Genetics – Lifeboat News: The Blog

Feb 18
2026

Germline mutations in DICER1 and DGCR8 can lead to a range of thyroid conditions

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.

Feb 18
2026

Comparative single-cell lineage bias in human and murine hematopoietic stem cells

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.

Feb 17
2026

Senescent astrocytes discovered in Alzheimer’s brains point to new treatment targets

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.

Feb 17
2026

New AI model could cut the costs of developing protein drugs

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.

Feb 17
2026

AI model learns yeast DNA ‘language’ to boost protein drug output

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.

Feb 17
2026

Ancestry-Associated Performance Variability of Open-Source AI Models for EGFR Prediction in Lung 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.

Feb 16
2026

Studies show 11 genetic variants affect gut microbiome

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.

Feb 16
2026

Discussing the implication of DNA methylation in human diseases

DNA methylation plays a critical role in gene expression regulation and has emerged as a robust biomarker of biological age. This modification will become heavier or site drift along with aging. Recently, it is termed epigenetic clocks—such as Horvath, Hannum, PhenoAge, and GrimAge—leverage specific methylation patterns to accurately predict age-related decline, disease risk, and mortality. These tools are now widely applied across diverse tissues, populations, and disease contexts. Beyond age-related loss of methylation control, accelerated DNA methylation age has been linked to environmental exposures, lifestyle factors, and chronic diseases, further reinforcing its value as a dynamic and clinically relevant marker of biological aging. DNA methylation is reshaping our understanding of aging and disease risk, with promising implications for preventive medicine and interventions aimed at promoting healthy longevity. However, it must be admitted that some challenges remain, including limited generalizability across populations, an unclear mechanism, and inconsistent longitudinal performance. In this review, we examine the biological foundations of DNA methylation, major advances in epigenetic clock development, and their expanding applications in aging research, disease prediction and health monitoring.

Aging is a complex, multifactorial process that affects nearly all biological systems. While chronological age simply measures the passage of time from birth, biological age reflects the functional state and health of an individual’s tissues and organs (Kiselev et al., 2025). This distinction is critical, as individuals of the same chronological age often exhibit markedly different biological conditions, disease risks, and mortality trajectories (Dugue et al., 2018). Therefore, biological age potentially serves as a more meaningful measure of aging-related decline and is increasingly used to assess overall health status, predict disease onset, and evaluate the effectiveness of interventions aimed at promoting healthy longevity (Dugue et al., 2018; Petkovich et al., 2017).

Among various biomarkers proposed to estimate biological age, epigenetic modifications—particularly DNA methylation—have emerged as one of the most reliable and informative (Dugue et al., 2018). In epigenetics, DNA methylation involves the addition of a methyl group to the 5′ position of cytosine residues, typically at CpG dinucleotides, which can regulate gene expression without altering the underlying DNA sequence. Moreover, DNA methylation can be accurately measured by sequencing at methylated sites with bisulfate treatment (Zhang et al., 2012). Age-related changes in DNA methylation pattern are not random; they occur at specific genomic locations. These methylated sites are picked and constitute come patterns, by which scientists can construct “epigenetic clocks” to precisely estimate a person’s biological age based on their DNA modification. As people grow older, their methylation profiles shift in predictable ways (Kiselev et al., 2025; Horvath, 2013; Horvath and Raj, 2018).