Proteins attached to the surface of group A and group B streptococcal human pathogens that contain tandemly arrayed Rib domains have been associated with invasive infections and proposed as potential vaccine candidates. Here, we present structures of the Rib domain, both isolated and in tandem. The Rib domain structure is revealed as a rare example of “domain atrophy” from the much more common immunoglobulin-like fold. Tandem Rib domains adopt a head-to-tail arrangement with limited interdomain flexibility, suggesting that the previously observed, and proposed immune evasion-related, variation in Rib domain number is likely to result in differential projection of the N-terminal host colonization domain from the bacterial surface. Streptococcus groups A and B cause serious infections, including early onset sepsis and meningitis in newborns. Rib domain-containing surface proteins are found associated with invasive strains and elicit protective immunity in animal models. Yet, despite their apparent importance in infection, the structure of the Rib domain was previously unknown. Structures of single Rib domains of differing length reveal a rare case of domain atrophy through deletion of 2 core antiparallel strands, resulting in the loss of an entire sheet of the β-sandwich from an immunoglobulin-like fold. Previously, observed variation in the number of Rib domains within these bacterial cell wall-attached proteins has been suggested as a mechanism of immune evasion. Here, the structure of tandem domains, combined with molecular dynamics simulations and small angle X-ray scattering, suggests that variability in Rib domain number would result in differential projection of an N-terminal host-colonization domain from the bacterial surface. The identification of 2 further structures where the typical B-D-E immunoglobulin β-sheet is replaced with an α-helix further confirms the extensive structural malleability of the Rib domain. Streptococcus (GBS) Streptococcus agalactiae is a leading cause of sepsis and meningitis in neonates and is associated with high morbidity and mortality in early onset disease (Streptococcus (GAS) Streptococcus pyogenes causes pharyngitis, scarlet fever, skin infections, cellulitis, necrotizing fasciitis, toxic shock syndrome, glomerulonephritis, and rheumatic fever  (UniProt: P72362) . Rib belnfection . In a confection . Rib repnfection –14.Lactobacillus acidophilus. Together, these 5 structures of homologous Rib domains, and further sequence analyses, show Rib is an example of domain atrophy  features in a diverse range of bacterial proteins and functional contexts  and is pS. agalactiae Rib; amino acids 230 to 308) and R28N  .Structures of single Rib domains with ∼38% sequence identity were solved: RibR .The Rib domain structure D and hidBacteroides thetaiotaomicron SusCD complex protein BT2262  having a Cα rmsd to RibR of 3.1 Å over 68 residues; other close structural homologs were also from the Ig-like superfamily. Comparison with the He_PIG family (PF05345)  in the top sheet of 4nzj (He_PIG) is replaced by a coil and 2 310 helices in the Rib structure. In common with He_PIG, strands A′, G, F, and C form the 4-stranded β-sheet. Thus, despite lacking 1 of the 2 β-sandwich sheets of the Ig fold, the Rib domain is clearly a member of the Ig-like fold superfamily, in agreement with the Pfam clan assignment (Pfam clan: CL0159). Given the narrow species distribution of the Rib family compared to the wide distribution of Ig-fold proteins, we suggest that Rib is an example of domain atrophy from an ancestral Ig fold, a rare phenomenon where core elements of secondary structure are lost (The protein structure comparison service PDBeFold at the European Bioinformatics Institute  identifiPF05345) E shows tL. acidophilus (UniProt: Q5FIM8) contains both long (L) and standard (S) Rib domains . Thus, we solved the structure of “Rib Long” (RibL) (residues 1165 to 1268) and “Rib Standard” (RibS) (residues 1269 to 1329) at resolutions of 1.07 Å and 1.25 Å, respectively , versus a Cα rmsd of 2.3 Å for RibL . As predicted, the Ig-fold D/E strands missing from RibR and R28N are also missing from RibS but are present in the RibL structure . Thus, RibL comprises a full Ig-like fold which likely represents the ancestral state of the Rib domain, and RibS is an example of evolutionarily recent domain atrophy from RibL; this is remarkable because there are very few cases where we have the structure of both atrophied and nonatrophied domains that are closely related in sequence.We hypothesized that some members of the Rib domain that have long insertions in the position of the characteristic Rib D-E strand atrophy A may ret domains  with 45%ectively . RibS beStreptococcus gordonii serine-rich repeat protein GspB (Streptococcus sanguinis contains a related domain with an rmsd of 2.4 Å to RibR (PDB ID code: 5eq2)  highlights the importance of these residues that appear to stabilize the β-turn and interaction between the central antiparallel strands.Further sequence analyses identified the “unique subdomain” from ein GspB  as a dise: 5eq2) . These se: 5eq2) C and D; e: 5eq2) . The tope: 5eq2) , and theThe aRib domain (Pfam family: PF18938) is sometimes found tandemly arrayed and often associated with other known cell surface domains, such as DUF1542, FIVAR, Rib, and GA domains; 1,180 proteins are found in UniProtKB (release 2019_01) to contain the aRib domain, and it is found primarily in Firmicutes (86%) and Actinobacteria (12%), with some sporadic cases found in other bacterial groups. This compares to 2,405 proteins containing the Rib domain, with 77% being found in Firmicutes, and 19% in Actinobacteria and 2% in Proteobacteria.SI Appendix, Fig. S4), this begins to shed light on the evolutionary pathway of a fold where an ancestral Ig-like fold (RibL) has lost core secondary structures (RibR) and diversified in structure (aRib). The replacement of the typical B-D-E β-sheet of an Ig fold by an α-helix in the aRib domains, and a combination of loops and short helices in Rib, shows the remarkable structural malleability of the Rib family. The evolutionary mechanism termed domain atrophy has been shown to be extremely rare (ms) of 78 °C and 88 °C for RibL and RibS, respectively , and 71 °C for RibR , RibL, and aRib form 3 distinct Rib subfamilies . The interdomain linker is short and consists of the residues DPRT . The similarity of the Tm of RibR and Rib2R . Our analysis displays high quality model:data fits for both RibR and Rib2R , with χ2 values of 1.3 in each case. Analysis of the Guinier regions of both plots gives radius of gyration (Rg) values of 14.7 Å and 26.5 Å, and distance distribution [P(r)] functions determine maximum particle dimensions (Dmax) of 57 Å and 97 Å for RibR and Rib2R, respectively. These calculated Dmax values are consistent with our structural models .To validate our RibR and Rib2R crystal structures, we conducted small angle X-ray scattering experiments with in-line size exclusion chromatography (SEC-SAXS)  for RibR and Rib2R from our SAXS data   B and C, max calculated from SAXS (97 Å) and the length of the crystal structure (98 Å). Modeling of the 3 interdomain rotation angles  suggests that, while a wide range of long axis (x) rotations (α) are accommodated, rotations around the y and z axes  occupy a more narrow distribution . MD model-to-SAXS data fits are consistent with an extended Rib2R solution conformation, with low χ2 values obtained for a wide range of α rotations  but for only a narrow distribution of β and γ angles . Models of multidomain Rib constructs using the MD-obtained range for α, β, and γ show an approximately linear dependence of Rg on Rib domain number  simulations of fully solvated Rib2R at room temperature (303 K) D and E. n number F and elon number G. Taken SI Appendix, Fig. S9), which suggests that most tandem Rib domains will be more constrained than in Rib2R and thus are likely to also form relatively rigid, extended arrays.Given the lack of interdomain stabilization in Rib2R, rigidity in the interdomain linker is likely to be influential in maintaining the interdomain interface. In alpha C (UniProt: Q02192), the linker residues are predicted to be PKPVP; the presence of 3 proline residues is likely to result in an even higher degree of restricted interdomain dynamics. It is interesting to consider how representative these linkers are of the Rib domain proteins in general. The most common linker length between tandem Rib domains is shorter at just 2 residues  structure revealed that the original Pfam analysis successfully predicted the C-terminal residues of the domain, but the N terminus of the HMM needed to be extended by ∼11 amino acids. This has been adjusted within the Pfam database, which now also includes new families representing the RibL and aRib domain. These changes, motivated by our structural analysis, will enhance the accuracy and completeness of annotation of Rib domains across known protein sequences.Staphylococcus aureus  coding for 2 repeats from the Rib protein . For this work, the coding sequence for a single Rib repeat  was amplified by PCR using primers . The product was subcloned into the pGEX-6P-1 vector  by restriction endonuclease digestion with enzymes NdeI and XhoI followed by ligation-dependent cloning, generating an in-frame fusion with an N-terminal glutathione S-transferase (GST) tag and human rhinovirus 3C protease specific linker (GST-3C-RibR). Owing to difficulties separating the GST tag from Rib2R during protein purification, Rib2R was amplified using primers  and incorporated downstream of a Hexa-His and 3C protease-specific linker sequence (MGSSHHHHHHSSGLEVLFQGPAM) in the pETYSBLIC3c vector backbone  to generate the sequence MGSSHHHHHHSSGLEVLFQGPLGS. The Escherichia coli optimized coding sequence for S. pyogenes R28 protein (UniProt: Q9XDB6) Rib domain  was synthesized (Integrated DNA technologies) and PCR amplified using primers  for cloning downstream of a His-tag and tobacco etch virus (TEV) protease-specific linker sequence (MGSSHHHHHHSSSENLYFQS) in the pET28a vector backbone by In-fusion cloning. The E. coli optimized coding sequences for RibL (UniProt: Q5FIM8 1165 to 1268) and RibS (residues 1269 to 1329) were synthesized (Genewiz) and inserted into the pETYSBLIC3c vector backbone  and the In-Fusion cloning method, as described for Rib2R (see above).A clone of backbone  by In-fu600 0.6 and expression induced by addition of 0.1 mM isopropyl β-d-1-thiogalactopyranoside incubated for 18 h at 30 °C with shaking. SeMet labeling was performed as described in ref. E. coli B834 (DE3) cells in minimal media supplemented with 40 μg⋅mL−1l-selenomethionine and purification performed as per the native protein. Cells were harvested by centrifugation at 4,500 × g, resuspended in phosphate-buffered saline (PBS), and lysed by sonication and lysate clarified by centrifugation at 48,000 × g 30 min at 4 °C. A GST-Trap4b column  was equilibrated in PBS, loaded with lysate at a flow rate of 1 mL⋅min−1, and washed with 50 mL of PBS. Bound protein was eluted in PBS supplemented with 10 mM reduced glutathione and was dialyzed against PBS for 18 h at 4 °C following the addition of 3C protease to remove the tags (1:100 wt/wt 3C:RibR). Cleaved RibR was concentrated by centrifugal filtration in a Vivaspin 3-kDa molecular weight cut-off (MWCO) filter (Sartorius) and separated from GST and 3C protease by size exclusion chromatography (SEC) using a Superdex 75 16/60 column  equilibrated in 20 mM sodium potassium phosphate buffer, pH 7.5. Elution of RibR was monitored by absorbance at 220 nm. RibR was subsequently dialyzed into 20 mM Tris, 50 mM NaCl at pH 7.5, and concentrated by centrifugal filtration with a Vivaspin 3-kDa MWCO PES membrane (Sartorius) for crystallization screening.GST-3C-RibR was transformed into BL21(DE3) cells, grown in lysogeny broth (LB) supplemented with ampicillin (100 μg/mL) to OD−1). Induction of protein expression, cell harvesting, and cell lysis were performed as described above. 6His-3C-Rib2R, 6His-3C-RibL, and 6His-3C-RibS were purified by standard immobilized metal affinity chromatography (IMAC) methods with gradient elution. The affinity tag was removed by application of 3C protease (1:100 wt/wt), and the sample was purified to homogeneity by SEC in buffer comprising 20 mM Tris, pH 8, 150 mM NaCl. pET28-His-TEV-R28N was transformed into BL21(DE3) cells, and protein expression, cell lysis, and purification were performed as described for Rib2R, with application of TEV (1:100 wt/wt) for removal of the poly-His tag and SEC buffer comprising 20 mM Tris, pH 7.5, 150 mM NaCl.pETYSBLIC3c-6His-3C-Rib2R, pETYSBLIC3c-6His-3C-RibL, or pETYSBLIC3c-6His-3C-RibS were transformed into BL21(DE3) cells and grown in LB supplemented with kanamycin , and 34 mg⋅mL−1 (RibL and RibS); 150 nL each of protein and reservoir solution were mixed using a Mosquito robot (TTP Labtech) into Swissci MRC 96-well trays, alongside 54 μL of reservoir solution. Crystallization trays were incubated at 291 K (RibR and RibR-SeMet) and 279 K . Crystals of RibR and SeMet RibR were obtained in the Index screen (Hampton Research) in 0.1 M citric acid, pH 3.5, and 2 M ammonium sulfate (RibR) and 3.5 M sodium formate, pH 7.0 (SeMet RibR). R28N crystallized in the JCSG+ screen , beamlines as shown in 32For the solution of RibL, a conserved fragment of RibR corresponding to residues 60 to 71 was selected as a molecular replacement model for PHASER  and gave2PO4, pH 7.4) and well-matched buffer were degassed for 10 min at 10 °C using a Thermovac degasser . Differential scanning calorimetry (DSC) scans were acquired on a MicroCal VP-DSC calorimeter  with a scan rate of 90 °C/h. The reversibility of thermal unfolding was verified by repetitive scans on the same sample. A buffer–buffer scan was subtracted from each protein scan, followed by normalization for protein concentration and subtraction of a progress baseline for sample baseline correction. Melting temperatures (Tms) were derived by fitting the unfolding endotherm using a 2-state unfolding model for data above 20 °C. Data analysis was performed using MicroCal Origin 5.0.For RibR and Rib2R, protein samples  instrument (NanoTemper) at a protein concentration of 1 mg/mL in 20 mM Tris, 150 mM NaCl, pH 7.5, using a sample volume of 10 µL. Samples were heated to 95 °C at a rate of 1 °C/min to generate an unfolding profile.The TMolecular dynamic (MD) simulations were performed starting from Rib2R chains A and C (PDB ID code: 6S5Y). Simulations were performed using the CHARMM36m  force fiq) of 0.01 Å−1 < q < 0.4 Å−1. Scattering intensity  was collected using a Pilatus 2M detector, with an incident beam energy of 12.4 keV and a beam-to-detector distance of 4,014 mm. RibR and Rib2R samples were injected onto an in-line Shodex KW402 column equilibrated in 10 mM Hepes, 150 mM NaCl, 3 mM KNO3, pH 7.5, both at concentrations of 10 mg⋅mL−1. Data processing and reduction and calculation of Rgc plots [Ln(I*q) vs. q2] were performed using the program Scatter (https://github.com/rambor/scatter3.git). Rg {Ln[I(q)] vs. q2} and distance distribution [P(r) vs. r] plots were calculated with Primus . Other data presented are available from the corresponding authors on reasonable request.Structures were deposited in the Protein Data Bank, Supplementary File


PRDM16) gene is a dominant transcriptional regulator that favors the “browning” of white adipocytes in rodents. Since the “browning” of white fat is important in pig in terms of producing heat fighting against cold environment, avoiding obesity, and improving meat quality, understanding the critical role that PRDM16 gene played in pig adipose “browning” and energy metabolism is of great significance. However, the constitution of pig fat differs a lot from rodents and human as they do not have brown adipose tissue (BAT) even in the newborn piglets. In this study, we isolated porcine primary preadipocytes and investigated the function of PRDM16 during preadipocytes differentiation. Our results showed that overexpression of the PR domain of PRDM16 repressed the differentiation of porcine preadipocytes, indicated by oil red O staining and the deposition of the triglyceride. Overexpression of the PR domain significantly increased the level of lipolysis and mitochondrial oxidative capacity detected by Western blotting during differentiation. Furthermore, we purified the protein coded by the PR domain and demonstrated that this protein has the H3K9me1 methyltransferase activity. In conclusion, the PR domain of the porcine PRDM16 gene repressed the mature of the porcine preadipocytes by promoting its oxidative activity.The positive regulatory domain containing 16 ( Adipose tissue is mainly divided into two types: white adipose tissue (WAT) and brown adipose tissue (BAT), which are mainly composed of white and brown adipocytes, respectively . The whiγ), C/EBPα, C/EBPβ, and epigenetic modification regulators including HDAC9 and SIRT1 [PRDM16), also called MDS1/EVI1-like gene 1(MEL1 gene), contains an N-terminal PR domain, which is the key functional domain of this gene. Baizabal et al. found that the PR domain is the vital component of the gene in regulating upper layer neuron migration by comparing reintroducing PRDM16 and the PRDM16 deleted the PR domain (△PRDM16) into the cKO radial glial cells [ PRDM16 played crucial roles in regulating the development of fat tissue. In human, the PRDM16 polymorphisms are associated with obesity as the A allele is dominant in the obese group [ PRDM16 mainly affected the “browning” of the white adipocyte when stimulated with cold stress, and knockout of this gene will lead a switch from subcutaneous to visceral fat [ PRDM16 regulated beige adipocyte development by suppressing the expression of white fat-selective genes, which recruited the histone methyltransferase Ehmt1 in young mice [ PRDM16 in the fibroblast cell model C2C12 also leads to repression of myogenic genes and adipogenic genes in both proliferation and differentiation stages, indicating PRDM16 switched the cell fate into brown adipocyte [ PRDM16 can be regulated by other posttranscriptional factors, such as miRNAs, sumoylation, and histone deacetylase 3(HDAC3) in the process of browning and thermogenesis in white adipocyte [ PRDM16 may work by its H3K9me1 transferase activity, as this domain of the PRDM family shares high homology with the SET domain, which possesses the activity of the histone methyltransferase [ PRDM16 functioned as the lysine methyltransferase (KMT) directing cytoplasmic H3K9me1 methylation, which methylated H3K9me1 and then can be converted into H3K9me3 in nucleus to reinforce the heterochromatin [Previous studies showed that although brown and beige cells shared a lot of common characters, both of them were abundant of mitochondria and are UCP1+ cells, and they derived from different precursors, as both white and beige adipocytes derived from the committed white adipocyte precursors, while the brown adipocytes derive form the same precursors with the muscle cells . As mentnd SIRT1 , 6. The al cells . Studiesse group . The stuung mice . Overexpdipocyte . Furtherdipocyte . The PR nsferase . This is UCP1 was disrupted about 20 million years ago [ UCP3 gene to induce the browning of white adipocyte as an evolution mechanism [ UCP1 gene, Zhao's group knocked the functional mice UCP1 into pig genome using CRISPR-Cas9 system, and found that the thermoregulation ability was improved; however, the white adipocytes were not “browned” [ PRDM16 in the differentiation of pig white adipocyte and energy metabolism, which may provide some new insight in the way of fighting with obesity and improving the living number of newborn piglets.The composition of pig adipocyte is quite different from rodents and human, and pig lacks the brown adipocyte while the key regulatorears ago , 18. Recechanism . FurtherAll animals used in this study were reared and euthanized with the approval of the College of Animal Science, South China Agricultural University. We followed the methods of Hou et al. 2017 that we published previously . All exp CTA CGCFor constructing the ectopic expression vector, porcine PR fragment was amplified by the primer pair PR-2 with NheI- BamH sites in the end and the PCR product was digested and cloned into the pcDNA3.1 vector. PRDM16-2F: 5′-μM cell strainer to remove large pieces, and the flow-through was centrifuged for 10 min at 500 × g to collect stromal-vascular fraction (SVF) cells in the pellet. The SVF cells were suspended in growth medium-DMEM/F12  containing 15% fetal bovine serum , 100,000 units/L of penicillin sodium, and 100 mg/L of streptomycin sulfate (GIBCO) and then plated onto 10 cm tissue culture dishes. For PR domain overexpression, preadipocytes with 60% confluent were incubated with pcDNA-3.1-PR or pcDNA-3.1 backbone plasmid for 6 h in growth medium. Then the medium was replaced with new growth medium and cells were maintained in growth medium for an additional 12 h before adipogenic differentiation induction. Preadipocytes were induced to differentiation by the induction medium containing 10% FBS, 0.5 mM isobutylmethylxanthine, 0.25 μM dexamethasone, 1 μg/mL insulin, and 1 μM rosiglitazone for 48 h. Two days later, cells were switched to a different medium containing 10% FBS and 1 μg/mL insulin. Differentiated cells were collected for gene expression detection, and culture media were used to conduct triglyceride assays. All chemicals for cell culture were purchased from Sigma-Aldrich .Porcine preadipocytes were isolated from subcutaneous adipose tissue of 1-day-old male Landrace pigs' back according to previous published protocols with modifications , 21–23.  g, 10 min), the supernatants were collected for triglyceride analysis following the manufacturer's instructions .Porcine preadipocytes were differentiated for 5 or 8 days and homogenized in ice-cold phosphate-buffered saline (PBS). After centrifugation . The pictures were taken under inverted microscope.μg/μl for SDS-PAGE gel running. The proteins were subjected to Western blotting analysis with the following antibodies: anti-PPARγ , anti-C/EBPα , anti-AP2 , anti-COX3 , anti-ELOVL , anti-ACOX1 , anti-HSL , anti-UCP3 , anti-β-actin . The blots were developed using HRP-conjugated secondary antibodies and ECL system. The relative expression levels were calculated by software Image J in the form of the band density relative to β-actin.The porcine preadipocytes on different differentiation stages were collected and the protein was extracted according to the instruction of RIPA lysis buffer . The concentration of protein was detected by BCA protein essay kit  and then adjusted to 1 GCTAGCCACTCCCAAGGAAGGCTCG -3′; RDM16-2R: 5′-TTGGGATCCCTAGAAGGTGGGCTCCTCGTCC-3′. The PCR amplification condition is as follows: 94°C 5 min; 94°C, 30 s, 63°C, 30 s, 72°C, 40 s, repeated 35 cycles; 72°C, 10 min. After transfection and sequencing for the insertion sequence, the vector was expressed in the E. coli BL21 (DE3) and the combined protein was purified using the Ni-NTC columns.The PR domain was cloned into the pET-28a vector which contains a his-tag. The fragment was amplified by the primer pair PR-2 with NheI(GCTAGC) - BamHIsites (GGATCC) and the stop codon (CTA) in the end and the PCR product was digested and cloned into the pET-28a vector. PRDM16-2F: 5′-TGμl of the total histone  was incubated with the purified PR-his combinant protein or his protein as the negative control in 30°Cfor 1 h. Western blotting with H3K9me1 antibody was used to detect the methyltransferase activity of the PR protein.10  t-test was carried out using the R (download website: https://www.r-project.org/).The levels of triglyceride deposition and gene relative expression were presented as mean ± standard error of the mean (SEM). Student'sWe isolated the porcine preadipocytes from subcutaneous adipose tissue of 1-day-old male Landrace pig and the pcDNA3.1-PR vector or the pcDNA3.1 vector was transfected when the cells reached the 60% confluence. After being incubated with the vector for 6 h, the cells were cultured in new growth medium and maintained for an additional 12 h before inducing for differentiation and the adipocytes in different time stages were collected for further molecular analysis. qPCR results showed that the PR domain was successfully transfected into porcine preadipocytes and expressed significantly higher than the blank vector . As show g, 10 min), the supernatants were collected for triglyceride analysis. The results showed that the PR domain overexpression significantly repressed triglyceride deposition on the differentiation day 8 compared to the control, while on the differentiation day 5, no significant repression effect was observed . After centrifugation , CCAAT enhancer binding protein alpha (C/EBPα), adipocyte protein 2 , and the brown fat-selective expressed genes, such as elongation of very long chain fatty acids (ELOVL) and mitochondrial electron transport gene Cytochrome C oxidase subunit III (COX3) by Western blot analysis in differentiated pig preadipocytes after transfecting the PR domain vector. The results demonstrated that the level of fatty acid metabolism was elevated by the PR domain, indicated by elevated expression of COX3 , to make out whether the hydrolysis of fatty acids in cytoplasm was elevated in adipocytes. The results demonstrated that UCP3 was elevated (0.89 ± 0.02 vs. 0.79 ± 0.04, P = 0.04 in Figures  P = 0.04 in Figures Since pig does not haveWe expressed the PR domain encoding protein by pET-28a vector, which carried a his-tag and can be further purified by the Ni-NTA column. As shown in  PRDM16 gene played in adipocyte differentiation and metabolism regulation would be beneficial not only for pharmaceutical development in fighting with obesity, but also for improving the living numbers of newborn farm animals who are weak in adapting the cold environment [ UCP1 gene as we reported previously [A detailed understanding of the role that theironment . Pig is eviously . As a re PRDM16 repressed the adipogenesis in porcine preadipocytes and promoted fatty acid oxidation, indicated by the Western blot analysis in γ; however, there was no difference on differentiation day 5, and on differentiation day 8 the protein of PPARγ was even more than the control group. This is reasonable as PPARγ is a glitazone receptor, which was required for the survival of differentiated adipocytes [γ was expressed at high level immediately after differentiation was induced and it promoted lipogenesis depending on the gene dosage, and its expression decreased when differentiation continued [ PRDM16 gene by retroviruses would significantly elevate the expression level of ELOVL [In this study, we found that overexpression of the PR domain ofipocytes . In miceontinued . Meanwhiof ELOVL . Furtherof ELOVL . PRDM16 may not only possesses the H3K9me1 methyltransferase activity, as in the study in C2C12 showing that knockout of the PR domain not only affected the level of H3K9me1, but also H3ac, H3K4me3, and H3K27me3 on the promoter of the MyoD gene, although it may occurr through a complex network [The PR domain coding protein has the H3K9me1 methyltransferase activity, although the target genes regulated still need further study. Notably, the PR domain of network .Based on our findings, we suggest a role for the PR domain of the PRDM16 gene in repressing the pig lipogenesis by promoting the lipolysis activity through H3K9me1 activity, although more investigations are still in need to clarify the mechanism behind.


Cohesin has essential roles in chromosome structure, segregation and repair. Cohesin binding to chromosomes is catalyzed by the cohesin loader, Mis4 in fission yeast. How cells fine tune cohesin deposition is largely unknown. Here, we provide evidence that Mis4 activity is regulated by phosphorylation of its cohesin substrate. A genetic screen for negative regulators of Mis4 yielded a CDK called Pef1, whose closest human homologue is CDK5. Inhibition of Pef1 kinase activity rescued cohesin loader deficiencies. In an otherwise wild-type background, Pef1 ablation stimulated cohesin binding to its regular sites along chromosomes while ablating Protein Phosphatase 4 had the opposite effect. Pef1 and PP4 control the phosphorylation state of the cohesin kleisin Rad21. The CDK phosphorylates Rad21 on Threonine 262. Pef1 ablation, non-phosphorylatable Rad21-T262 or mutations within a Rad21 binding domain of Mis4 alleviated the effect of PP4 deficiency. Such a CDK/PP4-based regulation of cohesin loader activity could provide an efficient mechanism for translating cellular cues into a fast and accurate cohesin response. Cohesin is a central player in chromosome biology. Defects in the cohesin pathway are linked to human pathologies such as sterility, cancer and severe developmental disorders . CohesinCohesin loading onto DNA requires ATP hydrolysis by cohesin, which is thought to trigger transient opening of the ring and DNA entrapment . The reaOnce loaded onto chromosomes, cohesin can either remain bound or removed. Wpl1 promotes cohesin release in a reaction requiring Pds5 and Psc3. All three proteins bind Rad21 and Wpl1 promotes DNA release by weakening the Rad21-Smc3 interface . DNA relAs opposed to sister-chromatid cohesion, cohesin loops may not require topological entrapment of DNA . CohesinIntriguingly, the kleisin subunit of cohesin is targeted by multiple phosphorylation events. In fission yeast, Rad21 shows multiple phospho-isoforms whose relative abundance fluctuates along the cell cycle . Our recpef1 in a genetic screen for mutants able to rescue the cohesin loader mutant mis4-367. Pef1 ablation or inhibition of its kinase activity increased cohesin deposition and rescued sister-chromatid cohesion defects of the mis4 mutant. In otherwise wild-type cells, Pef1 ablation increased the binding of both cohesin and its loader to their regular sites along chromosomes. Genetic analyses indicated that Pef1 acts through the phosphorylation of multiple targets. We identified one of these within the kleisin Rad21. Specifically, the Pef1/Psl1 complex phosphorylates Rad21 on T262 and preventing this phosphorylation event recapitulates in part the effects of Pef1 ablation. PP4 had the opposite effect. Its ablation lead to hyper-phosphorylated Rad21 and reduced cohesin deposition which is alleviated by Pef1 ablation or Rad21-T262A. Hence, phosphorylation of the kleisin negatively regulates cohesin loading, possibly by lowering the activity of the cohesin loader. Further supporting this notion, a genetic screen identified compensatory mutations that cluster within the catalytic domain of Mis4, in a previously described Rad21-binding region. Such a phosphorylation-based control may provide a fast, accurate and reversible way for regulating cohesin functions in response to cellular cues.Here, we report on the control of cohesin deposition by the opposite activities of the Pef1 CDK and PP4. Pef1 was first described as a PSTAIRE-related protein in fission yeast . The CDKmis4-367 allele encodes Mis4G1487D. This single amino acid change is located within the last HEAT repeat of the C-terminal catalytic domain (G1487D activity and restore growth at the restrictive temperature. Eleven mutants were isolated that distributed into four linkage groups. Genetic mapping and tiling array hybridization were used to identify the mutated locus in group 1. A single base substitution was found within the pef1 coding sequence. The amino acid change (N146S) is located within the catalytic site of the kinase suggesting the kinase activity was involved. Accordingly, deletion of the pef1 gene or inhibition of Pef1 kinase activity using an analog-sensitive allele (pef1-as) suppressed the ts growth defect of mis4-367  and arrested in S phase at the restrictive temperature using hydroxyurea (cc2) which is the site of kinetochore assembly, within the imr and dg repeats that flank the central core on either side and at tRNA-rich domains that delineate the centromere. The peri-centromere repeats are bound by Heterochromatin Protein 1 (Swi6 in fission yeast) that recruits cohesin, thus providing robust sister chromatid cohesion at centromeres , consistent with improved sister-chromatid cohesion as seen with the cen2 FISH assay , the centromere central core  and flanking heterochromatin domains . Most chromosome arm CARs did respond as well. A non-CAR (ars 3004) site did not show any enrichment for Mis4 and Rad21  in G2 Co-immunoprecipitation experiments indicated that the amount of Rad21 bound to Mis4 in G1-arrested cells was enhanced in the absence of Pef1 . The effpef1 deleted cells , truncated Rad21 peptides were used as substrates for in vitro kinase assays . A N-terRad21-T262 phosphorylation was detected in G1, S and G2-arrested cells . Pef1-asmis4-367. Individual deletion of the three cyclin genes indicated that pas1Δ was a poor mis4-367 suppressor; the deletion of clg1 had a weak effect while psl1Δ showed the strongest effect . By contrast, Mis4 binding was close to wild-type levels along chromosome arm sites and within the rDNA gene cluster (28S and NTS). No additive effect was seen in combination with pef1Δ, consistent with Rad21-T262 being a substrate of the CDK. Mis4 binding was marginally reduced in the phospho-mimicking mutant rad21-T262E but importantly, increased Mis4 binding in pef1Δ was reduced in a rad21-T262E background  an acetyl-mimicking allele of psm3 , PP4 activity stimulated cohesin loading while Pef1 restrained it. Consistent with the genetic data, poor Rad21 binding in the absence of PP4 was efficiently rescued by pef1Δ and to a lower extent by rad21-T262A  reduces cohesin loading while the non-phosphorylated state (pef1Δ and pef1Δ pph3Δ) has the opposite effect. The differences observed between pef1Δ and pef1Δ pph3Δ suggests that other kinase(s) may be contributing.To see the effect of PP4 and Pef1 in otherwise wild-type cells, we looked at Rad21 and Mis4 binding to G1 chromosomes by ChIP. PP4 ablation lead to an overall decrease of DNA-bound Rad21 and Mis4 . Converspph3 deleted cells and might be restored to some extent when pef1 was additionally deleted , suggesting that a similar regulatory mechanism may operate in human. Another argument came from the suppressors of the negative interaction between NNpsm3 and pph3Δ. Besides pef1 mutants and rad21-T262A, suppressor mutations were found in Mis4 and clustered within a Rad21-binding domain . Genomic DNA was extracted from the mutant strain UV12sup and the wild-type S. pombe reference strain (SP972) and co-hybridized to a CGH tiling array . The array spanned 1,290,000 bp of chromosome 3, from coordinates 117000 to 1065000 and from 1143000 to 1485000 (Genbank NC_003421.2 GI:63054406). DNA regions carrying candidate Single Nucleotide Polymorphisms (SNPs) were used to design a high-resolution tiling array . A single A to G SNP (N146S) was found within SPCC16C4.11 (pef1). The mutation was confirmed by PCR and DNA sequencing. The pef1 gene was deleted and genetic analyses showed that pef1Δ was allelic to UV12sup and suppressed the Ts phenotype of mis4-367.Suppressors of the pef1-as allele (pef1-F78A) was generated by in vitro mutagenesis. The DNA fragment carrying the mutated allele was transformed into a recipient strain in which the region of interest was substituted by the +ura4 marker (pef1d(260-320)::ura4+), and Ura- colonies were selected on 5-Fluoroorotic acid plates. The pef1-as allele in the selected strain was amplified by PCR and checked by sequencing. The inhibitor 1-NA-PP1  was added to the culture medium at 25 μM. The equivalent volume of solvent alone (DMSO) was added for the control sample.The rad21 alleles was done using the strategy described in The construction of DNA content was measured by flow cytometry with an Accuri C6 Flow cytometer after Sytox Green staining of ethanol-fixed cells . Data weRabbit polyclonal antibodies against Rad21, Psm1, Psm3, Psm3-K106Ac have been described previously . The mouE. coli EasyXpress, biotechrabbit) using plasmid DNA templates. The reaction was carried out at 37°C for 1 hr using 5–10 nM plasmid DNA in a total volume of 50 µL. Pef1-GFP was immunoprecipitated (IPed) from total cell extracts (5 × 108 cells) prepared in lysis buffer  supplemented with inhibitors . The IPed material was washed three times with 0.2 ml of lysing buffer without inhibitors, and twice with 1.5X kinase buffer . The CDK bound to the beads was recovered in 60 µL of the 1.5X kinase buffer. The kinase assay was done using 40 µL of CDK beads and 20 µL of in vitro generated Rad21-6HIS using the nonradioactive method described in For kinase assays, the Rad21-6HIS substrates were produced using a coupled transcription/translation reaction system  in cohesion regulation, via phosphorylation of the Rad21 cohesion subunit, thereby affecting the activity of the Mis4 cohesion loader.Decision letter after peer review:eLife. Your article has been reviewed by three peer reviewers, including Bruce Stillman as the Reviewing Editor and Reviewer #3, and the evaluation has been overseen by James Manley as the Senior Editor.Thank you for submitting your article "The CDK Pef1 and Protein Phosphatase 4 oppose each other for regulating cohesin binding to fission yeast chromosomes" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.Summary:In this manuscript, the authors use a genetic approach to identify a previously unknown level of posttranslational regulation of the chromosomal cohesin complex. Phosphorylation by the Pef1-CDK limits cohesin loading and/or stability on chromosomes while the counteracting PP4 phosphatase facilitates loading and/or increases the stability of cohesin binding. The authors identify one of the sites that is targeted by this mechanism as Rad21-T262. This is important information, if a comprehensive understanding of how cohesin is regulated on chromosomes is sought. The experiments are generally conducted and documented well and they support the authors conclusions. As such revisions are requested, taking into account the comments below.Major comment:1) The main concern relates to the physiological significance of the mode of regulation depicted in this manuscript. The effect on chromosomal cohesin levels are small, invisible by cytological means. Previously, the authors reported that the PP4 phosphatase destabilizes cohesin on chromosomes , the opposite of what is reported here. Stabilization in that case was achieved by Rad21 phosphorylation on positions S163/S164 by an unknown kinase. No doubt, the phospho-regulation of Rad21 is intricate. Nevertheless, as it stands, it remains unclear whether either S163/S164 or T262 phosphorylation is used to modulate cohesin behavior in response to any regulatory input. Some discussion on this point is requested. Major comment:1) The main concern relates to the physiological significance of the mode of regulation depicted in this manuscript. The effect on chromosomal cohesin levels are small, invisible by cytological means. Previously, the authors reported that the PP4 phosphatase destabilizes cohesin on chromosomes , the opposite of what is reported here. Stabilization in that case was achieved by Rad21 phosphorylation on positions S163/S164 by an unknown kinase. No doubt, the phospho-regulation of Rad21 is intricate. Nevertheless, as it stands, it remains unclear whether either S163/S164 or T262 phosphorylation is used to modulate cohesin behavior in response to any regulatory input. Some discussion on this point is requested.Our study reports a previously unknown level of cohesin regulation by a pair of cell cycle kinase/phosphatase. The identity of the input signals and the physiological outputs are indeed very exciting questions but these are still unanswered and form the basis of our current and future projects. As suggested we have made additional experiments that provide some additional information and have remodeled and expanded the Discussion section in that direction.


In the original article, there was an error.In the Discussion section, a small error was made in one of the quantum computations, which requires minor adjustment of the discussion. None of the empirical results, analyses, and other conclusions are affected.Discussion, paragraph 7. The original sentence was: For a double CF, we have: Prob(A), Prob(B)>Prob(A&B), that is a CF occurs for both conjuncts. This has been corrected to: For a double CF, we have: Prob(A), Prob(B) < Prob(A&B), that is a CF occurs for both conjuncts.A correction has been made to Discussion, paragraph 8. The original sentence was: For the participants with no legal background, we have a situation where Prob(A), Prob(B)>Prob(A&B), for when evaluating criminal cases for which the suspect was guilty for both crimes. This has been corrected to: For the participants with no legal background, we have a situation where Prob(A), Prob(B) < Prob(A&B), for when evaluating criminal cases for which the suspect was guilty for both crimes.A correction has been made to Discussion, paragraph 10. The original sentences were: The observed results require an initial representation for the mental space in a tensor product structure as above, but also a thought process which “mixes” thoughts and beliefs between the two crimes  individuals, but not for participants with more advanced levels of legal knowledge/experience with legal proceedings. As an empirical finding, this constitutes a salutary message regarding the ability of humans to embody rational decision making, in situations where there is a high expectation for such decision making. The double CF presents a challenge for decision models specifically developed to account for the CF and related fallacies. We focussed on one model, based on QPT. So far, QPT theory for the CF has been applied to the single CF, which is by far the most common finding. Modeling of the single CF with QPT involves incompatible questions, which lead to a psychological explanation based on how one question alters our perspective for the other. Regarding the double CF, we have outlined one possibility based on QPT, corresponding to compatible questions, and a “mixing” thought process; our outline was intended to simply show indicative calculations, noting that for a single CF only particular space structures will work.Psychologically this corresponds to a consideration of the two questions in a way that thoughts making each one individually more likely interfere with each other in the conjunctive case to produce probabilities inconsistent with CPT.The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.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.


Domain transfer learning aims to learn common data representations from a source domain and a target domain so that the source domain data can help the classification of the target domain. Conventional transfer representation learning imposes the distributions of source and target domain representations to be similar, which heavily relies on the characterization of the distributions of domains and the distribution matching criteria. In this paper, we proposed a novel framework for domain transfer representation learning. Our motive is to make the learned representations of data points independent from the domains which they belong to. In other words, from an optimal cross-domain representation of a data point, it is difficult to tell which domain it is from. In this way, the learned representations can be generalized to different domains. To measure the dependency between the representations and the corresponding domain which the data points belong to, we propose to use the mutual information between the representations and the domain-belonging indicators. By minimizing such mutual information, we learn the representations which are independent from domains. We build a classwise deep convolutional network model as a representation model and maximize the margin of each data point of the corresponding class, which is defined over the intraclass and interclass neighborhood. To learn the parameters of the model, we construct a unified minimization problem where the margins are maximized while the representation-domain mutual information is minimized. In this way, we learn representations which are not only discriminate but also independent from domains. An iterative algorithm based on the Adam optimization method is proposed to solve the minimization to learn the classwise deep model parameters and the cross-domain representations simultaneously. Extensive experiments over benchmark datasets show its effectiveness and advantage over existing domain transfer learning methods. Transfer learning is a machine learning problem which deals with data from two domains –6. One dℓ2 norm distance to match the two domains.In this case, it is very necessary to map the data points of both domains to a common data space so that they lie in the same distribution, and we can directly train a model for the target domain by using both domains' data points' representations. Another solution is to learn a model for the source domain first and then adapt it to the target domain. In this paper, we focus on the first solution where the data points are mapped to a common space. This solution aims to learn domain transferable representations for data points in different domains. Different representation learning methods have been applied for domain transferable representation learning, including multikernel learning –10, deepIn this paper, we study the problem of learning domain transfer representations. However, we do not consider the distribution matching of two domains but consider learning representations which can be directly generalized to two domains.  Theauthors in  present   The authors in  proposed  The authors in  proposed  The authors in  developeIn this section, we briefly introduce the state-of-the-art methods for transferable representation learning.All the above methods are based on the matching of two domains' distributions of the data representations. The two key components of this method are the representation of distributions and the metric of the mismatching of the two distributions. In this paper, we give up this framework and propose a completely different framework for domain transfer learning. We observed that for an ideal representation model across two different domains, from its output of one data point, we cannot tell which domain it is from. At the same time, we can separate it from its true class and the other classes according to its output of the cross-domain representation model. It means that the representation of a data point is independent of its domain, but closely relevant to its class. Thus, instead of measuring the mismatch of source and target domain distributions, we measure the independence of the representations and domain-belonging indicators of the data points. To measure the dependency between the representation and the domain indicator, we employ the mutual information. By minimizing the mutual information between them, we learn the domain-independent representations. Meanwhile, we also propose to maximize the margin of each data point so that it can be separated from data points from other classes and kept close to the data points from the same classes.ℓ2 norm term to control the complexity of the model. An iterative algorithm based on Adam is developed to solve the problem.Motivated by the above ideas, we propose a novel deep learning model for the representation of data points of transfer learning problems. Firstly, to enhance the ability to discriminate data points of different classes, we propose to learn a unique deep convolutional network for each class, named classwise convolutional representation model. This is different from traditional domain transfer representation models, which learns a common model for all classes. To make the outputs of this model independent from the domain indicators, we propose to minimize the mutual information between the representation model outputs and the domain indicators. The mutual information estimation is based on the probability of representations and conditional probability of domain indicators given representations. We develop novel estimators for the conditional probability of domain indicators given representations. The estimator is defined over the neighborhood of the data point of the given representation, and it calculates the normalized summation of the soft weights of the data points from the input domain. To make the outputs of the model to be discriminate, we proposed to maximize the margin of each data point in the corresponding class. The margin is defined as the difference between intraclass dissimilarity and the interclass dissimilarity. The intraclass dissimilarity is defined in an intraclass neighborhood which contains a set of neighboring data points from the same class, while the interclass dissimilarity is defined in an interclass neighborhood which contains a set of neighboring data points from the other classes. To learn the representation model parameters, we build a unified learning framework. The objective function is defined by combining the margins, mutual information, and a squared The overall diagram of the proposed learning framework for each class is given in For the first time, the idea of learning cross-domain representations which are independent of domains is proposed for transfer learning. Instead of learning representations and making the distributions of two domains' representations to match each other, we directly learn representations which are independent of their domain-belonging indicators. The mutual information is used to measure such dependency of representations and domain indicators, and it is minimized to seek the domain-independent representations.We develop a novel and practical representation learning method to minimize the mutual information between the data points' representations and domain indicators. The mutual information between representations and domain indicators of data points is estimated according to the probability of representation and the conditional probability of domain indicator given representation. We estimate the conditional probability of a data point's domain indicator given its representation over its neighborhood. It is calculated as a summation of the normalized Gaussian kernel based similarity measured of the data points in the neighborhood but from the considering domain.We propose a novel transfer learning framework for learning domain transfer deep representation models. It is a classwise model and we learn the parameters by simultaneously maximizing the margin of each data point of this class and minimizing the mutual information between the data points' representations and their domain indicators. An iterative algorithm is developed to learn the optimal representations and the parameters of the model to output these representations.Our contributions are of three folds:The paper is organized as follows: in In this section, we give a list of detailed definitions of the symbols used in the following sections.n training data points, denoted as 𝒳={X1,…, Xn}, where Xi={xi1,…, xini} is the i-th data point, which is composed of ni instances, and xij is the j-th instance of the i-th data point. For the computer vision problem, a data point is an image, and an instance is an image patch, while for the natural language process problem, a data point is a sentence, and an instance is the embedding vector of a word.We assume we have a set of L classes, the training set can be divided into subsets of L classes and a set of unlabeled data points whose class labels are not known yet. The training set can be denoted as follows:𝒳l is the subset of the l-th class and 𝒳U is the subset of the unlabeled data points.We consider a classification problem of l-th class, we learn a class-specific deep CNN model to represent the data point X,m dimensions as the class-specific convolutional vector, and Wl presents the parameters of the model.For the CNN model is good at extracting local patterns by utilizing a large number of sliding local filters, while in most domain transfer applications discussed in this paper, the local patterns play the most important role. For example, in the cross-domain image categorization task, for two images of different domains but containing the same object, the CNN model can capture the local region of the object with some local filters while ignoring the contexts which may vary in different domains. Another example is the text-related tasks; long sentences of the same topic may have different linguistic styles of different domains, but still contains short phrase, which could be captured by the CNN model effectively by its sliding local filters to extract features from short phrases.CNN model, compared to the other deep learning models, such as recurrent neural network (RNN), has a more efficient training process. The CNN model has a parallel structure, and the responses of a sliding filter are calculated independently from each other; thus, its computing can be easily paralleled by GPU. This is different from the RNN model which has a sequential structure, where the response of a node is calculated based on the response of the previous nodes, which makes its computing time longer than the CNN model.We choose to learn the convolutional representations due to the following two reasons:l-th class and the other classes as far as possible so that the classification performance can be improved. To this end, we propose to learn to discriminate convolutional representations for the data points of l-th class, by maximizing the local margin of each data point in this class. For the local margin of a data point in the l-th class, Xi ∈ 𝒳l is defined by its intraclass neighborhood and interclass neighborhood. The intraclass neighborhood is the set of κ nearest neighboring data points in the same class, 𝒳l:ℓ2 norm distance between their l-th class-specific convolutional representations is used to measure the distance of neighbors. Meanwhile, the interclass neighborhood is the set of κ nearest neighboring data points from a different class:Naturally, we hope the class-specific convolutional representations can separate the data points of the Xi, we use the convolutional representations of its class, the l-th class, even for the data points of the other classes. We further calculate an affinity measure for Xi and a data point Xj from 𝒩i+, according to their class-specific convolutional representations and a Gaussian kernel function:g(x)=exp(−‖x‖F2/2σ2).Note that to search for the interclass neighbors of Xi and data points in 𝒩i−:Similarly, we also calculate the interclass affinity between Xi is defined as the difference between the weighted intraclass convolutional dissimilarity and the interclass dissimilarity:The local margin of l-th class from the other classes. To this end, we minimize the following objective function of margins over the data points of the l-th class to learn the convolutional representation network parameters:We proposed to maximize the local margin to improve the ability to separate data points of the 𝒳S and 𝒳T; thus, 𝒳=𝒳S  ∪  𝒳T. We introduce a domain indicator for each data point Xi to present which domain it is from, πi ∈ {source, target}, where πi=source indicates that Xi is a source domain data point, while πi=target indicates that it is a target domain data point. Naturally, we hope the classwise convolutional representations of the source and target domains are mapped to a common space of the same distribution. To this end, we impose that the representations of the data points and their domain indicators are independent of each other so that from the presentation, we cannot measure which domain it is from. To measure the mutual dependence between the classwise representation z and the domain indicator π, we proposed to use the mutual information between them, I.Since we are considering the problem of domain transfer learning, the training data points are from a source domain and a target domain, and we denote the source domain training set as x, and y, the definition of mutual information of x and y is calculated by the double integral as follows:p is the joint probability function of x and y and p(x)(p(y)) is the probability function of x(y). For the discrete variables, the mutual information is calculated by the double sum:According to the probability theory and information theory, the mutual information between two variables is a measure of the mutual dependence between them. For two variables, DKL(p(x | y)p(x)) is the Kullback–Leibler divergence between p(x | y) and p(x) and p(x | y) is the conditional probability of x given y. Following equation  as follows:To estimate the mutual information over the training set, we propose to recalculate p(π | zi), and the probability of the convolutional representation, p(zi).In the following, we discuss how to estimate the conditional probability of domain indicator given the convolutional representation, π given a data point, we propose to calculate the density of π over the neighborhood of Xi. 𝒳i is the set of k-nearest neighbors,π over 𝒳i is calculated as the empirical distribution,δ(x)=1 if x is true, otherwise 0. According to equation (p(π | zi) is the weighted summation of δ(πj=π) over 𝒩i, and the weights are hard weight (1/|𝒩i|). We release the calculation of the weights as soft weight according to Gibbs distribution as follows:To estimate the probability of ribution,pπ ∣ zi=1j∈𝒩iωij=1 and ωij ≥ 0.The weights satisfy the constraints of ∑zi as follows:We assume the convolutional representations are evenly distributed; thus, we use a simple empirical distribution function to calculate the probability of z and π as follows:Substituting equations  and 15)15) in 1, we rewrTo simplify the equations, we introduce the following variables:hi, his, and hit as follows:We rewrite equation  with hi,I as follows:To learn a cross-domain representation to map the data of both domains to common space, we reduce the dependency of the domain indicator and the convolutional representation variables as much as possible. Since the mutual information measures the dependency, we minimize In this way, we hope that the learned representations are independent from the domains as much as possible so that it can be generalized to adapt to both domains.Wl‖F2 term is used to control the complexity of the model to prevent the overfitting problem and C1 and C2 are the tradeoff parameters. In the objective, the first term is a large margin corresponding term, while the second and third terms are corresponding to the entropies of location distribution of convolutional representations over the neighborhood specified by source and target domains. The fourth them is corresponding to the entropy of the overall location distribution of representations.To construct the learning framework for the domain adaptation problem based on the classwise deep CNN representation model, we combine the objects of equations  and 22)22) for tzi, i=1,…, n are the outputs of a deep CNN function, fl, while it also defines the neighborhoods and affinities. To solve the problem of equation  and ρ is its penalty parameter. We solve this problem by alternately updating the variables in an iterative algorithm.To solve this problem, we use the ADMM algorithm. Following ADMM, we have the following optimization problem:zi is conducted by solving the following ionization problem:ρ is a descent step parameter and ∇zio1(zi) is the gradient function regarding zi,The updating of Wl is conducted by solving the following minimization problem:Updating of We also use the backprorogation algorithm to solve this problem, based on the chain rule:The dual variable is updated by gradient ascent:In this section, we give the overall iterative learning algorithm of the proposed minimum mutual information transfer representation (MMITR) method. In this algorithm, it has an updating strategy similar to the expectation-maximization (EM) algorithm. In each iteration, we firstly fix domain transfer representations to update the inter- and intraclass affinity metrics in an E-step and then fix the inter- and intraclass affinity metrics to update the CNN parameters and the representations in an M-step. The iterations are stopped until a maximum iteration number is reached or the objective value reaches a threshold. The overall algorithm is described in X, to classify it, we calculate its classwise representation and corresponding margin regarding each class:When we have a new data point, http://www.amazon.com), Webcam (photos taken by web camera), and DSLR .  Office-31: this dataset has 4,652 images of 31 classes. The images are from three different domains: Amazon : this dataset is an image set for facial expression recognition. It has images of 123 subjects, and each subject is treated as a domain. This dataset has 593 videos in total, and for each video, there are about 20 frames. Each image of a face belongs to one of the 7 expression classes.  Amazon: this dataset is a text classification task dataset. The texts are from three different domains, and each domain is the review for products of books, DVD, and music. For each domain, there are 2,000 positive review texts and 2,000 negative review texts.In our experiments, we use the following datasets as benchmark datasets:In this experiment, we use each domain of a dataset as a target domain in turn and the remaining domains as the source domains. For each test domain, we use the leave-one-out protocol to split it into a training set and a test set. Each data point of a target domain is used as a test data point in turn, and the remaining data points are combined to form a training set. The training set of the target domain is randomly split to an unlabeled set and a labeled set, with equal size. The data points of the source domains are always treated as labeled in our setting. Our algorithm is performed over the training set to learn the parameters of the classwise representation model and the domain-independent representations and then used to classify the test data points. The classification accuracy is used to evaluate the performance of the algorithm.In our experiment, we first study the properties of the algorithm experimentally, including its sensitivity to the tradeoff parameters and its convergence property to iteration numbers. Then, we compare its performance to state-of-the-art domain transfer learning algorithms.(1) Sensitive to Tradeoff Parameters. There are two tradeoff parameters in our algorithm: C1 and C2. They are the weights of the mutual information term and the complexity reduction term in our object. We plot the accuracy of our algorithm regarding different values of C1, as shown in C1 increases. Since C1 is the weight of the mutual information term to measure how dependent the representation is from the domain, this indicates that a more domain-independent representation helps the classification in the target domain. Actually, the more independent the representation is from the domain, the better the data of different domains are merged. Thus, the source domain can benefit the learning problem in the target domain more. This phenomenon is even more obvious in the CK + dataset; when C1 grows from 1 to 10, the accuracy is boosted significantly. This is a piece of strong evidence how the minimum mutual information improves the transfer learning.C2 are shown in C2. Since the algorithm is not sensitive to the change of C2, the tuning of this parameter will be easy for a specific dataset. One only exception is the case when C2 varies between 1 and 10, the accuracy changes dramatically.The accuracy curves of classification with different values of (2) Convergence Analysis. Since our algorithm is an iterative algorithm, it is critical to know when to stop the iterations. We study the convergence of the algorithm by plotting the accuracy over different datasets with varying numbers of iterations in To solve a minimization problem in our algorithm, we employed the ADMM algorithm. To verify if the ADMM algorithm solves the optimization of the minimization problem effectively, we plot the object values of the learning problem with increasing numbers of iterations in We compare our algorithm, MMITR, to several state-of-the-art transfer learning algorithms, including the Deep Adaptation Network (DAN) , SelectiThe comparison of accuracy results is given in λ and the Entropy Penalty γ, and we set their values to 1 and 0.1, respectively. For the STM algorithm, there are two hyperparameters: C for the tradeoff between maximal margin and training loss and λ for the tradeoff between the SVM empirical risk and the domain mismatch loss. In this experiment, we set both their values to 1. For the SSKMDA algorithm, it has five tradeoff parameters between the model components: μ, η, β, γs, γt, and their value setting in our experiment are 10, 2, 0.1, 0.1, and 1, respectively. The DTMKL algorithm has only one hyperparameter: the regularization parameter C, and we set it to 0.5 in the experiments. For our algorithm MMITR, it has two tradeoff parameters, C1 and C2; for each benchmark dataset, we report the best results among the results obtained by using different values of C1 and C2.The conditions of the methods of the results reported in 6 are described in details as follows. For the DAN algorithm, it has two hyperparameters: the MMD Penalty In this paper, we proposed a novel framework for transfer learning. Not like the traditional transfer learning which tries to match the representations of source domain and target domain, we proposed to learn domain-independent representations. We argue to measure the dependency of learned deep representations and domain by mutual information and learn the domain-independent deep representations by minimizing the mutual information. We also proposed a practical estimation method for the mutual information between domain and deep representations. A classwise deep representation neural network work is trained under this framework and used to classify new data points. Experiments over benchmark datasets for transfer learning verify the effectiveness of the proposed method.The new concept proposed in this paper is a novel domain transfer learning framework which minimizes the mutual information between the domain transfer representations and the domain indicators so that the gaps among domains can be effectively leveraged and a common representation space is learned. The new method developed in this is a novel iterative learning algorithm to learn the domain transfer representations based on CNN models.


Construction and demolition (C&D) materials obtained from the demolition of buildings are proven to be qualified and sustainable subgrade fillers. The permanent deformation response of subgrade C&D materials under different moisture contents, degrees of compaction, deviator stresses, and confining pressures was revealed by carrying out dynamic triaxial texts. Then, using a four-factor and three-level orthogonal test and by calculating the Gray correlation degree of each factor, the influence degree of each factor on the permanent deformation was determined. The results indicated that two different response types of the permanent deformation of subgrade C&D materials, plastic shakedown and plastic creep, were identified as reason behind the increase in stress levels. Also, according to the Gray correlation analysis results, the permanent deformation of highway subgrade filled with C&D materials is influenced by the deviator stress most significantly, followed by moisture content, degree of compaction, and confining pressure. Finally, a permanent deformation prediction model about this kind of subgrade filler with a reasonable prediction accuracy was proposed. With the rapid advancement of urbanization, many countries have vast waste resources, including plastics, glass shards, metal shavings, and construction and demolition (C&D) materials . Survey Excessive permanent deformation of highway subgrade is likely to lead to distresses such as rutting and cracking in the asphalt pavement ,12. Therα1 and α2 are the regression coefficients, and N is the number of load cycles.After analyzing the influencing factors of permanent deformation, many researchers built various models to predict the permanent deformation and provide conveniences for engineering application. Initially, researchers proposed models of permanent deformation concerning load cycles. Barksdale found that the permanent deformation of silt and crushed stone showed a logarithmic growth trend with the increase of load cycles. He proposed the following half-logarithm model :(1)εp=αSubsequently, based on the RLT test results of the silt, Monisimith et al.  proposedatmσ is the reference stress (atmospheric intensity of pressure), atmσ= 100 kPa, octτ is the octahedral shear stress, octσ is the bulk stress, and α1 to α4 are the regression coefficients.Then, some classical prediction models were proposed based on Equation (2). The model is only of mathematical meaning and does not roundly reflect the stress and physical state of subgrade fillers. A rational prediction model should embody these factors synchronously. Therefore, Puppala et al. introduced the bulk stress and the octahedral shear stress into Equation (2) successively in 1999  and 2009w is the actual moisture content of subgrade, omcw is the optimal moisture content of subgrade, and α1 to α5 are the regression coefficients.The two prediction models established by Puppala considered the influence of stress state but ignored the influence of physical factors. Later, Zhang  found tha, b, c, and d are regression coefficients.Chow et al.  carried dσ is the deviator stress, rM is the resilient modulus, and I and J are the regression coefficients.In previous studies, the elastic behavior of subgrade fillers under load was also considered in the prediction model of permanent deformation. For example, the model includes the resilient modulus established by Rasul et al. , which cAs mentioned above, the main factors affecting the permanent deformation of subgrades are the load cycles, the stress state, and the physical state. However, previous studies on comprehensively analyzing these factors together and comparing their levels of influence are less common and insufficient. At the same time, in the prediction models shown in Equations (1)–(7), the effect of the degree of compaction on permanent deformation was not visibly reflected. Therefore, it is a challenge and meaningful to comprehensively analyze the impact of various factors and propose a straightforward and applicable model for predicting the permanent deformation of C&D materials.As a potential subgrade filler, the permanent deformation is an important evaluation index for C&D materials. At present, few pieces of literature report the permanent deformation of the subgrade filled with C&D materials. This study aims to reveal the permanent deformation response of C&D materials under repeated loads, investigate the influence of various factors on their permanent deformation, and finally propose a prediction method for permanent deformation of the subgrade built by C&D materials. The structure of this paper is as follows: the preparation process and the basic properties of the C&D materials are introduced in the forthcoming section. After that, an orthogonal test was designed to comprehensively investigate various factors and levels, and dynamic triaxial tests were carried out. Afterward, the Gray correlation degree of each factor was calculated to determine different influence degrees on the permanent deformation. Then, the applicability of some existing models to predict the permanent deformation of C&D materials was confirmed by data from the orthogonal test in this study. Next, based on the results of Gray correlation analysis, the existing model with good applicability was improved, and the new model for predicting the permanent deformation of C&D materials was verified for accuracy. The main findings are summarized in the last section.The C&D materials were taken from the Tongzhou–Daxing section of the capital area highway loop project located in Beijing, China. The preparation process of this subgrade fillers was as follows : firstlyAccording to field test results based on different mixing proportions, three batches of materials with particle sizes ranging from 0 to 4.75 mm, 4.75 to 9.5 mm, and 9.5 to 31.5 mm were mixed to form subgrade fillers in a mass ratio of 25%:25%:50%, so that the particles of different batches could be better interlocked to ensure higher strength of subgrade. 3, and the optimal moisture content (OMC) was 14.8%. The contents of organic and soluble salt were 1.90% and 0.376%, respectively. The values of CBR corresponding to 30, 50, and 98 times of compaction for each layer were 10.9%, 47.7%, and 98.6%, respectively. The particle size analysis for these materials is presented in The basic properties were tested by selecting some representative C&D materials at the construction site. The plastic limit was 22.5%, the liquid limit was 28.1%, and the natural moisture content was 12.7%. The compaction test results indicated that the maximum dry density was 1.843 g/cmIn China, the holistic bearing capacity of highway subgrade is evaluated according to the deflection value. In this study, the six continuous sections in the experiment section (K12 + 960–K13 + 060) were selected to measure deflections of the completed subgrade top by Beckman beam deflection tests (JTG E60-2008). The truck used in the test had a load of 100 kN and a tire pressure of 0.7 MPa, and test points were located at the centerline of subgrade. The permanent deformation characteristics of C&D materials were studied by dynamic triaxial tests. As mentioned above, four influencing factors were set as factor A (moisture content), factor B (degree of compaction), factor C (confining pressure), and factor D (deviator stress). Usually, the moisture content in subgrades will gradually increase and become stable finally . ElliottAccording to requirements for the specimen size in the dynamic triaxial test and the range of particle size of C&D materials, cylindrical specimens with a height of 300 mm and a diameter of 150 mm were prepared. Different moisture contents and degrees of compaction of specimens were set according to Dynamic triaxial tests were carried out after specimens were completed. The selected apparatus was the Dynatriax 100/14 static–dynamic triaxial test system made by the Italian Controls company  b. The cy(1)Elastic shakedown, where the applied cyclic stress is sufficiently small compared to the strength of the material, and the material response is purely elastic.(2)Plastic shakedown, where the rate of cumulative permanent deformations decreases gradually until it reaches a stabilized state, and the material gradually achieves a long-term steady-state response.(3)Plastic creep, where the permanent deformation continues to accumulate, and the material response to cyclic loads shows an unstable state; however, the material plastic response neither terminates nor leads to a sudden collapse.(4)Incremental collapse, where the applied cyclic stress is relatively large to cause the material to reach and exceed the yield stress, and the accumulated plastic deformations then increase rapidly with failure occurring in a small number of load cycles.The evolution of permanent deformation of subgrade fillers with load cycles is complex, and the response of the material under cyclic loads can be classified into four different categories according to Werkmeister et al. :(1)ElasThe permanent deformation under different conditions in In conclusion, the permanent deformation response of C&D materials exhibited a stress-dependent characteristic. It is worth mentioning that no obvious incremental collapse state was observed in the permanent deformation response of C&D materials; this infers that subgrade filled with C&D materials should have better stability and durability in the service period.Gray correlation analysis was used to determine the different influence degrees of various factors on dependent variables in complex systems. The geometric proximity between discrete sequences can be evaluated by the Gray correlation model . In the (1) The data series ρ is the coefficient distinguishing the proximity degree of X0 and iX.(2) A discrete function of the relational degree coefficient  was obtained by the following equation so that the relational degree between the comparison and reference sequences could be determined:iX to the reference sequence X0 was obtained by(3) The Gray relational degree of the comparison sequence After initiating treatment, the results are listed in The absolute difference between the two sequences was acquired byρ = 0.5 [∆max = 4.3557 and ∆min = 0.The Gray correlation coefficient was given by ρ = 0.5 . From TaX0 and the comparison sequence Xi. The calculation results were as follows:Finally, Equation (11) was used to calculate the Gray correlation degree of the reference sequence The order of the Gray correlation was Factor D > Factor A > Factor B > Factor C (deviator stress > moisture content > degree of compaction > confining pressure). Therefore, the influence of deviator stress on the permanent deformation of C&D materials was the most significant. Secondly, the effects of the moisture content and degree of compaction were similar because the Gray correlation degrees were approximate. It also could be found that the Gray correlation of confining pressure was slightly smaller than the other three factors; thus, the confining pressure had the least influence on the permanent deformation of C&D materials in this study.R2), Witczak et al. [R2 ≤ 0.69, a good fit is R2 = 0.7–0.89, and an excellent fit is R2 ≥ 0.9. Zhang et al. [Some models were selected for reference and comparison to establish a model for predicting the permanent deformation of C&D materials. By analyzing the prediction models mentioned above (Equations (1)–(7)), it can be seen that the first five models made it easy to obtain model parameters and the model form was simple and easy to be understood. Therefore, Equations (1)–(5) were selected in this study to determine the applicability of predicting the permanent deformation of C&D materials under different working conditions. k et al.  put forwg et al.  also useR2 for the first three equations were all lower than 0.4, which shows that these models were not applicable to describe the permanent deformation of C&D materials. The adjusted R2 values of Equations (4) and (5) were between 0.7 and 0.89 and root-mean-square errors were also significantly reduced. Although the prediction accuracy was increased encouragingly, this study still expects a better improvement.K represents the degrees of compaction of subgrade, and α6 is the regression coefficient.Based on the Gray correlation analysis, it was found that the degree of compaction had a non-negligible influence on the permanent deformation. However, Equations (4) and (5) did not consider the contribution of the degree of compaction to permanent deformation. Therefore, the form of Equation (5) was revised, and the degree of compaction reflecting the physical state of the material was taken into account as a new variable, and the new model was established as shown in Equation (15).The major influence factors, including the physical state, the constraint effect, and shear effect, and the load cycles were comprehensively reflected in the model. Therefore, the prediction model could be better understood. Moreover, the universal triaxial testing system and basic performance test were enough to get its parameters. The parameters and goodness of this model are shown in R2 value greater than 0.9 and the root-mean-square error remained at a low level. The new model provided an excellent fitting and was more suitable than Equations (4) and (5) for predicting the permanent deformation of C&D materials. In order to ensure the robustness of the new model, verifications were implemented via the permanent deformation data of C&D materials under different numbers of loads with the other nine different conditions, as shown in In this study, material basic properties tests and dynamic triaxial tests were carried out for C&D materials from northern China. The permanent deformation response of these materials under repeated loads was analyzed. Then, the different influence degrees of deviator stress, confining pressure, degree of compaction, and moisture content on the permanent deformation were determined according to the Gray correlation analysis. Finally, a new permanent deformation prediction model that comprehensively considers the stress state and physical state of the subgrade filler was established and verified. The following conclusions were drawn:(1) The permanent deformation response of C&D materials exhibits a stress-dependent characteristic. Upon increasing in stress levels, the permanent deformation response of C&D materials changed from plastic shakedown to plastic creep in this study.(2) The results of the Gray correlation analysis indicated that the deviator stress influences the permanent deformation of highway subgrade filled with C&D materials most significantly, followed by moisture content, degree of compaction, and confining pressure. However, the Gray correlation degrees of these factors are relatively close. Therefore, when analyzing the permanent deformation problem, the influence of these factors should be considered comprehensively.(3) According to the Gray correlation analysis, the permanent deformation of C&D materials is significantly influenced by the degree of compaction. Therefore, a new exponential model including load cycles, moisture content, degree of compaction, volume stress, and shear stress was established. The results showed that the proposed model, which has a high fitting degree, can provide reference for the prediction of the permanent deformation of highway subgrade filled with C&D materials.


Drosophila models showing a direct or indirect role in some neurodegenerative diseases.Ubiquitin-like/ubiquitin-associated proteins (UbL-UbA) are a well-studied family of non-proteasomal ubiquitin receptors that are evolutionarily conserved across species. Members of this non-homogenous family facilitate and support proteasomal activity by promoting different effects on proteostasis but exhibit diverse extra-proteasomal activities. Dysfunctional UbL-UbA proteins render cells, particularly neurons, more susceptible to stressors or aging and may cause earlier neurodegeneration. In this review, we summarized the properties and functions of UbL-UbA family members identified to date, with an emphasis on new findings obtained using  Protein homeostasis (proteostasis) is a fine-tuned process that controls the biogenesis, folding, trafficking and degradation of proteins ,2. ProteThe proteasome is a large multi-subunit complex that selectively degrades approximately 80–90% of misfolded or damaged cellular proteins in a ubiquitin (Ub)- and ATP-dependent manner and also provides proper Ub recycling ,4,5,6,7.The ubiquitin proteasome system (UPS) needs ubiquitylation to covalently mark substrates with Ub before degradation . The LysUPS mainly targets soluble and monomeric proteins over aggregated ones by a mechanism involving heat shock 70-kDa protein (HSP70) and the sequential actions of three classes of ubiquitin ligases: E1, E2, and E3. Degradation by UPS requires ubiquitin-activating enzyme (E1), which binds and activates Ub in an ATP-dependent mechanism. Ub is then transferred to an active site of ubiquitin-conjugating enzyme (E2), and ubiquitin protein ligase (E3) ultimately transfers Ub directly or indirectly from E2 to protein substrates .Specific subunits of 19S-regulating particles have been identified as Ub receptors with the ability to recognize free Ub and Ub-conjugated proteins. Rpn10 and Rpn13 are the most well-studied proteasomal Ub receptors and appear to be evolutionarily conserved across species from yeast to mammals ,23,24. NThe UbA domain binds mono- and poly-ubiquitin chains as well as ubiquitylated proteins, while the UbL domain interacts with the ubiquitin-interacting motif (UIM) domain of the proteasomal Ub receptor Rpn10. UbA domains contain a special structure of hydrophobic patches that provide stronger binding to Lys48-linked polyubiquitin chains than to mono-Ub ,26,27 FA. The UbAny attempts to catalog UbL-UbA-bearing proteins in a single group may be far from useful or precise. Beyond structural analogies and sequence similarities in their domains , these pwww.expasy.org) to search for the presence and localization of relative domains.The primary structures of UbL-UbA members were analyzed by Expasy Prosite , Parkinson’s disease (PD), frontotemporal dementia (FTD), Huntington’s disease (HD), Amyotrophic Lateral Sclerosis (ALS), and prion diseases, which are collectively classified as proteinopathies ,30,31,32Drosophila models showing direct or indirect roles in neurodegenerative diseases.In this review, we summarized the properties and functions of different UbL-UbA members, with an emphasis on new findings obtained using The DNA damage-inducible protein (Ddi1) was initially discovered and mostly studied in yeast ,36,37. TCaenorhabditis elegans (C. elegans) revealed that Ddi1 indirectly involves in proteasomal function by activating SKN1, a transcription factor related to mammalian Nrf1/2 [C. elegans exhibits the ability to cleave the proteasome activator SKN1 via its aspartic protease activity and activates an endoplasmic reticulum (ER)-associated isoform of SKN1 [Few Ddi substrates have been studied to date in mammals. A weak relationship was previously reported with the proteasomal regulatory protein Rpn1 ,46, and n Nrf1/2 . During  of SKN1 . Mammali of SKN1 ,49,50, sDdi1 gene of patients conferring a gain of Ddi function that may play role in this kind of dementia [Ddi1 appears to contribute to neurodegenerative diseases. For example, in a specific familial variant with the neuroradiological features of AD, but lacking amyloid-β deposits in the brain, whole-exome sequencing revealed a novel nonsense mutation in the dementia .Drosophila carries one Ddi1 homolog that was identified in 2011 as CG4420 and molecularly characterized as a ubiquitin receptor with the ability to bind to Rpn10 [to Rpn10 . CG4420 to Rpn10 .Drosophila models of Rngo may expand our understanding of the functions of Ddi1 in neurological diseases.By employing an unbiased ubiquitin proteomic approach, further studies demonstrated that Rngo was a target of the E3 ligase UBE3A  and, intNedd8 Ultimate Buster 1 (NUB1) is a predominately nuclear-located protein that is mainly involved in the regulation of ubiquitin-like proteins, such as Neural Precursor Cell Expressed Developmentally Down-regulated 8 (NEDD8) and FAT10, with high levels of NUB1 accelerating the degradation of NEDD8 B, FAT10,The N-terminal UbL domain of NUB1 may bind Rpn1 and Rpn10 proteasomal subunits, while the C terminus carries two UbA domains that are involved in the interaction with ubiquitin-like proteins, as described above. Recent findings demonstrated that the neddylation activity of NUB1 is under the control of the oncogenic E3 ubiquitin ligase Mdm2. Mdm2 acts as a positive regulator of NUB1 function because di-ubiquitylation on NUB1 lysine-159 by Mdm2 leads NUB1 to negatively regulate Nedd8 and neddylated target proteins .A longer unsliced variant of NUB1 (NUB1L) has also been identified and characterized . NUB1L dThe role of NUB1 in the regulation of neddylated substrates remains an area of active investigation. The biological outcomes of post-translational modifications such as ubiquitylation and neddylation appear to be diverse, from protein destruction to transcriptional regulation, subcellular localization, DNA repair, endocytosis, signal transduction, and autophagy. Neddylation of the tumor suppressor p53 is mediated by NUB1 in order to regulate p53 localization and inhibits its transcriptional activity rather than promote its degradation via proteasomes  Figure B.The involvement of NUB1 in neurological diseases associated with the formation of toxic aggregates has been intensively investigated. The immunohistochemical characterization of eosinophilic intranuclear inclusions (INI) in the brains of patients with intranuclear inclusion body disease (INIBD) revealed high levels of NUB1 and NEDD8, suggesting a role for neddylation in the formation of this class of protein aggregate . In patiThe role of NUB1 in the toxic aggregation of the microtubule-associated protein Tau has been also investigated. The abnormal phosphorylation and aggregation of Tau have been associated with neurodegenerative diseases, including AD and frontotemporal lobar degeneration (FTLD). Among the numerous kinases that phosphorylate Tau such as glycogen synthase kinase 3β (GSK3β) is strongly expressed in the brain and is involved in the hyperphosphorylation of Tau, with an increase in GSK3β levels being observed in AD ,73,74,75NUB1 abolishes the recruitment of GSK3β to Tau inclusions by disrupting the Tau-GSK3β interaction. Moreover, NUB1 lowers the GSK3β-mediated phosphorylation of Tau, leading to a reduction in Tau levels in intracellular inclusions . TherefoCG15445 gene is the Drosophila homolog of NUB1, which has been referred to as Drosophila NUB1 following the study by Lu and colleagues in which a Drosophila model of HD was employed to identify gene modifiers of the toxicity associated with high levels of the mutant form of the protein huntingtin (mHTT) [HTT gene that leads to the formation and neurotoxic accumulation of polyglutamine-expanded mHTT [Drosophila model of HD allowed Lu and colleagues to confirm that NUB1 reduced mHTT levels [The n (mHTT) . Huntingded mHTT . Genome-T levels . ConsistT levels . NUB1 alT levels .NUB1 was previously reported to be inducible by interferon-β (IFNβ) , and rodHR23 gene, hereafter called Rad23 is evolutionarily conserved among eukaryotic organisms. Rad23 A and B, both are essential for DNA repair and protein degradation [Mammalian cells express two homologs of yeast Rad23, the so-called “homolog of Rad23” (HR23) proteins. The radation ,80.The first indication that Rad23 is involved in proteolytic degradation via the proteasome was provided by the interaction between its UbL domain and the ubiquitin-binding site of S5a, a human homolog of yeast Rpn10 . Since tThe C-terminal portion of Rad23 contains two UbA domains  Figure , which aThe UbL domain of Rad23 also exhibits some regulatory activity because the phosphorylation of its serine residues may inhibit the interaction of Rad23 with the 26S proteasome , while iThe role of Rad23 in DNA repair has been extensively documented. The discrimination of the appropriate excision repair pathway according to the type of DNA lesion has proven to be the most important role for Rad23 .Rad23 has been found to be one of delivery factors for ubiquitylated ER substrates after extraction by Cdc48-Ufd1-Npl4 complex in which Cdc48 is a homologue of VCP in yeast . Rad23 wataxin-3 gene (ATXN3) leads to the translation of a pathogenic form with expanded polyQ referred to as SCA3 [ATXN3 expansion mutations.Previous studies demonstrated that the proteasomal degradation of ataxin-3 is regulated by a direct interaction between its UbS2 domain and Rad23 . Since t as SCA3 , Rad23 aRad23 gene was identified in Drosophila in 1999 by Nabirochkina EN et al. It was characterized as a gene involved in proteasomal-related functions due to its up-regulation caused reduction in proteasomal activity [Drosophila carries three Rad23 isoforms referred to as Rad23/1, 2, and 3, respectively  domains  contains one NH domains . Due to oteasome . Therefooteasome .More recently, in a proteome-wide interactome approach, KPC was shown to play a role in the regulation of HOXA2, a transcriptional factor belonging to the HOX family of proteins, which are fundamental for development . In contDrosophila is the isopeptidase-T-3 (ISOT-3A) protein, which contains two UbA domains on the C terminus, but lacks the UbL domain  is the most well-characterized of the five ubiquilins and perhaps the most well-studied UbL-UbA protein because mutations in the  ALS/FTD , and the ALS/FTD . Moreove ALS/FTD .UBQLN2 regulates the protein degradation of ubiquitylated targets not only by proteasomes ,116,117,In addition to their function in controlling protein turnover and homeostasis, UBQLNs have several other distinct activities that are important in various cellular pathways. UBQLNs associate with actin and intermediate filaments  and locaThe UbL domain of UBQLN2 binds to several proteasomal subunits and E3 ligase. It also interacts with the ubiquitin-interacting motif (UIM) of several endocytic proteins such as Eps15, Hrs, and STAT2/Hrp ,126,127.The large STI-like chaperonin-binding region in the central portion of UBQLN2 interacts with the HSP70-like chaperone Stch and is important for ubiquilin multimerization and facilitation of the ERAD pathway ,131. TheUBQLN1 or UBQLN2 leads to the accumulation of AD causative presenilin proteins by slowing down their proteasomal degradation [C9orf72 gene, which is a commonly found mutation in both familial and sporadic ALS [UBQLN2 mutations in ALS patients [UBQLN2 and TDP-43 mutations have also been consistently identified in ALS and FTD patients, with high levels of the UBQLN2 wild type or mutant enhancing the cytosolic accumulation of TDP-43 [Several lines of evidence have linked ubiquilins to diverse neurodegenerative diseases, such as AD ,133 as wradation ,136,137.radation ,139,140.adic ALS . A loss-patients . Moreovepatients ,143 and patients . Recent patients ,146,147.f TDP-43 ,149,150.Drosophila contains a single UBQLN gene homologue located on the X chromosome that is referred to as dUbqn and encodes a predicted protein of 547 amino acids [dUbqn induces various changes in synaptic morphology, resulting in defects in locomotion and learning ability [no acids , which s ability  , resulting in a cytoplasmic ubiquitin-positive (Ub+) TDP-43 pathology [ in vivo . The co-athology . Despiteathology .Drosophila has revealed an important role of UBQLNs in neurodegenerative diseases in which UBQLNs loss-of-function triggered TDP-43 gain-of-toxic function [The study in function . Furtherfunction .Neurons are highly susceptible to dysfunction in the ubiquitin-proteasome system, as demonstrated by the wide spectrum of neurodegenerative and neurodevelopmental disorders . Recent Increases in the pool of free ubiquitin or overexpressing specific ubiquitin ligases, such as the C-terminus of Hsc-70 interacting protein (CHIP) ,159,160,Alternatively, the use of small-molecule compounds to stimulate proteasome activity has also been investigated for its therapeutic value in the treatment of neurodegenerative diseases ,165,166.Based on the general nature of UPS and its extremely large spectrum of action, an overall increase in degradation by stimulating UPS activity may be accompanied by undesirable side effects. Therefore, the precise and selective targeting of disease-causing proteins for proteasomal degradation is expected to have more advantages than the general stimulation of UPS activity. The pursuit of the biological functions of UbL-UbA family members has highlighted the critical roles of these proteins in the control of proteasome activity and revealed that the genetic manipulation of ubiquitin receptors is an effective regulator of the turnover of some specific neurodegenerative disease-related proteins.Despite exciting new findings in this field, the functions of the UbL-UbA proteins have not yet been fully characterized and their targets or cross-talk with the proteasome remain unclear. Once the overall targets of each UbL-UbA family protein are fully identified, these proteins may become an attractive molecular knife to use in the onset of neurodegenerative diseases. As such, the functions of UbL-UbA proteins may be improved for selective delivery to proteasomes and the facilitated degradation of their aggregation-prone targets. Alternatively, abnormal and eventually toxic levels of UbL-UbA proteins may be modulated by specific targeting.Drosophila models will provide a novel insight into the possible beneficial use of UbL-UbA proteins as targets in the treatment of neurodegenerative diseases. Drosophila is an attractive in vivo model for investigating neurodegeneration for several reasons, including its short life span (~60 days), easy genetic manipulation, rapid screening for mutations, and similar biological complexity to that of mammals. Moreover, critical mechanisms in Drosophila neurodegeneration were proven to be regulated in a similar manner in humans. Drosophila is also an excellent in vivo model for screening biologically active compounds, as already evidenced by the numerous candidate drug studies that have been undertaken using Drosophila models of HD, AD, and PD [The use of , and PD .Drosophila has already been proven as a valid system to examine the involvement of NUB1 in mediating the degradation of mHTT, Rad23 in reducing the SCA3 pathology, and UBQLN2 in inducing the TDP-43 pathology. The further characterization of these models may lead to the identification of promising new therapies for these neurodegenerative diseases.As summarized above,