Quantitative Proteome Profiling Reveals Cellobiose-Dependent Protein Processing and Export Pathways for the Lignocellulolytic Response in Neurospora crassa

Exploring the heterogeneity of osteosarcoma cell characteristics and metabolic states and their association with clinical prognosis

Background

Osteosarcoma is a malignant tumor originating from mesenchymal bone tissue, characterized by high malignancy and poor prognosis. Despite progress in comprehensive treatment approaches, the five-year survival rate remains largely unchanged, highlighting the need to clarify its underlying mechanisms and discover new therapeutic targets.

Methods

This study utilized RNA sequencing data from multiple public databases, encompassing osteosarcoma samples and healthy controls, along with single-cell RNA sequencing data. Various methods were utilized, such as differential expression analysis of genes, analysis of metabolic pathways, and weighted gene co-expression network analysis (WGCNA), to pinpoint crucial genes. Using this list of genes, we developed and validated a prognostic model that incorporated risk signatures, and we evaluated the effectiveness of the model through survival analysis, immune cell infiltration examination, and drug sensitivity evaluation.

Results

We analyzed gene expression and metabolic pathways in nine samples using single-cell sequencing data. Initially, we performed quality control and clustering, identifying 21 statistically significant cell subpopulations. Metabolic analyses of these subpopulations revealed heterogeneous activation of metabolic pathways. Focusing on the osteoblastic cell subpopulation, we further subdivided it into six groups and examined their gene expression and differentiation capabilities. Differential expression and enrichment analyses indicated that tumor tissues were enriched in cytoskeletal and structural pathways. Through WGCNA, we identified core genes negatively correlated with four highly activated metabolic pathways. Using osteosarcoma patient data, we developed a risk signature model that demonstrated robust prognostic predictions across three independent cohorts. Ultimately, we performed a thorough examination of the model, which encompassed clinical and pathological characteristics, enrichment analysis, pathways associated with cancer markers, and scores of immune infiltration, highlighting notable and complex disparities between high-risk and low-risk populations.

Conclusion

This research clarifies the molecular mechanisms and metabolic features associated with osteosarcoma and how they relate to patient outcomes, offering novel perspectives and approaches for targeted therapy and prognostic assessment in osteosarcoma.

The Sordariomycetes: an expanding resource with Big Data for mining in evolutionary genomics and transcriptomics

Advances in genomics and transcriptomics accompanying the rapid accumulation of omics data have provided new tools that have transformed and expanded the traditional concepts of model fungi. Evolutionary genomics and transcriptomics have flourished with the use of classical and newer fungal models that facilitate the study of diverse topics encompassing fungal biology and development. Technological advances have also created the opportunity to obtain and mine large datasets. One such continuously growing dataset is that of the Sordariomycetes, which exhibit a richness of species, ecological diversity, economic importance, and a profound research history on amenable models. Currently, 3,574 species of this class have been sequenced, comprising nearly one-third of the available ascomycete genomes. Among these genomes, multiple representatives of the model genera Fusarium, Neurospora, and Trichoderma are present. In this review, we examine recently published studies and data on the Sordariomycetes that have contributed novel insights to the field of fungal evolution via integrative analyses of the genetic, pathogenic, and other biological characteristics of the fungi. Some of these studies applied ancestral state analysis of gene expression among divergent lineages to infer regulatory network models, identify key genetic elements in fungal sexual development, and investigate the regulation of conidial germination and secondary metabolism. Such multispecies investigations address challenges in the study of fungal evolutionary genomics derived from studies that are often based on limited model genomes and that primarily focus on the aspects of biology driven by knowledge drawn from a few model species. Rapidly accumulating information and expanding capabilities for systems biological analysis of Big Data are setting the stage for the expansion of the concept of model systems from unitary taxonomic species/genera to inclusive clusters of well-studied models that can facilitate both the in-depth study of specific lineages and also investigation of trait diversity across lineages. The Sordariomycetes class, in particular, offers abundant omics data and a large and active global research community. As such, the Sordariomycetes can form a core omics clade, providing a blueprint for the expansion of our knowledge of evolution at the genomic scale in the exciting era of Big Data and artificial intelligence, and serving as a reference for the future analysis of different taxonomic levels within the fungal kingdom.

Comparative Genomic Analysis Reveals the Functional Traits and Safety Status of Lactic Acid Bacteria Retrieved from Artisanal Cheeses and Raw Sheep Milk

Lactic acid bacteria (LAB) are valuable for the production of fermented dairy products. We investigated the functional traits of LAB isolated from artisanal cheeses and raw sheep milk, assessed their safety status, and explored the genetic processes underlying the fermentation of carbohydrates. Lactiplantibacillus plantarum had the largest and more functional genome compared to all other LAB, while most of its protein-encoding genes had unknown functions. A key finding of our analysis was the overall absence of acquired resistance genes (RGs), virulence genes (VGs), and prophages, denoting that all LAB isolates fulfill safety criteria and can be used as starter or adjunct cultures. In this regard, the identified mobile genetic elements found in LAB, rather than enabling the integration of RGs or VGs, they likely facilitate the uptake of genes involved in beneficial functions and in the adaptation of LAB in dairy matrices. Another important finding of our study was that bacteriocins and CAZymes were abundant in LAB though each species was associated with specific genes, which in turn had different activity spectrums and identified applications. Additionally, all isolates were able to metabolize glucose, lactose, maltose, and sucrose, but Lactiplantibacillus plantarum was strongly associated with the fermentation of rhamnose, mannose, cellobiose, and trehalose whereas Levilactobacillus brevis with the utilization of arabinose and xylose. Altogether these results suggest that to fully exploit the beneficial properties of LAB, a combination of strains as food additives may be necessary. Interestingly, biological processes involved in the metabolism of carbohydrates that are not of direct interest for the dairy industry may yield valuable metabolites or activate pathways associated with beneficial health effects. Our results provide useful information for the development of new probiotic artisanal cheeses and probiotic starter cultures.

Improving the quality of Napier grass silage with pyroligneous acid: Fermentation, aerobic stability, and microbial communities

The presence of undesirable microorganisms in silage always leads to poor fermentation quality and low aerobic stability. Pyroligneous acid (PA), a by-product of biochar production, is known to have strong antimicrobial and antioxidant activities. To investigate the effects of PA on fermentation characteristics, aerobic stability, and microbial communities, Napier grass was ensiled with or without 1 and 2% PA for 30 days and then aerobically stored for 5 days. The results showed that PA application decreased (P < 0.01) the pH value, ammonia nitrogen content, and number of undesirable microorganisms (coliform bacteria, yeasts, and molds) after 30 days of ensiling and 5 days of exposure to air. The temperature of the PA-treated group was stable during the 5-day aerobic test, which did not exceed room temperature more than 2°C. The addition of PA also enhanced the relative abundance of Lactobacillus and reduced that of Klebsiella and Kosakonia. The relative abundance of Candida was higher in PA-treated silage than in untreated silage. The addition of PA decreased the relative abundance of Kodamaea and increased that of Monascus after 5 days of exposure to air. The abundances of Cladosporium and Neurospora were relatively high in 2% PA-treated NG, while these genera were note observed in the control group. These results suggested that the addition of PA could improve fermentation characteristics and aerobic stability, and alter microbial communities of silage.