Expression of GA733-Fc Fusion Protein as a Vaccine Candidate for Colorectal Cancer in Transgenic Plants

The tumor-associated antigen GA733 is a cell-surface glycoprotein highly expressed in colorectal carcinomas. In this study, 3 recombinant genes were constructed as follows: GA733 tagged to the ER retention sequence KDEL (GA733K), GA733 fused to the immunoglobulin Fc fragment (GA733-Fc), and GA733-Fc fused to the ER retention sequence (GA733-FcK). Agrobacterium-mediated transformation was used to generate transgenic plants expressing recombinant genes. The presence of transgenes was confirmed by genomic PCR. Western blot, confocal immunofluorescence, and sandwich ELISA showed the expression of recombinant proteins. The stability, flexibility, and bioactivity of recombinant proteins were analyzed and demonstrated through N-glycosylation analysis, animal trials, and sera ELISA. Our results suggest that the KDEL retained proteins in ER with oligomannose glycan structure and enhanced protein accumulation level. The sera of mice immunized with GA733-FcK purified from plants contained immunoglobulins which were at least as efficient as the mammalian-derived GA733-Fc at recognizing human colorectal cancer cell lines. Thus, a plant system can be used to express the KDEL fusion protein with oligomannose glycosylation, and this protein induces an immune response which is comparable to non-KDEL-tagged, mammalian-derived proteins

Beclin 1 functions as a negative modulator of MLKL oligomerisation by integrating into the necrosome complex

Necroptosis is a form of regulated cell death caused by formation of the necrosome complex. However, the factors modulating this process and the systemic pathophysiological effects of necroptosis are yet to be understood. Here, we identified that Beclin 1 functions as an anti-necroptosis factor by being recruited into the necrosome complex upon treatment with TNF alpha, Smac mimetic, and pan-caspase inhibitor and by repressing MLKL oligomerisation, thus preventing the disruption of the plasma membrane. Cells ablated or knocked-out for Beclin 1 become sensitised to necroptosis in an autophagy-independent manner without affecting the necrosome formation itself. Interestingly, the recruitment of Beclin 1 into the necrosome complex is dependent on the activation and phosphorylation of MLKL. Biochemically, the coiled-coil domain (CCD) of Beclin 1 binds to the CCD of MLKL, which restrains the oligomerisation of phosphorylated MLKL. Finally, Beclin 1 depletion was found to promote necroptosis in leukaemia cells and enhance regression of xenografted-tumour upon treatment with Smac mimetics and caspase inhibitors. These results suggest that Beclin 1 functions as a negative regulator in the execution of necroptosis by suppressing MLKL oligomerisation

YH29407 with anti-PD-1 ameliorates anti-tumor effects via increased T cell functionality and antigen presenting machinery in the tumor microenvironment

Among cancer cells, indoleamine 2, 3-dioxygenase1 (IDO1) activity has been implicated in improving the proliferation and growth of cancer cells and suppressing immune cell activity. IDO1 is also responsible for the catabolism of tryptophan to kynurenine. Depletion of tryptophan and an increase in kynurenine exert important immunosuppressive functions by activating regulatory T cells and suppressing CD8+ T and natural killer (NK) cells. In this study, we compared the anti-tumor effects of YH29407, the best-in-class IDO1 inhibitor with improved pharmacodynamics and pharmacokinetics, with first and second-generation IDO1 inhibitors (epacadostat and BMS-986205, respectively). YH29407 treatment alone and anti-PD-1 (aPD-1) combination treatment induced significant tumor suppression compared with competing drugs. In particular, combination treatment showed the best anti-tumor effects, with most tumors reduced and complete responses. Our observations suggest that improved anti-tumor effects were caused by an increase in T cell infiltration and activity after YH29407 treatment. Notably, an immune depletion assay confirmed that YH29407 is closely related to CD8+ T cells. RNA-seq results showed that treatment with YH29407 increased the expression of genes involved in T cell function and antigen presentation in tumors expressing ZAP70, LCK, NFATC2, B2M, and MYD88 genes. Our results suggest that an IDO1 inhibitor, YH29407, has enhanced PK/PD compared to previous IDO1 inhibitors by causing a change in the population of CD8+ T cells including infiltrating T cells into the tumor. Ultimately, YH29407 overcame the limitations of the competing drugs and displayed potential as an immunotherapy strategy in combination with aPD-1

Data for analysis of mannose-6-phosphate glycans labeled with fluorescent tags

Mannose-6-phosphate (M-6-P) glycan plays an important role in lysosomal targeting of most therapeutic enzymes for treatment of lysosomal storage diseases. This article provides data for the analysis of M-6-P glycans by high-performance liquid chromatography (HPLC) and matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry. The identities of M-6-P glycan peaks in HPLC profile were confirmed by measuring the masses of the collected peak eluates. The performances of three fluorescent tags (2-aminobenzoic acid [2-AA], 2-aminobenzamide [2-AB], and 3-(acetyl-amino)-6-aminoacridine [AA-Ac]) were compared focusing on the analysis of bi-phosphorylated glycan (containing two M-6-Ps). The bi-phosphorylated glycan analysis is highly affected by the attached fluorescent tag and the hydrophilicity of elution solvent used in HPLC. The data in this article is associated with the research article published in “Comparison of fluorescent tags for analysis of mannose-6-phosphate glycans” (Kang et al., 2016 [1])

Post-Translational Regulation of ARF: Perspective in Cancer

Tumorigenesis can be induced by various stresses that cause aberrant DNA mutations and unhindered cell proliferation. Under such conditions, normal cells autonomously induce defense mechanisms, thereby stimulating tumor suppressor activation. ARF, encoded by the CDKN2a locus, is one of the most frequently mutated or deleted tumor suppressors in human cancer. The safeguard roles of ARF in tumorigenesis are mainly mediated via the MDM2-p53 axis, which plays a prominent role in tumor suppression. Under normal conditions, low p53 expression is stringently regulated by its target gene, MDM2 E3 ligase, which induces p53 degradation in a ubiquitin-proteasome-dependent manner. Oncogenic signals induced by MYC, RAS, and E2Fs trap MDM2 in the inhibited state by inducing ARF expression as a safeguard measure, thereby activating the tumor-suppressive function of p53. In addition to the MDM2-p53 axis, ARF can also interact with diverse proteins and regulate various cellular functions, such as cellular senescence, apoptosis, and anoikis, in a p53-independent manner. As the evidence indicating ARF as a key tumor suppressor has been accumulated, there is growing evidence that ARF is sophisticatedly fine-tuned by the diverse factors through transcriptional and post-translational regulatory mechanisms. In this review, we mainly focused on how cancer cells employ transcriptional and post-translational regulatory mechanisms to manipulate ARF activities to circumvent the tumor-suppressive function of ARF. We further discussed the clinical implications of ARF in human cancer

Deep Learning-Based End-to-End Language Development Screening for Children Using Linguistic Knowledge

Language development is inextricably linked to the development of fundamental human abilities. A language problem can result from abnormal language development in childhood, which has a severe impact on other elements of life. As a result, early treatment of language impairments in children is critical. However, because it is difficult for parents to identify atypical language development in their children, optimal diagnosis and treatment periods are frequently missed. Furthermore, the diagnosis process necessitates a significant amount of time and work. As a consequence, in this study, we present a deep learning-based language development screening model based on word and part-of-speech and investigate the effectiveness of a large-scale language model. For the experiment, we collected data from Korean children by transcribing the utterances of children aged 2, 4, and 6 years. Convolutional neural networks and the notion of Siamese networks, as well as word and part-of-speech information, were used to determine the language development level of children. We also investigated the effectiveness of employing KoBERT and KR-BERT among Korean-specific large-scale language models. In 5-fold cross-validation study, the proposed model has an average accuracy of 78.0%. Furthermore, contrary to predictions, the large-scale language models were shown to be ineffective for representing children’s utterances

Deep Learning-Based End-to-End Language Development Screening for Children Using Linguistic Knowledge

Language development is inextricably linked to the development of fundamental human abilities. A language problem can result from abnormal language development in childhood, which has a severe impact on other elements of life. As a result, early treatment of language impairments in children is critical. However, because it is difficult for parents to identify atypical language development in their children, optimal diagnosis and treatment periods are frequently missed. Furthermore, the diagnosis process necessitates a significant amount of time and work. As a consequence, in this study, we present a deep learning-based language development screening model based on word and part-of-speech and investigate the effectiveness of a large-scale language model. For the experiment, we collected data from Korean children by transcribing the utterances of children aged 2, 4, and 6 years. Convolutional neural networks and the notion of Siamese networks, as well as word and part-of-speech information, were used to determine the language development level of children. We also investigated the effectiveness of employing KoBERT and KR-BERT among Korean-specific large-scale language models. In 5-fold cross-validation study, the proposed model has an average accuracy of 78.0%. Furthermore, contrary to predictions, the large-scale language models were shown to be ineffective for representing children’s utterances

Enhanced sialylation and in vivo efficacy of recombinant human α-galactosidase through in vitro glycosylation

Human α-galactosidase A (GLA) has been used in enzymereplacement therapy for patients with Fabry disease. Weexpressed recombinant GLA from Chinese hamster ovary cellswith very high productivity. When compared to an approvedGLA (agalsidase beta), its size and charge were found to besmaller and more neutral. These differences resulted from thelack of terminal sialic acids playing essential roles in the serumhalf-life and proper tissue targeting. Because a simplesialylation reaction was not enough to increase the sialic acidcontent, a combined reaction using galactosyltransferase,sialyltransferase, and their sugar substrates at the same timewas developed and optimized to reduce the incubation time.The product generated by this reaction had nearly the samesize, isoelectric points, and sialic acid content as agalsidasebeta. Furthermore, it had better in vivo efficacy to degrade theaccumulated globotriaosylceramide in target organs of Fabrymice compared to an unmodified version. [BMB Reports 2013;46(3): 157-162