Development of oncolytic and gene therapy vectors based on adenovirus serotype 4 as an alternative to adenovirus serotype 5.

BACKGROUND: Adenoviral vectors are among the most frequently used vectors for gene therapy and cancer treatment. Most vectors are derived from human adenovirus (Ad) serotype 5 despite limited applicability caused by pre-existing immunity and unfavorable liver tropism, whereas the other more than 100 known human serotypes remain largely unused. Here, we screened a library of human Ad types and identified Ad4 as a promising candidate vector. METHODS: Reporter-gene-expressing viruses representative of the natural human Ad diversity were used to transduce an array of muscle cell lines and two- or three-dimensional tumor cultures. The time-course of transgene expression was monitored by fluorescence or luminescence measurements. To generate replication-deficient Ad4 vector genomes, successive homologous recombination was applied. RESULTS: Ad4, 17 and 50 transduced human cardiomyocytes more efficiently than Ad5, whereas Ad37 was found to be superior in rhabdomyocytes. Despite its moderate transduction efficiency, Ad4 showed efficient and long-lasting gene expression in papillomavirus (HPV) positive tumor organoids. Therefore, we aimed to harness the potential of Ad4 for improved muscle transduction or oncolytic virotherapy of HPV-positive tumors. We deleted the E1 and E3 transcription units to produce first generation Ad vectors for gene therapy. The E1- and E1/E3-deleted vectors were replication-competent in HEK293 cells stably expressing E1 but not in the other cell lines tested. Furthermore, we show that the Ad5 E1 transcription unit can complement the replication of E1-deleted Ad4 vectors. CONCLUSIONS: Our Ad4-based gene therapy vector platform contributes to the development of improved Ad vectors based on non-canonical serotypes for a broad range of applications.

Genomic characterization of a nematode tolerance locus in sugar beet.

BACKGROUND: Infection by beet cyst nematodes (BCN, Heterodera schachtii) causes a serious disease of sugar beet, and climatic change is expected to improve the conditions for BCN infection. Yield and yield stability under adverse conditions are among the main breeding objectives. Breeding of BCN tolerant sugar beet cultivars offering high yield in the presence of the pathogen is therefore of high relevance. RESULTS: To identify causal genes providing tolerance against BCN infection, we combined several experimental and bioinformatic approaches. Relevant genomic regions were detected through mapping-by-sequencing using a segregating F2 population. DNA sequencing of contrasting F2 pools and analyses of allele frequencies for variant positions identified a single genomic region which confers nematode tolerance. The genomic interval was confirmed and narrowed down by genotyping with newly developed molecular markers. To pinpoint the causal genes within the potential nematode tolerance locus, we generated long read-based genome sequence assemblies of the tolerant parental breeding line Strube U2Bv and the susceptible reference line 2320Bv. We analyzed continuous sequences of the potential locus with regard to functional gene annotation and differential gene expression upon BCN infection. A cluster of genes with similarity to the Arabidopsis thaliana gene encoding nodule inception protein-like protein 7 (NLP7) was identified. Gene expression analyses confirmed transcriptional activity and revealed clear differences between susceptible and tolerant genotypes. CONCLUSIONS: Our findings provide new insights into the genomic basis of plant-nematode interactions that can be used to design and accelerate novel management strategies against BCN.

Benchmarking antibody clustering methods using sequence, structural, and machine learning similarity measures for antibody discovery applications.

Antibodies are proteins produced by our immune system that have been harnessed as biotherapeutics. The discovery of antibody-based therapeutics relies on analyzing large volumes of diverse sequences coming from phage display or animal immunizations. Identification of suitable therapeutic candidates is achieved by grouping the sequences by their similarity and subsequent selection of a diverse set of antibodies for further tests. Such groupings are typically created using sequence-similarity measures alone. Maximizing diversity in selected candidates is crucial to reducing the number of tests of molecules with near-identical properties. With the advances in structural modeling and machine learning, antibodies can now be grouped across other diversity dimensions, such as predicted paratopes or three-dimensional structures. Here we benchmarked antibody grouping methods using clonotype, sequence, paratope prediction, structure prediction, and embedding information. The results were benchmarked on two tasks: binder detection and epitope mapping. We demonstrate that on binder detection no method appears to outperform the others, while on epitope mapping, clonotype, paratope, and embedding clusterings are top performers. Most importantly, all the methods propose orthogonal groupings, offering more diverse pools of candidates when using multiple methods than any single method alone. To facilitate exploring the diversity of antibodies using different methods, we have created an online tool-CLAP-available at (clap.naturalantibody.com) that allows users to group, contrast, and visualize antibodies using the different grouping methods.

Large-scale data mining of four billion human antibody variable regions reveals convergence between therapeutic and natural antibodies that constrains search space for biologics drug discovery.

The naïve human antibody repertoire has theoretical access to an estimated > 1015 antibodies. Identifying subsets of this prohibitively large space where therapeutically relevant antibodies may be found is useful for development of these agents. It was previously demonstrated that, despite the immense sequence space, different individuals can produce the same antibodies. It was also shown that therapeutic antibodies, which typically follow seemingly unnatural development processes, can arise independently naturally. To check for biases in how the sequence space is explored, we data mined public repositories to identify 220 bioprojects with a combined seven billion reads. Of these, we created a subset of human bioprojects that we make available as the AbNGS database (https://naturalantibody.com/ngs/). AbNGS contains 135 bioprojects with four billion productive human heavy variable region sequences and 385 million unique complementarity-determining region (CDR)-H3s. We find that 270,000 (0.07% of 385 million) unique CDR-H3s are highly public in that they occur in at least five of 135 bioprojects. Of 700 unique therapeutic CDR-H3, a total of 6% has direct matches in the small set of 270,000. This observation extends to a match between CDR-H3 and V-gene call as well. Thus, the subspace of shared ('public') CDR-H3s shows utility for serving as a starting point for therapeutic antibody design.

Adenoviral Vectors Armed with PAPILLOMAVIRUs Oncogene Specific CRISPR/Cas9 Kill Human-Papillomavirus-Induced Cervical Cancer Cells.

Human papillomaviruses (HPV) cause malignant epithelial cancers including cervical carcinoma, non-melanoma skin and head and neck cancer. They drive tumor development through the expression of their oncoproteins E6 and E7. Designer nucleases were shown to be efficient to specifically destroy HPV16 and HPV18 oncogenes to induce cell cycle arrest and apoptosis. Here, we used high-capacity adenoviral vectors (HCAdVs) expressing the complete CRISPR/Cas9 machinery specific for HPV18-E6 or HPV16-E6. Cervical cancer cell lines SiHa and CaSki containing HPV16 and HeLa cells containing HPV18 genomes integrated into the cellular genome, as well as HPV-negative cancer cells were transduced with HPV-type-specific CRISPR-HCAdV. Upon adenoviral delivery, the expression of HPV-type-specific CRISPR/Cas9 resulted in decreased cell viability of HPV-positive cervical cancer cell lines, whereas HPV-negative cells were unaffected. Transduced cervical cancer cells showed increased apoptosis induction and decreased proliferation compared to untreated or HPV negative control cells. This suggests that HCAdV can serve as HPV-specific cancer gene therapeutic agents when armed with HPV-type-specific CRISPR/Cas9. Based on the versatility of the CRISPR/Cas9 system, we anticipate that our approach can contribute to personalized treatment options specific for the respective HPV type present in each individual tumor.

Comparative gene-expression profiling of the large cell variant of gastrointestinal marginal-zone B-cell lymphoma.

Gastrointestinal (g.i.) large cell lymphoma is currently regarded as diffuse large B-cell lymphoma (DLBCL) despite a more favorable clinical outcome compared to other DLBCL. Cluster analyses on a transcriptome signature of NF-κB target genes of 30 g.i. marginal zone B-cell lymphomas (MZBL; 8 g.i. MZBL, 22 large cell MZBL - among them 9 with coexisting small cell component) and 6 DLBCL (3 activated B-cell like (ABC), 3 germinal center-like (GCB)) reveals a distinct pattern. The distinctiveness of large cell MZBL samples is further confirmed by a cohort of 270 available B-cell lymphoma and B-cell in silico profiles. Of the NF-κB genes analyzed, c-REL was overexpressed in g.i. MZBL. c-REL amplification was limited to 6/22 large cell MZBL including the large cell component of 2/9 composite small cell/large cell lymphomas, and c-Rel protein expression was found in the large cell compartment of composite lymphomas. Classification experiments on DLBCL and large cell MZBL profiles support the concept that the large cell MZBL is a distinct type of B-cell lymphoma.