Biology subjects

Belanger, D.

Publications and source records attributed to Belanger, D..

9 recordsLinked to original sources

bioRxiv2026

Multimodal transcriptomics and calcium imaging reveal a novel subset of polymodal nociceptors expressing the interleukin 1 receptor in mice

We previously established that sensory neurons in the mouse dorsal root ganglia (DRG) express a functional receptor for the proinflammatory cytokine interleukin (IL)-1. We also demonstrated that deletion of the IL-1 receptor type 1 gene, Il1r1, in TRPV1-expressing (+) neurons prevented pain in models of chronic inflammatory diseases such as multiple sclerosis, rheumatoid arthritis and osteoarthritis. Here, we found a marked sex difference in the abundance of IL-1R1+ neurons, which represented approximately 10% of all DRG neurons in females but only 5% in males. However, male mice exhibited stronger, longer-lasting mechanical hypersensitivity than females after IL-1{beta} injection into the cerebrospinal fluid. In vivo calcium imaging revealed that IL-1{beta}- responsive DRG neurons responded to cutaneous mechanical and capsaicin stimulation. By integrating spatial transcriptomics with single-cell RNA sequencing (scRNA-Seq), we identified a gene signature uniquely marking IL-1R1+ neurons, including genes such as Ada, Cysltr2, Gpr139, Htr1a, Htr1f, Il31ra, Nppb, Npy2r, Nts, P2rx2, Pde4c, and Sst, with Ada and Sst validated at the protein level. Omics analysis revealed that IL-1R1+ DRG neurons form a subset of non-peptidergic type 3 (NP3) sensory neurons, which are linked to inflammatory pain and itch. However, Il1r1 deletion did not affect itch responses to serotonin, histamine, or chloroquine, and these mediators failed to induce calcium activity in IL-1{beta}-responsive DRG neurons. Finally, scRNA-seq identified several genes upregulated in NP3 neurons after IL-1{beta} injection, including Alkal2, Bdnf, and Lcn2, associated with chronic inflammatory pain. Thus, our study unveils novel markers for IL-1R1+ nociceptors and reaffirms their selective role in inflammatory pain.

neuroscience↗

bioRxiv2026

Immunoglobulins are rapidly internalized by neurons after CNS injury and cleared through lysosomal degradation

Spinal cord injury (SCI) causes hemorrhage and blood-spinal cord barrier disruption, allowing blood-derived molecules to infiltrate the parenchyma. While immunoglobulins (Ig) are abundant plasma proteins, their distribution and cellular targets within the injured spinal cord remain poorly defined. Here, we show that circulating non-autoimmune immunoglobulins rapidly infiltrate the spinal cord after injury in mice and disseminate beyond the lesion core. IgG, IgM, and IgA accumulate within the parenchyma early post-injury, with IgG displaying the widest spatial distribution, reaching distant spinal segments within hours. Neurons are the predominant cell type internalizing immunoglobulins in the gray matter, whereas astrocytes exhibit moderate uptake in white matter. Intra-cisterna magna administration of fluorescent serum-derived IgG reveals that neurons and astrocytes internalize IgG under physiological conditions, independently of Fc receptor engagement. Although the neonatal Fc receptor (FcRn) has minimal impact on CNS IgG recycling, its genetic deletion significantly improves locomotor recovery after SCI. Both in vitro and in vivo, neurons clear IgG through lysosomal degradation. Following SCI, inhibition of lysosomal proteases with the clinically approved drug E64d increases CNS IgG retention without compromising locomotor recovery. These findings establish neurons as key targets of circulating immunoglobulins after CNS injury and reveal IgG uptake and clearance pathways that may be leveraged to improve therapeutic performance of monoclonal antibody treatments.

neuroscience↗

bioRxiv2024

Rapid ecosystem change after the cod collapse in Newfoundland & Labrador

The cod collapse of Newfoundland and Labrador in the early 1990s was predicated by collapses of the key forage fish species in the region, capelin, and the wider groundfish community. After these collapses, rapid changes in ecosystem structure and function have been observed. From the early 2000s to mid 2010s, invertebrates dominated ecosystem biomass and the snow crab fishery became important to the region. During the late 2010s, invertebrates declined as groundfish stocks increased, although not to pre collapse levels. We developed two ecosystem models representing 1) the Grand Banks and 2) the Newfoundland and Labrador Shelf ecosystem structure and function for 2018-2020. We compared these models with existing ecosystem models that represent the combined Grand Banks and Newfoundland and Labrador Shelf ecosystem for the pre collapse (1985-1987) and invertebrate dominated (2013-2015) periods. Variability in primary productivity, species biomass, and catch reveal that the two model regions are distinct in ecosystem structure and function. Primary and secondary production has increased in the most recent period, however capelin biomass - an important factor for cod recovery - has remained low. Harp seals play a strong top-down ecosystem role and may be contributing to impaired cod recovery. Since the decline in capelin abundance, Arctic cod became an important alternative prey on the Newfoundland and Labrador Shelf region compared to reliance on sand lance and squid on the Grand Banks. Capelin has not shown signs of recovery in either region. Overall, the ecosystem structure in 2018-2020 in both regions is closer to the pre-collapse state than it was in 2013-2015, and the rapid change indicates that the system is highly dynamic.

ecology↗

bioRxiv2024

ProtEx: A Retrieval-Augmented Approach for Protein Function Prediction

Mapping a protein sequence to its underlying biological function is a critical problem of increasing importance in biology. In this work, we propose ProtEx, a retrieval-augmented approach for protein function prediction that leverages exemplars from a database to improve accuracy and robustness and enable generalization to unseen classes. Our approach relies on a novel multi-sequence pretraining task, and a fine-tuning strategy that effectively conditions predictions on retrieved exemplars. Our method achieves state-of-the-art results across multiple datasets and settings for predicting Enzyme Commission (EC) numbers, Gene Ontology (GO) terms, and Pfam families. Our ablations and analysis highlight the impact of conditioning predictions on exemplar sequences, especially for classes and sequences less well represented in the training data.

bioinformatics↗

bioRxiv2024

Engineering of highly active and diverse nuclease enzymes by combining machine learning and ultra-high-throughput screening

Optimizing enzymes to function in novel chemical environments is a central goal of synthetic biology, but optimization is often hindered by a rugged, expansive protein search space and costly experiments. In this work, we present TeleProt, an ML framework that blends evolutionary and experimental data to design diverse protein variant libraries, and employ it to improve the catalytic activity of a nuclease enzyme that degrades biofilms that accumulate on chronic wounds. After multiple rounds of high-throughput experiments using both TeleProt and standard directed evolution (DE) approaches in parallel, we find that our approach found a significantly better top-performing enzyme variant than DE, had a better hit rate at finding diverse, high-activity variants, and was even able to design a high-performance initial library using no prior experimental data. We have released a dataset of 55K nuclease variants, one of the most extensive genotype-phenotype enzyme activity landscapes to date, to drive further progress in ML-guided design.

bioinformatics↗

bioRxiv2022

Tuned Fitness Landscapes for Benchmarking Model-Guided Protein Design

Advancements in DNA synthesis and sequencing technologies have enabled a novel paradigm of protein design where machine learning (ML) models trained on experimental data are used to guide exploration of a protein fitness landscape. ML-guided directed evolution (MLDE) builds on the success of traditional directed evolution and unlocks strategies which make more efficient use of experimental data. Building an MLDE pipeline involves many design choices across the design-build-test-learn loop ranging from data collection strategies to modeling, each of which has a large impact on the success of designed sequences. The cost of collecting experimental data makes benchmarking every component of these pipelines on real data prohibitively difficult, necessitating the development of synthetic landscapes where MLDE strategies can be tested. In this work, we develop a framework called SLIP ("Synthetic Landscape Inference for Proteins") for constructing biologically-motivated synthetic landscapes with tunable difficulty based on Potts models. This framework can be extended to any protein family for which there is a sequence alignment. We show that without tuning, Potts models are easy to optimize. In contrast, our tuning framework provides landscapes sufficiently challenging to benchmark MLDE pipelines. SLIP is open-source and is available at https://github.com/google-research/slip.

synthetic biology↗

bioRxiv2021

ProteInfer: deep networks for protein functional inference

Predicting the function of a protein from its amino acid sequence is a long-standing challenge in bioinformatics. Traditional approaches use sequence alignment to compare a query sequence either to thousands of models of protein families or to large databases of individual protein sequences. Here we instead employ deep convolutional neural networks to directly predict a variety of protein functions - EC numbers and GO terms - directly from an unaligned amino acid sequence. This approach provides precise predictions which complement alignment-based methods, and the computational efficiency of a single neural network permits novel and lightweight software interfaces, which we demonstrate with an in-browser graphical interface for protein function prediction in which all computation is performed on the users personal computer with no data uploaded to remote servers. Moreover, these models place full-length amino acid sequences into a generalised functional space, facilitating downstream analysis and interpretation. To read the interactive version of this paper, please visit https://google-research.github.io/proteinfer/ O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/461077v2_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@74f8f1org.highwire.dtl.DTLVardef@183ae0borg.highwire.dtl.DTLVardef@1754f3org.highwire.dtl.DTLVardef@1ca54f5_HPS_FORMAT_FIGEXP M_FIG C_FIG QR code for the interactive version of this preprint at https://google-research.github.io/proteinfer/

bioinformatics↗

bioRxiv2019

Critiquing Protein Family Classification Models Using Sufficient Input Subsets

In many application domains, neural networks are highly accurate and have been deployed at large scale. However, users often do not have good tools for understanding how these models arrive at their predictions. This has hindered adoption in fields such as the life and medical sciences, where researchers require that models base their decisions on underlying biological phenomena rather than peculiarities of the dataset introduced. In response, we propose a set of methods for critiquing deep learning models and demonstrate their application for protein family classification, a task for which high-accuracy models have considerable potential impact. Our methods extend the sufficient input subsets technique, which we use to identify subsets of features (SIS) in each protein sequence that are alone sufficient for classification. Our suite of tools analyzes these subsets to shed light on the decision-making criteria employed by models trained on this task. These tools expose that while deep models may perform classification for biologically-relevant reasons, their behavior varies considerably across choice of network architecture and parameter initialization. While the techniques that we develop are specific to the protein sequence classification task, the approach taken generalizes to a broad set of scientific contexts in which model interpretability is essential.

bioinformatics↗

bioRxiv2019

Using Deep Learning to Annotate the Protein Universe

Understanding the relationship between amino acid sequence and protein function is a long-standing problem in molecular biology with far-reaching scientific implications. Despite six decades of progress, state-of-the-art techniques cannot annotate 1/3 of microbial protein sequences, hampering our ability to exploit sequences collected from diverse organisms. In this paper, we explore an alternative methodology based on deep learning that learns the relationship between unaligned amino acid sequences and their functional annotations across all 17929 families of the Pfam database. Using the Pfam seed sequences we establish rigorous benchmark assessments that use both random and clustered data splits to control for potentially confounding sequence similarities between train and test sequences. Using Pfam full, we report convolutional networks that are significantly more accurate and computationally efficient than BLASTp, while learning sequence features such as structural disorder and transmembrane helices. Our model co-locates sequences from unseen families in embedding space, allowing sequences from novel families to be accurately annotated. These results suggest deep learning models will be a core component of future protein function prediction tools.

bioinformatics↗