Canine adenovirus type 2 (CAV2) is a nonhuman adenovirus with a known ability to infect human and canine cells. The cell surface receptors involved in CAV2 transduction are still unknown. Identification of these would provide valuable information to develop enhanced gene delivery tools and better understand CAV2 biology. CAV2 is erroneously grouped with Ad5 based on the knowledge that CAV2 may transduce using CAR. Therefore, we have evaluated CAV2 and Ad5 (CAV2GFP, Ad5G/L) infection patterns in various canine and human cell lines to determine their different tropisms. Our research demonstrates that CAV2 can successfully infect cells that Ad5 does not infect, and CAV2 infections do not correlate with CAR expression. CAV2 can infect cells that have a low or minimal expression of CAR. Our data suggest that CAV2 transduction is not dependent on the CAR receptor, and thus, it is crucial to find novel CAV2 receptors.

Extracting sufficient quantity and quality RNA from bone is essential for downstream application, such as transcriptomic sequencing, to evaluate gene expression. Isolation of RNA from bone presents... METHOD SUMMARY A combination of physical manipulation to remove exterior tissue, washing and centrifugation to remove cells and fat within the diaphysis, homogenization using a mortar and pestle on...

Osteosarcoma (OSA) is a highly aggressive bone tumor primarily affecting pediatric or adolescent humans and large-breed dogs. Canine OSA shares striking similarities with its human counterpart, making it an invaluable translational model for uncovering the disease’s complexities and developing novel therapeutic strategies. Tumor heterogeneity, a hallmark of OSA, poses significant challenges to effective treatment due to the evolution of diverse cell populations that influence tumor growth, metastasis, and resistance to therapies. In this study, we apply single-nuclei multiome sequencing, encompassing ATAC (Assay for Transposase-Accessible Chromatin) and GEX (Gene Expression, or RNA) sequencing, to a treatment-naïve primary canine osteosarcoma. This comprehensive approach reveals the complexity of the tumor microenvironment by simultaneously capturing the transcriptomic and epigenomic profiles within the same nucleus. Furthermore, these results are analyzed in conjunction with bulk RNA sequencing and differential analysis of the same tumor and patient-matched normal bone. By delving into the intricacies of OSA at this unprecedented level of detail, we aim to unravel the underlying mechanisms driving intra-tumoral heterogeneity, opening new avenues for therapeutic interventions in both human and canine patients. This study pioneers an approach that is broadly applicable, while demonstrating significant heterogeneity in the context of a single individual’s tumor.

Despite significant advances in cancer diagnosis and treatment, osteosarcoma (OSA), an aggressive primary bone tumor, has eluded attempts at improving patient survival for many decades. The difficulty in managing OSA lies in its extreme genetic complexity, drug resistance, and heterogeneity, making it improbable that a single-target treatment would be beneficial for the majority of affected individuals. Precision medicine seeks to fill this gap by addressing the intra- and inter-tumoral heterogeneity to improve patient outcome and survival. The characterization of differentially expressed genes (DEGs) unique to the tumor provides insight into the phenotype and can be useful for informing appropriate therapies as well as the development of novel treatments. Traditional DEG analysis combines patient data to derive statistically inferred genes that are dysregulated in the group; however, the results from this approach are not necessarily consistent across individual patients, thus contradicting the basis of precision medicine. Spontaneously occurring OSA in the dog shares remarkably similar clinical, histological, and molecular characteristics to the human disease and therefore serves as an excellent model. In this study, we use transcriptomic sequencing of RNA isolated from primary OSA tumor and patient-matched normal bone from seven dogs prior to chemotherapy to identify DEGs in the group. We then evaluate the universality of these changes in transcript levels across patients to identify DEGs at the individual level. These results can be useful for reframing our perspective of transcriptomic analysis from a precision medicine perspective by identifying variations in DEGs among individuals.

Authors are often faced with the decision of whether to maximize impact or minimize costs when publishing the results of their research. For example, to potentially improve impact via increased accessibility, many subscription-based journals now offer the option of paying a fee to publish open access (i.e., hybrid journals), but this solution excludes authors who lack the capacity to pay to make their research accessible. Here, we tested if paying to publish open access in a subscriptionbased journal benefited authors by conferring more citations relative to closed access articles. We identified 146,415 articles published in 152 hybrid journals in the field of biology from 2013-2018 to compare the number of citations between various types of open access and closed access articles. In a simple generalized linear model analysis of our full dataset, we found that publishing open access in hybrid journals that offer the option confers an average citation advantage to authors of 17.8 citations compared to closed access articles in similar journals. After taking into account the number of authors, journal impact, year of publication, and subject area, we still found that open access generated significantly more citations than closed access ( p < 0.0001). However, results were complex, with exact differences in citation rates among access types impacted by these other variables. This citation advantage based on access type was even similar when comparing open and closed access articles published in the same issue of a journal ( p < 0.0001). However, by examining articles where the authors paid an article processing charge, we found that cost itself was not predictive of citation rates ( p = 0.14). Based on our findings of access type and other model parameters, we suggest that, in most cases, paying for access does confer a citation advantage. For authors with limited budgets, we recommend pursuing open access alternatives that do not require paying a fee as they still yielded more citations than closed access. For authors who are considering where to submit their next article, we offer additional suggestions on how to balance exposure via citations with publishing costs.