Two morphologically similar halophilic strains, named USBA 874 and USBA 960^T, were isolated from water and sediment samples collected from the Zipaquirá salt mine in the Colombian Andes. Both isolates had non-spore-forming, Gram-stain-negative and motile cells that grew aerobically. The strains grew optimally at 30 °C, pH 7.0 and with 25 % NaCl (w/v). The isolates showed almost identical 16S rRNA gene sequences (99.0 % similarity). The predominant quinones of USBA-960^T were Q-8, Q-7 and Q-9. The major cellular fatty acids were C_19 : 0 cyclo ω8c, C_18 : 0 and C_16 : 0. According to 16S rRNA gene sequencing, the closest phylogenetic relatives are Salinisphaera species (similarity between 93.6 and 92.3 %), Abyssibacter profundi OUC007^T (88.6 %) and Oceanococcus atlanticus 22II-S10r2^T (88.7 %). In addition, the result of genome blast distance phylogeny analysis between strains USBA 874 and USBA 960^T, Salinisphaera halophila (YIM 95161^T), Salinisphaera shabanensis (E1L3A^T), Salinisphaera orenii (MK-B5^T) and Salinisphaera japonica (YTM-1^T) was 18.5 %. Other in silico species delineation analyses also showed low identity such as ANIb and ANIm values (<69.0 and <84.0 % respectively), TETRA (<0.81) and AAI values (<0.67). Genome sequencing of USBA 960^T revealed a genome size of 2.47 Mbp and a G+C content of 59.71 mol%. Phylogenetic analysis of strains USBA 874 and USBA 960^T indicated that they formed a different lineage within the family Salinisphaeraceae . Based on phenotypic and chemotaxonomic characteristics, phylogenetic analysis and DNA–DNA relatedness values, along with identity at whole genome level, it can be concluded that strains USBA 960^T and USBA 874 represent a novel genus of the family Salinisphaeraceae and the name Salifodinibacter halophilus gen. nov., sp. nov. is proposed. The type strain is USBA 960^T (CMPUJ U095^T=CECT 30006^T).

Previous studies revealed the potential of Labrenzia aggregata USBA 371 to produce cytotoxic metabolites. This study explores its metabolic diversity and compounds involved in its cytotoxic activity. Extracts from the extracellular fraction of strain USBA 371 showed high levels of cytotoxic activity associated with the production of diketopiperazines (DKPs). We purified two compounds and a mixture of two other compounds from this fraction. Their structures were characterized by 1D and 2D nuclear magnetic resonance (NMR). The purified compounds were evaluated for additional cytotoxic activities. Compound 1 (cyclo (l-Pro-l-Tyr)) showed cytotoxicity to the following cancer cell lines: breast cancer 4T1 (IC₅₀ 57.09 ± 2.11 µM), 4T1H17 (IC₅₀ 40.38 ± 1.94), MCF-7 (IC₅₀ 87.74 ± 2.32 µM), murine melanoma B16 (IC₅₀ 80.87 ± 3.67), human uterus sarcoma MES-SA/Dx5 P-pg (−) (IC₅₀ 291.32 ± 5.64) and MES-SA/Dx5 P-pg (+) (IC₅₀ 225.28 ± 1.23), and murine colon MCA 38 (IC₅₀ 29.85 ± 1.55). In order to elucidate the biosynthetic route of the production of DKPs and other secondary metabolites, we sequenced the genome of L. aggregata USBA 371. We found no evidence for biosynthetic pathways associated with cyclodipeptide synthases (CDPSs) or non-ribosomal peptides (NRPS), but based on proteogenomic analysis we suggest that they are produced by proteolytic enzymes. This is the first report in which the cytotoxic effect of cyclo (l-Pro-l-Tyr) produced by an organism of the genus Labrenzia has been evaluated against several cancer cell lines.

Melanoma-associated antigens (MAGEs) are cancer-testis antigens (CTAs) aberrantly expressed in multiple cancer types, including hepatocellular carcinoma (HCC), and associated with aggressive phenotypes. Although MAGE proteins are widely studied as cancer immunotherapy targets, their roles in HCC and the regulation of their expression during liver pathogenesis in mouse models, including dietary effects, remain poorly understood. We analyzed Mage gene expression in liver tissues from 78 C3H/HeJ mice with chronic diet-induced obesity. While type I MAGE genes are frequently expressed in human HCC, we found no evidence of their expression in mouse liver tumors, suggesting species-specific regulation. In contrast, type II Maged2, previously reported to be upregulated in human HCC, was significantly increased in mouse liver tumors. Analysis of human HCC samples from The Cancer Genome Atlas (TCGA) database confirmed MAGED2 upregulation and its association with patient prognosis. Together, these findings identify MAGED2 as a conserved marker of liver cancer in both humans and mice and emphasize the importance of cross-species comparative approaches for selecting appropriate models and accurately interpreting results, particularly for CTAs, which often evolved recently and in a species-specific manner.

The identification of cell types is a basic step of pipelines for Single-Cell RNA sequencing (scRNA-seq) data analysis. However, unsupervised clustering of cells from scRNA-seq data has multiple challenges: high dimensionality, sparseness of the expression matrix, and technical noise that generates false zero entries. In this study, we introduce new algorithms for clustering scRNA-seq data. The first algorithm builds a k-MST graph from distances obtained directly from the input data without dimensionality reduction. The computation follows an iterative procedure of k steps, calculating the edges of minimum spanning trees over different subgraphs obtained by removing edges selected in previous iterations. The Louvain algorithm is executed on the k-MST graph for cell clustering. We also explored an alternative based on neural networks, using an autoencoder to learn the parameters of a Gaussian mixture model. Benchmark experiments show that the algorithms have competitive accuracy, compared to previous solutions. Sequencing depth, number of cells and tissue types have important effects on the performance of the algorithms. Further experiments with scRNA-data taken from a patient with refractory epilepsy show that the autoencoder model achieved the best accuracy for this dataset, and the k-MST was competitive among graph-based approaches.

The identification of cell types is a basic step of the pipeline for Single-Cell RNA sequencing data analysis. However, unsupervised clustering of cells from scRNA-seq data has multiple challenges: the high dimensional nature of the data, the sparse nature of the gene expression matrix, and the presence of technical noise that can introduce false zero entries. In this study, we introduce new algorithms for clustering scRNA-seq data. The first algorithm builds a k -MST graph from distances obtained directly from the input data without dimensionality reduction. The computation follows an iterative procedure of k steps in which each step calculates and stores the edges of minimum spanning trees over different subgraphs obtained removing edges selected in previous iterations. The Louvain algorithm is executed on the k -MST graph for cell clustering. We also explored alternatives based on neural networks in which an autoencoder is used to learn the parameters of a Gaussian mixture model, aiming to improve the handling of clusters with different shapes and sizes. Benchmark experiments with simulated data and public datasets show that the algorithms proposed in this work have competitive accuracy, compared to previous solutions, but also that sequencing depth, number of cells and tissue types have important effects on the performance of the algorithms. Moreover, we performed further experiments with scRNA-data taken from a patient with refractory epilepsy. The AE-GMM model achieved the best accuracy for this dataset, and the k -MST ranked first among methods that do not require previous information on the expected number of clusters.