Journal of Biomolecular Techniques最新文献

The National Institutes of Health (NIH) offers many types of funding programs and opportunities to support biomedical research. The best known of these programs, the NIH Research Project Grant Program, or R01, supports investigator-initiated research projects. Another well-known funding mechanism is the NIH Shared Instrumentation Grant Program, also known as SIG or S10. This year marks the S10's 40th anniversary. To commemorate this triumphant milestone and a successful 40 years, let's first review how this legendary and highly impactful program started.

The functional annotation of gene lists is a common analysis routine required for most genomics experiments, and bioinformatics core facilities must support these analyses. In contrast to methods such as the quantitation of RNA-Seq reads or differential expression analysis, our research group noted a lack of consensus in our preferred approaches to functional annotation. To investigate this observation, we selected 4 experiments that represent a range of experimental designs encountered by our cores and analyzed those data with 6 tools used by members of the Association of Biomolecular Resource Facilities (ABRF) Genomic Bioinformatics Research Group (GBIRG). To facilitate comparisons between tools, we focused on a single biological result for each experiment. These results were represented by a gene set, and we analyzed these gene sets with each tool considered in our study to map the result to the annotation categories presented by each tool. In most cases, each tool produces data that would facilitate identification of the selected biological result for each experiment. For the exceptions, Fisher's exact test parameters could be adjusted to detect the result. Because Fisher's exact test is used by many functional annotation tools, we investigated input parameters and demonstrate that, while background set size is unlikely to have a significant impact on the results, the numbers of differentially expressed genes in an annotation category and the total number of differentially expressed genes under consideration are both critical parameters that may need to be modified during analyses. In addition, we note that differences in the annotation categories tested by each tool, as well as the composition of those categories, can have a significant impact on results.

We analyzed co-authorship patterns within the National Institutes of Health Center of Biomedical Research Excellence in Matrix Biology program from 2014 to 2022. In this study, we analyzed junior investigators, senior researchers, and research scientists within a shared core facility. Social network analysis techniques were applied to evaluate the co-authorship network based on journal publications from members of the center. The results indicated that co-authorship network visualization and analysis is a useful tool for understanding the relationship between a shared core facility and young investigators within a research center. Young investigators collaborated with and relied upon the individual research scientists of the shared core facility to serve as contributing members of their extended research team. This reliance on the shared core facility effectively increases the size and productivity of the research team led by the young investigator. Our results indicate that shared core facility staff may serve as hubs within the network of biomedical researchers, particularly at institutions with a growing research emphasis. Listen to this article.

Immunoglobulin G (IgG) is the main immunoglobulin in human serum, and its biological activity is modulated by glycosylation on its fragment crystallizable region. Glycosylation of IgGs has shown to be related to aging, disease progression, protein stability, and many other vital processes. A common approach to analyze IgG glycosylation involves the release of the N-glycans by PNGase F, which cleaves the linkage between the asparagine residue and the innermost N-acetylglucosamine (GlcNAc) of all N-glycans except those containing a 3-linked fucose attached to the core GlcNAc. The biological significance of these glycans necessitates the development of accurate methods for their characterization and quantification. Currently, researchers either perform PNGase F deglycosylation on intact or trypsin-digested IgGs. Those who perform PNGase F deglycosylation on trypsin-digested IgGs argue that proteolysis is needed to reduce steric hindrance, whereas the other group states that this step is not needed, and the proteolytic step only adds time. There is minimal experimental evidence supporting either assumption. The importance of obtaining complete glycan release for accurate quantitation led us to investigate the kinetics of this deglycosylation reaction for intact IgGs and IgG glycopeptides. Statistically significant differences in the rate of deglycosylation performed on intact IgGs and trypsin-digested IgGs were determined, and the rate of PNGase F deglycosylation on trypsin-digested IgGs was found to be 3- to 4-times faster than on intact IgG.