Antibody and antibody fragments site-specific conjugation using new Q-tag substrate of bacterial transglutaminase.

During the last few years Antibody-Drug Conjugates (ADCs) have become one of the most active and very promising therapeutic weapons. Lessons learned from the traditional chemical conjugations (via lysine or cysteine residues of the antibodies) and the clinical studies of the developed ADCs have recently paved the way to the improvement of the conjugation technologies. Use of site-specific conjugation is considered as the promising path for improving the design and development of homogeneous ADCs with controlled Drug-Antibody ratio (DAR). Moreover, some of these conjugations can be applied to antibody fragments such as Fab, scfv and VHH for which random and chemical conjugation showed significant limitations. In this study, we identified a novel small peptide substrate (Q-tag) with high affinity and specificity of bacterial transglutaminase which can be genetically fused to different formats of antibodies of interest for the development of enzymatic site-specific conjugation we named "CovIsolink" platform. We describe the synthesis of chemically defined drugs conjugation in which the site and stoichiometry of conjugation are controlled using a genetically encoded Q-tag peptide with specific amino acids which serves as a substrate of bacterial transglutaminase. This approach has enabled the generation of homogeneous conjugates with DAR 1,7 for full IgG and 0,8 drug ratio for Fab, scfv and VHH antibody fragments without the presence of significant amounts of unconjugated antibody and fragments. As a proof of concept, Q-tagged anti Her-2 (human IgG1 (Trastuzumab) and the corresponding fragments (Fab, scfv and VHH) were engineered and conjugated with different aminated-payloads. The corresponding Cov-ADCs were evaluated in series of in vitro and in vivo assays, demonstrating similar tumor cell killing potency as Trastuzumab emtansine (Kadcyla®) even with lower drug-to-antibody ratio (DAR).

An attentive joint model with transformer-based weighted graph convolutional network for extracting adverse drug event relation.

Adverse drug event (ADE) relation extraction is a crucial task for drug safety surveillance which aims to discover potential relations between ADE mentions from unstructured medical texts. To date, the graph convolutional networks (GCN) have been the state-of-the-art solutions for improving the ability of relation extraction task. However, there are many challenging issues that should be addressed. Among these, the syntactic information is not fully exploited by GCN-based methods, especially the diversified dependency edges. Still, these methods fail to effectively extract complex relations that include nested, discontinuous and overlapping mentions. Besides, the task is primarily regarded as a classification problem where each candidate relation is treated independently which neglects the interaction between other relations. To deal with these issues, in this paper, we propose an attentive joint model with transformer-based weighted GCN for extracting ADE Relations, called ADERel. Firstly, the ADERel system formulates the ADE relation extraction task as an N-level sequence labelling so as to model the complex relations in different levels and capture greater interaction between relations. Then, it exploits our neural joint model to process the N-level sequences jointly. The joint model leverages the contextual and structural information by adopting a shared representation that combines a bidirectional encoder representation from transformers (BERT) and our proposed weighted GCN (WGCN). The latter assigns a score to each dependency edge within a sentence so as to capture rich syntactic features and determine the most influential edges for extracting ADE relations. Finally, the system employs a multi-head attention to exchange boundary knowledge across levels. We evaluate ADERel on two benchmark datasets from TAC 2017 and n2c2 2018 shared tasks. The experimental results show that ADERel is superior in performance compared with several state-of-the-art methods. The results also demonstrate that incorporating a transformer model with WGCN makes the proposed system more effective for extracting various types of ADE relations. The evaluations further highlight that ADERel takes advantage of joint learning, showing its effectiveness in recognizing complex relations.

Biochemical Characterisation of Human Transglutaminase 4.

Transglutaminases are protein-modifying enzymes involved in physiological and pathological processes with potent therapeutic possibilities. Human TG4, also called prostate transglutaminase, is involved in the development of autoimmune and tumour diseases. Although rodent TG4 is well characterised, biochemical characteristics of human TG4 that could help th e understanding of its way of action are not published. First, we analysed proteomics databases and found that TG4 protein is present in human tissues beyond the prostate. Then, we studied in vitro the transamidase activity of human TG4 and its regulation using the microtitre plate method. Human TG4 has low transamidase activity which prefers slightly acidic pH and a reducing environment. It is enhanced by submicellar concentrations of SDS suggesting that membrane proximity is an important regulatory event. Human TG4 does not bind GTP as tested by GTP-agarose and BODIPY-FL-GTPγS binding, and its proteolytic activation by dispase or when expressed in AD-293 cells was not observed either. We identified several potential human TG4 glutamine donor substrates in the AD-293 cell extract by biotin-pentylamine incorporation and mass spectrometry. Several of these potential substrates are involved in cell-cell interaction, adhesion and proliferation, suggesting that human TG4 could become an anticancer therapeutic target.

SemBioNLQA: A semantic biomedical question answering system for retrieving exact and ideal answers to natural language questions.

BACKGROUND AND OBJECTIVE: Question answering (QA), the identification of short accurate answers to users questions written in natural language expressions, is a longstanding issue widely studied over the last decades in the open-domain. However, it still remains a real challenge in the biomedical domain as the most of the existing systems support a limited amount of question and answer types as well as still require further efforts in order to improve their performance in terms of precision for the supported questions. Here, we present a semantic biomedical QA system named SemBioNLQA which has the ability to handle the kinds of yes/no, factoid, list, and summary natural language questions. METHODS: This paper describes the system architecture and an evaluation of the developed end-to-end biomedical QA system named SemBioNLQA, which consists of question classification, document retrieval, passage retrieval and answer extraction modules. It takes natural language questions as input, and outputs both short precise answers and summaries as results. The SemBioNLQA system, dealing with four types of questions, is based on (1) handcrafted lexico-syntactic patterns and a machine learning algorithm for question classification, (2) PubMed search engine and UMLS similarity for document retrieval, (3) the BM25 model, stemmed words and UMLS concepts for passage retrieval, and (4) UMLS metathesaurus, BioPortal synonyms, sentiment analysis and term frequency metric for answer extraction. RESULTS AND CONCLUSION: Compared with the current state-of-the-art biomedical QA systems, SemBioNLQA, a fully automated system, has the potential to deal with a large amount of question and answer types. SemBioNLQA retrieves quickly users' information needs by returning exact answers (e.g., "yes", "no", a biomedical entity name, etc.) and ideal answers (i.e., paragraph-sized summaries of relevant information) for yes/no, factoid and list questions, whereas it provides only the ideal answers for summary questions. Moreover, experimental evaluations performed on biomedical questions and answers provided by the BioASQ challenge especially in 2015, 2016 and 2017 (as part of our participation), show that SemBioNLQA achieves good performances compared with the most current state-of-the-art systems and allows a practical and competitive alternative to help information seekers find exact and ideal answers to their biomedical questions. The SemBioNLQA source code is publicly available at https://github.com/sarrouti/sembionlqa.

An adverse drug effect mentions extraction method based on weighted online recurrent extreme learning machine.

BACKGROUND AND OBJECTIVE: Automatic extraction of adverse drug effect (ADE) mentions from biomedical texts is a challenging research problem that has attracted significant attention from the pharmacovigilance and biomedical text mining communities. Indeed, deep learning based methods have recently been employed to solve this issue with great success. However, they fail to effectively identify the boundary of mentions. In this paper, we propose a weighted online recurrent extreme learning machine (WOR-ELM) based method to overcome this drawback. METHODS: The proposed method for ADE mentions extraction from biomedical texts is divided into two stages: span detection and ADE mentions classification. At the first stage, we identify the boundary of the mentions irrespective of their types with a WOR-ELM in a given sentence. At the second stage, another WOR-ELM is used to classify the identified mentions to the appropriate type. Both stages use the concatenation of character-level and word-level embeddings as features. The character-level embedding is obtained using a modified online recurrent extreme learning machine, whereas the word-level embedding is obtained from a pre-trained model. RESULTS: Several experiments were carried out on a well-known ADE corpus to evaluate the effectiveness and demonstrate the usefulness of the proposed method. The obtained results show that our method achieves an F-score of 87.5%, which outperforms the current state-of-the-art methods. CONCLUSIONS: Our research results indicate that the proposed method for adverse drug effect mentions extraction from text can significantly improve performance over existing methods. Our experiments show the effectiveness of incorporating word-level and character level embeddings as features for WOR-ELM. They also illustrate the benefits of using IOU segment to represent ADE mentions.

Optimised methods (SDS/PAGE and LC-MS) reveal deamidation in all examined transglutaminase-mediated reactions.

Transglutaminases (TGs) are a family of structurally and functionally related enzymes that catalyse calcium-dependent post-translational modifications of proteins through protein-protein crosslinking, amine incorporation, or deamidation. For many years deamidation mediated by TGs was considered to be a side reaction, but recently substrate-specific deamidations have been reported. Here we describe an optimised SDS/PAGE assay for the easy and rapid monitoring of the TG reaction with small peptides. The relative proportion of deamidation to transamidation was evaluated by densitometric analysis and confirmed by nano-liquid chromatography-nano-electrospray ionisation MS. We further investigated the effect of reaction conditions on transamidation and deamidation of TG1, TG2 and blood coagulation factor XIII A-subunit (FXIII-A) enzymes using a panel of glutamine-containing peptide substrates. The ratio of transamidation to deamidation was enhanced at high excess of the acyl-acceptor substrate and increasing pH. In addition, it was influenced by peptide substrates as well. Whereas deamidation was favoured at low cadaverine concentrations and acidic pH, no significant effect of calcium was observed on the ratio of transamidation/deamidation. Under our experimental conditions, deamidation always occurred in vitro even at high excess of the acyl-acceptor substrate, and the reaction outcome was shifted to deamidation at neutral pH. Our results provide clear evidence of the deamidation in the TG reaction, and may serve as an important approach for in vivo analysis of deamidation to better understand the role of TGs in biological events.

Generation and characterization of novel anti-DR4 and anti-DR5 antibodies developed by genetic immunization.

Development of therapeutic antibodies in oncology has attracted much interest in the past decades. More than 30 of them have been approved and are being used to treat patients suffering from cancer. Despite encouraging results, and albeit most clinical trials aiming at evaluating monoclonal antibodies directed against TRAIL agonist receptors have been discontinued, DR4 or DR5 remain interesting targets, since these receptors are overexpressed by tumour cells and are able to trigger their death. In an effort to develop novel and specific anti-DR4 and anti-DR5 antibodies with improved properties, we used genetic immunization to express native proteins in vivo. Injection of DR4 and DR5 cDNA into the tail veins of mice elicited significant humoral anti-DR4 and anti-DR5 responses and fusions of the corresponding spleens resulted in numerous hybridomas secreting antibodies that could specifically recognize DR4 or DR5 in their native forms. All antibodies bound specifically to their targets with a very high affinity, from picomolar to nanomolar range. Among the 21 anti-DR4 and anti-DR5 monoclonal antibodies that we have produced and purified, two displayed proapoptotic properties alone, five induced apoptosis after cross-linking, four were found to potentiate TRAIL-induced apoptosis and three displayed antiapoptotic potential. The most potent anti-DR4 antibody, C#16, was assessed in vivo and was found, alone, to inhibit tumour growth in animal models. This is the first demonstration that DNA-based immunization method can be used to generate novel monoclonal antibodies targeting receptors of the TNF superfamily that may constitute new therapeutic agents.

Correction: Characterization of Transglutaminase 2 activity inhibitors in monocytes in vitro and their effect in a mouse model for multiple sclerosis.

[This corrects the article DOI: 10.1371/journal.pone.0196433.].

HUWE1 E3 ligase promotes PINK1/PARKIN-independent mitophagy by regulating AMBRA1 activation via IKKα.

The selective removal of undesired or damaged mitochondria by autophagy, known as mitophagy, is crucial for cellular homoeostasis, and prevents tumour diffusion, neurodegeneration and ageing. The pro-autophagic molecule AMBRA1 (autophagy/beclin-1 regulator-1) has been defined as a novel regulator of mitophagy in both PINK1/PARKIN-dependent and -independent systems. Here, we identified the E3 ubiquitin ligase HUWE1 as a key inducing factor in AMBRA1-mediated mitophagy, a process that takes place independently of the main mitophagy receptors. Furthermore, we show that mitophagy function of AMBRA1 is post-translationally controlled, upon HUWE1 activity, by a positive phosphorylation on its serine 1014. This modification is mediated by the IKKα kinase and induces structural changes in AMBRA1, thus promoting its interaction with LC3/GABARAP (mATG8) proteins and its mitophagic activity. Altogether, these results demonstrate that AMBRA1 regulates mitophagy through a novel pathway, in which HUWE1 and IKKα are key factors, shedding new lights on the regulation of mitochondrial quality control and homoeostasis in mammalian cells.

Characterization of Transglutaminase 2 activity inhibitors in monocytes in vitro and their effect in a mouse model for multiple sclerosis.

The neurodegenerative disease multiple sclerosis (MS) is pathologically characterized by the massive influx of immune cells into the central nervous system. This contributes to demyelination and axonal damage which causes symptoms such as motor and cognitive dysfunctions. The migration of leukocytes from the blood vessel is orchestrated by a multitude of factors whose determination is essential in reducing cellular influx in MS patients and the experimental autoimmune encephalomyelitis (EAE) animal model. The here studied enzyme tissue Transglutaminase (TG2) is present intracellularly, on the cell surface and extracellularly. There it contributes to cellular adhesion and migration via its transamidation activity and possibly by facilitating cellular interaction with the extracellular matrix. Previous data from our group showed reduced motor symptoms and cellular infiltration after using a pharmacological TG2 transamidation activity inhibitor in a rat EAE model. However, it remained elusive if the cross-linking activity of the enzyme resulted in the observed effects. To follow-up, we now characterized two new small molecule TG2 activity inhibitors, BJJF078 and ERW1041E. Both compounds are potent inhibitor of recombinant human and mouse Transglutaminase enzyme activity, mainly TG2 and the close related enzyme TG1. In addition they did not affect the binding of TG2 to the extracellular matrix substrate fibronectin, a process via which TG2 promotes cellular adhesion and migration. We found, that ERW1041E but not BJJF078 resulted in reduced EAE disease motor-symptoms while neither caused apparent changes in pathology (cellular influx), Transglutaminase activity or expression of inflammation related markers in the spinal cord, compared to vehicle treated controls. Although we cannot exclude issues on bioavailability and in vivo efficacy of the used compounds, we hypothesize that extracellular TG1/TG2 activity is of greater importance than (intra-)cellular activity in mouse EAE pathology.