Biology subjects

Blomberg, P.

Publications and source records attributed to Blomberg, P..

4 recordsLinked to original sources

bioRxiv2025

Single-Cell Transcriptomics Reveals Dynamics of NK Cell Expansion in a Feeder Cell-Free Culture of PBMCs - Implications for Immunotherapy

Natural killer (NK) cell therapies hold great promise for cancer treatment; however, donor-to-donor heterogeneity in the ex vivo expansion process remains a critical bottleneck in their supply. This study aimed to identify factors influencing donor variability in a two-week long ex vivo NK cell expansion from PBMCs, analyzed across three donors. Single-cell transcriptomics was applied to investigate the distribution of cell types and phenotypes, as well as trajectory inference and differential gene expression. Our results identified that several factors were associated with the variability in the final NK cell fraction and expansion, and that their influence was prevalent between culture day 3 and 8. Compared to high final NK cell fraction, a culture with low final NK cell fraction exhibited an upregulation of some stress and inflammatory genes and an increase in one specific subcluster of the NK cells already on culture day 3. It showed a low score of CD56Bright CD16- phenotype and high score of CD56Dim CD16+ phenotype. It had also an increased presence of cytotoxic CD8+ Tm cells. Among the observed subclusters of CD8+ Tm cells, it exhibited a higher presence of a subcluster associated with a less differentiated and less cytotoxic phenotype as well as a lower prevalence of a subcluster associated with chemokine and cytotoxic genes. Finally, it had a major expansion of one of the CD8+ Tm cells subclusters annotated as NK-like T cell and characterized by a high CCR5 mRNA expression while the levels of CCL3, CCL4, and CCL5 mRNA were downregulated. The present findings point towards a potential link between CCL signaling and improved NK cell expansion performance, including possible markers for further investigations, and suggest future strategies to increase the final NK cell fraction and expansion based on donor-specific markers.

bioengineering↗

bioRxiv2024

Anti-gene oligonucleotides targeting Friedreichs ataxia expanded GAA:TTC repeats increase Frataxin expression

Friedreichs ataxia (FRDA) is a progressive, autosomal recessive ataxia caused, in the majority of cases, by homozygous expansion of GAA*TTC triplet-repeats in the first intron of the frataxin (FXN) gene. GAA*TTC repeat expansion results in the formation of non-B DNA intramolecular triplex structure (H-DNA) as well as changes in the epigenetic landscape at FXN loci and heterochromatin formation. Expansion of intronic GAA*TTC repeats is associated with reduced levels of FXN mRNA and protein resulting in disease development. Previously, we reported that DNA-binding anti-gene oligonucleotides (AGOs) targeting the GAA*TTC repeat expansion abolished H-DNA formation. Here, we demonstrate that targeting repeat-expanded chromosomal DNA using single-strand locked nucleic acid (LNA)-DNA mixmer AGOs increases FXN mRNA and protein expression in patient-derived cells. We examined numerous LNA-DNA AGOs and found that the design, length and their LNA composition have a high impact on the effectiveness of the treatment. Collectively, our results demonstrate the unique capability of specifically designed ONs targeting the GAA*TTC DNA repeats to upregulate FXN gene expression.

molecular biology↗

bioRxiv2023

Reconstruction of compartmentalized genome-scalemetabolic models using deep learning for over 800fungi

Eukaryotic metabolism is organized into subcellular compartments enclosed by lipid-membranes. Since most metabolites do not move freely across the membranes, the compartmentalization influences metabolic pathway connectivities. Thus, compartmentalizing the pathways also in genome-scale metabolic models (GEMs) is essential for accurate predictions of eukaryotic cells metabolic phenotypes. Compartmentalization has manually been introduced into the model Eukaryote GEMs like for yeast Saccharomyces cerevisiae. Non-model organisms GEMs can be automatically reconstructed from genome data. However, the existing GEM reconstruction methods do not introduce compartmentalization into the models. To that end, we integrated our novel deep learning protein localization prediction and protein functional annotation into a top-down GEM reconstruction for automatically creating species-specific compartmentalized GEMs. We developed also a universal fungal GEM for top-down reconstruction of species-specifically compartmentalized GEMs and reconstructed models for 834 species. The novel protein localization prediction outperformed the state of the art in classifying proteins into multiple compartments and integrating the enzyme localization predictions to functional annotations improved the top-down model reconstruction. Interestingly, the clustering of the fungal GEMs reconstructed with predicted species-specific enzyme localization resembled more closely the phylogenetic relationships of the species than that of the GEMS without the compartmentalization. Compartmentalization of metabolism in fungi differs from other eukaryotes but the enzyme subcellular localizations vary also among fungal species. The Fungal kingdom encompasses species important for human health, environment, and industrial biotechnology. The reconstructed fungal GEM set offers valuable tools for e.g., predicting metabolic phenotypes of e.g., mushrooms or single-cellular fungi, the roles of eukaryotes in microbial communities, designs optimizing eukaryotic hosts for industrial chemical production, and identifying drug targets against pathogenic fungi. Beyond fungi, the compartmentalized GEM reconstruction method allows combining other universal GEMs for developing cell type specific GEMs for higher eukaryotes including plants, insects, and mammals.

systems biology↗

bioRxiv2017

Principal Metabolic Flux Mode Analysis

MotivationIn the analysis of metabolism using omics data, two distinct and complementary approaches are frequently used: Principal component analysis (PCA) and Stoichiometric flux analysis. PCA is able to capture the main modes of variability in a set of experiments and does not make many prior assumptions about the data, but does not inherently take into account the flux mode structure of metabolism. Stoichiometric flux analysis methods, such as Flux Balance Analysis (FBA) and Elementary Mode Analysis, on the other hand, produce results that are readily interpretable in terms of metabolic flux modes, however, they are not best suited for exploratory analysis on a large set of samples.\n\nResultsWe propose a new methodology for the analysis of metabolism, called Principal Metabolic Flux Mode Analysis (PMFA), which marries the PCA and Stoichiometric flux analysis approaches in an elegant regularized optimization framework. In short, the method incorporates a variance maximization objective form PCA coupled with a Stoichiometric regularizer, which penalizes projections that are far from any flux modes of the network. For interpretability, we also introduce a sparse variant of PMFA that favours flux modes that contain a small number of reactions. Our experiments demonstrate the versatility and capabilities of our methodology.\n\nAvailabilityMatlab software for PMFA and SPMFA is available in https://github.com/ aalto-ics-kepaco/PMFA.\n\nContactsahely@iitpkd.ac.in, juho.rousu@aalto.fi, Peter.Blomberg@vtt.fi, Sandra.Castillo@vtt.fi\n\nSupplementary informationDetailed results are in Supplementary files. Supplementary data are available at https://github.com/aalto-ics-kepaco/PMFA/blob/master/Results.zip.

bioinformatics↗