Biology subjects

Marcus, E.

Publications and source records attributed to Marcus, E..

9 recordsLinked to original sources

bioRxiv2025

The genetic basis for DNA methylation variation across tissues and development

The mechanisms by which genetic variation shapes the epigenome across cell types and developmental stages have remained elusive. Here, we define a unifying developmental framework for DNA methylation programming, grounded in genome-wide methylation and genetic variation data from both mouse and human. In mice, we identify thousands of differentially methylated regions (DMRs) linked to sequence polymorphisms that disrupt transcription factor binding. These DMRs are programmed either during implantation or later in organogenesis, revealing two distinct periods of epigenetic regulation. Extending this logic to humans, we analyze our atlas of over 200 WGBS samples from 39 purified cell types and map 33,574 regions where common SNPs control allele-specific methylation. These include both early-established and cell-type-specific loci, many of which colocalize with eQTLs, enhancers, silencers, and disease-associated variants. Our results uncover a widespread mechanism by which genetic variation influences the regulatory landscape, linking sequence, methylation, and transcription across tissues. This cross-species atlas of sequence-dependent methylation not only clarifies the logic and timing of epigenetic programming, but also provides a foundational resource for deciphering non-coding variants in development, complex disease, and regenerative medicine.

genomics↗

bioRxiv2025

Integrating computational protein structure predictions and genetic dependencies yields an atlas of human multi-protein complexes (AHMPC)

Knowledge of which proteins interact to form functional complexes in cells is essential for understanding molecular mechanisms in biology. Structure prediction methods recently allowed to compute the Human Interactome of likely binary protein interactions. We combine computational predictions with orthogonal functional data from the Dependency Map that estimate the correlation between genetic dependencies and vulnerabilities of the corresponding gene pairs. This revealed groups of proteins that likely form larger complexes. Clustering analysis followed by AlphaFold3 multi-protein complex predictions and AlphaBridge analysis provided the basis to construct and atlas of human multi-protein complexes (AHMPC), currently encompassing 354 high-confidence predicted multi-protein complexes. These include well-known assemblies and new ones - such as a complex involving SYS1, JTB, and ARFRP1 that we validate experimentally, suggesting an unexpected role of JTB in Golgi traficking. To enable the research community to explore the AHMPC and enable further discovery, we cluster all complexes using functional and disease-related embeddings, demonstrate how structured prompts allow validation by large language models (LLMs), and make all structures and analysis available online as an open resource at https://ahmpc.eu/.

biochemistry↗

bioRxiv2025

A functionally validated TCR-pMHC database for TCR specificity model development

Accurate prediction of TCR specificity forms a holy grail in immunology and large language models and computational structure predictions provide a path to achieve this. Importantly, current TCR-pMHC prediction models have been trained and evaluated using historical data of unknown quality. Here, we develop and utilize a high-throughput synthetic platform for TCR assembly and evaluation to assess a large fraction of VDJdb-deposited TCR-pMHC entries using a standardized readout of TCR function. Strikingly, this analysis demonstrates that claimed TCR reactivity is only confirmed for 50% of evaluated entries. Intriguingly, the use of TCRbridge to analyze AlphaFold3 confidence metrics reveals a substantial performance in distinguishing functionally validating and non-validating TCRs even though AlphaFold3 was not trained on this task, demonstrating the utility of the validated VDJdb (TCRvdb) database that we generated. We provide TCRvdb as a resource to the community to support training and evaluation of improved predictive TCR specificity models.

immunology↗

bioRxiv2024

Pervasive divergence in protein thermostability is mediated by both structural changes and cellular environments

Temperature is a universal environmental constraint and organisms have evolved diverse mechanisms of thermotolerance. A central feature of thermophiles relative to mesophiles is a universal shift in protein stability, implying that it is a major constituent of thermotolerance. However, organisms have also evolved extensive buffering systems, such as those that disaggregate and refold denatured proteins and enable survival of heat shock. Here, we show that both cellular and protein structural changes contribute to divergence in protein thermostability between two closely related Saccharomyces species that differ by 8{degrees}C in their thermotolerance. Using thermal proteomic profiling we find that 85% of S. cerevisiae proteins are more stable than their S. uvarum homologs and there is an average shift of 1.6{degrees}C in temperature induced protein aggregation. In an interspecific hybrid of the two species, S. cerevisiae proteins retain their thermostability, while the thermostability of their S. uvarum homologs is enhanced, indicating that cellular context contributes to protein stability differences. By purifying orthologous proteins we show that amino acid substitutions underlie melting temperature differences for two proteins, Guk1 and Aha1. Amino acid substitutions are also computationally predicted to contribute to stability differences for most of the proteome. Our results imply that coordinated changes in protein thermostability impose a significant constraint on the time scales over which thermotolerance can evolve.

evolutionary biology↗

bioRxiv2024

Single-cell spatial atlas of high-grade serous ovarian cancer unveils MHC class II as a key driver of spatial tumor ecosystems and clinical outcomes

The tumor microenvironment (TME) is a complex network of interactions between malignant and host cells, yet its orchestration in advanced high-grade serous ovarian carcinoma (HGSC) remains poorly understood. We present a comprehensive single-cell spatial atlas of 280 metastatic HGSCs, integrating high-dimensional imaging, genomics, and transcriptomics. Using 929 single-cell maps, we identify distinct spatial domains associated with phenotypically heterogeneous cellular compositions, and demonstrate that immune cell co-infiltration at the tumor-stroma interface significantly influences clinical outcomes. To uncover the key drivers of the tumor ecosystem, we developed CEFIIRA (Cell Feature Importance Identification by RAndom forest), which identified tumor cell-intrinsic MHC-II expression as a critical predictor of prolonged survival, independent of clinicomolecular profiles. Validation with external datasets confirmed that MHC-II-expressing cancer cells drive immune infiltration and orchestrate spatial tumor-immune interactions. Our atlas offers novel insights into immune surveillance mechanisms across HGSC clinicomolecular groups, paving the way for improved therapeutic strategies and patient stratification.

cancer biology↗

bioRxiv2024

Complementation testing identifies causal genes at quantitative trait loci underlying fear related behavior

Knowing the genes involved in quantitative traits provides a critical entry point to understanding the biological bases of behavior, but there are very few examples where the pathway from genetic locus to behavioral change is known. Here we address a key step towards that goal by deploying a test that directly queries whether a gene mediates the effect of a quantitative trait locus (QTL). To explore the role of specific genes in fear behavior, we mapped three fear-related traits, tested fourteen genes at six QTLs, and identified six genes. Four genes, Lsamp, Ptprd, Nptx2 and Sh3gl, have known roles in synapse function; the fifth gene, Psip1, is a transcriptional co-activator not previously implicated in behavior; the sixth is a long non-coding RNA 4933413L06Rik with no known function. Single nucleus transcriptomic and epigenetic analyses implicated excitatory neurons as likely mediating the genetic effects. Surprisingly, variation in transcriptome and epigenetic modalities between inbred strains occurred preferentially in excitatory neurons, suggesting that genetic variation is more permissible in excitatory than inhibitory neuronal circuits. Our results open a bottleneck in using genetic mapping of QTLs to find novel biology underlying behavior and prompt a reconsideration of expected relationships between genetic and functional variation.

genetics↗

bioRxiv2023

A connection between the ribosome and two S. pombe tRNA modification mutants subject to rapid tRNA decay

tRNA modifications are crucial in all organisms to ensure tRNA folding and stability, and accurate translation in the ribosome. In both the yeast Saccharomyces cerevisiae and the evolutionarily distant yeast Schizosaccharomyces pombe, mutants lacking certain tRNA body modifications (outside the anticodon loop) are temperature sensitive due to rapid tRNA decay (RTD) of a subset of hypomodified tRNAs. Here we show that for each of two S. pombe mutants subject to RTD, mutations in ribosomal protein genes suppress the temperature sensitivity without altering tRNA levels. Prior work showed that S. pombe trm8{Delta} mutants, lacking 7-methylguanosine, were temperature sensitive due to RTD and that one class of suppressors had mutations in the general amino acid control (GAAC) pathway, which was activated concomitant with RTD, resulting in further tRNA loss. We now find that another class of S. pombe trm8{Delta} suppressors have mutations in rpl genes, encoding 60S subunit proteins, and that suppression occurs with minimal restoration of tRNA levels and reduced GAAC activation. Furthermore, trm8{Delta} suppression extends to other mutations in the large or small ribosomal subunit. We also find that S. pombe tan1{Delta} mutants, lacking 4-acetylcytidine, are temperature sensitive due to RTD, that one class of suppressors have rpl mutations, associated with minimal restoration of tRNA levels, and that suppression extends to other rpl and rps mutations. However, although S. pombe tan1{Delta} temperature sensitivity is associated with some GAAC activation, suppression by an rpl mutation does not significantly inhibit GAAC activation. These results suggest that ribosomal protein mutations suppress the temperature sensitivity of S. pombe trm8{Delta} and tan1{Delta} mutants due to reduced ribosome concentrations, leading to both a reduced requirement for tRNA, and reduced ribosome collisions and GAAC activation. Results with S. cerevisiae trm8{Delta} trm4{Delta} mutants are consistent with this model, and fuel speculation that similar results will apply across eukaryotes.

molecular biology↗

bioRxiv2023

STAPLER: Efficient learning of TCR-peptide specificity prediction from full-length TCR-peptide data

The prediction of peptide-MHC (pMHC) recognition by {beta} T-cell receptors (TCRs) remains a major biomedical challenge. Here, we develop STAPLER (Shared TCR And Peptide Language bidirectional Encoder Representations from transformers), a transformer language model that uses a joint TCR{beta}- peptide input to allow the learning of patterns within and between TCR{beta} and peptide sequences that encode recognition. First, we demonstrate how data leakage during negative data generation can confound performance estimates of neural network-based models in predicting TCR - pMHC specificity. We then demonstrate that, because of its pre-training and fine-tuning masked language modeling tasks, STAPLER outperforms both neural network-based and distance-based ML models in predicting the recognition of known antigens in an independent dataset, in particular for antigens for which little related data is available. Based on this ability to efficiently learn from limited labeled TCR- peptide data, STAPLER is well-suited to utilize growing TCR - pMHC datasets to achieve accurate prediction of TCR - pMHC specificity.

bioinformatics↗

bioRxiv2017

Transparency in Authors’ Contributions and Responsibilities to Promote Integrity in Scientific Publication

In keeping with the growing movement in scientific publishing toward transparency in data and methods, we argue that the names of authors accompanying journal articles should provide insight into who is responsible for which contributions, a process should exist to confirm that the list is complete, clearly articulated standards should establish whether and when the contributions of an individual justify authorship credit, and those involved in the generation of scientific knowledge should follow these best practices.\n\nTo accomplish these goals, we recommend that journals adopt common and transparent standards for authorship, outline responsibilities for corresponding authors, adopt the CRediT (Contributor Roles Taxonomy)1 methodology for attributing contributions, include this information in article metadata, and encourage authors to use the digital persistent identifier ORCID.2 Furthermore, we suggest that research institutions have regular open conversations on authorship criteria and ethics and that funding agencies adopt ORCID and accept CRediT. Scientific societies should further authorship transparency by promoting these recommendations through their meetings and publications programs.

scientific communication and education↗