Sip1, a Novel RS Domain-Containing Protein Essential for Pre-mRNA Splicing

Previous studies have shown that protein-protein interactions among splicing factors may play an important role in pre-mRNA splicing. We report here identification and functional characterization of a new splicing factor, Sip1 (SC35-interacting protein 1). Sip1 was initially identified by virtue of its interaction with SC35, a splicing factor of the SR family. Sip1 interacts with not only several SR proteins but also with U1-70K and U2AF65, proteins associated with 5′ and 3′ splice sites, respectively. The predicted Sip1 sequence contains an arginine-serine-rich (RS) domain but does not have any known RNA-binding motifs, indicating that it is not a member of the SR family. Sip1 also contains a region with weak sequence similarity to the Drosophila splicing regulator suppressor of white apricot (SWAP). An essential role for Sip1 in pre-mRNA splicing was suggested by the observation that anti-Sip1 antibodies depleted splicing activity from HeLa nuclear extract. Purified recombinant Sip1 protein, but not other RS domain-containing proteins such as SC35, ASF/SF2, and U2AF65, restored the splicing activity of the Sip1-immunodepleted extract. Addition of U2AF65 protein further enhanced the splicing reconstitution by the Sip1 protein. Deficiency in the formation of both A and B splicing complexes in the Sip1-depleted nuclear extract indicates an important role of Sip1 in spliceosome assembly. Together, these results demonstrate that Sip1 is a novel RS domain-containing protein required for pre-mRNA splicing and that the functional role of Sip1 in splicing is distinct from those of known RS domain-containing splicing factors.Pre-mRNA splicing takes place in spliceosomes, the large RNA-protein complexes containing pre-mRNA, U1, U2, U4/6, and U5 small nuclear ribonucleoprotein particles (snRNPs), and a large number of accessory protein factors (for reviews, see references 21, 22, 37, 44, and 48). It is increasingly clear that the protein factors are important for pre-mRNA splicing and that studies of these factors are essential for further understanding of molecular mechanisms of pre-mRNA splicing.Most mammalian splicing factors have been identified by biochemical fractionation and purification (3, 15, 19, 31–36, 45, 69–71, 73), by using antibodies recognizing splicing factors (8, 9, 16, 17, 61, 66, 67, 74), and by sequence homology (25, 52, 74).Splicing factors containing arginine-serine-rich (RS) domains have emerged as important players in pre-mRNA splicing. These include members of the SR family, both subunits of U2 auxiliary factor (U2AF), and the U1 snRNP protein U1-70K (for reviews, see references 18, 41, and 59). Drosophila alternative splicing regulators transformer (Tra), transformer 2 (Tra2), and suppressor of white apricot (SWAP) also contain RS domains (20, 40, 42). RS domains in these proteins play important roles in pre-mRNA splicing (7, 71, 75), in nuclear localization of these splicing proteins (23, 40), and in protein-RNA interactions (56, 60, 64). Previous studies by us and others have demonstrated that one mechanism whereby SR proteins function in splicing is to mediate specific protein-protein interactions among spliceosomal components and between general splicing factors and alternative splicing regulators (1, 1a, 6, 10, 27, 63, 74, 77). Such protein-protein interactions may play critical roles in splice site recognition and association (for reviews, see references 4, 18, 37, 41, 47 and 59). Specific interactions among the splicing factors also suggest that it is possible to identify new splicing factors by their interactions with known splicing factors.Here we report identification of a new splicing factor, Sip1, by its interaction with the essential splicing factor SC35. The predicted Sip1 protein sequence contains an RS domain and a region with sequence similarity to the Drosophila splicing regulator, SWAP. We have expressed and purified recombinant Sip1 protein and raised polyclonal antibodies against the recombinant Sip1 protein. The anti-Sip1 antibodies specifically recognize a protein migrating at a molecular mass of approximately 210 kDa in HeLa nuclear extract. The anti-Sip1 antibodies sufficiently deplete Sip1 protein from the nuclear extract, and the Sip1-depleted extract is inactive in pre-mRNA splicing. Addition of recombinant Sip1 protein can partially restore splicing activity to the Sip1-depleted nuclear extract, indicating an essential role of Sip1 in pre-mRNA splicing. Other RS domain-containing proteins, including SC35, ASF/SF2, and U2AF65, cannot substitute for Sip1 in reconstituting splicing activity of the Sip1-depleted nuclear extract. However, addition of U2AF65 further increases splicing activity of Sip1-reconstituted nuclear extract, suggesting that there may be a functional interaction between Sip1 and U2AF65 in nuclear extract.     

Human OS-9, a Lectin Required for Glycoprotein Endoplasmic Reticulum-associated Degradation, Recognizes Mannose-trimmed N-Glycans

In the endoplasmic reticulum (ER), lectins and processing enzymes are involved in quality control of newly synthesized proteins for productive folding as well as in the ER-associated degradation (ERAD) of misfolded proteins. ER quality control requires the recognition and modification of the N-linked oligosaccharides attached to glycoproteins. Mannose trimming from the N-glycans plays an important role in targeting of misfolded glycoproteins for ERAD. Recently, two mammalian lectins, OS-9 and XTP3-B, which contain mannose 6-phosphate receptor homology domains, were reported to be involved in ER quality control. Here, we examined the requirement for human OS-9 (hOS-9) lectin activity in degradation of the glycosylated ERAD substrate NHK, a genetic variant of α1-antitrypsin. Using frontal affinity chromatography, we demonstrated that the recombinant hOS-9 mannose 6-phosphate receptor homology domain specifically binds N-glycans lacking the terminal mannose from the C branch in vitro. To examine the specificity of OS-9 recognition of N-glycans in vivo, we modified the oligosaccharide structures on NHK by overexpressing ER α1,2-mannosidase I or EDEM3 and examined the effect of these modifications on NHK degradation in combination with small interfering RNA-mediated knockdown of hOS-9. The ability of hOS-9 to enhance glycoprotein ERAD depended on the N-glycan structures on NHK, consistent with the frontal affinity chromatography results. Thus, we propose a model for mannose trimming and the requirement for hOS-9 lectin activity in glycoprotein ERAD in which N-glycans lacking the terminal mannose from the C branch are recognized by hOS-9 and targeted for degradation.Recognition and sorting of improperly folded proteins is essential to cell survival, and hence, an elaborate quality control system is found in cells. ER⁴ quality control is well characterized with respect to the N-linked oligosaccharides regulating the folding and degradation of newly synthesized proteins in the ER (1). Immediately after polypeptides enter the ER, Glc₃Man₉GlcNAc₂ (G3M9) precursor oligosaccharides are covalently attached and subsequently processed. Terminally misfolded proteins are removed from the ER by the ERAD machinery (1–4). Aberrant conformers are recognized, retrotranslocated to the cytosol, and degraded by the ubiquitin-proteasome system (5, 6). Processing of mannose residues from the N-linked oligosaccharides acts as a timer for the recognition of misfolded glycoproteins in the ER lumen (1, 7). ER α1,2-mannosidase I (ER ManI) in mammals and ER α-mannosidase in yeast preferentially trim mannose residues from the middle branch of N-glycans, generating the Man₈GlcNAc₂ (M8) isomer B (M8B) (8). In mammals, further mannose processing is required as a signal for degradation (1, 9, 10), whereas the presence of M8B is sufficient to signal degradation in yeast (11). The postulated lectin EDEMs in mammals, their yeast homolog Htm1p/Mnl1p, and the yeast MRH domain-containing lectin Yos9p have all been proposed to recognize glycoproteins targeted for degradation (12).The role of Yos9p in glycoprotein ERAD was identified using a genetic screen in Saccharomyces cerevisiae (13). Yos9p, a homolog of hOS-9, contains an MRH domain (14) and functions as a lectin. Yos9p recognizes substrates of the ERAD-lumenal pathway (15–17), generating a large ER membrane complex containing the Hrd1p-Hrd3p ubiquitin ligase core complex (18–20). The M8B and Man₅GlcNAc₂ (M5) N-glycans are predicted to function as ligands for Yos9p (17). Bipartite recognition of both glycan and polypeptide by Yos9p has also been reported (15).Recent studies revealed that two mammalian MRH domain-containing lectins, OS-9 and XTP3-B, are ER luminal proteins involved in ER quality control and form a large complex containing the HRD1-SEL1L ubiquitin-ligase in the ER membrane (21–24). The components of the complex are similar to yeast, suggesting evolutionary conservation, although the molecular mechanisms underlying the role of OS-9 and XTP3-B remain elusive. Studies using lectin mutants have suggested that the MRH domains are required not for binding to ERAD substrates but for interactions with SEL1L (21), which has multiple N-glycans (25, 26). Additionally, lectin activity appears to be dispensable for hOS-9 binding to misfolded glycoproteins (21, 24). Thus, to understand the role of hOS-9 in the ER quality control pathway, the specific carbohydrate structures recognized by the hOS-9 MRH domain need to be identified, and the requirement of the lectin domain in substrate recognition needs to be determined.In the present study we demonstrate that the lectin activity of hOS-9 is required for enhancement of glycoprotein ERAD. We identified the N-glycan structures recognized by the recombinant hOS-9 MRH domain in vitro by frontal affinity chromatography (FAC). Using a model ERAD substrate, NHK (27), we show that the ability of hOS-9 to enhance ERAD in vivo depends on the oligosaccharides present on NHK, consistent with the FAC results.     

Identification of Tumor-associated Autoantigens for the Diagnosis of Colorectal Cancer in Serum Using High Density Protein Microarrays

There is a mounting evidence of the existence of autoantibodies associated to cancer progression. Antibodies are the target of choice for serum screening because of their stability and suitability for sensitive immunoassays. By using commercial protein microarrays containing 8000 human proteins, we examined 20 sera from colorectal cancer (CRC) patients and healthy subjects to identify autoantibody patterns and associated antigens. Forty-three proteins were differentially recognized by tumoral and reference sera (p value <0.04) in the protein microarrays. Five immunoreactive antigens, PIM1, MAPKAPK3, STK4, SRC, and FGFR4, showed the highest prevalence in cancer samples, whereas ACVR2B was more abundant in normal sera. Three of them, PIM1, MAPKAPK3, and ACVR2B, were used for further validation. A significant increase in the expression level of these antigens on CRC cell lines and colonic mucosa was confirmed by immunoblotting and immunohistochemistry on tissue microarrays. A diagnostic ELISA based on the combination of MAPKAPK3 and ACVR2B proteins yielded specificity and sensitivity values of 73.9 and 83.3% (area under the curve, 0.85), respectively, for CRC discrimination after using an independent sample set containing 94 sera representative of different stages of progression and control subjects. In summary, these studies confirmed the presence of specific autoantibodies for CRC and revealed new individual markers of disease (PIM1, MAPKAPK3, and ACVR2B) with the potential to diagnose CRC with higher specificity and sensitivity than previously reported serum biomarkers.Colorectal cancer (CRC)¹ is the second most prevalent cancer in the western world. The development of this disease takes decades and involves multiple genetic events. CRC remains a major cause of mortality in developed countries because most of the patients are diagnosed at advanced stages because of the reluctance to use highly invasive diagnostic tools like colonoscopy. Actually only a few proteins have been described as biomarkers in CRC (carcinoembryonic antigen (CEA), CA19.9, and CA125 (1–3)), although none of them is recommended for clinical screening (4). Proteomics analysis is actively used for the identification of new biomarkers. In previous studies, the use of two-dimensional DIGE and antibody microarrays allowed the identification of differentially expressed proteins in CRC tissue, including isoforms and post-translational modifications responsible for modifications in signaling pathways (5–8). Both approaches resulted in the identification of a collection of potential tumoral tissue biomarkers that is currently being investigated.However, the implementation of simpler, non-invasive methods for the early detection of CRC should be based on the identification of proteins or antibodies in serum or plasma (9–13). There is ample evidence of the existence of an immune response to cancer in humans as demonstrated by the presence of autoantibodies in cancer sera. Self-proteins (autoantigens) altered before or during tumor formation can elicit an immune response (13–19). These tumor-specific autoantibodies can be detected at early cancer stages and prior to cancer diagnosis revealing a great potential as biomarkers (14, 15, 20). Tumor proteins can be affected by specific point mutations, misfolding, overexpression, aberrant glycosylation, truncation, or aberrant degradation (e.g. p53, HER2, NY-ESO1, or MUC1 (16, 21–25)). In fact, a number of tumor-associated autoantigens (TAAs) were identified previously in different studies involving autoantibody screening in CRC (26–28).Several approaches have been used to identify TAAs in cancer, including natural protein arrays prepared with fractions obtained from two-dimensional LC separations of tumoral samples (29, 30) or protein extracts from cancer cells or tissue (9, 31) probed by Western blot with patient sera, cancer tissue peptide libraries expressed as cDNA expression libraries for serological screening (serological analysis of recombinant cDNA expression libraries (SEREX)) (22, 32), or peptides expressed on the surface of phages in combination with microarrays (17, 18, 33, 34). However, these approaches suffer from several drawbacks. In some cases TAAs have to be isolated and identified from the reactive protein lysate by LC-MS techniques, or in the phage display approach, the reactive TAA could be a mimotope without a corresponding linear amino acid sequence. Moreover, cDNA libraries might not be representative of the protein expression levels in tumors as there is a poor correspondence between mRNA and protein levels.Protein arrays provide a novel platform for the identification of both autoantibodies and their respective TAAs for diagnostic purposes in cancer serum patients. They present some advantages. Proteins printed on the microarray are known “a priori,” avoiding the need for later identifications and the discovery of mimotopes. There is no bias in protein selection as the proteins are printed at a similar concentration. This should result in a higher sensitivity for biomarker identification (13, 35, 36).In this study, we used commercially available high density protein microarrays for the identification of autoantibody signatures and tumor-associated antigens in colorectal cancer. We screened 20 CRC patient and control sera with protein microarrays containing 8000 human proteins to identify the CRC-associated autoantibody repertoire and the corresponding TAAs. Autoantibody profiles that discriminated the different types of CRC metastasis were identified. Moreover a set of TAAs showing increased or decreased expression in cancer cell lines and paired tumoral tissues was found. Finally an ELISA was set up to test the ability of the most immunoreactive proteins to detect colorectal adenocarcinoma. On the basis of the antibody response, combinations of three antigens, PIM1, MAPKAPK3, and ACVR2B, showed a great potential for diagnosis.     

Splitting the BLOSUM Score into Numbers of Biological Significance

Mathematical tools developed in the context of Shannon information theory were used to analyze the meaning of the BLOSUM score, which was split into three components termed as the BLOSUM spectrum (or BLOSpectrum). These relate respectively to the sequence convergence (the stochastic similarity of the two protein sequences), to the background frequency divergence (typicality of the amino acid probability distribution in each sequence), and to the target frequency divergence (compliance of the amino acid variations between the two sequences to the protein model implicit in the BLOCKS database). This treatment sharpens the protein sequence comparison, providing a rationale for the biological significance of the obtained score, and helps to identify weakly related sequences. Moreover, the BLOSpectrum can guide the choice of the most appropriate scoring matrix, tailoring it to the evolutionary divergence associated with the two sequences, or indicate if a compositionally adjusted matrix could perform better.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29]     

Loop 2 Structure in Glycine and GABAA Receptors Plays a Key Role in Determining Ethanol Sensitivity

The present study tests the hypothesis that the structure of extracellular domain Loop 2 can markedly affect ethanol sensitivity in glycine receptors (GlyRs) and γ-aminobutyric acid type A receptors (GABA_ARs). To test this, we mutated Loop 2 in the α1 subunit of GlyRs and in the γ subunit of α1β2γ2GABA_ARs and measured the sensitivity of wild type and mutant receptors expressed in Xenopus oocytes to agonist, ethanol, and other agents using two-electrode voltage clamp. Replacing Loop 2 of α1GlyR subunits with Loop 2 from the δGABA_AR (δL2), but not the γGABA_AR subunit, reduced ethanol threshold and increased the degree of ethanol potentiation without altering general receptor function. Similarly, replacing Loop 2 of the γ subunit of GABA_ARs with δL2 shifted the ethanol threshold from 50 mm in WT to 1 mm in the GABA_A γ-δL2 mutant. These findings indicate that the structure of Loop 2 can profoundly affect ethanol sensitivity in GlyRs and GABA_ARs. The δL2 mutations did not affect GlyR or GABA_AR sensitivity, respectively, to Zn²⁺ or diazepam, which suggests that these δL2-induced changes in ethanol sensitivity do not extend to all allosteric modulators and may be specific for ethanol or ethanol-like agents. To explore molecular mechanisms underlying these results, we threaded the WT and δL2 GlyR sequences onto the x-ray structure of the bacterial Gloeobacter violaceus pentameric ligand-gated ion channel homologue (GLIC). In addition to being the first GlyR model threaded on GLIC, the juxtaposition of the two structures led to a possible mechanistic explanation for the effects of ethanol on GlyR-based on changes in Loop 2 structure.Alcohol abuse and dependence are significant problems in our society, with ∼14 million people in the United States being affected (1, 2). Alcohol causes over 100,000 deaths in the United States, and alcohol-related issues are estimated to cost nearly 200 billion dollars annually (2). To address this, considerable attention has focused on the development of medications to prevent and treat alcohol-related problems (3–5). The development of such medications would be aided by a clear understanding of the molecular structures on which ethanol acts and how these structures influence receptor sensitivity to ethanol.Ligand-gated ion channels (LGICs)² have received substantial attention as putative sites of ethanol action that cause its behavioral effects (6–12). Research in this area has focused on investigating the effects of ethanol on two large superfamilies of LGICs: 1) the Cys-loop superfamily of LGICs (13, 14), whose members include nicotinic acetylcholine, 5-hydroxytryptamine₃, γ-aminobutyric acid type A (GABA_A), γ-aminobutyric acid type C, and glycine receptors (GlyRs) (10, 11, 15–20) and 2) the glutamate superfamily, including N-methyl d-aspartate, α-amino-3-hydroxyisoxazolepropionic acid, and kainate receptors (21, 22). Recent studies have also begun investigating ethanol action in the ATP-gated P2X superfamily of LGICs (23–25).A series of studies that employed chimeric and mutagenic strategies combined with sulfhydryl-specific labeling identified key regions within Cys-loop receptors that appear to be initial targets for ethanol action that also can determine the sensitivity of the receptors to ethanol (7–12, 18, 19, 26–30). This work provides several lines of evidence that position 267 and possibly other sites in the transmembrane (TM) domain of GlyRs and homologous sites in GABA_ARs are targets for ethanol action and that mutations at these sites can influence ethanol sensitivity (8, 9, 26, 31).Growing evidence from GlyRs indicates that ethanol also acts on the extracellular domain. The initial findings came from studies demonstrating that α1GlyRs are more sensitive to ethanol than are α2GlyRs despite the high (∼78%) sequence homology between α1GlyRs and α2GlyRs (32). Further work found that an alanine to serine exchange at position 52 (A52S) in Loop 2 can eliminate the difference in ethanol sensitivity between α1GlyRs and α2GlyRs (18, 20, 33). These studies also demonstrated that mutations at position 52 in α1GlyRS and the homologous position 59 in α2GlyRs controlled the sensitivity of these receptors to a novel mechanistic ethanol antagonist (20). Collectively, these studies suggest that there are multiple sites of ethanol action in α1GlyRs, with one site located in the TM domain (e.g. position 267) and another in the extracellular domain (e.g. position 52).Subsequent studies revealed that the polarity of the residue at position 52 plays a key role in determining the sensitivity of GlyRs to ethanol (20). The findings with polarity in the extracellular domain contrast with the findings at position 267 in the TM domain, where molecular volume, but not polarity, significantly affected ethanol sensitivity (9). Taken together, these findings indicate that the physical-chemical parameters of residues at positions in the extracellular and TM domains that modulate ethanol effects and/or initiate ethanol action in GlyRs are not uniform. Thus, knowledge regarding the physical-chemical properties that control agonist and ethanol sensitivity is key for understanding the relationship between the structure and the actions of ethanol in LGICs (19, 31, 34–40).GlyRs and GABA_ARs, which differ significantly in their sensitivities to ethanol, offer a potential method for identifying the structures that control ethanol sensitivity. For example, α1GlyRs do not reliably respond to ethanol concentrations less than 10 mm (32, 33, 41). Similarly, γ subunit-containing GABA_ARs (e.g. α1β2γ2), the most predominantly expressed GABA_ARs in the central nervous system, are insensitive to ethanol concentrations less than 50 mm (42, 43). In contrast, δ subunit-containing GABA_ARs (e.g. α4β3δ) have been shown to be sensitive to ethanol concentrations as low as 1–3 mm (44–51). Sequence alignment of α1GlyR, γGABA_AR, and δGABA_AR revealed differences between the Loop 2 regions of these receptor subunits. Since prior studies found that mutations of Loop 2 residues can affect ethanol sensitivity (19, 20, 39), the non-conserved residues in Loop 2 of GlyR and GABA_AR subunits could provide the physical-chemical and structural bases underlying the differences in ethanol sensitivity between these receptors.The present study tested the hypothesis that the structure of Loop 2 can markedly affect the ethanol sensitivity of GlyRs and GABA_ARs. To accomplish this, we performed multiple mutations that replaced the Loop 2 region of the α1 subunit in α1GlyRs and the Loop 2 region of the γ subunit of α1β2γ2 GABA_ARs with corresponding non-conserved residues from the δ subunit of GABA_AR and tested the sensitivity of these receptors to ethanol. As predicted, replacing Loop 2 of WT α1GlyRs with the homologous residues from the δGABA_AR subunit (δL2), but not the γGABA_AR subunit (γL2), markedly increased the sensitivity of the receptor to ethanol. Similarly, replacing the non-conserved residues of the γ subunit of α1β2γ2 GABA_ARs with δL2 also markedly increased ethanol sensitivity of GABA_ARs. These findings support the hypothesis and suggest that Loop 2 may play a role in controlling ethanol sensitivity across the Cys-loop superfamily of receptors. The findings also provide the basis for suggesting structure-function relationships in a new molecular model of the GlyR based on the bacterial Gloeobacter violaceus pentameric LGIC homologue (GLIC).