Biology subjects

Chen, K. M.

Publications and source records attributed to Chen, K. M..

6 recordsLinked to original sources

bioRxiv2024

Contrastive pre-training for sequence based genomics models

MotivationIn recent years deep learning has become one of the central approaches in a number of applications, including many tasks in genomics. However, as models grow in depth and complexity, they either require more data or a strategic initialization technique to improve performance. ResultsIn this project, we introduce cGen, a novel unsupervised, model-agnostic contrastive pretraining method for sequence-based models. cGen can be used before training to initialize weights, reducing the size of the dataset needed. It works through learning the intrinsic features of the reference genome and makes no assumptions on the underlying structure. We show that the embeddings produced by the unsupervised model are already informative for gene expression prediction and that the sequence features provide a meaningful clustering. We demonstrate that cGen improves model performance in various sequence-based deep learning applications, such as chromatin profiling prediction and gene expression. Our findings suggest that using cGen, particularly in areas constrained by data availability, could improve the performance of deep learning genomic models without the need to modify the model architecture. Contactogt@cs.princeton.edu Availability and implementationSource code available at github.com/ksenia007/cGen

genomics↗

bioRxiv2024

Targeting PD-L1 in solid cancer with myeloid cells expressing a CAR-like immune receptor

Myeloid cells are prevalent in solid cancers, but they frequently exhibit a pro-tumor phenotype, hindering cancer immunotherapy. Their abundance makes engineered myeloid cell therapy an intriguing approach to tackle challenges posed by solid cancers, such as tumor trafficking and infiltration along with tumor cell heterogenicity and immunosuppressive tumor microenvironment (TME). Solid cancers often upregulate the checkpoint molecule PD-L1 to evade immune responses. Thus, we devised an adoptive cell therapy strategy based on myeloid cells expressing a Chimeric Antigen Receptor (CAR)-like immune receptor (CARIR). The extracellular domain of CARIR is derived from the natural inhibitory receptor PD-1, while the intracellular domain(s) are derived from CD40 and/or CD3{xi}. To assess the efficacy of CARIR-engineered myeloid cells, we conducted proof-of-principle experiments using co-culture and flow cytometry-based phagocytosis assays in vitro. Additionally, we employed a fully immune-competent syngeneic tumor mouse model to evaluate the strategys effectiveness in vivo. Co-culturing CARIR-expressing human monocytic THP-1 cells with PD-L1+ target cells lead to upregulation of the co-stimulatory molecule CD86 along with expression of proinflammatory cytokines TNF-1 and IL-1{beta}. Moreover, CARIR expression significantly enhanced phagocytosis of multiple PD-L1+ human solid tumor cell lines in vitro. Similar outcomes were observed with CARIR-expressing human primary macrophages. In experiments conducted on Balb/c mice bearing aggressive 4T1 mammary tumors, infusing murine myeloid cells expressing a murine version of CARIR significantly slowed tumor growth and prolonged survival. Taken together, our results demonstrate that adoptive transfer of PD-1 CARIR-engineered myeloid cells may be an effective strategy in treating PD-L1+ solid tumors. Graphic Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=105 SRC="FIGDIR/small/577873v1_ufig1.gif" ALT="Figure 1"> View larger version (15K): org.highwire.dtl.DTLVardef@1657eborg.highwire.dtl.DTLVardef@e61ae3org.highwire.dtl.DTLVardef@ef3df4org.highwire.dtl.DTLVardef@cc90ea_HPS_FORMAT_FIGEXP M_FIG C_FIG In BriefWe described here an adoptive cell therapy approach employing PD-L1-specific CAR-like immune receptor (CARIR) modified myeloid cells as a potential immune cell therapy strategy for treating PD-L1+ solid cancer. O_LICARIR expression directed human THP-1 macrophages to recognize PD-L1+ target cells, which led to an upregulation of co-stimulatory molecule CD86 and production of proinflammatory cytokines TNF- and IL-1{beta}. C_LIO_LICARIR expression in human THP-1 macrophages had increased % phagocytosis and killing against PD-L1+ tumor cells in vitro. C_LIO_LIAdoptive transfer of CARIR transduced myeloid cells in immunocompetent syngeneic mice with established aggressive 4T1 tumor significantly slowed tumor growth and prolonged survival. C_LI

immunology↗

bioRxiv2021

A sequence-based global map of regulatory activity for deciphering human genetics

Sequence is at the basis of how the genome shapes chromatin organization, regulates gene expression, and impacts traits and diseases. Epigenomic profiling efforts have enabled large-scale identification of regulatory elements, yet we still lack a sequence-based map to systematically identify regulatory activities from any sequence, which is necessary for predicting the effects of any variant on these activities. We address this challenge with Sei, a new framework for integrating human genetics data with sequence information to discover the regulatory basis of traits and diseases. Our framework systematically learns a vocabulary for the regulatory activities of sequences, which we call sequence classes, using a new deep learning model that predicts a compendium of 21,907 chromatin profiles across >1,300 cell lines and tissues, the most comprehensive to-date. Sequence classes allow for a global view of sequence and variant effects by quantifying diverse regulatory activities, such as loss or gain of cell-type-specific enhancer function. We show that sequence class predictions are supported by experimental data, including tissue-specific gene expression, expression QTLs, and evolutionary constraints based on population allele frequencies. Finally, we applied our framework to human genetics data. Sequence classes uniquely provide a non-overlapping partitioning of GWAS heritability by tissue-specific regulatory activity categories, which we use to characterize the regulatory architecture of 47 traits and diseases from UK Biobank. Furthermore, the predicted loss or gain of sequence class activities suggest specific mechanistic hypotheses for individual regulatory pathogenic mutations. We provide this framework as a resource to further elucidate the sequence basis of human health and disease.

genomics↗

bioRxiv2020

Genome-wide landscape of RNA-binding protein dysregulation reveals a major impact on psychiatric disorder risk

Despite the strong genetic basis of psychiatric disorders, the molecular origins of these diseases are still largely unmapped. RNA-binding proteins (RBPs) are responsible for most post-transcriptional regulation, from splicing to translational to localization. RBPs thus act as key gatekeepers of cellular homeostasis, especially in the brain. Here, we leverage a deep learning approach to interrogate variant effects genome-wide, and discover that the dysregulation of RBP target sites is a principal contributor to psychiatric disorder risk. We show that specific modes of RBP regulation are genetically linked to the heritability of psychiatric disorders, and demonstrate that diverse RBP regulatory functions are reflected in distinct genome-wide negative selection signatures. Notably, RBP dysregulation has a stronger impact on psychiatric disorders than common coding region variants and explains heritability not currently captured by large-scale molecular QTL studies (expression QTLs and splicing QTLs). We share genome-wide profiles of RBP target site dysregulation, which we used to identify DDHD2 as a candidate schizophrenia risk gene, in a public web server. This resource provides a novel analytical framework to connect the full range of RNA regulation to complex disease.

genetics↗

bioRxiv2018

Selene: a PyTorch-based deep learning library for sequence-level data

To enable the application of deep learning in biology, we present Selene (https://selene.flatironinstitute.org/), a PyTorch-based deep learning library for fast and easy development, training, and application of deep learning model architectures for any biological sequences. We demonstrate how Selene allows researchers to easily train a published architecture on new data, develop and evaluate a new architecture, and use a trained model to answer biological questions of interest.

bioinformatics↗

bioRxiv2017

PathCORE: Visualizing globally co-occurring pathways in large transcriptomic compendia

BackgroundInvestigators often interpret genome-wide data by analyzing the expression levels of genes within pathways. While this within-pathway analysis is routine, the products of any one pathway can affect the activity of other pathways. Past efforts to identify relationships between biological processes have evaluated overlap in knowledge bases or evaluated changes that occur after specific treatments. Individual experiments can highlight condition-specific pathway-pathway relationships; however, constructing a complete network of such relationships across many conditions requires analyzing results from many studies. ResultsWe developed PathCORE-T framework by implementing existing methods to identify pathway-pathway transcriptional relationships evident across a broad data compendium. PathCORE-T is applied to the output of feature construction algorithms; it identifies pairs of pathways observed in features more than expected by chance as functionally co-occurring. We demonstrate PathCORE-T by analyzing an existing eADAGE model of a microbial compendium and building and analyzing NMF features from the TCGA dataset of 33 cancer types. The PathCORE-T framework includes a demonstration web interface, with source code, that users can launch to (1) visualize the network and (2) review the expression levels of associated genes in the original data. PathCORE-T creates and displays the network of globally co-occurring pathways based on features observed in a machine learning analysis of gene expression data. ConclusionsThe PathCORE-T framework identifies transcriptionally co-occurring pathways from the results of unsupervised analysis of gene expression data and visualizes the relationships between pathways as a network. PathCORE-T recapitulated previously described pathway-pathway relationships and suggested experimentally testable additional hypotheses that remain to be explored.

bioinformatics↗