Biology subjects

Makrodimitris, S.

Publications and source records attributed to Makrodimitris, S..

10 recordsLinked to original sources

bioRxiv2026

Cell-free DNA Fragmentation Profiling at Transcription Start Sites Improves upon Cancer-Type-Specific Region Selection for Cancer Detection

MotivationAccurate discrimination between healthy individuals and patients with cancer using minimally invasive liquid biopsies could improve cancer diagnosis and monitoring. Circulating cell-free DNA (cfDNA) is a promising biomarker, since fragmentation patterns reflect chromatin organization and have been used to interrogate regulatory regions such as transcription start sites (TSSs). Classification approaches typically rely on hypothesis-driven selection of genomic regions based on literature or external tissue data. Therefore, they assume that tumor-derived cfDNA constitutes the dominant diagnostic signal, potentially overlooking a systemic, genome-wide shift in the cfDNA pool. ResultsWe present a data-driven framework that identifies discriminative genomic loci directly from cfDNA whole-genome sequencing data. Using fragmentomic features captured at TSSs within a nested cross-validation framework, the model outperforms ichorCNA and hypothesis-driven baselines in distinguishing healthy from colorectal and breast cancer samples (AUROC 0.95 {+/-} 0.039). Performance was maintained in a pan-cancer setting across seven malignancies (AUROC 0.946 {+/-} 0.032) and generalized to previously unseen cancer types within the same cohorts (AUROC 0.934 {+/-} 0.006). While validation in an independent external cohort showed a performance gap (AUROC 0.694), the data-driven model was consistently competitive with baseline methods. These results indicate that robust cancer detection is enabled by integrating distributed genome-wide fragmentation patterns rather than restricting analysis to predefined regions. Availability and implementationScripts to reproduce the results are available at https://github.com/brmprnk/comp/ Contacti.b.pronk@tudelft.nl Supplementary informationavailable at NAR Online.

bioinformatics↗

bioRxiv2025

From Background to Signal: Simultaneously derived Copy Numbers from Methylation-Dependent Sequencing cell-free DNA

Cell-free DNA (cfDNA) analysis offers a powerful, minimally invasive approach to improve cancer care by measuring tumor-specific genomic and epigenetic alterations. Here, we demonstrate the versatility of MeD-seq, a methylation-dependent sequencing assay, for comprehensive cfDNA analysis, including DNA methylation profiling, chromosomal copy number (CN) alterations, and tumor fraction (TF) estimation. MeD-seq-derived CN profiles and TF estimates from 38 colorectal cancer with liver metastases (CRLM) and 5 ovarian cancer patients were highly comparable to shallow whole-genome sequencing (sWGS) validating our approach. For 120 CRLM patients we used MeD-seq CN and TF information in an improved Differential Methylation Model which detected additional significantly Differentially Methylated Regions (DMRs) correlating with TF estimates. Using the identified DMR sets we were subsequently able to distinguish healthy blood donors from CRLM patients with low amounts of circulating tumor DNA (ctDNA) as well. These findings show MeD-seq as an affordable platform for detecting cancer-specific signals directly from plasma without prior tissue-based information. Future work could expand its application to other cancer types, solidifying MeD-seq as a versatile tool for cfDNA profiling. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=159 SRC="FIGDIR/small/633371v2_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@bd440eorg.highwire.dtl.DTLVardef@1befbb7org.highwire.dtl.DTLVardef@17fe9d2org.highwire.dtl.DTLVardef@d0101a_HPS_FORMAT_FIGEXP M_FIG Graphical Abstract of MeD-seq and Copy Number Informed Differential Methylation Analysis Liquid biopsy samples are collected, followed by cfDNA extraction, methylation-dependent enzymatic digestion, Whole Genome Sequencing (WGS) and in silico read selection. Background reads (lacking the recognition site of the methylation-dependent restriction ezyme) are utilized for CN profiling and TF estimation. Methylated reads (with the recognition site of the methylation-dependent restriction enzyme) are counted in CpG islands and integrated with CN information to perform Differential Methylation profiling, enabling a comprehensive assessment of methylation in cfDNA. C_FIG

bioinformatics↗

bioRxiv2023

An in-depth comparison of linear and non-linear joint embedding methods for bulk and single-cell multi-omics

Multi-omic analyses contribute to understanding complex biological processes, but also to making reliable predictions about, for example, disease outcomes. Several linear joint dimensionality reduction methods exist, but recently neural networks are more commonly used to embed different-omics into the same non-linear manifold. We compared linear to non-linear joint embedding methods using bulk and single-cell data. For modality imputation, non-linear methods had a clear advantage. Comparisons in downstream supervised tasks lead to the following insights: First, concatenating the principal components of each modality is a competitive baseline for multi-modal prediction. If only one modality was available at test time, joint embeddings yielded significant performance improvements with respect to a unimodal predictor. Second, imputed omics profiles can be fed to classifiers trained on real data with limited performance drops. Overall, the product-of-experts architecture performed well in most tasks while a common encoder of concatenated modalities performed poorly.

bioinformatics↗

bioRxiv2023

Benchmarking Variational AutoEncoders on cancer transcriptomics data

Deep generative models, such as variational autoencoders (VAE), have gained increasing attention in computational biology due to their ability to capture complex data manifolds which subsequently can be used to achieve better performance in downstream tasks, such as cancer type prediction or subtyping of cancer. However, these models are difficult to train due to the large number of hyperparameters that need to be tuned. To get a better understanding of the importance of the different hyperparameters, we examined six different VAE models when trained on TCGA transcriptomics data and evaluated on the downstream task of cluster agreement with cancer subtypes. We studied the effect of the latent space dimensionality, learning rate, optimizer and initialization on the quality of subsequent clustering of the TCGA samples. We found {beta}-TCVAE and DIP-VAE to have a good performance, on average, despite being more sensitive to hyperparameters selection. Based on these experiments, we derived recommendations for selecting the different hyperparameters settings. In addition, we examined whether the learned latent spaces capture biologically relevant information. Hereto, we correlated the different representations with various data characteristics such as age, days to metastasis, immune infiltration, and mutation signatures. We found that for all models the latent factors, in general, do not uniquely correlate with one of the data characteristics even for models specifically designed for disentanglement.

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bioRxiv2022

Cell type deconvolution of methylated cell-free DNA atthe resolution of individual reads

Cell-free DNA (cfDNA) are DNA fragments originating from dying cells that are detectable in bodily fluids, such as the plasma. Accelerated cell death, for example caused by disease, induces an elevated concentration of cfDNA. As a result, determining the cell type origins of cfDNA molecules can provide information about an individuals health. In this work, we aim to increase the sensitivity of methylation-based cell type deconvolution by adapting an existing method, CelFiE, which uses the methylation beta values of individual CpG sites to estimate cell type proportions. Our new method, CelFEER, instead differentiates cell types by the average methylation values within individual reads. We additionally improved the originally reported performance of CelFiE by using a new approach for finding marker regions that are differentially methylated between cell types. This approach compares the methylation values over 500 bp regions instead of at single CpG sites and solely takes hypomethylated regions into account. We show that CelFEER estimates cell type proportions with a higher correlation (r2 = 0.94{+/-}0.04) than CelFiE (r2 = 0.86{+/-} 0.09) on simulated mixtures of cell types. Moreover, we found that it can find a significant difference between the skeletal muscle cfDNA fraction in four ALS patients and four healthy controls.

bioinformatics↗

bioRxiv2021

The power of universal contextualised protein embeddings in cross-species protein function prediction

Computationally annotating proteins with a molecular function is a difficult problem that is made even harder due to the limited amount of available labelled protein training data. A recently published supervised molecular function predicting model partly circumvents this limitation by making its predictions based on the universal (i.e. task-agnostic) contextualised protein embeddings from the deep pre-trained unsupervised protein language model SeqVec. SeqVec embeddings incorporate contextual information of amino acids, thereby modelling the underlying principles of protein sequences insensitive to the context of species. We applied the existing SeqVec-based molecular function prediction model in a transfer learning task by training the model on annotated protein sequences of one training species and making predictions on the proteins of several test species with varying evolutionary distance. We show that this approach successfully generalises knowledge about protein function from one eukaryotic species to various other species, proving itself an effective method for molecular function prediction in inadequately annotated species from understudied taxonomic kingdoms. Furthermore, we submitted the performance of our SeqVec-based prediction models to detailed characterisation, first to advance the understanding of protein language models and second to determine areas of improvement. Author summaryProteins are diverse molecules that regulate all processes in biology. The field of synthetic biology aims to understand these protein functions to solve problems in medicine, manufacturing, and agriculture. Unfortunately, for many proteins only their amino acid sequence is known whereas their function remains unknown. Only a few species have been well-studied such as mouse, human and yeast. Hence, we need to increase knowledge on protein functions. Doing so is, however, complicated as determining protein functions experimentally is time-consuming, expensive, and technically limited. Computationally predicting protein functions offers a faster and more scalable approach but is hampered as it requires much data to design accurate function prediction algorithms. Here, we show that it is possible to computationally generalize knowledge on protein function from one well-studied training species to another test species. Additionally, we show that the quality of these protein function predictions depends on how structurally similar the proteins are between the species. Advantageously, the predictors require only the annotations of proteins from the training species and mere amino acid sequences of test species which may particularly benefit the function prediction of species from understudied taxonomic kingdoms such as the Plantae, Protozoa and Chromista.

bioinformatics↗

bioRxiv2020

Unsupervised protein embeddings outperform hand-crafted sequence and structure features at predicting molecular function

MotivationProtein function prediction is a difficult bioinformatics problem. Many recent methods use deep neural networks to learn complex sequence representations and predict function from these. Deep supervised models require a lot of labeled training data which are not available for this task. However, a very large amount of protein sequences without functional labels is available. ResultsWe applied an existing deep sequence model that had been pre-trained in an unsupervised setting on the supervised task of protein function prediction. We found that this complex feature representation is effective for this task, outperforming hand-crafted features such as one-hot encoding of amino acids, k-mer counts, secondary structure and backbone angles. Also, it partly negates the need for deep prediction models, as a two-layer perceptron was enough to achieve state-of-the-art performance in the third Critical Assessment of Functional Annotation benchmark. We also show that combining this sequence representation with protein 3D structure information does not lead to performance improvement, hinting that three-dimensional structure is also potentially learned during the unsupervised pre-training. AvailabilityImplementations of all used models can be found at https://github.com/stamakro/GCN-for-Structure-and-Function. Contactameliavm@ugr.es Supplementary informationSupplementary data are available online.

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bioRxiv2019

Sparsity of Protein-Protein Interaction Networks Hinders Function Prediction in Non-Model Species

Physical interaction between two proteins is strong evidence that the proteins are involved in the same biological process, making Protein-Protein Interaction (PPI) networks a valuable data resource for predicting the cellular functions of proteins. However, PPI networks are largely incomplete for non-model species. Here, we tested to what extened these incomplete networks are still useful for genome-wide function prediction. We used two network-based classifiers to predict Biological Process Gene Ontology terms from protein interaction data in four species: Saccharomyces cerevisiae, Escherichia coli, Arabidopsis thaliana and Solanum lycopersicum (tomato). The classifiers had reasonable performance in the well-studied yeast, but performed poorly in the other species. We showed that this poor performance can be considerably improved by adding edges predicted from various data sources, such as text mining, and that associations from the STRING database are more useful than interactions predicted by a neural network from sequence-based features.

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bioRxiv2019

A NINE-YEAR LONGITUDINAL STUDY IN THE BLOOD OF A SUPERCENTENARIAN REVEALS AN EXTENSIVE SUBCLONAL ARCHITECTURE AND ONGOING SUBCLONAL DYNAMICS

The aged hematopoietic system is characterized by decreased immuno-competence and by a reduced number of hematopoietic stem cells (HSCs) that actively generates new blood cell (age-related clonal hematopoiesis, ARCH). While both aspects are commonly associated with an increased risk of aging-related diseases, it is currently unknown to what extent these aspects co-occur during exceptional longevity. Here, we investigated these aspects in blood cells of an immuno-hematopoietically normal female who reached 111 years. Blood samples were collected across a 9-year period at ages 103, 110 and 111 years. We applied several genetic sequencing approaches to investigate clonality in peripheral blood samples and sorted cell subsets. Immuno-competence was characterized using flow cytometry, T-cell receptor excision circle (TREC) assays, and in vitro proliferation assays. We identified a single DNMT3A-mutated HSC clone with a complex subclonal architecture and observed ongoing subclonal dynamics within the 9-year timeframe of our sampling. The mutated HSC generated 78-87% myeloid cells, 6-7% of the B-cells, 6% of CD8+ T-cells, and notably 22% of the CD4+ T-cells. Intriguingly, we found that T-cells were capable of robust proliferation when challenged in vitro. Moreover, we observed a surprisingly high TREC content, indicative of recent generation of naive T-cells. Concluding, we observed long-term stability of extreme ARCH with ongoing clonal dynamics combined with functional T-cell immunity. Our results indicate that extreme ARCH does not compromise immuno-competence and that a clonally expanded CD4+ T-cell subset may serve as a potential hallmark of the supercentenarian immune system. Key pointsO_LILongitudinal blood sampling from a female aged 103-111 revealed a dynamic clonal hematopoiesis contributing to myeloid and lymphoid subsets C_LIO_LIDespite the highly advanced age and extreme clonal hematopoiesis we observed functional T-cell immunity C_LI

immunology↗

bioRxiv2019

Metric Learning on Expression Data for Gene Function Prediction

MotivationCo-expression of two genes across different conditions is indicative of their involvement in the same biological process. However, using RNA-Seq datasets with many experimental conditions from diverse sources introduces batch effects and other artefacts that might obscure the real co-expression signal. Moreover, only a subset of experimental conditions is expected to be relevant for finding genes related to a particular Gene Ontology (GO) term. Therefore, we hypothesize that when the purpose is to find similar functioning genes that the co-expression of genes should not be determined on all samples but only on those samples informative for the GO term of interest.\n\nResultsTo address both types of effects, we developed MLC (Metric Learning for Co-expression), a fast algorithm that assigns a GO-term-specific weight to each expression sample. The goal is to obtain a weighted co-expression measure that is more suitable than the unweighted Pearson correlation for applying Guilt-By-Association-based function predictions. More specifically, if two genes are annotated with a given GO term, MLC tries to maximize their weighted co-expression, and, in addition, if one of them is not annotated with that term, the weighted co-expression is minimized. Our experiments on publicly available Arabidopsis thaliana RNA-Seq data demonstrate that MLC outperforms standard Pearson correlation in term-centric performance.\n\nAvailabilityMLC is available as a Python package at www.github.com/stamakro/MLC\n\nContacts.makrodimitris@tudelft.nl\n\nSupplementary informationSupplementary data are available online.

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