Both IGFBP‐3‐ and TGF‐β‐stimulated PPase activities in cell lysates are abolished when cells are co‐treated with TGF‐β/IGFBP‐3 antagonist or RAP (LRP‐1/TβR‐V antagonist). However, the IGFBP‐3‐stimulated PPase activity, but not TGF‐β‐stimulated PPase activity, is sensitive to inhibition by okadaic acid (OA). In addition, OA or PP2Ac siRNA reverses IGFBP‐3 growth inhibition, but not TGF‐β growth inhibition, in Mv1Lu and 32D cells. These suggest that IGFBP‐3‐ and TGF‐β‐stimulated PPases are identical to PP2A and PP1, respectively. By Western blot/phosphorimager/immunofluorescence‐microscopy analyses, IGFBP‐3 and TGF‐β stimulate TβR‐V‐mediated IRS‐2‐dependent activation and cytoplasm‐to‐nucleus translocation of PP2Ac and PP1c, resulting in dephosphorylation of p130/p107 and pRb, respectively, and growth arrest. Small molecule TGF‐β enhancers, which potentiate TGF‐β growth inhibition by enhancing TβR‐I–TβR‐II‐mediated canonical signaling and thus activating TβR‐V‐mediated tumor suppressor signaling cascade (TβR‐V/IRS‐2/PP1/pRb), could be used to prevent and treat carcinoma.The TGF‐β type V receptor (TβR‐V) mediates growth inhibition by IGFBP‐3 and TGF‐β in epithelial cells and loss of TβR‐V expression in these cells leads to development of carcinoma. The mechanisms by which TβR‐V mediates growth inhibition (tumor suppressor) signaling remain elusive. Previous studies revealed that IGFBP‐3 and TGF‐β inhibit growth in epithelial cells by stimulating TβR‐V‐mediated IRS‐1/2‐dependent activation and cytoplasm‐to‐nucleus translocation of IGFBP‐3‐ or TGF‐β‐stimulated protein phosphatase (PPase), resulting in dephosphorylation of pRb‐related proteins  or pRb, and growth arrest. To define the signaling, we characterized/identified the IGFBP‐3‐ and TGF‐β‐stimulated PPases in cell lysates and nucleus fractions in Mv1Lu cells treated with IGFBP‐3 and TGF‐β, using a cell‐free assay with  A549 cellshuman Caucasian lung carcinoma cellsCDKcyclin‐dependent kinaseCHO cellsChinese hamster ovary epithelial cellsD32 cellsmurine 32D myeloid cellsEMTepithelial mesenchymal transitionIGF‐1insulin‐like growth factor‐1IGF‐2insulin‐like growth factor‐1IGFBP‐3insulin‐like growth factor‐binding protein‐3IRinsulin receptorIRS‐1/2insulin receptor substrate‐1/2LRP‐1low density lipoprotein receptor‐related protein‐1Mv1Lu cellsmink lung epithelial cellsOAokadaic acidp107p130, pRb‐related proteinsPAI‐1plasminogen activator inhibitor‐1PP1protein phosphatase 1cPP136‐kDa PP1 catalytic subunitPP2Aprotein phosphatase 2APP2A‐B56a 56‐kDa substrate‐recognition B subunit of PP2AcPP2A37‐kDa PP2A catalytic subunitPPaseprotein phosphatasepRbretinoblastoma protein (p105)RAPreceptor‐associated proteinsiRNAsmall interfering RNATGF‐βtransforming growth factor‐βTβR‐Itype I TGF‐β receptorTβR‐IItype II TGF‐β receptorTβR‐IIItype III TGF‐β receptorTβR‐Vtype V TGF‐β receptor125βst to 65th of human TGF‐β1TGF‐β peptide antagonist containing amino acid residues 411d (0.3 µg/ml or 10 nM) for binding to TβR‐V,Insulin‐like growth factor‐binding protein‐3 (IGFBP‐3) is a growth regulator which exhibits IGF‐dependent and ‐independent growth inhibitory activities in target cells.−/− cells) and H1299 human non‐small cell lung carcinoma cells.−/− cells express both TβR‐I and TβR‐II, and respond to TGF‐β‐stimulated TβR‐I/TβR‐II‐mediated transcriptional activation of extracellular matrix (ECM)‐related genes, such as PAI‐1.−/− cells exhibit a spindle‐shaped fibroblastoid morphology, frequently observed in invasive carcinoma cells.−/− cells with TβR‐V/LRP‐1 cDNA confers sensitivity to either TGF‐β or IGFBP‐3 growth inhibition and restores normal squamous epithelial morphology.IGFBP‐3 and TGF‐β do not inhibit growth in cells lacking TβR‐V, such as homozygous LRP‐1‐deficient mouse embryonic fibroblasts (PEA‐13 cells), CHO cells deficient in LRP‐1  and pRb (p105) in the nucleus, respectively, in epithelial cells and growth arrest.We previously demonstrated that IGFBP‐3 and TGF‐β inhibit growth in epithelial cells by stimulating TβR‐V‐mediated tumor suppressor signaling which involves IRS‐1/2‐dependent activation and cytoplasm‐to‐nucleus translocation of IGFBP‐3‐ or TGF‐β‐stimulated protein phosphatase (PPase), and dephosphorylation of retinoblastoma family proteins in the nucleus, resulting in cell growth arrest.22.132P]ATP, [32P]‐orthophosphate and [methyl‐3H] thymidine (67 Ci/mmol) were purchased from ICN Biochemicals . Okadaic acid (OA) was purchased from Tocris. IGFBP‐3 and TGF‐β1 (TGF‐β) were purchased from Peprotech. Insulin (A11382II) was purchased from Gibco. Primary antibodies against IRS‐1 (sc‐398), IRS‐2 (sc‐390761), PP1c  (sc‐7482), pRb (p105) (sc‐65230), p107 (sc‐250), p130 (sc‐374521), phosphorylated Smad2 (P‐Smad2) (sc‐135644), Ser 270‐phosphorylated IRS‐1/2 (P‐IRS‐1/2) (sc‐17192), β‐actin (sc‐47778), and lamin B (sc‐6216) were purchased from Santa Cruz Biotechnology. Rabbit antibodies against N‐ and C‐terminal of human LRP‐1 (TβR‐V) were purchased from Sigma Chemical Co. and Abcam, respectively. Rabbit polyclonal antibodies against hyperphosphoryrated Rb (P‐Rb) (#8516) and PP2Ac  (#2038) were purchased from Cell Signaling Technology. Alexa Fluor 488‐ and 594‐conjugated secondary antibodies were purchased from Thermo Fisher. Secondary antibodies conjugated with horseradish peroxidase  and enhanced chemiluminescence (ECL) kit (Perkin‐Elmer Life Sciences) were used to develop immunoblots. TGF‐β peptide antagonist [β125], a dual TGF‐β/IGFBP‐3 antagonist, was synthesized as previously described.All chemicals used in the experiments were prepared as a 10 mM stock solution in DMSO. The final concentration of DMSO in all experiments was 0.1% or lower, which had no effect on IGFBP‐3 and TGF‐β activity. Human receptor‐associated protein (RAP) was provided by Dr. Dudley K. Strickland .  ATP (200 cpm/pmol), 10 mM MgCl2, and 1.5 Unit/ml of the catalytic subunit of protein kinase A in a final volume of 3 ml. After overnight incubation at room temperature, the solution was filtered on a column (1.5 × 20 cm) of Sephadex 50G equilibriated in 50 mM Tris–HCl containing 10% glycerol and 1 mM benzamidine. Before stimulation with IGFBP‐3 or TGF‐β, cells were treated with or without 25 µg/ml of RAP (receptor‐associated protein) and 30 µg/ml of TGF‐β peptide antagonist (β125) in serum‐free DMEM or DMEM/Ham's F‐12 medium for 10 min. The cells were stimulated with IGFBP‐3 (0.6 µg/ml) or TGF‐β (40 pM) for 3 hr. The cells were washed with cold phosphate‐buffered saline (PBS), detached with 50 mM Tris–HCl pH 7.0 containing 0.25 M sucrose, 5 mM EDTA, and pelleted at 1,500 rpm for 5 min at 4°C. The cells were then lysed in 50 µl of homogenization buffer .32P‐labeled substrate in a final volume of 0.05 ml. Reactions in triplicates were initiated with the 32P‐labeled casein at 30°C, and after a 10 min reaction period, 0.1 ml of 10% trichloroacetic acid (TCA) was added. The mixture was centrifuged at 12,000 g for 2 min in a microcentrifuge. About 0.1 ml of the supernatant was then added to 1 ml scintillation counting liquid, and radioactivity was determined.The PPase activity assay mixtures were composed of 50 mM Tris–HCl, pH 7.0 containing 10% glycerol, 1 mM benzamidine, 0.1 mM PMSF, 14 mM mercaptoethanol, 0.1 mg of bovine serum albumin (BSA), PPase‐containing sample (cell lysates or nucleus extracts containing 5 µg protein), and 2–103 cpm; 200 cpm/pmol phosphate). This non‐specific PPase activity was subtracted from the total PPase activity in the cell lysates from cells treated with IGFBP‐3 or TGF‐β in order to estimate IGFBP‐3‐stimulated or TGF‐β‐stimulated PPase activity. For this reason, the mean (±SD) of the non‐specific PPase activity from triplicates was taken as 0 cpm in cells treated with vehicle only.The lysates from cells treated with vehicle only exhibited non‐specific PPase activity (IGFBP‐3‐ or TGF‐β‐independent PPase activity with certain ~102.4c and hyperphosphorylated pRb (P‐Rb), the images were analyzed in three dimensions using an AxioObserver Z1 Apotome microscope (Zeiss). Colocalization was evaluated in single optical planes taken through the entire z‐axis of each cell. All images were acquired using identical intensity and photodetector gain to allow quantitative comparisons of relative levels of immunoreactivity between samples. All images were cropped and sized using ImageJ.One milliliter of culture media containing approximately 5,000–10,000 Mv1Lu cells was added to a 35 mm culture dish containing a square coverslip. Mv1Lu cells grown on coverslips were treated with IGFBP‐3 or TGF‐β. Cells were then fixed in 4% paraformaldehyde for 15 min followed by permeabilization. Fixed cells were blocked with 5% BSA in PBS for 20 min at room temperature (RT) and then incubated with an appropriate primary antibody solution overnight at 4°C. Fixed cells were incubated with Alexa Fluor‐conjugated secondary antibodies for 1 hr at RT. Samples were observed with a Zeiss AxioObserver Z1 microscope (Zeiss), and images were captured using AxioVision Rev 4.6 software. To determine the nuclear localization and the colocalization of PP12.52, 0.1 mM EDTA, 1 mM dithiothreitol (DTT), and 0.5 mM phenylmethylsulfonyl fluoride . The homogenates were centrifuged for 30 s at 500 g at 4°C to eliminate any unbroken tissue. The supernatants were incubated on ice for 20 min, vortexed for 30 s after the addition of 50 μL of 10% Nonidet P‐40 , and then centrifuged for 1 min at 5,000 g at 4°C. The crude nucleus pellet was suspended in 200 μL of ice‐cold extraction buffer  and incubated on ice for 30 min, mixed frequently, and centrifuged at 12,000 g at 4°C for 15 min. The supernatants were collected as nucleus extracts for Western blot and PPase activity assay. Protein concentration was determined using a bicinchoninic acid assay kit with BSA as the standard .Nuclear extracts of the cells were prepared by hypotonic lysis followed by high salt extraction. Briefly, cell pellets were homogenized in 0.5 mL of ice‐cold lysis buffer, composed of 10 mM HEPES pH 7.9, 10 mM KCl, 2 mM MgCl2.6c siRNA oligonucleotide corresponding to nucleotide sequence 5’‐xxx‐3’ (ON‐TARGETplus SMARTpool Cat #: L‐040657–00) and negative control siRNA were obtained from Dharmacon. PP2Ac siRNA and negative control siRNA were resuspended in in RNase‐free water and stored at −80°C. Transfection of siRNA was carried out using electroporation . Three million cells in 600 µl of RPMI 1640 were incubated with siRNA in a 0.4 cm cuvette for 5 min on ice before electroporation . After additional 5‐min incubation on ice, cells were re‐suspended in 12 ml of RPMI 1640 supplemented with glutamine and 10% FCS  without antibiotic. Antibiotics (1% penicillin/streptomycin) were added at 6 hr after electroporation. All measurements were performed at 24 or 72 hr after transfection.Murine PP2A2.7c siRNA knocked‐down 32D cells were determined by the measurement of [methy‐3H] thymidine incorporation into cellular DNA as described previously.3H] thymidine incorporation into cellular DNA was determined by incubation of cells with [methy‐3H] thymidine for 6 hr.Growth of OA‐treated Mv1Lu cells and PP2A2.86 cells) were lysed with 100 µl, 50 mM Tris–HCl, pH 7.0 containing 1% Triton X‐100, 150 mM NaCl, 5 mM EDTA, and 0.1 mM PMSF. Cell lysates were subjected to 7.5% SDS‐PAGE and Western blotting using specific antibodies (Santa Cruz Biotechnology) as described previously.Seventy‐two hours after siRNA transfection, 32D and Mv1Lu cells (3 × 102.96 cells) grown in 6‐well plate were washed and incubated in phosphate‐free DMEM for 1 hr to deplete intracellular phosphate. After 2 hr of incubation with [32P] orthophosphate at 37°C in a CO2 incubator, cells were treated with 1 µg/ml of IGFBP‐3 and/or OA (and RAP) for 16 hr. Cell lysates were prepared by suspending cells in 600 µl of lysis buffer and p130 or p107 was immunoprecipitated with a rabbit polyclonal antibody against the N‐terminal domain of p130 or p107. The p130 or p107 antibody complex was captured with a protein G‐coated agarose beads. The immunoprecipitated proteins were resolved using 7.5% SDS‐PAGE. The gel was dried and autoradiographed by a phosphorimager.Mv1Lu and 32D cells (3 × 102.10t‐test was used for determining the significance of a difference between two (vehicle only and sample) means. It was mainly used to compare the means between two groups (vehicle only and one specific concentration of IGFBP‐3 or TGF‐β). The values were presented as mean ± SD. p < 0.05 was considered significant.Two‐tailed unpaired Student's 33.132P‐phosphorylated casein, which was generated by 32P‐phosphate‐labeling (32P‐labeling) of casein (dephosphorylated) with protein kinase A in the presence of γ‐32P‐ATP, was incubated with cell lysates of Mv1Lu cells treated with or without IGFBP‐3 or TGF‐β1 (TGF‐β). After incubation, 32P‐phosphate released from 32P‐casein via the action of stimulated PPase in cell lysates and nucleus extracts were separated from remaining 32P‐casein by 10% trichloroacetic acid (TCA) precipitation in the presence of a carrier protein (BSA). The 32P‐phosphate released was recovered in the supernate of the 10% TCA solution. The IGFBP‐3‐ and TGF‐β‐stimulated PPase activities were estimated by subtracting the radioactivity of 32P‐phosphate released by cell lysates or nucleus extracts of cells treated without IGFBP‐3 or TGF‐β from that released by cell lysates or nucleus extracts of cells treated with IGFBP‐3 or TGF‐β. Using this assay, we characterized the kinetics, IGFBP‐3 or TGF‐β concentration dependence and OA sensitivity of the IGFBP‐3‐ or TGF‐β‐stimulated PPase activity in Mv1Lu cells. As shown in Figure d (50 pM) of TGF‐β binding to TβR‐V3 cpm; 200 cpm/pmol phosphate)  Figure . However) Figure . OA at 1) Figure . These r3.2125)−/− cells, respectively). As shown in Figure 125 alone stimulated non‐specific (IGFBP‐3‐ or TGF‐β‐independent) PPase activity  PPase activity which was statistically indifferent from that treated with RAP or β125 alone, suggesting that RAP or β125 completely abolished the IGFBP‐3‐ and TGF‐β‐stimulated PPase activities  and did not exhibit IGFBP‐3‐stimulated PPase activity. IGFBP‐3 and RAP appeared to exert additive effects on stimulating non‐specific (IGFBP‐3‐independent) PPase activity in CHO‐LRP‐1−/− cells  seen in wild‐type CHO‐K1 cells  which exhibited a spindle‐shaped fibroblastoid morphology, frequently observed in invasive carcinoma cells.−/− cells (carcinoma cells) appeared to greatly upregulate non‐specific (IGFBP‐3‐independent) PPase activity in these CHO‐LRP‐1−/− cells.The TβR‐V has been identified as the IGFBP‐3 receptor which mediates its IGF‐independent growth inhibitory activity.y Figure . Co‐treas Figure . In Figus Figure . Howevers Figure . LRP‐1  induced by IGFBP‐3 and TGF‐β. As shown in Figure c), a 65‐kDa scaffolding A subunit, and a 56‐kDa substrate‐recognizing B subunit (PP2A‐B56).c siRNA transfection on IGFBP‐3‐induced growth inhibition in these murine 32D cells. This PP2Ac siRNA was developed based on the murine sequence. 32D cells were transfected with control siRNA, 2 and 4 nM PP2Ac siRNA by electroporation and treated with IGFBP‐3. As shown in Figure c siRNA (2 and 4 nM) reversed the growth inhibition induced by IGFBP‐3   . This is consistent with the inability of murine PP2Ac siRNA to significantly downregulate mink PP2Ac  Figure  as deter3.5c siRNA in murine 32D cells. These suggest that the IGFBP‐3‐stimulated PPase is identical to PP2A. We hypothesized that IGFBP‐3 induces growth inhibition by stimulating IRS‐2‐dependent activation and cytoplasm‐to‐nucleus translocation of PP2Ac in Mv1Lu cells. To test this, Mv1Lu cells were treated with 0, 2, and 10 nM , IGFBP‐3 for 2 hr. The cytoplasm and nucleus fractions in treated cells were then isolated and subjected to 7.5% SDS‐PAGE followed by Western blot analysis. As shown in Figure c in the nucleus fraction by 1.5‐ to 1.7‐fold (n = 3) as compared to that in cells treated with vehicle only (0 nM IGFBP‐3). These results suggest that IGFBP‐3 promotes cytoplasm‐to‐nucleus translocation of PP2Ac (likely as the IRS‐2 complex) in Mv1Lu cells.As described above, IGFBP‐3‐stimulated PPase activity and IGFBP‐3‐induced growth inhibition are blocked or reversed by co‐treatment with very low concentrations of OA in Mv1Lu and 32D cells and by transfection with PP2A3.632P‐orthophosphate at 37°C for 1 hr, washed, and incubated with 0.3 µg/ml (10 nM) IGFBP‐3 in the presence of excess unlabeled orthophosphate in the medium. After 2 hr at 37°C, 32P‐labeled cell lysates were immunoprecipitated with specific antibodies to p130 and p107 and analyzed by 7.5% SDS‐PAGE and quantified by a phosphorimager . As shown in Figure PP2A plays a critical multi‐faceted role in the regulation of the cell cycle. It has been implicated in dephosphorylation of two retinoblastoma protein (pRb)‐related proteins, p130 and p107, which interact primarily with E2F4 and E2F5 and are most active in G0‐the quiescent phase of the cell cycle.3.7c) and at least one regulatory subunit which directs PP1c to different substrates or sites. To test this, we performed immunofluorescence microscopy in Mv1Lu cells treated with 40 pM TGF‐β at 37˚C for 0 and 1 hr using antibodies to TβR‐V (LRP‐1) and PP1c , which is the PP1c‐dephosphorylated product of pRb in the nucleus. To demonstrate the subsequent cytoplasm‐to‐nucleus translocation of PP1c and its effect on dephosphorylation of pRb in the nucleus, we performed immunofluorescence analysis in Mv1Lu cells treated with 40 pM TGF‐β at 37˚C for 0, 1, and 2 hr using specific antibodies to PP1c and hyperphosphorylated pRb (P‐Rb). We reasoned that TGF‐β promotes nucleus accumulation of PP1c and should accordingly decrease the amount of P‐Rb, its target substrate, in the nucleus. PP1 specifically dephosphorylates pRb in the nucleus of target cells.c and P‐Rb in the nucleus  present in the cytoplasm and nucleus fractions are identified as hyperphosphorylated (as a slow‐migrating form) and hypophosphorylated (as a fast‐migrating form) forms of pRb, respectively, based on its mobility on 7.5% SDS‐PAGE.3.8c, PP2Ac, pRb (Rb), phosphorylated Smad2 (P‐Smad2), phosphorylated IRS‐1/2 , lamin B, and β‐actin using 7.5% SDS‐PAGE and quantitative Western blot analysis with specific antibodies to PP1c, PP2Ac, and others after subcellular cytoplasm/nucleus fractionation of Mv1Lu and A549 cells treated with 40 pM TGF‐β for 0, 1, and 2 hr. As shown in Figure c , P‐IRS‐2 and P‐Smad2 by 1.5‐ to 2‐fold (n = 3) in the nucleus fraction in Mv1Lu and A549 cells. In contrast, TGF‐β did not significantly increase the amount of PP2Ac in the nucleus fraction in these cells. Interestingly, Rb present in the cytoplasm and nucleus fractions were identified as phosphorylated (as a slow‐migrating form) and dephosphorylated (as a fast‐migrating form) forms of Rb, respectively, based on its mobility on 7.5% SDS‐PAGE.c and P‐IRS‐2, and correspondingly increases the amount of dephosphorylated Rb (as a fast‐migrating form of Rb), which is the PP1‐dephosphorylated product of Rb, in the nucleus.To further support the hypothesis that TGF‐β stimulates cytoplasm‐to‐nucleus translocation of PP1, we determined the subcellular localization of PP1c Figure , dephospc Figure , P‐IRS‐2c Figure , and P‐Sc Figure  in the nc Figure e,h. Afte4c docking motifs (FxxR/KxR/K),Here, we have provided evidence revealing that IGFBP‐3 inhibits growth in epithelial cells by stimulating the TβR‐V‐mediated growth inhibition signaling cascade which involves IRS‐2, PP2A, and pRb‐related proteins, p130 or p107. We propose a revised version of our previously published model−/− cells with LRP‐1 (TβR‐V) cDNA restores the growth inhibitory response to IGFBP‐3 and TGF‐β, and normal epithelial morphologySeveral lines of evidence suggest that IGFBP‐3 acts as a potential tumor suppressor gene. First, aberrant promoter methylation of IGFBP‐3 gene, which silences its expression, is detected in human gastric cancer, colorectal cancer, breast cancer, and malignant mesothelioma cancer.125‐labeled IGFBP‐3 affinity labeling (binding/cross‐linking) followed by immunoprecipitation using antiserum to TβR‐V in epithelial cells and other cell types.125‐labeled IGFBPs affinity labeling.d of 10 nM.125, which contains a minimal active site motif of WS/CXD in TGF‐β and IGFBP‐3 molecules, blocks TGF‐β and IGFBP‐3 binding to TGF‐β receptors in epithelial cells and reverses growth inhibition induced by either TGF‐β or IGFBP‐3 in these cells.d of IGFBP‐3 binding to TMEM219/IGFBP‐3R has been estimated to be 125 nM.TβR‐V is the only cell surface IGFBP‐3 receptor identified by IThe transcriptional activation and growth inhibition activities of TGF‐β have generally been thought to be mediated by the canonical TβR‐I/TβR‐II/Smad2/3/4 signaling cascade.c, resulting in dephosphorylation of pRb (p105) in the nucleus, as demonstrated by Western blot analysis following subcellular fractionation (to yield cytoplasm and nucleus fractions) and immunofluorescence analysis. (8) PP1 as well as PP2A are the master regulators of the eukaryotic cell cycle.We previously proposed a model for the mechanism by which TGF‐β inhibits growth in epithelial cells by binding to a site between cell surface subdomains I and II of TβR‐V in target cells. In this model, TGF‐β stimulates sequential association of IRS‐1 or IRS‐2 and a Ser/Thr‐specific PPase with the cytoplasmic tail of TβR‐V by inducing TβR‐V dimerization via its covalently linked homodimeric structure. In the ternary complexes, the Ser/Thr‐specific PPase becomes activated and dephosphorylate IRS‐1/2. Dephosphorylated IRS‐1‐PPase or IRS‐2‐PPase binary complexes dissociate from the cytoplasmic tail of TβR‐V and undergo IRS‐1/2‐dependent translocation from cytoplasm to the nucleus where the PPase dephosphorylates pRb (retinoblastoma protein) or pRb‐related proteins, resulting in growth arrest. This model lacked the identity of PPase, IRS‐1/2, and retinoblastoma family proteins which are involved in TGF‐β‐stimulated TβR‐V‐mediated tumor suppressor (growth inhibition) signaling.c docking motifs of FrssfR (residues 438–443) and FefRpR (residues 298–303), respectively (PP1c docking affinity: IRS‐2 > IRS‐1). IRS‐2 appears to comprise of overlapping high‐affinity PP1 and PP2A docking motifs of FefRpR (residues 298–303) and LkeLfE (residues 294–299), respectively, suggesting that PP1 and PP2A docking to IRS‐2 are mutually exclusive. After dephosphorylation of IRS‐2 by activated PP1 in the ternary complex, dephosphorylated IRS‐2‐PP1 binary complexes dissociate from the cytoplasmic tail of TβR‐V and enter the nucleus via the nucleus‐targeting function of IRS‐2. In the nucleus, PP1 dephosphorylates pRb (p105), resulting in cell growth arrest. In normal epithelial cells, TGF‐β potently inhibits cell growth (~100% growth inhibition at 1–5 pM) by stimulating the TβR‐V‐mediated signaling cascade (TβR‐V/IRS‐2/PP1/pRb) to dephosphorylate (activate) pRb by PP1 in concert with canonical TGF‐β signaling (TβR‐I/TβR‐II/Smad2/3/4).c itself possesses a high‐affinity PP2A‐B56 docking motif of LlrLfE (residues 82–87), on the way to bind to TβR‐I (Figure c also recruits PP2A to form the high‐affinity‐bound TβR‐1‐PPc‐PP2A complex. PP2A recruited by TβR‐1 as the high‐affinity‐bound PP1‐PP2A complexc‐PP2A by TβR‐I to suppress non‐Smad signaling.In this model Figure , TGF‐β, I Figure , PP1c alTGF‐β is known to act as a tumor suppressor and a tumor promoter during tumorigenesis. The mechanism of switching TGF‐β from a tumor suppressor to a tumor promoter in the process of tumorigenesis remains unclear.Activation of tumor suppressors or their signaling for the treatment of human cancers has been a long sought, yet elusive, strategy for an effective therapy.The authors declare that there are no competing financial interest associated with this work.C.L.C. performed most of the experiments in the Department of Biochemistry and Molecular Biology, Saint Louis University School of Medicine. F.W.H. was involved in identifying the negative regulation of non‐Smad pathways by TβR‐V. S.S.H and J.S.H. were involved in research designing and finalizing the manuscript.


Poephila cincta) at fledgling and adult stages. Our results revealthat in both the fledgling and the adult, cranial musculature shows clear and complexpartitioning in the Musculus adductor mandibulae externus that isconsistent with other families within Passeriformes. We quantified jaw-muscle sizes andfound that the adult showed a decrease in muscle mass in comparison to the fledglingindividual. We propose that this could be the result of low sample size or a physiologicaleffect of parental care in Passeriformes. Our study shows that high-resolutionvisualization techniques are informative at revealing morphological discrepancies forstudies that involve small specimens such as Passeriformes especially with carefulspecimen selection criteria.Synopsis Dietary requirements and acquisition strategies change throughout ontogenyacross various clades of tetrapods, including birds. For example, birds hatch withcombinations of various behavioral, physiological, and morphological factors that placethem on an altricial–precocial spectrum. Passeriformes (=songbirds) in particular, afamily constituting approximately more than half of known bird species, displays the mostdrastic difference between hatchling and adults in each of these aspects of their feedingbiology. How the shift in dietary resource acquisition is managed during ontogenyalongside its relationship to the morphology of the feeding apparatus has been largelyunderstudied within birds. Such efforts have been hampered partly due to the small size ofmany birds and the diminutive jaw musculature they employ. In this study, we used standardand diffusible iodine-based contrast-enhanced computed tomography in conjunction withdigital dissection to quantify and describe the cranial musculature of the Black-throatedFinch  và với chụp cắt lớp tăng cường độ tương phản dựa trên i-ốt khuếch tán , bên cạnh với việc giải phẫukỹ thuật số, để định lượng và mô tả hệ cơ đầu của loài Di cổ đen  ở giai đoạn rời tổ và khi trưởng thành. Kết quả phântích cho thấy, ở cả chim non mới rời tổ và chim trưởng thành, hệ cơ đầu có sự phân chiaphức tạc và rõ rệt tại bó cơ cắn ngoài (Musculus adductor mandibulaeexternus), tương tự các họ chim khác trong bộ Sẻ. Khi định lượng kích thước củabó cơ này, chúng tôi phát hiện khối lượng cơ của cá thể trưởng thành thấp hơn của cá thểmới rời tổ. Đây có thể là hệ quả của cỡ mẫu nhỏ hoặc là một tác động sinh lý trong quátrình chăm con của thành viên bộ Sẻ. Nghiên cứu của chúng tôi cho thấy các kỹ thuật trựcquan hóa với độ phân giải cao rất hữu dụng trong việc phân biệt hình thái ở những nghiêncứu dùng mẫu vật có kích thước nhỏ như chim thuộc bộ Sẻ, nhất là khi có phương pháp lựachọn mẫu vật kĩ lưỡng.Nhiều nhánh động vật có tứ chi, bao gồm cả chim, có nhu cầu dinh dưỡng và chiến thuậtkiếm ăn liên tục thay đổi xuyên suốt quá trình sinh trưởng của chúng. Ví dụ, chim non nởdưới sự tác động của các yếu tố hành vi, sinh lý, và hình thái để xác định thời điểm chúngcó thể tự kiếm ăn. Bộ Sẻ  Throughout ontogeny, shifts in food resource acquisition are common across vertebrates. Such chThe avian feeding apparatus is part of a kinetic skull held together by ligaments and withmotions powered by a series of muscles  that enhPoephila cincta), a seed-eating songbird in theOld-World tropics and Australasia with a short, thick, and conical bill. Juvenile orsub-adult birds rapidly achieve average adult size or larger as a combined result of thesteady diet from their parents while not having to perform many energy-consuming tasks, suchas flying and active foraging  were each acquired as deceased individuals from the Tulsa Zoo, Inc.. The fledgling had not molted to adult plumage and was∼50 days from hatching at time of death based on records kept by the Tulsa Zoo, whereas theadult black-throated finch had mature plumage and was >180 days old at the time of death.The finch specimens were initially stored frozen. All specimens were chemically fixed in 10%neutral buffered formalin for ∼2 weeks. Specimens were then pre-stain CT-scanned to captureskull morphologies, using grayscale thresholding in Avizo© version9.0, 9.3, and 9.5  to generate skeletal models.Computed tomographic data were collected on a GE phoenixv|tome|x s240 high-resolution microfocusCT system  at the American Museum of Natural HistoryMicroscopy and Imaging Facility  and on a Nikon XTH 225 ST high-resolutionmicrofocus CT system  at DENTSPLY’s Research and Design Facility .All unstained specimens were scanned at resolutions of <70-μ isometric voxel sizes toobtain the degree of detail necessary to identify bony landmarks. All scanning parametersare listed in One fledgling (unknown sex) and one adult female black-throated finch (2KI) for 10 or 14 days  of Lugol’s iodine  to calculate the mass of each jaw muscle. The left-sidemusculature was reconstructed in both specimens for consistency. The following muscles were3D rendered for the black-throated finch based on work by Musculus adductor mandibulae externus caudalis(MAMEC); M. adductor mandibulae externus rostralis lateralis (MAMERL);M. adductor mandibulae externus rostralis medialis (MAMERM); M.adductor mandibulae externus rostralis temporalis (MAMERT); M. adductormandibulae externus ventralis (MAMEV); M. adductor mandibulaecaudalis (MAMC); M. depressor mandibulae (MDM); M.protractor pterygoideus (MPP); M. protractor quadratus (MPQ);M. pseudotemporalis profundus (MPsP); M. pseudotemporalissuperficialis (MPsS); M. pterygoideus dorsalis (MPtD); andM. pterygoideus ventralis (MPtV) , using the “Measurements” tool. During image-stack processing, weutilized Fiji  to crop, rotate, and re-slicethe global axes of the image stack so that they were orthogonal in the standard anatomicalplanes. Following segmentation of the pre-injection skeletal scans, the diceCT image stackswere processed secondarily. The anatomy of the skull and left-side jaw musculature wasmanually reconstructed in Avizo based on grayscale value differences. Each muscle was firstdelineated in the plane that was easiest to discern and evaluate, which in this case was thetransverse plane, to differentiate it from adjacent muscle bellies before more thoroughsegmentation was performed. The initial step generated a tubular schematic of muscles andtheir attachment points. A more thorough segmentation was then performed on each muscleusing a combination of the “Brush” tool and the “Interpolation” tool. When the thoroughsegmentation in one plane of view was performed, at least one other view was simultaneouslymonitored in order to identify, corroborate, and confirm the muscle boundaries seen in theprimary plane view. Muscle boundaries were determined based on sharp differentiation betweengrayscale values that usually denotes muscles and dense, unstained connective tissues ormuscles and bones/cartilage  and os opticus  fiber morphologies  in comparison to thefledgling specimen of the same species. Several lines of reasoning supported that the CTimage stack of the adult finch did not have any apparent distortion that could be due tomuscle shrinkage from the iodine staining. In the scan, the muscle fibers did not appearstraightened and “rigid,” and no prominent gaps were apparent between adjacent muscle fiberbundles. The space between the jaw muscles of the fledgling were more prominent than in theadult, but the fledgling’s brain tissues are still flushed against the cranial cavity see.LikewisOther factors that could have contributed to this mass difference might be intraspecificdifferences such that the adult female we sampled was a particularly small individual orthat the fledgling was a particularly large individual. Because this species is not reportedas sexually dimorphic, we interpret that the sex of the fledgling should not have been afactor regarding overall mass differences. However, breeding conditions might be animportant consideration for the adult. Other female songbirds, such as house wrens, havebeen reported to lose mass during breeding season either for contributing body tissues tooffspring production  or due tBetter criteria for specimen collection, including information about sex and in what seasonthe specimen was collected, is stressed for collecting small songbirds because slightdifferences can contribute to apparently significant changes. Further research shouldincorporate additional specimens —including hatchlings, additionalfledglings, and male and female adults—to fully explore these findings. These factors arealso important considering that digital dissection is a time-consuming process, and the lownumber of individuals in this study limits some interpretations. Digital dissection speedcan be improved by utilizing interpolation tools  along wiobab007_Supplementary_DataClick here for additional data file.


Motivation: SNP-based kinship analysis with genome-wide relationship estimation and IBD segment analysis methods produces results that often require further downstream process- ing and manipulation. A dedicated software package that consistently and intuitively imple- ments this analysis functionality is needed.Results: Here we present the skater R package for SNP-based kinship analysis, testing, and evaluation with R. The skater package contains a suite of well-documented tools for importing, parsing, and analyzing pedigree data, performing relationship degree inference, benchmarking relationship degree classification, and summarizing IBD segment data.Availability: The skater package is implemented as an R package and is released under the MIT license at https://github.com/signaturescience/skater. Documentation is available at https://signaturescience.github.io/skater. R version: R version 4.0.4 (2021-02-15)skater package version: 0.1.0 or KING, and methods that rely on identity by descent (IBD) segment detection, such as GERMLINE, hap-IBD, and IBIS. Recent efforts focusing on benchmarking these methods have been aided by tools for simulating pedigrees and genome-wide SNP data. Analyzing results from genome-wide SNP-based kinship analysis or comparing analyses to simulated data for benchmarking have to this point required writing one-off analysis functions or utility scripts that are seldom distributed with robust documentation, test suites, or narrative examples of usage. There is a need in the field for a well-documented software package with a consistent design and API that contains functions to assist with downstream manipulation, benchmarking, and analysis of SNP-based kinship assessment methods. Here we present the skater package forSNP-basedkinshipanalysis,testing, andevaluation withR.Inferring familial relationships between individuals using genetic data is a common practice in population genetics, medical genetics, and forensics. There are multiple approaches to estimating relatedness between samples, including genome-wide measures, such as those implemented in PlinkUse Cases section below. The package adheres to “tidy” data analysis principles, and builds upon the tools released under the tidyverse R ecosystem.The skater package provides an intuitive collection of analysis and utility functions for SNP-based kinship analysis. Functions in the package include tools for importing, parsing, and analyzing pedigree data, performing relationship degree inference, benchmarking relationship degree classification, and summarizing IBD segment data, described in full in thehttp://CRAN.R-project.org/package=skater. Users can install skater in R by executing the following code:install.packages("skater")The skater package is hosted in the Comprehensive R Archive Network (CRAN) which is the main repository for R packages:https://github.com/signaturescience/skater. The development version may contain new features which are not yet available in the version hosted on CRAN. This version can be installed using theinstall_github function in the devtools package:install.packages("devtools")devtools::install_githubAlternatively, the development version of skater is available on GitHub atWhen installing skater, other packages which skater depends on are automatically installed, including magritr, tibble, dplyr, tidyr, readr, purrr, kinship2, corrr, rlang, and others. that many R users will already have installed. Use cases are demonstrated in detail below. In summary, the skater package has functions for:•Reading in various output files produced by commonly used tools in SNP-based kinship analysis•Pedigree parsing, manpulation, and analysis•Relationship degree inference•Benchmarking and assessing relationship classification accuracy•IBD segment analysis post-processingMinimal system requirements for installing and using skater include R (version 3.0.0 or higher) and several tidyverse packageshttps://signaturescience.github.io/skater/.A comprehensive reference for all the functions in the skater package is available atskater package provides a collection of analysis and utility functions forSNP-basedkinshipanalysis,testing, andevaluation as anR package. Functions in the package include tools for working with pedigree data, performing relationship degree inference, assessing classification accuracy, and summarizing IBD segment data.library(skater)The and other genetic analysis tools is the.fam file. A.fam file is a tabular format with one row per individual and columns for unique IDs of the mother, father, and the family unit. The package includesread_fam to read files in this format:famfile <- system.filefam <- read_fam(famfile)fam## # A tibble: 64 x 6##   fid    id          dadid        momid       sex affected##   <chr>   <chr>        <chr>        <chr>      <int>   <int>## 1  testped1 testped1_g1-b1-s1 0          0           1     1## 2  testped1 testped1_g1-b1-i1 0          0           2     1## 3  testped1 testped1_g2-b1-s1 0          0           1     1## 4  testped1 testped1_g2-b1-i1 testped1_g1-b1-s1 testped1_g1-b1-i1 2     1## 5  testped1 testped1_g2-b2-s1 0          0           1     1## 6  testped1 testped1_g2-b2-i1 testped1_g1-b1-s1 testped1_g1-b1-i1  2     1## 7  testped1 testped1_g3-b1-i1 testped1_g2-b1-s1 testped1_g2-b1-i1  2     1## 8  testped1 testped1_g3-b2-i1 testped1_g2-b2-s1 testped1_g2-b2-i1  1     1## 9  testped2 testped2_g1-b1-s1 0          0           2     1## 10 testped2 testped2_g1-b1-i1 0          0           1     1## #… with 54 more rowsPedigrees define familial relationships in a hierarchical structure. One of the common formats used by PLINK.fam formated files can then be translated to thepedigree structure used by thekinship2 package. The “fam” format may include multiple families, and thefam2ped function will collapse them all into atibble with one row per family:peds <- fam2ped(fam)peds## # A tibble: 8 x 3##   fid    data          ped##   <chr>   <list>        <list>## 1 testped1 <tibble [8 x 5]> <pedigree>## 2 testped2 <tibble [8 x 5]> <pedigree>## 3 testped3 <tibble [8 x 5]> <pedigree>## 4 testped4 <tibble [8 x 5]> <pedigree>## 5 testped5 <tibble [8 x 5]> <pedigree>## 6 testped6 <tibble [8 x 5]> <pedigree>## 7 testped7 <tibble [8 x 5]> <pedigree>## 8 testped8 <tibble [8 x 5]> <pedigree>Family structures imported fromtibble is nested by family ID. Thedata column contains the individual family information, while theped column contains the pedigree object for that family. Using standard tidyverse operations, the resulting tibble can be unnested for any particular family:peds %>% dplyr::filter(fid=="testped1") %>% tidyr::unnest(cols=data)## # A tibble: 8 x 7##   fid    id           dadid        momid   sex affected   ped##   <chr>   <chr>        <chr>        <chr>  <int>   <dbl> <list>## 1 testped1 testped1_g1-b1-s1 <NA>         <NA>      1       1 <pedig~## 2 testped1 testped1_g1-b1-i1 <NA>         <NA>      2       1 <pedig~## 3 testped1 testped1_g2-b1-s1 <NA>         <NA>      1       1 <pedig~## 4 testped1 testped1_g2-b1-i1 testped1_g1-b1-s1 testped1_~  2       1 <pedig~## 5 testped1 testped1_g2-b2-s1 <NA>         <NA>      1       1 <pedig~## 6 testped1 testped1_g2-b2-i1 testped1_g1-b1-s1 testped1_~  2       1 <pedig~## 7 testped1 testped1_g3-b1-i1 testped1_g2-b1-s1 testped1_~  2       1 <pedig~## 8 testped1 testped1_g3-b2-i1 testped1_g2-b2-s1 testped1_~  1       1 <pedig~In the example above, the resultingmar orcex can be used to adjust aesthetics):peds$ped[[1]]## Pedigree object with 8 subjects## Bit size= 4plot, cex=.7)A single pedigree can also be inspected or visualized Theped2kinpair function takes a pedigree object and produces a pairwise list of relationships between all individuals in the data with the expected kinship coefficients for each pair.Theped2kinpair(peds$ped[[1]])## # A tibble: 36 x 3##   id1          id2            k##   <chr>         <chr>        <dbl>##  1 testped1_g1-b1-s1 testped1_g1-b1-s1 0.5##  2 testped1_g1-b1-i1 testped1_g1-b1-s1 0##  3 testped1_g1-b1-s1 testped1_g2-b1-s1 0##  4 testped1_g1-b1-s1 testped1_g2-b1-i1 0.25##  5 testped1_g1-b1-s1 testped1_g2-b2-s1 0##  6 testped1_g1-b1-s1 testped1_g2-b2-i1 0.25##  7 testped1_g1-b1-s1 testped1_g3-b1-i1 0.125##  8 testped1_g1-b1-s1 testped1_g3-b2-i1 0.125##  9 testped1_g1-b1-i1 testped1_g1-b1-i1 0.5## 10 testped1_g1-b1-i1 testped1_g2-b1-s1 0## # … with 26 more rowsThe function can be run on a single family:kinpairs <- peds %>% dplyr::mutate) %>% dplyr::select %>% tidyr::unnest(cols=pairs)kinpairs## # A tibble: 288 x 4##   fid     id1         id2            k##   <chr>    <chr>        <chr>        <dbl>##  1 testped1 testped1_g1-b1-s1 testped1_g1-b1-s1 0.5##  2 testped1 testped1_g1-b1-i1 testped1_g1-b1-s1 0##  3 testped1 testped1_g1-b1-s1 testped1_g2-b1-s1 0##  4 testped1 testped1_g1-b1-s1 testped1_g2-b1-i1 0.25##  5 testped1 testped1_g1-b1-s1 testped1_g2-b2-s1 0##  6 testped1 testped1_g1-b1-s1 testped1_g2-b2-i1 0.25##  7 testped1 testped1_g1-b1-s1 testped1_g3-b1-i1 0.125##  8 testped1 testped1_g1-b1-s1 testped1_g3-b2-i1 0.125##  9 testped1 testped1_g1-b1-i1 testped1_g1-b1-i1 0.5## 10 testped1 testped1_g1-b1-i1 testped1_g2-b1-s1 0## # … with 278 more rowsThis function can also be mapped over all families in the pedigree:ped2kinpair over allped objects in the inputtibble, and that relationships are not shown for between-family relationships.Note that this mapsped2kinpair or other kinship estimation software.The skater package includes functions to translate kinship coefficients to relationship degrees. The kinship coefficients could come fromdibble function creates adegreeinferencetibble, with degrees up to the specifiedmax_degree (default=3), expected kinship coefficient, and lower (l) and upper (u) inference ranges as defined in Manichaikul et al. Degree 0 corresponds to self/identity/monozygotic twins, with an expected kinship coefficient of 0.5, with inference range >=0.354. Anything beyond the maximum degree resolution is considered unrelated (degreeNA). Note also that while the theoretical upper boundary for the kinship coefficient is 0.5, the inference range for 0-degree  extends to 1 to allow for floating point arithmetic and stochastic effects resulting in kinship coefficients above 0.5.dibble## # A tibble: 5 x 4##  degree   k     l     u##  <int>  <dbl>   <dbl>  <dbl>## 1   0 0.5    0.354  1## 2   1 0.25   0.177  0.354## 3   2 0.125   0.0884  0.177## 4   3 0.0625  0.0442 0.0884## 5  NA 0     -1     0.0442Themax_degree default is 3. Change this argument to allow more granular degree inference ranges:dibble (max_degree = 5)## # A tibble: 7 x 4##  degree   k    l    u##  <int>  <dbl>  <dbl>  <dbl>## 1   0 0.5   0.354  1## 2   1 0.25  0.177  0.354## 3   2 0.125  0.0884 0.177## 4   3 0.0625 0.0442 0.0884## 5   4 0.0312 0.0221 0.0442## 6   5 0.0156 0.0110 0.0221## 7  NA 0    -1     0.0110The degree inferencedibble function will emit a warning withmax_degree >=10, and will stop with an error at >=12.Note that the distance between relationship degrees becomes smaller as the relationship degree becomes more distant. Thekin2degree function infers the relationship degree given a kinship coefficient and amax_degree up to which anything more distant is treated as unrelated. Example first degree relative:kin2degree## [1] 1Thekin2degree## [1] NAExample 4th degree relative, but using the default max_degree resolution of 3:kin2degree## [1] 4Example 4th degree relative, but increasing the degree resolution:kin2degree function is vectorized over values ofk, so it can be used inside of amutate on atibble of kinship coefficients:# Get two pairs from each type of relationship we have in kinpairs:kinpairs_subset <- kinpairs %>% dplyr::group_by(k) %>% dplyr::slice(1:2)kinpairs_subset## # A tibble: 10 x 4## # Groups:  k [5]##   fid     id1          id2             k##   <chr>    <chr>        <chr>          <dbl>##  1 testped1 testped1_g1-b1-i1 testped1_g1-b1-s1 0##  2 testped1 testped1_g1-b1-s1 testped1_g2-b1-s1 0##  3 testped1 testped1_g3-b1-i1 testped1_g3-b2-i1 0.0625##  4 testped2 testped2_g3-b1-i1 testped2_g3-b2-i1 0.0625##  5 testped1 testped1_g1-b1-s1 testped1_g3-b1-i1 0.125##  6 testped1 testped1_g1-b1-s1 testped1_g3-b2-i1 0.125##  7 testped1 testped1_g1-b1-s1 testped1_g2-b1-i1 0.25##  8 testped1 testped1_g1-b1-s1 testped1_g2-b2-i1 0.25##  9 testped1 testped1_g1-b1-s1 testped1_g1-b1-s1 0.5## 10 testped1 testped1_g1-b1-i1 testped1_g1-b1-i1 0.5# Infer degree out to third degree relatives:kinpairs_subset %>% dplyr::mutate )## # A tibble: 10 x 5## # Groups:  k [5]##   fid     id1          id2            k  degree##   <chr>    <chr>        <chr>         <dbl>  <int>##  1 testped1 testped1_g1-b1-i1 testped1_g1-b1-s1 0      NA##  2 testped1 testped1_g1-b1-s1 testped1_g2-b1-s1 0      NA##  3 testped1 testped1_g3-b1-i1 testped1_g3-b2-i1 0.0625    3##  4 testped2 testped2_g3-b1-i1 testped2_g3-b2-i1 0.0625    3##  5 testped1 testped1_g1-b1-s1 testped1_g3-b1-i1 0.125    2##  6 testped1 testped1_g1-b1-s1 testped1_g3-b2-i1 0.125    2##  7 testped1 testped1_g1-b1-s1 testped1_g2-b1-i1 0.25     1##  8 testped1 testped1_g1-b1-s1 testped1_g2-b2-i1 0.25     1##  9 testped1 testped1_g1-b1-s1 testped1_g1-b1-s1 0.5     0## 10 testped1 testped1_g1-b1-i1 testped1_g1-b1-i1 0.5     0TheOnce estimated kinship is converted to degree, it may be of interest to compare the inferred degree to truth. When aggregated over many relationships and inferences, this approach can help benchmark performance of a particular kinship analysis method.confusion_matrix function from Clark to provide standard contingency table metrics  with a new reciprocal RMSE (R-RMSE) metric. Theconfusion_matrix function on its own outputs a list with four objects:1.tibble with calculated accuracy, lower and upper bounds, the guessing rate and p-value of the accuracy vs. the guessing rate.A2.tibble with contingency table statistics calculated for each class. Details on the statistics calculated for each class can be reviewed on the help page for?confusion_matrix.A3.matrix with the contingency table object itself.A4.vector with the reciprocal RMSE (R-RMSE). The R-RMSE represents an alternative to classification accuracy when benchmarking relationship degree estimation and is calculated using the formula in (1). Taking the reciprocal of the target and predicted degree results in larger penalties for more egregious misclassifications  than misclassifications at more distant relationships . The +0.5 adjustment prevents division-by-zero when a 0th-degree  relative pair is introduced.AThe skater package adapts akinpairs data from above and randomly flip ~20% of the true relationship degrees:# Function to randomly flip levels of a factor randomflip <- function ifelse(runif(length(x))<p, sample(unique(x)), x)# Infer degree (truth/target) using kin2degree, then randomly flip 20% of themset.seed(42)kinpairs_inferred <- kinpairs %>% dplyr::mutate) %>% dplyr::mutate) %>% dplyr::mutate(degree_inferred=randomflip (degree_truth))kinpairs_inferred## # A tibble: 288 x 6##   fid    id1          id2         k    degree_truth degree_inferred##   <chr>   <chr>        <chr>        <dbl> <chr>     <chr>## 1 testped1 testped1_g1-b1-s1 testped1_g1-b1-s1 0.5  0       0## 2 testped1 testped1_g1-b1-i1 testped1_g1-b1-s1 0   unrelated  unrelated## 3 testped1 testped1_g1-b1-s1 testped1_g2-b1-s1 0   unrelated  unrelated## 4 testped1 testped1_g1-b1-s1 testped1_g2-b1-i1 0.25 1       1## 5 testped1 testped1_g1-b1-s1 testped1_g2-b2-s1 0   unrelated  unrelated## 6 testped1 testped1_g1-b1-s1 testped1_g2-b2-i1 0.25 1       1## 7 testped1 testped1_g1-b1-s1 testped1_g3-b1-i1 0.125 2       2## 8 testped1 testped1_g1-b1-s1 testped1_g3-b2-i1 0.125 2       1## 9 testped1 testped1_g1-b1-i1 testped1_g1-b1-i1 0.5  0       0## 10 testped1 testped1_g1-b1-i1 testped1_g2-b1-s1 0   unrelated  unrelated## #… with 278 more rowsTo illustrate the usage, this example will start with theconfusion_matrix function will return all four objects noted above:confusion_matrix## $Accuracy## # A tibble: 1 x 5##  Accuracy ‘Accuracy LL‘ ‘Accuracy UL‘ ‘Accuracy Guessing‘ ‘Accuracy P-value‘##    <dbl>      <dbl>      <dbl>          <dbl>          <dbl>## 1   0.812      0.763      0.856          0.333        1.09e-62#### $Other## # A tibble: 6 x 15##   Class      N ‘Sensitivity/Re~ ‘Specificity/TN~ ‘PPV/Precision‘ NPV  ‘F1/Dice‘##   <chr>  <dbl>        <dbl>        <dbl>      <dbl> <dbl>   <dbl>## 1 0     64         0.75        0.964      0.857 0.931   0.8## 2 1     72         0.806        0.944      0.829 0.936   0.817## 3 2     48         0.833        0.967      0.833 0.967   0.833## 4 3      8         0.75        0.936       0.25  0.992   0.375## 5 unrelated 96         0.854        0.958      0.911 0.929   0.882## 6 Average  57.6        0.799        0.954      0.736 0.951   0.741## # … with 8 more variables: Prevalence <dbl>, Detection Rate <dbl>,## #  Detection Prevalence <dbl>, Balanced Accuracy <dbl>, FDR <dbl>, FOR <dbl>,## #  FPR/Fallout <dbl>, FNR <dbl>#### $Table##       Target## Predicted  0  1  2 3 unrelated##  0      48  4  2 1      1##  1      5 58  4 0      3##  2      0  3 40 1      4##  3      8  4  0 6      6##  unrelated 3  3  2 0     82#### $recip_rmse## [1] 0.4665971Next, running thepurrr::pluck can be used to isolate just the contingency table:confusion_matrix %>% purrr::pluck("Table")##       Target## Predicted  0  1  2 3 unrelated##  0      48  4  2 1      1##  1      5 58  4 0      3##  2      0  3 40 1       4##  3      8  4  0 6       6##  unrelated 3  3  2 0      82Standard tidyverse functions such asconfusion_matrix function includes an argument to output in a tidy (longer=TRUE) format, and the example below illustrates how to spread contingency table statistics by class:confusion_matrix %>% purrr::pluck("Other") %>% tidyr::spread  %>% dplyr::relocate  %>% dplyr::mutate_if## # A tibble: 14 x 7##   Statistic           ‘0‘   ‘1‘   ‘2‘   ‘3‘  unrelated  Average##   <chr>             <dbl>  <dbl>  <dbl>  <dbl>    <dbl>   <dbl>##  1 Balanced Accuracy      0.86  0.88  0.9   0.84     0.91   0.88##  2 Detection Prevalence    0.19  0.24  0.17  0.083    0.31   0.2##  3 Detection Rate       0.17  0.2   0.14  0.021    0.28   0.16##  4 F1/Dice            0.8   0.82  0.83  0.38     0.88   0.74##  5 FDR              0.14  0.17  0.17  0.75     0.089   0.26##  6 FNR              0.25  0.19  0.17  0.25     0.15   0.2##  7 FOR              0.069  0.064  0.033  0.0076    0.071   0.049##  8 FPR/Fallout         0.036  0.056  0.033  0.064     0.042   0.046##  9 N              64   72   48    8      96    58## 10 NPV              0.93  0.94  0.97  0.99     0.93   0.95## 11 PPV/Precision        0.86  0.83  0.83  0.25     0.91   0.74## 12 Prevalence          0.22  0.25  0.17  0.028    0.33   0.2## 13 Sensitivity/Recall/TPR  0.75  0.81  0.83  0.75     0.85   0.8## 14 Specificity/TNR       0.96  0.94  0.97  0.94     0.96   0.95The and IBIS detect shared IBD segments between individuals. The skater package includes functionality to take those IBD segments, compute shared genomic centimorgan (cM) length, and converts that shared cM to a kinship coefficient. In addition to inferred segments, these functions can estimate “truth” kinship from simulated IBD segments. Theread_ibd function reads pairwise IBD segments from IBD inference tools and from simulated IBD segments. Theread_map function reads in genetic map in a standard format which is required to translate the total centimorgans shared IBD to a kinship coefficient using theibd2kin function. See?read_ibd and?read_map for additional details on expected format.Tools such as hap-IBD,read_ibd function reads in the pairwise IBD segment format. Input to this function can either be inferred IBD segments from hap-IBD (source="hapibd") or simulated segments (source="pedsim"). The first example below uses data in thehap-ibd output format:hapibd_filepath <- system.filehapibd_seg <- read_ibdhapibd_seg## # A tibble: 3,954 x 6##   id1          id2           chr   start    end  length##   <chr>         <chr>        <dbl>   <dbl>   <dbl>  <dbl>##  1 testped1_g1-b1-s1 testped1_g3-b1-i1   1 197661576 234863602  47.1##  2 testped1_g2-b2-i1 testped1_g3-b1-i1   1 197661576 231017545  39.8##  3 testped1_g3-b1-i1 testped1_g3-b2-i1   1 197661576 212799139  20.3##  4 testped3_g1-b1-s1 testped3_g3-b2-i1   1  2352146  10862397  17.7##  5 testped3_g2-b2-i1 testped3_g3-b2-i1   1  2352146  10862397  17.7##  6 testped1_g1-b1-s1 testped1_g2-b1-i1   1  3328659  64123868  86.4##  7 testped1_g1-b1-s1 testped1_g3-b1-i1   1  3328659  33476811  51.2##  8 testped1_g2-b2-s1 testped1_g3-b2-i1   1  5003504  32315147  45.9##  9 testped2_g1-b1-i1 testped2_g3-b1-i1   1 240810528 248578622  15.9## 10 testped2_g1-b1-i1 testped2_g2-b2-i1   1 241186056 249170711  15.5## # … with 3,944 more rowsTheread_map. Software for IBD segment inference and simulation requires a genetic map. The map loaded for kinship estimation should be the same one used for creating the shared IBD segment output. The example below uses a minimal genetic map that ships withskater:gmap_filepath <- system.filegmap <- read_map(gmap_filepath)gmap## # A tibble: 28,726 x 3##    chr value      bp##   <dbl> <dbl>    <dbl>##  1   1 0     752721##  2   1 0.0292 1066029##  3   1 0.0829 1099342##  4   1 0.157  1106473##  5   1 0.246  1152631##  6   1 0.294  1314015##  7   1 0.469  1510801##  8   1 0.991  1612540##  9   1 1.12  1892325## 10   1 1.41  1916587## # … with 28,716 more rowsIn order to translate the shared genomic cM length to a kinship coefficient, a genetic map must first be read in withibd2kin function takes the segments and map file and outputs atibble with one row per pair of individuals and columns for individual 1 ID, individual 2 ID, and the kinship coefficient for the pair:ibd_dat <- ibd2kinibd_dat## # A tibble: 196 x 3##   id1          id2           kinship##   <chr>         <chr>          <dbl>##  1 testped1_g1-b1-i1 testped1_g1-b1-s1 0.000316##  2 testped1_g1-b1-i1 testped1_g2-b1-i1 0.261##  3 testped1_g1-b1-i1 testped1_g2-b2-i1 0.263##  4 testped1_g1-b1-i1 testped1_g2-b2-s1 0.000150##  5 testped1_g1-b1-i1 testped1_g3-b1-i1 0.145##  6 testped1_g1-b1-i1 testped1_g3-b2-i1 0.133##  7 testped1_g1-b1-i1 testped2_g1-b1-i1 0.000165##  8 testped1_g1-b1-i1 testped2_g1-b1-s1 0.000323##  9 testped1_g1-b1-i1 testped2_g2-b1-i1 0.000499## 10 testped1_g1-b1-i1 testped2_g2-b1-s1 0.000318## # … with 186 more rowsTheThe skater R package provides a robust software package for data import, manipulation, and analysis tasks typically encountered when working with SNP-based kinship analysis tools. All package functions are internally documented with examples, and the package contains a vignette demonstrating usage, inputs, outputs, and interpretation of all key functions. The package contains internal tests that are automatically run with continuous integration via GitHub Actions whenever the package code is updated. The skater package is permissively licensed (MIT) and is easily extensible to accommodate outputs from new genome-wide relatedness and IBD segment methods as they become available.1.http://CRAN.R-project.org/package=skater.Software available from:2.https://github.com/signaturescience/skater.Source code available from:3.https://doi.org/10.5281/zenodo.5761996.Archived source code at time of publication:4.Software license: MIT License.SDT, VPN, and MBS developed the R package.All authors contributed to method development.SDT wrote the first draft of the manuscript.All authors assisted with manuscript revision.All authors read and approved the final manuscript.No competing interests were disclosed.This work was supported in part by award 2019-DU-BX-0046 (Dense DNA Data for Enhanced Missing Persons Identification) to B.B., awarded by the National Institute of Justice, Office of Justice Programs, U.S. Department of Justice and by internal funds from the Center for Human Identification. The opinions, findings, and conclusions or recommendations expressed are those of the authors and do not necessarily reflect those of the U.S. Department of Justice. This article describes an R package that provides a suite of tools for performing kinship and IBD segment analysis. Key features include methods for reading and analyzing pedigree data, inferring relationships, benchmarking relationship degree classification, and IBD segment analyses. The use cases in this article and the R reference manual on CRAN together provide exemplary documentation for skater. Users unfamiliar with pedigree-style analyses and/or who desire a clean, well documented interface for performing these analyses can find a great deal of utility in this R package.Are the conclusions about the tool and its performance adequately supported by the findings presented in the article?YesIs the rationale for developing the new software tool clearly explained?YesIs the description of the software tool technically sound?YesAre sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others?YesIs sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool?YesReviewer Expertise:Relatedness in large datasets.I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. ad hoc scripts for parsing, cleaning, and manipulating various result files. Arguably, the best solution to this problem is to collect related scripts into an R package, which can be properly documented, version-controlled, and potentially shared with others. Theskater package presented in this paper is precisely such a package, aimed at the users of several popular software for relatedness estimation, including KING, PLINK, hap-IBD, and others. A specific goal of the package is to provide tools that work consistently across these programs.For many researchers, myself included, R is the language-of-choice when it comes to downstream analysis and tidying-up the output of other programs. This inevitably leads to a disorganized body of The paper is written in clear language, well structured, and easy to follow. Parts of the paper resemble a user manual, including detailed code examples. I enjoyed playing around with the package, which is well organized and documented. In particular, I appreciate the thorough input checks and helpful error messages when things go wrong. The package may not offer a ton of novel methods yet, but I can certainly see myself using it the next time I run some of these relatedness programs.It would be helpful to clarify what sort of applications skater is intended for. The introduction mentions "relationship inference in population genetics, medical genetics, and forensics", but that seems overly broad. The focus is on simple measures like the kinship coefficient and relatedness degree  suggesting large-scale applications rather than small-scale pedigree analysis as e.g. in forensic case work. Indeed, all the programs referenced in the paper are intended for large-scale population studies. Furthermore, it seems to be an underlying assumption that all individuals are noninbred , and also that they are human (by the hardcoded 3560 cM genome length). To be clear, I think these limitations and assumptions are perfectly fine, but it would be better to state (or discuss) them more clearly.A connection to "SNP-based methods" is mentioned repeatedly, but never really explained. Is there one? I note at least one of the cited programs (hap-IBD) is designed to work with sequencing data. Does it matter for skater how the kinship estimates were obtained?The function `confusion_matrix` produces an impressive array of summary stats when comparing inferred kinship degrees to true ones. This is great, but I wonder about the origin of this function. In my understanding, it is a modified version of a function from another package, confusionMatrix, written by Michael Clark (not an author of the paper). Several related functions appear to be copied verbatim into skater. All of this is clearly marked in the code, with Clark rightfully listed as author, and Clark's package does come with a permissive license (MIT), but I still find it a bit odd. Why not simply import it? That way, bug fixes and improvements in the original version would automatically propagate to skater, avoiding the confusion of multiple versions. If the problem is that confusionMatrix is not on CRAN, perhaps one could reach out to make this happen?In the example illustrating `confusion_matrix`, the authors construct a dataset by modifying a previous one, by "randomly flipping ~20% of the true degrees". The term "flip" sounds misplaced to me here, since the variable isn't dichotomous. Also, the procedure doesn't change 20%, only 0.2 * 4/5 = 16% on average, since the new values are generated from the complete set and stay unchanged with probability 1/5. Regardless of these minor issues, I must admit I found the example rather artificial. It would be much more interesting to see confusion_matrix applied to a real dataset! That could also motivate some comments on how its output should be interpreted. Finally, I cannot resist offering a couple of suggestions for the skater package itself:The `read_fam` for reading .fam files is very strict, insisting on space-separated columns and disallowing any format deviations. I note that e.g. PLINK, and several other R packages that read pedigree files, allow variations like tab-separated columns and missing phenotype column; perhaps this could be useful in the skater package too.Separating parent-offspring pairs from full sibs is a crucial step in many relatedness studies, and often possible by a simple analysis of the output of e.g. KING. Does skater offer such differentiation? If not, it might make a nice addition to the package in the future. Below are a few issues I have with the manuscript.Are the conclusions about the tool and its performance adequately supported by the findings presented in the article?PartlyIs the rationale for developing the new software tool clearly explained?YesIs the description of the software tool technically sound?YesAre sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others?YesIs sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool?YesReviewer Expertise:Statistical genetics, pedigree analysis, RI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. The authors developed an R package to evaluate the performance of different relatedness inference tools. Both the R package and the manuscript are user-friendly and easy to follow. This tool should be timely and valuable to geneticists  who have a need to compare different relatedness inference tools and choose a tool that performs the best for their own genetic datasets. The manuscript is very well written.How about only one parent available, with Father ID 1234 and mother ID 0? This is allowed and considered as PLINK format. How does the proposed tool handle it, e.g., ask the user to reformat it?How about both parents only with IDs available. E.g., Father ID is 1234 and mother ID is 5678, and none of them have appeared again in Individual IDs?  Again, the comment above is quite minor and may only require some clarification. The overall quality of this manuscript is great. One very minor comment is about the format requirement for the developed tool. Is the required format basically PLINK with expectations that some commonly used PLINK formats are still not allowed, or all PLINK format is acceptable? E.g.,Are the conclusions about the tool and its performance adequately supported by the findings presented in the article?YesIs the rationale for developing the new software tool clearly explained?YesIs the description of the software tool technically sound?YesAre sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others?YesIs sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool?YesReviewer Expertise:My area of research is statistical genetics, specifically developing methods and tools for relatedness inference.I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.


Scientific Reports 10.1038/srep20968, published online 10 February 2016Correction to: This Article contains errors in Figure 4 and 6.In the assembly of Figure 4B, images from Figure 2C were inadvertently incorporated for the “shRNA-sfrp2”, “shRNA-sfrp2 + miR218”, and “miR218 inhibitor” panels.For Figure 4C, the flow cytometry data for the “shRNA-sfrp2 + miRNA217 inhibitor” and “miR218 inhibitor” panels were incorrect.In Figure 6A, incorrect images were used for the “mimic con” condition.The correct Figures


MicroRNAs (miRNAs) participate in the reactivation of γ‐globin expression in β‐thalassemia. However, the miRNA transcriptional profiles of pediatric β‐thalassemia remain unclear. Accordingly, in this study, we assessed miRNA expression in pediatric patients with β‐thalassemia.Differentially expressed miRNAs in pediatric patients with β‐thalassemia were determined using microRNA sequencing.Hsa‐miR‐483‐3p, hsa‐let‐7f‐1‐3p, hsa‐let‐7a‐3p, hsa‐miR‐543, hsa‐miR‐433‐3p, hsa‐miR‐4435, hsa‐miR‐329‐3p, hsa‐miR‐92b‐5p, hsa‐miR‐6747‐3p and hsa‐miR‐495‐3p were significantly upregulated, whereas hsa‐miR‐4508, hsa‐miR‐20a‐5p, hsa‐let‐7b‐5p, hsa‐miR‐93‐5p, hsa‐let‐7i‐5p, hsa‐miR‐6501‐5p, hsa‐miR‐221‐3p, hsa‐let‐7g‐5p, hsa‐miR‐106a‐5p, and hsa‐miR‐17‐5p were significantly downregulated in pediatric patients with β‐thalassemia. After integrating our data with a previously published dataset, we found that hsa‐let‐7b‐5p and hsa‐let‐7i‐5p expression levels were also lower in adolescent or adult patients with β‐thalassemia. The predicted target genes of hsa‐let‐7b‐5p and hsa‐let‐7i‐5p were associated with the transforming growth factor β receptor, phosphatidylinositol 3‐kinase/AKT, FoxO, Hippo, and mitogen‐activated protein kinase signaling pathways. We also identified 12 target genes of hsa‐let‐7a‐3p and hsa‐let‐7f‐1‐3p and 21 target genes of hsa‐let‐7a‐3p and hsa‐let‐7f‐1‐3p, which were differentially expressed in patients with β‐thalassemia. Finally, we found that hsa‐miR‐190‐5p and hsa‐miR‐1278‐5p may regulate hemoglobin switching by modulation of the B‐cell lymphoma/leukemia 11A gene.The results of the study show that several microRNAs are dysregulated in pediatric β‐thalassemia. Further, the results also indicate toward a critical role of let7 miRNAs in the pathogenesis of pediatric β‐thalassemia, which needs to be investigated further. p‐value <0.05, 111 microRNAs were upregulated in β‐thalassemia patients, while 85 microRNAs were downregulated in β‐thalassemia patients. Those microRNAs could clearly distinguish the normal cohorts from the β‐thalassemia patients. Hsa‐miR‐2100‐3p, hsa‐microRNA‐15a‐5p, hsa‐microRNA‐16‐5p, and hsa‐miR‐503‐5p were all downregulated in pediatric β‐thalassemia patients. We also found five let7 microRNAs hsa‐let‐7b‐5p, hsa‐let‐7i‐5p, hsa‐let‐7f‐5p, hsa‐let‐7e‐5p, and hsa‐let‐7d‐5p and were downregulated in pediatric thalassemia patients.We performed microRNA sequencing to identify the microRNA expression profiling of pediatric β‐thalassemia. Totally, 530 microRNAs were identified. Based on criteria of fold change >1.5 and  The red cells were lysed using PAXgene Blood RNA Kit. The remaining mononuclear cells were used for further RNA isolation. The information and clinical conditions of the participants were also collected.2.2Total RNA from mononuclear cells was isolated using a miRNeasy Mini Kit (Qiagen) according to the manufacturer's protocol. Briefly, mononuclear cells were lysed using lysis reagent, and 140 μl chloroform was added. The upper aqueous phase was then mixed with 100% ethanol, and the mixture was transferred to the column, washed, and eluted with RNase‐free water.2.3Total RNA was used to prepare the miRNA sequencing library. After linker ligation, cDNA synthesis, and polymerase chain reaction (PCR) amplification, 135–155‐bp PCR amplification fragments were selected. The library was denatured into single‐stranded DNA, captured on an Illumina flow cell, amplified into clusters, and sequenced for 51 cycles using an Illumina NextSeq 500 sequencer (Illumina).2.4http://bioconductor.org/packages/release/bioc/html/edgeR.html) in R statistics software,p value less than 0.05, and CPM greater than or equal to 1.After sequencing, Solexa Chastity software was used for quality control. The linkers were removed using Cutadapt,2.5https://github.com/CJ‐Chen/TBtools), respectively.Volcano plots and Venn diagrams were generated using Fancy Volcano Plot and Wonderful Venn in TBtools software  in R statistics software.Unsupervised heatmaps were generated using “pheatmap” package http://www.targetscan.org/vert_72/)The targets of miRNAs were predicted using miRDB (2.8https://david.ncifcrf.gov).p < 0.05.The enriched biological processes and KEGG signaling pathways were determined using the Database for Annotation, Visualization, and Integrated Discovery Web site . Microarray expression was analyzed using R software .The gene expression matrix from patients with β‐thalassemia and normal controls was deposited in the GSE56088 dataset from the GEO Web site (2.10let7 miRNAs and predicted target genes was created using Cytoscape (http://www.cytoscape.org/).A network of 2.11t tests. Statistical significance was set at p < 0.05.Box plots were generated using GraphPad Prism software. Statistical analysis was performed using Student's 33.1Peripheral blood samples from five children diagnosed with β‐thalassemia in our hospital and five healthy children were collected to identify differentially expressed miRNAs. The clinical characteristics of β‐thalassemia and normal individuals are shown in Table Next, we performed miRNA sequencing to identify the miRNA expression profile of pediatric β‐thalassemia. In total, 530 miRNAs were identified; among these, 111 miRNAs were upregulated, whereas 85 miRNAs were downregulated in patients with β‐thalassemia. Three hundred and 330 miRNAs showed no differential expression in pediatric β‐thalassemia and normal cohorts, respectively Figure . All difhsa‐miR‐210 regulates γ‐globin expression through the transcription factor BCL11A,miR‐15a and miR‐16‐1 elevate γ‐globin expression through the transcription factor MYB.hsa‐miR‐503 has been shown to be downregulated in patients with β‐thalassemia.hsa‐miR‐2100‐3p, hsa‐miR‐15a‐5p, hsa‐miR‐16‐5p, and hsa‐miR‐503‐5p were all downregulated in pediatric patients with β‐thalassemia compared with that in normal individuals  β receptor signaling pathway, and somatic stem cell population maintenance /AKT, FoxO, Hippo, and mitogen‐activated protein kinase (MAPK) signaling pathways  of BCL11A (Figure hsa‐miR‐1278‐5p targeted the 3′ UTRs of BCL11A (Figure hsa‐miR‐190‐5p and hsa‐miR‐1278‐5p were both downregulated in patients with β‐thalassemia compared with those in normal controls (Figure BLC11A is a critical transcription factor that regulates hemoglobin switching.A Figure . In addiA Figure . Moreoves Figure .hsa‐miR‐190‐5p and hsa‐miR‐1278‐5p. In addition to BCL11A, hsa‐miR‐190‐5p also targeted ZNF99, FNDC3A, ORC4, PFDN4, ZNF382, EPC2, PHF20L1, ASAP2, WDR44, ZNF529, NHLRC2, ZFC3H1, CHMP7, YTHDF3, and TAPBP genes (Figure hsa‐miR‐190‐5p also targeted NRBF2, ZRANB2, PAX8, DNAJB14, TIPARP, LHX6, IGF2BP2, and STK40 (Figure IGF2BP2 and STK40 were also target genes of hsa‐let‐7a‐3p and hsa‐let‐7f‐1‐3p (Figure hsa‐miR‐190‐5p and hsa‐miR‐1278‐5p in hemoglobin switching and β‐thalassemia need to be studied in greater detail.We further identified the target genes of s Figure . Further0 Figure . Interesp Figure . However4let7 miRNAs, that is, hsa‐let‐7b‐5p, hsa‐let‐7i‐5p, hsa‐let‐7f‐5p, hsa‐let‐7e‐5p, and hsa‐let‐7d‐5p, may be involved in the reactivation of γ‐globin expression and HbF synthesis in pediatric and adult patients with β‐thalassemia.β‐Thalassemia is a heterogeneous disease, and the clinical manifestations of β‐thalassemia in pediatric and adult patients may be different.LIN28B/let7 axis directly regulates BCL11A expression to promote hemoglobin switching.hsa‐let‐7a and hsa‐let‐7b reactivated the expression of HbF in erythroid cells.hsa‐let‐7a was upregulated in pediatric β‐thalassemia, whereas hsa‐let‐7b was downregulated in pediatric β‐thalassemia. The functions of hsa‐let‐7i‐5p, hsa‐let‐7f‐5p, hsa‐let‐7e‐5p, and hsa‐let‐7d‐5p in the regulation of γ‐globin expression or HbF synthesis in erythroid cells have not been reported. Furthermore, we identified 21 target genes of hsa‐let‐7b‐5p and hsa‐let‐7i‐5p, which were differentially expressed in patients with β‐thalassemia. We also found that the target genes of hsa‐let‐7b‐5p and hsa‐let‐7i‐5p were associated with the PI3K/AKT, FoxO, Hippo, and MAPK signaling pathways. How those pathways involved in the pathology of β‐thalassemia should be further studied.Consistent with these observations, reports have shown that the Hsa‐miR‐210 and hsa‐let‐7b‐5p regulate γ‐globin expression through BCL11A.miR‐15a and miR‐16‐1 elevate γ‐globin expression through the transcription factor MYB.hsa‐miR‐210, hsa‐let‐7b‐5p, miR‐15a, and miR‐16‐1 were all downregulated in β‐thalassemia. We believe that our data could help to identify more miRNAs associated with the BCL11A transcription factor. Indeed, our findings showed that two miRNAs, that is, hsa‐miR‐190‐5p and hsa‐miR‐1278‐5p, may regulate hemoglobin switching by targeting BCL11A. However, the functions of these miRNAs should be studied further.BCL11A,let7 miRNAs and their target genes are abnormally dysregulated in pediatric β‐thalassemia. However, there were some limitations to the integrated analysis of the different datasets. Because of differences in cohorts and approaches, our analysis could not fully reveal the miRNA profiles associated with pediatric and adult β‐thalassemia. In addition, identification of differentially expressed miRNA target genes in patients with β‐thalassemia using the GSE56088 dataset may also have some bias. In our subsequent studies, we will collect a large cohort of β‐thalassemia cases comprising patients of different ages and perform miRNA sequencing and mRNA sequencing simultaneously. Additionally, the functions of hsa‐let‐7i‐5p, hsa‐let‐7f‐5p, hsa‐let‐7e‐5p, and hsa‐let‐7d‐5p in the regulation of γ‐globin expression will be studied in greater detail.To the best of our knowledge, this is the first study to identify differentially expressed miRNAs, particularly in pediatric β‐thalassemia. Our results suggest that The authors declare that they have no conflicts of interest.HW performed the data analysis and wrote the manuscript. MH collected the blood samples. SY, YL, and YH helped with the collection of blood samples. HL and LP designed the study and supervised the work.


With the course of this research, it will prove that subsemigroup, the set of all right h-bi-ideals, and set of all left h-bi-ideals are bands for h-regular semiring. Moreover, it will be demonstrated that if semigroup of all h-bi-ideals (B(H), ∗) is semilattice, then H is h-Clifford. This research will also explore the classification of minimal h-bi-ideal.Semigroups are generalizations of groups and rings. In the semigroup theory, there are certain kinds of band decompositions which are useful in the study of the structure of semigroups. This research will open up new horizons in the field of mathematics by aiming to use semigroup of  Primary idea of semigroup and monoid is given by Wallis . Wallis h-ideals, a new class of ideals proposed by Iizuka  ={{x1 + x2 + x3 ⋯ +xn} ∈ {u} ∪ {u2} ∪ uHu}. Then, B[u] be a subsemiring of semiring H. Further, ∀h ∈ H and x, y ∈ {u} ∪ {u2} ∪ uHu. We get xhy ∈ Hu which refers that B[u]HB[u]⊆B[u] and thus B[u] be a bi-ideal of H.Let H be a semiring and let take u from H. Then, the principal h-bi-ideal of H generated by u is given by:Let x, y∈Bh(u). Then, ∃m, n ∈ H and u ∈ H such that x + u + u2 + umu + w = u + u2 + umu + w and y + u + u2 + unu + w′ = u + u2 + unu + w′ for w ∈ H. Then, we have:H, +) is semillatice, wherexy ∈ Bh(u). Also, x + y ∈ Bh(u). Thus, Bh(u) is a subsemiring of H. Similarly, xmy ∈ Bh(u) for all x, y ∈ Bh(u) and m ∈ H. Thus, Bh(u) is a bi-ideal of H. Indeed h−closure of B[u]. Hence, Bh(u) is a h-bi-ideal of H. Let u ∈ B and let B be a h-bi-ideal of semiring H. Let x ∈ Bh(u), then there exists m ∈ H such that:Let u ∈ B implies that u + u2 + umu + w ∈ B and so, x ∈ B. Hence, Bh(u)⊆B. Thus, Bh(u) is the least h-bi-ideal of H, which contains u.Now, h-bi-ideals, the set of all left h-ideals, and the set of all right h-ideals shall be each semigroup with respect to the product of subsets of H specified in the usual way: ∗:P(H) × P(H)⟶P(H) by The set of all H. Acknowledge receipt by P(H) of all subsets of H. We represent set of all h-bi-ideal by B(H), set of all left h-ideal by ℒ(H), and set of right h-ideal by ℛ(H). We define a binary operation ∗:P(H) × P(H)⟶P(H) by Instead of defining binary operation on these three sets individually, we consider them to be semigroups of all subsets of MN = {mn | m ∈ M, n ∈ N}; so,As P(H) is a semigroup under the usual operation ∗. Also, since L1 and L2 are left h-ideal, then h-ideal. So, ∗:P(H) × P(H)⟶P(H) induced a binary operation on ℒ(H); in the interests of comfort, we signify with the same symbol “∗.” Thus, ℒ(H) is a semigroup under the defined operation “∗.” Analogously, (ℛ(H) is a semigroup under the same binary operation “∗.” Now, we can show that the same holds for B(H).We will check that M and N be the h-bi-ideals of semiring H. Let m ∈ M and n ∈ N such thatj + j′ = j′′and mn + mn = mn. Since  is semillatice, soj1 = mnj′ + jmn + jj′. ⇒u + v & uv both belong to h ∈ H, we have uhv + mnhmn + j1h = mnhmn + j1h implies that h-closure of MN & h-subset of H. Thus, h-bi-ideal of H. This shows that B(H) is a semigroup.Let S = {0, d, e, f} defined by the following tables. Now, consider the semiring h-ideal is S itself. Obviously, this h-ideal is h-bi-ideal which forms a semigroup.The only H be a semigroup, then H is said to be a regular semigroup if for each t ∈ H, there exists u′ ∈ H such that t = tu′t. Bourne [S to be regular if u′, v′ ∈ H exists for each a ∈ H that t + tu′t = tv′t. Adhikari [k-regularity; let H be a semiring, if  is a semillatice, then t + tu′t = tv′t. Adding tu′t + tv′t to both sides, that t + t(u′ + v′)t = t(u′ + v′)t. We can say that a semiring H ∈ HL+ is called k-regular if and only if for any t ∈ H, there is u′ ∈ H such that t + tu′t = tu′t.Suppose . Bourne  has descAdhikari  defined H is called h-regular if ∀t′ ∈ H, then ∃u′, v′ ∈ H such that t′ + t′u′t′ + w = t′v′t′ + w, for w ∈ H.A semiring H be a semiring, if  is a semillatice, then t + tu′t+w = tv′t + w. Adding tu′t + tv′t to both sides, then t + t(u′ + v′)t + w = t(u′ + v′)t + w. We can say that a semiring H ∈ HL+ is called h-regular if and only if for any t ∈ H, there is u′ ∈ H such that t + tu′t + w = tu′t + w.Let H. H is h-regularh-bi-ideal B of H, For every h-bi-ideal M of H, For every generalized The following conditions are identical for the semiring iii)⇒(ii) We know that every h-bi-ideal is a generalized h-bi-ideal from definition, so it is obvious.(i)⇒(iii) Assume that H is h-regular and let M be the generalized h-bi-ideal of H. Then, MHM⊆M refers to u ∈ M, since H is h-regular, then there exists m′ ∈ H such that u + um′u + w = um′u + w for w ∈ H. Now, um′u ∈ MHM implies that (ii)⇒(i) Let u′ ∈ H, consider the h-bi-ideal Bh(u′). Then, n1, n2, n3, n4 ∈ Bh(u′), and h1, h2 ∈ H such thatn = n1 + n2 + n3 + n4 ∈ Bh(u′) and h = h1 + h2 ∈ H. Hence, ∃h ∈ H such thatt = u′ + u′2 + u′h2 + u′2h2 + h3u′2 + h2w′ + w′h2, and w′w′ = w. Thus, H is h-regular.(ℛ(H) and (ℒ(H)) are bands for h-regular semiring H.Subsemigroup ℛ(H) and ℒ(H) are two subsemigroups. Let R′ ∈ ℛ(H) and u ∈ R′, then ∃m ∈ H such that u + umu + w = umu + w for w ∈ H. Now,Let um ∈ R′H⊆R′ implies that R′⊆R′2. Also, it is obvious that R′2⊆R′. Thus, it can be written as R′ = R′2. Hence, ℛ(H) is a band.ℒ(H) is a band that can prove dually.Similarly, h-bi-ideals B(H) is a product of ℛ(H) and ℒ(H) semigroups. Bear in mind that ℛ(H)ℒ(H) = {RL | R′ ∈ ℛ(H), L′ ∈ ℒ(H)}.In the following theorem, we will prove that semigroup of all B(H) = ℛ(H)ℒ(H) for h-regular semiring H.Show that B(H) = ℛ(H)ℒ(H), we have to show that B(H)⊆ℛ(H)ℒ(H) and ℛ(H)ℒ(H)⊆B(H). Suppose R′ ∈ ℛ(H) and L′ ∈ ℒ(H) and we represent B is h-subsemiring of H. Now, B ∈ B(H). Hence, ℛ(H)ℒ(H)⊆B(H).To prove that B ∈ B(H). We represent L′ is left h-ideal of H. Now, R′ is right h-ideal of H. Now, H is h-regular, and we know that H is h-regular iffh-bi-ideal B of H. So, B(H)⊆ℛ(H)ℒ(H). Thus, B(H) = ℛ(H)ℒ(H). Hence, proved.Now, suppose h-bi-ideals of the h-Clifford semiring and the left h-Clifford semiring.In this segment, we are characterizing the semigroup of all H be a semiring, then H is called a h-Clifford semiring ifH be a h-regular semiring, i.e., for every t ∈ H, then ∃u, v ∈ H such that t + tut + w = tvt + w; for h ∈ Ht ∈ HLet e of semiring H is h-idempotent of H if e + e2 + w = e2 + w for w ∈ H. Eh(H) is representation of set of all h-idempotents.An element h-regular semiring , the given following statements are identicalH be h-Clifford semiringu ∈ H(ii)h-ideals as well as all right h-ideals are two sided and (iii) All left h-ideal and for all right h-ideals R(iv)h-ideals L1, L2 of H and h-ideals R1, R2 of H(v)In i)⇒(ii) Suppose H is h-Clifford and we want to show that u, v ∈ H, then ∃t ∈ H such thatH is h-regular, then there is h1, h2 ∈ H such that u + uh1u + w = uh1u + w and v + vh2v + w′ = vh2v + w′. Now, u + uh1u + uh1tv + w + w′ = uh1u + uh1tv + w + w′; this implies that u + uh1u + uh1tv + w = uh1u + uh1tv + w, where w + w′ = w. Hence, by  is a semillatice, these implies thatx = h1 + h2 + h1t + h2t. Thus, ux and vx are h-idempotents. Since H is a h-Clifford semiring, there are s1, s2 in H such that u + vs1v + w = vs1v + w and vxu + us2u + w′′ = us2u + w′′for w′′ ∈ H. Hence, ⇒(iii) Suppose that u ∈ H. For this, let t1 ∈ H such that v + ut1 + w′ = ut1 + w′ for w′ ∈ H. Since H is h-regular, there is t2 ∈ H for which u + ut2u + w = ut2u + w. Thus, there is t = t1 + t2 ∈ H such that v + ut + w′ = ut + w′ and u + utu + w = utu + w. This implies that x ∈ H such that ut + xu + w = xu + w. Now, v + ut + xu + w′ = ut + xu + w′ implies that v + xu + w′ = xu + w′, that is, (iii)⇒(iv) Lets take u from left h-ideal L, and t ∈ H, then L is two-sided h-ideal. Similarly, this will be hold for right h-ideal R. Let M and N are two h-ideals of H. Then, MN contain in M∩N. Let u ∈ M∩N, since H is h-regular, there is h ∈ H such that u + utu + w = utu + w for w ∈ H. So, u will be in (iv)⇒(v) This is trivial.(v)⇒(i) Let u ∈ H and e be h-idempotent in H. Then, from (v), we can write H is h-regular, there is t ∈ H such that ue + uetue + w1 = uetue + w1 for w1 ∈ H and so,(ue + ((uet)u)(e(tue)) + w1 = ((uet)u)(e(tue)) + w1 implies that p ∈ H such that ue + pu + w2 = pu + w2. Now, ⇒(vi) This is trivial.(vi)⇒(i) Let u ∈ H and e ∈ Eh(H). Since H is h-regular semiring, there is t ∈ H such that ue + uetue + w5 = uetue + w5 for w5 ∈ H, and so,(ue + (uet)u(etu)e + w = (uet)u(etu)e + w implies that p ∈ H such that ue + pu + w6 = pu + w6. Hence, , ∗) is semilattice, then prove that H is h-Clifford.Let B(H) is semillatice, then H is h-regular semiring. Let u, v ∈ H, then uv ∈ Bh(u)Bh(v) = Bh(v)Bh(u) implies that uv + rs + w = rs + w for some r ∈ Bh(v) and s ∈ Bh(u) and for w ∈ H; since H is h-regular, there are x, y ∈ H such that r + vxv + w′ = vxv + w′ and s + uyu + w′′ = uyu + w′′ for w′, w′′ ∈ H. Then, we have uv + rs + w = rs + wz = xvuy and w′′′ = vxvw′ + w′uyu + w′w′′ ∈ H.Assume that H is a h-Clifford semiring.Hence, h-bi-ideals B(H) is a left normal band if and only if semiring is a left h-Clifford.The set of all H be h-Clifford semiring. Let L, M, N ∈ B(H) and l ∈ L, m ∈ M, and n ∈ N, we have t + lmn + w = lmn + w for w ∈ H. Since H is h-regular; there is s ∈ H such that lmn + lmnslmn + w1 = lmnslmn + w1 for w1 ∈ H, which implies that t + lmnslmn + w = lmnslmn + w, from which we get t + lxlmnxnsls1n + w = lxlmnxnsls1n + w as l + lxl + w′ = lxl + w′, n + nxn + w′′ = nxn + w′′, and mn + s1m + w′′′ = s1m + w′′′ for some x, s1 ∈ H and w′, w′′, w′′′ ∈ H. Again, since lm + s2l + w1 = s2l + w1 and nsls1 + s3n + w2 = s3n + w2; for some s2, s3 ∈ H and w1, w2 ∈ H, after rearranging the above expression, we get t + (lxs2l)(nxs3n)m + w = (lxs2l)(nxs3n)m + w. Thus, L∗M∗N⊆L∗N∗M; similarly, it can be proved that L∗N∗M is subset of L∗M∗N. Hence, L∗M∗N = L∗N∗M, and so, B(H) is a left normal band.Let h-bi-ideals B(H) be a left normal band and we are to prove that H is a left h-Clifford. Thus, H is h-regular, we know that semigroup B(H) is regular under ∗ iff H is h-regular. Using this result, let u, v ∈ H, then there is t ∈ H such that uv + uvtuv + w = uvtuv + w which implies that B(H) is a left normal band, then uv + opq + w′ = opq + w′, where o ∈ Bh(uvt), p ∈ Bh(v), q ∈ Bh(u), and w′ ∈ H. Since H is h-regular, there is h ∈ H such that q + uhu + w′′ = uhu + w′′. Now, uv + opq + w′ = opq + w′ implies that uv + (opuh)u + w′′′ = (opuh)u + w′′′, for w′′′ ∈ H. Thus, H is left h-Clifford.Conversely, suppose that set of all h-ideals in this section and will work on semiring of minimal left h-ideals and minimal right h-ideals. Also, we will look at those semirings H ∈ HL+ that have a minimal left(right)h-ideal, and a minimal h-bi-ideal. Bm(H) stands for the class of all minimal h-bi-ideals. Likewise, we will denote minimal left and minimal right h-ideals with ℒm(H) and ℛm(H).We will define semiring in terms of minimal H be a semiring and N be the h-bi-ideal of H, then N be a minimal h-bi-ideal if for every nontrivial h-bi-ideal U of H, U⊆N follows that U = N.Let N⊆H is a minimal h-bi-ideal of H if and only if h-ideal R1, L1 of H, respectively.Let ∅≠N⊆H, suppose that N is a minimal h-bi-ideal of H, then h-ideal and left h-ideal, respectively. Let us take R2⊆R1 is a right h-ideal of H, then h-bi-ideal of H, this follows that N; R2 = R1. Thus, h-ideal of H. Likewise, h-ideal of H, since h-bi-ideal and N is a minimal h-b-ideal of Let ∅≠Bm(H) is set of all minimal h-bi-ideals, then Bm(H) is a subsemigroup of B(H).If h-bi-ideal M1and M2 in Bm(H). Let G ∈ B(H) be such that M1, we have Bm(H) would be subsemigroup of B(H).Suppose two minimal ℒm(H) and ℛm(H) are subsemigroup of B(H) which can justify easily.As the same pattern, the set h-ideals is ℒm(H) and L1∈ℒ(H), then L1∗Lm′ = Lm′, where Lm′ is in ℒm(H). Hence, ℒm(H) be a left ideal of semigroup, i.e., (ℒ(H), ∗), in particular a subsemigroup. Further, (ℒm(H), ∗) be a right zero band.(a) If a nonempty class of all minimal left h-ideals is ℛm(H) and R1∈ℛ(H), then R1∗Rm′ = Rm′, where Rm′ is in ℛm(H). Hence, ℛm(H) be a right ideal of semigroup, i.e., (ℛ(H), ∗), in particular, a subsemigroup. Further, (ℛm(H), ∗) be a left zero band.(b) If a nonempty class of all minimal right L1 ∈ ℒ(H) and Lm′∈ℒm(H), take the left h-ideal L1∗Lm′. Then, L1∗Lm′ implies Lm′ and by minimality of Lm′ shows L1∗Lm′ = Lm′.(a) For some (b) This proceeds dually.h-bi-ideal Bm(H) for a semiring H is nonempty, then ∀Mm, Gm ∈ Bm(H), and B ∈ B(H); there are Mm∗B∗Gm ∈ Bm(H), Mm∗Mm = Mm, and Mm∗B∗Mm = Mm. As a consequence, semigroup's bi-ideal is the set of all minimal h-bi-ideals (Bm(H), ∗), in particular, a subsemigroup. Further, (Bm(H), ∗) is rectangular band.If set of all minimal Bm(H) is set of all minimal h-bi-ideal and B(H) denotes the set of h-bi-ideal, then suppose Mm, Gm ∈ Bm(H) and B ∈ B(H). Denote the bi-ideal Mm∗B∗Gm by G. Let F ∈ B(H) such that F⊆G, then Mm shows that Mm = F∗Mm. Similarly, Gm = Gm∗F; therefore, G = F. Thus, the h-b-ideal Mm∗B∗Gm ∈ Bm(H). Furthermore, Bm(H) be a subsemigroup of B(H). As a result, Bm(H) is bi-ideal of B(H). For some Mm ∈ Bm(H), let us assume the h-bi-ideal Mm∗Mm. Then, Mm shows that Mm∗Mm = Mm. Since, also, Mm∗Mm∗Mm is h-bi-ideal, from Mm it means that Mm∗B∗Mm = Mm.Let h-regular semiring along their h-ideals. The different subclasses of the h-regular semiring are described by their h-bi-ideal semigroup. We have shown that B(H) = ℛ(H)ℒ(H) for h-regular semiring H. We have characterized a new class of semigroup of h-bi-ideals in h-Clifford semiring. Lastly, we worked on minimal h-ideal along their h-bi-ideals and proved that Bm(H) is a rectangular band. This research further can be extended with h-quasi ideals and k-ideals. The other idea of its extension is in the field of biology by using in growth-fragmentation-coagulation equation.This research represents