Prunus accessions. The trnH-psbA and trnL-trnF intergenic spacers were able to distinguish and identify only four Prunus species. The average aligned length was 316–352 bp and 701–756 bp for trnH-psbA and trnL-trnF, respectively. The overall evolutionary divergence was higher in trnH-psbA than trnL-trnF. The transition/transversion bias (R) recorded as 0.59 in trnL-trnF and 0.89 in trnH-psbA. The number of invariable sites, nucleotide diversity (Pi), and the average number of nucleotide differences (k) was higher in the trnH-psbA region. The trnL-trnF records was above the other region in the number of variable sites, number of singleton variable sites, and the parsimony informative sites. Phylogenetic relationships among the 25 accessions of Prunus species were investigated. Most of the different Prunus species clustered in a homogenized distribution in both regions, except for the plum (P. domestica) accession (African Rose) was assigned with the peach (P. persica) accessions. The two intergenic cpDNA trnH-psbA and trnL-trnF were able to distinguish and identify the four Prunus species accessions.Two intergenic spacers cpDNA barcoding regions were used to assess the genetic diversity and phylogenetic structure of a collection of 25  A potential method to meet this identification is DNA barcoding, which is the identification of species by a short universal DNA sequence that exhibits a sufficient level of variation to discriminate among species , 2. The r plants –9. Recenr plants .Prunus (or stone fruits) belongs to family Rosacea, is an economically important genus with approximately 200 species, grown in moderate regions [P. domestica L.), japanese plum , sweet cherry (P. avium L.), sour cherry (P. cerasus L.), peach (P. persica (L.) Batsch), nectarine (P. persica var. nucipersica (Suckow) C. K. Schneid.), almond (P. dulcis (Mill.) D. A. Webb.), and apricot (P. armeniaca L.) [Prunus persica includes peach and nectarine. The nectarine (P. perscica var. nucipersica) is a mutant strain of peach (P. persica), with special unique fruit characteristics [Prunus genome is relatively small with about 250–300 Mbp [Prunus chromosomes is (x = 8). Almond (P. dulcis), peach (P. persica), apricot (P. armeniaca L.), sweet cherry (P. avium L.), Japanese plum  are diploids (2n = 2 × = 16). Unless the European plum (P. domestica L.) is hexaploidy (2n = 6 × = 48), it is supposed resulted from the tetraploid species (P. spinosa L.) and the diploid species (P. cerasifera Ehrh.) [ regions . The mosiaca L.) . Prunus eristics . Prunus –300 Mbp . The basa Ehrh.) . The cora Ehrh.) .Prunus accessions grown in Egypt conserved in the National Gene Bank, utilizing two intergenic DNA barcoding regions (trnH-psbA and trnL-trnF).The overall goal of this study is to assess the genetic diversity and phylogenetic structure of a collection of 25 Prunus genotypes belonging to 5 species grown in Egypt, collected from different locations. The twenty-five Prunus accessions were collected, conserved, and maintained in the gene bank greenhouses. The samples used in this study are demonstrated in Table The current research conducted using 25 Prunus samples were identified using two chloroplast DNA intergenic regions (trnH-psbA and trnL-trnF). The PCR reaction amplifications were performed on BioRad™ T100 thermal Cycler (No. 1861096), in 25 µl reaction volume, containing 2X of EmeraldAmp® MAX PCR mix (RR320A), 50 ng gDNA, and 20pMol for each primer. The primer sequence and thermocycling profile of PCR are demonstrated in Table The genomic DNA (gDNA) of the samples was extracted using Qiagen DNeasy kit (cat No. 69104). The DNA was quantified using NanoDrop™ OneC (cat No. 840-329700) and adjusted to 50 ng/µl and used in the reactions. The twenty-five different DNA sequencing was carried out by Potsdam, Institute of Biochemistry and Biology  using an ABI sequencer. All sequences were submitted to NCBI GenBank, USA. GenBank provided accession numbers for the nucleotide sequences of each accession for each of the two loci, as demonstrated in Table trnH-psbA and trnL-trnF for the two loci were subjected to NCBI–BLASTN online tool http://blast.ncbi.nlm.nih.gov/Blast.cgi [Prunus species in this database. Alignments of sequence were achieved by MUSCLE algorithm [The sequences of last.cgi  to checklgorithm . The evolgorithm , other plgorithm .trnH-psbA and trnL-trnF loci, respectively. The trnH-psbA over all evolutionary divergence was higher (0.05) than in trnL-trnF (0.007). The transition/transversion bias (R) recorded as 0.59 and 0.89 in trnL-trnF and trnH-psbA, respectively.The average aligned length was 316–352 bp and 701–756 bp, for trnH-psbA than in trnL-trnF . While, the number of variable (polymorphic) and singleton variable sites were lower (18 and 6) in trnH-psbA than in the other loci (77 and 47). The nucleotide diversity (Pi) and the average number of nucleotide differences (k) in trnH-psbA was lower than the other region. Meanwhile, the number of parsimony informative sites was higher (30) in trnL-trnF than the other region (12), Table The number of invariable sites was higher in trnH-psbA loci length across the twenty-five Prunus accessions ranged from 316 to 352 bp. The nucleotide frequencies for A, T, C and G was 37.6%, 37.6%, 12.4% and 12.4%, respectively. The rate of different transitional substitutions from G to A was equal to those from C to T (16.73). On the other hand, the transversionsal substitution rates was equal as it recorded 10.44 for transversion from T to A, from C to A, and from G to T. While, it reached 3.44 in transversion from G to C, results shown in Table The trnH-psbA chloroplast region  was assigned among the European plum (P. domestica) species accessions in the phylogenetic tree. European plum accessions  were clustered away from the related species accessions. Also, African Rose European plum accession was clustered among the peach accessions. Almond (P. dulcis) samples were homogenized and grouped together in the same group, where the two local accessions (Hash and Adm) were closer to each other than the other two samples. Peach (P. persica) and Nectarine (P. persica var. nucipersica) were grouped in the similar group. The apricot (P. armeniaca) accessions (Hammaway and Canino) constructed together, as they were closer to each other than the other accessions.The Japanese plum accession , and equal in G and C as 17.01%. The lowest rate of transitional substitution events was 5.14 for transition substitution from G to C. While, it was equal rate (9.98) in the transition substitution from T to A, from C to A, and from G to T. The transversion substitution from C to T had the equal value (13.04) as for the value of transversion from G to A  accessions. Second: the Japanese plum  was assigned amomg the European plum (P. domstica) species accessions. The apricot (P. armeniaca) accessions clustered together in two groups, as accessions  clustered in the first group, while accessions  clustered in the second. The European plum (P. domestica) species accessions were clustered in a homogenized groups, except the Japanese plum accession  was assigned with the succari European plum species accession. The almond (P. dulcis) species accessions were grouped together in a related cluster. The peach (P. persica) and nectarine (P. persica var. nucipersica) accessions were grouped in a related groups, where the nectarine (P. persics var. nucipesica) acession was in the same group with Florida Prince, and Early Grand peach. the two accessions  were clustered in a distant groups. The African Rose (European plum) species accession was clustered in the same group with Florida Prince, Early Grand, and Nectarine peach accessions.The tic Fig.  clusteretrnH-psbA and trnL-trnF sequences for the twenty-five Prunus accessions.The concatenated (combined) sequences were assembled and aligned from Prunus species for the 25 Prunus accessions. The most noted observation was that most Prunus species clustered together with the same relative species. Except, the European plum (P. domestica) accession (African Rose) which was assigned with the peach (P. persica) accessions. Also, the Japanese plum  accession assigned with the European plum (P. domestica) accessions.The concatenated-based phylogentic tree Fig.  demonstr(P. dulcis) accessions were clustered together, as the local accessions (Adm and Hash) were near to each other. The apricot (P. armeniaca) accessions samples were clustered together at the same group. The peach (P. persica) and the nectarine (P. persica var. nucipersica) accession samples were related to each other.The two accessions of European plum (Bokra and English) grouped away from the other relative European plum accessions, as thesse accessions were used only for pollination not for commercial purposes. The almond Prunus species [trnH-psbA region sucessefully to detect the fraud of apricot kernels to the almond valuable oil.Teberlet et al.  proposed species , 25, 26. species  used trntrnLUAA-trnFGAA and trnH-psbA, which was first proposed by Teberlet et al. [Prunus species, and were able to characterize the different accessions. The trnL-trnF region had higher values in number of polymorphic sites, number of singleton variable sites, number of parsimony informative sites, nuclotide diversity, and average number of nucleotide differences. Meanwhile, trnH-psbA had evolutionary divergence, transition/transversion bias, monomorphic sites, and sequence conservation values higher than the second region.In the present study, the intergenic chloroplast regions t et al. , were abP. salicina species, as P. salicina species was assigned with P. domestica species. The most notable observation in the phylogentic clusters was that the African Rose European plum accession, was distantly away from the related species, near to peach species accessions. Since this accession breeding ancestors had peach parents (data not published). The Japanese plum accession  is less resolved here as it was assigned among the European plum species (P. domestica) accessions, it could be for the selections proceeded for this adapted old-local variety. The nectarine accession (P. persica var. nucipersica) was assigned properly with peach species (P. persica) accessions, as nectarine is a mutant strain of peach [P. dulcis) and peach (P. persica) is closer to each other, as it was evolutionary hybridized [trnL-trnF regions to identify different Prunus species, indicated little variations because of the monophyletic divergence of Prunus. Batnini et al. [trnL-trnF and trnH-psbA regions in studying the genetic diversity among different Prunus species, resulting in high variability among studied species, with higher average than our obtained results.The two intergenic regions were able to identify only four species, and were not able to identify of peach . It was bridized . Bortiribridized  used trni et al.  used trnPrunus collection. This step is a cornerstone in identifying the conserved Punus germplasm, which will help in the crop development, sustainable use and impeovement of Prunus.The current research constructed the phylogentic relationships of


Dose-response associations showed the strongest effect estimates in neonates whose mothers continued smoking ≥5 cigarettes per day . Furthermore, smoking during the first trimester only was associated with altered neonatal metabolite ratios involved in the Krebs cycle and oxidative stress, whereas continued smoking during pregnancy was associated with inflammatory, transsulfuration, and insulin resistance markers . Thus, fetal tobacco exposure seems associated with neonatal metabolite profile adaptations. Whether these changes relate to later life metabolic health should be studied further.Fetal tobacco exposure has persistent effects on growth and metabolism. The underlying mechanisms of these relationships are yet unknown. We investigated the associations of fetal exposure to maternal smoking with neonatal metabolite profiles. In a population-based cohort study among 828 mother-infant pairs, we assessed maternal tobacco use by questionnaire. Metabolite concentrations of amino acids, non-esterified fatty acids, phospholipids and carnitines were determined by using LC-MS/MS in cord blood samples. Metabolite ratios reflecting metabolic pathways were computed. Compared to non-exposed neonates, those exposed to first trimester only tobacco smoking had lower neonatal mono-unsaturated acyl-alkyl-phosphatidylcholines (PC.ae) and alkyl-lysophosphatidylcholines (Lyso.PC.e) 18:0 concentrations. Neonates exposed to continued tobacco smoking during pregnancy had lower neonatal mono-unsaturated acyl-lysophosphatidylcholines (Lyso.PC.a), Lyso.PC.e.16:0 and Lyso.PC.e.18:1 concentration   Tobacco smoking during pregnancy is a major clinical and public health problem worldwide . SmokingWe examined the associations of fetal tobacco exposure with neonatal metabolite profiles from umbilical cord blood in a population-based cohort among 828 children. We also explored whether any sex specific associations exist.This study was embedded in the Generation R Study, a population-based on a prospective cohort study from fetal life until adulthood in Rotterdam, the Netherlands . The stuAs previously described, information on maternal smoking was collected by postal questionnaires in early pregnancy , mid-pregnancy , and late pregnancy  . In earlp < 0.001). Exposure to environmental smoking was assessed in each questionnaire as the number of hours per day that mothers were exposed to environmental smoke at home and at work. For environmental smoking, the following categories were used: no, occasionally, and daily.Paternal information on tobacco use was assessed by maternal report and paternal self-report during the first trimester of pregnancy. We used maternal reports of paternal tobacco use, given that less fathers (86.4%) completed questionnaires. The inter-rater reliability between maternal and paternal self-reporting was high  were collected by midwife and obstetrician, as described previously . The liqIndividual metabolites were clustered in general metabolite groups based on chemical structure , and in metabolite subgroups based on chemical structure and biological relevance (amino acids: branched-chain amino acids (BCAA), aromatic amino acids (AAA), essential amino acids, non-essential amino acids; NEFA; PC.aa; PC.ae; Lyso.PC.a; Lyso.PC.e; SM: saturated, mono-unsaturated, poly-unsaturated; Carn.a: short-chain, medium-chain, long-chain) . The sumIndividual metabolite concentrations, sums and ratios were square root transformed to normalize metabolite concentrations. To enable comparison of the effect estimates, individual metabolite concentrations and sums and ratios were standardized by calculating standard deviation scores (SDS).Potential covariates were selected based on previous studies and presented as a directed acyclic graphic  1,10,1313,43. Int-test or Mann-Whitney U test for continuous variables. Second, we estimated the associations of maternal smoking during pregnancy  with neonatal metabolite groups, subgroups and individuals using multiple linear regression models. All models were adjusted for maternal age, education, alcohol use, pre-pregnancy BMI, psychopathology score and fetal sex (confounder model). We examined whether these associations were explained by birth weight or gestational age at birth by additionally adjusting for them, since neonatal metabolic profiles correlate with these birth characteristics (birth outcomes model). To examine potential sex-specific associations, we tested the statistical interaction between maternal smoking and offspring sex, and, if significant interactions were present after taking multiple testing, performed stratified analysis by sex. To account for multiple testing, we applied Benjamin-Hochberg correction using an overall false discovery rate of (FDR) < 5% [p-values (p < 0.05) were considered in this specific analysis. As sensitivity analyses, first, we used similar analytical approaches to examine the associations of paternal tobacco smoking with neonatal metabolite profiles (excluding mothers who smoked in pregnancy), and the associations of environmental smoking with neonatal metabolite profiles (excluding parents who smoked in pregnancy). Second, we additionally adjusted the associations between fetal tobacco exposure and neonatal metabolite profiles for first trimester blood folate concentrations. The percentage of missingness in the covariates ranged from 0% to 6.5%, with the exception of psychopathology score (12.2%). To account for missing covariates, we used a multiple imputation approach using the multivariate imputation by chained equations (mice) package to estimate missing information of the covariates in 25 datasets and 100 iterations [First, we assessed the subject characteristics and performed a non-response analysis by comparing children with and without metabolomics data using a chi-squared test for categorical variables and a Student’s DR) < 5% . Becauseerations . All staerations .Of all mothers, 23.8% used tobacco during pregnancy, of whom 9.3% smoked in the first trimester only and 14.5% continued smoking during pregnancy . Mothersp-values < 0.05)   A,B. No d < 0.05) A,B. Mate < 0.05) C. Dose-r < 0.05) C. None o < 0.05) .p-values < 0.05)   A,B. Dose < 0.05) B. Matern < 0.05) C. Matern < 0.05) C. Overal < 0.05) .p-values < 0.05). Among girls only, maternal continued smoking during pregnancy was associated with lower neonatal overall, mono-unsaturated and saturated Lyso.PC.e concentrations   A. Overal < 0.05) B.First, we did not observe associations of exposure to paternal tobacco smoking or environmental smoking with neonatal metabolite profiles (data not shown). Second, the associations of maternal smoking during pregnancy with neonatal metabolite profiles were not explained by first trimester blood folate concentrations (data not shown).Fetal tobacco exposure is associated with alterations in the neonatal phospholipid profile. Neonates exposed to tobacco in the first trimester only had lower neonatal mono-unsaturated PC.ae and Lyso.PC.e.18:0 concentrations, whilst neonates exposed to tobacco throughout pregnancy had lower neonatal mono-unsaturated Lyso.PC.a, Lyso.PC.e.16:0, and Lyso.PC.e.18:1 concentrations. This study reveals differences in the metabolic signatures of neonates exposed to tobacco during pregnancy, both in the first trimester only and continued, as compared to those not exposed using advanced metabolomics analysis.Fetal tobacco exposure leads to early developmental adaptations, such as changes in the physiology, metabolism, and structure of various organ systems . MaternaWe observed that neonates exposed to tobacco in the first trimester only had lower mono-unsaturated PC.ae and Lyso.PC.e.C18:0 concentrations, whilst neonates exposed to tobacco throughout pregnancy had lower mono-unsaturated Lyso.PC.a, Lyso.PC.e.C16:0, and Lyso.PC.e.C18:1 concentrations. Dose-response associations showed the strongest effect estimates in neonates whose mothers continued smoking ≥5 cigarettes. Thus, our study showed that fetal tobacco exposure, both in the first trimester only and continued, is associated with alterations in the neonatal phospholipid profile. Since phospholipids play a key role in embryonic and fetal development, a critical period in which tissues and organs develop, lower phospholipid concentrations may be the result of undesirable structural and functional changes in the metabolism of the unborn child  ,53. ConsA human prospective study in Germany among 40 mother-infant pairs showed that smoking during pregnancy alters both the maternal and neonatal metabolome . Partly We did not observe associations of exposure to paternal tobacco smoking or environmental smoking with neonatal metabolite profiles. These findings suggest that the associations of fetal exposure to tobacco smoke with neonatal metabolite profiles are based on direct maternal smoking only. However, future studies with larger numbers of subjects may enable the identification of potential smaller effect estimates related to the associations of passive maternal smoking on neonatal metabolite profiles.Several molecular and biological processes may represent the underlying mechanism between prenatal tobacco exposure and altered neonatal metabolome profiles. We observed that neonates exposed to tobacco in the first trimester only showed alterations in the metabolite ratios marking process of the Krebs cycle and oxidative stress, whereas those neonates exposed to tobacco throughout pregnancy showed alterations in the inflammatory response, transsulfuration and insulin resistance markers . In lineIncreased inflammatory response, oxidative stress, and lipid metabolism remains poorly understood in the placenta and fetus despite its association with various pathological conditions, such as IUGR, cardiovascular, endocrine and metabolic disorders . Our stuThe strengths of our study are the prospective data collection from early pregnancy onwards, allowing the collection of information on tobacco per trimesters, as well as extensive information on potential confounders. Some limitations need to be discussed. First, the metabolomics data was only available in a subgroup of our multi-ethnic cohort, which consisted of Dutch participants only and relatively high educated and healthy participants as compared to the full cohort . EthniciOur findings suggest that fetal tobacco exposure is associated with alterations in the neonatal phospholipid profile, mainly PC.ae, Lyso.PC.a, and Lyso.PC.e, and alterations in the metabolite ratios marking processes of Krebs cycle, oxidative stress, inflammatory response, lipid metabolism, and insulin resistance. Clearly, understanding the neonatal metabolome profile may yield new targets to predict, prevent and treat cardiovascular, endocrine and metabolic disease in later life.


Primulaceae, Primulawolongensissp. nov. from Wolong National Nature Reserve in Sichuan Province, China. It is very rare and currently only known from its type locality. The new species belongs to subsection Chartacea of the section Petiolares on account of lacking bud scales at flowering, being efarinose and having distinct petiolate leaves with more or less rounded lamina. The new species can be differentiated from other members of the subsection by leaf blade margin dentate, and leaf veins which are not raised, scape shorter than or equal to pedicels, yellow flowers and location of stamens of the corolla tube at thrum flower. Molecular phylogenetic analysis based on nuclear ribosome internal transcribed spacer (nrITS) demonstrated that P.wolongensis was sister to subgen. Auriculastrum. Primulawolongensis is currently known from a single location in Wolong Town, and its conservation status is assessed as Data Deficient (DD).This paper describes and illustrates a new species of  Primula L. is the most widespread genus in Primulaceae. It contains about 500 species mainly distributed throughout the moister and cooler regions of the northern hemisphere  Smith & Forrest, subsect. Davidii  Craib, subsect. Griffithii Smith & Forrest, subsect. Petiolaris-Sonchifoli Smith & Forrest and subsect. Tongolensis Smith & Forrest  in molecular phylogenetic analysis were retrieved from GenBank. Total DNA was extracted exclusively from silica-gel dried leaves via a Plant DNA Isolation Kit (Cat.No.DE-06111). We used the same primers as nrITS) through polymerase chain reaction (PCR). All DNA samples were sent to TSINGKE Biotech Co. Ltd  for sequencing and then deposited to GenBank under the accession number OP901719.Except for the newly generated sequence of ML) analysis based on nrITS dataset using IQ-TREE v1.4.2 (UFboot)  and aligned using MAFFT v7.475  with defE v1.4.2  with bra(UFboot) .Primulawolongensis was sister to subgen. Auriculastrum  A.R.Mast & Reveal, sect. Parryi W.W.Sm. ex Wendelbo and sect. Suffrutescens A.J.Richards. Apart from the sect. Amethystina, which occurs in the Himalayan-Transverse Mountains, species of all other sections of this subgenus are found in Europe, North America, and northern Japan through to British Columbia. The position of P.wolongensis in the genus Primula is clearly divergent between morphological and molecular evidence, and more evidence is needed to understand the evolutionary history of the species.The molecular phylogenetic tree showed that Taxon classificationPlantaeEricalesPrimulaceaeW.B.Ju, Bo Xu & X.F.Gaosp. nov.0C0E648E-E4E9-5D7C-84F7-0C3006978959urn:lsid:ipni.org:names:77311678-1Chartacea, the new species is easily recognized by the following combination of characters: leaf blade margin dentate, leaf veins which are not raised, scape which is shorter or equal with pedicel, corolla yellow and location of stamens of the corolla tube at thrum flower. The new species is morphologically similar to P.arunachalensis Basak & Maiti and P.fenghwaiana C.M.Hu & G.Hao, but can be easily distinguished from P.arunachalensis by its shorter rootstocks, petioles 3–5× as long as leaf blade (versus 1–2× as long as leaf blade), reticulation of veins obscure on both surfaces , corolla lobes apex emarginate (versus corolla lobes margin denticulate to lacerate), heterostylous (versus homostylous). Compared with P.fenghwaiana, the difference of the new species is the petioles 3–5× as long as the leaf blade (versus ca.2/3 the length of the leaf blade), leaf blade base cordate , leaf blade margin irregular dentate (versus margin remotely denticulate), scapes and pedicels sparsely short-stalked glandular (versus densely covered with minute glandular hairs), corollas yellow (versus pink to white), the position of stamens at thrum flower tube on the middle (versus on the apex).Amongst the Chinese members of subsect. 31°04'N, 103°11'E, elevation ca. 3400 m, 18 May 2021 (fl./fr.), Y.H. Cheng & H.Q. Lin XuBo2771 . , is suggested here.The species has so far only been found at its type locality in Wolong Town, Wenchuan county, Sichuan Province. It grows in the cracks of steep wet cliffs covered with moss above the tree-line.DD). Currently, only one population with more than 100 individuals has been found in the type locality. According to the guidelines for using the IUCN Red List categories and criteria (DD)’. Further explorations in the adjacent mountainous tracts are necessary for an adequate assessment.Data Deficient (criteria , the conPrimulasectionPetiolares on account of globose capsule included within the calyx-tube that does not open by valves but apically crumbling at membrane apex at maturity (Figs Following ity Figs –4.Chartacea by having glabrous and efarinose plants, at flowering time devoid of basal bud-scales, more or less rounded blades and slender petioles.Further morphological analysis shows that the new species is allied with subsect. Chartacea, the new species, is morphologically most similar to P.arunachalensis and P.fenghwaiana in its bud-scales lacking at anthesis, slender petioles, rounded blades, and scape shorter than or equal to pedicels, but can be recognized by almost absent rhizomes, morphological features of leaves, inflorescences, and flowers. Further morphological comparisons among P.wolongensis, P.arunachalensis and P.fenghwaiana are shown in Table Including the newly described here, there are nine species reported so far for this subsection . Amongst


Galega orientalis, a leguminous herb in the Fabaceae family, is an ecologically and economically important species widely cultivated for its strong stress resistance and high protein content. However, genomic information of Galega orientalis has not been reported, which limiting its evolutionary analysis. The small genome size makes chloroplast relatively easy to obtain genomic sequence for phylogenetic studies and molecular marker development. Here, the chloroplast genome of Galega orientalis was sequenced and annotated. The results showed that the chloroplast genome of G. orientalis is 125,280 bp in length with GC content of 34.11%. A total of 107 genes were identified, including 74 protein-coding genes, 29 tRNAs and four rRNAs. One inverted repeat (IR) region was lost in the chloroplast genome of G. orientalis. In addition, five genes  were lost compared with the chloroplast genome of its related species G. officinalis. A total of 84 long repeats and 68 simple sequence repeats were detected, which could be used as potential markers in the genetic studies of G. orientalis and related species. We found that the Ka/Ks values of three genes petL, rpl20, and ycf4 were higher than one in the pairwise comparation of G. officinalis and other three Galegeae species , which indicated those three genes were under positive selection. A comparative genomic analysis of 15 Galegeae species showed that most conserved non-coding sequence regions and two genic regions (ycf1 and clpP) were highly divergent, which could be used as DNA barcodes for rapid and accurate species identification. Phylogenetic trees constructed based on the ycf1 and clpP genes confirmed the evolutionary relationships among Galegeae species. In addition, among the 15 Galegeae species analyzed, Galega orientalis had a unique 30-bp intron in the ycf1 gene and Tibetia liangshanensis lacked two introns in the clpP gene, which is contrary to existing conclusion that only Glycyrrhiza species in the IR lacking clade (IRLC) lack two introns. In conclusion, for the first time, the complete chloroplast genome of G. orientalis was determined and annotated, which could provide insights into the unsolved evolutionary relationships within the genus Galegeae. As the main semi-autonomous genetic organelle in plant cells, chloroplasts contain their own independent DNA (cpDNA) and play an important role in carbon fixation, oxygen production and regulation of many metabolic pathways . ChloropmatK gene [Trifolium [Fabaceae, the third largest family of angiosperms after Orchidaceae and Asteraceae, contains many species with wide distribution ,10. CurratK gene . Among tatK gene . As a reatK gene . In the rifolium . HoweverGalega orientalis is a member of the Galegeae tribe in Papilionoideae, which belongs to the genus Galega along with Galega officinalis. It is a perennial cool-season forage legume species mainly naturally distributed in grassland and subalpine meadows at an altitude of 330–1800 m around the Black Sea region extending from southeastern Europe through the Caucasus to western Asia [Medicago sativa, Trifolium pratense) and excellent disease and pest resistance [galegine content [G. orientalis is a nectar plant useful for producing excellent quality of honey, and is also used to extract dimethylbiguanide to treat diabetes as a medicinal plant [G. orientalis has been introduced and promoted in many countries [ern Asia ,16. It hsistance ,18, good content . In addial plant ,21. Therountries .Galega species has been reported [Galega, which seriously hampers our understanding of molecular identification, evolution and phylogeny of Galegeae tribe [G. officinalis is the only Galega species with an available complete chloroplast genome [Galega species would greatly enrich the cp genome of this genus. Here, we sequenced the complete chloroplast genome of G. orientalis, and compared it with the chloroplast genome of G. officinalis as well as with the cp genomes of other six genera species in Galegeae: Astragalus, Calophaca, Caragana, Oxytropis, Tibetia and Glycyrrhiza. We also elucidated the evolutionary relationships within the genus Galega in Galegeae based on their complete cp genomes. Our results will provide important information and markers for phylogenetic and evolutionary studies in Galegeae.However, the improvement of biomass trait by transgenic technique in reported . Currentae tribe ,24. Espet genome . TherefoGalega orientalis were collected from Hongyuan County, Sichuan, China. DNA was extracted from leaves immediately after drying with silica gel. Total genomic DNA was extracted from 500 mg of dried leaves using the TruSeq DNA Sample Preparation Kit . In accordance with the Illumina manual , sheared low-molecular-weight DNA fragments were used to construct paired-end libraries. Sequencing of the completed libraries was performed on an Illumina HiSeq 2500 using a PE150 sequencing strategy and 300 bp insert size. The data that support the findings of this study have been deposited into CNGB Sequence Archive (CNSA) [The young leaves of e (CNSA)  of Chinae (CNSA)  with accGalega orientalis was eliminated. Sequences with Q20 less than 60% were eliminated. Low quality sequences at 3′ end, and sequences with length less than 60 bp were eliminated. Based on the reference genome of Galega officinalis (NC_051885.1), cp DNA was assembled. Briefly, sequences were assembled into Contigs using MetaSPAdes software [G. orientalis and G. officinalis were verified using NCBI BLAST, with the following parameters: E value less than 1 × 10−5 identity values of nearly 100%, and almost no gap. Then, CPGAVAS2 was used to annotate chloroplast genes [The sequence containing more than 3 unidentified bases in the raw data of software , and nonst genes . The codst genes  was usedst genes  for predst genes .The REPuter program  was usedG. orientalis in Galegeae  were analyzed based on Ka/Ks value using KaKs Calculator v2.0 [G. orientalis and G. officinalis cp genomes, the complete chloroplast genome sequence was aligned with MAFFT v7.402 [G. orientalis and 14 Galegeae species in the shuffle-LAGAN mode and with G. orientalis annotation as a reference.In the present study, the shared protein coding genes of 14 closely related species of tor v2.0 . FurtherT v7.402  and thenT v7.402  softwareT v7.402  softwareCrotalaria albida (NC_061361.1), Crotalaria pallida (NC_053562.1), Piptanthus concolor (NC_051876.1), Piptanthus nepalensis (NC_056139.1), Ammopiptanthus mongolicus (MK704436.1), Ammopiptanthus nanus (NC_034743.1), Lotus corniculatus (MT528596), Carmichaelia australis (MF597719), Vicia sepium (NC_039595), Lupinus micranthus (KU726828), Clitoria ternatea (MN709849.1), Cicer arietinum (NC_011163.1), Medicago sativa (KU321683), Medicago hybrida (NC_027153.1), Onobrychis viciifolia (MW007721.1) and Trifolium alexandrinum (MN857160) were used to construct a phylogenetic tree. Multiple sequence alignment of the 31 complete cp genome sequences was performed using MAFFT [Pterocarpus santalinus (MN251250.1) and Arachis hypogaea (NC_037358.1) as outgroups, with a bootstrap number of 1000 [Complete chloroplast genome sequences of 15 Galegeae species and another 16 legume species, including anus NC_0743.1, Los NC_0561.1, AmmopGalega orientalis, among which Q20 > 96% and Q30 > 90%. After removing adaptor sequences and low-quality sequences, 1,283,368 clean reads were remained. Both G. orientalis and G. officinalis cp genomes were found lacked IR region. The cp genome size of G. orientalis and G. officinalis was 125,280 and 125,086 bp, with an overall GC content of 34.11% and 34.18%, respectively  in the original sequencing data of ectively . Based oG. orientalis, including 74 protein-coding genes, 29 tRNA and four rRNA. Overall, 14 genes contained introns, of which nine encoded proteins, and five encoded tRNA. Of these, 13 genes had one intron, whereas ycf3 had two. ycf1 and trnK-UUU had the smallest intron (30 bp) and largest intron (2475 bp), respectively , 26 palindromic repeats (30–72 bp), three reverse repeats (30–31 bp) and three complement repeats (30 bp). Among them, the maximum number of repeats (N = 170) was located in the ycf1-trnN-GUU region. In addition, 73.8% of the repeat sequence occurred in the intergenic spacer region (IGS), 14.3% in coding regions , 11.9% in the introns of ycf1, ndhA, ndhB, and rpoC1 . All dinucleotide repeats were composed of the AT/TA motif. In total, 43 and 21 SSRs were distributed in IGS and CDS regions of G. orientalis, respectively  were detected in -repeats B. Overalectively A. Howeveicinalis B, but CGicinalis .The Ka/Ks ratio was used to evaluate the degree of selection constraint on each gene and estimate the selective pressure of protein-coding genes. Ka/Ks > 1 indicates the gene is under positive selection, Ka/Ks = 1 indicates the gene is under neutral selection and Ka/Ks < 1 indicates purification selection.G. orientalis and species belonging to six other genera  to 1.33757 (petL). Among them, the Ka/Ks value of rpl20 gene between G. orientalis and G. officinalis was >1 (1.00749). However, the Ka/Ks values of rpl20 among G. orientalis and other species of Galegeae were all lower than 0.5 . Caragana ycf4 gene ratio was also >1 (1.04846). These results indicated that rpl20 gene was positively selected within Galega, but was under strong purification selection pressure in other Galegeae genera. petL and ycf4 genes were positively selected between G. orientalis and Calophaca. The ycf4 gene evolved under beneficial mutations between G. orientalis and Caragana  to synonymous (Ks) ratio (Ka/Ks) of 71 effectively shared protein-coding genes in r genera . The resthan 0.5 . The Ka/Caragana .G. orientalis and 14 other Galegeae species. The results showed that the cp genome size in Galegeae species ranged from 122,210 bp (Oxytropis diversifolia) to 129,331 bp (Caragana korshinskii) and GC content ranged from 33.94%  to 34.68% (Tibetia liangshanensis). G. orientalis differed significantly from other species in the length of the CDS and non-coding regions  than in other regions. In addition to ndhD-ccsA, ndhE-psaC, petB-petD, trnS-GGA-ycf3, trnK-UUU-matK and psaJ-rpl33, almost all CNS regions showed variation. In addition, we found a high degree of sequence variation in the accD, rpoC2 and ycf4 genes. In the UTR region of the rrn23 gene, the degree of regional variation in the CNS was similar between G. orientalis and G. officinalis but significantly different among 14 species. Overall, the degree of sequence variation in conserved non-coding sequence regions was significantly higher than that in the coding regions  for phylogenetic analysis  with strong sequence differences and high Pi values to construct phylogenetic trees of 15 Calegeae species by comparing chloroplast genomes of G. orientalis and G. officinalis. The results showed that the phylogenetic topology of Galegeae species based on ycf1  were grouped together in one cluster , suggesting that they are more closely related.In addition, we selected two genes  were found only in certain species like Trifolium and Medicago [InfA, which is considered to be the most transferable gene in the cp genome, and only exists in 24 angiosperms lineages, was absent in the cp genome of any known Galegeae species [Compared with angiosperms with intact chloroplast tetrad structure, Medicago . InfA, w species .G. orientalis and G. officinalis. In addition, forward repeat was the most common type and most repeats were distributed in the IGS region, suggesting their potential as genetic markers for phylogenetic studies [G. orientalis and G. officinalis chloroplast genomes with A/T motif repeats being dominant. An extremely strong A/T bias in SSR loci has also been observed in other legumes such as Vigna radiate [Arachis hypogaea [In the present study, we found high similarity in the types and number of tandem repeats between  studies . As the  studies . We obse radiate  and Arachypogaea , which mG. orientalis was compared with 14 other Galegeae species. In the protein-coding region (CDS), only ycf1 and rpl23 genes showed strong differences. At the same time, ycf1 gene had a higher Ka/Ks value, suggesting that it is under less purifying selection pressure. The accelerated evolution rate of petL gene was observed in Galega. The ycf4 gene has undergone adaptive evolution between G. orientalis and Calophaca/Caragana. It is also found in Lathyrus, Pisum and Vavilovia [ndhD-ccsA, ndhE-psaC, petB-petD, trnS-GGA-ycf3, trnK-UUU-matK and psaJ-rpl33. These hypervariable genomic regions were considered to be useful markers for elucidating the phylogenetic relationships among the Galegeae species. The ycf1 gene also showed potential as a genetic marker for Quercus bawanglingensis [atpB-rbcL was used to assess the germplasm differentiation of P. lunatus [trnW-CCA-petG and psbN-psbH were also used for phylogenetic and domestication studies of Astragalus species [G. orientalis. Furthermore, we found that the clpP gene had the highest Pi value in the chloroplast genomes of G. orientalis and G. officinalis and could be used for phylogenetic analysis and population genetic study of Galega species.Chloroplast genome is useful for the study of species evolution and classification. The CNS showed more obvious hypervariable regions than the other regions when avilovia ,55. Howeingensis . atpB-rb lunatus , trnW-CC species . The polG. orientalis and G. officinalis are closely related, clustered into one group, and were grouped in the same clade with Lotus corniculatus. These results indicate that besides the Galega family species, the Lotus also has a close evolutionary relationship with the G. orientalis. However, in Galegeae, the species of the Tibetia did not belong to the same clade as other genera, which was also confirmed in the phylogenetic tree constructed based on clpP gene. This may be due to the absence of IR region in the ancestral species of the Galegeae species, resulting in a series of alternations (including high rearrangements and duplications) during evolution, leading to differences among congeners [Based on complete chloroplast genome sequences of 31 legume species, we determined the position of the genus in the Papilionoideae using phylogenetic analysis, and the topological structure was relatively consistent. ongeners .Galega orientalis was sequenced and annotated, and compared with the cp genome of other Galegeae species. The results showed that the length and structure of the chloroplast genome of G. orientalis were similar to those of G. officinalis, but the length of the non-coding region varied significantly. mVISTA analysis results showed that the mutation rate of the non-coding region was higher in G. orientalis than other Galegeae species, and the coding region was more conserved. Some hypervariable regions were identified, which could be used to distinguish species. In addition, we identified two hotspot genes (ycf1 and clpP) that contained rich information for species identification and phylogenetic reconstruction of Galegeae, as well as provide insights into the evolution of Galegeae species.In this study, the complete chloroplast genome of


Over the past decade, the association between oral health, intestinal microbiota, and systemic diseases has been further validated. Some oral microbial species have been isolated from pathological intestine mucosa or feces and identified as biomarkers for intestinal diseases. A small proportion of oral microbiome passes through or colonizes the lower gastrointestinal tract, even in healthy individuals. Opportunistic pathogens from the oral cavity may expand and participate in the occurrence and progression of intestinal diseases when the anatomical barrier is disrupted. These disruptors interact with the intestinal microbiota, disturbing indigenous microorganisms, and mucosal barriers through direct colonization, blood circulation, or derived metabolite pathways. While interacting with the host’s immune system, oral-derived pathogens stimulate inflammation responses and guide the transition of the intestinal microenvironment from a healthy state to a pre-disease state. Therefore, the oral-gut microbiome axis sheds light on new clinical therapy options, and gastrointestinal tract ecology balance necessitates simultaneous consideration of both oral and gut microbiomes. This review summarizes possible routes of oral microbes entering the intestine and the effects of certain oral bacteria on intestinal microbiota and the host’s immune responses. However, many current studies separately focus on these two regions in an organ-specific manner without considering microbial translocation. The oral contains various microhabitats that are exposed or not exposed to the air. Tooth surfaces, tongue, mucous membrane, and gums are habitats of aerobe microbes whereas periodontal pockets mainly resident facultative anaerobic and anaerobic microbes, potentially act as complementary reservoirs for opportunistic enteric pathogens. However, the prerequisites for ectopic colonization of oral microbes are harsh. The majority of oral pathobionts are inactivated by gastric acidity, bile acid, colonization resistance of native microbiotas, and the immune system. As a validation, proton pump inhibitors (PPIs) users and achlorhydria patients exhibit an abnormal increase in gut colonization by oral pathobionts . In receHundreds of proteins and peptides released by oral microbes could be metabolized into a variety of bioactive by-products, many of which are toxic . ConsequIt is not fully understood the exact oral microbes that influence gut microbiota and the mechanism of these intruders participating in intestinal diseases, further elucidations are still required. As the oral microbiome can exhibit a stable state of at least 3 months, and the gut microbiome for up to 5 years, the crosstalk between oral and intestinal microecology is pathological and clinically valuable . In this22.1Streptococcus, Veillonella, Actinomyces, and Haemophilus) that result in endogenous infection emerged in the GI tract in health and disease status, with high levels of variation across individuals  have been proven positively related to pancreatic cancer risk  in CRC tissues, while butyrate-producing and anti-inflammatory microbes that originally lived in the intestine decreased correspondingly  and Fusobacteria (Fusobacterium) potentially modulate colon motility    . Primary (NAFLD) . Even inrtality) .2.2via the circulatory system , indicating more clinical pathologic examinations are still required . Vasapol samples . Whereas samples . Moreove samples .3F. nucleatum colonization in the intestine and significantly induces systemic humoral IgG and IgM antibody responses  to distant locations   . Proinflesponse)   .44.1P. gingivalis is a major pathogen for periodontitis and extensively involves in systemic diseases. To disrupt host-microbial homeostasis of the oral cavity, P. gingivalis only needs a microscopic colonization level   promotes microbial molecules to disrupt cytoplasm and establishes continuous infection in mucosal epithelial tissues , including endotoxemia, intestinal inflammation, and systemic hypo-inflammation   activate antigen-presenting cells, initiating intestinal inflammation and immune reactions. Via stimulating TLR2/4 and inducing Th17 cells, P. gingivalis and its metabolites heighten the mRNA expression of TNF-α and IL-1β in the colon  level  in vitro and in vivo, P. gingivalis triggers low-grade systemic inflammation and intestinal dysbiosis, promoting CRC neoplasia progression  up-regulation in colon carcinoma cells and mediates deep inhibition of T cells   . Extrace tissues . In miceammation . Regardimicutes) . Pyriculpathways . In P. ghe serum . In termhe colon . Regiona1) level . Next, t1) level . In termammation . Consistion mice . Comparerognosis . Via actgression . Moreovend NOD2) .4.2F. nucleatum normally resides in the intestine but increases synchronously with common oral microorganisms during CRC. 40% of the CRC patients detected identical F. nucleatum strains in both tumor tissue and saliva  and potential new subspecies are isolated from the intestine of colorectal cancer patients, and the major encoding virulence factors for F. uncleatum showed evidence of horizontal gene transfer  activate TLR/MyD88/NF-κB pathway, promoting the secretion of a series of proinflammatory cytokines, including IL-8, TNF, keratinocyte-derived chemokine (KC), IL-6, IFN-γ, and monocyte chemoattractant protein-1 (MCP-1) , Fap2 , RadD (autotransporter) and FomA) recruit tumor-infiltrating immune cells, generating tumor microenvironment and participating in immunosuppression and tumorigenesis (F. nucleatum modifies colonic Th17 cell frequency and IL-17 expression recombinant free fatty acid receptor 2 (FFAR2)-dependently  . The col (MCP-1) . In addinization . Anotherand IL-6 . Regardiigenesis . F. nuclendently . Moreoveendently . ENO1 gerognosis . Besidesifficile .4.3Klebsiella is an opportunistic pathogen in the GI and respiratory tracts. Although a few Klebsiella physiologically colonizes in the intestine, in pathological states such as periodontitis, it demonstrates signs of ectopic colonization from the oral. Researchers found that saliva K. pneumonia of IBD patients rapidly establishes colonization in mice’s intestines, triggering a strong inflammatory response  , rather than intestinal native microbes-reactive, translocate into the intestine and affect colitis progress (Klebsiella promotes the secretion of IL-1β (mainly from inflammatory macrophages) via cysteine-11-mediated inflammatory vesicles, activating intestinal inflammasome  and cecal metabolome (SCFAs) are altered  . In micefections . Th17 ceprogress . Moreoveammasome . For the altered . Whereasignaling .4.4S. pneumoniae and S. mutans. Whereas human co-salivary Streptococcus lacks functional BlpRH pairs, the couples bacteriocin production and competence commitment is directly guided by the competence signaling system ComRS, which is the undetermined basis for its optimal adaptation, potentially explaining the high prevalence of S. salivarius in the human GI tract  activation in small intestinal epithelial cells in vitro  revealed S. salivarius’s protective ability to diminish intestinal epithelial inflammation  was observed in the intestine of CRC patients   . Additio levels. .4.6V. parvula is an anaerobic commensal and opportunistic pathogen that plays a crucial role in bacteria adhesion and biofilm formation for oral and gut microbiomes. Type V secreted autotransporters that typically exist in diderm bacteria determine this character. Whereas inactivation of the gene coding for a poorly characterized metal-dependent phosphohydrolase HD domain protein in Firmicutes could inhibit autotransporter-mediated biofilm formation  patients   increases Th17 cell activation and luminal secretory IgA  . Actuallnhibited . When retory IgA . In perifunction .4.7C. Albicans dominate among the cultivable mycobiota that longitudinally persists. Intestinal C. Albicans probably as commensals constantly supplemented from the oral cavity, frequent teeth cleaning dramatically reduces fecal C. Albicans abundance -β-D-glucan (BG) and bacteria from the gut into the systemic circulation  that weakens mucosal innate and adaptive immune responses  has been proven useful for treating C. Albicans infection and maintaining intestinal homeostasis, which negatively regulates PPARγ and TLR-4  ecological network, which mediates the pathogenesis of precancerous gastric atrophy (GA) and intestinal metaplasia (IM)  is discovered to alter vascular and colonic BH4/nNOS/NRF2 pathways and to damage vascular relaxation and colonic motility  . RegardiC either . Gemellas polyps . Intestis polyps . In termmotility . In addirmicutes .5P. gingivalis, F. nucleatum, P. intermedia, P. nigrescens, C. albicans, etc.) showed potential origin from the oral cavity via direct enteral translocation or through the circulatory system. Using deep learning algorithms, the fecal microbiome could be predicted by the oral microbiome of the same individual in a new bioinformatic tool  are closely linked with the intestinal healthy state. In recent years, several intestinal pathobionts (tic tool . For ent16S rRNA sequencing and conventional bacterial identification and quantification methods are suitable for identifying well-characterized and abundant genera. However, frequently using 1% as the threshold for excluding the low-abundant organisms, some oral-origin microbiome species in the intestine may escape detection unconsciously. The SourceTracker results showed that the percentage of intestinal bacteria from the tongue-coated microflora was within 1% . MoreoveThe study of oral-gut microbes has gained popularity, not only for its role in intestinal and systemic diseases that have gradually been explored but also for its offering new approaches to conveniently detectable biomarkers and clinical therapy methods. Various probiotics, prebiotics, and even herbal medicines that work by modifying gut microbes’ variations have been proven of positive therapeutic outcomes. Both saliva and gut microbes are potential biomarkers for diagnosis. This clue indicates an overall idea to manage oral health and intestinal diseases coordinatingly in the future, and this approach is still in its infancy. As the attention to oral health grows, studying the effect of oral microbes on the intestine opens up new insight into preventing and controlling intestinal diseases and discovering new therapies.YL, ZL, and XP wrote and edited the manuscript. ZL and XP critically reviewed the manuscript and provided insightful discussions and ideas. YL and ZL contributed equally to this work and share first authorship. All authors contributed to the article and approved the submitted version.


Diabetes mellitus (DM) is a group of metabolic diseases marked by hyperglycemia, which increases the risk of systemic infections. DM patients are at greater risk of hospitalization and mortality from bacterial, viral, and fungal infections. Poor glycemic control can result in skin, blood, bone, urinary, gastrointestinal, and respiratory tract infections and recurrent infections. Therefore, the evidence that infections play a critical role in DM progression and the hazard ratio for a person with DM dying from any infection is higher. Early diagnosis and better glycemic control can help prevent infections and improve treatment outcomes. Perhaps, half (49.7%) of the people living with DM are undiagnosed, resulting in a higher frequency of infections induced by the hyperglycemic milieu that favors immune dysfunction. Novel diagnostic and therapeutic markers for glycemic control and infection prevention are desirable. High-throughput blood-based immunoassays that screen infections and hyperglycemia are required to guide timely interventions and efficiently monitor treatment responses. The present review aims to collect information on the most common infections associated with DM, their origin, pathogenesis, and the potential of immunoproteomics assays in the early diagnosis of the infections. While infections are common in DM, their role in glycemic control and disease pathogenesis is poorly described. Nevertheless, more research is required to identify novel diagnostic and prognostic markers to understand DM pathogenesis and management of infections. Precise monitoring of diabetic infections by immunoproteomics may provide novel insights into disease pathogenesis and healthy prognosis. Diabetes Mellitus (DM) is a complex metabolic disorder characterized by abnormal immune-inflammatory processes that increase vulnerability to severe infections. About 463 million people have DM worldwide, and future projections predict that this figure will grow to 700 million by 2045 . More thChlamydia pneumoniae, herpes simplex virus (HSV), and Helicobacter pylori, enhance the risk of cardiovascular events and have the strongest relationship with insulin resistance  infections. S. aureus is one of the most isolated pathogens in diabetic patients, causing meningitis, sepsis, bacteremia, skin infections, and nasal carriage. Considering S. aureus clinical significance, the pathogen will be discussed thoroughly in the DM infection pathogenesis and immunoproteomics sections.The most common sites of infection and their common inhabitant pathogens are discussed in the following sections. However, we highlight that patients . The patS. aureus, with a prevalence rate of 50% in DFI. S. aureus was the most frequently (26.9%) isolated pathogen, followed by P. aeruginosa (20.9%). Similarly, S. aureus in 35% of postoperative infections  among patients undergoing vascular surgery (40% of whom were DM patients). In DM1 patients, S. aureus mostly colonizes the nose and skin  are often present in infections with a more severe grade and systemic impact. S. aureus has been observed to co-exist in many cases with A. baumannii, which is also associated with a higher incidence (24.4%) of major amputation in patients with DFI  and surgical site infections. In diabetic patients, DFIs account for up to 20% of hospital admissions and 17.4% of hospital-related deaths . Althougand skin . The bacogenesis . Dunyachwith DFI .Staphylococcus aureus grows and secretes virulent factors in glucose-rich diabetic conditions, where insulin deficiency prevents or delays immune response  are strictly responsible for glucose transportation  in DM, indicating a physiologically relevant role of these molecules in DM , glucosaminidase (Gmd), and the iron-regulated surface determinant proteins . Therefore, antibody responses against bacterial proteins could predict infection outcomes in patients with S. aureus induced SSTIs.response . Staphylortation . Vitko ees in DM . In diabStreptococcus pneumonia, Mycobacterium tuberculosis, S. aureus, Candida albicans, and influenza virus (Pseudomonas spp. (26–32%) S. aureus (14–16%) Klebsiella spp. (13–14%), E. coli (12–13%), and Enterobacter spp. (8–12%) (S. aureus carriage (persistent or intermittent) in 20 to 50% of healthy people with a high probability of severe and recurrent infections, particularly in diabetic patients   . In The  (8–12%) . The naspatients . Further7–34.2%) . Toxic sl cavity . SchmidtMany other respiratory pathogens are also associated with DM. The prevalence of pulmonary tuberculosis is 4–5 times higher in diabetic patients than in healthy subjects . LikewisE. coli, Klebsiella spp., S. aureus, and C. albicans are the most isolated pathogens from UTIs in DM patients (E. coli (23.2%), Coagulase negative Staphylococci (12.5%), Enterococcus spp. (10.7%), C. albicans (17.9%) and Non-albicans Candida spp. (16.1%) were the most frequently isolated pathogens. Similarly, DM patients are more likely to get urinary tract infections caused by frequent urination and high glucose levels that provide a suitable growing environment for the pathogens . High urpatients . RecentlE. coli attach to urinary tract epithelium through toll-like receptor 5 and ascend the urinary tract against urine flow , S. aureus (15%) Streptococcus sp. (10.2%), Staphylococcus sp. (9.4%), Enterococcus sp. (7.84%), and Klebsiella (5.5%) in DM2 patients. The infection rate was far higher in uncontrolled DM2 than in controlled DM2. S. aureus, have a better glycolytic flux during infection, enhancing pathogen survival and pathogenicity rate .DM increases the risk of enteric infections and intestinal barrier dysfunction, elevating systemic infection risk. The GI tract hosts a complex microbial community, including archaea, bacteria, fungi, protozoans, and viruses, that dynamically influence immune, endocrine, and metabolic homeostasis . Gao et glycemia . Chen etSeveral enteric virus infections are also associated with DM pathogenesis. Viral infections are the instigator of gut dysbiosis and disruption of intestinal homeostasis that lead to subclinical enteropathy before DM1 onset . FabianiAggregatibacter actinomycetemcomitans, P. gingivalis, Tannerella forsythia, and T. denticola, are associated with hyperglycemia and contribute to periodontal disease development and gingivitis  that enter the systemic circulation and interfere with insulin signaling, causing insulin antagonism and pancreatic β-cell destruction , which inhibits insulin binding to insulin receptors and prevents GLUT4 from being transported  to establish severe infection. Particularly, S. aureus has increased glucose import capabilities, which aid the bacteria in optimizing glycolytic flux during infection and pathogenesis , another popular superantigen from  failure . This no failure .Proteomics and immunoproteomics studies of human body fluids and infection-specific antibodies offer complementary knowledge and hold the potential for developing novel biomarkers for DM infection . ProteomE. coli, S. typhimurium, and K. pneumoniae that are otherwise difficult to identify. Similarly, S. aureus exoproteome in SSTI, bacteremia, and healthy carriage using 2-DGE, 2D-immunoblotting, and MS/MS (MS2). Using different proteomics techniques, the authors discovered a diverse exoproteome profile that varied significantly across infection sites. Differential expression of the Isd protein and host cytokine/chemokine response pattern were particularly important in understanding S. aureus infection. A. baumannii for vaccine development. Rational proteomics analysis discovered higher anti-OmpA antibody titers in in vivo testing of the OmpA antigen in diabetic mice, protecting the mice from intravenous infection with A. baumannii , with only 85B and CFP10 revealing a significant difference  system, allowing efficient and reproducible analysis of protein interactions (Legionella pneumophila antigen proteins (SidM and LidA). NAPPA immunoproteomic analysis of H. pylori revealed IgG humoral immune response, characterized by a panel of anti-Ggt, -HslU, -NapA, and-CagA antibodies to differentiate gastric cancer patients from healthy control , and nucleic acid programmable protein array (NAPPA) are a few of the other novel antigen expression arrays that are recently developed for high throughput host immune response screening and autoimmune analysis . NAPPA uractions . The queractions . Using N control . SimilarM. tuberculosis  to quantify up to 64 antigens across a broad dynamic spectrum . However, these studies did not observe differences in antibody responses through both studies had diabetic and non-diabetic patients infected with S. aureus , which allows for multiplex analysis of up to 500 analytes, is also gaining popularity in immunoproteomics and infection studies. The technology offers flexible options for optimization and multiplexing by binding pathogen antigens with the microspheres to study antibody responses, as previously reported for EBV, papillomavirus, and rculosis . Jiménezspectrum . The arrbacteria . Another vaccine . The auttrations . The sturesponse . Antibodresponse . ParticuS. aureus 4-Antigen (SA4Ag) Vaccine , Luminex immunoassays are being used to study humoral immune response against S. aureus CP5, CP8, rmClfA, and rMntC proteins . Compared with the controls, infection severity was much worse in both mouse models of diabetes, with DM2 mice generally having lower anti-S. aureus IgG response and higher IgM response.To clinically validate the proteins . Furtherproteins . Althougulations . FarnswoS. aureus IgG levels in the serum of DFI patients. The study showed strong prognostic and diagnostic potential of IgG antibodies against S. aureus IsdA, IsdH, Amd, Gmd, and Hla antigens in DFI with more than 0.85 ROC-AUC for anti-IsdA and anti-IsdH antibodies. The findings suggest that early detection of S. aureus exoproteins using multiplex immune assays improves disease prognosis and holds promise for vaccine development.Staphylococcus aureus is the most contagious and lethal pathogen in SSTIs and septicemia. Both proteomics and immunoproteomics complement each other and have the potential to discover new biomarkers. The lack of reliable infection markers suggests that the diagnostic scope of immunoproteomics, which uses both infection and host marker molecules, should be expanded. Immunoproteomics holds promise for early infection diagnosis, emphasizes the protective function of the adaptive immune system, and promotes further research in producing a clinically successful S. aureus vaccine. The above-mentioned latest proteomics and immunoproteomics techniques represent a new milestone in sensitivity, selectivity, and automation analytics. As we can now screen large cohorts for antibodies to pathogens and autoimmune antibodies, a new chapter is being opened in studying the role of antibodies in various diseases. Immunoproteomics techniques can plausibly identify new targets for vaccines and can also be used to conclude mimicry of pathogenic proteins, which has been poorly studied and was not possible on a large scale. However, recent immunoproteomics study of antigenic mimicry of viral and bacterial antigens, as investigated for autoantibody response against blood coagulation proteins  12th Cycle grant # NPRP12S-0318-190392 from the Qatar National Research Fund (a member of Qatar Foundation). The findings herein reflect the work and are solely the responsibility of the authors.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.


The rapid spread of COVID-19 in Ethiopia was attributed to joint effects of multiple factors such as low adherence to face mask-wearing, failure to comply with social distancing measures, many people attending religious worship activities and holiday events, extensive protests, country election rallies during the pandemic, and the war between the federal government and Tigray Region. This study built a system dynamics model to capture COVID-19 characteristics, major social events, stringencies of containment measures, and vaccination dynamics. This system dynamics model served as a framework for understanding the issues and gaps in the containment measures against COVID-19 in the past period (16 scenarios) and the spread dynamics of the infectious disease over the next year under a combination of different interventions (264 scenarios). In the counterfactual analysis, we found that keeping high mask-wearing adherence since the outbreak of COVID-19 in Ethiopia could have significantly reduced the infection under the condition of low vaccination level or unavailability of the vaccine supply. Reducing or canceling major social events could achieve a better outcome than imposing constraints on people’s routine life activities. The trend analysis found that increasing mask-wearing adherence and enforcing more stringent social distancing were two major measures that can significantly reduce possible infections. Higher mask-wearing adherence had more significant impacts than enforcing social distancing measures in our settings. As the vaccination rate increases, reduced efficacy could cause more infections than shortened immunological periods. Offsetting effects of multiple interventions (strengthening one or more interventions while loosening others) could be applied when the levels or stringencies of one or more interventions need to be adjusted for catering to particular needs . Since the emergence of the first case of COVID-19 on 13 March 2020 in Ethiopia, as of 6 January 2022, this country had reported 436,586 confirmed cases and 6,988 deaths Reviewers' comments:Reviewer's Responses to Questions  Comments to the Author1. Is the manuscript technically sound, and do the data support the conclusions?  The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: Partly**********  2. Has the statistical analysis been performed appropriately and rigorously?   Reviewer #1: No**********  3. Have the authors made all data underlying the findings in their manuscript fully available?PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.  The Reviewer #1: No**********  4. Is the manuscript presented in an intelligible fashion and written in standard English?  PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #1: No**********  5. Review Comments to the Author  Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. Reviewer #1: Comments to AuthorsCurrently, COVID-19 is a major public health issue and assessing the dynamic impacts of non-pharmaceutical and pharmaceutical intervention measures against COVID-19 is an important area of study. The authors try to investigate system dynamics model to capture COVID-19 characteristics, major social events, stringencies of containment measures, and vaccine dynamics. To improve the quality of the paper, I suggest to address the following issues:Abstract- Avoid abbreviation from the abstract. i.e. SD, SDMBackground- The background section didn’t intensively explore literatures related with this study.- Many statements written without citing references that support the statement. Such us line 64-66, line 72-78, Line- This section didn’t motivate why doing this study is important and what are the limitation of other similar studies, i.e. Ejigu et al (2021)- Need proof reading. There are many grammatical issues across the paper.i.e., line 61 “only were 1.4%....”.line 161 “This study is to better understand…”Method- Line 161 is not a meaningful sentence. “This study is to better understand…”- Motivation to the considered “Model structure and formulations” were not clearly stated. Under what assumption this model is valid? How you get the model parameter input parameters?- Add links/hyperlinks (cite websites) from where you got the data under the “Data collection and quality” subsection.- Further in this section, you need to explaina) How the Immunological period (days) 240 and 180 determined.b) How the vaccine efficacy determinedc) Briefly explain how other parameters also fixed-Result and discussion- Many of the assumptions set for the scenarios are far away the reality. For example, line 272-273, it was stated that “.. adherence level in wearing face was assumed 55%” which is really far away from the reality in the context of Ethiopia. As far as I know, 75% of the population in Ethiopia are living in rural areas and majority of them didn’t her about facemask. Further, in line 295 “…. adherence level of mask-wearing .”- Revise the assumed adherence level related with face masking, social distancing,… in the context of Ethiopia.- Line 297 “…total of 968 days”, but in line 205 “… total of 603 days” which one is correct.- Finding of other studies related with this work were not well discussed.- Limitation of the study not clearly stated. i.e., the quality of the data and others not clearly addressed.**********  6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.If you choose “no”, your identity will remain anonymous but your review may still be made public.Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.  Reviewer #1: Nohttps://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, AttachmentComments to authors.pdfSubmitted filename: Click here for additional data file. 13 Jun 2022First of all, we would like to sincerely thank the editor and anonymous reviewers for their comments and constructive critiques. We appreciate the opportunity to address the comments by submitting a revision. We have considered all comments with care, and we believe the revisions address these comments and have greatly improved the paper. In this revision report, we explain the responses to each comment in detail. We have reproduced the original comments in black and typed our responses in blue for clarity. Responses to Reviewer #11. - Avoid abbreviation from the abstract. i.e. SD, SDMResponse:Thank you for mentioning this point. We have changed those abbreviations in the abstract to their full names. 2. -The background section didn’t intensively explore literatures related with this study.Response:We appreciate that you pointed this out. We further searched relevant studies and summarized them in the revised manuscript. Relevant references are listed below. References: 1. Ayele W, Tesfaye L, Abagero A, Taye G, Abate B, Habtamu T, et al. COVID 19 Epidemic trajectory modeling results for Ethiopia. Ethiop J Heal Dev. 2021;35: 25–32.2. Ayele TA, Fentahun N, Tiruneh SA, Haile M, Zeru T, Meseret G, et al. Spatial variation and level of compliance on COVID-19 Prevention strategies in Amhara region, Ethiopia: Observational survey. Ethiop J Heal Dev. 2021;35.3. Endriyas M, Kawza A, Alano A, Hussen M, Shibru E. COVID-19 prevention practices in urban setting during early introduction of the disease: results from community survey in SNNP Region, Ethiopia. BMJ Open. 2021;11. doi:10.1136/bmjopen-2020-0473734. Haftom M, Petrucka PM. Determinants of Face Mask Utilization to Prevent Covid-19 Pandemic among Quarantined Adults in Tigrai Region, Northern Ethiopia, 2020. Clin Nurs Res. 2021;30: 1107–1112. doi:10.1177/105477382110132195. Tucho GT, Kumsa DM. Universal Use of Face Masks and Related Challenges During COVID-19 in developing countries. Risk Manag Healthc Policy. 2021;14: 511–517. doi:10.2147/RMHP.S2986876. Zewude B, Melese B, Addis E, Solomon W. Changing patterns of compliance with protective behavioral recommendations in the post first-round COVID-19 vaccine period among healthcare workers in Southern Ethiopia. Risk Manag Healthc Policy. 2021;14: 3575–3587. doi:10.2147/RMHP.S3256997. Bushira KM. Modeling the effectiveness of social distancing interventions on the epidemic curve of coronavirus disease in Ethiopia. Model earth Syst Environ. 2021; 1–11. doi:10.1007/s40808-021-01190-9https://doi.org/10.1371/journal.pone.02571128. Deressa W, Worku A, Abebe W, et al. Social distancing and preventive practices of government employees in response to COVID-19 in Ethiopia. PLOS ONE. 2021; 16(9): e0257112:1-22. 9. Fikrie A, Amaje E, Golicha W. Social distancing practice and associated factors in response to COVID-19 pandemic at West Guji Zone, Southern Ethiopia, 2021: A community based cross-sectional study. PLoS One. 2021;16. doi:10.1371/journal.pone.026106510. Hailu W, Derseh L, Hunegnaw MT, Tesfaye T, Angaw DA. Compliance, barriers, and facilitators to social distancing measures for prevention of coronavirus disease 2019 in Northwest Ethiopia, 2020. Curr Ther Res Exp. 2021;94. doi:10.1016/j.curtheres.2021.10063211. Tolu LB, Ezeh A, Feyissa GT. How prepared is Africa for the COVID-19 pandemic response? The case of Ethiopia. Risk Manag Healthc Policy. 2020;13: 771–776. doi:10.2147/RMHP.S2582733. - Many statements written without citing references that support the statement. Such us line 64-66, line 72-78, LineResponse:Thank you for raising this point. We have double-checked the whole paper and added citations at relevant places. 4. - This section didn’t motivate why doing this study is important and what are the limitation of other similar studies, i.e. Ejigu et al (2021)Response:Thank you for the suggestions. We have added a paragraph deliberating this point. 5. - Need proof reading. There are many grammatical issues across the paper. i.e., line 61 “only were 1.4%....”. line 161 “This study is to better understand…”Responses: Many thanks to the reviewer for indicating this situation. We agree that improving the writing is very important for guaranteeing the quality of the manuscript. We have invited professional English-editing service to edit and polish the manuscripts. 6. - Line 161 is not a meaningful sentence. “This study is to better understand…”Responses: Thank you for helping improve the writing of this manuscript, we have revised this sentence and checked other places as well. 7. - Motivation to the considered “Model structure and formulations” were not clearly stated. Under what assumption this model is valid? How you get the model parameter input parameters?Responses: Thank you for mentioning this. We did have some elaboration on the motivation for using the extended SEIR model in “Introduction” and subsection of “Model structure and formulations” Built on previous research, our extended SEIR model tends to include not only the state variables of susceptible population, exposed population, symptomatic patients, asymptomatic patients, mild cases, severe cases, deaths, recovered cases, but also the sub-structure of hospitalized patients, un-hospitalized patients, vaccine administration. Piecewise calibration was conducted to estimate necessary model parameters, given that the cumulative infections and cumulative deaths did not follow an expectedly exponential growth curve. Historical data were split into two parts with one part being used for building the model. And the model was then verified and validated by the other part of the data. As for the model parameters, some of the parameters were obtained through the fitting with historical and they were verified through repetitive calibrations. Besides, the value settings for other parameters were done by referring to literature, which were clearly presented in Table 1. 8. - Add links/hyperlinks (cite websites) from where you got the data under the “Data collection and quality” subsection.Responses: Thank you for advising on this point, we have put the link in the relevant place of the “Data collection and quality” subsection. 9. - - Further in this section, you need to explaina) How the Immunological period (days) 240 and 180 determined.b) How the vaccine efficacy determined c) Briefly explain how other parameters also fixedResponses: Thank you for mentioning this. For a) and b), we have listed literature that indicated the source of the value  and [3-6] for b)). In this revision, we added several more references. Please also refer to the literature provided below. Regarding c), some of the parameters were obtained through the fitting with historical and they were verified through repetitive calibrations. Besides, the value settings for other parameters were done by referring to literature, which was clearly presented in Table 1. References:1. Khoury DS, Cromer D, Reynaldi A, et al. Neutralizing antibody levels are highly predictive of immune protection from symptomatic SARS-CoV-2 infection. Nat Med 2021;27: 1205-112. Tenforde MW, Self WH, Naioti EA, et al. Sustained effectiveness of Pfizer BioNTech and Moderna vaccines against COVID-19 associated hospitalizations among adults-United States, March, July 2021. Morb Mortal Wkly Rep. 2021; 70(34), 1156.https://doi.org/10.1136/bmj.n10883. Bernal JL, Andrews N, Gower C, et al. Effectiveness of the Pfizer-BioNTech and Oxford-AstraZeneca vaccines on COVID-19 related symptoms, hospital admissions, and mortality in older adults in England: test negative case-control study. BMJ-British Med J. 2021; 373:n1088. https://doi.org/10.1056/NEJMoa21017654. Dagan N, Barda N, Kepten E, et al. BNT162b2 mRNA Covid-19 vaccine in a nationwide mass vaccination setting. N Engl J Med. 2021; 384(15):1412-1423. https://doi.org/10.1056/NEJMoa21077155. Jara A, Undurraga EA, Gonzalez C, et al. Effectiveness of an inactivated SARS-CoV-2 Vaccine in Chile. N Engl J Med. 2021; 385(10):875-884. https://doi.org/10.3390/vaccines90201196. Shekhar R, Sheikh A, Upadhyay S, et al. COVID-19 vaccine acceptance among health care workers in the United States. Vaccines. 2021;9,119. 10. - - - Many of the assumptions set for the scenarios are far away the reality. For example, line 272-273, it was stated that “.. adherence level in wearing face was assumed 55%” which is really far away from the reality in the context of Ethiopia. As far as I know, 75% of the population in Ethiopia are living in rural areas and majority of them didn’t her about facemask. Further, in line 295 “…. adherence level of mask-wearing .”- Revise the assumed adherence level related with face masking, social distancing… in the context of Ethiopia.Responses: Thanks to the reviewer for pointing this out. We agree that the compliance level to face mask-wearing in Ethiopia was very low, especially in those poor rural areas at the outset of COVID-19. However, with the supply of face masks, the level of compliance witnessed an obvious increase, which increased from 30% to as high as 62% (IHME). We also searched the literature and found results from some surveys done by Ethiopian scholars showing that the levels of adherence  were more than 50%. Ayele Wondimu et al. evaluated the spread dynamics of COVID-19 in Ethiopia under the assumptions of face mask utilization of 20%, 40%, and 60%.Therefore, the percentages used in our scenarios represented relatively reasonable assumptions. References:1. IHME, Mask use in Ethiopia. Accessed on June 5th, 2022 at https://covid19.healthdata.org/ethiopia?view=mask-use&tab=trend2. Endriyas M, Kawza A, Alano A, Hussen M, Shibru E. COVID-19 prevention practices in urban setting during early introduction of the disease: Results from community survey in SNNP Region, Ethiopia. BMJ Open. 2021;11. doi:10.1136/bmjopen-2020-0473733. Haftom M, Petrucka PM. Determinants of Face Mask Utilization to Prevent Covid-19 Pandemic among Quarantined Adults in Tigrai Region, Northern Ethiopia, 2020. Clin Nurs Res. 2021;30: 1107–1112. doi:10.1177/105477382110132194. Ayele W, Tesfaye L, Abagero A, Taye G, Abate B, Habtamu T, et al. COVID 19 Epidemic trajectory modeling results for Ethiopia. Ethiop J Heal Dev. 2021;35: 25–32.11. - Line 297 “…total of 968 days”, but in line 205 “… total of 603 days” which one is correct.Responses: Thank you for mentioning this point. Our study has two parts with one focusing on counterfactual analysis that has a total of 603 days and the other focusing on the prediction that covers one year (365 days). Therefore, the total simulation days considering prediction are 968 days. To reduce confusion, we have made changes at the corresponding place. 12. - Finding of other studies related with this work were not well discussed.Responses: Thank you so much for pointing this out. We have added relevant studies in the Introduction, Results, and Discussion parts. 13. - - Limitation of the study not clearly stated. i.e., the quality of the data and others not clearly addressed.Responses: Thanks for your suggestions. We have added limitations at the end of the discussion part.AttachmentResponse to Reviewers.docxSubmitted filename: Click here for additional data file. 27 Jun 2022Assessing the dynamic impacts of non-pharmaceutical and pharmaceutical intervention measures on the containment results against COVID-19 in EthiopiaPONE-D-22-02713R1Dear Dr. Liu,We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. 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For more information, please contact Kind regards,Vinícius Silva BeloAcademic EditorPLOS ONEAdditional Editor Comments :Reviewers' comments: 8 Jul 2022PONE-D-22-02713R1 Assessing the dynamic impacts of non-pharmaceutical and pharmaceutical intervention measures on the containment results against COVID-19 in Ethiopia Dear Dr. Liu:I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. onepress@plos.org.If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact plosone@plos.org. If we can help with anything else, please email us at Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staffon behalf ofDr. Vinícius Silva Belo Academic EditorPLOS ONE