The prevalence of autoimmunity in the U.S. has increased recently for undetermined reasons. Little is known about associations between autoimmunity and environmental causes.In a large representative sample of the U.S. population, we expanded our prior exploratory study of how exposures to selected xenobiotics and dioxin-like (DL) mixtures relate to antinuclear antibodies (ANA), the most common biomarker of autoimmunity.We analyzed cross-sectional data on 12,058 participants aged ≥ 12 years from three time periods of the National Health and Nutrition Examination Survey between 1988 and 2012, of whom 14% were ANA-positive. We used lognormal regression models and censored-data methods to estimate ANA associations with xenobiotic concentrations overall and in sex, age, and race/ethnicity subgroups. Our analyses adjusted for potential confounders and appropriately handled concentrations below detection limits.Observed ANA associations were positive for most DL compounds and nonDL polychlorinated biphenyls (PCBs), negative for most phthalates, and mixed for other xenobiotic classes. After correcting for multiple comparisons, some associations remained statistically significant. In subgroup analyses, the most significant finding was a positive ANA association with N-acetyl-S-(2-hydroxy-3-butenyl)-L-cysteine (MHB2) in males, followed by positive associations with 2,2′,3,5′-tetrachlorobiphenyl (PCB 44), 2,2′,4,5′-tetrachlorobiphenyl (PCB 49), and 2,2′,3,4′,5′,6-hexachlorobiphenyl (PCB 149) in 12–19 year-olds, and with 3,4,4′,5-tetrachlorobiphenyl (PCB 81), 2,2′,3,3′,4,4′,5,5′,6-nonachlorobiphenyl (PCB 206), and N-acetyl-S-(phenyl)-L-cysteine (PMA) in Mexican Americans. Negative associations were found with mono-benzyl phthalate (MBzP) in 20–49 year-olds and mono-n-butyl phthalate (MnBP) in 12–19 year-olds. In overall analyses, combining stratum-specific results across race/ethnicity strata revealed a positive ANA association with PCB 81 and a negative ANA association with N-acetyl-S-(2-hydroxyethyl)-L-cysteine (HEMA).This study identified potential associations between ANA and various xenobiotics. Further investigation to confirm these observations and elucidate effects of certain xenobiotics on immune regulation could have important mechanistic, preventive, and treatment implications for a variety of immune-mediated disorders. AlthougThe most common clinical biomarkers of AID are antinuclear antibodies (ANA) directed against many cellular components . AutoantAlso, though we employ essentially the same analytical approach as before, the ANA data now come from a more appropriate assay, and we adjust for additional covariates. Prior studies conducted by other researchers usually modeled an indicator of ANA positivity as a covariate-adjusted function of xenobiotic concentration, which can be problematic when some concentrations are below the limit of detection (LOD). Un-detectable concentrations were typically excluded or replaced by constant values, such as LOD/2, which can lead to bias and inefficiency. In comparison, our approach reverses the roles of the ANA indicator and the xenobiotic concentration , while treating concentrations below the LOD as censored. This method improves the analysis by allowing application of censored-data techniques, by making full use of the observed data, and by avoiding bias and underestimation of variance .2.2.1.We initially assessed ANA in 13,519 persons sampled from five NHANES cycles: 1988–1991 , 1999–2000 , 2001–2002 , 2003–2004 , and 2011–2012 . Participants, all of whom provided biospecimens and completed questionnaires, were ≥ 12 years old and weighted to be nationally representative of the noninstitutionalized U.S. population. Available data included demographics, health covariates, measured factors such as height and weight, and constructed variables such as body mass index (BMI) and poverty income ratio (PIR). The NHANES protocol and this study were approved by the human subjects Institutional Review Board of the U.S. Centers for Disease Control and Prevention (CDC), and all participants gave written informed consent. For additional information about the NHANES, see their website .2.2.Serum samples were evaluated by indirect immunofluorescence at a 1:80 dilution using the NOVA Lite HEp-2 ANA slide with DAPI kit  and a highly specific fluorescein isothiocyanate conjugated secondary antibody (goat anti-human IgG). Images captured via the NOVA View automated fluorescence microscope system (INOVA Diagnostics) were stored digitally. Immunofluorescence staining intensities were assigned integer grades from 0 to 4, relative to standard references, with nonzero grades indicating ANA positivity . All sam2.3.We initially considered all 253 xenobiotics measured in biospecimens from any NHANES cycle with ANA data . SeveralOur study included a diverse set of persistent and non-persistent xenobiotics or their metabolites, which were measured in serum, urine, or whole blood see . See tab2.4.The 192 xenobiotics we analyzed were categorized into 21 classes. These included seven polychlorinated dibenzo-p-dioxins (PCDDs), five polychlorinated dibenzofurans (PCDFs), three non-ortho dioxin-like polychlorinated biphenyls (NODL PCBs), six mono-ortho dioxin-like polychlorinated biphenyls (MODL PCBs), 29 non-dioxin-like polychlorinated biphenyls (nonDL PCBs), 14 volatile organic compounds (VOCs), 22 volatile organic compound metabolites (VOC Metabolites), 29 metals and metalloids , 14 phthalates and phthalate alternative metabolites , 10 polycyclic aromatic hydrocarbon metabolites (PAH Metabolites), 10 organochlorine pesticides and metabolites (Pesticides), two herbicides and metabolites (Herbicides), two fungicides and metabolites (Fungicides), four pyrethroid insecticide metabolites (Pyrethroid Insecticides), six organophosphorus insecticides classified as dialkyl phosphate metabolites (OP/DPM Insecticides), four organophosphorus insecticides classified as specific pesticides and metabolites (OP/Other Insecticides), two insect repellents and metabolites (Insect Repellents), nine perfluoroalkyl and polyfluoroalkyl substances (PFAS), three perchlorate and other anions (Anions), nine personal care and consumer product chemicals and metabolites (Consumer Products), and two tobacco alkaloids and metabolites (Tobacco Biomarkers).2.5.We paid special attention to compounds with dioxin-like (DL) activity, which have well-documented immunotoxic effects  and impa2.6.As in our prior study , we adju2.7.Large proportions of nondetects complicate the usual modeling of ANA positivity as a covariate-adjusted function of xenobiotic concentration. As in our earlier analysis , we addrWe used the LIFEREG procedure in SAS  to perform lognormal regression analyses, where the response was either an individual xenobiotic concentration or a mixture TEQ concentration. Xenobiotics measured in urine were modeled on a creatinine basis to account for dilution, while concentrations of lipophilic xenobiotics were modeled on a per lipid basis. Our analyses used categorical covariates for all factors except age, which we modeled by a restricted cubic spline . We anal2.8.The ANA regression coefficient gauges the association between ANA positivity and xenobiotic concentration, and its exponentiated value is the ratio of mean concentrations for ANA-positive versus ANA-negative persons . A mean We assessed each xenobiotic’s association with ANA in the general population and in subgroups. We estimated the overall ANA/xenobiotic association by fitting the full model to data on all participants (which adjusts for covariate main effects) and also by combining results across sex-by-age strata or race/ethnicity strata . This second method derived a weighted average of stratum-specific MCR estimates, using inverse variance estimates as weights; thus, larger samples had greater influence. We also performed separate analyses within sex, age, and race/ethnicity subgroups, which may identify xenobiotics that only correlate with ANA in certain demographic categories. We note that combining results across strata to produce overall assessments of ANA-relevant xenobiotics can help avoid criticisms of bias due to focusing on selective findings from individual subgroups.2.9.An individual xenobiotic concentration below the LOD is left censored, but a mixture TEQ concentration is interval censored if any component concentration is below its LOD . If a xe2.10.The NHANES data were obtained from multistage stratified cluster samples . The LIF2.11.FDR) and we employed a critical value of 0.1 for declaring statistical significance. Thus, in each group, we used the FDR correction to control for assessing many ANA/xenobiotic associations.In addition to reporting uncorrected P-values (P) to help identify ANA/xenobiotic associations for further study, we also corrected for multiple comparisons, separately in each of nine demographic groups . Specifically, we applied the false discovery rate (FDR) correction of 3.3.1.Overall, 13.7%  of the NHANES participants were ANA positive, consistent with previous unadjusted ANA prevalence estimates of 14.4% , 13.8% , 12.9% , and 13.3.2.−7) and with N-acetyl-S-(phenyl)-L-cysteine (PMA) in Mexican Americans (P = 4.5×10−5), and a negative ANA association with mono-benzyl phthalate (MBzP) in 20–49 year-olds (P = 4.6×10−5). All three remained significant after correcting for multiple comparisons, with PFDR values of 0.000048, 0.0080, and 0.0086, respectively. Summaries of all ANA/xenobiotic associations, overall and in subgroups, are depicted in The ANA/xenobiotic associations of greatest magnitude in The following subsections explore xenobiotic classes, and the xenobiotics within them, with respect to both the statistical significance and magnitude of observed ANA associations.3.3.Only 21 of the 26 DL xenobiotics had sufficient data for our analysis: seven PCDDs, five PCDFs, three NODL PCBs, and six MODL PCBs . In addiIn the full sample, all MCR estimates equaled or exceeded 1.0, suggesting positive ANA associations with DL xenobiotics and their mixtures . This coFDR = 0.013) remained statistically significant after correcting for multiple comparisons (FDR = 0.057), which may have been driven by the association in Mexican Americans.Some subgroup analyses also suggested positive ANA associations with DL xenobiotics and their mixtures, especially in males  and non-parisons  and 5. Wparisons , the ove3.4.−3), and 11 other nonDL PCBs also had uncorrected P-values below 0.05, but none remained statistically significant after correcting for multiple comparisons in the full-sample analysis  was the most significant , PCB 49 , and 2,2′,3,4′,5′,6-hexachlorobiphenyl (PCB 149) , and in Mexican Americans for 2,2′,3,3′,4,4′,5,5′,6-nonachlorobiphenyl (PCB 206) .Among the demographic subgroups, eight ANA associations with nonDL PCBs in males, five in females, six in 12–19 year-olds, one in 20–49 year-olds, four in ≥ 50 year-olds, six in non-Hispanic Whites, and one in Mexican Americans had uncorrected P-values below 0.05 , and 4b.3.5.Only 14 of the 43 VOCs had adequate data for analysis. Of those, none appeared associated with ANA overall, and while four showed weak evidence (P < 0.05) of a positive ANA association in subgroups , and 4c,FDR = 4.8×10−5) and with PMA in Mexican Americans , and both had fairly large MCR estimates . Of the 33 metal/matrix combinations, 29 had adequate data, with MCR estimates ranging from 0.75 to 1.38 , and 4e.3.7.FDR = 0.0086) and with mono-n-butyl phthalate (MnBP) in 12–19 year-olds    and 5.3.8.None of the 10 PAH metabolites showed an overall association with ANA, though two showed weak evidence (P < 0.05) of an ANA association in certain subgroups , and 4f.3.9.trans-nonachlor in males and the negative ANA associations with trans-nonachlor in females, with p,p’-dichlorodiphenyltrichloroethylene  in ≥ 50 year-olds, and with beta-hexachlorocyclohexane in non-Hispanic Blacks had an uncorrected P < 0.05, but none of these associations were statistically significant after correcting for multiple comparisons. The MCR estimates for the 10 pesticides ranged from 0.68 to 1.42 (Ten of the 15 organochlorine pesticides (but neither carbamate pesticide) had adequate data. Only the positive ANA association with  to 1.42 , and 4g.ortho-phenylphenol showed weak evidence of a negative ANA association in Mexican Americans, but not after correcting for multiple comparisons. Three of the five pyrethroid insecticide metabolites had adequate data and cis-3--2,2-dimethyl cyclopropane carboxylic acid (cis-DCCA) showed weak evidence of a positive ANA association in non-Hispanic Whites, but not after correcting for multiple comparisons. All six organophosphorus insecticides in the dialkyl phosphate metabolites class had adequate data, but only diethylthiophosphate (DETP) in non-Hispanic Blacks showed an association (negative) with ANA, which was not statistically significant after correcting for multiple comparisons. Finally, two of the three insect repellents had adequate data, but only DEET acid showed evidence of an ANA association (P < 0.05), which was positive both overall and in males, but it did not remain statistically significant after correcting for multiple comparisons. Though not statistically significant, several of these xenobiotics had large positive MCR estimates , in ≥ 50 year-olds , and in non-Hispanic Blacks , none of which were statistically significant after correcting for multiple comparisons.FDR = 0.029) and a negative ANA association with tin in 12–19 year-old females . When sex and age were considered individually, there was some evidence of an ANA association with molybdenum for females but little for ≥ 50 year-olds, and only weak evidence of an ANA association with tin for 12–19 year-olds and none for females  and a negative overall association with N-acetyl-S-(2-hydroxyethyl)-L-cysteine (HEMA) . Ignoring statistical significance, ANA had the greatest positive overall association with DEET acid  and the greatest negative overall association with butyl paraben .When simultaneously analyzing all participants , uncorrected P-values suggested several xenobiotics might be associated with ANA, but none of these associations remained statistically significant after correcting for multiple comparisons . Alterna3.16.FDR = 4.8×10−5), PMA in Mexican Americans , PCB 81 in Mexican Americans , PCB 206 in Mexican Americans , PCB 44 in 12–19 year-olds , PCB 49 in 12–19 year-olds , and PCB 149 in 12–19 year-olds , while ANA appeared negatively associated with MBzP in 20–49 year-olds  and MnBP in 12–19 year-olds . Focusing only on magnitude, ANA was most positively associated with ethylbenzene in Mexican Americans  and most negatively associated with alachlor mercapturate in males . Considering both statistical significance and effect size, the positive ANA/MHB2 association in males and the negative ANA/MnBP association in 12–19 year-olds were the most extreme in magnitude while remaining statistically significant after correcting for multiple comparisons.Within single-factor demographic subgroups, ANA associations with nine xenobiotics remained statistically significant after correcting for multiple comparisons  and 5. O3.17.FDR < 0.1) are listed in Possible exposure sources for, and toxicologic associations with, the xenobiotics having ANA associations of greatest statistical significance after correcting for multiple comparisons  associations with ANA, even though not all were statistically significant. Some associations observed in the full sample were reinforced by similar findings in multiple subgroups, whereas other overall associations appeared driven by a particular demographic.In the full sample, xenobiotics in several classes consistently had ANA associations in the same direction; correlations among these xenobiotics could be relevant, but an extensive correlation analysis was beyond our scope. Overall, DL compounds were positively associated with ANA, as were the PCDD, PCDF, NODL PCB, and MODL PCB classes (and the various TEF mixtures). Similarly, ANA associations with nonDL PCBs also were consistently positive. In addition to the consistency in direction, many of these positive ANA associations initially appeared statistically significant (P < 0.05), though few remained significant after correcting for multiple comparisons. In contrast, some classes suggested mainly negative associations with ANA, most notably phthalates. Other classes were either too small to draw general conclusions about overall ANA associations or else exhibited a mix of positive and negative associations, most of which were not statistically significant after correcting for multiple comparisons.Many subgroup analyses supported the class-specific results based on all participants, though the smaller sample sizes often led to weaker statistical significances. The consistently positive ANA associations estimated for DL compounds and nonDL PCBs were mirrored in both sexes , adults (ages ≥ 20 years), and non-Hispanic Whites. Likewise, the generally negative ANA associations with phthalates overall were also observed in several subgroups. Occasionally, however, subgroup analyses led to findings that differed from those in the full sample. For example, ANA appeared to be negatively associated with PAH metabolites in females and non-Hispanic Blacks but showed little evidence of an association in either direction for all participants combined or within the other subgroups.FDR = 4.8 × 10−5). The estimated MCR was 2.12 , suggesting that the average concentration of MHB2 in men with ANA was more than twice that in men without ANA. Though statistical significances were less, estimated ANA associations with MHB2 also were positive in the full sample and in several other subgroups. The MCR estimates ranged from 0.75 in females to 2.12 in males, suggesting a possible sex effect with higher exposures in males, which is consistent with the probable sources (FDR = 0.0080). The estimated MCR was 1.82 , suggesting that the average concentration of PMA in Mexican Americans with ANA was nearly twice that in those without ANA. Both of these xenobiotics are VOC metabolites; MHB2 is a metabolite of 1,3-butadiene and PMA is a known irritant that is a metabolite of benzene , five involved PCBs. Specifically, PCB 44, PCB 49, and PCB 149 were positively associated with ANA in 12–19 year-olds, while PCB 81 and PCB 206 were positively associated with ANA in Mexican Americans. Ignoring statistical significance, nearly all ANA associations with these five PCBs were positive, both in the full sample and in subgroups. There are limited data regarding the immune impacts of specific PCBs, but most studies suggest immunosuppressive effects or disruptions in immune homeostasis, which may allow for increased or more persistent infections or other immune stimulatory processes that may relate to these positive ANA associations . The estimated MCR for MBzP was 0.70 , suggesting that the average concentration of MBzP in 20–49 year-olds with ANA was only about two-thirds of that in those without ANA. Though statistical significances were less, ANA associations with MBzP were consistently negative in the full sample and all but one subgroup, with MCR estimates ranging from 0.70 to 1.02. Similarly, all ANA associations with the related chemical MnBP were negative, with MCR estimates ranging from 0.62 to 0.92; the most statistically significant negative ANA association with MnBP was in 12–19 year-olds . Both MBzP and MnBP are metabolites of phthalates used in the manufacture of polyvinyl chloride plastics and belong to a group of endocrine-disrupting chemicals suspected of having numerous impacts on human health, including influences on the immune system -6, IL-10, chemokine CXCL8, and tumor necrosis factor (TNF)-α by stimulated monocytes/macrophages, as well as the secretion of IL-6 by stimulated T cells. Also, The most statistically significant negative associations were between ANA and two metabolites of phthalates, MBzP in 20–49 year-olds and MnBP in 12–19 year-olds, both of which remained significant after correcting for multiple comparisons  and a negative overall ANA association with HEMA .Despite evidence of ANA/xenobiotic associations in several subgroups, the same was not necessarily true for the population as a whole. After correcting for multiple comparisons, none of the xenobiotics were statistically significantly associated with ANA when analyzing the full sample directly, though two showed evidence of an overall ANA association when stratifying by race/ethnicity and then combining results across those strata . The latOne strength of the present study is its large size. Compared with our earlier study of ANA/xenobiotic associations , which iThe current study had several other strengths. As in our earlier study, exposures were objectively measured  rather than being self-reported (in surveys). Compared to conventional analyses that substitute specific values  risk for autoimmunity, and which mechanisms are involved.MMC4MMC3MMC6MMC2MMC5MMC1


Nicotiana tabacum L.) is an economic crop and a model organism for studies on plant biology and genetics. A population of 271 recombinant inbred lines (RIL) derived from K326 and Y3, two elite flue-cured tobacco parents, has been constructed to investigate the genetic basis of agronomic traits in tobacco. Six agronomic traits including natural plant height (nPH), natural leaf number (nLN), stem girth (SG), inter-node length (IL), length of the largest leaf (LL) and width of the largest leaf (LW) were measured in seven environments, spanning the period between 2018 and 2021. We firstly developed an integrated SNP-indel-SSR linkage map with 43,301 SNPs, 2,086 indels and 937 SSRs, which contained 7,107 bin markers mapped on 24 LGs and covered 3334.88 cM with an average genetic distance of 0.469cM. Based on this high-density genetic map, a total of 70 novel QTLs were detected for six agronomic traits by a full QTL model using the software QTLNetwork, of which 32 QTLs showed significant additive effects, 18 QTLs showed significant additive-by-environment interaction effects, 17 pairs showed significant additive-by-additive epistatic effects and 13 pairs showed significant epistasis-by-environment interaction effects. In addition to additive effect as a major contributor to genetic variation, both epistasis effects and genotype-by-environment interaction effects played an important role in explaining phenotypic variation for each trait. In particular, qnLN6-1 was detected with considerably large main effect and high heritability (Nt16g00284.1, Nt16g00767.1, Nt16g00853.1, Nt16g00877.1 were predicted as pleiotropic candidate genes for five traits.Tobacco ( According to FAO-statistics, approximately 5.9 million tons of tobacco were produced worldwide in 2020, and China accounted for 36.3% of the total yields (Tobacco (l yields . Tobaccol yields . Therefol yields . Howeverl yields .Nicotiana sylvestris (2n = 24) and Nicotiana tomentosiformis (2n = 24) about 200,000 years ago  and 271 F7 generation individuals. The materials were planted at Yanhe  and Shilin  field experiment stations using complete random design with 5 replications, and were cultivated according to local technical measures for quality tobacco production. Six agronomic traits, including natural plant height (nPH), natural leaf number (nLN), stem girth (SG), inter-node length (IL), length of the largest leaf (LL) and width of the largest leaf (LW), were measured in 2018, 2019, 2020 and 2021 years in Yanhe, and in 2018, 2019 and 2020 in Shilin. Seven different combinations of location and year were treated as environments denoted as E1 (2018 Shilin), E2 (2018 Yanhe), E3 (2019 Shilin), E4 (2019 Yanhe), E5 (2020 Shilin), E6 (2020 Yanhe) and E7 (2021 Shilin). Six agronomic traits were investigated on the day 65 after planting in the field (first green fruit stage) according to the tobacco industry standard YC/T 369-2-10.The recombinant inbred lines (RILs) were generated from two elite flue-cured tobacco parents Y3 and K326. Y3 is a backbone cultivated variety that originated from Zimbabwe with elite agronomic traits and complicated parental sources. K326, whose genome has been assembled , was int2.2Variance components analysis and heritability estimation were performed based on the following mixed linear model,khiy is the phenotypic value of the i-th replication of the k-th individual in the h-th environment; μ is the population mean; kg is the genotypic value of the k-th genotype, random effect, subjected to a normal distribution khge the effect of the h-th environment, random, subjected to the normal distribution khge the interaction effect between the k-th genotype and the h-th environment, subjected to the normal distribution khiε is the residual effect of the individual, random, mmer module of sommer R package (rcorr module of Hmisc R package (https://cran.r-project.org/web/packages/Hmisc/index.html) was employed to calculate Pearson correlation coefficients between six studied traits: (1) phenotypic correlation coefficients with khiy for each environment, respectively; (2) genetic correlation coefficients with k-th individual by population mean, k-th individual predicted by BLUP.where  package  was appl2.37 individuals, the parents, and one F1 generation) was extracted from the leaves of 7-week-old tobacco seedlings by Qiagen DNeasy Plant Mini Kits  and sonicated into ~350 bp fragments via a Covaris M220 system . The paired end DNA fragments were used to generate DNA Nano Balls via LM-PCR and rolling circle amplification. The sequencing library was constructed according to the requirements of the BIGSEQ-500 platform and sequenced with PE100 .The raw data were filtered by SOAPnuke (v1.5.6) with the default parameters except “-l 40 -q 0.2”. The genome of Nicotiana tabacum (K326 cultivar) was used as a reference genome . After e2.51 generation were filtered based on DP ≥10 and GQ ≥25. (2) SNPs of the 271 RIL F7 individuals were filtered based on DP ≥3. (3) SNPs that were absent in the parents and F1 generation were removed. (4) SNPs that exhibited a missing rate of over 20% in the 271 RIL F7 individuals were eliminated. (5) Triallelic or tetraallelic sites were deleted. For indels, (1) Indels of the parents and F1 generation were filtered based on DP ≥10 and GQ ≥40. (2) Indels of the 271 RIL F7 individuals were filtered based on DP ≥3 and GQ ≥10. (3) Indels that were absent in the parents and F1 generation, were removed. (4) Indels that exhibited a missing rate of over 20% in the 271 RIL F7 individuals were eliminated. (5) Triallelic or tetraallelic sites were deleted. (6) Indels over 5bp were removed. After quality controls, there were 10,057,282 high-quality SNPs and 569,946 indels remained for subsequent analysis. Then, we screened out 1,626,811 SNPs and 71,130 indels which exhibited two different homozygous genotypes in two parents (aa and bb) and heterozygous (ab) in F1 (hereinafter referred to as aa × bb & F1-h types); consequently, high quality linkage map will be guaranteed after eliminating the influence of uncertainty in genotype because of the high heterozygosity of Y3.SNPs and indels were called using GATK (GATK3.3.0) and filtered according to the following criteria : For SNPA total of 937 SSRs were provided by 2.61-h types was over whelming for most software used to construct genetic linkage maps. Therefore, we clustered the markers that mapped on the same scaffold, kept one marker per 100 Kb as the low recombination rate within a short physical distance, and selected out 44,804 SNPs and 2,163 indels. Together with 937 published SSRs, a total of 47,904 markers were selected uniformly for genetic linkage map construction in software LepMap3 . The genetic distance of each LG was calculated by the OrderMarkers2 module. The genetic linkage density map was visualized using R package in LinkageMapView  that were aa × bb & F LepMap3 . In detaeMapView .2.7a) of each QTL, additive-by-additive epistatic effect (aa) of each paired QTLs, treated as fixed effects, and their corresponding environment interaction effects (ae and aae) as random effects. Suppose a trait is controlled by s segregating QTL in which t pair of QTL involved interaction. Then, the phenotypic value of the m-th replication of the k-th genotypes in the h-th environment (hkmy) can be expressed by the following mixed linear model,A saturated genetic model was adopted for modeling the genetic architecture of complex traits from multi-environment trials, which includes additive effect random effect, hijaae the interaction effect of the ijaa and the h-th environment with coefficient hijku(=ikxikx), random effect, hkmε is the residual effect of the individual, random, where, QTLNetwork 2.0 software were employed to detect possible QTLs by the mixed-linear-model-based composite interval mapping (MCIM) method  with 20,000 Gibbs sampler iterations.) method . One- an2.8https://solgenomics.net/ftp/genomes/Nicotiana_tabacum/edwards_et_al_2017/annotation/Nitab-v4.5_gene_models_Scf_Edwards2017.gff) . The function of candidate genes was retrieved from the database UniProtKB/Swiss-Prot (ftp://ftp.ebi.ac.uk/pub/databases/uniprot/knowledgebase/uniprot_sprot.fasta.gz) using Protein BLAST (BLASTp) provided by NCBI, and selected on basis of E value = 1E−5.QTL intervals, determined by two adjacent markers in linkage map, were dissected for prediction of candidate gene(s) associated with the respective QTLs by bioinformatic analysis. Candidate genes in QTL regions and their corresponding protein sequence were selected from the annotation of K326 reference genome sequence (33.1α=0.05)  to 57.72% (nLN), indicating that traits like nLN and nPH were more stable across environments, compared with the traits like LL and LW Table 1.3.2We finally obtained 46,324 markers to generate a high-resolution genetic linkage map that contained 7,107 bin markers (defined as the marker with least genotype missing rate when there were multiple markers at the same genetic distance) mapped on 24 LGs and covered 3334.88 cM with an average genetic distance of 0.469cM Table 2.3.3According to the critical F-values to declare QTLs with significant single locus effects  or to declare epistatic QTLs with significant epistatic effects , a total of 70 QTLs consisting of 33 single-locus QTLs Table 3 a) main effects and/or additive-by-environment interaction (ae) effects were detected for six traits. Most of the QTLs exhibited small additive effects, which were regarded as minor-effect QTLs, and about 70% of them explained less than 2% of the phenotypic variance  at these seven QTLs all contributed negative effects ; in contrast, those of the alleles from the parent Y3 (qq) contributed positive effects  environments accounted for 4.40% of the phenotypic variance (ae effects exhibited the same (positive or negative) effect direction as those of the main effects of the QTLs, while some of them showed the opposite. For example, qLL6-1 decreased the length of the largest leaf, whereas its interaction effects with environment E4 and E6 increased the value of the trait.Although significant additive by environment interaction effects (ae) were detected for more than half of QTLs, their contributions to phenotypic variation hae2 wer3.4aa) effects and/or epistasis-by-environment interaction (aae) effects for six traits, in which 8 pairs had aa effects only, 4 pairs had aae effects only and 9 pairs had both aa and aae effects. However, 21 epistatic QTLs which involved 37 loci contributed insignificant additive-additive epistasis effects  between qnLN2 and qnLN4-2 in E3/E6 was in the same direction with that of the aa effect, while its interaction with E5 effected in opposite. Both aa and aae effects contributed limited magnitude to phenotypic variance, mostly less than 1%  effects explained the biggest part of the phenotypic variance which ranged from 31.65% to 47.82%, followed by genetic (G) main effects and genotype-by-environment interaction (GE) effects. While for nLN, the genetic effect was the major component of the phenotypic variance, wherein the additive effect of 3.5In terms of general genetic main effects, P2 (Y3) performed much better than P1 (K326) in most agronomic traits ignoring gene by environment interaction  in Arabidopsis thaliana  in Arabidopsis thaliana  in Arabidopsis thaliana in Arabidopsis thaliana expression levels; BRH1 mediates BRs signaling pathway, which also influence FLC expression; SOK2 is one part of a three-gene cluster containing FLC, UFC and DFC, all of three genes are associated with FLC. Encoding a MADS-box transcription factor, FLC is a major repressor of flowering in Arabidopsis  would be observed in results. For six traits, a total of 17 epistasis QTL pairs were detected. These results proved that non-additive epistatic interactions are an important contributor to trait variation, which has been documented by numerous studies before (ae and aae) under multiple environments, which could be utilized in genetic improvement of tobacco to breed environment-specific or strong environment adaptability elite varieties in future breeding program. Compared with those models with QTL additive effects only, this model indeed offered us new insights into understanding of genetic architecture of quantitative traits. However, this study found that the additive effects were the main force accounting for heritability and provided guidance for further studies and utilization of the QTLs, such as, fine mapping, cloning and marker-assisted selection. Such as qnPH6-5, qnLN6-1, qnLN6-3, qSG6-1, qIL6-3 and qLW6-2, they contributed relatively larger additive effects compared with additive-environment interaction effects, indicating selection on them is valid across environments; while, qnPH6-2 exhibited similar magnitude of a and ae effects, indicating it could be selectively utilized in different environment. As we conjectured that nLN, nPH, SG, IL, LW were controlled by several major effect QTLs/genes, especially for nLN, whose main effects were considerable compared with that of environment interaction; therefore, these major QTLs are promising and should be paid more attention in following-up utilization in breeding higher-yield varieties.Considering of involvement of gene-gene, gene-environment interaction in the genetic variation of complex traits, we used a full-QTL model to integrate the effects of additive QTLs, epistasis and GE interactions into one mapping system provided by QTLNetwork2.0, which has not been taken into consideration in most QTL studies of agronomic traits in tobacco. The QTL main effects are treated as fixed, which is valid across different environment; while, the QTL-environment interaction effects as random, which are deviation from the average effect of QTL effects across all environments, zero summation of which is assumed. Therefore, some positive or negative QTL-environment interaction effects  like our study, it is still a challenge to distinguish candidate genes of target trait from a lot of annotated genes in QTL regions. Therefore, further studies such as fine mapping using secondary mapping populations is required to narrow down the target QTL. Including single segment substitution lines (SSSLs), near isogenic lines (NILs) and chromosome segment substitution lines (CSSLs), secondary mapping populations are identical to the recurrent parent for all trait loci except the target QTLs, which can eliminate the interference of genetic background and improve the sensitivity and accuracy of miming and prediction of candidate genes in QTL. What’s more, functional validation of candidate genes with molecular biology techniques on the levels of gene expressions, proteins and metabolites should be implemented to understand the genetic regulation mechanism of traits, which is valuable to guide us for designing superior genotype with better performance of agronomic traits.https://www.ebi.ac.uk/ena/browser/view/PRJEB59363).The data presented in the study are deposited in the European Nucleotide Archive (ENA) repository, accession number PRJEB59363 (ZT and BX designed the study; ZT and MX wrote the manuscript; MX, QZ, FL, TZ, YL and HX analyzed the data; ZT, DF, XC and BX conducted the field experiment; HX and BX revised the manuscript. All authors contributed to the manuscript and approved the submitted version.


Microglia are macrophages present in the brain that function as the primary and most important source of immune response in the central nervous system (CNS). Regardless of their multitasking role, our knowledge regarding their molecular heterogeneity is limited; due to technical restrictions, it is only possible to measure gene expression in cell populations, not individual cells, with the results reflecting average mRNA levels. Therefore, recent scientific approaches have focused on single-cell techniques such as single-cell RNA sequencing (scRNAseq), a powerful technique that enables the delineation of transcriptomic cell-to-cell differences, revealing subpopulations with distinct molecular and functional characteristics. Here, we summarize recent studies that focused on transcriptomic microglial subpopulation clustering and classify them into three distinct groups based on age, spatial distribution, and disease. Additionally, we cross-compare populations from different studies to identify expressional and functional overlaps between them. Microglia are the resident macrophages of the brain, acting as the primary and main source of immune response in the central nervous system (CNS). They enter the developing CNS at early embryonic stages ,2 and prMicroglial activation is commonly considered to be any morphological or biochemical modification from the naive state, although polarization can be complex or tailored to a particular disease or developmental state. Despite their multitasking role, our knowledge about microglial molecular heterogeneity is limited due to technical restrictions, meaning that gene expression must be measured in cell populations instead of individual cells, reflecting average mRNA levels . TherefoThe development of sequencing technologies has led to a variety of scRNAseq techniques being presented in recent years, greatly aiding our understanding of dynamic gene expression at the single-cell level. CEL-seq2, Drop-seq, MARS-seq, SCRB-seq, Smart-seq, and Smart-seq2 are six popular scRNAseq techniques . AlthougIn this review, we summarize recent relevant studies, concentrating on microglial subpopulation clustering and further categorizing them into three distinct groups associated with age, region, and disease. Additionally, we cross-compare populations from different studies to identify expressional and functional overlaps between them.CX3CR1), cluster of differentiation receptors , and ionized calcium-binding adapter molecule 1 (IBA-1). According to current knowledge, transmembrane protein 119 (TMEM119), purinergic receptor P2Y12R, olfactomedin-like 3 (OLFML3), and spalt-like transcription factor 1 (SALL1) are the most precise general homeostatic microglia core indicators [Nowadays, the selection of microglial-relevant markers is a scientific difficulty because there is an enormous amount of information on numerous sets of markers that is available for such investigations. Thus, it is crucial to examine the functions and tissue state of certain microglial phenotypes with precision. Regardless of the present cell phenotype, certain general microglia markers can be found. The most frequently used homeostatic markers include the fractalkine receptor  indicative of an activated/phagocytic microglial phenotype was enriched in one of the clusters (cluster Hs7). These data indicate that cells in cluster Hs7  was depleted in this cluster and in fact, Hs7 microglia were specifically detected in lesion types with active demyelination.Multiple sclerosis (MS) is an auto-immune disease illustrated by inflammation and focal areas of demyelination, leading to axonal degeneration and neuronal death . To testster Hs7 B are micLpl, Apoe, Spp1, and Axl), and cluster Mm4 expressed genes associated with interferon responses . Expression of the Mm3 marker gene set was significantly enriched in cluster Hs7, and in fact, both showed activation/phagocytosis profiles [To test the hypothesis that Hs7 cells are associated with demyelination, they profiled brain macrophages in a demyelinated and remyelinated cuprizone mouse model. Two clusters associated with demyelination were identified: Mm3 and Mm4 C. Clusteprofiles . Taken tCtsc three days after FNX, while 14 days after FNX microglia recovered to homeostatic conditions. In contrast, C12 (demyelination) and C13 (remyelination) gene expression patterns remained almost unaltered after ten weeks of cuprizone treatment. C12 and C13 disease-associated clusters presented a range of expression markers in common, such as Apoe, Axl, Igf1, Lyz2, Itgax, Gpnmb, and Apoc1. C12 overexpressed Fam20c, Cst7, Ccl6, Fn1, Ank, Psat1, and Spp1 and C13 showed an induced expression of Cybb and the MHC class II genes Cd74, H2-Aa, and H2-Ab1.In the same direction, Masuda et al. isolated microglia from both healthy and disease mouse models in order to investigate their involvement in neurodegenerative and neuroinflammatory disorders. Consequently, microglial plasticity and response were assessed utilizing two different neurodegenerative mouse models: a model of toxic demyelination after cuprizone treatment and a facial nerve axotomy (FNX) disease model. The toxic demyelination model causes the loss of oligodendrocytes in the corpus callosum , while tCST3, similarly to mouse C9 and C10 clusters, and the HHu-C4 cluster revealed high expression levels of the chemokine genes CCL4 and CCL2 and the zinc finger transcription factors EGR2 and EGR3.Further scRNAseq studies by Masuda and colleagues focused on human microglia isolated from healthy brain tissue, as well as tissue from histologically confirmed early MS patients. Clustering revealed four distinct classes of healthy microglia (HHu-C1 to HHu-C4) and seven classes from MS-related tissues (Hu-C2 to Hu-C8). Healthy HHu-C1 and HHu-C2 clusters showed strong upregulation of CCL2, CCL4, EGR2 and other chemokine and cytokine genes, which suggests that these microglia were pre-activated. Three clusters that were enriched in MS microglia  were separated from the homeostatic clouds on t-SNE plots [APOE and MAFB, whereas the expression of microglial core genes was downregulated or even absent . Finally, Hu-C8 showed strong expression of the SPP1, PADI2 and LPL, genes, similar to C12 microglia associated with demyelination in mice. Early MS-related Hu-C5, Hu-C6, and Hu-C7 clusters consisted entirely of transcriptionally “healthy” microglial cells. These clusters showed the highest expression levels of the microglial core genes and were therefore considered to represent homeostatic states of microglia in early MS stages. The Hu-C4 cluster, which contained both homeostatic and MS-related microglia, was characterized by reduced expression levels of the core signature genes and elevated levels of NE plots . These cn absent B. The HuOf note, canonical correlation analysis of mouse and human microglia orthologues confirmed that clusters Hu-C2, Hu-C3, and Hu-C8 are associated with MS patients and have an expression profile similar to demyelination (C12) and remyelination (C13) clusters in FNX and cuprizone mouse models .Emphasizing myeloid diversity during neuroinflammation, Jordão et al. performed scRNAseq in brain tissues from healthy and EAE  mice, one of the most common MS animal models . MoleculBhlhe41, Gpr34, Sall1, Hexb, Olfml3, Siglech, P2ry12, P2ry13, Serpine2, and Sparc. daMG subsets displayed lower expression of P2ry12, Slc2a5, and Maf and higher expression of Ly86, Ccl2, Cxcl10, and Mki67, showing proliferative potency [P2ry12, Tmem119, and Selplg, and upregulated MD-1. Olfml3 and Sparc were the only core microglial genes that remained stable through neuroinflammation, showing that these genes may serve as robust microglial markers in health and disease [Apoe, Cd74, and Ctsb and a low proliferation rate. On the other hand, daMG3 upregulated Cxcl10, Tnf, Ccl4, and APC genes, whereas daMG4 expressed high levels of Ccl5, Ctss, and Itm2b. daMG3 and daMG4 displayed high proliferative capacity. Upregulation of Cd74 in daMG2, Cxcl10 in daMG3, and Ccl5 in daMG4 suggest an interaction between them, possibly at different activation stages [The results showed that all microglial populations  expressed  potency . Variati disease . daMG2 pn stages .Alzheimer’s Disease (AD) is an age-related neurodegenerative disease characterized by progressive memory loss and cognitive dysfunction, which is usually histologically demonstrated by the accumulation of amyloid-beta (Aβ) plaques in the parenchyma, the formation of neurofibrillary tangles, and neuroinflammation . Using a mouse model (5XFAD) that expresses five human familial Alzheimer’s disease (AD) gene mutations at different age stages, Keren-Shaul et al. identified two novel microglial subtypes, exhibiting a unique molecular expression signature related to AD and other neurodegenerative conditions, termed as disease-associated microglia (DAM). Massively parallel scRNAseq from both AD and wild type brain tissue revealed a map of ten distinct populations based on cluster-specific expression patterns of the 500 most variable genes. Among those populations, a large group of microglia were identified and further subcategorized into three subpopulations . Microglia from groups II and III constitute distinctive microglial subsets associated only with the AD phenotype, in contrast with group I, which represents homeostatic microglia.P2ry12/P2ry13, Cx3cr1, and Tmem119 and upregulation of various known AD risk factors  in DAM clusters. Additionally, DAMs express unique genes, such as Cd9, Itgax (Cd11c), Clec7a, and Cd63, indicating them as potential disease markers  analysis from all major disease stages , this study pointed out that microglia can proceed from the homeostatic to the disease-associated phenotype through a transitional state as a function of disease progression . Compari program . On the In the same framework, Mathys et al. determined the transcriptomic diversity of microglial cells isolated from the hippocampus of CK control and CK-p25 AD-like mice, at four timepoints  during the progression of neurodegeneration. Clustering revealed multiple distinct microglial subsets (Clusters 1–8) constituted of cell populations from specific time points. Thus, cluster 2 contained cells isolated from the CK control and 0-week CK-p25 mice, clusters 3 and 7 mainly included 1-week CK-p25 cells, and cluster 6 was composed of 2-week and 6-week CK-p25 cells. This clustering exhibited various microglial cell states during neurodegeneration that grouped separately from microglia isolated from CK control mice. Therefore, these neurodegeneration states were divided into two groups: an early-response state formed mainly from 1-week CK-p25 cells (Clusters 3 and 7) and a late-response state composed mainly of 2-week and 6-week CK-p25 cells (Cluster 6).Top2a, Uhrf1, Rrm2, Rad51, and Chaf1b. Cell cycle-related genes such as Top2a, Spc25, Plk1, Nusap1, and Ndc80 were upregulated in cluster 7 as well. These clusters displayed significant differences in cell cycle phases, indicating high proliferation in response to neurodegeneration [A comparison of early response (cluster 3 and 7) and late response (cluster 6) states with homeostatic microglia (cluster 2) pointed out how microglia respond over time in neurodegeneration progress. Gene Ontology (GO) analysis showed that early response cluster 3 was characterized by over-represented cell-cycle and DNA replication and by repair genes such as neration .H2-D1, H2-Q5) and MHC II  components, many interferon response genes, such as Irf7 and Ifitm3, and genes related to the GO term “defense response to virus”, such as Oasa1a, Rsad2, and Zbp1. A comparison of early-response cluster 3 and late-response cluster 6 revealed that a large fraction of genes, including Ccl3, Ccl4, Cxcl16, and Mif, were significantly over-expressed in both clusters. In contrast to clusters 3 and 7, cell cycle genes were not represented in late response cluster 6, in which immune response-related genes were over-expressed instead C. Genes As previously mentioned, cluster 6 was characterized by its antiviral and interferon response gene expression module, although there was a notable variation over individual cells, with a subset displaying a fold induction module score higher than the average. In a subset of cells, the MHC II module was at least one order of magnitude higher than the average, whereas in another subset ribosomal protein-encoding genes presented a much smaller distribution. Thus, these data suggest the existence of at least two different reactive microglial phenotypes in neurodegeneration .C1QA, C1QB, C1QC, and GPR34. The results revealed that clusters 1 and 2 represent homeostatic microglial states, implementing housekeeping tasks of the CNS parenchyma. Cluster 3 appears as a subset enriched in genes related to cellular stress. Cluster 4 is enriched in genes related to the interferon response signaling pathway , while clusters 5 and 6 overexpress genes such as IL-10, IL-4, and IL-13 that are related to anti-inflammatory responses. Cluster 8 exhibits the highest expressional diversity of transcriptional factors and cell surface molecule encoding genes, whereas cluster 9 is enriched in genes associated with the cell cycle (CREB and ATF), suggesting that it may constitute a pool of proliferating microglial cells [CD74, and displayed a frequency reduction in AD tissues.Additionally, in a study conducted by Olah et al., a unique subset of microglia  has been detected that is characterized by the enrichment of genes usually lacking in AD patients B. In detal cells . Of noteAs microglia sense and disseminate inflammatory signals, they coordinate immune responses . AlthougP2ry13/P2ry12, Tmem119, Mef2c, Fcrls, Gpr34, and Singlech, whereas classical pro-inflammatory genes such as Ccl2, Gpr84, Tnf, Irg1, and Nfkbia were upregulated  and two related to LPS injection (“main LPS” and “subset LPS”), named inflammatory-associated microglia (IAMs). GO term analysis of upregulated genes in LPS-injected microglia showed “translation”, “protein folding”, “ribosome biogenesis”, and “immune system process” involvement, whereas downregulated genes uncovered a significant enrichment in “regulation of TGF-β receptor signaling pathway” . IAMs diegulated C. The “Snditions .Traumatic brain injury (TBI) represents one of the most substantial causes of fatality and disability in ages under 40 years in developed countries ,36. It iP2ry12 and Cx3cr1 and distinctive over-expression of Birc5 (a cell proliferation marker), Cxcl10 (an interferon response gene), Ccl4, and Apoe [Hammond et al. analyzed a great number of white matter isolated microglial cells after TBI, derived from mice exposed to a focal demyelinating injury caused by LPC injection. Saline-injected adult (P100) mice samples and untreated P100 whole-brain samples were used as control states. Two main distinct injury-responsive microglial subtypes were revealed, IR1 and IR2 C. The IRand Apoe .Ifitm3, Isg15, Ifi27l2a) and other immune-related genes , with a corresponding reduction in homeostatic genes , whereas cluster 8 was characterized by interferon- (Ifi27l2a) and damage-related genes   in the development from acute to chronic pathogenesis. Increased inflammation and type-1 interferon signaling was linked to TBI’s influence on microglia. A subset of microglia (Cluster 6) expressed significant levels of interferon-responsive genes  C. AccordSpp1, Igf1, and Clec7a19), and embryonic microglial cells , suggesting that a subpopulation of de-differentiated homeostatic microglia are being activated during injury in order to repair the wound and restore homeostasis [Further scRNAseq and functional studies contacted by Li et al. uncovered a unique microglial subset , found only in neonatal mice, that orchestrates the injury response process and can lead to scar-free healing of spinal cord injuries. In more detail, when activated in response to injury, MG3 microglia can temporarily secrete fibronectin, a protein that forms scaffolds of extracellular matrix in order to ligate the injured ends of the spinal cord and express peptidase inhibitors involved in the termination of the inflammatory process . MG3 miceostasis . Gliomas are diffusely developing brain tumours that arise from astroglial or oligodendroglial progenitor cells, and are characterized by strong innate immune infiltration of various myeloid cells . To profCX3CR1 and TMEM119), C2 strongly expressed MHC-II and antiviral immunity genes , and C6 and C7 downregulated CX3CR1 and upregulated integrin-receptor-binding protein and metabolism genes  [scRNAseq revealed nine major subpopulations (Clusters C1-C9) with variations over the transcriptional spectrum. C3 upregulated the microglial core genes  . GO termnd IL1B) .CX3CR1 and SELPLG and higher expression of metabolic, inflammatory, and interferon-associated genes, such as CD163, APOE, LPL, IFI27, and IFITM3  were created after the analysis. Clusters C9, C11, and C12 were control-enriched, clusters C3-C7, and C10 were mixed, and clusters C13 and C14 were almost exclusively composed of glioma-associated microglia (GAMs). C13 and C14 were characterized by lower expression of microglial core genes such as d IFITM3 B. Mixed class I” .Although much of the study on microglia has focused on adult brain activities, data from developmental and aging research suggest that the nature of these processes changes over time. Aging, rather than inducing a universal program, drives a distinct transcriptional course in each cell population . Thus, sMatcovitch-Natan et al. identified sixteen different microglial populations (I–XVI) across different temporal phases. Four main age-related microglial categories were formed: microglia derived from the yolk sac (I-III), embryonic microglia from different time points, termed early microglia (IV-XI), pre-microglia, which include all postnatal age stages (XII-XV), and adult microglia (XVI).Mcm5 was uniquely expressed in early microglial cells, Csf1 displayed a pre-microglial marker, while Mafb, Jun, Fos, and Mef2a were strongly correlated with adult microglia [Fcgrt, Lyz2, and Pf4, which were found to be expressed in several of the early microglial clusters (IV and V) as well. Dab2 and other cell cycle regulatory and chromatin remodeling genes were found to be specifically expressed in early microglial classes, but not at later time points. Canonical transcriptional factors such as Egr1 and Sall1 were initially expressed in pre-microglia subpopulations and further induced in adulthood [Most of the microglial subsets expressed a characteristic gene pattern, pointing out their temporal specificity and dynamics across different developmental stages, while several classes showed partial expression overlap . Mcm5 waicroglia . Clusterdulthood . Within the same framework, Hammond et al. analyzed a great number of microglial cells isolated from mice during development . Clustering revealed nine differentiated microglial subtypes across all ages and conditions, termed clusters 1–9, and two clusters of old age microglia (OA2 and OA3 ). Fcrls, P2ry12, Cx3cr1, Trem2, and C1qa were significantly expressed by most of the microglial clusters, while only C1qa, Fcrls, and Trem2 were universally expressed. In contrast, clusters 3 and 4 showed an important downregulation of P2ry12, Cx3cr1, and Tmem119 transcripts. Younger-aged clusters displayed various unique markers, such as Arg1 (cluster 1), Rrm2, Mcm6, and Pcna (cluster 2a), Ube2c, Birc5, and H2afx (cluster 2b), Cenpa, Hist1h2bc, and Ccnb2 (cluster 2c), Fabp5 (cluster 3), Spp1 (cluster 4), Hmox1 (cluster 5), and Ms4a7 (cluster 6). Cluster 3 overexpressed a wide range of other genes, such as Mif, Ldha, Tpi1, Spp1, Gpnmb, Igf1, Pkm, Lgals1, Aldoa, Ftl1, and Eno1, highlighting their association with glycolysis and potential functional overlap with macrophages in cell growth, motility, inflammation, and immunomodulation [Mrc1, Ccr1, Dab2) while at the same time over-expressing transcripts found in mature microglia . This suggests the idea that cluster 6 represents an intermediate state between microglia and their brain border neighbours, which comes to a symphony with the idea that these two cell types are derived from the same pool of yolk sac hematopoietic progenitors and migrate to the brain at the same time in development [Ccl4.The embryonic and early postnatal stages (E14.5 and P5) showed the greatest diversity, whereas aging clusters showed a major redistribution of microglial states . Homeostdulation ,42,43. Celopment . In addiLgals3, Cst7, Ccl4, Ccl3, Il1b, Id2, and Atf3  in Hammond’s et al. study. OA2 microglia uniquely expressed several inflammatory signals that were absent in clusters 1–9. This cluster showed high expression of and Atf3 . Clusternd Oasl2 . These gP2ry12 and Salc2a5. Likewise, by performing scRNAseq in six regions  of embryonic (E14.5), postnatal (P7), and adult (P60) brain tissues, Li et al. uncovered microglial heterogeneity among different developmental stages. Clustering revealed seven microglial populations (clusters 0–6) expressing microglial signature genes, such as P2ry12 slightly lower in cluster 6. Cluster 6 was characterized by IEG expression, such as Fos and Egr1  and higher expression of Mt1, Fth1, Ftl1, Tmsb4x, Pfn1, Cfl1, Rps14, Rps18, Rps29, and Rpl35 , with P7-C1 expressing them at lower levels. In scRNAseq analysis, cell cycle genes can hide cell-to-cell variations, masking functional relevant differences. Bearing this in mind, Li et al. re-clustered P7 microglial cells by removing cell cycle effects. The results revealed three clusters, P7-C0, P7-C1, and P7-C2, showing early postnatal microglial heterogeneity. P7-C2 was characterized by lower expression of microglial core genes  related to age and development. To investigate spatial and temporal microglia heterogeneity, Masuda and colleagues performed massively parallel single-cell analysis in microglial cells from multiple regions of the embryonic (16.5), juvenile (3 weeks), and adult (16 weeks) mouse CNS.Ctsb, Ctsd, and Lamp1, suggesting increased lysosomal activity [Apoe, whereas in C6 the microglial subset genes Tmsb4x, Eef1a1, and Rpl4 appear to be overexpressed [Examination of microglial transcriptional heterogeneity during development showed two major developmental stages with distinct expression patterns: embryonic microglia and postnatal microglia (including cells from both juvenile and adult mouse brains). Clusters C1-C6 mainly consisted of embryonic microglia and were distributed across the forebrain and cerebellum. C1 and C2 showed high expression of the lysosome-related genes activity  . This microglial population was detected only in the early postnatal brain and was identified specifically in the subcortical axon tracks of the corpus callosum in the forebrain and cerebellum (ATM—Axon Tracks Microglia), regulating the growth and fasciculation of axons and refining synapses in a circuit- and region-specific manner. This cluster was further characterized by high enrichment of genes associated with immune cell activation, lysosomal activity, and phagocytosis, such as  regions .Igf1 and Itgax . This transcriptional pattern indicates that these genes may function as potential markers under different conditions, defining specific microglial states. In addition to these core genes, each microglial subtype expresses different transcriptomic patterns related to its specific functional role in CNS, confirming multiple definable states of microglia over the course of age, spatial distribution, and disease. Interestingly, certain genes show a strong correlation with a variety of microglial clusters, belonging to the same main group and participating in similar molecular mechanisms. Genes such as Cd9, Lpl, Itgax, Ctsb, Cd63, Fth1, Apoe, Cst7, Spp1, and Axl. The expression profile between cluster 6 and DAM III was even more similar, indicating that these clusters represent an advanced stage of neurodegeneration. It is interesting to note that research over the past 20 years has revealed both positive and negative impacts of microglia in AD. The detection of DAM cells in proximity to Aβ plaques in the study by Keren et al. demonstrated their potential possible neuroprotective role in AD. However, microglia have been reported as detrimental in tau models of AD [Across disease-associated microglia, we found a significant association between the DAM clusters of Keren’s et al. and Mathys’ et al. late-response cluster 6 A. These ls of AD . Taken tCd63, Lyz2, and Apoe and downregulation of P2ry12, P2ry13, and Tmem119), significant differences were noticed. For example, Tyrobp and Trem2 were highly downregulated in IAMs and over-represented in DAMs. Based on expression profiling, IAMs showed elevated inflammatory reactivity, whereas DAMs displayed phagocytic and lysosomal gene identity. These distinct expression patterns may reflect different functional approaches to inflammation and neurodegeneration. Collectively, these results suggest that microglia under inflammation show molecular signals which are different from disease-associated profiles, supporting the idea that microglial modulation takes place through a very specific regulatory program [Despite the fact that DAM clusters demonstrate various similarities with other disease-associated subtypes, distinct gene expression patterns may appear due to different pathological situations. For instance, DAMs and Sousa’s et al. IAM clusters both represent neuropathological conditions B. Althou program .Spp1, Igf1, Gpnmb, Lilrb4, Clec7a, Cd9, Lpl, Cd63, Fabp5, Itgax, Lgals3, Apoe, Lyz2, and Tyrobp and downregulated the microglial core genes P2ry12 and Tmem119. Although these clusters derive from distinct functional categories, similarities between them support the idea that gene expression during development matches the transcriptional profile of aging and neurodegeneration through reactivation of certain genes [Of note, high transcriptional overlap between DAMs and Li et al.’s PAMs was detected C. Both cin genes .Ccl2, Ccl3, Ccl4, Ccl7, Ccl9, Ccl12, and Tnf).Regarding age-associated microglial subtypes, studies have revealed higher heterogeneity between young-aged clusters compared to old-aged microglia ,40. ThisIt is worth mentioning that protocols for enzymatic dissociation of brain tissue as well as different isolation approaches could possibly affect the gene expression signature. Microglial cells, which demonstrate elevated plasticity and dimensionality, are considered to be particularly sensitive to stimulation of their microenvironment, and thus are highly susceptible to ex vivo gene expression artifacts. As a result, it is critical to establish methods to prevent this aberrant transcriptional state. Recent studies have developed new digestion protocols using cold-active enzymes ,51. NotaIn conclusion, scRNAseq techniques have shown for the first time the increased variation in microglial cell populations, especially in humans, where heterogeneity is significant, in contrast to mouse and other species models, which demonstrate one largely dominant microglial type . StudiesInstances of inflammatory processes in the aging and diseased brains highlight the significance of proper microglial phenotypic description. The data presented in this study provide a thorough and useful overview of the state of the art and simplify the selection of more appropriate and precise markers for in-depth investigation of microglia and neuroinflammatory pathways in diverse pathophysiological circumstances. Recently, regulating microglial phenotypes has emerged as a treatment strategy in several CNS illnesses. The data acquired here are useful for selecting the right descriptors or study goals and will enable more accurate identification of microglial states in research while describing immunological pathways in the brain. In any case, our knowledge, for now, remains limited, and further study is needed.


Recent findings suggest a correlation between COVID-19 and diabetes, although the underlying causes are still little understood. COVID-19 infection tends to induce severe symptoms in patients with underlying diabetes, increasing their mortality rate. Moreover, COVID-19 itself appears to be a diabetogenic factor. In addition, mental health conditions, such as depression due to lockdown and anxiety about infection, were found to affect glycemic control and immunity, highlighting the importance of mental health care during the pandemic. Mind–Body Intervention (MBI), which includes meditation, yoga, and qigong, has emerged as a tool for mental health management due to its effects on stress reduction and the promotion of mental and physical well-being. Here, we review the latest randomized controlled trials to determine the effects of MBI on glycemic control and the immune system and discuss the underlying mechanisms by which MBI facilitates the virtuous cycle of stress management, glycemic control, and immune modulation. Furthermore, we examine the actual utilization of MBI during the COVID-19 pandemic era through recent studies. With proper online education, non-pharmacological MBI may be more widely used as an important tool for self-health care that complements the usual treatment of COVID-19 patients and survivors. Patients with pre-existing conditions including diabetes may have a worse prognosis from coronavirus disease 2019 (COVID-19) infections. During the pandemic, lockdown-mediated social isolation, uncertainty, anxiety and reduced physical activity can aggravate diabetes-specific emotional distress, which may decrease both compliance to therapeutic regimens and glycemic control. Poor mental health and socioeconomic issues caused by COVID-19 or post-COVID may also increase the risk of type 2 diabetes mellitus (T2DM). Non-pharmacological therapeutic approaches such as Mind–Body Intervention (MBI), although mainly used as a stress reduction strategy, were also reported for their modulating effects on both glycemic control and immune function, suggesting their potential use in self-health care during the pandemic. Given the relatively recent occurrence of COVID-19, not many studies have examined the application of MBIs for the prevention or recovery of COVID-19. Here, we approach this review by systematically examining the effects of MBI on factors correlated with disease severity and mortality from COVID-19 such as glycemic state and immunological factors. Following this, we explore studies that examined the effects of MBI on depression, anxiety, and stress associated with poor immune function, which can aggravate negative clinical outcomes of COVID-19. Lastly, we discuss current studies that have examined the effects of COVID-19 on the physical and mental health of COVID-19 patients, as well as caregivers. We hope that this study can provide a basis for the practical use of MBI not only for the management of mental health, but also in glycemic control and immunity during the pandemic.Recent studies indicated a possible bidirectional relationship between COVID-19 and diabetes. Diabetes patients have an increased risk of exhibiting severe COVID-19 clinical outcomes, that is, higher rates of hospitalization and mortality ,3,4. SevAs COVID-19 continues to spread globally, leading to a substantial number of confirmed cases, health care crises have emerged in several cities worldwide, causing an increase in individuals needing home care for COVID-19 infection and other pre-existing diseases. During this pandemic, public education regarding non-pharmacological MBI techniques complementary to regular therapy would help self-care and health promotion for affected individuals and the general public. This paper is a comprehensive review of recent studies on the effects of MBIs on glycemic control, the immune system and mental health, together with their potential underlying mechanisms. Moreover, it includes recent studies regarding the application of MBI in COVID-19 patients/survivors and health care providers, patients with other diseases, and healthy populations during the current pandemic.MBIs  include a wide range of strategies such as meditation, yoga, tai chi, and qigong. Many of these strategies often emphasize focusing on the present moment, resulting in stress reduction, mental health improvements, and beneficial effects on physical health ,8,9,10. Although this study is not a systematic review, in order to provide transparency in the search and selection process, we followed the guidelines of the Preferred Reporting Items for the Systematic Reviews and Meta-Analysis (PRISMA) statement . PubMed Articles were eligible if they were (1) randomized controlled trials (RCTs), (2) published in English, (3) published within the last 5 years. Moreover, they were included if they reported (4) a Mind–Body Intervention as a main intervention, (5) glucose-related biochemical measures as research outcomes, and (6) immune function-related measures as research outcomes. Studies were ineligible if they were (1) case reports, protocol papers, reviews or theoretical articles; (2) book chapters; and (3) studies whose main interventions were not MBIs. In addition, studies were excluded (4) if the study outcomes were not related to glycemic or immune function, (5) if the number of participants in the study was fewer than 20, and (6) if studies were retracted. Articles were screened for eligibility by specific inclusion and exclusion criteria. From the selected articles, information was extracted by assessing the full text.n = 13); acupuncture/accupressure intervention (n = 3); protocols (n = 3); lifestyle intervention (n = 1); no MBI interventions (n = 8); outcomes not related to glycemic or immune function (n = 6); nutrients intervention (n = 2); drug intervention (n = 1); retracted (n = 1); reviews (n = 2)  .To assess study quality, checklists of the National Heart, Lung, and Blood Institute (NHLBI) were used  with minA number of studies reported the effects of MBIs on blood glucose, glycated hemoglobin levels, and insulin resistance in a variety of people, including those with T2DM, metabolic syndrome, overweight/obesity, or cardiovascular risk, some of which reported beneficial outcomes .n = 48) [n = 87) showed that 3 months of qigong or tai chi did not induce significant changes compared to stretching (control group) [n = 48) [One study showed that 3 months of yoga intervention lowered blood glucose and glycated hemoglobin (HbA1c) levels in T2DM patients, whereas standard walking exercises had no such effects (n = 48) . Howeverl group) . For typ[n = 48) . The fre[n = 48)  while a [n = 48) ,24 if hon = 246) [n = 260) [n = 54) [Several studies have reported on the application of MBIs on blood sugar management among people with metabolic syndrome. Tai chi significantly reduced fasting blood glucose (FBG) and HbA1c levels compared to brisk walking and usual activity in this population (n = 246) . In anot[n = 54) . Conside[n = 54) , changesn = 37) [n = 86) [n = 33) [n = 170) [n = 57) [n = 120) [MBIs have been reported to be beneficial in lowering blood glucose levels of other susceptible populations, such as people with pre-diabetes, obesity, family history of T2DM, and high risk of ischemic stroke. An RCT involving women with pre-diabetes showed that 3 months of a diabetic yoga protocol significantly reduced the level of HbA1c and FPG levels compared to the waitlist control n = 37) . In wome[n = 86) . Meditat[n = 33) . Individ[n = 33) , which h . In womn = 170) , suggestn = 170) ,40,41,42[n = 57) . This im[n = 57) . Therefon = 60) [n = 47) [However, MBI was not effective for glucose regulation of cardiac patients under cardiac rehabilitation. In an RCT involving patients undergoing cardiac rehabilitation following acute coronary events, 3 months of yoga practice showed no difference in FBG compared to the usual care (n = 60) . In anot[n = 47) .Although many studies have reported the beneficial effects of MBI on glycemic regulation, variations in its effects have been noted perhaps due to the different health states of subject populations, their education level, and the intensity, frequency, and period of MBI performance, warranting further studies to understand which conditions most effectively regulate glucose level to improve the use MBI for self-care.Glycemic regulation interacts with the innate immune system. Diabetes is accompanied by systemic low-grade inflammation, and chronic activation of the innate immune system dysregulates insulin secretion and contributes to diabetes complications . MBI hasn = 48) [During infection, pro-inflammatory markers—C-reactive protein (CRP), interleukin-6 (IL-6) as well as interferon-gamma-inducible protein-10 (IP-10)—respond acutely. In chronic inflammatory conditions, such as diabetes, systemic levels of these markers remained increased for an extended period of time ,74. Moren = 48) . In addin = 48) .+ T lymphocytes, %CD8+ T lymphocytes, %CD19+ B lymphocytes, %CD14+ monocytes, and %NK cells) [For healthy people, acute psychological stress increases circulating levels of peripheral blood natural killer (NK) cells, which are major constituents of the innate immune system, implying a potential for the development of allergic and autoimmune disorders . On the K cells) , implyinn = 20) [n = 55) [Mental conditions such as depression and anxiety are strongly linked to low-grade systemic inflammation . In RCTsn = 20) . Among o[n = 55) . The lev[n = 55)  and tai [n = 55) . Another[n = 55) . In an R[n = 55) . For MBIn = 124) [+ B-lymphocyte number were increased and the percentages of CD3+ or CD3+8+ T lymphocytes were decreased, while IL-6 and IL-8 were not changed at post-intervention compared to baseline (n = 166) [n = 322) [n = 44) [n = 48) [Multiple trials sought to determine the potential benefits of MBI for immune modulation in cancer patients. While breast cancer patients undergoing active therapy did not show significant differences in CRP levels by MBSR , breast n = 124) . In anotn = 166) . Six ween = 322) . There w[n = 44) . In anot[n = 48) . Most ofn = 72) [n = 166) [n = 24) [Rheumatoid arthritis, an autoimmune disease, has been shown to accelerate immune system aging and reduce immune cell function with chronic low-grade systemic inflammation. Two recent RCTs showed that yoga intervention promoted beneficial effects in reducing inflammation in patients with rheumatoid arthritis. In one RCT, the levels of systemic inflammation markers, including erythrocyte sedimentation rate and CRP, and pro-inflammatory cytokines, such as IL-6, IL-17A, and TNF-α, were lowered, whereas the levels of an anti-inflammatory cytokine transforming growth factor beta (TGF-β) and immunomodulatory marker human leukocyte antigen-G (HLA-G) were increased (n = 72) . In anotn = 166) . Some MB[n = 24) . In thesn = 260) [n = 110) [n = 47) [n = 70) [n = 82) [n = 177) [Studies reported that dysregulated inflammation can be observed in wide range of disease conditions, such as metabolic syndrome , myocardn = 110) . In pati[n = 47) . Fibromy[n = 82) . In HIV-n = 177) .The above results indicate that MBIs have positive impacts on immune-modulation in some conditions, suggesting their possible efficacy in treating symptoms of chronic inflammation or dysregulated immune response.Studies in both animals and humans have shown that physiological responses to psychological stress are associated with T2DM  and immuPsychological stress-induced secretions of hormones and neurotransmitters, such as cortisol, epinephrine, and norepinephrine, affect immune cells, causing changes in the immune system during stress ,96. ChroConsidering the effects of psychological stress on blood glucose levels and immune function as described above, stress management through MBI may play a protective role against the stress-induced acceleration of diabetes, systemic inflammation, or immune system dysregulation. Despite the varying types of MBI, the key benefits of MBI commonly include stress reduction, mood improvements, and improved sleep ,104,105.According to a recent systematic review and meta-analysis of 51 studies with 18,917 patients, survivors of COVID-19 exhibit neuropsychiatric symptoms, including sleep disturbances (27.4%), fatigue (24.4%), objective cognitive impairment (20.2%), anxiety (19.1%), and post-traumatic stress (15.7%) at a mean of 77 days (range 14–182 days) after COVID-19 . Aside fAmong the general population, COVID-19 lockdowns have led to multiple psychological symptoms, such as anxiety, depression, and anger . People Poor mental health states, such as depression, anxiety, and stress, have been associated with poor immune functions such as low-grade inflammation. In a meta-analysis that compared 5166 patients with depression and 5083 controls, patients with depression exhibited a significantly higher pro-inflammatory state  than controls . A meta-It is well-established that MBI techniques  induce the relaxation of the body and mind by activating the parasympathetic nervous system (PNS), leading to beneficial effects on mental health for a variety of different people  or depression, etc.). A meta-analysis involving 1076 participants from 19 studies found that mindfulness meditation interventions improved depressive symptoms in older adults . MeditatSince the COVID-19 outbreak, studies on MBI in patients with COVID-19, other diseases, and the general public have contributed to investigating the health benefits of MBI, which can be applied through self-health management techniques. In general, several MBIs often include some common components, for example, breathing practices. This common feature of MBI makes it an attractive approach for complementary therapy against COVID-19 and long COVID, as its major symptoms have been associated with pulmonary function and breathing. COVID-19 can cause severe pulmonary disease. One study showed that among the 156 participants across five large Norwegian hospitals, one-third of participants experienced a reduction in oxygen uptake (V’O2peak < 80%) during cardiopulmonary exercise testing 3 months after COVID-19 discharge . TherefoAn RCT conducted in the COVID-19 clinic of a tertiary hospital with 44 patients found that deep breathing exercises in the triflo group exhibited a significantly shorter hospitalization time, lower anxiety levels, higher oxygen saturation levels (SpO2), and a higher quality of life compared to the usual care group . Among pn = 30) [During the COVID-19 pandemic, several cities worldwide experienced a health care crisis, which impacted the treatment of patients with other disease besides COVID-19 and public health in general. Given that MBI is non-pharmacological and easy to access, it can be used as a self-health management technique not only for COVID-19 patients/survivors but also a wide range of populations, including healthy individuals. In fact, an RCT involving 101 obstetrics and gynecology patients during the COVID-19 pandemic showed that a mobile meditation app significantly reduced perceived stress, depression, anxiety, and sleep disturbance compared to standard care . During n = 30) .As MBIs are non-invasive/non-pharmacological interventions, and their intensity, frequency and education details can be customized according to the target populations, they are currently widely used with target-modified versions for various target populations, including patients with diabetes, hypertension, cancer, affective disorders, PTSD, etc. A wide age range is also applicable as the methods can be modified for the target age. There are no gender limitations in the application for these methods. Once a method is properly learned from a certified expert of a certain MBI program, one can freely practice by oneself for one’s own health management for a lifetime. The personal and social burden in health management and recovery increased throughout the pandemic. Organizations for public health can communicate the benefits, safety, precautions of MBIs more actively and support MBI education for the public in order to improve individuals’ mental and physical health condition at home, especially in times of difficulty in use for public health services during the pandemic.Multiple limitations exist in the current study. As this was not a systematic review or meta-analysis, statistical conclusions could not be made. Investigations of both glycemic and immune-related outcomes within a single study have rarely been performed; therefore, the papers included in the current study mostly report outcomes related to only one aspect (either glycemic or immune-related outcomes). In the included papers, there is a wide variety of MBI methods and participant characteristics. Therefore, a specific MBI that has beneficial effects on one health aspect  does not guarantee its effects for another aspect  as well. For future studies, RCTs handling both aspects within specified populations should be performed.Although much of the relationship between COVID-19 and diabetes still remains unclear, evidence regarding such an association has emerged. COVID-19 may induce diabetes, while pre-existing diabetes can exacerbate COVID-19 symptoms. MBI reduces psychological stress and can improve mental and physical health. Accumulated studies suggest that MBI may have positive effects on glycemic control and immune modulation potentially through stress reduction, followed by reduced HPA axis/SNS activation and increased PNS activity. This suggests that MBI may potentially improve the prognosis of COVID-19, which may cause diabetes or exacerbate symptoms among those with pre-existing diabetes.