Discovery of Selenocysteine as a Potential Nanomedicine Promotes Cartilage Regeneration With Enhanced Immune Response by Text Mining and Biomedical Databases

Transcriptome profiling of differentiating adipose-derived stem cells across species reveals new genes regulating adipogenesis

Adipose-derived stem cells (ADSCs) that are enriched in adipose tissue with multilineage differentiation potential have become an important tool in therapeutic research and tissue engineering. Certain breeds of sheep exhibit a unique fat tail trait such that tail tissue accounts for approximately 10 % of body weight and can provide an excellent source of ADSCs. Here, we describe isolation of primary ADSCs from ovine embryonic fat tail tissues that displayed high self-renewal capacity, multilineage differentiation and excellent adipogenic ability. Through transcriptome analysis covering ADSCs differentiating into adipocytes, 37 transcription factors were involved in early transcriptional events that initiate a regulatory cascade of adipogenesis; the entire adipogenic activity consists of a reduction in proliferation ability and upregulation of genes related to lipid generation and energy metabolism, as well as several genes associated with myogenesis. Furthermore, Comparative transcriptome analysis across species (sheep, human, and mouse) revealed enhanced basal metabolic ability in differentiating ovine ADSCs, which may relate to the excellent adipogenic capability of these cells. We also identified a small evolutionarily conserved gene set, consisting of 21 and 22 genes exhibiting increased and decreased expression, respectively. Almost half (20) of these genes have not previously been reported to regulate adipogenesis in mammals. In this study, we identified important regulators that trigger ovine adipocyte differentiation, main biological pathways involved in adipogenesis as well as the evolutionarily conserved genes governing adipogenic process across species. Our study provides a novel excellent biomaterial and novel genes regulating adipogenesis for cellular transplantation therapy and investigations of fat metabolism.

Regulation of secretory pathway kinase or kinase-like proteins in human cancers

Secretory pathway kinase or kinase-like proteins (SPKKPs) are effective in the lumen of the endoplasmic reticulum (ER), Golgi apparatus (GA), and extracellular space. These proteins are involved in secretory signaling pathways and are distinctive from typical protein kinases. Various reports have shown that SPKKPs regulate the tumorigenesis and progression of human cancer via the phosphorylation of various substrates, which is essential in physiological and pathological processes. Emerging evidence has revealed that the expression of SPKKPs in human cancers is regulated by multiple factors. This review summarizes the current understanding of the contribution of SPKKPs in tumorigenesis and the progression of immunity. With the epidemic trend of immunotherapy, targeting SPKKPs may be a novel approach to anticancer therapy. This study briefly discusses the recent advances regarding SPKKPs.

Machine learning approaches for biomolecular, biophysical, and biomaterials research

A fluent conversation with a virtual assistant, person-tailored news feeds, and deep-fake images created within seconds-all those things that have been unthinkable for a long time are now a part of our everyday lives. What these examples have in common is that they are realized by different means of machine learning (ML), a technology that has fundamentally changed many aspects of the modern world. The possibility to process enormous amount of data in multi-hierarchical, digital constructs has paved the way not only for creating intelligent systems but also for obtaining surprising new insight into many scientific problems. However, in the different areas of biosciences, which typically rely heavily on the collection of time-consuming experimental data, applying ML methods is a bit more challenging: Here, difficulties can arise from small datasets and the inherent, broad variability, and complexity associated with studying biological objects and phenomena. In this Review, we give an overview of commonly used ML algorithms (which are often referred to as "machines") and learning strategies as well as their applications in different bio-disciplines such as molecular biology, drug development, biophysics, and biomaterials science. We highlight how selected research questions from those fields were successfully translated into machine readable formats, discuss typical problems that can arise in this context, and provide an overview of how to resolve those encountered difficulties.

Glutamine Uptake via SNAT6 and Caveolin Regulates Glutamine-Glutamate Cycle

SLC38A6 (SNAT6) is the only known member of the SLC38 family that is expressed exclusively in the excitatory neurons of the brain. It has been described as an orphan transporter with an unknown substrate profile, therefore very little is known about SNAT6. In this study, we addressed the substrate specificity, mechanisms for internalization of SNAT6, and the regulatory role of SNAT6 with specific insights into the glutamate-glutamine cycle. We used tritium-labeled amino acids in order to demonstrate that SNAT6 is functioning as a glutamine and glutamate transporter. SNAT6 revealed seven predicted transmembrane segments in a homology model and was localized to caveolin rich sites at the plasma membrane. SNAT6 has high degree of specificity for glutamine and glutamate. Presence of these substrates enables formation of SNAT6-caveolin complexes that aids in sodium dependent trafficking of SNAT6 off the plasma membrane. To further understand its mode of action, several potential interacting partners of SNAT6 were identified using bioinformatics. Among them where CTP synthase 2 (CTPs2), phosphate activated glutaminase (Pag), and glutamate metabotropic receptor 2 (Grm2). Co-expression analysis, immunolabeling with co-localization analysis and proximity ligation assays of these three proteins with SNAT6 were performed to investigate possible interactions. SNAT6 can cycle between cytoplasm and plasma membrane depending on availability of substrates and interact with Pag, synaptophysin, CTPs2, and Grm2. Our data suggest a potential role of SNAT6 in glutamine uptake at the pre-synaptic terminal of excitatory neurons. We propose here a mechanistic model of SNAT6 trafficking that once internalized influences the glutamate-glutamine cycle in presence of its potential interacting partners.