Biology subjects

Helmer-Citterich, M.

Publications and source records attributed to Helmer-Citterich, M..

9 recordsLinked to original sources

bioRxiv2026

LAYERED GENOMIC COMPOSITION AND LINGUISTIC CONTINUITY IN GRECIA SALENTINA

Grecia Salentina (GS) is the label assigned to a geographic area in Southern Italy where a Greek dialect has historically been spoken. The origins of this Greek-speaking community, whether dating from ancient or Medieval settlement, are a controversial and essentially unsolved issue. To evaluate which historical-linguistic scenario is best supported on genomic grounds, we used whole genome sequencing of 27 GS natives and 21 controls, embedding them in a 799 modern samples dataset in which Greece and Anatolia were evenly represented, an uncommon feature in the genomic literature on the area. We considered different aspects of the genetic structuring, at different spatial and temporal scales. Analyses also included 4021 ancient samples from the literature. All analyses pointed to the same conclusion: the GS genomic profile displays the same characteristic features of the rest of Southern Italy, with no definite evidence for a single source population from outside Italy. Correspondingly, we detected strong genetic connectivity among GS municipalities and between GS and the rest of Italy. Only a subset of our analyses suggested a slightly increased affinity of GS with Greece, or with the Anatolian peninsula. These results indicate that GS has been all but a genetically closed community, and that the Greek language did not act as a barrier to gene flow. By endorsing a ''transient mobility model'' we support the most recent linguistic hypothesis: the prolonged resilience of the Greek language in GS, maintained in situ and periodically rejuvenated through recurrent external inputs from the Greek and Byzantine worlds.

genetics↗

bioRxiv2026

Cross-Attention Over RNA And Protein Sequences Enables Generalizable Interaction Prediction

Computational predictions are essential to characterize the RNA-protein interaction landscape, yet a persistent gap between benchmark performance and practical utility suggests that current models have limited generalization capabilities. To address this issue, we present CORAL (Cross-attention for RNA-protein Association Learning), a deep learning framework for the prediction of RNA-protein interactions that integrates pretrained protein (ESM-2) and RNA (DNABERT2) language models through bidirectional cross-attention with Low-Rank Adaptation fine-tuning. We also introduce a benchmarking framework that rigorously addresses the problem of data redundancy between training and test sets, which greatly inflates model performances reported in the literature. To this end we adopt three partitioning strategies of increasing stringency: conventional random splits, pairwise non-redundant splits, and component-wise non-redundant splits. CORAL maintains an F1 score of 0.65 under the most stringent component-wise evaluation, compared to 0.47 for the next-best method retaining discriminative behavior. Interpretability analyses further reveal that the cross-attention mechanism captures biologically meaningful features of molecular recognition at two complementary structural scales: at atomic resolution, specific attention heads systematically attend to structurally defined contact positions, showing 26% elevated attention at interface residues across 309 experimentally resolved complexes (p < 0.001); at the domain level, protein-side attention localizes to annotated RNA-binding domains across 94 % of 462 proteins examined (median within-protein Cohens d {approx} 0.94). Together, these findings establish that current RPI prediction benchmarks substantially inflate performance estimates and demonstrate that cross-modal attention architectures yield improved generalization alongside mechanistically interpretable representations.

bioinformatics↗

bioRxiv2025

The Integrated Cellular and Molecular Landscape of Autoimmunity

We performed a large-scale immunogenomic analysis of [~]13,000 transcriptomic profiles from 10 autoimmune diseases, integrating publicly available datasets from both blood and tissue. Using meta-analysis, we identified core immune mechanisms underlying autoimmunity, including strong interferon responses, inflammation, and adaptive immune suppression, alongside disease-specific signatures. To enhance biological interpretability, we derived higher-order immune features - such as cell type proportions, cytokine levels, pathway activity, transcription factor regulation, and miRNA activity - and organized them into 15 coherent immune modules. These modules enabled systematic cross-disease comparisons, revealing shared and distinct immunopathological patterns. The inflammation module, in particular, was associated with disease severity and predicted treatment response across multiple conditions. This modular framework offers a powerful tool for understanding immune dysregulation and advancing precision medicine in autoimmune diseases. To support reproducibility and enable others to build upon this work, we developed an interactive app to explore and download the complete dataset and associated results.

bioinformatics↗

bioRxiv2023

Human lncRNAs harbor conserved modules embedded in different sequence contexts

We analyzed the structure of human long non-coding RNA (lncRNAs) genes to investigate whether the non-coding transcriptome is organized in modular domains, as is the case for protein-coding genes. To this aim, we compared all known human lncRNA exons and identified 340 pairs of exons with high sequence and/or secondary structure similarity but embedded in a dissimilar sequence context. We grouped these pairs in 106 clusters based on their reciprocal similarities. These shared modules are highly conserved between humans and the four great ape species, display evidence of purifying selection and likely arose as a result of recent segmental duplications. Our analysis contributes to the understanding of the mechanisms driving the evolution of the non-coding genome and suggests additional strategies towards deciphering the functional complexity of this class of molecules. Author summaryThe Human genome includes more than 18,000 genes coding for RNAs that are not translated into proteins, called long non-coding RNAs (lncRNA). Mounting evolutionary and experimental evidence shows that a large amount of these RNAs have a specific function, mainly as regulators of a diverse set of biological processes. Here we set out to investigate whether these genes have a modular organization similar to that of protein-coding genes. Accordingly, we compared the sequence of all the exonic regions of human lncRNAs and identified 106 clusters of non-repetitive exonic modules shared between this class of genes. These modules display evidence of purifying selection, are highly conserved between humans and the four great ape species, and may represent distinct functional units that have been shuffled among multiple lncRNA genes, in a manner similar to the exon-shuffling process that is observed in the coding genome.

evolutionary biology↗

bioRxiv2023

Unveiling the signaling network of FLT3-ITD AML improves drug sensitivity prediction

Currently, the identification of patient-specific therapies in cancer is mainly informed by personalized genomic analysis. In the setting of acute myeloid leukemia (AML), patient-drug treatment matching fails in a subset of patients harboring atypical internal tandem duplications (ITDs) in the tyrosine kinase domain of the FLT3 gene. To address this unmet medical need, here we develop a systems-based strategy that integrates multiparametric analysis of crucial signaling pathways, and patient-specific genomic and transcriptomic data with a prior-knowledge signaling network using a Boolean-based formalism. By this approach, we derive personalized predictive models describing the signaling landscape of AML FLT3-ITD positive cell lines and patients. These models enable us to derive mechanistic insight into drug resistance mechanisms and suggest novel opportunities for combinatorial treatments. Interestingly, our analysis reveals that the JNK kinase pathway plays a crucial role in the tyrosine kinase inhibitor response of FLT3-ITD cells through cell cycle regulation. Finally, our work shows that patient-specific logic models have the potential to inform precision medicine approaches.

cancer biology↗

bioRxiv2023

NRF2 connects Src tyrosine kinase to ferroptosis resistance in glioblastoma

Glioblastoma (GBM) is a severe brain tumor characterized by an extremely poor survival rate of patients. GBM cancer cells escape to standard therapeutic protocols consisting of combination of ionizing radiation (IR) and alkylating drugs that trigger DNA damage, by rewiring of signaling pathways. In recent years, the upregulation of factors that counteract ferroptosis has been highlighted as a major driver of cancer resistance to IR, although the molecular connection between the activation of oncogenic signaling and the modulation of ferroptosis has not been clarified yet. Here we provide the first evidence for a molecular connection between the constitutive activation of tyrosine kinases and resistance to ferroptosis. Src tyrosine kinase, a central hub on which Receptor Tyrosine Kinases deregulated signaling converge in cancer, leads to the stabilization and activation of NRF2 pathway, thus promoting resistance to IR-induced ferroptosis. These data suggest that the upregulation of Src-NRF2 axis may represent a vulnerability for combined strategies that, by targeting ferroptosis resistance, enhance radiation sensitivity in glioblastoma.

cancer biology↗

bioRxiv2021

Conserved exchange of paralog proteins during neuronal differentiation

Gene duplication enables the emergence of new functions by lowering the general evolutionary pressure. Previous studies have highlighted the role of specific paralog genes during cell differentiation, e.g., in chromatin remodeling complexes. It remains unexplored whether similar mechanisms extend to other biological functions and whether the regulation of paralog genes is conserved across species. Here, we analyze the expression of paralogs across human tissues, during development and neuronal differentiation in fish, rodents and humans. While ~80% of paralog genes are co-regulated, a subset of paralogs shows divergent expression profiles, contributing to variability of protein complexes. We identify 78 substitutions of paralog pairs that occur during neuronal differentiation and are conserved across species. Among these, we highlight a substitution between the paralogs SEC23A and SEC23B subunits of the COPII complex. Altering the ratio between these two proteins via RNAi-mediated knockdown is sufficient to influence neuron differentiation. We propose that remodeling of the vesicular transport system via paralog substitutions is an evolutionary conserved mechanism enabling neuronal differentiation.

systems biology↗

bioRxiv2020

An Eutherian-Specific microRNA Controls the Translation of Satb2 in a Model of Cortical Differentiation

Cerebral cortical development is controlled by key transcription factors that specify the neuronal identities in the different cortical layers. These transcription factors are crucial for the identity of the different neurons, but the mechanisms controlling their expression in distinct cells are only partially known. Here we investigate the expression and stability of the mRNAs of Tbr1, Bcl11b, Fezf2, Satb2 and Cux1 in single developing mouse cortical cells. We focus on Satb2 and find that its mRNA expression occurs much earlier than its protein synthesis and in a set of cells broader than expected, suggesting an initially tight control of its translation, which is subsequently de-repressed at late developmental stages. Mechanistically, Satb2 3UTR modulates protein translation of GFP reporters during mouse corticogenesis. By in vitro pull-down of Satb2 3UTR-associated miRNAs, we select putative miRNAs responsible for SATB2 inhibition, focusing on those strongly expressed in early progenitor cells and reduced in late cells. miR-541, an Eutherian-specific miRNA, and miR-92a/b are the best candidates and their inactivation triggers robust and premature SATB2 translation in both mouse and human cortical cells. Our findings indicate that RNA interference plays a major role in the timing of cortical cell identity and may be part of the toolkit involved in specifying supra-granular projection neurons.

developmental biology↗

bioRxiv2020

COTAN: Co-expression Table Analysis for scRNA-seq data

Estimating co-expression of cell identity factors in single-cell transcriptomes is crucial to decode new mechanisms of cell state transition. Due to the intrinsic low efficiency of single-cell mRNA profiling, novel computational approaches are required to accurately infer gene co-expression in a cell population. We introduce COTAN, a statistical and computational method to analyze the co-expression of gene pairs at single cell level, providing the foundation for single-cell gene interactome analysis.

bioinformatics↗