A studyAn important finding in our study was that HIV infected women were significantly younger at the time of diagnosis. HIV infection is a risk factor for CC and it is estimated that infected women are six times more likely to get cervical cancer compared to women without HIV . InfectiLimitations of this study include a possible sampling bias as indicated above as the study was targeting cancer cases and many women reported symptoms possibly related to cancer diagnosis that might have led to a greater screening services attendance. We also assume an operational study bias related to CDT compliance because of persistent effort to follow up on all women with CC diagnosis, including telephone visits and even physical home visits to rural areas. In contrast mortality or survival rates might be under- or overestimates as despite our tracing efforts follow-up information were often not obtainable.In line with the call for global action made by the WHO to eliminate cervical cancer we aim to provide real-world data to assess the current cervical cancer care situation, as well as barriers and compliance in a resource poor-setting to support evidence based global health policies. Countries like Tanzania with up-scaling CC services are challenged by high numbers of diagnosed CC cases and sparse – however increasing—availabilities of decentralized CDT infrastructure providing cancer care and treatment. In combination with frequent late-stage presentation especially palliative care, e.g., symptom control currently mainly limited to pain relief, should be improved. CC education, reduction of stigmatization, fear, and improved patient-focused health service provision is needed to increase trust and compliance to the existing health care services. In our study we could demonstrate that CDT interventions have a clinical impact on survival also for remote settings. In combination with preventive cancer screening and HPV vaccination programmes, CDT imposes challenges on African health care systems, also in respect to health care system cost considerations as recently summarized in a WHO technical document for Tanzania . Especia


Escherichia coli, the predominant bacterial species isolated from the carcasses. A total of 400 broiler carcasses condemned because of cellulitis (100), scratches (100), hepatitis (100), and healthy control carcasses (100) were selected. Samples of meat, pathological lesion, and bone marrow of each carcass were obtained for microbial analysis. From the analyzed samples, 469 bacterial isolates were recovered with E. coli accounting for 45.8%, followed by Aeromonas spp. (27.9%), in particular A. veronii. The highest rate of bacterial isolation was observed in carcasses condemned with cellulitis, whereas carcasses with hepatitis had the lowest rate of bacterial isolation. Forty-four E. coli isolates originating from different sample types were selected for whole genome sequencing. A clonal relationship was shown between E. coli from different sample types of the same carcass condemned with cellulitis and scratches. A major clade of E. coli was found in carcasses condemned with cellulitis with isolates containing mdf(A), tet(A), and blaTEM-1B genes that confer resistance to macrolides, tetracycline, and ampicillin, respectively. E. coli in this clade all belonged to ST117 and clustered with E. coli isolates previously collected from dead chickens and carcasses condemned due to cellulitis in Denmark, Finland, and the United Kingdom. Bacterial evaluation results of carcasses condemned with cellulitis, scratches (moderate to severe skin lesion), and acute hepatitis confirmed the need for total condemnation of carcasses with these pathological findings. A similar evaluation should be done for carcasses affected with chronic hepatitis, and minor scratches lesions.Meat inspection is important to ensure food safety and protect public health. Visual inspection of slaughtered carcasses for pathological changes should be supported by bacteriological analysis to determine whether the entire carcass or parts of it should be condemned. The aim of this study was to determine the bacterial species present in different sample types from condemned broiler carcasses. Furthermore, we investigated the genetic characteristics, zoonotic potential, and relatedness of  Poultry meat inspection is primarily conducted to ensure that safe and wholesome meat is produced for human consumption. Data obtained from poultry meat inspection are also used for the surveillance of animal welfare conditions and animal diseases . Meat inIn Denmark, the Danish Veterinary and Food Administration is responsible for performing meat inspection in the abattoir, and to record inspection data. The meat inspection data is entered into the Danish Quality Assurance System (KIK) database which is managed by the broiler industry and also contains other data from breeding to retail for all individual broiler flocks. A recent study by Scratches are seen during post-mortem inspection as an injury that usually occurs at the caudal part of the limb. Findings such as scratches, as well as injuries, hematomas, footpad dermatitis, breast blisters, and hock burn provide further information on welfare conditions during rearing, transportation, and handling of broilers . A positFindings of acute hepatitis will result in condemnation of the carcass and the organs . Acute hEscherichia coli is the most common cause of bacterial disease in poultry resulting in significant health problems and economic losses (E. coli (ExPEC)  . Phyloge (ExPEC) . ExPEC s (ExPEC)  Thus, po (ExPEC) . Moreovelth risk .Condemnations by food inspections at slaughterhouses account for the second largest proportion  of the total food waste generated in the EU . In DenmE. coli commonly isolated from pathological lesions of condemned carcasses were determined as was the genetic relatedness of the E. coli isolated from the meat, lesions, and bone marrow of individual carcasses.Therefore, the overall aim of our study was to provide knowledge about the bacterial flora and food safety aspects of broiler carcasses condemned at abattoirs. We characterized the bacteria and possible systemic infections associated with the most common causes of condemnation. Antimicrobial resistance and genetic characteristics including possession of virulence genes in A total of 400 condemned and healthy carcasses were collected during five monthly visits to a main slaughterhouse in Denmark from October 2020 to February 2021. Three hundred carcasses condemned due to cellulitis, scratches, and acute hepatitis were obtained, 100 of each. In addition, 100 healthy carcasses were randomly selected. Carcasses condemned due to cellulitis and scratches were collected from the slaughter line after the defeathering stage, while healthy carcasses, and carcasses condemned with hepatitis were collected after the evisceration stage where the pluck was removed. The broiler carcasses originated from 28 different poultry farms representing 43 flocks and 41 houses.Carcasses were selected from the slaughter line immediately after being condemned by meat inspection of the plucks and placed individually in a sterile plastic bag that was labeled as “cellulitis,” “scratches,” “hepatitis,” and “control.” Collected carcasses condemned with scratches represented carcasses with moderate to severe skin lesions. Whereas carcasses affected with hepatitis were selected from those with acute hepatitis. Immediately after collection, the carcasses were transferred and maintained at 4°C in a refrigerator at the slaughterhouse. The carcasses were subsequently transported in an insulated box with ice packs to a laboratory of the University of Copenhagen, Denmark. Upon arrival sampling processing and bacterial isolation was initiated within 48 h of carcass collection.From every condemned carcass three samples were obtained, one from the lesion, one from the meat (breast muscle), and one from the bone marrow of the femur. From each healthy carcass, samples were collected from meat, bone marrow, subcutaneously (control for cellulitis and scratches), and from the liver. A heated branding iron was used to sterilize the surfaces before sampling the breast muscle and liver. Subsequently, a sterile metal swab with cotton placed at the edge which was inserted into the sterilized tissue and streaked onto a 5% blood agar plate consisting of blood agar base  and sterile bovine blood. Samples obtained from cellulitis and scratches were taken directly from the lesion. The outer surface of the inflamed skin of carcasses with cellulitis was aseptically incised carefully to access the lesion (fibrin/pus). Subsequently, the sterile metal swab was inserted in the lesion and then streaked onto a blood agar plate. Samples from scratches were carefully obtained from the deep lesion by cutting around the damaged skin. The sterile metal swab was placed in the lesion and then streaked onto a blood agar plate. Samples from healthy chickens were obtained from under the skin, by cutting gently the outer layer and inserting the sterile metal swab under the skin in a new area that had not been touched or exposed, and then streaked onto a blood agar plate. Samples from bone marrow were obtained after removing the muscle carefully around femur, then cutting the end of the femur with bone shears, and subsequently, the metal swab was carefully inserted directly into the bone marrow and streaked onto a blood agar plate. The bone shears was sterilized in 70% alcohol solution before and after use. Isolation of bacteria from the bone marrow was considered as a systemic infection. During the 5 months of sampling, data about the total number of slaughtered chickens approved for human consumption as well as condemned carcasses were obtained from the Danish Quality Assurance System (KIK) database.The blood agar plates were incubated at 37°C under aerobic conditions for 24 to 48 h. From plates with growth, up to five colonies were sub-cultured to obtain pure cultures. On agar plates with colonies displaying different morphologies, one specimen of each colony type was obtained for pure culture and bacterial identification. Blood agar plates containing less than five colonies were discarded.Matrix-Assisted Laser Desorption Ionization Time-of-Flight Mass Spectrometry (MALDI-TOF MS) from VITEK® MS  was used to confirm the species of the bacterial isolates.p was considered statistically significant if the value was ≤0.05.A Chi-square test was used to determine the significance of systemic infection occurrence (isolation of bacteria from bone marrow) in the three causes of condemnation  when compared to control samples. A value of E. coli isolates originating from carcasses condemned with cellulitis (20 isolates), scratches (12 isolates), and hepatitis (12 isolates) was subjected to antimicrobial susceptibility analysis to the following antimicrobials: ampicillin (AMP) (dilution range 64–1 μg/mL), azithromycin (AZI) (64–2 μg/mL), cefotaxime (CTA) (4–0.25 μg/mL), ceftazidime (CTZ) (8–0.5 μg/mL), chloramphenicol (CHL) (128–8 μg/mL), ciprofloxacin (CIP) (8–0.015 μg/mL), colistin (COL) (16–1 μg/mL), gentamicin (GEN) (32–0.5 μg/mL), meropenem (MER) (16–0.03 μg/mL), nalidixic acid (NAL) (128–4 μg/mL), sulfamethoxazole (SME) (1024–8 μg/mL), tetracycline (TET) (64–2 μg/mL), tigecycline (TIG) (8–0.25 μg/mL), and trimethoprim (TRI) (32–0.25 μg/mL). The minimum inhibitory concentrations (MIC) were determined by the broth microdilution method according to ISO-standard (20776–1). Escherichia coli ATCC25922 was used as a control strain. Results interpretation was done using the Thermo Scientific™ Sensititre™ SWIN™ Software System. The breakpoints from EUCAST version 11.0 for Enterobacteriaceae were used.Forty-four E. coli isolates were cultured onto blood agar and incubated at 37°C for 24 h. A pure bacterial colony was then transferred to Luria broth  and incubated in a shaking incubator at 37°C for 13 h. Genomic DNA was extracted using the bacterial DNA extraction kit Maxwell® RSC Cultured Cells DNA Kit  following the manufacturer’s protocol. Quality control of the extracted DNA was assessed using NanoDrop 8,000, Qubit  and by gel electrophoresis in a 1% agarose gel stained with ethidium bromide. Whole genome sequencing was performed according to standard protocols with Illumina MiSeq . The raw reads were submitted to NCBI GenBank Database under the project accession number PRJNA888805.The 44 In silico assembled sequences were analyzed using different tools from the servers of the Center for Genomic Epidemiology (CGE)We used the following software for analyzing the data from the whole genome sequencing. The quality of raw reads was evaluated using FastQC version v0.11.9 . TrimminE. coli strains with other E. coli strains obtained elsewhere in Europe. Our 44 strains were therefore included in a phylogenetic analysis with 120 publicly available E. coli genomes. Seventy of these publicly available genomes were human isolates originating from Denmark, whereas 50 other genomes were isolates from poultry obtained in Denmark, Sweden, Finland, and the United Kingdom. Details and accession numbers of the public genomes included in the phylogenetic analysis are shown in  were called with Snippy v4.6.0Salmonella spp. as registered in the national surveillance program as according to Danish laws any flock positive for Salmonella spp. cannot be slaughtered in Denmark.During the study period from October 2020 to February 2021, a total of 17,331,511 chickens were slaughtered at the slaughterhouse. A total of 17,125,632 carcasses were healthy and approved for human consumption during the post-mortem inspection, while 205,879 (1.2%) carcasses were condemned. The main causes of carcass condemnation include scratches , cellulitis , and hepatitis . All slaughtered flocks were free of E. coli was the most common bacterial species isolated from all sample types , followed by Aeromonas spp. . Among the isolated Aeromonas spp., A. veronii was predominant , followed by A. sobria (11.5%), A. hydrophila (10.7%), A. caviae (3.8%), A. ichthiosmia (1.5%), and A. salmonicida (0.8%).At two initial visits to the slaughterhouse, all samples were processed and agar plates incubated under both aerobic and anaerobic conditions. Both incubations yielded agar plates with bacterial colonies; however, when the identity of such colonies were confirmed by MALDI-TOF MS there were no differences in the bacterial species identified. We, therefore, decided only to culture agar plates under aerobically conditions in subsequent sampling. A total of 469 bacterial isolates were recovered from the 400 carcasses. E. coli (60.9%) was the most frequently isolated species from the carcasses with cellulitis followed by Aeromonas spp. (20.6%) and P. mirabilis (12.5%)  . The comllulitis . Cellulillulitis . Howeverllulitis . A recenllulitis . In additer spp. . These sp < 0.001)  among carcasses with cellulitis compared to those condemned due to other causes and the control group (< 0.001) . Isolatiarcasses . This maAeromonas spp. (37.5%) was the most frequently isolated species from all sample types, followed by E. coli (29.9%) and P. mirabilis (23.6%)  . Intact  (23.6%) . Litter crobiota . Thus, scrobiota .p < 0.001)  . Unlike arcasses . Visual arcasses . To reduE. coli (47.8%), Aeromonas spp. (23.2%) and S. aureus (11.6%)  . The com studies . Aeromonepatitis . Anothernas spp. . S. aurebroilers . Our fin poultry . E. faecotential .p = 0.05)  . E. coliarcasses . OverallE. coli (38.9%), Aeromonas spp. (30.5%), P. mirabilis (18.6%), and S. aureus (8.5%); findings that corroborate previous studies (Bacteria were isolated from the skin (25.0%), liver (23.0%), bone marrow (7.0%), and meat (1.0%) of the healthy control carcasses. The most common species were  studies . During  studies . Carcass studies . Fecal cThe presence of bacteria under the skin of about 25.0% of the healthy control carcasses can be explained by contamination during the scalding process. The skin and feather follicles of the carcasses are often dilated during this high-temperature exposure which permits the entry of bacteria from the feathers into the skin . MoreoveE. coli was isolated from the liver, under the skin and bone marrow of three carcasses, and the same bacterial species was isolated from the liver and bone marrow in one carcass , sulfamethoxazole (27.3%), tetracycline (27.3%), nalidixic acid (22.8%), trimethoprim (16%), ciprofloxacin (9.1%) and gentamicin (2.3%). All ecycline .blaTEM family including blaTEM-1B (n = 12), blaTEM-1C (n = 6), blaTEM-220 (n = 2), blaTEM-106 (n = 2), blaTEM-135 (n = 2), blaTEM-126 (n = 1), and blaTEM-127 (n = 1). The lack of ESBL resistance is likely because third-generation cephalosporins have not been used in Danish poultry production for many years  was detected in 25% of the E. coli isolates all of which also showed phenotypic sulphonamide resistance. Aminoglycoside resistance genes were detected in 13.6% of the isolates, including aph(6)-Id (n = 5), aph(3″)-Ib (n = 5), aph(3′)-Ia (n = 2), aadA1 (n = 2), aadA5 (n = 1), and aac(3)-Via (n = 1). One isolate carrying aadA1 and aac(3)-Via was resistant to gentamicin. The trimethoprim resistance genes dfrA1 (n = 3), dfrA14 (n = 2), dfrA15 (n = 2), and dfrA17 (n = 1) were detected in 16% of the isolates and the tetracycline resistance genes tet(A) (n = 9) and tet(B) (n = 3) were detected in 25% of the isolates. The macrolide resistance gene mdf(A) was detected in all isolates, while the sitABCD operon, encoding resistance to disinfectants, particularly hydrogen peroxide was detected in 77.3% of the isolates. The single E. coli isolate that harbored a cmlA1 gene was not phenotypically resistant to chloramphenicol. Ten isolates that had a known mutation in gyrA (S83L), a mutation that confers resistance to quinolones were also observed to show phenotypic resistance to nalidixic acid.The sulphonamide resistance gene E. coli over the last 5 years  for broilers including an increasing nalidixic acid resistance and decreasing chloramphenicol and trimethoprim resistance among  5 years . In geneE. coli from the condemned broilers when compared to other European countries  and 24 different serotypes identified among the 44 E. coli sequenced isolates (E. coli isolated from cellulitis samples (45%), whereas ST48 was the second most prevalent ST (9.1%). ST69 was isolated from liver samples from condemned carcasses (hepatitis) and skin samples of carcasses condemned with scratches. Seven of the 44 isolates (16%) were serotype O78:H4 and most belonged to phylogroup G (27.3%) with only one isolate belonging to phylogroup E. In accordance with our findings, the ST117 type has previously been associated with cellulitis in Danish poultry farms (Multi-locus sequence typing (MLST) and serotyping revealed a high diversity among the isolates . The mosry farms . High mory farms . ST69 hary farms . The majE. coli (APEC), while the rest are mostly found in all E. coli including commensal strains. None of the ST117 O78:H4 isolates could be classified as an EXPEC or as a uropathogenic E. coli (UPEC) strain. However, ST117 O45:H4 (sample ID:36), and ST177 O161:H4 (ID:6) revealed more virulence genes known to be associated with infections in humans, and can therefore be classified as EXPEC and UPEC. Ten additional strains , four of them originating from cellulitis, five from scratches, and one from hepatitis could be also classified as an EXPEC and a UPEC. Three isolates were classified as an EXPEC  including one isolate from scratches lesions and two from liver samples. Two isolates (ID: 20 and 39) from carcasses with cellulitis and hepatitis were classified as UPEC. Moreover, these EXPEC and UPEC isolates were closely related to public available clinical isolates from diseased chickens and humans as observed in the phylogenetic analysis (n = 34), IncFII (n = 19), Col156 (n = 11), and IncFIC(FII) (n = 9) . The first seven genes have been reported to be associated with avian pathogenic analysis . All of  (n = 9) .Phylogenetic analysis revealed that our isolates constituted a genetically heterogeneous population with isolates distributed in five different clades. We observed a close genetic relatedness among isolates obtained from carcasses condemned with cellulitis . For insE. coli ST117 O78:H4 clones appeared to be circulating among broilers in poultry farms in the Nordic countries. It was concluded that the spread of this clone was vertically through grandparents of broilers that were reared in Sweden and then imported and spread at poultry farms importing day-old chickens from Sweden , i.e., isolates from meat and bone marrow of two carcasses. Of the carcasses condemned with hepatitis, only one carcass had isolates from the lesion and bone marrow (ID: 34–35), however, these two isolates displayed no genetic relatedness . A majorm Sweden . Since im Sweden .E. coli isolates that were obtained from bone marrow and samples of cellulitis and meat of the same carcass. Furthermore, the WGS analysis confirmed the presence of known virulence genes which indicates that the E. coli strains are pathogenic. Our findings thus support the hypothesis that the E. coli initially was associated with the development of cellulitis, and then spread to cause systemic infection. Four E. coli isolates from lesions and bone marrow of two carcasses condemned with scratches were also closely genetically related. However, follow-up studies are needed to confirm if E. coli from samples of scratches cases are able to develop into systemic infections.There was a close relationship among the E. coli was the main isolated bacterial species from all sample types from condemned carcasses, indicating that infection caused by E. coli is the main cause of carcass condemnation in Danish broilers. A clonal relationship was found between E. coli isolated from different sample types of the same carcass condemned with cellulitis and scratches. This suggests that an infection may be initiated in the tissue of the lesion, and then gradually develop into a systemic infection which can be detected through the isolation of bacteria in the bone marrow and muscle (meat) samples. A dominant clade including 12 isolates was found to be closely related and primarily associated with systemic infection. Isolates from this clade belonged to ST117 and clustered together with strains isolated from diseased and condemned broiler carcasses from Scandinavian countries and United Kingdom. Further research is needed to assess risk factors and how to control the transmission of this clade.Overall, a total of 469 bacterial isolates were recovered from different sample types of the 400 carcasses collected. Bacteria were more frequently isolated from carcasses condemned with cellulitis (184/469) as compared with carcasses condemned with hepatitis (69/469). The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/AA, AD, JC, and MS designed the study. AA collected all samples, performed various laboratory analyses, and wrote the first draft of the manuscript. JC and YH provided guidance on laboratory analysis. AA and YH performed bioinformatic analysis. AD supervised the study. All authors revised the manuscript and approved the final version.This work was supported by King Faisal University, Ministry of Education Kingdom of Saudi Arabia which provided the financial cost of the research and a Ph.D. scholarship for AA, project number: (1160961001), 50-KU (Copenhagen University), (30368200) Section of Food Safety and Zoonoses.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.