Biology subjects

Celik, S.

Publications and source records attributed to Celik, S..

12 recordsLinked to original sources

bioRxiv2024

Cardiomyocyte antisense transcription regulates exon usage in the elastic spring region of Titin to modulate sarcomere function

BackgroundThe spring-like sarcomere protein Titin (TTN) is a key determinant of cardiac passive stiffness and diastolic function. Alternative splicing of TTN I-band exons produce protein isoforms with variable size and elasticity, but the mechanisms regulating TTN exon skipping and isoform composition in the human heart are not well studied. Non-coding RNA transcripts from the antisense strand of protein-coding genes have been shown to regulate alternative splicing of the sense gene. The TTN gene locus harbours >80 antisense transcripts with unknown function in the human heart. The aim of this study was to determine if TTN antisense transcripts play a role in alternative splicing of TTN. MethodsRNA-sequencing and RNA in situ hybridization (ISH) of cardiac tissue from unused organ donor hearts (n=7) and human induced pluripotent stem cell-derived cardiomyocytes (iPS-CMs) were used to determine the expression and localization of TTN antisense transcripts. The effect of siRNA-mediated knock down of TTN antisense transcripts on TTN exon usage in iPS-CMs was determined using RNA-sequencing. Live cell imaging with sarcomere tracking was used to analyze the effect of antisense transcript knock down on sarcomere length, organization and contraction dynamics. RNA ISH, immunofluorescence and high content microscopy was performed in iPS-CMs to study the interaction between antisense transcripts, TTN mRNA and splice factor protein RBM20. ResultsIn mapping TTN antisense transcription, we found that TTN-AS1-276 was the predominant transcript in the human heart and that it was mainly localized in cardiomyocyte nuclear chromatin. Knock down of TTN-AS1-276 in human iPS-CMs resulted in decreased interaction between the splicing factor RBM20 and TTN pre-mRNA, decreased TTN I-band exon skipping, and markedly lower expression of the less elastic TTN isoform N2B. The effect on TTN exon usage was independent of sense-antisense exon overlap and polymerase II elongation rate. Furthermore, knockdown resulted in longer sarcomeres with preserved alignment, improved fractional shortening and relaxation times. ConclusionsWe demonstrate a role for the cardiac TTN antisense transcript TTN-AS1-276 in facilitating alternative splicing of TTN and regulating sarcomere properties. This transcript could constitute a target for improving cardiac passive stiffness and diastolic function in conditions such as heart failure with preserved ejection fraction.

molecular biology↗

bioRxiv2024

A deep profile of gene expression across 18 human cancers

Clinically and biologically valuable information may reside untapped in large cancer gene expression data sets. Deep unsupervised learning has the potential to extract this information with unprecedented efficacy but has thus far been hampered by a lack of biological interpretability and robustness. Here, we present DeepProfile, a comprehensive framework that addresses current challenges in applying unsupervised deep learning to gene expression profiles. We use DeepProfile to learn low-dimensional latent spaces for 18 human cancers from 50,211 transcriptomes. DeepProfile outperforms existing dimensionality reduction methods with respect to biological interpretability. Using DeepProfile interpretability methods, we show that genes that are universally important in defining the latent spaces across all cancer types control immune cell activation, while cancer type-specific genes and pathways define molecular disease subtypes. By linking DeepProfile latent variables to secondary tumor characteristics, we discover that tumor mutation burden is closely associated with the expression of cell cycle-related genes. DNA mismatch repair and MHC class II antigen presentation pathway expression, on the other hand, are consistently associated with patient survival. We validate these results through Kaplan-Meier analyses and nominate tumor-associated macrophages as an important source of survival-correlated MHC class II transcripts. Our results illustrate the power of unsupervised deep learning for discovery of cancer biology from existing gene expression data.

bioinformatics↗

bioRxiv2023

High-resolution genome-wide mapping of chromosome-arm-scale truncations induced by CRISPR-Cas9 editing

CRISPR-Cas9 editing is a scalable technology for mapping of biological pathways, but it has been reported to cause a variety of undesired large-scale structural changes to the genome. We performed an arrayed CRISPR-Cas9 scan of the genome in primary human cells, targeting 17,065 genes for knockout with 101,029 guides. High-dimensional phenomics reveals a "proximity bias" in which CRISPR knockouts bear unexpected phenotypic similarity to knockouts of biologically-unrelated genes on the same chromosome arm, recapitulating both canonical genome structure and structural variants. Transcriptomics connects proximity bias to chromosome-arm truncations. Analysis of published large-scale knockout and knockdown experiments confirms that this effect is general across cell types, labs, Cas9 delivery mechanisms, and assay modalities, and suggests proximity bias is caused by DNA double-strand-breaks with cell cycle control in a mediating role. Finally, we demonstrate a simple correction for large-scale CRISPR screens to mitigate this pervasive bias while preserving biological relationships.

genomics↗

bioRxiv2023

RxRx3: Phenomics Map of Biology

The combination of modern genetic perturbation techniques with high content screening has enabled genome-scale cell microscopy experiments that can be leveraged to construct maps of biology. These are built by processing microscopy images to produce readouts in unified and relatable representation space to capture known biological relationships and discover new ones. To further enable the scientific community to develop methods and insights from map-scale data, here we release RxRx3, the first ever public high-content screening dataset combining genome-scale CRISPR knockouts with multiple-concentration screening of small molecules (a set of FDA approved and commercially available bioactive compounds). The dataset contains 6-channel fluorescent microscopy images and associated deep learning embeddings from over 2.2 million wells that span 17,063 CRISPR knockouts and 1,674 compounds at 8 doses each. RxRx3 is one of the largest collections of cellular screening data, and as far as we know, the largest generated consistently via a common experimental protocol within a single laboratory. Our goal in releasing RxRx3 is to demonstrate the benefits of generating consistent data, enable the development of the machine learning methods on this scale of data and to foster research, methods development, and collaboration. For more information about RxRx3 please visit RxRx.ai/rxrx3

systems biology↗

bioRxiv2022

Biological Cartography: Building and Benchmarking Representations of Life

1The continued scaling of genetic perturbation technologies combined with high-dimensional assays such as cellular microscopy and RNA-sequencing has enabled genome-scale reverse-genetics experiments that go beyond single-endpoint measurements of growth or lethality. Datasets emerging from these experiments can be combined to construct perturbative "maps of biology", in which readouts from various manipulations (e.g., CRISPR-Cas9 knockout, CRISPRi knockdown, compound treatment) are placed in unified, relatable embedding spaces allowing for the generation of genome-scale sets of pairwise comparisons. These maps of biology capture known biological relationships and uncover new associations which can be used for downstream discovery tasks. Construction of these maps involves many technical choices in both experimental and computational protocols, motivating the design of benchmark procedures to evaluate map quality in a systematic, unbiased manner. Here, we (1) establish a standardized terminology for the steps involved in perturbative map building, (2) introduce key classes of benchmarks to assess the quality of such maps, (3) construct maps from four genome-scale datasets employing different cell types, perturbation technologies, and data readout modalities, (4) generate benchmark metrics for the constructed maps and investigate the reasons for performance variations, and (5) demonstrate utility of these maps to discover new biology by suggesting roles for two largely uncharacterized genes. 2 Author SummaryWith the proliferation of genetic perturbation, laboratory robotics, computer vision and sequencing technologies, a growing number of researchers are producing datasets that capture digital readouts of cellular responses to genetic perturbations at the full-genome-scale. Since each of these efforts utilizes different cellular models, experimental approaches, terminology, code bases, analysis methods and quality metrics, it is exceptionally difficult to reason through the pros and cons of possible design choices or even discuss the primary considerations when embarking on such an endeavor. These datasets can be powerful discovery tools to look at known biological relationships and uncover new associations in an unbiased manner, but only when paired with a computational pipeline to assemble the data into a digestible format. Moreover, there is great promise in looking across these data to highlight commonalities and differences that may be attributed to experimental or analytical approaches or the biological context. Therefore, a unified framework is necessary to align this nascent field and speed progress in assessing technologies and methods. In this work we define a unified framework for building and benchmarking these perturbative maps, benchmark four different datasets assembled into 18 different maps, explore the impact of different design decisions and demonstrate how these maps can be used to elucidate gene functions. The framework we propose highlights the necessary steps for building any such map - embedding, filtering, aligning, aggregating and relating the data across perturbations. For benchmarking, we propose two main types of metrics and give examples which highlight the impact of different processing pipelines. Finally, we explore these maps to demonstrate their utility for confirming known biological relationships and nominating annotations for genes with unknown function. We expect that this work will positively impact the nascent field of perturbative map building by enabling easier comparisons within and between technologies and methods through a shared language. Additionally, the associated code base is openly available and flexible enough to be easily extended with new methods, so we hope that it will become a resource for future researchers working on developing both laboratory and computational methodology. While there are too many confounding variables to make recommendations on the strengths of different technologies and cellular models at this time, highlighting that fact may prompt studies designed with the goal of directly comparing methods while holding other confounding variables fixed. Moreover, as the number of perturbative maps grows, the field will naturally consider the advantages of combining maps across modalities and the framework provided here can also help guide the evaluation of those efforts.

bioinformatics↗

bioRxiv2022

Principled Feature Attribution for Unsupervised Gene Expression Analysis

As interest in unsupervised deep learning models for the analysis of gene expression data has grown, an increasing number of methods have been developed to make these deep learning models more interpretable. These methods can be separated into two groups: (1) post hoc analyses of black box models through feature attribution methods and (2) approaches to build inherently interpretable models through biologically-constrained architectures. In this work, we argue that these approaches are not mutually exclusive, but can in fact be usefully combined. We propose a novel unsupervised pathway attribution method, which better identifies major sources of transcriptomic variation than prior methods when combined with biologically-constrained neural network models. We demonstrate how principled feature attributions aid in the analysis of a variety of single cell datasets. Finally, we apply our approach to a large dataset of post-mortem brain samples from patients with Alzheimers disease, and show that it identifies Mitochondrial Respiratory Complex I as an important factor in this disease.

bioinformatics↗

bioRxiv2021

Uncovering expression signatures of synergistic drug response using an ensemble of explainable AI models

Complex machine learning models are poised to revolutionize the treatment of diseases like acute myeloid leukemia (AML) by helping physicians choose optimal combinations of anti-cancer drugs based on molecular features. While accurate predictions are important, it is equally important to be able to learn about the underlying molecular basis of anti-cancer drug synergy. Explainable AI (XAI) offers a promising new route for data-driven cancer pharmacology, combining highly accurate models with interpretable insights into model decisions. Due to the highly correlated, high-dimensional nature of cancer transcriptomic data, however, we find that existing XAI approaches are suboptimal when applied naively to large transcriptomic datasets. We show how a novel approach based on model ensembling helps to increase the quality of explanations. We then use our method to demonstrate that a hematopoietic differentiation signature underlies synergy for a variety of anti-AML drug combinations.

cancer biology↗

bioRxiv2020

Unified AI framework to uncover deep interrelationships between gene expression and Alzheimer's disease neuropathologies

Deep neural networks offer a promising approach for capturing complex, non-linear relationships among variables. Because they require immense sample sizes, their potential has yet to be fully tapped for understanding complex relationships between gene expression and human phenotypes. Encouragingly, a growing number of diseases are being studied through consortium efforts. Here we introduce a new analysis framework, namely MD-AD (Multi-task Deep learning for Alzheimers Disease neuropathology), which leverages an unexpected synergy between deep neural networks and multi-cohort settings. In these settings, true joint analysis can be stymied using conventional statistical methods, which (1) require "harmonized" phenotypes (i.e., measured in a highly consistent manner) and (2) tend to capture cohort-level variations, obscuring the subtler true disease signals. Instead, MD-AD incorporates multiple related phenotypes sparsely measured across cohorts, and learns complex, non-linear interactions between genes and phenotypes not discovered using conventional expression data analysis methods (e.g., component analysis and module detection), enabling the model to capture subtler signals than cohort-level variations. Applied to the largest available collection of brain samples (N=1,758), we demonstrate that MD-AD learns a truly generalizable relationship between gene expression program and AD-related neuropathology. The learned program generalizes in several important ways, including recapitulation of the disease progress in animal models and across tissue types, and we show that such generalizability is not achieved by previous statistical paradigms. Its ability to identify genes with high non-linear relevance to neuropathology enabled us to identify a sex-specific relationship between neuropathology and immune response across microglia, providing a nuanced context for association between inflammatory genes and AD.

systems biology↗

bioRxiv2018

Explainable machine learning prediction of synergistic drug combinations for precision cancer medicine

Although combination therapy has been a mainstay of cancer treatment for decades, it remains challenging, both to identify novel effective combinations of drugs and to determine the optimal combination for a particular patients tumor. While there have been several recent efforts to test drug combinations in vitro, examining the immense space of possible combinations is far from being feasible. Thus, it is crucial to develop datadriven techniques to computationally identify the optimal drug combination for a patient. We introduce TreeCombo, an extreme gradient boosted tree-based approach to predict synergy of novel drug combinations, using chemical and physical properties of drugs and gene expression levels of cell lines as features. We find that TreeCombo significantly outperforms three other state-of-theart approaches, including the recently developed DeepSynergy, which uses the same set of features to predict synergy using deep neural networks. Moreover, we found that the predictions from our approach were interpretable, with genes having well-established links to cancer serving as important features for prediction of drug synergy.

cancer biology↗

bioRxiv2018

MD-AD: Multi-task deep learning for Alzheimer’s disease neuropathology

Systematic modeling of Alzheimers Disease (AD) neuropathology based on brain gene expression would provide valuable insights into the disease. However, relative scarcity and regional heterogeneity of brain gene expression and neuropathology datasets obscure the ability to robustly identify expression markers. We propose MD-AD (Multi-task Deep learning for AD) to effectively combine heterogeneous AD datasets by simultaneously modeling multiple phenotypes with shared layers. MD-AD leads to an 8% and 5% reduction in mean squared error over MLP for predicting counts of two AD hallmarks: plaques and tangles. It also leads to a 40% and 30% reduction in classification error over MLP for two common staging systems for AD: CERAD score and Braak stage. Additionally, MD-ADs network representation tends to better capture known metabolic pathways, including some AD-related pathways. Together, these results indicate that MD-AD is particularly useful for learning expressive network representations from heterogeneous and sparsely labeled AD data.

systems biology↗

bioRxiv2018

A computational framework identifying concordant gene expression-neuropathology associations reveals Complex I as a potential Alzheimer’s disease therapeutic target

Identifying gene expression markers for Alzheimers disease (AD) neuropathology through meta-analysis is a complex undertaking because available data are often from different studies and/or brain regions involving study-specific confounders and/or region-specific biological processes. Here we introduce a novel probabilistic model-based framework, DECODER, leveraging these discrepancies to identify robust biomarkers for complex phenotypes. Our experiments present: (1) DECODERs potential as a general meta-analysis framework widely applicable to various diseases (e.g., AD and cancer) and phenotypes (e.g., Amyloid-{beta} (A{beta}) pathology, tau pathology, and survival), (2) our results from a meta-analysis using 1,746 human brain tissue samples from nine brain regions in three studies -- the largest expression meta-analysis for AD, to our knowledge --, and (3) in vivo validation of identified modifiers of A{beta} toxicity in a transgenic Caenorhabditis elegans model expressing AD-associated A{beta}, which pinpoints mitochondrial Complex I as a critical mediator of proteostasis and a promising pharmacological avenue toward treating AD.

systems biology↗

bioRxiv2018

DeepProfile: Deep learning of patient molecular profiles for precision medicine in acute myeloid leukemia

We present the DeepProfile framework, which learns a variational autoencoder (VAE) network from thousands of publicly available gene expression samples and uses this network to encode a low-dimensional representation (LDR) to predict complex disease phenotypes. To our knowledge, DeepProfile is the first attempt to use deep learning to extract a feature representation from a vast quantity of unlabeled (i.e, lacking phenotype information) expression samples that are not incorporated into the prediction problem. We use Deep-Profile to predict acute myeloid leukemia patients in vitro responses to 160 chemotherapy drugs. We show that, when compared to the original features (i.e., expression levels) and LDRs from two commonly used dimensionality reduction methods, DeepProfile: (1) better predicts complex phenotypes, (2) better captures known functional gene groups, and (3) better reconstructs the input data. We show that DeepProfile is generalizable to other diseases and phenotypes by using it to predict ovarian cancer patients tumor invasion patterns and breast cancer patients disease subtypes.

bioinformatics↗