Amin Emad

2025-08-19

bioRxiv (preprint)

Multi-Modal Protein Representation Learning with CLASP

Nicolas Bolouri

2025-08-12

bioRxiv (preprint)

A flaw in using pre-trained pLLMs in protein-protein interaction inference models

With the growing pervasiveness of pre-trained protein large language models (pLLMs), pLLM-based methods are increasingly being put forward f… (see more)or the protein-protein interaction (PPI) inference task. Here, we identify and confirm that existing pre-trained pLLMs are a source of data leakage for the downstream PPI task. We characterize the extent of the data leakage problem by training and comparing small and efficient pLLMs on a dataset that controls for data leakage (“strict”) with one that does not (“non-strict”). While data leakage from pre-trained pLLMs cause measurable inflation of testing scores, we find that this does not necessarily extend to other, non-paired biological tasks such as protein keyword annotation. Further, we find no connection between the context-lengths of pLLMs and the performance of pLLM-based PPI inference methods on proteins with sequence lengths that surpass it. Furthermore, we show that pLLM-based and non-pLLM-based models fail to generalize in tasks such as prediction of the human-SARS-CoV-2 PPIs or the effect of point mutations on binding-affinities. This study demonstrates the importance of extending existing protocols for the evaluation of pLLM-based models applied to paired biological datasets and identifies areas of weakness of current pLLM models.

2025-04-23

bioRxiv (preprint)

A flaw in using pre-trained pLLMs in protein-protein interaction inference models

With the growing pervasiveness of pre-trained protein large language models (pLLMs), pLLM-based methods are increasingly being put forward f… (see more)or the protein-protein interaction (PPI) inference task. Here, we identify and confirm that existing pre-trained pLLMs are a source of data leakage for the downstream PPI task. We characterize the extent of the data leakage problem by training and comparing small and efficient pLLMs on a dataset that controls for data leakage (“strict”) with one that does not (“non-strict”). While data leakage from pre-trained pLLMs cause measurable inflation of testing scores, we find that this does not necessarily extend to other, non-paired biological tasks such as protein keyword annotation. Further, we find no connection between the context-lengths of pLLMs and the performance of pLLM-based PPI inference methods on proteins with sequence lengths that surpass it. Furthermore, we show that pLLM-based and non-pLLM-based models fail to generalize in tasks such as prediction of the human-SARS-CoV-2 PPIs or the effect of point mutations on binding-affinities. This study demonstrates the importance of extending existing protocols for the evaluation of pLLM-based models applied to paired biological datasets and identifies areas of weakness of current pLLM models.

2025-04-23

bioRxiv (preprint)

Refining sequence-to-expression modelling with chromatin accessibility

Orsolya Lapohos

Gregory Fonseca

2025-04-21

bioRxiv (preprint)

Divergent responses to SARS-CoV-2 infection in bronchial epithelium with pre-existing respiratory diseases

Justine Oliva

Manon Ruffin

Claire Calmel

Aurélien Gibeaud

Andrés Pizzorno

Clémence Gaudin

Solenne Chardonnet

Viviane de Almeida Bastos

Manuel Rosa-Calatrava

Antoine Soulé

Simon Rousseau

Harriet Corvol

Olivier Terrier

Loïc Guillot

2025-02-01

iScience (published)

Circulating IL-17F, but not IL-17A, is elevated in severe COVID-19 and leads to an ERK1/2 and p38 MAPK-dependent increase in ICAM-1 cell surface expression and neutrophil adhesion on endothelial cells

Jérôme Bédard-Matteau

Antoine Soulé

Katelyn Yixiu Liu

Lyvia Fourcade

Douglas D. Fraser

Simon Rousseau

Severe COVID-19 is associated with neutrophilic inflammation and immunothrombosis. Several members of the IL-17 cytokine family have been as… (see more)sociated with neutrophilic inflammation and activation of the endothelium. Therefore, we investigated whether these cytokines were associated with COVID-19.We investigated the association between COVID-19 and circulating plasma levels of IL-17 cytokine family members in participants to the Biobanque québécoise de la COVID-19 (BQC19), a prospective observational cohort and an independent cohort from Western University (London, Ontario). We measured the in vitro impact of IL-17F on intercellular adhesion molecule 1 (ICAM-1) cell surface expression and neutrophil adhesion on endothelial cells in culture. The contribution of two Mitogen Activated Protein Kinase (MAPK) pathways was determined using small molecule inhibitors PD184352 (a MKK1/MKK2 inhibitor) and BIRB0796 (a p38 MAPK inhibitor).We found increased IL-17D and IL-17F plasma levels when comparing SARS-CoV-2-positive vs negative hospitalized participants. Moreover, increased plasma levels of IL-17D, IL-17E and IL-17F were noted when comparing severe versus mild COVID-19. IL-17F, but not IL-17A, was significantly elevated in people with COVID-19 compared to healthy controls and with more severe disease. In vitro work on endothelial cells treated with IL-17F for 24h showed an increase cell surface expression of ICAM-1 accompanied by neutrophil adhesion. The introduction of two MAPK inhibitors significantly reduced the binding of neutrophils while also reducing ICAM-1 expression at the surface level of endothelial cells, but not its intracellular expression.Overall, these results have identified an association between two cytokines of the IL-17 family (IL-17D and IL-17F) with COVID-19 and disease severity. Considering that IL-17F stimulation promotes neutrophil adhesion to the endothelium in a MAPK-dependent manner, it is attractive to speculate that this pathway may contribute to pathogenic immunothrombosis in concert with other molecular effectors.

2024-10-18

Frontiers in Immunology (published)

Circulating IL-17F, but not IL-17A, is elevated in severe COVID-19 and leads to an ERK1/2 and p38 MAPK-dependent increase in ICAM-1 cell surface expression and neutrophil adhesion on endothelial cells

Jérôme Bédard-Matteau

Antoine Soulé

Katelyn Yixiu Liu

Lyvia Fourcade

Douglas D. Fraser

Simon Rousseau

Background Severe COVID-19 is associated with neutrophilic inflammation and immunothrombosis. Several members of the IL-17 cytokine family h… (see more)ave been associated with neutrophilic inflammation and activation of the endothelium. Therefore, we investigated whether these cytokines were associated with COVID-19. Methods We investigated the association between COVID-19 and circulating plasma levels of IL-17 cytokine family members in participants to the Biobanque québécoise de la COVID-19 (BQC19), a prospective observational cohort and an independent cohort from Western University (London, Ontario). We measured the in vitro impact of IL-17F on intercellular adhesion molecule 1 (ICAM-1) cell surface expression and neutrophil adhesion on endothelial cells in culture. The contribution of two Mitogen Activated Protein Kinase (MAPK) pathways was determined using small molecule inhibitors PD184352 (a MKK1/MKK2 inhibitor) and BIRB0796 (a p38 MAPK inhibitor). Results We found increased IL-17D and IL-17F plasma levels when comparing SARS-CoV-2-positive vs negative hospitalized participants. Moreover, increased plasma levels of IL-17D, IL-17E and IL-17F were noted when comparing severe versus mild COVID-19. IL-17F, but not IL-17A, was significantly elevated in people with COVID-19 compared to healthy controls and with more severe disease. In vitro work on endothelial cells treated with IL-17F for 24h showed an increase cell surface expression of ICAM-1 accompanied by neutrophil adhesion. The introduction of two MAPK inhibitors significantly reduced the binding of neutrophils while also reducing ICAM-1 expression at the surface level of endothelial cells, but not its intracellular expression. Discussion Overall, these results have identified an association between two cytokines of the IL-17 family (IL-17D and IL-17F) with COVID-19 and disease severity. Considering that IL-17F stimulation promotes neutrophil adhesion to the endothelium in a MAPK-dependent manner, it is attractive to speculate that this pathway may contribute to pathogenic immunothrombosis in concert with other molecular effectors.

2024-10-18

Frontiers in Immunology (published)

A long-context RNA foundation model for predicting transcriptome architecture

Ali Saberi

Benedict Choi

Sean Wang

Aldo Hernández-Corchado

Mohsen Naghipourfar

Arsham Mikaeili Namini

Vijay Ramani

Hamed S. Najafabadi

Hani Goodarzi

Linking DNA sequence to genomic function remains one of the grand challenges in genetics and genomics. Here, we combine large-scale single-m… (see more)olecule transcriptome sequencing of diverse cancer cell lines with cutting-edge machine learning to build LoRNASH, an RNA foundation model that learns how the nucleotide sequence of unspliced pre-mRNA dictates transcriptome architecture—the relative abundances and molecular structures of mRNA isoforms. Owing to its use of the StripedHyena architecture, LoRNASH handles extremely long sequence inputs (∼65 kilobase pairs), allowing for quantitative, zero-shot prediction of all aspects of transcriptome architecture, including isoform abundance, isoform structure, and the impact of DNA sequence variants on transcript structure and abundance. We anticipate that our public data release and proof-of-concept model will accelerate varying aspects of RNA biotechnology. More broadly, we envision the use of LoRNASH as a foundation for fine-tuning of any transcriptome-related downstream prediction task, including cell-type specific gene expression, splicing, and general RNA processing.

2024-08-27

bioRxiv (preprint)

GRouNdGAN: GRN-guided simulation of single-cell RNA-seq data using causal generative adversarial networks

Yazdan Zinati

Abdulrahman Takiddeen

We introduce GRouNdGAN, a gene regulatory network (GRN)-guided causal implicit generative model for simulating single-cell RNA-seq data, in-… (see more)silico perturbation experiments, and benchmarking GRN inference methods. Through the imposition of a user-defined GRN in its architecture, GRouNdGAN simulates steady-state and transient-state single-cell datasets where genes are causally expressed under the control of their regulating transcription factors (TFs). Training on three experimental datasets, we show that our model captures non-linear TF-gene dependences and preserves gene identities, cell trajectories, pseudo-time ordering, and technical and biological noise, with no user manipulation and only implicit parameterization. Despite imposing rigid causality constraints, it outperforms state-of-the-art simulators in generating realistic cells. GRouNdGAN learns meaningful causal regulatory dynamics, allowing sampling from both observational and interventional distributions. This enables it to synthesize cells under conditions that do not occur in the dataset at inference time, allowing to perform in-silico TF knockout experiments. Our results show that in-silico knockout of cell type-specific TFs significantly reduces cells of that type being generated. Interactions imposed through the GRN are emphasized in the simulated datasets, resulting in GRN inference algorithms assigning them much higher scores than interactions not imposed but of equal importance in the experimental training dataset. Benchmarking various GRN inference algorithms reveals that GRouNdGAN effectively bridges the existing gap between simulated and biological data benchmarks of GRN inference algorithms, providing gold standard ground truth GRNs and realistic cells corresponding to the biological system of interest. Our results show that GRouNdGAN is a stable, realistic, and effective simulator with various applications in single-cell RNA-seq analysis.

2024-05-14

Nature Communications (published)

Deciphering lineage-relevant gene regulatory networks during endoderm formation by InPheRNo-ChIP.

Chen Su

William A Pastor

Deciphering the underlying gene regulatory networks (GRNs) that govern early human embryogenesis is critical for understanding developmental… (see more) mechanisms yet remains challenging due to limited sample availability and the inherent complexity of the biological processes involved. To address this, we developed InPheRNo-ChIP, a computational framework that integrates multimodal data, including RNA-seq, transcription factor (TF)-specific ChIP-seq, and phenotypic labels, to reconstruct phenotype-relevant GRNs associated with endoderm development. The core of this method is a probabilistic graphical model that models the simultaneous effect of TFs on their putative target genes to influence a particular phenotypic outcome. Unlike the majority of existing GRN inference methods that are agnostic to the phenotypic outcomes, InPheRNo-ChIP directly incorporates phenotypic information during GRN inference, enabling the distinction between lineage-specific and general regulatory interactions. We integrated data from three experimental studies and applied InPheRNo-ChIP to infer the GRN governing the differentiation of human embryonic stem cells into definitive endoderm. Benchmarking against a scRNA-seq CRISPRi study demonstrated InPheRNo-ChIP's ability to identify regulatory interactions involving endoderm markers FOXA2, SMAD2, and SOX17, outperforming other methods. This highlights the importance of incorporating the phenotypic context during network inference. Furthermore, an ablation study confirms the synergistic contribution of ChIP-seq, RNA-seq, and phenotypic data, highlighting the value of multimodal integration for accurate phenotype-relevant GRN reconstruction.

2024-01-01

Briefings Bioinform. (published)

INTREPPPID - An Orthologue-Informed Quintuplet Network for Cross-Species Prediction of Protein-Protein Interaction

An overwhelming majority of protein-protein interaction (PPI) studies are conducted in a select few model organisms largely due to constrain… (see more)ts in time and cost of the associated “wet lab” experiments. In silico PPI inference methods are ideal tools to overcome these limitations, but often struggle with cross-species predictions. We present INTREPPPID, a method which incorporates orthology data using a new “quintuplet” neural network, which is constructed with five parallel encoders with shared parameters. INTREPPPID incorporates both a PPI classification task and an orthologous locality task. The latter learns embeddings of orthologues that have small Euclidean distances between them and large distances between embeddings of all other proteins. INTREPPPID outperforms all other leading PPI inference methods tested on both the intra-species and cross-species tasks using strict evaluation datasets. We show that INTREPPPID’s orthologous locality loss increases performance because of the biological relevance of the orthologue data, and not due to some other specious aspect of the architecture. Finally, we introduce PPI.bio and PPI Origami, a web server interface for INTREPPPID and a software tool for creating strict evaluation datasets, respectively. Together, these two initiatives aim to make both the use and development of PPI inference tools more accessible to the community. GRAPHICAL ABSTRACT

2024-01-01

Briefings Bioinform. (published)