Protocol for isolating mice skeletal muscle myoblasts and myotubes via differential antibody validation.

Isolation of skeletal muscles allows for the exploration of many complex diseases. Here, we present a protocol for isolating mice skeletal muscle myoblasts and myotubes that have been differentiated through antibody validation. We describe steps for collecting and preparing murine skeletal tissue, myoblast cell maintenance, plating, and cell differentiation. We then detail procedures for cell incubation, immunostaining, slide preparation and storage, and imaging for immunofluorescence validation.

Components of Isolated Skeletal Muscle Differentiated Through Antibody Validation.

Isolation of skeletal muscles allows for the exploration of many complex diseases. Fibroblasts and myoblast play important roles in skeletal muscle morphology and function. However, skeletal muscles are complex and made up of many cellular populations and validation of these populations is highly important. Therefore, in this article, we discuss a comprehensive method to isolate mice skeletal muscle, create satellite cells for tissue culture, and use immunofluorescence to validate our approach.

In the Age of Machine Learning Cryo-EM Research is Still Necessary: A Path toward Precision Medicine.

Machine learning has proven useful in analyzing complex biological data and has greatly influenced the course of research in structural biology and precision medicine. Deep neural network models oftentimes fail to predict the structure of complex proteins and are heavily dependent on experimentally determined structures for their training and validation. Single-particle cryogenic electron microscopy (cryoEM) is also advancing the understanding of biology and will be needed to complement these models by continuously supplying high-quality experimentally validated structures for improvements in prediction quality. In this perspective, the significance of structure prediction methods is highlighted, but the authors also ask, what if these programs cannot accurately predict a protein structure important for preventing disease? The role of cryoEM is discussed to help fill the gaps left by artificial intelligence predictive models in resolving targetable proteins and protein complexes that will pave the way for personalized therapeutics.

Improving automatic GO annotation with semantic similarity.

BACKGROUND: Automatic functional annotation of proteins is an open research problem in bioinformatics. The growing number of protein entries in public databases, for example in UniProtKB, poses challenges in manual functional annotation. Manual annotation requires expert human curators to search and read related research articles, interpret the results, and assign the annotations to the proteins. Thus, it is a time-consuming and expensive process. Therefore, designing computational tools to perform automatic annotation leveraging the high quality manual annotations that already exist in UniProtKB/SwissProt is an important research problem RESULTS: In this paper, we extend and adapt the GrAPFI (graph-based automatic protein function inference) (Sarker et al. in BMC Bioinform 21, 2020; Sarker et al., in: Proceedings of 7th international conference on complex networks and their applications, Cambridge, 2018) method for automatic annotation of proteins with gene ontology (GO) terms renaming it as GrAPFI-GO. The original GrAPFI method uses label propagation in a similarity graph where proteins are linked through the domains, families, and superfamilies that they share. Here, we also explore various types of similarity measures based on common neighbors in the graph. Moreover, GO terms are arranged in a hierarchical manner according to semantic parent-child relations. Therefore, we propose an efficient pruning and post-processing technique that integrates both semantic similarity and hierarchical relations between the GO terms. We produce experimental results comparing the GrAPFI-GO method with and without considering common neighbors similarity. We also test the performance of GrAPFI-GO and other annotation tools for GO annotation on a benchmark of proteins with and without the proposed pruning and post-processing procedure. CONCLUSION: Our results show that the proposed semantic hierarchical post-processing potentially improves the performance of GrAPFI-GO and of other annotation tools as well. Thus, GrAPFI-GO exposes an original efficient and reusable procedure, to exploit the semantic relations among the GO terms in order to improve the automatic annotation of protein functions.

GrAPFI: predicting enzymatic function of proteins from domain similarity graphs.

An amendment to this paper has been published and can be accessed via the original article.

Primary vitreoretinal lymphomas display a remarkably restricted immunoglobulin gene repertoire.

Primary vitreoretinal lymphoma (PVRL) is a high-grade lymphoma affecting the vitreous and/or the retina. The vast majority of cases are histopathologically classified as diffuse large B-cell lymphoma (DLBCL) and considered a subtype of primary central nervous system lymphoma (PCNSL). To obtain more insight into the ontogenetic relationship between PVRL and PCNSL, we adopted an immunogenetic perspective and explored the respective immunoglobulin gene repertoire profiles from 55 PVRL cases and 48 PCNSL cases. In addition, considering that both entities are predominantly related to activated B-cell (ABC) DLBCL, we compared their repertoire with that of publicly available 262 immunoglobulin heavy variable domain gene rearrangement sequences from systemic ABC-type DLBCLs. PVRL displayed a strikingly biased repertoire, with the IGHV4-34 gene being used in 63.6% of cases, which was significantly higher than in PCNSL (34.7%) or in DLBCL (30.2%). Further repertoire bias was evident by (1) restricted associations of IGHV4-34 expressing heavy chains, with κ light chains utilizing the IGKV3-20/IGKJ1 gene pair, including 5 cases with quasi-identical sequences, and (2) the presence of a subset of stereotyped IGHV3-7 rearrangements. All PVRL IGHV sequences were highly mutated, with evidence of antigen selection and ongoing mutations. Finally, half of PVRL and PCNSL cases carried the MYD88 L265P mutation, which was present in all 4 PVRL cases with stereotyped IGHV3-7 rearrangements. In conclusion, the massive bias in the immunoglobulin gene repertoire of PVRL delineates it from PCNSL and points to antigen selection as a major driving force in their development.