Antenatal depression has immense public health importance, as it can adversely affect both the mother and child health. The problem contributes to the disease burden in both developed and developing countries. Despite this, it is less investigated and not getting the necessary attention in the study setting.  The aim of the study was to assess the prevalence of antenatal depression and associated factors among women attending antenatal care (ANC) service in Kochi Health Center, Jimma town, southwest Ethiopia, 2019.  Institutional based cross-sectional survey was conducted on 314 pregnant women attending Kochi Health Center from February 15 to April 15, 2019. A systematic random sampling technique was used to include the study participants. Antenatal depression was assessed using the Patient Health Questionnaire (PHQ-9) tool. Data was collected through face-to-face interviews using a pretested and structured questionnaire. Descriptive statistics was done to summarize the dependent and independent variables. Moreover, the chi-square test analysis was done to determine the association between the outcome and explanatory variables. P < 0.00001).  A total of 314 pregnant women participated in the study, making a response rate of 96.7%. The study has revealed a total of 52 (16.6%) of the respondent had antenatal depression. A chi-square test of independence analysis showed a significant association between antenatal depression and marital status, family history of depression, pregnancy planning, history of abortion, social support, and intimate partner violence ( The study has shown that the prevalence of antenatal depression was high and associated with multiple psychosocial, clinical, and obstetric factors. Therefore, screening pregnant women for depression and the provision of necessary mental health services is recommended to mitigate the adverse health outcome of the problem. Globally, more than 350 million people of all ages suffer from depression, and more women are affected by depression than men , 2. AccoCommon mental disorders such as depression are one of the public health problems of the developing world. Studies have shown that depression and substance abuse are rampant, overwhelmingly disabling, follows chronic courses when untreated, and can lead to increased morbidity and mortality , 5.Depressed women are more likely to have unhealthy practices during pregnancy, poor nutrition due to a lack of appetite, poor weight gain, and risk for intrauterine growth. Depressed women are less compliant with prenatal care and feel less invested in the care toward their pregnancy –8.Antenatal depression is a condition that describes a range of physical and emotional changes that many women can have during pregnancy. Some of these symptoms include fatigue, crying, hopelessness, anxiety, problems concentrating, changes in appetite, sleeping difficulty, lack of interest in daily activities, and suicide. It is estimated that 25-35% of pregnant women have depressive symptoms, and 20% of them may meet the diagnostic criteria for major depression at some time during pregnancy , 10.It is a multifaceted illness that has varying consequences for a woman's mental health, physical health, the family's functioning, and her children's –13. AnteUntreated depression during pregnancy has been also associated with poor pregnancy and birth outcomes such as maternal preeclampsia, low birth weight, smaller head circumference, increased risk of premature delivery, increased surgical delivery interventions, and lower Appearance, Pulse, Grimace, Activity, and Respiration(APGAR) scores and is considered to be the strongest risk factor for postpartum depression –18.In the developing world, antenatal depression is a critical public health problem because of its intergenerational impact on the mother, infants, and children . One in Many studies which have done in the topic area in Ethiopia were from the different regions of the country, having the knowledge of socio-cultural-economic difference among the regions in the country; this study will give an insight towards antenatal depression and associated factor among pregnant women attending ANC service in Jimma town, southwest Ethiopia. Despite national and international organizations focus on maternal mental health, antenatal depression remains a neglected issue in healthcare systems and given low priority in the study setting. Moreover, there is a paucity of evidence on antenatal depression. Indeed, better evidence is needed to support greater priority to mental health care and to design appropriate interventions. Additionally, the findings of this study will serve as a baseline for further research in the area.Kochi Health Center is located in Jimma town, which is found in Oromia regional state, southwest of Ethiopia, 352 km far away from the capital city, Addis Ababa. The health center is owned by the government and serving almost ten kebeles (lowest administrative unit) in the town. The health center has started health service in December 2000 and provides services such as outpatient treatment, ANC service, maternal and child health service, delivery service, antiretroviral therapy service, HIV/AIDS prevention from mother to child service, and public health services with an outreach program for a total of 11503 population in the catchment area. During the study, there were 1287 pregnant women who had ANC follow-up in the health center. The study was conducted from February 15 to April 15, 2019.The institution-based cross-sectional study design was conducted on randomly sampled pregnant women attending ANC services during the study period.n = [(Z α/2)2 p (1 − p)]/d2, by assuming the prevalence of antenatal depression among pregnant woman rate 50% to get the maximum sample size, 5% margin of error, and 95% confidence level of certainty . Accordingly, n = (1.96)2 × 0.5(1 − 0.5)/(0.05)2 = 384. Since the total population is less than ten thousand, finite population correction formula was used to get the desired sample size. Hence, nf = n/(1 + n/N); hence, nf = 384/(1 + 384/1287) = 295. Finally, with the addition of a 10% nonresponse rate, the total sample size was 325.The sample size was computed through a single population proportion formula, K = N/n, where K is a periodic interval, N is the total population, and n is the estimated sample size, accordingly, K = 1287/325; approximately, every fourth pregnant women attended ANC service during the data collection period was included in the study.A systematic random sampling technique was used to include pregnant women attending the Kochi Health Center ANC clinic during the data collection period. The periodic interval was calculated using the formula Data were collected by five trained midwives through a face-to-face interview. A structured questionnaire was developed through reviewing available relevant literature adapted from previous similar studies. The instrument includes items related to sociodemographic, antenatal depression, social support, obstetric and clinical parameters, intimate partner violence, and substance-related characteristics. Patient health questionnaire (PHQ-9) was used to screen antenatal depression symptom severity; accordingly, PHQ-9-score of 0-4 indicates minimal depression, 5-9 mild depression, 10-14 moderate depression, 15-19 moderately severe depression, and 20-27 as severe depression. A score of 10 and above is considered as a cutoff score for depression . PHQ-9 hRegular supervision was made by the data collection supervisors to ensure all necessary data were properly collected. Each day of data collection, the filled questionnaires were checked manually first for completeness and consistency then inserted into the computer twice. A pilot test was conducted to identify the impending problems on data collection tools on 5% of respondents at the Bocho-bore Health Center; the necessary correction of the questionnaire was done accordingly to the pilot test result; the data collected during pilot test were not included in the analysis of the main study.P value of less than 0.05 were considered to have a statistically significant association with the outcome variable. The final result was organized through text narration and table form.The collected data were cleaned, coded, and entered into the Epi-data version 3.1 data entry software and exported to the Statistical Package for the Social Sciences (SPSS) version 20 statistical software for analysis. Descriptive statistics were done to summarize the dependent and independent variables. A chi-square test of association was performed to identify factors associated with the outcome variable. Finally, variables with a The study was reviewed and approved by the Institutional Review Board (IRB) of Jimma University. Furthermore, permission was obtained from the Kochi Health Center for data collection. Participants were fully informed about the aims of the study, and written informed consent was sought before the interview. Privacy and confidentiality were ensured throughout the process of the study.This study was conducted in accordance with the Declaration of Helsinki.A total of 314 of pregnant women gave their consent to participate in this study, resulting in a response rate of 96.7%. The majority of the respondents 121 (38.5%) were between the ages of 25 and 29 years old and were housewives 176 (56.1%). Most of the respondents 141 (44.9%) were Muslim, attended primary school 116 (36.9%), and earn less than one thousand birr (around 29 USD) monthly 171 (54.5%) .Nearly all 289 (92%) pregnancies were planned, and three-quarters of pregnant women were multigravida 230 (73.2%). Almost half of the women were on second trimester 158 (50.4%), and 11 (3.5%) had a history of abortion. Moreover, 41 (13%) reported perceived complications during a previous pregnancy, and 34 (10.8%) of the respondents were current substance abusers. Almost one-fifth of the respondents were reported having currently known medical illness, and among them, more than two-thirds 41 (70.7%) were currently taking medication .Approximately one-third 94 (29.9%) of the total respondents reported as they have good friends who always support them, and nearly half of the participants described as their husband or partner who always helps them a lot. Additionally, two-thirds of the respondents were reported as having a conflict with their husband or partner sometimes 109 (34.6), and about 141 (44.9%) replied as feeling always controlled by their husband or partner .A total of 53 (16.9%) of the study participants reported that their husband or partner has done things to scare or intimidate them, and nearly one-fifth 61 (19.4%) of the respondents described that they were hit, slapped, or thrown something that could hurt them by their husband or partner. Moreover, 16 (5.1%) reported that they were forced or pressured by their husband or partner to have sexual intercourse when they did not want to .A total of 52 (16.6%) of the respondent had depression; among them, 35 (11.1%), 11 (3.5%), and 6 (1.9%) had moderate, moderately severe, and severe depression, respectively .X2  =72.1353, P = <0.00001, and family history of depression, X2  =29.2109, P < 0.00001. Likewise, the analysis result has shown the association between antenatal depression and pregnancy planning, X2  =69.4507, P < 0.00001. Additionally, statistically significant association was found between antenatal depression and history of abortion, X2  = 26.0251, P < 0.00001; social support, X2  = 86.1944, P < 0.00001; and intimate partner violence, X2  = 41.5347, P < 0.00001  of the pregnant woman had antenatal depression during the current pregnancy. This result is nearly similar to 16% in the longitudinal study done in the US  but muchIn this study, marital status was found to have a significant association with antenatal depression; this is similar with the studies done in Poland , AbeokutSimilarly, family history of depression was associated with antenatal depression; this finding is similar with the studies done in Dare Salam Tanzania ; an umbrIn the current study, pregnancy planning was found to have an association with antenatal depression similarly; consistent findings were reported from the study done in Hyderabad, Pakistan , and NigIn this study, social support was associated with antenatal depression; similar findings were reported from the study done in the USA , KwaZuluA significant association was found between intimate partner violence and antenatal depression; similar reports were obtained from the studies done in Hyderabad, Pakistan , and KwaThis study has found the association between history of abortion and antenatal depression; similar findings were reported from the study done in the US , NetherlThe prevalence of antenatal depression among pregnant women attending ANC service in Kochi Health Center was high and significantly associated with marital status, family history of depression, pregnancy planning, history of abortion, social support, and intimate partner violence. Integration of mental health service with ANC follow-up service and screening pregnant women for possible depression has paramount importance for a healthy pregnancy and prevention of the possible adverse health outcomes on the mother and the fetus.


The most relevant targets for preeclampsia were: AR, VDR, SLC6A2, NOS3 and CHRM4, while ABCG2, ERBB2, CES1 and REN led to the most relevant off-targets. A virtual screening of the DrugBank database identified estradiol, estriol, vitamins E and D, lynestrenol, mifrepristone, simvastatin, ambroxol, and some antibiotics and antiparasitics as drugs with potential application in the treatment of preeclampsia.Preeclampsia is a hypertensive disorder that occurs during pregnancy. It is a complex disease with unknown pathogenesis and the leading cause of fetal and maternal mortality during pregnancy. Using all drugs currently under clinical trial for preeclampsia, we extracted all their possible targets from the DrugBank and ChEMBL databases and labeled them as “targets”. The proteins labeled as “off-targets” were extracted in the same way but while taking all antihypertensive drugs which are inhibitors of ACE and/or angiotensin receptor antagonist as query molecules. Classification models were obtained for each of the 55 total proteins (45 targets and 10 off-targets) using the TPOT pipeline optimization tool. The average accuracy of the models in predicting the external dataset for targets and off-targets was 0.830 and 0.850, respectively. The combinations of models maximizing their virtual screening performance were explored by combining the desirability function and genetic algorithms. The virtual screening performance metrics for the best model were: the Boltzmann-Enhanced Discrimination of ROC (BEDROC) Preeclampsia is a complex pregnancy related disease with unknown pathogenesis. During pregnancy, preeclampsia appears after 20 weeks of gestation in two possible scenarios: early or late preeclampsia onset . The worAny drug for the treatment of preeclampsia will have, in general, two possible options (which are not necessarily independent of each other): 1) to be a preventive drug for use early in pregnancy to prevent a possible preeclampsia manifestation or 2) to be a drug capable of handling symptoms and/or disease progression. The first option includes the use of low-dose aspirin in women with high risk of preeclampsia  to be a  and the  to be a Because we are dealing with pregnancy, de novo drug design presents several important drawbacks not only during in vitro assays but also during clinical trials. These difficulties are highlighted by the lack of clinical trials during pregnancy involving drugs discovery . TherefoTarget based approach. The disease target is known and this information is used for drug repurposing. These methods usually use a variety of data sources, i.e., protein-protein interaction and drug-target interaction networks.Drugs-Drugs similarity based approach: The similarity between drugs is explored  to identify new candidates.Gene signature based approach : TheThe strategies for drug repurposing can be grouped in several ways discussed in the recent reviews by Lofti Shahreza et al., and Pushpakom et al. ,11. For In preeclampsia, the targets related with the disease or even its pathogenesis are unknown. More important, even if some targets are proposed considering recent research on preeclampsia, it is possible that no drugs had been previously tested in clinical trials for them. Therefore, we do not really know if that target could be desirable for a possible drug treatment. However, following this target-based approach, Shuyu Zhao et al.  recentlyAn alternative strategy (mixing target and ligands based approaches) can be to provide the current information for the drugs that can be safely used during pregnancy and preeclampsia. That is, (1) to extract all targets associated with the drugs currently on clinical trials for preeclampsia and (2) to combine them with the undesirable targets derived from the drugs that cannot be used during pregnancy and that are associated with the treatment of hypertension. This proposed strategy requires a multi-target and multi-objective model potentially capable of identifying drugs candidates for the treatment of preeclampsia.From the 19 drugs obtained from the ClinicalTrials for preeclampsia treatment or management and from the 41 antihypertensive drugs following the ACE inhibition and angiotensin receptor antagonist mechanisms, we obtained 155 unique proteins combining DrugBank and ChEMBL information. For the 155 possible proteins, we extracted and curated  all active/inactive compounds from the ChEMBL database. After all filtering and curation steps, we finally obtained 55 proteins and 147,456 compounds-proteins interactions. In this final dataset there are 45 target and 10 off-targets. However, from these 45 targets, 10 are common to both groups. These common proteins are: EGFR, PHG1, SLC6A2, PPARG, ADORA3, CYP2C9, CYP3A4, CYP2C19, ABCB1 and CYP2C8. This means that some drugs used in preeclampsia share targets with those used for hypertension treatment. Because all drugs are actually used in preeclampsia, these targets were included in the “target” group and removed from the “off-target” set.All of the targets for preeclampsia are: CHRM4, PDE5A, FYN, AR, HDAC2, CACNA1C, VDR, SCN5A, IKBKB, CHRM5, ESR1, ADRB2, CHRM2, ADRB1, HTR1A, CHRM1, ADORA1, PTGS2, CASP3, OPRK1, SLC6A3, PPARA, ESR2, CHRM3, ADORA2A, EDNRA, CYP2D6, CYP1A2, NR1I2, NOS1, CCR2, SCN9A, NOS2, CASP1 and NOS3. On the other hand, the final list of off-targets is: ACE, ERBB2, BCHE, F2, CES1, AGTR1, REN, MMP9, MMP2 and ABCG2.In order to comprehend the relationship between the 55 proteins with preeclampsia and hypertension, we performed a pathway enrichment analysis. The analysis showed that “Pathway in Cancer” and “Renin Secretion”, “Endocrine resistance”, “Calcium signaling pathway” and “Estrogen signaling pathway” are common between targets and off-target genes while the Renin-Angiotensin system is mainly enriched with off-targets . A totalSeveral metabolic pathways that were detected as being enriched by our 55 proteins had also been found in previous bioinformatics analyses of transcriptomic data in preeclampsia. The HIF-1 signaling pathways are known to be involved in preeclampsia. Actually, placenta hypoxia  is one of the events involved in the proposed mechanism of preeclampsia pathogenesis ,14. OtheMoreover, the enrichment analysis clearly indicates that hypertension related pathways are also in contact with proteins related to the preeclampsia group . This means that some drugs could have an effect (which is probably unknown) on different proteins and metabolic pathways. This polypharmacological mechanism implies that some drugs used for hypertension treatment though the ACE inhibition could also affect, for example, pathways in diabetes, TNF signaling or even arginine metabolism. More importantly, from the space of the desirable proteins in preeclampsia (targets) some of them could have different risk levels to interlink with undesirable targets (off-targets). So, the complexity of the disease as well as the necessity of avoiding undesirable pathways interconnected through desirable targets, support our strategy of using a multi-objective modeling approach. In other words, we need to find desirable drugs while reducing their risk to interact with undesirable targets.The goal of this research is to find molecules that selectively inhibit targets from the preeclampsia group. Using the TPOT pipeline modeling strategy, we found predictive models for each of the 55 proteins. The final list of the obtained models with all the performance metrics is available in tpot_CHEMBLID.zip).The average accuracy for the off-target models in cross-validation analysis was found to be ACC-CV = 0.880 while ACC = 0.851 in the external data of the same group. Moreover, the average values for precision and recall in the external data were 0.850 and 0.863, respectively. Regarding the models for the targets in preeclampsia similar results to the off-targets were found with ACC-CV = 0.835 and ACC = 0.829 when predicting the external dataset. The average values for precision and recall in the external data for this group of targets were 0.838 and 0.833, respectively. Detailed information for each model can be found in 1% = 31.55 and the Area Under the Accumulation Curve (AUAC) = 0.831. This result means that the best virtual screening model is capable of enriching the top 1% of the ranked list with desirable drugs almost 32-fold more than a random model.Once the 55 models were obtained, we needed to go through multi-objective model construction. This needed to prioritize desirable targets and avoid undesirable ones. However, we did not know a priori the influence of each protein toward a preeclampsia drug that avoids interactions with off-targets. In order to determine this, we ran the genetic algorithm for feature selection (models/proteins selection) in order to obtain the optimal set of proteins for the virtual screening. The optimization problem needed to combine the different targets (using the desirability function) in a way so that the virtual screening prioritizes the drugs from the clinical trials related to preeclampsia . The genetic algorithm population comprises 4000 models that evolved for 3000 generations. The fitness function used in the genetic algorithms was the maximization of BEDROC values. The enrichment parameters of the best performing model were: Boltzmann-Enhanced Discrimination of ROC (BEDROC) = 0.258, the Enrichment Factor (EF)Even when the best model is good, different models  could identify different combinations of targets and off-targets for accomplishing a desirable screening performance. Therefore, instead of using the best model, we selected the top 275 models  for the evaluation of the targets’ relevance. We expected in this multi-objective model that desirable targets as well as undesirable targets would be differently selected and consequently that their relevance could change. Because different models could provide different sets of selected targets, and because an individual analysis of each of the 275 models is computationally expensive, we first performed a cluster analysis to group similar models A. BasicaConsidering the obtained dendrogram, we selected four clusters of models. All models in each cluster were used to score the frequency of selection of the preeclampsia targets and off-targets. In this context, the amount of models selecting preeclampsia targets or off-targets was considered to be a measure of their significance for the early recognition of desirable drugs B.Each cluster of models did not provide exactly the same ranking of targets/off-targets. However, they were very similar. We performed another grouping of models using 7 clusters instead of 4 (data not shown) that provided similar results to those obtained with 4 clusters. Among preeclampsia targets, AR and VDR were always selected among the top 5 targets in all clusters. Progesterone and estrogen receptors are known to be involved in preeclampsia pathogenesis  and vitaIn the group of off-targets, it is interesting to note that REN is better ranked than ACE. Actually in clusters 1 to 3, REN is among the top prioritized off-targets. Similarly, ABCG2 (the most relevant in the four clusters) is involved in bile secretion. Polymorphisms in ABCG2 had been associated with uricemia , which iScreeningData.txtIn the process of optimization through genetic algorithms to finally obtain the multi-objective model (or models in our case), each of the 55 models predicted the probability of the 14,686 molecules being active/inactive against all 55 proteins  to be unpractical at this point. Repurposing strategies based on gene signatures had been successfully applied for repurposing topiramate  and statAs previously discussed, several proteins in the target space derived from the drugs related to preeclampsia management  enrich mi) of each repurposed drug was obtained in each cluster. Therefore, in order to explore the general similarities of the desirability values between each cluster, a correlogram was computed (Considering that each cluster (even when not being very different) could probably prioritize different molecules, we decided to perform the repurposing across each cluster. Thus, the global desirability  flunisolide and budesonide which are corticoids with anti-inflammatory effects, (2) ambroxol and bromhexine with a mucolytic effect, (3) oxymetazoline and phenylephrine which are α-adrenergic agonists and (4) chlorcyclizine which is an antihistaminic of the phenylpiperazine class (inhibitors of histamine-H1 receptor). Out of these drugs, flunisolide, budesonide, bromhexine and phenylephrine are part of the 36 drugs common to all clusters. Antihistaminic drugs do not increase the risk of fetal malformation or spontaneous abortion and could improve maternal outcomes, including a reduction of preeclampsia ,41,42. TThe group of drugs related to cardiovascular system are: phenylephrine, cyclopenthiazide (both are found in the list of 36 common drugs), acetyldigitoxin, simvastatin, ouabain, cholesterol, lidoflazine and hydrocortisone valerate. Cyclopenthiazide is described as a type of diuretics and is also used in hypertension management. The use of diuretics during pregnancy is well known, specially nifedipine  which isIn the group of anti-infective drugs and in our list of repurposed drugs common to all models, we found the antibiotics erythromycin, abacavir, clarithromycin, azithromycin, voriconazole roxithromycin and cefiderocol. None of these drugs, except for azithromycin, have received focus on their possible effect in preeclampsia in any previous study. Azithromycin  with a current clinical trial (NCT03233880) has indirect evidence of facilitating preeclampsia management . InteresIn our ATC codes (and repurposed drugs) there are other two interesting groups: genito urinary system and sex hormones , as well as alimentary tract and metabolism drugs . The hormone imbalance during pregnancy is well known and is a reason behind the proposal of progesterone as preventive candidate . The identified drugs are: desogestrel, mifepristone, ethinylestradiol, estriol, ulipristal, lynestrenol , nystatin, estradiol, tolterodine, norethynodrel, estradiol cypionate and estradiol valerate. Nystatin is an antifungal drug used for candidiasis treatment and no evidence related to preeclampsia was found. In this big group of drugs, desogestrel, ulipristal and lynestrenol are contraceptives. Moreover, a recent study shows that a reduction of 2-methoxyestradiol levels is related to clinical severity in preeclampsia ,57 and iIn the other highly representative ATC category, alimentary tract and metabolism, we found the following drugs: ergocalciferol, loperamide, sibutramine, drostanolone propionate , calcifediol, vitamin E, cholecalciferol, rolapitant and DL-alpha-tocopherol. From these drugs, ergocalciferol, calcifediol, DL-alpha-tocopherol and cholecalciferol  are related to vitamin D or D3. Vitamin D is already known to be a drug for preeclampsia prevention and was used in our initial set from clinical trials. However, the finding of vitamin E is interesting. Vitamin E together with vitamin C or independently had been found to be protective for preeclampsia ,64,65. PWe consider that the methodology proposed here for drug-repurposing is innovative. The identification of the proteins of interest is carried out using the known drugs under clinical trials for preeclampsia. Based on these proteins, a profile of predicted interactions was computed. Our strategy aims at the identification of new drugs for preeclampsia that have a similar profile of interactions with proteins to those already in use or in clinical trials for preeclampsia management. At the same time, our approach is designed to avoid negative effects of the repurposed drugs. To the best of our knowledge, this is one of the few attempts of accomplishing a completely computational approach for drug repurposing in preeclampsia.Antihypertensive drugs following the mode of action through ACE inhibition and angiotensin receptor antagonist should not be used during pregnancy because of undesirable effects . Therefore, these targets should be avoided. The groups of targets associated with this type of drugs are labeled here as “Off-Targets”. A total of 41 antihypertensive drugs with those modes of actions and the corresponding targets were identified in DrugBank 5.1.8 . These d(1)All reported interactions with  were extracted from the ChEMBL database version 27.(2)All extracted interactions were labeled as active if interaction values are lower than 10 μM and as inactive otherwise.(3)If more than one report (active or inactive) was available for the same compound-target interaction, then the final criterion (active or inactive) was assigned considering 75% agreement to occur among reports. That is, if at least 75% of the reports did not agree on the same criterion, then the relevant compound was discarded.Additional targets were also extracted from ChEMBL version 27  for theshttps://clinicaltrials.gov/ct2/home), all records related to clinical trials for preeclampsia (prevention and/or treatment) were identified . A manual curation was done considering the following criteria: (1) Combined treatments were excluded, (2) treatments using proteins or inorganic salts were excluded, (3) all clinical trials evaluating drugs in post-partum or during labor conditions were excluded, (4) drugs in phase 1 or 2 (when information is available) were excluded and (5) when results data were present, drugs with negative results were excluded. A total of 19 drugs were finally selected: aspirin, labetalol, lidocaine, methyldopa, nicardipine, nifedipine, pravastatin, progesterone, sildenafil, diltiazem, coenzyme q10, arginine, vitamin D, azithromycin, docosahexaenoic acid, esomeprazole, urapidil, citruline and metformin. The same procedure described above for off-targets identification was applied to these 19 drugs, yielding 120 targets relevant for preeclampsia.From the ClinicalTrials database . For these 155 targets, we extracted all compounds from ChEMBL reporting active and inactive interactions with them, as described above. One dataset per protein including all compounds for which an interaction with it could be established was thus created. For all the compound-target interactions datasets, we performed the following curation steps sequentially: (1) removal of all of the 2214 approved drugs as well as the 13 and 41 drugs used for defining the targets and off-targets groups, (2) removal of all proteins with less than 100 active and 100 inactive compounds. These filters were applied to 55 total proteins and 147,456 compounds-proteins interactions. In this final dataset, there are 35 target, 10 off-targets and 10 targets common to both groups. The common targets were considered as “targets” and therefore 45 proteins were finally classified as “targets” and 10 proteins as “off-targets”.chemblid_data.zip, where each target is identified with its ChEMBL version 27 identification number).All active/inactive compounds for each one of the 55 proteins can be found in Each of the 55 proteins was separately modeled as follows:(1) Each compound was codified as a vector of 1024 features using the Morgan Circular Fingerprint of radius 4 using RDKit sklearn.decomposition) from scikit-learn Python library. With the reduced number of variables, a clustering procedure using the K-means algorithm was carried out. The KMeans function from sklearn.cluster module available in the same Python library was used for clustering. K-mean was performed with a number of clusters in the range 3 – (nPCA+1), where nPCA is the number of principal components obtained in the previous step. For the selection of the optimal number of clusters, we used the maximum value of the Silhouette coefficient. For this computation, we used the function silhouette_score from sklearn.metrics. Finally, extraction of molecules matching the size of the minority class was done by using random selection from each cluster in proportion to the size of the cluster.(2) For unbalanced datasets, the majority class (active or inactive) was reduced to obtain a balanced dataset. For this, a principal component analysis (on the 1024 descriptors) was performed. We kept as many principal components as needed to obtain an explained variance higher than 90% using the PCA function  Form each balanced dataset, 25% was randomly separated for external validation.TPOTClassifier  and accuracy as scoring metrics. Because each model is computed separately, the final pipeline was different for each model. The final model obtained for each target was used to compute its accuracy, precision, recall and f1-score on the external dataset.(4) The TPOT pipeline optimization was used for model constructions . TPOT ischemblid_data_training_external.zip. Additionally, the python script TPOTtestDataFile.py was used to compute the external partition and to identify the optimal model from TPOT optimization. This pipeline optimization generates an optimal model for each target. The optimal models can be found in the tpot_CHEMBLID.zip file, which is the python script used for screening molecules against each target.All active/inactive compounds selected as external sample for each one of the 55 proteins can be found chemblid_data_notused.zip). Therefore, none of these molecules were involved in the model construction. This dataset for virtual screening comprises a total of 14,686 molecules and is provided as ScreeningData.txt).The virtual screening dataset was constructed using: (1) The 19 drugs derived from ClinicalTrials under investigation for preeclampsia treatment and the 41 antihypertensive drugs (used to define off-targets), (2) all active compounds in the external dataset of all the Off-Target proteins and (3) all active compounds excluded in the data balancing for the creation of the training dataset of off-targets compounds (tpot_CHEMBLID.zip). However, we should keep in mind that probably not all targets have the same contribution to the virtual screening. For example, some targets and/or off-targets could be more promiscuous than others and consequently will have a high weight, leading to its inclusion or exclusion in the task of selecting the best drugs. Therefore, after having all models for the 55 proteins, a new question emerged: what is the best way to combine them in order to ensure the maximal recovery of the desirable drugs?All of the 14,686 molecules were evaluated by all 55 models . The genetic algorithm was run using the predictions of the 55 models across the 14,686 compounds. The population size for the genetic algorithm was set to 4000 individuals that evolve for 3000 generations with a mutation probability of 0.4. Individuals were codified as bit strings with a length of 55 bits, one bit per input model. A position was set to 0 or 1 if an individual excludes or includes, respectively, the corresponding target from the virtual screening aggregation. The objective function used of the genetic algorithm was based on a desirability function (Equation (1)) and BEDROC computation as described below.i) was computed as:P and NO are the number of targets for preeclampsia and off-targets, respectively.NFirst, the desirability of each compound  was computed. Thus, BEDROC was maximized during the genetic algorithm evolution.i and a relative ranking xi = ri/N in the whole ranked list. Given these definitions, the area under the accumulation curve AUAC was computed as:The virtual screening performance was evaluated by computing the Area Under the Accumulation Curve (AUAC), the Boltzmann-Enhanced Discrimination of ROC (BEDROC) and the Enrichment Factor (EF) ,77. In bThe EF metric was defined as the number of desirable compounds found in a fraction The maximum value that EF can take is In the above equations, Ra is the rate of desirables molecules in the dataset (n/N) and α is a parameter ensuring that active compounds ranked at the beginning of the ordered list get higher weights than those at the tail. The α parameter was computed from the equation:GA_VS_Model.py and GA_VS.py were used for genetic algorithm optimization based on the probability prediction of each compound to each of the 55 targets .The python scripts When dealing with several genes, it is important to have a statistical strategy capable of establishing their relevance for specific biological processes or metabolic pathways. Enrichment analysis is the most used strategy to accomplish this task. We used ToppCluster  for thisClustersInformations.txt). The target/off target relevance was computed for both clustering strategies. The relevance of all targets (Targets and Off-targets) across each cluster was evaluated as: i,j is 1 if the target “i” was selected in the model “j” and 0 otherwise. In other words, the relevance of a particular targets is the normalized frequency of appearance of the target across all the models belonging to a particular cluster.The selection of the best multi-objective model for screening compounds using genetic algorithms produces several possible models. All models with BEDORC ≥ 0.15 (α = 160.9) were used to analyze the target relevance. However, not all models obtained with this BEDROC cutoff will necessarily include the same set of targets/off-targets. Therefore, in order to consider the possible heterogeneity of the obtained models, a clustering strategy was applied. A cluster analysis on the selected models in the final population of the genetic algorithm with best performance was done using the list of targets and off-targets as a fingerprint. The hierarchical clustering was done using the Euclidian distance with the identification of 4 and 7 clusters. Each model and cluster distribution can be found in DrugBank_Prob_Pred.txt in DrugBankModelsClusters.py script. This process yielded several desirability values for a particular drug (inside each cluster). The global desirability (GDi) of a drug in a particular cluster was computed by the average of all desirability in the cluster. This results in a consensus strategy that integrates the decisions of all multi-objective models rather than only the best model reported by the genetic algorithms in each cluster.Using the 55 models, we computed the active/inactive probability of all approved drugs (see https://www.whocc.no/).For each of the top selected drugs (50 per cluster), we identified their ATC codes (Level 1 and Level 2) using annotations in DrugBank 5.1.8, PubChem  and the The average accuracy of the models obtained for preeclampsia targets and off-target were 0.830 and 0.85 in the external dataset, respectively. We integrated all models using a desirability function and genetic algorithm to optimize the combination of models, leading to the bets initial enrichment in virtual screening. The performance metrics for the best performing model in virtual screening were: BEDROC = 0.258, EF1% = 31.55 and AUAC = 0.831.The analysis of the final models points toward a relevance of AR, VDR, SLC6A2, NOS3 and CHRM4 as targets for preeclampsia. Similarly, the most relevant off-targets identified were: ABCG2, ERBB2, CES1 and REN. Among the top ranked repurposed drugs, estradiol, estriol, vitamins E and D, lynestrenol, mifrepristone, simvastatin, ambroxol and some antibiotics and antiparasitics seem to be safe and in some cases well supported in scientific literature as potential therapeutic alternatives for preeclampsia. On the other hand, the majority of the potential drugs identified herein remain completely unstudied focusing on their effects on pregnancy, preeclampsia or even placentation and we consider that they deserve further investigation.


Background: Diabetes mellitus (DM) is associated with increased risk of sudden cardiac death (SCD), particularly in patients with heart failure with preserved ejection fraction (HFpEF). However, there are no known biomarkers in the population with DM and HFpEF to predict SCD risk.Objectives: This study was designed to test the hypothesis that osteopontin (OPN) and some proteins previously correlated with OPN, low-density lipoprotein receptor (LDLR), dynamin 2 (DNM2), fibronectin-1 (FN1), and 2-oxoglutarate dehydrogenase-like (OGDHL), are potential risk markers for SCD, and may reflect modifiable molecular pathways in patients with DM and HFpEF.Methods: Heart tissues were obtained at autopsy from 9 SCD victims with DM and HFpEF and 10 age and gender-matched accidental death control subjects from a Finnish SCD registry and analyzed for the expression of OPN and correlated proteins, including LDLR, DNM2, FN1, and OGDHL by immunohistochemistry.Results: We observed a significant upregulation in the expression of OPN, LDLR, and FN1, and a marked downregulation of DNM2 in heart tissues of SCD victims with DM and HFpEF as compared to control subjects (p < 0.01).Conclusions: The dysregulated protein expression of OPN, LDLR, FN1, and DNM2 in patients with DM and HFpEF who experienced SCD provides novel potential modifiable molecular pathways that may be implicated in the pathogenesis of SCD in these patients. Since secreted OPN and soluble LDLR can be measured in plasma, these results support the value of further prospective studies to assess the predictive value of these plasma biomarkers and to determine whether tuning expression levels of OPN and LDLR alters SCD risk in patients with DM and HFpEF. Heart failure with preserved ejection fraction (HFpEF) is the most common form of heart failure, affecting more than 3 million adults in the United States, and is represented by multiple subgroups or phenotypes . Type 2 A common cause of non-ischemic SCD in younger subjects is primary myocardial fibrosis , 5, whicOsteopontin (OPN) is a matricellular pro-fibrotic phosphorylated glycoprotein whose upregulation contributes to various pathological diseases such as cancers, chronic kidney disease, atherosclerosis, and adverse cardiac remodeling . In expeIn HF patients at high risk for SCD, plasma OPN and Galectin-3 levels were associated with sustained ventricular tachycardia and ventricular fibrillation . Plasma In this study, we investigated the expression levels of OPN and correlated proteins, dynamin2 (DNM2), low density lipoprotein (LDLR), 2-oxoglutarate dehydrogenase-like (OGDHL), and fibronectin-1 (FN1), in the hearts of SCD victims who had both DM and HFpEF. The correlation of the selected proteins with OPN is based on our previous findings that these proteins were positively or negatively correlated with OPN expression in the heart and/or kidney in a mouse model of HFpeF , 14. OurA death was classified as sudden if it was either a witnessed event within 6 h of the onset of symptoms or an unwitnessed death within 24 h when the subject was last seen alive in a normal state of health. The current criteria for SCD were chosen to cover as many subjects with SCD as possible, considering that many subjects with SCD would be found dead and would have been missed with the 1 h definition for SCD originally proposed by Hinkle and Thaler . All of Autopsied human heart samples from the left ventricular free wall from 9 SCD victims with DM and HFpEF and 10 previously healthy individuals who died as a result of an accident were obtained from a Finnish SCD registry as paraffin-embedded sections and analyzed for the expression of seven proteins by immunohistochemistry see .The study complies with the Declaration of Helsinki and was approved by the Ethics Committee of Northern Ostrobothnia Hospital District and the National Authority for Medicolegal Affairs . Consent from next of kin was waived by the Ethics Committee since according to the Finnish law, medicolegal autopsy does not require consent.The slides with paraffin-embedded heart sections were incubated for 45 min at 70 degrees, then dewaxed with two 5 min xylene washes and hydrated by 3 min graded ethanol washes of 100% (twice), 95, 80, and 70%, followed by two 4 min water immersions. Heat-mediated antigen retrieval was then done using 1X Citrate Antigen Retrieval Buffer (Ab93678) in a steamer for 75 min followed by permeabilization using 0.2% Triton X-100 (Sigma-Aldrich) in PBS for 30 min, and then blocking with 10% donkey serum in 1% TBST for 45 min. The slides were then incubated with the following antibodies for 2 h at room temperature: osteopontin , low density lipoprotein receptor , dynamin 2 , fibronectin 1 , 2-oxoglutarate dehydrogenase-like , very low density lipoprotein receptor , and desmoplakin . Slides were then washed with PBS and staining was detected by incubation with various species-specific biotinylated secondary antibodies  at room temperature for 30 min, followed by DAB peroxidase (HRP) amplification . Primary and secondary antibody incubations were done in 10% donkey serum in 1% Tris-Buffered Saline, 0.1% Tween® 20 Detergent (TBST). Each single-antibody staining was repeated 3 times (on 3 different slides from the same paraffin block). No-primary control staining was performed with each of the secondary antibodies used.The stained slides were scanned at 20-X magnification using the Olympus VS120–L100 Virtual Slide Microscope . Images were quantified using ImageJ (NIH) by 2 independent examiners blinded to the groups. For each sample, 5 images (of 5 fields) were used for quantification and the average positive area normalized to the total stained area (as percentage) was used for statistical analysis.P-values were calculated using unpaired Student's t-tests and p < 0.05 was considered significant. Repeated symbols represent p-values of different orders of magnitude, i.e., *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001. Similar significant differences were confirmed by two independent examiners.For all experiments, N refers to the number of individual subjects. All data are expressed as mean ± SEM. p < 0.05, p < 0.0001, We have previously shown that OPN and LDLR are highly expressed in the renal tubules of a mouse model of HFpEF, and that OPN knockcout reduced the pathologically high levels of LDLR and improved renal and cardiac function , 14. We p < 0.05, We have previously shown that OPN and DNM2 are highly expressed in the renal tubules of a mouse model of HFpEF, and that OPN knockcout reduced the pathologically high levels of DNM2 and improved renal and cardiac function , 14. Whep < 0.05, We have previously shown that in a mouse model of pressure overload, treatment with an OPN aptamer prevented cardiomyocyte hypertrophy and cardiac fibrosis, blocked OPN downstream signaling, and reduced expression of extracellular matrix proteins including FN1 . CardiacWe recently reported that OPN negatively regulates the expression of mitochondrial enzyme, OGDHL in the heart of a mouse model of HFpEF, and that OGDHL expression is downregulated in cardiac biopsies from patients with HFpEF and DM . We inveDiabetes Mellitus (DM) is a major public health problem affecting ~29.1 million Americans. DM is present in about 45% of patients with heart failure with preserved ejection fraction (HFpEF). Furthermore, SCD occurs in ~20% of HFpEF patients . Notablyin vivo. Therefore, we analyzed the protein expression levels of both OPN and its related molecules (previously shown to be correlated to OPN expression), as well as other proteins not previously correlated to OPN, to serve as negative controls.OPN is a matricellular protein that has been implicated in many inflammatory and profibrotic events, especially in the heart. In patients with severe aortic stenosis, elevated OPN levels correlated with increased rates of atrial arrhythmia, and an increased risk of death . In our 2-adrenergic receptor in cardiac cells  , 27. In Our team previously reported that the DSP gene, which codes for the structural protein Desmoplakin, harbors 2 variants associated with sudden cardiac death in subjects with primary myocardial fibrosis . FindingThe dysregulation of OPN, LDLR, DNM2, and FN1 protein levels in heart tissues . The patients/participants provided their written informed consent to participate in this study.KJ-W performed the experiments. MP, DR, KY, and LS analyzed the data and prepared the figures. MP, DR, KY, AL, AM, RG, LT, JG, RM, JJ, and LS interpreted the results of experiments. MP, DR, and KY drafted the manuscript. MP, DR, KY, KJ-W, AL, AM, RG, LT, JG, RM, JJ, and LS edited and revised the manuscript and approved the final version of the manuscript. All authors contributed to the article and approved the submitted version.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.


In the field of sewage treatment, the identification of polyphosphate-accumulating organisms (PAOs) usually relies on biological experiments. However, biological experiments are not only complicated and time-consuming, but also costly. In recent years, machine learning has been widely used in many fields, but it is seldom used in the water treatment. The present work presented a high accuracy support vector machine (SVM) algorithm to realize the rapid identification and prediction of PAOs. We obtained 6,318 genome sequences of microorganisms from the publicly available microbial genome database for comparative analysis (MBGD). Minimap2 was used to compare the genomes of the obtained microorganisms in pairs, and read the overlap. The SVM model was established using the similarity of the genome sequences. In this SVM model, the average accuracy is 0.9628 ± 0.019 with 10-fold cross-validation. By predicting 2,652 microorganisms, 22 potential PAOs were obtained. Through the analysis of the predicted potential PAOs, most of them could be indirectly verified their phosphorus removal characteristics from previous reports. The SVM model we built shows high prediction accuracy and good stability. Phosphorus (P) is one of the key element controlling the normal functioning of many organisms in the ecosystem , as the most economical strategy was widely used in wastewater treatment plants (WWTP) . New long-chain genome sequences were obtained by splicing the genome sequences in MBGD database to serve as training databases.Step 2: Data processing was conducted to get a series of s the similarity between genome sequences, based on the database formed from MBGD (Step 1). Minimap2 was used to compare the obtained genome sequences in pairs, and read the overlap. By calculating the overlap, the similarity between genome sequences was achieved.*6,318. The accuracy of the SVM model is verified by grouping the microorganisms reasonably. In addition, over 2,000 species of bacteria were identified based on this SVM model to predict the potential PAOs.Step 3: The SVM model based on the prediction of PAOs was established. Through feature selection of the above similarity, we got a matrix of 1http://mbgd.genome.ad.jp/). As its unique feature, MBGD allows users to perform orthology analysis among any specified set of organisms  is one of the most successful machine learning algorithm in the prediction field developed by Vapnik . It has w represents the weight vector, b is the bias parameter, and x indicates the input vector in the sample space. Taking binary classification problem as an example, to correctly classify samples, all samples are required to meet the following constraints:where i and penalty factor C are added. The optimization function and constraints are as follows:However, in practice, some abnormal points that cannot be linearly separated often exist. In order to solve this problem, a slack variable ξFor simplifying the calculation, the above equation can be transformed into solving the saddle point of the Lagrange equation by using the Lagrange multiplier. By applying the duality theorem, the final optimization problem is presented as:i is Lagrange multiplier.where λThis problem can be solved by using quadratic programming. The final linear discriminant function that is used for the classification of new data can be achieved as follows:*6318. Since the feature dimension we got is already very high, there is no need to perform higher-dimensional mapping by other kernel functions. Therefore, in this method, we employed the LN function to train our predictive models, which is defined as:However, after mapping the original space into the higher dimensional feature space, different inner product kernel functions will form different algorithms. As we know, four types of kernel functions are available in SVM including polynomial kernel (PL), sigmoid kernel (SIG), radial basis function (RBF) and linear kernel (LN). Besides, the prediction accuracy would be also enhanced with appropriate kernel function. In the previous stage, we have performed a pairwise comparison of the gene sequences of the 6,318 strains in MBGD. By calculating the similarity, we get a matrix of 1The kernel function is used to transform data into two classes consisting of unknown bacteria and PAOs {0, 1}.The optimal classification function can be constructed as follows:*6318 to complete the feature selection.Experiments adopted an MBGD data which combined the Reference Sequences (RefSeq) database of National Center for Biotechnology Information (NCBI), the original GenBank entry, and the Gene Trek in Prokaryote Space (GTPS) provided by DNA Data Bank of Japan (DDBJ). The genome sequences of 6,318 microorganisms were provided by MBGD. However, the genome sequence of each microorganism provided by MBGD is divided into several thousand groups that cannot be directly compared. Therefore, we made a reasonable splicing of the microbial genome sequences, so that each microbial obtained a long chain of genome sequence. Feature selection was used before SVM algorithm for choosing a subset of the features that is clean of redundancies. Moreover, it is necessary to select highly correlated features over other features to improve the prediction accuracy in the SVM model. After obtaining the microbial genome sequences, minimap2 which is a general-purpose alignment program to map DNA or long mRNA sequences against a large reference database was used to compare the genome sequences, and read the overlap. By calculating the overlaps, the similarity between the microbial genome sequences were available. We obtained a matrix of 1All the 6,318 microbial genome sequences were provided by MBGD, including 1,833 known PAOs, such as Dechloromonas, Pseudomonas, Rhodopseudomonas, and Klebsiella. The remaining 4,485 microbes have not been proved whether they are PAOs. All the 1,833 known PAOs and the remaining 4,485 microbes were selected to form the sample database of the SVM model. All the data were classified into , 0 for the microorganisms that are uncertain whether they are PAOs, and one for the PAOs. Subsequently, the features obtained were trained by the SVM classifier, which were then evaluated with 10-fold cross-validation to test the accuracy of the algorithm . 10-foldDue to the high accuracy and good stability of the SVM model, it can be used to predict PAOs. In this experiment, we selected 1,833 of all known PAOs, and randomly selected 1,833 from the remaining microorganisms as the negative samples to test the model. Subsequently, we made predictions on the remaining 2,652 microorganisms and identified potential PAOs that may not be discovered .Anoxybacillus flavithermus WK1, Arcanobacterium haemolyticum DSM 20,595, Anoxybacillus sp. B7M1 b7m1, Anoxybacillus sp. B2M1 b2m1, Arthrobacter phenanthrenivorans Sphe 3, Amphilbacillus xylanus NBRC 15112, Brevibacillus brevis NBRC NBRC 100599, Corallococcus coralloides DSM 2259, Arthrospira sp. PCC 8005, Salmonella enterica subsp. Enterica serovar Choleraesuis SCSA50, Campylobacter jejuni subsp. jejuni RM3196, Pannonibacter phragmitetus 31801, Helicobacter pylori ML1, Shigella flexneri 2a 981, Lactobacillus plantarum JBE245, Sulcia muelleri PUNC, Jannaschia sp. CCS1, Kinetoplastibacterium oncopeltii TCC290E, Thiomonas sp. 3As, Teredinibacter turnerae T7901, Wolbachia endosymbiont TRS, Wolbachia endosymbiont of Drosophila simulans wHa. All the above 22 kinds of microorganisms are potential PAOs which have not been reported, and their characteristics are analyzed as follows.It can be seen from the prediction results that we have predicted 22 potential PAOs, namely: A. flavithermus WK1 was isolated from the waste water drain at the Wairakei geothermal power station in New Zealand  was released in 2013, and the type strain was confirmed to utilize xylan from oat spelt and larchwood, and to grow between pH 8 and pH 10  under extreme conditions  for its nutritive value and oxygenic properties in the Micro-Ecological Life Support System Alternative (MELiSSA) life support system. It is a photosynthetic prokaryote that plays a crucial role in the Earth's nitrogen and phosphorus cycles. Due to the unique photosynthesis, it has a certain absorption effect on phosphorus (Janssen et al., S. enterica subsp. Enterica serovar Choleraesuis SCSA50, C. jejuni subsp. jejuni RM3196 and S. flexneri 2a 981 can cause severe invasive disease in humans just like food-borne bacterial gastroenteritis and diarrheal disease (Hughes and Cornblath, H. pylori ML1 is associated with chronic active type B gastritis and peptic ulcer diseases (Chaun, L. plantarum JBE245 is a member of L. plantarum which is isolated from malolactic fermentation of apple juice (Heo and Uhm, L. plantarum strains possess organophosphorus pesticide-degrading activity (Li et al., L. plantarum JBE245 was probably an undiscovered PAOs. S. muelleri PUNC, T. turnerae T7901, K. oncopeltii TCC290E, W. endosymbiont TRS, and W. endosymbiont of D. simulans wHa are as intracellular endosymbionts found in specialized cells in organisms (James and Ballard, s Chaun, . These pA. flavithermus WK1, Anoxybacillus sp. B7M1 b7m1, Anoxybacillus sp. B2M1 b2m1, and A. xylanus NBRC 15112, the accuracy of SVM model is proved indirectly. There are also some microorganisms, just like A. haemolyticum DSM 20595, A. phenanthrenivorans Sphe 3, and C. coralloides DSM 2259, belonging to Actinobacteria or part of gene sequences are the same as Actinobacteria. Some work have been reported that most of the Actinobacteria may be PAOs. Some others are the endosymbionts ingesting P from the hosts to maintain the growth, but not common in activated sludge. It is worth noting that L. plantarum JBE245, belonging to L. plantarum, has the degradation characteristics for organophosphorus. All of them have been predicted by SVM model which can be seen in the The following information can be obtained by analyzing the predicted PAOs. Although Anoxybacillus and Amphibacillus are the members of Bacillus which is considered as PAOs, they are classified separately in MBGD. Through the recognition of *6,318 was achieved. The features were trained by the SVM model, and then they were evaluated with 10-fold cross-validation. The average accuracy we got is 0.9628 ± 0.019. Therefore, the SVM model shows high prediction accuracy and good stability. By predicting 2,652 microorganisms, 22 potential PAOs were obtained. Through the analysis of the predicted potential PAOs, most of them could be indirectly verified their phosphorus removal characteristics from previous reports. This result also further confirmed the accuracy of the model we built. This development illustrated a novel and efficient method to accelerate the screening and identification of microorganism. We also look forward to expanding this work to predict other functional bacteria to provide more possibilities for water treatment.The present work presented a high accuracy SVM algorithm to realize the rapid identification and prediction of PAOs. In this work, all the 6,318 microbial genome sequences were obtained from MBGD. Before the SVM model was established, minimap2 was used to compare the genome sequences. By calculating the similarity between the microbial genome sequences, a matrix of 1http://mbgd.genome.ad.jp.Publicly available datasets were analyzed in this study. This data can be found here: BL wrote the paper and did the experiments. JN provided ideas of this work and supervised this work. XZu revised this manuscript. XZh researched the literature. QX organized the data. All authors contributed to the article and approved the submitted version.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.


TM screening software, using homemade accurate mass databases, was proposed as a source of chemical descriptors, to address the characterization, classification, and authentication of paprika. A total of 126 paprika samples from different production regions—Spain (La Vera PDO and Murcia PDO) and the Czech Republic, each including different flavor varieties, were analyzed. UHPLC-HRMS polyphenolic profiles showed to be good chemical descriptors to achieve paprika classification and authentication, based on the production region, through principal component analysis and partial least squares regression-discriminant analysis, with classification rates of 82%, 86%, and 100% for La Vera PDO, Murcia PDO, and the Czech Republic, respectively. In addition, a perfect classification was also accomplished among the flavor varieties for the Murcia PDO and Czech Republic samples. By employing the UHPLC-HRMS polyphenolic and capsaicinoid profiles as chemical descriptors, acceptable discrimination among La Vera PDO flavor varieties was also achieved.Society’s interest in the quality of food products with certain attributes has increased, the attribute of a Protected Designation of Origin (PDO) being an effective tool to guarantee the quality and geographical origin of a given food product. In Spain, two paprika production areas with PDO (La Vera and Murcia) are recognized. In the present work, targeted UHPLC-HRMS polyphenolic and capsaicinoid profiling through the TraceFinder Capsicum, which belongs to the Solanaceae family [C. annuum, C. chinense, C. baccatum, C. frutescens, and C. pubescens, grown in different parts of the world, including wild, semi-domestic, and domestic ones; Capsicum annuum is the most common among these [Paprika is a red powder spice with a very characteristic flavor obtained after the drying and grinding of certain varieties of red peppers of the genus e family . Within ng these ,2. Papring these ,4,5,6.Nowadays, food manufacturers, as well as the general public, are increasingly concerned about the quality of food with certain attributes, and as a consequence, the demand for food products of a specific geographical origin has increased. Within this context, the attribute of a Protected Designation of Origin (PDO) is an effective tool to guarantee the quality and geographical origin of a given food product. According to the European Union Council Regulation (EC) No. 510/2006 , PDO estCapsicum genus, but the drying process and the fruit varieties change, which also provides specific organoleptic characteristics. Thereby, the red peppers used for the production of “Pimentón de la Vera” belong to the varieties of Ocales  and Bola. Three different flavor varieties are produced, depending on the type of red peppers employed—sweet, spicy, and bittersweet paprika. Additionally, La Vera paprika is characterized by its smoky aroma and taste achieved during the drying process using smoke produced with oak or holm oak wood. In contrast, pepper fruits used for the Murcia PDO paprika belong mainly to the Bola variety, which is sweet and lightweight, and is dried under the sun.In Spain, there are two paprika production areas with PDO attributes recognized by the European Union—the region of La Vera in Cáceres (Extremadura) and the region of Murcia ,9,10. Dep-coumaric, and p-hydroxybenzoic acids [Capsicum species depends on the concentration of capsaicinoids, particularly capsaicin, in the pepper fruit. Usually, sweet paprika has very low capsaicinoid contents and their level increases in the spicy paprika.Paprika is characterized by containing a large number of bioactive compounds with great beneficial health properties, such as carotenoids (provitamins), ascorbic acid (vitamin C), tocopherols (vitamin E), capsaicinoids, and phenolic compounds . Among tic acids . The preic acids ,20,21,22At present, there are several methods for the determination of polyphenolic and phenolic compounds, such as global assays for the evaluation of the total content by colorimetric or fluorimetric methods, or more specific ones, based on separation by capillary electrophoresis, liquid chromatography, and gas chromatography. However, liquid chromatography with UV or electrochemical detection, or that coupled with mass spectrometry is nowadays the technique of choice ,30,31,32It is worth noting that the content of paprika bioactive substances, such as polyphenols and capsaicinoids might differ due to multiple parameters, such as the pepper variety, the climate conditions, growing areas, water resources, ripening stage, agronomy conditions, pre- and postharvest treatments, etc. . As a rev/v) as an extractant agent. As a first approach, 53 polyphenols and phenolic compounds were monitored using the TraceFinderTM software v3.3 , employing a previously established accurate mass database [This work aims for the application of a targeted-UHPLC-HRMS method using an Orbitrap analyzer for polyphenolic and capsaicinoid profiling, and the use of the obtained profiles as sample chemical descriptors, to address the classification and authentication of paprika samples as a function of both production regions and flavor varieties. For this purpose, a total of 126 paprika samples belonging to La Vera PDO, Murcia PDO (Spain), and the Czech Republic were analyzed through the proposed methodology, after applying a simple sample extraction procedure utilizing water:acetonitrile 20:80 , as previously reported [m/z 193.0506) of three selected sweet paprika samples belonging to different production regions . As can be seen, remarkable differences in both signal profile and relative abundances are obtained, depending on the origin of the samples. As a first approach, chromatographic sample raw data was then processed with the TraceFinderTM software (Thermo Fisher Scientific) to obtain the corresponding targeted-UHPLC-HRMS polyphenolic profiles, by using an accurate mass database of 53 polyphenols and phenolic compounds that were previously characterized [5 was established in the screening software to consider a positive match for a given polyphenolic compound in the analyzed samples. In addition, compound confirmation was only granted if all the above-mentioned confirmation criteria  were accomplished. After raw data processing with the TraceFinderTM screening software (Thermo Fisher Scientific), a report was provided for each analyzed sample, depicting the peak areas of all polyphenolic compounds detected and confirmed  report obtained for the La Vera PDO sweet paprika sample). UHPLC-HRMS polyphenolic profiles consisting of the peak areas extracted by the TraceFinderTM software (Thermo Fisher Scientific) in the analyzed paprika and quality control (QC) samples were then obtained.As previously commented, the present work aimed to develop a targeted-UHPLC-HRMS profiling method to obtain discriminant paprika chemical descriptors that able to address sample classification regarding both the paprika production region (and PDO) and the paprika flavor variety. For this purpose, the paprika samples were analyzed through reversed-phase chromatography using a C18 column under universal gradient elution, with water and acetonitrile (both with 0.1% formic acid) as the mobile phase components, after a simple sample extraction with water:acetonitrile 20:80 , when using the UHPLC-HRMS polyphenolic profiling of the 53 studied compounds, as chemical descriptors for PCA a. In conA supervised classificatory chemometric analysis of the paprika samples was done by employing PLS-DA. Thus, while the X-data matrix was similar to that used in the PCA without the QCs, the Y-data matrix indicated the membership of each paprika sample. A total of 4 LVs were required for the construction of the PLS-DA model. The study of the PLS-DA loading plot allowed several phenolic and polyphenolic compounds to be identified, such as vanillic acid, homogentisic acid, umbelliferon, resveratrol, (+)-catechin, arbutin, chlorogenic acid, and veratric acid, which contributed to the separation of the La Vera PDO paprika samples from the other paprikas. Ursolic acid, rosmanol, and cirsimaritin seemed to be the most discriminant variables for the classification of the Murcia PDO samples, while the presence of luteolin-7-O-β-D-glucuronide, 2,5-dihydroxybenzoic acid, gallic acid, and rosmarinic acid seemed to be enhanced in the Czech Republic samples.A 3D PLS-DA model was also built to classify the analyzed samples, according to both production region and flavor variety; the obtained results are shown in PLS-DA models for each paprika production region was also independently evaluated, to study the distribution of samples according to each paprika flavor varieties, and the best score and loadings plots obtained are depicted in p-coumaric acid were the most discriminant for the spicy La Vera PDO samples; and rutin hydrate and luteolin-7-O-β-D-glucuronide were the compounds that were more discriminant for the bittersweet La Vera PDO samples. In relation to the other two groups of paprika samples, sample classification by PLS-DA according to the flavor varieties was perfectly accomplished. Polydatin, rosmanol, ellagic acid, carnosic acid, ursolic acid, genkwanin, and carnosol were among the polyphenolic and phenolic compounds responsible for the discrimination of Murcia PDO spicy paprika  La Vera PDO vs. the other samples, (ii) Murcia PDO vs. the other samples, and (iii) the Czech Republic vs. the other samples. For this purpose, the PLS-DA model was built with a calibration set composed of 70% of the samples belonging to each class, while the other 30% of the samples were used as the test set for prediction purposes.As can be seen, acceptable classification results were obtained. When targeting the La Vera PDO samples vs. all the other samples a, 82% ofTM software (Thermo Fisher Scientific).As previously discussed, UHPLC-HRMS polyphenolic profiling resulted in good chemical descriptors to address the paprika sample classification and authentication regarding the paprika PDO and production region, and also according to the different flavor varieties commercialized, for both the Murcia PDO and Czech Republic paprika. However, only a slight tendency was achieved in the discrimination of the three La Vera PDO paprika varieties a. With tTM screening software (Thermo Fisher Scientific), with both polyphenolic and capsaicinoids accurate mass databases. Again, a threshold signal of 1.0 × 105 was established in the screening software, to consider a positive match for a given targeted compound in the analyzed samples. Compound confirmation was only granted if all established confirmation criteria were established, i.e., accurate mass measurements lower than 5 ppm, and an isotopic pattern agreement higher than 85% for both profiled families of compounds. Retention times and product ion spectra agreement was also employed for the polyphenolic compounds, as characterization with commercially available standards was previously accomplished. UHPLC-HRMS polyphenolic and capsaicinoid profiles, consisting of peak areas extracted by the TraceFinderTM software (Thermo Fisher Scientific) in the analyzed paprika samples, were then obtained and subjected to PLS-DA.Paprika UHPLC-HRMS raw data was then processed with the TraceFinderFocusing only on the La Vera PDO paprika flavor varieties , Unless otherwise stated, all chemicals, standards, and reagents employed in this work were of analytical grade. Fifty-three polyphenolic and phenolic standards belonging to different families  characterized by HRMS in a previous work , were obLC-MS grade water, methanol, acetonitrile, formic acid (98–100%), and acetone were also obtained from Sigma-Aldrich, and hydrochloric acid (37%) was obtained from Merck .Stock standard solutions of all compounds (∼1000 mg/L) were prepared in methanol in amber glass vials. Intermediate working solutions were prepared weekly from these stock standard solutions through appropriate dilution with water. All stock solutions were kept in the refrigerator at 4 °C, for not more than 1 month.® Express C18 porous shell reversed-phase column  provided by Supelco  was employed for the chromatographic separation. A binary system of solvents, based on a 0.1% formic acid aqueous solution (Solvent A) and a 0.1% formic acid in acetonitrile (Solvent B) was used to establish the elution gradient at a mobile phase flow rate of 0.3 mL/min. The elution gradient employed was as follows: 0–1 min, isocratic conditions at 10% B, 1–20 min, linear gradient from 10% to 95% B; 20–23 min, isocratic step at 95% B; 23–24 min back to initial conditions at 10% B; and from 24 to 30 min, isocratic elution at initial conditions for column re-equilibration. An injection volume of 10 µL (full-loop mode) was employed, and the column was kept at room temperature.Sample analyses were performed using a UHPLC system  equipped with a quaternary pump, an autosampler, and a column oven. The UHPLC system was coupled to a Q-Exactive Orbitrap HRMS system (Thermo Fisher Scientific), with a heated electrospray ionization source (HESI-II) operating in a negative ionization mode. An Ascentism/z range from 100 to 1500 at a mass resolution of 70,000 full width at half-maximum , an automatic gain control (AGC) target (number of ions to fill the C-Trap) of 2.5 × 105, and a maximum injection time (IT) of 200 ms. The full MS scan mode was followed by a data-dependent scan mode operating in a product ion scan, obtained by applying stepped normalized collision energies (NCE) of 17.5, 35, and 52.5 eV, with a fixed first m/z value of 50 Da for the registered m/z product ion range. This data-dependent scan mode was activated with an intensity threshold of 1.0 × 105, with a quadrupole isolation window of 0.5 m/z. At this stage, a mass resolution of 17,500 FWHM (at m/z 200), an AGC value of 2.0 × 105, and an IT value of 200 ms were employed.Nitrogen was employed as sheath gas, sweep gas, and auxiliary gas for the HESI-II ionization source at flow rates of 60, 0, and 10 a.u. (arbitrary units), respectively. HESI-II heater temperature and capillary voltage were set at 350 °C and −2.5 kV, respectively. The capillary instrument temperature was maintained at 320 °C, and an S-Lens RF level at 50 V was used. The Orbitrap analyzer was tuned and calibrated every 3 days, by employing a commercially available calibration solution for this purpose (Thermo Fisher Scientific). HRMS spectra were acquired in full MS scan mode with an A total of 126 paprika samples purchased from local markets in Spain and the Czech Republic were analyzed. Samples from the Czech Republic were also analyzed in order to demonstrate the applicability of the proposed methodology, to discern the different countries of origin among the samples. Among them, 72 samples were from La Vera PDO , 24 samples from the Murcia PDO (12 sweet and 12 spicy flavors), and 30 samples were from the Czech Republic .v/v) solution, by stirring in a vortex mixer  for 1 min, and by sonication  for 15 min. Then, centrifugation for 30 min at 4500 rpm  was performed, and the supernatant was transferred into a 2 mL glass injection vial, after filtration through 0.45 µm nylon filters . The extracts were kept in the freezer at −18 °C, until analysis.Sample treatment was carried out, following a previously described method ,29,46. IA quality control (QC) solution was prepared by mixing 50 µL of each paprika sample extract. This QC solution was used to evaluate the repeatability of the proposed method and the robustness of the chemometric results.All paprika samples were analyzed randomly with the proposed UHPLC-HRMS method to prevent the variations caused by the sequence duration. Moreover, a QC and an instrumental chromatographic blank of acetonitrile were also analyzed every 10 paprika samples.TM v3.3 software (Thermo Fisher Scientific), by applying two user-targeted accurate mass database lists: (i) one list comprised the 53 previously characterized polyphenol and phenolic compounds [TM software (Thermo Fisher Scientific) to assess the presence of the targeted compounds in the analyzed samples.UHPLC-HRMS raw chromatographic data were processed by the TraceFinderompounds , and  obtained from Eigenvector Research . A theorTM software (Thermo Fisher Scientific). These polyphenolic profiles were then employed as sample chemical descriptors. The dimension of the data matrix was 126 (samples + QCs) × 53 analyte peak areas. When both, polyphenolic and capsaicinoid profiling was considered, the peak area values of the 12 studied capsaicionids and capsinoids were also incorporated in the data matrix, providing a matrix dimension of 140 (samples + QCs) × 65 analyte peak areas. In all cases, normalization pretreatment concerning the overall analyte concentration was applied to provide similar weights to all samples. Y-data matrix in the PLS–DA models was defined by the membership of each sample in the corresponding class. Scatter plots of scores and loadings from principal components (PCs), when PCA was used and from latent variables (LVs), for PLS-DA, were employed to study the distribution of samples and variables . Information regarding correlations and dependences for the targeted compounds with the paprika products analyzed was, thus, visualized. The most appropriate number of LVs was established by considering the first significant minimum point of the cross-validation (CV) error from a Venetian blind approach. Additionally, the applicability of the built chemometric models was proved by validating them on an independent prediction set. In particular, the validation of the PLS-DA models was carried out by using 70% of a sample group as the calibration set, while the remaining 30% constituted the prediction set.For polyphenolic profiling, X-data matrices to be treated by PCA and PLS-DA consisted of the peak area values of the 53 polyphenol and phenolic acids found and confirmed in the analyzed QCs and the paprika samples, using the TraceFinderTM screening software v3.3. (Thermo Fisher Scientific) without the requirement of target compound quantitation through commercially available standards, was proposed with the aim of obtaining a feasible, simple, and cheaper methodology. For this purpose, two homemade accurate mass databases consisting of 53 polyphenolic compounds, and 12 capsaicinoid and capsinoid compounds, were employed.In the present work, targeted UHPLC-HRMS (Orbitrap) polyphenolic and capsaicinoid profiles proved to be adequate sample chemical descriptors for the characterization, classification, and authentication of paprika samples, according to both their PDO and production region and their flavor varieties. UHPLC-HRMS bioactive compound profiling using the TraceFinderExploratory analysis through PCA and a classification study through PLS-DA, using the obtained UHPLC-HRMS polyphenolic profiles, showed good discrimination capabilities among the different paprika PDO and production regions, in general, with acceptable PLS-DA classification ratios. Additionally, perfect discrimination and authentication capabilities were also accomplished for the Murcia PDO and the Czech Republic flavor varieties. In contrast, only a slight discrimination was achieved for the three commercially available La Vera PDO flavor varieties.The capability of the proposed methodology to address the La Vera PDO flavor variety classification and authentication clearly improved when UHPLC-HRMS polyphenolic profiles were combined with the UHPLC-HRMS capsaicinoid and capsinoid profiles. A perfect separation of the spicy flavor variety from the other two groups, as expected by the high presence of capsaicinoid-based compounds in the spicy group of samples, and an acceptable discrimination between both sweet and bittersweet La Vera PDO varieties were accomplished.Therefore, the proposed target-UHPLC-HRMS polyphenolic and capsaicinoid profiling methodology resulted in a feasible, simple, and relatively cheap alternative to address the characterization, classification, and authentication of paprika samples.