Maureen A. Su find the epigenetic regulator UTX complexes with transcription factors TCF1 and STAT3 to promote pathogenicity of long-lived, stem-like progenitor T cells in models of type 1 diabetes (T1D)
1Department of Microbiology, Immunology, and Molecular Genetics, UCLA David Geffen School of Medicine, Los Angeles, California, USA.
2Department of Pediatrics and.
3Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Prostate cancer often develops very slowly. For the vast majority, this is a disease that you live well with, without the need for treatment, but some get an aggressive variant with recurrence of cancer even after surgery. The disease behaves very differently from patient to patient. Understanding what makes the cancer aggressive is crucial for better diagnostics and treatment, says the author.
Aggressive cancer has its own gene expression: The researchers identified a pattern in the gene expression of the tumor itself in prostate tissue in patients with a high risk of recurrence and spread. This signature can become a new tool for distinguishing between patients who need intensive care and those who can manage with less intensive follow-up.
Inflammation of apparently healthy tissue: Signs of inflammation and changes in metabolic processes were also found in the normal tissue close to the cancerous tumor. These glands had high activity of neurotransmitters that attract immune cells, and an increased occurrence of a cell type that can trigger inflammatory reactions. At the same time, the levels of important substances had decreased, suggesting that the gland had lost its normal function.
“Aggressive prostate cancer appears to be associated with inflammation in the area around the cancer cells, combined with specific genetic signatures and metabolic changes in the prostate tissue. This knowledge can provide better methods for early identification of patients at high risk,” says the author. ScienceMission sciencenewshighlights.
The research lays a foundation for the possibility that aggressive prostate cancer can probably be detected through a few drops of semen or blood in the long term.
Prostate cancer is the most common form of cancer among men in Western countries.
Here, Barbara Rivera & team report on the benign-to-malignant progression route in DICER1/DGCR8-thyroid lesions, identifying a DICER1-cancer epi-signature using multi-omic profiling:
The image depicts a thyroid lesion from a sporadic DICER1 case with immunofluorescent staining for pan-cytokeratin (green) and vimentin (red). Enclosed areas represent selected regions of interest.
1Program in Molecular Mechanisms and Experimental Therapy in Oncology (Oncobell), Bellvitge Biomedical Research Institute (IDIBELL), L’Hospitalet de Llobregat, Barcelona, Spain.
2Genetics Program, Faculty of Biology, and.
3Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, University of Barcelona, Barcelona, Spain.
A comparative single-cell analysis reveals similarities and differences in lineage bias between human and murine hematopoietic stem cells. This work deepens our understanding of how lineage commitment is regulated across species and provides a valuable framework for translating insights from mouse models to human hematopoiesis.
The commitment of hematopoietic stem cells (HSC) to myeloid, erythroid, and lymphoid lineages is influenced by microenvironmental cues, and governed by cell-intrinsic and epigenetic characteristics that are unique to the HSC population. To investigate the nature of lineage commitment bias in human HSC, mitochondrial single-cell assay for transposase-accessible chromatin (ATAC)-sequencing was used to identify somatic mutations in mitochondrial DNA to act as natural genetic barcodes for tracking the ex vivo differentiation potential of HSC to mature cells. Clonal lineages of human CD34+ cells and their mature progeny were normally distributed across the hematopoietic lineage tree without evidence of significant skewing. To investigate commitment bias in vivo, mice were transplanted with limited numbers of long-term HSC (LT-HSC). Variation in the ratio of myeloid and lymphoid cells between donors was suggestive of a skewed output but was not altered by increasing numbers of LT-HSC. These data suggest that the variation in myeloid and lymphoid engraftment is a stochastic process dominated by the irradiated recipient niche with minor contributions from cell-intrinsic lineage biases of LT-HSC.
Hematopoietic stem cells (HSC) are classically considered to have the capacity for complete regeneration of the hematopoietic compartment. More recent analyses indicate additional complexity and heterogeneity in the HSC compartment, with lineage-restricted or lineage-biased HSC considered a feature of mammalian hematopoiesis.1–13 A partial differential equation model to study relationships between hematopoietic stem and progenitor cells (HSPC) emphasizes that myeloid bias cannot be accounted for solely by short-term HSC bias during inflammation but rather involves a combination of HSC and progenitor cell biases.14 Central to the concept of lineage bias is an assumption that cells used for studying HSC commitment are HSC and not multipotent progenitors or lineage-committed progenitors. Changes in differentiation of cells downstream of the long-term HSC (LT-HSC) must also be evaluated when considering the potential lineage bias of a LT-HSC.
Researchers from the NeuroAD group (Neuropathology of Alzheimer’s Disease) within the Department of Cell Biology, Genetics and Physiology at the University of Málaga, also affiliated with IBIMA–BIONAND Platform and CIBERNED, have made a pioneering breakthrough in the fight against this disease by identifying astrocytes as a promising cellular target for the development of future therapies.
The study demonstrates, for the first time, the presence of senescent astrocytes—cells that remain alive but have lost their functional capacity—in the brains of Alzheimer’s patients, positioning this cellular aging process as a key mechanism in neurodegeneration.
The research, published in the journal Journal of Neuroinflammation, was led by Dr. Antonia Gutiérrez, Professor of Cell Biology and Principal Investigator of the NeuroAD group, together with Dr. Juan Antonio García León, Associate Professor of Cell Biology. Other contributors to the study include Laura Cáceres, Laura Trujillo, Elba López, Elisabeth Sánchez, and Inés Moreno.
Industrial yeasts are a powerhouse of protein production, used to manufacture vaccines, biopharmaceuticals, and other useful compounds. In a new study, MIT chemical engineers have harnessed artificial intelligence to optimize the development of new protein manufacturing processes, which could reduce the overall costs of developing and manufacturing these drugs.
Using a large language model (LLM), the MIT team analyzed the genetic code of the industrial yeast Komagataella phaffii — specifically, the codons that it uses. There are multiple possible codons, or three-letter DNA sequences, that can be used to encode a particular amino acid, and the patterns of codon usage are different for every organism.
The new MIT model learned those patterns for K. phaffii and then used them to predict which codons would work best for manufacturing a given protein. This allowed the researchers to boost the efficiency of the yeast’s production of six different proteins, including human growth hormone and a monoclonal antibody used to treat cancer.
Industrial yeasts are a powerhouse of protein production, used to manufacture vaccines, biopharmaceuticals, and other useful compounds. In a new study, MIT chemical engineers have harnessed artificial intelligence to optimize the development of new protein manufacturing processes, which could reduce the overall costs of developing and manufacturing these drugs.
Using a large language model (LLM), the MIT team analyzed the genetic code of the industrial yeast Komagataella phaffii—specifically, the codons that it uses. There are multiple possible codons, or three-letter DNA sequences, that can be used to encode a particular amino acid, and the patterns of codon usage are different for every organism.
The new MIT model learned those patterns for K. phaffii and then used them to predict which codons would work best for manufacturing a given protein. This allowed the researchers to boost the efficiency of the yeast’s production of six different proteins, including human growth hormone and a monoclonal antibody used to treat cancer.
Open-source AI models for LungCancer EGFR mutation prediction showed high accuracy overall but reduced performance in Asian patients and pleural samples, indicating the need for broader validation.
Importance Artificial intelligence (AI) models are emerging as rapid, low-cost tools for predicting targetable genomic alterations directly from routine pathology slides. Although these approaches could accelerate treatment decisions in lung cancer, little is known about whether their performance is consistent across diverse patient populations and tissue contexts.
Objective To evaluate the performance and generalizability of 2 open-source AI pathology models for predicting EGFR mutation status in lung adenocarcinoma (LUAD) across independent cohorts and ancestral subgroups.
Design, Setting, and Participants This cohort study included patients with LUAD from 2 cohorts: Dana-Farber Cancer Institute (DFCI) from June 2013 to November 2023, and a European-based trial (TNM-I) from August 2016 to February 2022. All patients had paired next-generation sequencing data and hematoxylin-eosin–stained whole-slide images. In the DFCI cohort, genetic ancestry was inferred using germline genotype data. Data analyses were performed from July 2025 to September 2025.
In two new studies on 28,000 individuals, researchers are able to show that genetic variants in 11 regions of the human genome have a clear influence on which bacteria are in the gut and what they do there. Only two genetic regions were previously known. Some of the new genetic variants can be linked to an increased risk of gluten intolerance, hemorrhoids and cardiovascular diseases.
The studies are published in the journal Nature Genetics.
The community of bacteria living in our gut, or gut microbiome, has become a hot research area in recent years because of its great significance for health and disease. However, the extent to which our genes determine which bacteria are present in the intestines has been unclear. Until now, it has only been possible to link a few genetic variants to the composition of the gut microbiome with certainty.