Cardiovascular complications due to accelerated arterial stiffening and atherosclerosis are the leading cause of morbimortality in Western society. Both pathologies are frequently associated with vascular calcification. Pathologic calcification of cardiovascular structures, or vascular calcification, is associated with several diseases  and is a common consequence of aging. Calcium phosphate deposition, mainly in the form of hydroxyapatite, is the hallmark of vascular calcification and can occur in the medial layer of arteries , in the atheroma plaque , and cardiac valves . Although various mechanisms have been proposed for the pathogenesis of vascular calcification, our understanding of the pathogenesis of calcification is far from complete. However, in recent years, some risk factors have been identified, including high serum phosphorus concentration (hyperphosphatemia) and defective synthesis of pyrophosphate (pyrophosphate deficiency). The balance between phosphate and pyrophosphate, strictly controlled by several genes, plays a key role in vascular calcification. This review summarizes the current knowledge concerning phosphate and pyrophosphate homeostasis, focusing on the role of extracellular pyrophosphate metabolism in aortic smooth muscle cells and macrophages. Pathologic calcification of cardiovascular structures, or vascular calcification, is associated with several diseases , and is a common consequence of aging ,2. VascuIn blood vessels, calcified deposits are found in distinct layers of the aortic wall and are associated with specific pathologies. Intimal calcification occurs in atherosclerotic lesions and is associated with vascular smooth muscle cells and macrophages ; whereasDifferent mechanisms regarding the pathogenesis of vascular calcification have been proposed , includiInorganic phosphate is essential for a variety of cellular processes, such as energy metabolism, bone formation, and synthesis of biomolecules, including phospholipids and nucleic acids. However, elevated serum phosphorus (in the form of inorganic phosphate) has emerged as a key risk factor for vascular calcification ,8. Durin3P04), (2) dihydrogen phosphate ion (H2PO4−), (3) hydrogen phosphate ion (HPO42−), and (4) phosphate ion (PO42−); (see a1 = 2.1), moderately weak with respect to the second (pKa2 = 6.9), and very weak with respect to the third (pKa3 = 12.4). Under strongly basic or acidic conditions, the phosphate ion or trihydrogen phosphate dominates, respectively. In extracellular fluid (pH = 7.4), only H2PO4− and HPO42− ions are present in significant amounts in a proportion of 1:4, respectively. Whereas, in cytosol (pH = 7) and lysosome (pH = 4.8), this proportion is inverted .The formation and deposition of inorganic minerals within or outside the cells of various organisms is known as biological mineralization or biomineralization. Biomineralization in hard tissues (such as in bone or dentine) is normally considered a physiological process; however, the accumulation of inorganic minerals in soft tissues  is considered pathological or ectopic biomineralization. Under normal conditions, the soft tissues are not mineralized, but due to aging and other pathological conditions, soft tissues become calcified, which leads to morbidity and mortality. The main biominerals found in mineralized vertebrate connective tissue are calcium-phosphate salts. In an aqueous system of calcium and phosphate, there are several known non-ion-substituted calcium phosphates, which have also been found in calcified tissues. The phosphate ion is a central phosphorus atom surrounded by four oxygen atoms in a tetrahedral arrangement. In biological systems, it is found as a free phosphate ion in solution (inorganic phosphate) or bound with different biological molecules, including proteins, sugars, lipids, and nucleic acid. Aqueous inorganic phosphate exists in four forms according to its triprotic equilibrium: (1) trihydrogen phosphate ion (H2−); see . Inorgan2PO4)2), (2) dicalcium phosphate anhydrous (CaHPO4), and (3) β-tricalcium phosphate (β -Ca3(PO4)2) [2PO4)2 and CaHPO4 are hydrated to form their hydrated forms 2H2O) and dicalcium phosphate dihydrate (CaHPO42H2O). CaHPO42H2O, also called Brushite, is often found in calcified tissues, whereas Ca(H2PO4)2, Ca(H2PO4)2H2O, CaHPO4, and β -Ca3(PO4)2 have never been found in calcifications [Notably, various phosphates-calcium salts are obtained by charge neutralizing these different inorganic phosphate ions in the presence of a calcium ion , includi3(PO4)2) ,23. Bothications ,25.10(PO4)6(OH)), the main component of bone and calcified tissues; along with two of its precursors 6nH2O, and octocalcium phosphate, Ca8H2(PO4)65H2O) [9(PO4)6 clusters  that appear to be energetically favored compared to (Ca3(PO4) and Ca6(PO4)4 clusters [2+ and ATP are critical for the stabilization of amorphous calcium phosphate [The final product of the calcium-phosphate salts’ reaction in neutral or basic solutions is crystalline hydroxyapatite (Ca4)65H2O) ,26. Notaclusters ,22. Therclusters ,28  calcinosis, (2) dystrophic calcification, and (3) metastatic calcification. In the presence of normal homeostasis of phosphate, calcinosis and/or dystrophic calcification occur, most often in subcutaneous tissues, skin, and related connective tissues, whereas, in the second case, calcification occurs in degenerated or necrotic tissue. Metastatic calcification occurs in normal tissues when the calcium levels are elevated in serum.In adults, normal phosphate concentration in serum or plasma is mainly 2.5 to 4.5 mg/dL (0.81 to 1.45 mmol/L). Elevated serum phosphate (hyperphosphatemia) is a key risk factor for pathologic calcification in cardiovascular structures . TherefoMany different foods contain phosphorus, including vegetables, grains, legumes, eggs, fish, and meats. In addition, phosphate additives such as phosphoric acid, sodium phosphate, and sodium polyphosphate are present in many processed food products. Phosphate deficiency is rare in Europe, and is rarely the result of low dietary intakes. However, phosphate is also available in dietary supplements containing only phosphate o supplements containing phosphate in combination with other ingredients, including vitamins and minerals.Recommended dietary daily phosphorus intake in healthy adults (>18 years old) is 700 mg. However, daily phosphorus intake varies between 700 and 2000 mg. After absorption, phosphorus is transported across cell membranes as phosphate. And in extracellular fluid (including plasma), phosphate undergoes one of three fates: (1) elimination, mainly by the kidneys, (2) transport into cells, or (3) deposition in bone or soft tissue see .In healthy adults, oral phosphate intake is balanced mainly by phosphate excretion in the urine and feces. In this case, different factors play an important role in the control of phosphate homeostasis, including phosphate excretion and absorption by the kidneys, intestines, and bone. Although the kidney is the major regulator of phosphate homeostasis, plasma phosphate levels are altered by intestinal phosphate absorption. Notably, in normal adults, between 75% and 85% of the daily phosphate filtered by the glomerulus is reabsorbed by the renal tubules  ,37.An increased absorption or decreased phosphate excretion can induce a relatively small elevation in serum phosphate, which has been correlated with the presence of calcified vessels due to an increase in calcium-phosphate crystal formation and saturation in the inhibition. Several diseases have been correlated with the dysregulation of phosphate homeostasis, including osteoporosis, diabetes mellitus, hyperparathyroidism, vitamin D (hyper-and hypovitaminosis), and chronic renal disease .m ≈ 0, 1 or less) but show differences in their affinities for H2PO4− and HPO42− ions [Cellular phosphate levels are controlled by sodium-phosphate co-transporters (NaPi) ,39. The 42− ions ,41. OrigThe SCL34 family comprises three members , which are expressed in the small intestine (NaPi-IIb) and the kidney (NaPi-IIa and NaPi-IIc), two important sites for the control of phosphate homeostasis . NaPi-IIThe roles of sodium phosphate cotransporters in pathophysiology have not been well defined, but their important role in controlling phosphate homeostasis and intracellular phosphate levels for the synthesis of macromolecules and energy metabolism make them an important target to study.In vertebrates, plasma and other extracellular fluids are supersaturated with phosphate and calcium, causing a tendency for spontaneous calcium-phosphate precipitation ,6,12 se. TherefoMatrix Gla protein is a mineral-binding extracellular matrix protein synthesized mainly by vascular smooth muscle cells and chondrocytes, the first protein recognized as an inhibitor of vascular calcification in vitro and in vivo . Matrix Fetuin-A is a circulating plasma glycoprotein that also has the capacity to bind calcium and has anti-inflammatory properties . NotablyOsteopontin, a sialic acid-rich glycoprotein first purified from the bone, is a known noncollagenous bone matrix protein that regulates calcification ,52. LikeOn the other hand, extracellular pyrophosphate is the major endogenous physicochemical inhibitor of calcium-phosphate crystal formation and growth, both in vitro and in vivo . ExtraceThe currently known enzymes and transporters involved in extracellular pyrophosphate metabolism include members of the ecto-nucleotide pyrophosphatase/phosphodiesterase, tissue-nonspecific alkaline phosphatase, ecto-5′-nucleotidase, equilibrative nucleoside transporters, phosphate transporters (NaPi), progressive ankylosis proteins, and pump/channels that release ATP extracellularly, including the multi-drug resistance-associated protein 6 ,64,65,66Pyrophosphate is mainly produced during the extracellular hydrolysis of ATP ,67,68. TMoreover, pyrophosphate is degraded to phosphate mainly by tissue non-specific alkaline phosphatase (TNAP) in tissues and extracellular fluids see . Cells oAnother enzyme involved in vascular calcification is the membrane-bound ecto-5′nucletotidase , which preferentially binds AMP and converts it to adenosine and phosphate see . MutatioFinally, in 2000 two additional new genes were identified that could play an important role in controlling tissue calcification and arthritis: progressive ankylosis protein and multidrug resistance-associated protein 6. However, the molecular mechanisms remain in part unknown. First, it was reported that mutations in the progressive ankylosis gene cause a severe form of generalized joint calcification and arthritis . Loss ofPhosphate and pyrophosphate concentration , known risk factors for vascular calcification, is strictly controlled by a complex interplay of genes . ProgresPseudoxanthoma elasticum, a heritable disorder of connective tissue characterized by calcification of the elastic fibers in skin, arteries, and retina. MRP6/ABCC6 is a member of the superfamily of ABC transporters, composed of several related pumps that can transport various molecules across extra- and intra-cellular membranes, including glutathione-S-conjugates and cyclic nucleotides. This suggests that MPR6/ABCC6 may act as a pump that releases endogenous, low molecular weight inhibitors of calcium phosphate deposits in fluids outside cells, such as ATP or citrate; however, this has not been thoroughly demonstrated.The second-gen reported is multidrug resistance-associated protein 6 (MRP6), also known as ATP-binding cassette sub-family C member 6 (ABCC6) ,85. It wIn the healthy aortic wall, the pyrophosphate synthesis from ATP hydrolysis is several times faster than pyrophosphate hydrolysis ,68. SincOn the other hand, calcification is a very common complication of atherosclerosis that involves aortic smooth muscle cells, monocyte infiltration, and macrophage accumulation within the artery wall . In respNotably, in a macrophage/vascular smooth muscle cell in vitro co-culture system, macrophages enhance the calcifying capacity of vascular smooth muscle cells by inducing phenotypic changes, including matrix mineralization and increment in TNAP activity ,89. MoreIn a recent study , the autFinally, a study also shows that hyperphosphatemia can activate macrophages, forming a different and new macrophage type . PhosphaExtracellular pyrophosphate is a major endogenous calcification inhibitor, and its metabolism plays a key role in vascular calcification. The use of pyrophosphate as a therapeutic is limited by its pharmacokinetics and the fact that stable analogs inhibit bone formation. However, strategies to raise pyrophosphate levels by targeting its metabolism may have promise as therapies in the future.


R. sativus.Fusarium wilt (FW) is a fungal disease that causes severe yield losses in radish production. The most effective method to control the FW is the development and use of resistant varieties in cultivation. The identification of marker loci linked to FW resistance are expected to facilitate the breeding of disease-resistant radishes. In the present study, we applied an integrated framework of genome-wide association studies (GWAS) using genotyping-by-sequencing (GBS) to identify FW resistance loci among a panel of 225 radish accessions, including 58 elite breeding lines. Phenotyping was conducted by manual inoculation of seedlings with the FW pathogen, and scoring for the disease index was conducted three weeks after inoculation during two constitutive years. The GWAS analysis identified 44 single nucleotide polymorphisms (SNPs) and twenty putative candidate genes that were significantly associated with FW resistance. In addition, a total of four QTLs were identified from F2 population derived from a FW resistant line and a susceptible line, one of which was co-located with the SNPs on chromosome 7, detected in GWAS study. These markers will be valuable for molecular breeding programs and marker-assisted selection to develop FW resistant varieties of  R. sativus L., 2n = 18) belong to the Brassicaceae family, and are an economically important crop that is globally utilized. The swollen taproot, young leaves, fresh sprouts, and immature siliques are eaten as vegetables, and are consumed raw, pickled, dried, and cooked . We. We48]. To validate the significant markers detected from GWAS, we conducted QTL analysis associated with FW resistance. Firstly, we derived a 133 F2 population derived from ‘G193′ (resistant) and ‘G190′ (susceptible), and constructed a genetic map by genotyping by sequencing (GBS). From the 133 samples, 2 runs of sequencing produced about 359 million (~54.3 Gb) DNA reads. Among them, 8.56 million reads (~2.38%) failed to determine samples while demultiplexing steps. In average, 2.42 million reads per sample was used for mapping to the pseudomolecule reference sequence of radish, recording 79.0% of the overall alignment rate . The initial variant calling pipeline produced 261,769 variants including InDel, and continued filtering steps with GATK, finally reporting 118,407 SNPs. To reduce the number of SNPs for linkage mapping, we conducted further filtering steps with VCFtools to control MAF, missing rate, and read depth level, which resulted in 2372 SNPs. Finally, duplication and segregation ratio filtering resulted in 2248 SNPs for linkage map construction . The lenqFWR 3 on chromosome 7 explained most of the phenotypic variation of 18.84%, with an additive effect of 0.68, which co-located with the significant SNPs (GBS-FW 25 to 28) from GWAS study. These SNP markers were located in Rs404770, which encoded the glycosyltransferase family protein .Two parental lines showed contrasting characteristics for disease resistance . QTL analysis identified a total of 4 QTLs, conferring FW resistance that were distributed on chromosomes 3, 6, 7, and 8 . The LODGenome-wide association study has been used to detect putative functional markers and candidate genes related to complex traits of various plant species ,49,50. GIn this study, a 225 GBS panel comprised of 167 accessions selected from 1237 radish germplasms based on their morphology, and 58 elite breeding lines, were evaluated for FW resistance . Ten accRadishes have been cultivated around the world and diversified in terms of their morphology, i.e., root and leaf shape, root size and root color . For exaThe GBS sequencing results were used to perform population structure analysis for the 225 accessions under an admixed model using the STRUTURE program. In the analysis of population structure, three clusters were confirmed (K = 3) a,b. Mostr2 greater than 0.2 was 226 kb, although it is an out-crossing crop (For linkage disequilibrium (LD), this was affected by many different factors, such as natural selection, domestication, founding events, genetic diversity, and population stratification ,56,57. Iing crop d. The loIn this study, a GWAS identified 42 genomic regions strongly associated with FW resistance using the MLM (QKmodel) ; Table 1qFW6 on LG4 (qFW4 (Fwr1) on LG3  which exhibited high LOD value ranging 4.34 to 8.84 during three trials in the previous study  on chromosome 5 was finely mapped [Fwr1 by sequencing and expression analyses. Here, GBS-FW39 located in intergenic region was most close to Fwr1 on chromosome 5 (qFWR 3 on chromosome 7 (PVE 18.84%) was co-located with the significant SNPs (GBS-FW 25 to 28) from the GWAS study. These SNP markers were located in Rs404770, which gene encoded the glycosyltransferase family protein. Glycosyltransferase gene sequences were retrieved from NCBI databases for in silico-based screening of biotic stress-responsive genes [F. oxysporum infection [Recently, a major FW resistance QTL  on chromosome 1 contributes resistance to F. oxysporum f. sp. matthioli and f. sp. raphani, which are causal agents of FW in Arabidopsis and radish, respectively [RFO1 encodes a member of the wall-associated kinase family of receptor-like kinases (RLKs), and contributes to immunity against F. oxisporum infection in the root vascular cylinder [Raphanus genome and A. thaliana on chromosome 1 [Raphanus genome that was homologous to RFO1 in A. thaliana, which is consistent with the previous reports [Foc-Bol region of Brassica oleracea was designated to be involved in qFW4 in the previous study, and this region is designated to chromosome 5 of Raphanus genome using flanking marker information [qFW4 (In ectively . RFO1 encylinder ,72. Compmosome 1 . However reports . On the ormation ,73. GBS-on [qFW4 . Further


Furthermore, animal proteins  showed increasing trends in CRP levels compared to whey protein isolate. Caution must be considered regarding these results as controlled, non-randomized, trials were included in the analysis, which may have contributed to high risk of bias. Future research should focus on protein types and the impact they have on kidney disease progression and inflammation markers.Proteins, especially plant proteins, may reduce inflammation among adults with chronic kidney disease (CKD). This systematic review and meta-analysis were conducted to evaluate the effect protein types  have on inflammation markers  among adults with varying stages of CKD. The Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA) was conducted to identify articles from inception until January 2021, utilizing six databases. Controlled trials that compared the effects of different protein types were analyzed using random-effects meta-analysis. Quality assessment and risk of bias of the included articles were assessed by using Cochrane risk of bias instrument and ROBINS-I. Out of the 10 studies that met the criteria, there was a decreasing trend in CRP levels when consuming plant proteins compared to animal proteins among non-dialysis participants. There was a statistically significant decrease when comparing animal proteins to unspecified proteins in CRP levels among dialysis participants [Hedges’ g = 2.11; 95% CI 1.12, 3.11;  Chronic kidney disease (CKD) has long been considered a silent and neglected killer compared to more prominent non-communicable diseases such as cardiovascular disease and diabetes . GloballCKD is characterized as a low-grade chronically inflamed state that has five progressive stages. In the first two stages, patients are asymptomatic, thus may go undiagnosed ,3,4. As Medical nutrition therapy is an important part of CKD management and is typically combined with weight management and anabolic pharmacotherapy . At latep-cresyl sulfate, TNF-α), and oxidative stress ” AND “Dietary protein intake or dietary intervention OR dietary animal protein OR dairy product OR eggs OR meat OR protein dietary supplement OR edible insects OR dietary plant protein OR vegan diet OR vegetarian diet OR high protein diet OR protein restriction diet [MeSH terms]” NOT “animal model OR rat OR mice OR rabbit OR cancer OR gut microbiome OR Mesoamerican Nephropathy OR Pregnant women OR children OR infant OR Urinary tract problems OR Polycystic Kidney Disease OR bacteria OR fungi [MeSH terms]”.This combination of terms was carried out in the other databases using the search function for all topics. The researchers identified other relevant studies by searching the reference list of the retrieved studies, as well as conducting a second scan in January 2021. The search was completed for peer-reviewed articles written in English and Spanish with no time restrictions in order to encompass the vast literature available. Literature searchers were combined into DistillerSR , software to assist in screening and removing duplicate studies. Initially, 20,439 articles were identified for a total of 10 articles that could be included in this meta-analysis. Any discrepancies were discussed with the research team. See Studies were included if they met the following criteria: (1) study design was controlled clinical trial , controlled non-RCT, observational controlled); (2) included humans; (3) included participants over 18 years of age; (4) intervention included PP and/or AP; (5) included the amount and/or frequency that participants consumed these proteins; and (6) primary outcomes included inflammation markers such as CRP, IL-6, and/or TNF-α. Articles were excluded if the above criteria were not met. Additional, exclusion factors were non-peer reviewed articles, studies that were qualitative in design, conference abstracts, books, and unpublished registered clinical trials. All titles and abstracts were screened independently by two reviewers , and full-text studies that were considered relevant were included for further review. Three reviewers  independently reviewed all full-text articles, and any discrepancies were resolved with consensus. These discrepancies surrounded protein types and inflammation outcome variables. Data from the studies that fulfilled the eligibility criterion were collected onto Microsoft Excel: (1) first author’s last name and date of publication, (2) location and population size, (3) design and duration of the study, (4) intervention components, (5) inflammation marker measurement and (6) primary outcome(s). Quality and risk of bias assessment of the identified RCTs followed the Cochrane risk of bias instrument . This toQuality and risk of bias assessment of the identified non-RCTs followed the Risk of Bias in Non-Randomized Studies–of Interventions (ROBINS-I) . This in2 statistic were completed. Statistical heterogeneity was determined at a chi-square (p < 0.01) and an I2 value of at least 50%. A test of heterogeneity was performed when three or more studies were included in the meta-analysis. Assessment of statistically significant heterogeneity between primary outcome studies using the chi-square test and Ip-value threshold for statistical significance was set at 0.05 for effect size, and p ≤ 0.05 was considered significant.Studies were grouped based on intervention and mode of treatment , AP group (intervention) and unspecified protein group (control) or PP group (intervention) and AP group (control) on inflammation markers. Following the guidelines provided by the Cochrane Handbook, non-RCTs were included due to the small number of RCTs available in the area of interest . Data frn = 3) [n = 3) [n = 1) [n = 1) [n = 1) [n = 1) [n = 4) [n = 1) [n = 4) [n = 1) [Studies were conducted in Italy (n = 3) ,44,45, U [n = 3) ,47,48, B [n = 1) , China ( [n = 1) , Malaysi [n = 1) , and Por [n = 1) . Study d [n = 4) ,47,48,51 [n = 1) , control [n = 4) ,50,52, on = 3) [n = 7) [n = 4) [n = 3) [n = 4) [n = 6) [n = 5) [n = 2) [n = 3) [n = 2) [n = 3) [n = 2) [n = 2) [All studies documented the amount of PP and/or AP within the intervention. Studies provided proteins based on kilogram of body weight (n = 3) ,44,45 or [n = 7) ,50,51,52 [n = 4) ,50,51,52 [n = 3) ,47,48. S [n = 4) ,50,51,52 [n = 6) ,47,48. P [n = 5) ,48,49,52 [n = 2) ,45 or pr [n = 2) ,49, retu [n = 3) ,48,51, d [n = 2) . Aside fn = 5) [n = 4) [n = 1) [n = 3) [n = 1) [n = 1) [n = 5) [For the analysis of inflammation markers, studies were inconsistent in the methods to measure and analyze them. Studies either did not mention how blood samples were collected (n = 5) ,47,51,52 [n = 4) ,48,49,50 [n = 1) . Additio [n = 3) ,46,49, i [n = 5) ,47,50,52Based on the Cochrane risk of bias tool, four RCT studies ,47,48,51Based on results from ROBINS-I, two studies were considered at low to moderate risk of bias due to comparability with RCTs, although information was lacking about how participants were randomized into the intervention and control groups ,52. Two p < 0.001). Visual inspection of the funnel plots revealed little evidence of asymmetry, and thus little evidence of publication bias (AP compared with non-specific protein CHD participants: CRP p < 0.000). For RCTs that included either non-CHD or CHD participants, chi-square and I2 were not reported for CRP, TNF-α and IL-6, as two studies or less focused on these inflammation markers with either PP and AP or AP and unspecified protein. Unclear risk domains predominated for selection bias due to inadequate details provided in the methodologies of the studies. For the studies that included CHD participants and compared AP with unspecified protein on CRP, the chi-square test indicated significant heterogeneity for between groups analysis , TNF-α (n = 3), and IL-6 (n = 2). To minimize bias when combining RCTs with controlled, non-randomized trials in the meta-analysis, no analysis could be conducted with non-CHD participants as only two RCTs compared PP to AP [n = 3) [The ten studies focused on inflammation markers based on protein types, measuring CRP  ,50,52. g = 2.11; 95% CI 1.12, 3.11; p < 0.001), as shown in The three controlled, non-randomized, studies comparing AP to unspecified proteins in a total of 158 CHD participants demonstrated statistically significant difference in CRP levels, favouring the unspecified proteins (Hedges’ n = 1) [n = 2) [Upon further analysis of each individual study, for the non-RCT  and two n = 1)  or stati [n = 2) ,45. In t [n = 2) ,48. For  [n = 2) ; whereasFor the RCT studies that included CHD participants and compared AP to non-specified protein, there was a non-statistical decrease in CRP levels favouring non-specified proteins ,51. FurtAs has been stated before, plant proteins may have the ability to reduce inflammation markers among adults with CKD, more so than animal proteins. This systematic review and meta-analysis compared the effect of protein types on inflammation markers  among adults with varying stages of CKD. Results from the meta-analysis showed a statistically significant decrease in CRP levels when comparing AP to unspecified proteins, favouring the unspecified proteins. Further individual analysis of the studies showed that there was a decreased trend in CRP levels comparing the whey only AP snacks to the animal products that consisted of red meat , or a con = 60), participants were exposed to three different dietary regimens: a free diet for 3 months that consisted of 50–70 g of AP and 15–20 g of PP, a Mediterranean diet for 6 months that consisted of 30–40 g of AP and 40–50 g of PP, and a vegetarian diet for 6 months that consisted of 0 g of AP and 30–40 g of PP. Adherence was assessed through weekly dietary interviews and through 24-h weekly urine tests. Results showed that the vegetarian diet significantly reduced CRP levels compared to the free and Mediterranean diets. The researchers attributed this to the low serum levels of urea that suppressed the rise in inflammation markers [Plant proteins contain between 5.7% –26.9% (wheat) of the amino acid glutamic acid and 80–100 mM of potassium within the cytoplasm of the plant . When th markers .n = 17) [On the other hand, Siefker et al. (2006) demonstrated an increasing trend in CRP levels when comparing a 4-day consumption of soy protein powder supplement (25 g/day) to whey protein powder (25 g/day) over 30 days among adults on CHD (n = 17) . Soy pron = 17)  comparedn = 17) . As the n = 17) ,57. Anotn = 17) , thus, tn = 17) . Howevern = 17) . Furthern = 17) ,60,61.n = 74), results demonstrated that consumption of 30 g of whey protein with nutrition counselling over 6 months showed a decreasing trend in CRP levels [n = 17). Results showed an increased trend in CRP levels when participants consumed whey protein supplement (25 g) compared to soy protein isolate (25 g) that was consumed four times a week for four weeks [In this systematic review and meta-analysis, an increasing trend was observed in CRP levels when participants consumed AP alone–red meat or in combination–egg albumin, milk proteins and whey protein. However, in one study that provided whey protein isolate as a supplement, results showed a decreasing trend in CRP levels . In the P levels . A recenP levels . Whey prP levels ,63,64,65P levels . This, tur weeks . The disp= 0.002) [Modest effects seen on inflammation markers may have been related to the participants’ characteristics. Participants within these studies were in the later stages of CKD pre-dialysis ,44,45 or= 0.002) . While t= 0.002) . This le= 0.002) ,69. As tn = 399) associated perceived (think you know) to objective  nutrition knowledge with estimated Glomerular Filtration Rates (eGFR). Results showed that adults who had higher nutrition knowledge had a higher eGFR and less severe CKD compared to adults with lower nutritional knowledge [In the advanced stages of CKD, recommendations are to monitor potassium, phosphorus, sodium, and protein . Foods rnowledge . Therefon = 2019) who were at high risk for breast-cancer risk, consuming 10% more calories after 5 pm the night prior to a morning blood draw resulted in elevated CRP levels [Another factor that contributed to the results were the methods used to collect and analyze the inflammation markers. In this meta-analysis, blood samples were used to analyze inflammation markers; however, studies were inconsistent regarding when and how these samples were collected. Four studies collected these labs’ pre-dialysis treatments, but did not specify if these samples were collected during fasting, or identify the amount and types of foods consumed prior to drawing these samples. The timing and frequency of consuming meals may have an impact on CRP levels. Based on a study of females (P levels , the reaP levels , which rP levels .To our knowledge, this is the first systematic review and meta-analysis that focused on identifying the impact protein types have on inflammation markers among adults with varying stages of CKD. This systematic review and meta-analysis do have limitations. The inclusion criteria targeted a specific chronic disease and specific outcomes, thus limited studies were included. Additionally, the inclusion of controlled, non-randomized, trials in the meta-analysis, may have contributed to high risk of bias. Furthermore, these trials were heterogeneous regarding their study design, number of participants, evaluation measures and techniques used, thus reducing ability to compare. One of the strengths of this review is that there was evidence to suggest that AP contributed to elevations in inflammation markers and that PP may reduce these. This pinpoints research priority areas and specific methodologies to improve the consistency of clinical data in future research. This meta-analysis showed that protein types and amounts need to be considered when examining the progression of CKD, especially on inflammation markers.In conclusion, results from this meta-analysis showed that animal proteins compared to unspecified protein types increase CRP levels, and that there was a decreasing trend in inflammation markers when comparing plant proteins to animal proteins among adults with varying stages of CKD. However, the literature does not confidently portray this due to various confounding factors. Future research should focus on RCTs to compare protein types and their subsequent effect on inflammation markers among the CKD population to aid in guiding researchers and practitioners.


Mitogen-activated protein kinase (MAPK) cascades play crucial roles in almost all biological processes in plants. They transduce extracellular cues into cells, typically through linear and sequential phosphorylation and activation of members of the signaling cascades. However, accumulating data suggest various regulatory mechanisms of plant MAPK cascades in addition to the traditional phosphorylation pathway, in concert with their large numbers and coordinated roles in plant responses to complex ectocytic signals. Here, we highlight recent studies that describe the uncanonical mechanism of regulation of MAPK cascades, regarding the activation of each tier of the signaling cascades. More particularly, we discuss the unusual role for MAPK kinase kinases (MAPKKKs) in the regulation of MAPK cascades, as accumulating data suggest the non-MAPKKK function of many MAPKKKs. In addition, future work on the biochemical activation of MAPK members that needs attention will be discussed. The mitogen-activated protein kinase (MAPK) cascade is a three-tiered system composed of three protein kinases: MAPK kinase kinase (MAPKKK), MAPK kinase (MAPKK), and MAPK . These aArabidopsis thaliana) genome [Oryza sativa) genome [Plant genomes encode relatively more MAPK members than yeast and mammal genomes. Over 60 MAPKKKs, 20 MAPKKs, and 10 MAPKs are found in the dicotyledonous model plant Arabidopsis  genome , and 75 ) genome ,5,6. TheSolanum lycopersicum), Arabidopsis, and rice, indicate uncanonical regulatory mechanisms of MAPK cascades, the elucidation of the underlying mechanism of the activation of MAPK cascades has become increasingly important. In this review, we discuss various alternative regulatory mechanisms of plant MAPK cascades in addition to the traditional phosphorylation pathway.MAPK cascades have been reported to be involved in various biological processes in plants, as well as reviewed previously and very recently ,8,9,10. The activation of a classical MAPK cascade is initiated by the phosphorylation and activation of MAPKKK. To date, various proteins have been found to activate MAPK cascades, and some of them directly bind MAPKKKs; however, only a few receptor-like kinases (RLKs) have been determined to directly phosphorylate and activate MAPKKKs.Nicotiana tabacum) cells was identified. This signaling cascade, which comprises NPK1 (a MAPKKK), NQK1 , and NRK1 , regulates cell division through sequential phosphorylation [Approximately two decades ago, a complete MAPK cascade that regulates cytokinesis in tobacco , SSP, activates the YDA (MAPKKK)-MKK4/MKK5-MPK3/MPK6 cascade to regulate embryonic patterning ,21 Tabl. In this2+)/calmodulin-regulated RLK, activates a potential MAPK cascade mediated by MEKK1 (a MAPKKK) to increase cold responsive genes expression [During cold stress, Arabidopsis CRLK1, a calcium  by plant pattern-recognition receptors (PRRs), is termed pattern-triggered immunity (PTI) . At leasPseudomonas syringae pv. tomato, host protein kinases Pto and Fen bind to the pathogen effectors AvrPto or AvrPtoB, and the host NLR Prf binds to Pto and Fen to indirectly recognize pathogen effectors and trigger ETI [The second layer of plant innate immunity is termed effector-triggered immunity (ETI), and is initiated by the direct or indirect interaction between plant intercellular nucleotide-binding leucine-rich repeat receptors (NLRs) and pathogen-secreted effectors . RLCKs agger ETI ,39,40,41gger ETI ,43,44. Fgger ETI . Althouggger ETI   motif . This acPlant MAPKKKs can be simply grouped into MEKK-like and Raf-like MAPKKKs based on their sequence similarity ,3. Most 2O2) in a MEKK2-dependent manner [In Arabidopsis, a complete MAPK cascade (MEKK1-MKK1/MKK2-MPK4) functions downstream of RLCKs in response to pathogen infection ,33,34,35t manner ,48,49,50t manner ,47. MEKKt manner ,51 , which is associated with the PRR complex and functions as a co-receptor of RLKs in the mekk1-mkk1/mkk2-mpk4 cell death pathway [Another Arabidopsis Raf-like MAPKKK, EDR1, negatively regulates innate immunity ,75. Furt cascade . Therefo cascade ,76. In d cascade . These r pathway ,78, an a pathway .MKD1 results in susceptibility to pathogens and a decreased degree of activation of MPK3/MPK6 in response to phytotoxins. As the mkk1 mutant and MKK5RNAi transgenic plants mimic the mkd1 mutants in response to pathogens, it was concluded that MKD1, MKK1/MKK5, and MPK3/MPK6 form a signaling cascade in plant responses against pathogens [In addition to EDR1, another Arabidopsis Raf-like MAPKKK, MKD1, also contributes to plant immunity via its association with the MKK1/MKK5-MPK3/MPK6 cascade . MKD1 inathogens .raf36-mediated disease resistance supports the Raf36-MKK2 signaling cascade. Indeed, Raf36 kinase activity is involved in the interaction with MKK2 and the modulation of plant disease resistance, although whether and how Raf36 kinase activity affects MKK2 function in the defense response remain unknown.In addition, Arabidopsis Raf36 (a Raf-like MAPKKK), which possesses kinase activity and belongs to the Raf-like kinase group , was recOsEDR1-knock out mutants, the phosphorylation and kinase activity of OsMPKK10.2 toward its downstream MAPK OsMPK6, is enhanced [OsMPKK10.2 or OsMPK6 compromised osedr1-mediated disease resistance place OsEDR1 upstream of the OsMPKK10.2-OsMPK6 cascade [OsEDR1, OsMPKK10.2, and OsMPK6 are also involved in rice drought resistance [In rice plants, OsEDR1, the ortholog of Arabidopsis EDR1, is involved in the regulation of the MAPK cascade in rice disease resistance. OsEDR1 is a Raf-like MAPKKK and negatively regulates rice resistance to bacterial pathogens ,81. OsEDenhanced . Genetic cascade . Remarka cascade . Finallysistance ,83, and Another Raf-like MAPKKK in rice plants, OsILA1, is also involved in the regulation of the MAPK cascade in rice resistance against bacterial pathogens. OsILA1 was initially identified as a regulator of the rice lamina joint through the interaction with and phosphorylation of CCCH-tandem zinc-finger transcription factors ,85. HoweAltogether, these results suggest that, although some Raf-like kinases possess kinase activities and can phosphorylate MAPKKs in vitro or even in planta, they do not function as canonical MAPKKKs to activate MAPK cascades. However, they do function as regulators of MAPK cascades. In fact, it was previously thought that classifying these kinases as Raf-like kinases might lead to the misconception that these kinases function as MAPKKKs . TherefoA wealth of data, showing that MAPK cascades function in plant biological processes, support the hypothesis that MAPKK or MAPK is always phosphorylated and activated by the upstream MAPKKK or MAPKK, in a signaling cascade. However, several reports have indicated that plant MAPKKs or MAPKs can be phosphorylated or activated directly by protein kinases, rather than through MAPKKKs or MAPKKs, or by other alternative mechanisms which do not include MAPK components.tmk1tmk4 double mutants [In auxin-controlled cell division patterns during lateral root development, Arabidopsis transmembrane kinases TMK1 and TMK4, a group of RLKs, directly interact with and phosphorylate MKK4 and MKK5  Table 3Table 3.  mutants . Thus, tIn rice bacterial-pathogen resistance, OsMPKK10.2 can be phosphorylated and activated by non-MAPKKKs . It has 2+/calmodulin-dependent protein kinase (CCaMK) [In the abscisic acid (ABA) signaling-pathway in rice, another MAPKK, OsMKK1, is directly phosphorylated and activated by OsDMI3, a Ca (CCaMK)   residues within the TXY motif . However2O2-mediated MAPK signaling [For instance, in Arabidopsis, NDPK2, an NDP kinase, is associated with Hignaling . NDPK2 bignaling .Furthermore, Arabidopsis MPK6 can also be activated by unusual modes. For example, MPK6 activation is induced when Arabidopsis is exposed to NaCl . Further2S) modifies cysteines of MPK4 by persulfidation, and promotes MPK4 kinase activity to enhance Arabidopsis resistance to cold stress [In Arabidopsis responses to cold stress, MPK4 is required in alleviating cell damages . Later, d stress . Persulfd stress . HoweverOther MAPKs have also been found to be directly activated in plants without the intervention of MAPKKs. For example, Arabidopsis MPK9, which is also induced by salt stress, autophosphorylates its TXY motif and other sites in the C-terminal domain in an MPKK-independent manner  Table 3Table 3, 2+-binding proteins (CaMs) bound to MPK8 in a Ca2+-dependent manner [Several calmodulins in Arabidopsis act as Cat manner . This bit manner .2+ has been found to be associated with OsMPK3  in rice plants [2+-dependent protein kinase CPK18 interacts with and phosphorylates OsMPK3 (ZmCPK11 decreased ZmMPK5 (the orthologue of rice OsMPK3) expression and kinase activity [Similarly, Cae plants . During s OsMPK3 . Such phactivity , leadingRecently, MPK15 was found to confer Arabidopsis resistance to fungal pathogens . MPK15 iMounting evidence proves that the number of MAPK members have expanded in land plants with functions in complex growth regulation and adaptation to dynamic environmental conditions ,111,112.As many MAPK cascades are genetically located downstream of RLKs ,10,113, For other MAPK members referring to MAPKKs and MAPKs, various proteins and metabolites are involved in their regulation. Many MAPKKKs or non-MAPKKKs affect MAPKKs protein accumulation or kinase activity directly or indirectly, and many non-MAPKKs or metabolites activate MAPKs through direct phosphorylation or binding, showing unusual modes of action that are independent of or coordinated with sequential phosphorylation mechanisms. These non-traditional regulation modes of MAPK cascades allow for the coordinated control of plant growth regulation or stress responses and require further in-depth analysis.


A total of 3584 DEGs were detected in LM23. A total of 3306 DEGs, including 863 DEGs in roots and 2443 DEGs in shoots, were specifically expressed in XM26 or showed huge differences between XM26 and LM23 (log2FC ratio > 3). These were selected for gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses. The calcium-mediated plant–pathogen interaction, MAPK signaling, and phosphatidylinositol signaling pathways were enriched in XM26 but not in LM23. We also verified the expression of important genes involved in these pathways in the two varieties using qRT-PCR. A total of 156 transcription factors were identified among the DEGs, and their expression patterns were different between the two varieties. Our findings suggest that calcium-related pathways play different roles in the two varieties, eliciting different tolerances to low-nitrogen stress.Nitrogen plays a crucial role in wheat growth and development. Here, we analyzed the tolerance of wheat strains XM26 and LM23 to low-nitrogen stress using a chlorate sensitivity experiment. Subsequently, we performed transcriptome analyses of both varieties exposed to low-nitrogen (LN) and normal (CK) treatments. Compared with those under CK treatment, 3534 differentially expressed genes (DEGs) were detected in XM26 in roots and shoots under LN treatment ( Arabidopsis or rice. Owing to the complexity of the wheat genome, research on wheat NUE-related regulatory networks remains limited.Nitrogen (N) and nitrogen fertilizers play key roles in the growth and development of crops and crop yield, respectively . HoweverTabZIP60 enhances the activity of NADH-dependent GOGAT, which in turn enhances the absorption of nitrogen in wheat nitrate tunctions , and ABAunctions  respond  pathway . In rice pathway .Arabidopsis significantly changes the expression of pathway genes in primary and secondary metabolism, cell growth, hormone response, protein synthesis, signal transduction, and transcriptional regulation [Transcriptome analysis is a rapid and effective method for studying regulatory networks in crops ,23. A trgulation . A transgulation .Here, to understand the molecular mechanism of low-nitrogen stress response using low-nitrogen-tolerant wheat varieties, to screen more nitrogen-related genes, and to decipher the basis for nitrogen-efficient breeding, we screened two wheat varieties resistant or sensitive to low nitrogen through chlorate sensitivity experiments. The DEGs of the two wheat varieties in response to low-nitrogen stress were identified. We also identified three pathways that may be important in response to low-nitrogen stress in wheat, namely the calcium-mediated plant–pathogen interaction, MAPK signaling, and phosphatidylinositol signaling pathways.3−), a nitrate analog, shares the same absorption and assimilation pathways as nitrate, whereas chlorate becomes toxic when it is reduced to chlorite. Therefore, chlorate sensitivity can be used for phenotypic identification to mimic plant uptake or to assimilative activity for nitrate, providing rapid identification of differential phenotypes [3). The growth inhibition rate indicated that the wheat variety with the highest inhibition rate of chlorate was XM26 and that the wheat variety with the lowest inhibition rate of chlorate was LM23  condition, the shoot height, leaf length, dry weight, N content, and N accumulation in both wheat varieties decreased; however, root elongation increased under low N treatment LN; . The anaThe roots and shoots of both wheat varieties were harvested at 12 h after CK and LN treatments. A transcriptome sequence was subsequently completed using the Illumina HiSeq platform. The number of clean reads for each sample varied from 58.35 million to 78.05 million, with a mean of 65.20 million. The content of Q20 for each sample varied from 97.92% to 98.56%, with a mean of 98.35%. The content of Q30 for each sample varied from 93.84% to 95.42%, with a mean of 94.90%. The GC content (Base G + Base C) for each sample varied from 49.68% to 54.19%, with a mean of 52.06%. The 88.61% and 70.71% reads from the shoot and root samples were uniquely mapped. Based on these quality control data, the transcriptome data were deemed suitable for subsequent analyses.p < 0.05, and |log2(fold change)| > 1 were defined as DEGs. Consequently, 2639 DEGs were identified in the shoots of XM26 under LN conditions compared with the CK and included 1773 and 866 upregulated and downregulated genes, respectively. Among the DEGs of XM26, there were 895 DEGs in the roots, including 565 and 330 upregulated and downregulated genes, respectively. We identified 2664 DEGs in LM23 shoots following LN treatment compared with the genes in CK, including 1583 and 1081 upregulated and downregulated genes, respectively. A total of 920 DEGs were identified in LM23 roots, including 553 upregulated and 367 downregulated genes , calmodulin-binding protein 60 B-like (CBP60), CDP-diacylglycerol-serine O-phosphatidyltransferase 2 isoform X2 (PSS2), group II HKT transporter (HKT9), serine/threonine-protein kinase SAPK3, DIBOA-glucoside dioxygenase BX6-like (BX6), and transcription factor MYB108-like (MYB108). The qRT-PCR and RNA-Seq findings were highly consistent, indicating the reliability of the RNA-Seq data  value > 3 between the two wheat varieties for further analysis. We performed a gene ontology (GO) enrichment analysis of a total of 2443 DEGs in shoots and 863 DEGs in roots. We classified DEGs into three categories: biological processes (BP), molecular functions (MF), and cellular components (CC). In shoots, the GO terms of the metallic, cellular, and single-organic processes were significantly enriched for BP. The significantly enriched GO terms in MF were catalytic activity, binding, and nucleic acid binding transcription factor activity. The significant enrichment terms in CC were cell, cell part, and organelle. In roots, the significant enrichment terms in BP were metabolic, cellular, and single-organism processes. The significant enrichment terms in MF were catalytic activity, binding, and transporter activity. The GO terms, including the cell, cell part, and membrane, were significantly enriched in CC a,b. DEGsCALM (CLM9), CDPK , RbOH (RBOHC), and WRKY33 belonging to the plant–pathogen interaction pathway were upregulated in XM26 shoots; however, there was no significant difference in LM23 , was also upregulated in XM26 but its level unchanged in LM23. Therefore, we speculate that the Ca2+-mediated phosphatidylinositol signaling pathway responds to LN-stress, and different expressions of DGK5 and CML9 might have different tolerances between XM26 and LM23  signaling pathway; phosphatidylinositol signaling pathway; fatty acid degradation pathway; flavonoid biosynthesis; alpha-linolenic acid metabolism; glyoxylate and dicarboxylate metabolism; glycerolipid metabolism; and cutin, suberine, and wax biosynthesis were significantly different between XM26 and LM23, suggesting that these genes may be related to different tolerances to low-nitrogen stress for the two wheat varieties . Genes s in LM23 . Interes in LM23 . Transcrn shoots . In LM23 qRT-PCR . In the ncreased , the cal in LM23 . Similarand LM23 . Transcr23 roots , suggestHST was significantly upregulated in the shoots of XM26 but showed no significant difference in LM23. Another flavonoid biosynthesis-related gene CHS2 was significantly upregulated in XM26 but was downregulated in LM23 , including 35 WRKY, 29 ERF, 26 BHLH, 18 zinc, and 24 transcription factors belonging to other families. A total of 24 transcription factors were found among root DEGs . These DEGs included eight zinc, five ERF, four WRKY, and seven other family transcription factors . The expTaMPK14 plays an important role in regulating low-nitrogen stress tolerance in plants, and TaMPK14 overexpression in tobacco showed higher NUE [AtMPK3 is significantly induced by low-temperature stress in Arabidopsis [CML9 and DGK5 were significantly upregulated in XM26 and were unchanged in LM23 (3) [3 and diacylglycerol (DG), which convert extracellular signals into intracellular signals [AtCML9 in the phosphatidylinositol signaling system is involved in salt-stress tolerance through its effect on the ABA-mediated pathway [DGK5 promotes responses to cold in Arabidopsis [2+ pathway-related CML9 and DGK5 may be involved in the regulation of response to low-nitrogen stress in different wheat varieties.We found that MAPK pathway-related proteins such as MPK3 and ACS1 were significantly upregulated in XM26, whereas the expression of these genes was not different in LM23, suggesting that the MAPK pathway was enhanced in resistant wheat varieties under low-nitrogen stress . MAPK-regher NUE . AtMPK3 bidopsis . Therefo in LM23 . CalciumLM23 (3) . The pho signals . AtCML9  pathway . DGK5 prbidopsis . Therefo2+-related genes, such as CPK7, CPK16, CML9, RBOHC, and WRKY33, in the plant–pathogen interaction pathway  participate in the regulation of NRT1.1 function [Arabidopsis, nitrate triggers a unique Ca2+-CDPK signal through six CPKac  [2+ participates in the plant–pathogen interaction pathway and phosphatidylinositol signaling system. Interestingly, MPK3 was previously characterized as being upstream of the transcription factor WRKY33 [We found other Ca pathway . AtWRKY3tter NUE . Many gefunction . In ArabCPK32ac) . Calciumr WRKY33 . Thus, w3 treatment than under normal treatment 2, and 1 mM Ca(NO3)2 was used in the CK treatment. An amount of 0.1 mM Ca(NO3)2 was used in the LN treatment, and CaCl2·2H2O was used to supplement Ca and to maintain its concentration at 2.5 mM. After 30 d, the phenotype and nitrogen content were determined.The wheat materials used in this experiment were obtained from the Chinese Academy of Agricultural Sciences. Wheat seeds were sterilized with 2% Hperature . Three dperature . At the perature . The pH 3 instead of Ca(NO3)2, and the chlorate toxicity experiment was conducted in a one-leaf one-heart period. After 4 d, the seedling height was measured and the chlorate inhibition rate was calculated according to the following formula: chlorate inhibition rate on plant growth = (control treatment height − chlorate treatment height)/control treatment height × 100% [The toxicity experiment was conducted using 4 mM KClOt × 100% .For RNA-Seq samples, XM26 and LM23 seeds were grown under the above conditions and samples were collected after treatment with LN (0.2 mM) and CK (2 mM) for 12 h, respectively. In a preliminary experiment, we found that approximately 12 h after chlorate treatment, the phenotype of XM26 began to change (the leaf tip began to wilt), and there was a significant difference between the two varieties at 20 h a. Therefp < 0.05, and |log2 (fold change)| > 1 were set as the thresholds for identifying DEGs [http://www.geneontology.org/, accessed on 9 November 2021), and pathway enrichment analysis was performed based on the KEGG database  [RNA isolation, cDNA library construction, and Illumina sequencing were performed with the assistance of Genedenovo Biotechnology . Clean reads were obtained by removing adapter-containing, N-containing greater than 10%, all A-base, and low-quality reads from the raw data. The clean reads were mapped to a cDNA database from the wheat variety Chinese Spring (IWGSC_RefSeq_v1.1). The expression level of each gene was measured using the number of fragments per kilobase per million reads (FPKM). Differences in the RNA transcript levels were analyzed using DESeq2 and edgeR. ing DEGs . GO enrier 2021) ,43. All −ΔΔT method. The experiments were performed in triplicate. The primers used in this study are listed in Total RNA was extracted using the KKFast Plant RNApure Kit , and RNA was reverse transcribed to cDNA  using the TransScript One-Step gDNA Removal and cDNA Synthesis SuperMix kit. The quantitative reverse transcription polymerase chain reaction (qRT-PCR) was performed using TaqPro Universal SYBR qPCR Mastermix . The qRT-PCR conditions were 30 s at 95 °C, followed by 40 cycles of 95 °C for 10 s and 58 °C for 30 s. Relative gene expression was calculated using the 2www.spss.com, accessed on 9 November 2021). Significance was set at p < 0.05.The seedling height, root length, leaf length, dry weight, N content, and N accumulation were measured. Each dry weight value is the total dry weight of the four seedlings. The nitrogen content was determined using the Kjeldahl method . FollowiIn this study, two wheat varieties with different low-nitrogen tolerances were screened through a chlorate sensitivity test, and DEGs were identified in these varieties in response to low-nitrogen stress. We speculate that three pathways, namely the calcium-mediated plant–pathogen interaction pathway, the MAPK signaling pathway, and the phosphatidylinositol signal pathway, might be important in the response of wheat to low-nitrogen stress. These results provide useful information for future research on molecular breeding of high-nitrogen-efficiency wheat.


VPS13C (P = 0.01) was opposite. Finally, we developed an online database named Gene4PD (http://genemed.tech/gene4pd) which integrated published genetic data in PD, the PAGs, and 63 popular genomic data sources, as well as an online pipeline for prioritize risk variants in PD. In conclusion, Gene4PD provides researchers and clinicians comprehensive genetic knowledge and analytic platform for PD, and would also improve the understanding of pathogenesis in PD.Parkinson’s disease (PD) is a complex neurodegenerative disorder with a strong genetic component. A growing number of variants and genes have been reported to be associated with PD; however, there is no database that integrate different type of genetic data, and support analyzing of PD-associated genes (PAGs). By systematic review and curation of multiple lines of public studies, we integrate multiple layers of genetic data  and age at onset in PD. We integrated five layers of genetic data (8302 terms) with different levels of evidences from more than 3,000 studies and prioritized 124 PAGs with strong or suggestive evidences. These PAGs were identified to be significantly interacted with each other and formed an interconnected functional network enriched in several functional pathways involved in PD, suggesting these genes may contribute to the pathogenesis of PD. Furthermore, we identified 10 genes were associated with a juvenile-onset (age ≤ 30 years), 11 genes were associated with an early-onset (age of 30–50 years), whereas another 10 genes were associated with a late-onset (age > 50 years). Notably, the AAOs of patients with loss of function variants in five genes were significantly lower than that of patients with deleterious missense variants, while patients with  PRKN  is the second most common neurodegenerative disease . PD, parPRKN , PINK1  have provided insight into the genetic basis of PD by identifying and replicating risk loci . Some diParkinson’s disease is an age-dependent neurodegenerative condition, and the age at onset (AAO) is a robust phenotypic measure compared to motor and non-motor phenotypes . Many prTo overcome this limitation and facilitate studies of the genetic basis of PD, we integrated multiple layers of genetic and genomic data of PD and prioritized several PAGs, we developed a convenient integrative genomic database named as Gene4PD, which can facilitate queries and analysis of genetic and genomic data with an intuitive graphical user interface that will benefit physicians and researchers.We collected five types of genomic data related to PD from the PubMed database with following search items: (1) rare variants identified from PD patients with “Parkinson × [Title/Abstract] AND [mutation (Title/Abstract) or variant (Title/Abstract)] AND gene [Title/Abstract]”; (2) CNVs in PD with “Parkinson × [Title/Abstract] AND [copy number variation (Title/Abstract) or CNV (Title/Abstract)]”; (3) associated SNPs identified from GWAS with “Parkinson × [Title/Abstract] AND GWAS[Title/Abstract]”; (4) differential expressed genes (DEGs) with “Parkinson × [Title/Abstract] AND transcriptome [Title/Abstract]”; (5) differentially expressed DNA methylation genes (DMGs) with “Parkinson × [Title/Abstract] AND DNA methylation [Title/Abstract].” We then screened out articles related to PD according to the exclusion criteria: (i) studies that focused on molecular mechanisms, rather than genetic studies; (ii) studies without original data for genomic information, which may cite from published studies, such as reviews or meta-analysis; and the inclusion criteria: (i) studies reported the genomic information  of PD, (ii) studies reported the differential expressed genes of PD, (iii) studies reported the differentially expressed DNA methylation genes of PD. Then, we manually extracted available genomic, AAO and other clinical information.To investigate the functional genetic variants identified in patients with PD, ANNOVAR  was usedp-values. A combined evidence score for each gene was calculated by summing up the evidence scores of all integrated studies. All genes integrated in this study were classified into five grades: high confidence (score ≥ 20), strong associated (score of 10–20), suggestive associated (score of 5–10), minimal evidence (score of 3–5), and uncertain evidence (score < 3)  .1  as described in our previous study . To ensu1  with a cThe AAO data and genetic data of each gene were downloaded from Gene4PD. We then analyzed the relationship between the AAO and PAGs from three perspectives. First, we aggregated and compared genes with more than five AAO terms to assess the association between AAO and PAGs. All genes were classified into one of three sections: juvenile-onset (≤30 years), early-onset (30–50 years), or late-onset (>50 years), according to the median of the AAO. Second, we compared the AAO with different functional types of variants [loss-of-function (LoF) and deleterious missense variants (Dmis)] in the same gene to investigate the contribution of different types of functional variants (LoF and Dmis) to the AAO of PD.Gene4PD database3 by combining Vue with a PHP-based web framework laravel to construct as a user-friendly web interface. Moreover, the front and back separation model was used for website development. The front end is based on vue and uses the UI Toolkit element, which supports all modern browsers across platforms, including Microsoft Edge, Safari, Firefox, and Google Chrome, and the back end is based on laravel, a PHP web framework. Gene4PD is compatible with all major browser environments and different operating systems such as Windows, Linux, and Mac. The data are stored in the MySQL database.By integrating all the collected genomic information as well as the annotation for each variant/gene from 63 data sources , we deveThrough systematic review and curation of multiple lines of public studies, we reviewed more than 3,000 publications, 487 of which met the quality control and collection criteria. Genetic information such as gene symbols, chromosomes, locations, reference base, altered base, and hereditary modes were collected, and other basic information and clinical data, including sample ID, PubMed ID, methods of detecting variants, country, race, gender, AAO, functional study, subtype of disease, detail description of clinical phenotypes, and sporadic/familiar types, were also integrated. We catalogd five types of genetic data (8302 terms) related to PD: (1) 2,252 rare variants, including 954 non-redundant rare variants, in 226 genes from 327 available publications; (2) 139 CNVs in 34 genes from 94 publications; (3) 1,237 associated SNPs in 640 genes from 42 studies; (4) 2,926 DEGs from 8 publications; (5) 657 DMGs from 7 publications  Figure . SpecifiGCH1, RAB39B, CHCHD2, MAPT, TH, and ASNA1, were also regarded as potential PD-causing genes. The remaining two known PD-causing genes were classified into strong associated genes (HTRA2) or suggestive associated genes (UCHL1), as they showed lower replication.We then developed a weighted scoring system to prioritize PAGs by combining all above genetic evidence. As a result, we prioritized 25 high confidence genes (score ≥ 20), 38 strong associated genes (score of 10–20), 61 suggestive associated genes (score of 5–10), 88 minimal evidence genes (score of 3–5), and 3,791 uncertain evidence genes (score < 3) . We founP < 1.0 × 10–6, P < 1.0 × 10–6, P = 0.098, P = 2.5 × 10–5, We performed a permutation test to assess the functional correlations of the 124 associated genes (score ≥ 5). Most of them were disease-causing genes or risk-genes from GWAS. As a result, we observed 48 of 63 high-confidence or strong PAGs (–10), regulation of autophagy , behavior , regulation of cellular catabolic process   , were reSLC6A3, TH, L2HGDH, SLC30A10, DNAJC6, GCH1, FBXO7, ATP13A2, SYNJ1, and PLA2G6) were associated with a juvenile-onset (age ≤ 30 years), 11 genes  were associated with an early-onset (age of 30–50 years), whereas another 10 genes  were associated with a late-onset (age > 50 years). Although different PAGs had specific AAO characteristics, we noted that there were large differences in the AAO for some genes, such as for PRKN, PINK1, SNCA, and LRRK2, suggesting that there are other factors that contribute to the AAO.The integrated genetic data and AAO data from the Gene4PD database provide an unprecedented opportunity to comprehensively identify the vital association between the AAO and PAGs on a large scale. Therefore, we analyzed 31 PAGs with more than five AAO items in each gene . After sGCH1 (P = 2.63 × 10–5), PINK1 (P = 2.31 × 10–3), PRKN (P = 4.08 × 10–3), FBXO7 (P = 0.02), and ATP13A2 (P = 0.03), were significantly lower than patients with Dmis, while the AAO of patients with LoF in VPS13C (P = 0.01) was higher than patients with Dmis, whereas four other genes including PARK7 (P = 0.06), SNCA (P = 0.12), RAB39B (P = 0.96), and TMEM230 (P = 0.27) did not present significant differences  contributing to the AAO of PD, we further performed pairwise comparison to analyze the differences in the AAO between patients with LoF and patients with Dmis for each gene. As a result, 10 genes with more than three AAO items both in LoF and Dmis were selected to perform a comparison analysis. We found that the AAO of patients with LoF in five genes, including ferences . More PD4 which integrated rare variants, CNVs, associated SNPs, DEGs, and DMGs related PD and more than 63 popular genomic and genetic data sources  allgorithms , ExAC  TheOne of the main advantages of Gene4PD is that it provides an interface for analyzing genetic data according to the specific needs of the user . The anaWith the development of NGS, the number of multi-omics studies has greatly increased, such as genomics, genetics, epigenetics, and transcriptomics, for revealing the pathogenesis of PD, resulting in the identification of numerous associated genes . The abiCHCHD2, RAB39B, and RIC3. CHCHD2 (SNCA, PINK1, LRRK2, PARK7, VPS35 in the network. RAB39B with the known PD-causing genes SNCA and LRRK2 is consistent with its α-synuclein pathology (We further performed several bioinformatics analyses to validate the functional association of the 124 PAGs based on PPI data. First, we observed significant associations among the high-confidence/strong PAGs, among the suggestive PAGs, and between the two classes of experimental evidences. Second, we developed an interconnected functional network containing 88 PAGs and enriched in several known PD-associated functional pathways. In the functional network, we highlighted several PAGs that interacted with known PD-causing genes, such as . CHCHD2  has beenathology . These rTo enable PD researchers to make full use of the reported genetic data, more than six online PD-related database such as the variation databases MDSGene , PDGene ATP13A2 (PLA2G6 (FBXO7 (PRKN (PINK1 (PARK7 (PD has been widely recognized as an age-dependent neurodegenerative condition. The variation in phenotypes and genotypes can be related to the AAO of PD . PreviouATP13A2 , PLA2G6  (PLA2G6 , FBXO7 (6 (FBXO7 , PRKN (DO7 (PRKN , PINK1 (N (PINK1 , and PAR1 (PARK7 . Based oThere were some limitations to this study. First, by satisfying the quality control, we collected as much related literature as possible. Although all data collectors, who were rigorously trained to ensure the consistency of collected data, were researchers or PhD students with a strong background in clinical genetics, there may have been some omissions. We encourage researchers to contact us to refine missing data of PD. We will keep to update it annually. Second, the scoring system used in this study may not be powerful enough, and thus we encourage users to analyze genetic data with the re-score function in Gene4PD or to download data for re-analysis. Third, the integrated approach in this study may be biased, and the prioritized genes need to be validated in population and verified the pathogenic mechanism with cell or animal experiments. Fourth, we did not get much data on AAO of PD. We hope to collect more AAO data in the future to make our analysis results to be more reliable. Last, clinical information besides the AAO must be considered to determine the factors contributing to the genotype-phenotype correlations of PD. However, most articles do not provide detailed phenotypic information, which poses a challenge for a meta-analysis of the relationship between clinical phenotypes and genes. In this regard, we encourage researchers to provide details on clinical phenotypes in the context of genetic studies.In conclusion, we catalogd different types of genetic data from many publications related to PD and prioritized 124 functionally related PAGs with different lines of evidence, which were used to construct the database and analysis tool Gene4PD, suggesting that integrating multiple genetic data is useful for prioritizing novel associated genes. In addition, we characterized the genetic landscape of the prioritized PAGs, providing insight into the pathology of PD. Gene4PD is expected to provide researchers and clinicians comprehensive genetic knowledge and analytic platform for PD, and would also improve the understanding of pathogenesis in PD.http://genemed.tech/gene4pd/.Publicly available datasets were analyzed in this study. This data can be found here: BL, GZ, BT, and JL: study design. BL, GZ, QZ, YX, ZW, ZF, BLu, LQ, YWZ, RZ, LJ, HP, YH, XW, TL, YZ, YW, QC, and JL: literature search and data collection. BL, GZ, QZ, YX, BT, and JL: figures. BL, GZ, ZF, BLu, XW, TL, YZ, ZL, JG, and JL: data analysis. BL, GZ, ZW, YW, QC, BT, and JL: data interpretation. BL, GZ, QZ, YX, ZW, ZF, BLu, LQ, YWZ, RZ, LJ, HP, YH, XW, TL, YZ, YW, QC, ZL, JG, BT, and JL: writing of the manuscript draft and all revision stages. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Thyroid cancer (TC) is the most common endocrine malignancy, and the incidence is increasing very fast. Surgical resection and radioactive iodine ablation are major therapeutic methods, however, around 10% of differentiated thyroid cancer and all anaplastic thyroid carcinoma (ATC) are failed. Comprehensive understanding the molecular mechanisms may provide new therapeutic strategies for thyroid cancer. Even though genetic heterogeneity is rigorously studied in various cancers, epigenetic heterogeneity in human cancer remains unclear.A total of 405 surgical resected thyroid cancer samples were employed . Twenty-four genes were selected for methylation screening, and frequently methylated genes in thyroid cancer were used for further validation. Methylation specific PCR (MSP) approach was employed to detect the gene promoter region methylation.AP2, CDH1, DACT2, HIN1, and RASSF1A) are found frequently methylated (>30%) in thyroid cancer. The five genes panel is used for further epigenetic heterogeneity analysis. AP2 methylation is associated with gender (P < 0.05), DACT2 methylation is associated with age, gender and tumor size , HIN1 methylation is associated to tumor size (P < 0.05) and extra-thyroidal extension (P < 0.01). RASSF1A methylation is associated with lymph node metastasis (P < 0.01). For heterogeneity analysis, AP2 methylation heterogeneity is associated with tumor size (P < 0.01), CDH1 methylation heterogeneity is associated with lymph node metastasis (P < 0.05), DACT2 methylation heterogeneity is associated with tumor size (P < 0.01), HIN1 methylation heterogeneity is associated with tumor size and extra-thyroidal extension . The multivariable analysis suggested that the risk of lymph node metastasis is 2.5 times in CDH1 heterogeneous methylation group . The risk of extra-thyroidal extension is almost 3 times in HIN1 heterogeneous methylation group .Five genes (Five of twenty-four genes were found frequently methylated in human thyroid cancer. Based on 5 genes panel analysis, epigenetic heterogeneity is an universal event. Epigenetic heterogeneity is associated with cancer development and progression. SOX17 and DACT2, the key components of Wnt signaling, were found frequently in thyroid cancer (GPX3 was found associated with thyroid cancer metastasis (Thyroid cancer (TC) is the most common endocrine malignancy and the incidence is 3.4% of all cancers . Papillad cancer . Methylatastasis . Phenotytastasis . Subpoputastasis . For cantastasis . Howevertastasis . The contastasis . The occtastasis .ITH plays an important role in chemotherapeutic resistance . The besIn this study, we evaluated epigenetic heterogeneity by examining promoter region methylation in a panel of genes in primary thyroid cancer. These genes are frequently methylated in thyroid and other cancers, and they are involved in different cancer-related signaling.n = 130), stage II (n = 4), and stage III (n = 1). All of the tissue samples were immediately snap frozen in liquid nitrogen and preserved at −80°C before analysis. All samples were collected following the guidelines approved by the Institutional Review Board of the Beijing Cancer Hospital.A total of 405 samples from 135 cases of thyroid cancer  were obtained in Beijing Cancer Hospital from 2018 to 2020 . For hetGenomic DNA was extracted from frozen tissues, digested with protease K and then extracted using the standard phenol/chloroform procedure . MethylaMGMT, TIMP3, DAPK, MLH1, TMEM176A, DIRAS1, SFRP1, SFRP2, HIN1, AP2, ER, DACT2, CDH1, RASSF1A, SOX17, GATA4, BCL6B, GPX3, CRBP-1, p16, RUNX3, RARβ, CDX2, and WIF1. All these genes were found frequently methylated in esophageal, colorectal, lung, gastric and hepatic cancers by our previous studies and others, except for AP2 (an important transcription factor). These genes were well characterized and reported to be involved in different signaling pathways. Among which, AP2, CDH1, DACT2, HIN1, and RASSF1A genes were found frequently methylated (>30%) and selected for heterogeneity analysis. The workflow is shown in To screen representative genes for heterogeneity analysis, twenty-four genes were selected as discovery group, including P < 0.05∗, P < 0.01∗∗, or P < 0.001∗∗∗ was regarded as statistically significant. Data were analyzed by SPSS 22.0 software.The Chi-square or Fisher exact-test was used to analyze the association of gene methylation status and clinical factors. Logistic regression analysis was used to analyze the association of methylation heterogeneity or clinical factors with lymph node Metastasis and Extrathyroidal extension. AP2, CDH1, DACT2, HIN1, and RASSF1A) are methylated more than 30% in thyroid cancer. These five genes panel is selected as validation group for methylation heterogeneity analysis.To explore epigenetic heterogeneity in thyroid cancer, 24 genes, which were found frequently methylated in other cancers, were selected as discovery group to detect 46 cases of thyroid cancer. As shown in AP2, CDH1, DACT2, HIN1, and RASSF1A genes is examined by MSP.To evaluate the intratumor epigenetic heterogeneity in thyroid cancer, three different tumor samples are obtain from isolated locations in the same patient. Totally 405 cancer samples are gained from 135 patients. The methylation status of AP2 methylation is associated with gender (P < 0.05), while no association were found between AP2 methylation and age, tumor size, tumor location, TNM stage, lymph node metastasis, and extra-thyroidal extension . No association are found between CDH1 methylation and gender, age, tumor size, tumor location, TNM stage, lymph node metastasis, and extra-thyroidal extension . DACT2 methylation is associated with age, gender and tumor size , while no association is found between DACT2 methylation and tumor location, TNM stage, lymph node metastasis, and extra-thyroidal extension . HIN1 methylation is associated with tumor size (P < 0.05) and extra-thyroidal extension (P < 0.01), while no association is found between HIN1 methylation and age, tumor location, TNM stage, and lymph node metastasis . RASSF1A methylation is associated with lymph node metastasis (P < 0.01), while no association is found between RASSF1A methylation and age, tumor size, tumor location, TNM stage, and extra-thyroidal extension .The association of promoter region methylation and clinical factors is analyzed by Chi-square tests, including gender, age, tumor size, tumor location, TNM stage, lymph node metastasis, and extra-thyroidal extension. As shown in RASSF1A, CDH1, AP2, HIN1, and DACT2 genes. The results suggest that methylation heterogeneity is a common event in human thyroid cancer.Unmethylation or methylation in all three samples from the same individual is regarded as homogeneity, while unmethylation or methylation in one or two samples from the same individual is regarded as methylation heterogeneity. As shown in AP2 methylation heterogeneity is associated with tumor size (P < 0.01), no association are found between AP2 methylation heterogeneity and gender, age, tumor location, TNM stage, lymph node metastasis and extra-thyroidal extension . CDH1 methylation heterogeneity is associated with lymph node metastasis (P < 0.05), no association are found between CDH1 methylation heterogeneity and gender, age, tumor size, tumor location, TNM stage and extra-thyroidal extension . DACT2 methylation heterogeneity is associated with tumor size (P < 0.01), no association are found between DACT2 methylation heterogeneity and gender, age, tumor location, TNM stage, lymph node metastasis and extra-thyroidal extension . HIN1 methylation heterogeneity is associated with tumor size and extra-thyroidal extension , no association are found between HIN1 methylation heterogeneity and gender, age, tumor location, TNM stage and lymph node metastasis . No association are found between RASSF1A methylation heterogeneity and gender, age, tumor size, tumor location, TNM stage, lymph node metastasis and extra-thyroidal extension . Above results demonstrate that methylation heterogeneity of three genes  is associated with tumor size.The association of methylation heterogeneity and clinical factors is further analyzed. As shown in CDH1 methylation heterogeneity, age and tumor size are associated with lymph node metastasis, independently  in 34 tumors , heterogeneous methylation was found in 70% of the cases , HIN1 methylation is associated with tumor size (P < 0.05) and extra-thyroidal extension (P < 0.01), RASSF1A methylation is associated with lymph node metastasis (P < 0.01). The results suggest that methylation of DACT2, HIN1, and RASSF1A increases the malignance of thyroid cancer. Further analysis find that methylation heterogeneity of AP2, DACT2, and HIN1 are associated with tumor size. The results suggest that epigenetic heterogeneity is increasing with tumor growth. It indicates that the phenotype of cancer cells is varied in different tumor stages. Thus, the therapeutic strategies need according to the phenotypes of cancer cells, which are determined by epigenetic changes. The multivariable analysis suggested that CDH1 methylation heterogeneity is associated with lymph node metastasis and HIN1 methylation heterogeneity is associated with extra-thyroidal extension. The results suggest that CDH1 and HIN1 methylation heterogeneity may increase tumor metastasis. Methylation heterogeneity of these two genes may serve as prognostic marker for thyroid cancer.Genomic heterogeneity has been well studied in various cancers . Howeverhe cases . Among 9CDH1, AP2, HIN1, and DACT2) of the five detected genes in thyroid cancer. The results suggest that epigenetic heterogeneity is a universal mechanism of cancer development. It is notable that epigenetic heterogeneity is associated with tumor size or tumor metastasis. For the first time, we find that epigenetic heterogeneity is related to cancer development. Based on “BRCAness” principle, our recent study found that methylation of NRN1 was a novel synthetic lethal marker for PI3K-Akt-mTOR and ATR inhibitors in human esophageal cancer (The conventional clinical therapeutics is the “one-size-fits-all-approach.” However, the ultimate aim of precision medicine is to enable clinicians to accurately and efficiently identify the most effective preventative or therapeutic intervention for a specific patient. Epigenetic switches play important roles in carcinogenesis and tumor progression, and epigenetic switches are reversible . In thisl cancer . It is rIn conclusion, 5 of 24 genes were found frequently methylated in human thyroid cancer. Based on the 5 genes panel analysis, epigenetic heterogeneity is an universal event. Epigenetic heterogeneity is associated with cancer development and progression.The original contributions presented in the study are included in the article/All samples were collected following the guidelines approved by the Institutional Review Board of the Beijing Cancer Hospital. The patients/participants provided their written informed consent to participate in this study.CZ, MZ, and QW designed the project and performed the experiments. MZ and MG wrote the manuscript. MG has made significant contributions to the concept of this study. BL and JJ take part in data interpretation. All authors read and approved the final manuscript.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.


Background and Objectives: Trifarotene is a topical retinoid selective for retinoic acid receptor gamma that was recently approved for treatment of acne vulgaris. We performed a gene expression analysis to identify the molecular and cellular impact of trifarotene treatment on acne papules.Methods: In this open-label prospective study, subjects with moderate inflammatory acne of the back were treated with trifarotene 0.005% or vehicle cream on dedicated areas for 27 days, and 4 biopsies were collected from each subject . Large scale gene expression profiling of the biopsies was performed using Affymetrix technology, treatment-specific gene expression profiles were generated using statistical modeling, and pathway analysis was performed. Using single-cell RNAseq data, in silico deconvolution of transcriptomics data was performed to identify cellular signatures.Results: We discovered a unique set of 67 genes modulated by trifarotene that are primarily involved in cellular migration, inflammation, and extracellular matrix reorganization. Changes in cellular expression were similar in both trifarotene-treated and spontaneously-resolving lesions. However, only trifarotene treatment impacted SPP1+ macrophages, a subset of highly proliferative macrophages recently identified in fibrotic tissue.Conclusions: These results show that trifarotene has a novel action in acne treatment by affecting epidermal and immune components of acne pathogenesis. Cutibacterium, and (iv) complex inflammatory mechanisms that involve both innate and acquired immunity  increased sebum production, (ii) abnormal keratinization and cornification of the sebaceous follicular duct, (iii) colonization of the hair follicles by microbes—putatively immunity , 2. Trifimmunity . Topicalimmunity . Topicalimmunity –8. Howev studies –12. To dTo enhance understanding of the mode of action of trifarotene in acne, skin biopsies were collected from lesional and non-lesional skin of patients with acne vulgaris treated with trifarotene or vehicle, and whole transcriptome gene expression profiling was performed. In addition, gene signatures of the major cell types present in skin biopsies were determined from a publicly available single cell RNAseq dataset , and celpost-hoc analysis of an additional subset of data collected as part of Aubert et al.  method  and custom R scripts. Affymetrix U133 Plus 2.0 chips  were normalized using the multi-array average (RMA) method . Low exp) method . Candidahttps://www.qiagenbioinformatics.com/products/ingenuity-pathway-analysis)  software  . IPA wasnalysis) .We used single-cell RNAseq data from Hughes et al. to determine the major cell types present in skin biopsies and the associated cell markers . Acne anGene markers specific to each class of macrophage were derived from the literature. For M0, M1 and M2 macrophages, we used markers as described in Newman et al. . For SPPM0 macrophage: ACP5, BHLHE41, C5AR1, CCDC102B, CCL22, CCL7, COL8A2, CSF1, CXCL3, CXCL5, CYP27A1, DCSTAMP, GPC4, HK3, IGSF6, MARCO, MMP9, NCF2, PLA2G7, PPBP, QPCT, SLAMF8, SLC12A8, TNFSF14, VNN1. M1 macrophage: ACHE, APOBEC3A, APOL3, APOL6, ARRB1, CCL19, CCL5, CCR7, CD38, CD40, CHI3L1, CXCL10, CXCL11, CXCL13, CXCL9, CYP27B1, DHX58, HESX1, IDO1, IFI44L, IL2RA, KIAA0754, KYNU, LAG3, LAMP3, LILRA3, LILRB2, NOD2, PLA1A, PTGIR, RASSF4, RSAD2, SLAMF1, SLC2A6, SOCS1, TLR7, TNFAIP6, TNIP3, TRPM4. M2 macrophage: AIF1, ALOX15, CCL13, CCL14, CCL23, CD209, CD4, CFP, CLEC10A, CLEC4A, CRYBB1, FES, FRMD4A, FZD2, GSTT1, HRH1, HTR2B, MS4A6A, NME8, NPL, P2RY13, PDCD1LG2, RENBP, WNT5B. SPP1 macrophage: MERTK, CD14, SPP1, CD68, LYZ.Each cell type gene signature was used to define a cell type average gene expression in each of our clinical study samples, calculated as the average of the log2 expression values of these markers. Boxplots were used to visualize the distribution of cell type gene expression scores between clinical groups. Statistical significance was assessed using Wilcoxon rank sum test. Graphs were generated using the ggpubr data visualization library.To assess the mode of action of trifarotene, we first derived gene expression profiles of (1) acne lesion at baseline (papule signature), (2) previously lesional skin after spontaneous resolution of the papule (vehicle signature), and (3) previously lesional skin after resolution of a papule following trifarotene treatment (trifarotene signature). Papule signature was defined as genes differentially expressed when comparing the papule biopsy before treatment vs. non-involved skin biopsy, spontaneous resolution of a papule (vehicle signature) was defined as genes differentially expressed between vehicle treated area and a papule biopsy before treatment, and trifarotene signature as genes differentially expressed between trifarotene treated area and a papule biopsy before treatment. The number of unique annotated genes found significantly differentially expressed in each comparison is presented in the Venn diagram .z-score > 2.5, P < 0.001 for all these pathways). IPA diseases and functions analyses of the acne gene signature also revealed that cell migration and activation were among the major processes upregulated in acne papules , suggesting inhibition of retinoid X receptor pathways  in acne papules (z-score = 1.46 for Trifarotene gene signature) and induced a concomitant reduction in neuroinflammation pathway, leukocyte extravasation signaling, cellular movement, and immune cell trafficking  and tissue remodeling   .When gene expression in acne lesions that spontaneously resolved  was analyzed, the pattern included changes in pathways and processes similar to those seen with trifarotene treatment. A total of 287 genes that were upregulated in acne papules were downregulated in both the trifarotene and vehicle signatures . These dA total of 67 genes were uniquely affected by trifarotene–these genes did not appear in the spontaneously resolving acne lesion signature . Figure Of the 67 genes regulated by trifarotene, there were only four genes upregulated: long intergenic non-protein coding RNA 1224 (LINC01224), alpha-internexin (INA), gamma-aminobutyric acid receptor subunit alpha-4 (GABRA4) and peroxisomal membrane protein 4 (PXMP4) . There iTo identify if these pathways were specific to trifarotene treatment, we analyzed the 69 genes uniquely regulated in spontaneously resolved lesions. The major downregulated genes in spontaneously resolved papules included inflammation related genes like chemokine CXCL10 and beta defensin 103 (DEFB103A/DEFB103B), transcobalamin-1 (TCN1), and epithelial-related genes including S100A7A, S100A9, SPRR3, SERPINB3, and SERPINB4 . PathwayTo further investigate the effects of trifarotene on the various cell types present in skin biopsies, we obtained specific gene signatures through the re-analysis of publicly available single cell RNAseq data from healthy and acne skin biopsies . We ide+) macrophages, were increased in lesional skin and tended back toward normal levels with spontaneous lesion resolution or trifarotene-induced lesion resolution. As shown in + macrophages vs. spontaneously resolved acne papules (P = 0.040).Cell type scoring analysis results showed that all subsets of macrophages, including the relatively newly identified SPP1/MERTK activated  . While whe lungs . The inche lungs , 31. FurSoluble osteopontin (OPN) is a multi-functional cytokine found elevated in psoriasis and other inflammatory diseases. It binds to integrins and CD44 on immune cells and plays a major role in cell adhesion, migration, and modulation of both Th1 and Th17 responses . OPN mayThe chemokine CXCL13 and its receptor, CXCR5, play a central role in driving humoral immunity during infection and vaccine responses , 35. OriC. acnes induces production of various MMPs  selective agonist, regulates several unique genes and pathways but whether these are driven by RARγ vs. other RARs remains an intriguing question. While there is limited information on the role of the other retinoids in driving anti-inflammatory modules in acne, a cursory look suggests that the data is indeed controversial. Jalian et al.  reportedThe dataset presented in this study can be found in Gene Expression Omnibus under accession number GSE107232.The studies involving human participants were reviewed and approved by CHU Nantes. The patients/participants provided their written informed consent to participate in this study.BD, SB-R, AK, and JKK were involved in study design and analysis. SB-R designed the computational framework and analyzed and interpreted the results. RC, VJ, and JKK aided in interpreting results and contributed to the writing of the manuscript. All authors discussed the results, read, and approved the final manuscript.Financial support for this study was provided by Galderma.RC, VJ, and JKK are employees of Galderma; SB-R is an employee of Syneos Health and consultant for Galderma; BD and AK are investigators and consultants for Galderma.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.