Biology subjects

Akalin, A.

Publications and source records attributed to Akalin, A..

At least 37 records · Page 2Linked to original sources

bioRxiv2020

Parallel genetics of regulatory sequences in vivo

Understanding how regulatory sequences control gene expression is fundamental to explain how phenotypes arise in health and disease. Traditional reporter assays inform about function of individual regulatory elements, typically in isolation. However, regulatory elements must ultimately be understood by perturbing them within their genomic environment and developmental- or tissue-specific contexts. This is technically challenging; therefore, few regulatory elements have been characterized in vivo. Here, we used inducible Cas9 and multiplexed guide RNAs to create hundreds of mutations in enhancers/promoters and 3' UTRs of 16 genes in C. elegans. To quantify the consequences of mutations on expression, we developed a targeted RNA sequencing strategy across hundreds of mutant animals. We were also able to systematically and quantitatively assign fitness cost to mutations. Finally, we identified and characterized sequence elements that strongly regulate phenotypic traits. Our approach enables highly parallelized, functional analysis of regulatory sequences in vivo.

genetics↗

bioRxiv2020

Single-cell-resolved dynamics of chromatin architecture delineate cell and regulatory states in wildtype and cloche/npas4l mutant zebrafish embryos

DNA accessibility of cis regulatory elements (CREs) dictates transcriptional activity and drives cell differentiation during development. While many of the genes that regulate embryonic development have been described, the underlying CRE dynamics controlling their expression remain largely unknown. To address this, we applied single-cell combinatorial indexing ATAC-seq (sci-ATAC-seq) to whole 24 hours post fertilization (hpf) stage zebrafish embryos and developed a new computational tool, ScregSeg, that selects informative genome segments and classifies complex accessibility dynamics. We integrated the ScregSeg output with bulk measurements for histone post-translational modifications and 3D genome organization, expanding knowledge of regulatory principles between chromatin modalities. Sci-ATAC-seq profiling of npas4l/cloche mutant embryos revealed novel cellular roles for this hemato-vascular transcriptional master regulator and suggests an intricate mechanism regulating its expression. Our work constitutes a valuable resource for future studies in developmental, molecular, and computational biology.

genomics↗

bioRxiv2020

Bulk and single-cell gene expression profiling of SARS-CoV-2 infected human cell lines identifies molecular targets for therapeutic intervention

The coronavirus disease 2019 (COVID-19) pandemic, caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is an ongoing global health threat with more than two million infected people since its emergence in late 2019. Detailed knowledge of the molecular biology of the infection is indispensable for understanding of the viral replication, host responses, and disease progression. We provide gene expression profiles of SARS-CoV and SARS-CoV-2 infections in three human cell lines (H1299, Caco-2 and Calu-3 cells), using bulk and single-cell transcriptomics. Small RNA profiling showed strong expression of the immunity and inflammation-associated microRNA miRNA-155 upon infection with both viruses. SARS-CoV-2 elicited approximately two-fold higher stimulation of the interferon response compared to SARS-CoV in the permissive human epithelial cell line Calu-3, and induction of cytokines such as CXCL10 or IL6. Single cell RNA sequencing data showed that canonical interferon stimulated genes such as IFIT2 or OAS2 were broadly induced, whereas interferon beta (IFNB1) and lambda (IFNL1-4) were expressed only in a subset of infected cells. In addition, temporal resolution of transcriptional responses suggested interferon regulatory factors (IRFs) activities precede that of nuclear factor-{kappa}B (NF-{kappa}B). Lastly, we identified heat shock protein 90 (HSP90) as a protein relevant for the infection. Inhibition of the HSP90 charperone activity by Tanespimycin/17-N-allylamino-17-demethoxygeldanamycin (17-AAG) resulted in a reduction of viral replication, and of TNF and IL1B mRNA levels. In summary, our study established in vitro cell culture models to study SARS-CoV-2 infection and identified HSP90 protein as potential drug target for therapeutic intervention of SARS-CoV-2 infection.

systems biology↗

bioRxiv2020

Single-nucleus transcriptomics reveals functional compartmentalization in syncytial skeletal muscle cells

Syncytial skeletal muscle cells contain hundreds of nuclei in a shared cytoplasm. We investigated nuclear heterogeneity and transcriptional dynamics in the uninjured and regenerating muscle using single-nucleus RNA-sequencing (snRNAseq) of isolated nuclei from muscle fibers. This revealed distinct nuclear subtypes unrelated to fiber type diversity, completely novel subtypes as well as the expected ones at the neuromuscular and myotendinous junctions. In fibers of the Mdx dystrophy mouse model, new subtypes emerged, among them nuclei expressing a repair signature that were also abundant in the muscle of dystrophy patients, and a nuclear population associated with necrotic fibers. Finally, modifications of our approach revealed the compartmentalization in the rare and specialized muscle spindle. Our data identifies new nuclear compartments of the myofiber and defines a molecular roadmap for their functional analyses; the data can be freely explored on the MyoExplorer server (https://shiny.mdc-berlin.net/MyoExplorer/).

developmental biology↗

bioRxiv2020

Cell-type specialization in the brain is encoded by specific long-range chromatin topologies

Neurons and oligodendrocytes are terminally differentiated cells that sustain cascades of gene activation and repression to execute highly specialized functions, while retaining homeostatic control. To study long-range chromatin folding without disturbing the native tissue environment, we developed Genome Architecture Mapping in combination with immunoselection (immunoGAM), and applied it to three cell types from the adult murine brain: dopaminergic neurons (DNs) from the midbrain, pyramidal glutamatergic neurons (PGNs) from the hippocampus, and oligodendroglia (OLGs) from the cortex. We find cell-type specific 3D chromatin structures that relate with patterns of gene expression at multiple genomic scales, including extensive reorganization of topological domains (TADs) and chromatin compartments. We discover the loss of TAD insulation, or TAD melting, at long genes (>400 kb) when they are highly transcribed. We find many neuron-specific contacts which contain accessible chromatin regions enriched for putative binding sites for multiple neuronal transcription factors, and which connect cell-type specific genes that are associated with neurodegenerative disorders such as Parkinsons disease, or specialized functions such as synaptic plasticity and memory. Lastly, sensory receptor genes exhibit increased membership in heterochromatic compartments that establish strong contacts in brain cells. However, their silencing is compromised in a subpopulation of PGNs with molecular signatures of long-term potentiation. Overall, our work shows that the 3D organization of the genome is highly cell-type specific, and essential to better understand mechanisms of gene regulation in highly specialized tissues such as the brain.

molecular biology↗

bioRxiv2020

PHF3 regulates neuronal gene expression through the new Pol II CTD reader domain SPOC

The C-terminal domain (CTD) of the largest subunit of RNA polymerase II (Pol II) is a regulatory hub for transcription and RNA processing. Here, we identify PHD-finger protein 3 (PHF3) as a new CTD-binding factor that negatively regulates transcription and mRNA stability. The PHF3 SPOC domain preferentially binds to CTD repeats phosphorylated on Serine-2 and PHF3 tracks with Pol II across the length of genes. PHF3 competes with TFIIS for Pol II binding through its TFIIS-like domain (TLD), thus inhibiting TFIIS-mediated rescue of backtracked Pol II. PHF3 knock-out or PHF3 SPOC deletion in human cells result in gene upregulation and a global increase in mRNA stability, with marked derepression of neuronal genes. Key neuronal genes are aberrantly expressed in Phf3 knock-out mouse embryonic stem cells, resulting in impaired neuronal differentiation. Our data suggest that PHF3 is a prominent effector of neuronal gene regulation at the interface of transcription elongation and mRNA decay.

biochemistry↗

bioRxiv2020

Pdap1 protects mature B lymphocytes from stress-induced cell death and promotes antibody gene diversification

The establishment of protective humoral immunity is dependent on the ability of mature B cells to undergo antibody gene diversification while adjusting to the physiological stressors induced by activation with the antigen. Mature B cells diversify their antibody genes by class switch recombination (CSR) and somatic hypermutation (SHM), which are both dependent on efficient induction of activation-induced cytidine deaminase (AID). Here, we identified PDGFA-associated protein 1 (Pdap1) as an essential regulator of cellular homeostasis in mature B cells. Pdap1 deficiency leads to sustained expression of the integrated stress response (ISR) effector activating transcription factor 4 (Atf4) and induction of the ISR transcriptional program, increased cell death, and defective AID expression. As a consequence, loss of Pdap1 reduces germinal center B cell formation and impairs CSR and SHM. Thus, Pdap1 protects mature B cells against chronic ISR activation and ensures efficient antibody diversification by promoting their survival and optimal function.

immunology↗

bioRxiv2019

A novel IKK- and proteasome-independent mechanism of RelA activation triggers senescence associated secretome via transcriptional repression of NFKBIA

The I{kappa}B kinase (IKK) - NF-{kappa}B pathway is activated as part of the DNA damage response and controls both resistance to apoptosis and inflammation. How these different functions are achieved remained unknown. We demonstrate here that DNA double strand breaks elicit two subsequent phases of NF-{kappa}B activation in vivo and in vitro, which are mechanistically and functionally distinct. RNA-sequencing reveals that the first phase controls anti-apoptotic gene expression, while the second drives expression of senescence-associated secretory phenotype (SASP) genes. The first, rapidly activated phase is driven by the ATM-PARP1-TRAF6-IKK cascade, which triggers proteasomal destruction of I{kappa}B and is terminated through I{kappa}B (NFKBIA) re-expression. The second phase is activated days later in senescent cells but is independent of IKK and the proteasome. An altered phosphorylation status of p65, in part driven by GSK3{beta}, results in transcriptional silencing of NFKBIA and IKK-independent, constitutive activation of NF-{kappa}B in senescence. Collectively, our study reveals a novel physiological mechanism of NF-{kappa}B activation with important implications for genotoxic cancer treatment.

cell biology↗

bioRxiv2019

Janggu: Deep Learning for Genomics

MotivationIn recent years, numerous applications have demonstrated the potential of deep learning for an improved understanding of biological processes. However, most deep learning tools developed so far are designed to address a specific question on a fixed dataset and/or by a fixed model architecture. Adapting these models to integrate new datasets or to address different hypotheses can lead to considerable software engineering effort. To address this aspect we have built Janggu, a python library that facilitates deep learning for genomics applications.\n\nResultsJanggu aims to ease data acquisition and model evaluation in multiple ways. Among its key features are special dataset objects, which form a unified and flexible data acquisition and pre-processing framework for genomics data that enables streamlining of future research applications through reusable components. Through a numpy-like interface, the dataset objects are directly compatible with popular deep learning libraries, including keras. Furthermore, Janggu offers the possibility to visualize predictions as genomic tracks or by exporting them to the BIGWIG format. We illustrate the functionality of Janggu on several deep learning genomics applications. First, we evaluate different model topologies for the task of predicting binding sites for the transcription factor JunD. Second, we demonstrate the framework on published models for predicting chromatin effects. Third, we show that promoter usage measured by CAGE can be predicted using DNase hyper-sensitivity, histone modifications and DNA sequence features. We improve the performance of these models due to a novel feature in Janggu that allows us to include high-order sequence features. We believe that Janggu will help to significantly reduce repetitive programming overhead for deep learning applications in genomics, while at the same time enabling computational biologists to assess biological hypotheses more rapidly.\n\nAvailabilityJanggu is freely available under a GPL-v3 license on https://github.com/BIMSBbioinfo/janggu or via https://pypi.org/project/janggu

bioinformatics↗

bioRxiv2019

Functional Annotation of Human Long Non-Coding RNAs via Molecular Phenotyping

Long non-coding RNAs (lncRNAs) constitute the majority of transcripts in the mammalian genomes and yet, their functions remain largely unknown. We systematically knockdown 285 lncRNAs expression in human dermal fibroblasts and quantified cellular growth, morphological changes, and transcriptomic responses using Capped Analysis of Gene Expression (CAGE). Antisense oligonucleotides targeting the same lncRNA exhibited global concordance, and the molecular phenotype, measured by CAGE, recapitulated the observed cellular phenotypes while providing additional insights on the affected genes and pathways. Here, we disseminate the largest to-date lncRNA knockdown dataset with molecular phenotyping (over 1,000 CAGE deep-sequencing libraries) for further exploration and highlight functional roles for ZNF213-AS1 and lnc-KHDC3L-2.

genomics↗

bioRxiv2019

Single-cell RNA-sequencing of Herpes simplex virus 1-infected cells identifies NRF2 activation as an antiviral program

Herpesvirus infection initiates a range of perturbations in the host cell, which remain poorly understood at the level of individual cells. Here, we quantified the transcrips of single human primary fibroblasts during the first hours of lytic infection with HSV-1. By applying a generalizable analysis scheme, we defined a precise temporal order of early viral gene expression and found unexpected bifurcations and bottlenecks. We identified individual host cell genes and pathways relevant in early infection by combining three different computational approaches: gene and pathway overdispersion analysis, prediction of cell-state transition probabilities as well as future cell states. One transcriptional program, which was turned on in infected cells and correlated with increased resistance to infection, implicated the transcription factor NRF2. Consequently, Bardoxolone methyl, a known NRF2 agonist, impaired virus production, suggesting that NRF2 activation restricts the progression of viral infection. Our study provides novel insights into early stages of HSV-1 infection and serves as a general blueprint for the investigation of heterogenous cell states in virus infection.

systems biology↗

bioRxiv2019

The conserved histone chaperone LIN-53 links lifespan and healthspan regulation in C. elegans

Whether extension of lifespan provides an extended time without health deteriorations is an important issue for human aging. However, to which degree lifespan and healthspan regulation might be linked is not well understood. Chromatin factors could be involved in linking both aging aspects, as epigenetic mechanisms bridge regulation of different biological processes. The epigenetic factor LIN-53 (RBBP4/7) is required for safeguarding cell identities in Caenorhabditis elegans as well as mammals and for preventing memory loss and premature aging in humans. LIN-53 is a histone chaperone that associates with different chromatin-regulating complexes. We show that LIN-53 interacts with the Nucleosome remodeling and deacteylase (NuRD)-complex in C. elegans muscles to promote healthy locomotion during aging. While mutants for other NuRD members show a normal lifespan, animals lacking LIN-53 die early because LIN-53 depletion affects also the Histone deacetylase complex Sin3, which is required for a normal lifespan. To determine why lin-53 and sin-3 mutants die early, we performed transcriptome and metabolome analysis and found that levels of the disaccharide Trehalose are significantly decreased in both mutants. As Trehalose is required for normal lifespan in C. elegans, lin-53 and sin-3 mutants could be rescued by either feeding with Trehalose or increasing Trehalose levels via the Insulin/IGF1 signaling pathway. Overall, our findings suggest that LIN-53 is required for maintaining lifespan and promoting healthspan through discrete chromatin regulatory mechanisms. Since both LIN-53 and its mammalian homologs safeguard cell identities, it is conceivable that its implication in lifespan and healthspan regulation is also evolutionarily conserved.

cell biology↗

bioRxiv2018

Evaluation of colorectal cancer subtypes and cell lines using deep learning

Colorectal cancer (CRC) is a common cancer with a high mortality rate and a rising incidence rate in the developed world. The disease shows variable drug response and outcome. Molecular profiling techniques have been used to better understand the variability between tumours as well as cancer models such as cell lines. Drug discovery programs use cell lines as a proxy for human cancers to characterize their molecular makeup and drug response, identify relevant indications and discover biomarkers. In order to maximize the translatability and the clinical relevance of in vitro studies, selection of optimal cancer models is imperative. We have developed a deep learning based method to measure the similarity between CRC tumors and other tumors or disease models such as cancer cell lines. Our method efficiently leverages multi-omics data sets containing copy number alterations, gene expression and point mutations, and learns latent factors that describe the data in lower dimension. These latent factors represent the patterns across gene expression, copy number, and mutational profiles which are clinically relevant and explain the variability of molecular profiles across tumours and cell lines. Using these, we propose a refined colorectal cancer sample classification and provide best-matching cell lines in terms of multi-omics for the different subtypes. These findings are relevant for patient stratification and selection of cell lines for early stage drug discovery pipelines, biomarker discovery, and target identification.

bioinformatics↗

bioRxiv2018

Long non-coding RNAs defining major subtypes of B cell precursor acute lymphoblastic leukemia

Recent studies implicated that long non-coding RNAs (lncRNAs) may play a role in the progression and development of acute lymphoblastic leukemia, however, this role is not yet clear. In order to unravel the role of lncRNAs associated with B-cell precursor Acute Lymphoblastic Leukemia (BCP-ALL) subtypes, we performed transcriptome sequencing and DNA methylation array across 82 BCP-ALL samples from three molecular subtypes (DUX4, Ph-like, and Near Haploid or High Hyperdiploidy). Unsupervised clustering of BCP-ALL samples on the basis of their lncRNAs on transcriptome and DNA methylation profiles revealed robust clusters separating three molecular subtypes. Using extensive computational analysis, we developed a comprehensive catalog of 1235 aberrantly dysregulated BCP-ALL subtype-specific lncRNAs with altered expression and methylation patterns from three subtypes of BCP-ALL. By analyzing the co-expression of subtype-specific lncRNAs and protein-coding genes, we inferred key molecular processes in BCP-ALL subtypes. A strong correlation was identified between the DUX4 specific lncRNAs and activation of TGF-{beta} and Hippo signaling pathways. Similarly, Ph-like specific lncRNAs were correlated with genes involved in activation of PI3K-AKT, mTOR, and JAK-STAT signaling pathways. Interestingly, the relapse-specific differentially expressed lncRNAs correlated with the activation of metabolic and signaling pathways. Finally, we showed a set of epigenetically altered lncRNAs facilitating the expression of tumor genes located at their cis location. Overall, our study provides a comprehensive set of novel subtype and relapse-specific lncRNAs in BCP-ALL. Our findings suggest a wide range of molecular pathways are associated with lncRNAs in BCP-ALL subtypes and provide a foundation for functional investigations that could lead to new therapeutic approaches.\n\nAuthor SummaryAcute lymphoblastic leukemia is a heterogeneous blood cancer, with multiple molecular subtypes, and with high relapse rate. We are far from the complete understanding of the rationale behind these subtypes and high relapse rate. Long non-coding (lncRNAs) has emerged as a novel class of RNA due to its diverse mechanism in cancer development and progression. LncRNAs does not code for proteins and represent around 70% of human transcripts. Recently, there are a number of studies used lncRNAs expression profile in the classification of various cancers subtypes and displayed their correlation with genomic, epigenetic, pathological and clinical features in diverse cancers. Therefore, lncRNAs can account for heterogeneity and has independent prognostic value in various cancer subtypes. However, lncRNAs defining the molecular subtypes of BCP-ALL are not portrayed yet. Here, we describe a set of relapse and subtype-specific lncRNAs from three major BCP-ALL subtypes and define their potential functions and epigenetic regulation. Our data uncover the diverse mechanism of action of lncRNAs in BCP-ALL subtypes defining how lncRNAs are involved in the pathogenesis of disease and the relevance in the stratification of BCP-ALL subtypes.

cancer biology↗

bioRxiv2018

Reproducible genomics analysis pipelines with GNU Guix

In bioinformatics, as well as other computationally-intensive research fields, there is a need for workflows that can reliably produce consistent output, independent of the software environment or configuration settings of the machine on which they are executed. Indeed, this is essential for controlled comparison between different observations or for the wider dissemination of workflows. Providing this type of reproducibility, however, is often complicated by the need to accommodate the myriad dependencies included in a larger body of software, each of which generally come in various versions. Moreover, in many fields (bioinformatics being a prime example), these versions are subject to continual change due to rapidly evolving technologies, further complicating problems related to reproducibility. Here, we propose a principled approach for building analysis pipelines and managing their dependencies. As a case study to demonstrate the utility of our approach, we present a set of highly reproducible pipelines for the analysis of RNA-seq, ChIP-seq, Bisulfite-seq, and single-cell RNA-seq. All pipelines process raw experimental data, and generate reports containing publication-ready plots and figures, with interactive report elements and standard observables. Users may install these highly reproducible packages and apply them to their own datasets without any special computational expertise beyond the use of the command line. We hope such a toolkit will provide immediate benefit to laboratory workers wishing to process their own data sets or bioinformaticians seeking to automate all, or parts of, their analyses. In the long term, we hope our approach to reproducibility will serve as a blueprint for reproducible workflows in other areas. Our pipelines, along with their corresponding documentation and sample reports, are available at http://bioinformatics.mdc-berlin.de/pigx

bioinformatics↗

bioRxiv2017

netSmooth: Network-smoothing based imputation for single cell RNA-seq

Single cell RNA-seq (scRNA-seq) experiments suffer from a range of characteristic technical biases, such as dropouts (zero or near zero counts) and high variance. Current analysis methods rely on imputing missing values by various means of local averaging or regression, often amplifying biases inherent in the data. We present netSmooth, a network-diffusion based method that uses priors for the covariance structure of gene expression profiles on scRNA-seq experiments in order to smooth expression values. We demonstrate that netSmooth improves clustering results of scRNA-seq experiments from distinct cell populations, time-course experiments, and cancer genomics. We provide an R package for our method, available at: https://github.com/BIMSBbioinfo/netSmooth.

bioinformatics↗

bioRxiv2017

FACT sets a barrier for cell fate reprogramming in C. elegans and Human

O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC=\"FIGDIR/small/185116_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (59K):\norg.highwire.dtl.DTLVardef@15ae7aaorg.highwire.dtl.DTLVardef@11f5c08org.highwire.dtl.DTLVardef@1d32442org.highwire.dtl.DTLVardef@f1808f_HPS_FORMAT_FIGEXP M_FIG C_FIG The chromatin regulator FACT (Facilitates Chromatin Transcription) is essential for ensuring stable gene expression by promoting transcription. In a genetic screen using C. elegans we identified that FACT maintains cell identities and acts as a barrier for transcription factor-mediated cell fate reprogramming. Strikingly, FACTs role as a reprogramming barrier is conserved in humans as we show that FACT depletion enhances reprogramming of fibroblasts into stem cells and neurons. Such activity of FACT is unexpected since known reprogramming barriers typically repress gene expression by silencing chromatin. In contrast, FACT is a positive regulator of gene expression suggesting an unprecedented link of cell fate maintenance with counteracting alternative cell identities. This notion is supported by ATAC-seq analysis showing that FACT depletion results in decreased but also increased chromatin accessibility for transcription factors. Our findings identify FACT as a cellular reprogramming barrier in C. elegans and humans, revealing an evolutionarily conserved mechanism for cell fate protection.

developmental biology↗

bioRxiv2017

Mutations In Disordered Regions Cause Disease By Creating Endocytosis Motifs

Mutations in intrinsically disordered regions (IDRs) of proteins can cause a wide spectrum of diseases. Since IDRs lack a fixed three-dimensional structure, the mechanism by which such mutations cause disease is often unknown. Here, we employ a proteomic screen to investigate the impact of mutations in IDRs on protein-protein interactions. We find that mutations in disordered cytosolic regions of three transmembrane proteins (GLUT1, ITPR1 and CACNA1H) lead to an increased binding of clathrins. In all three cases, the mutation creates a dileucine motif known to mediate clathrin-dependent trafficking. Follow-up experiments on GLUT1 (SLC2A1), a glucose transporter involved in GLUT1 deficiency syndrome, revealed that the mutated protein mislocalizes to intracellular compartments. A systematic analysis of other known disease-causing variants revealed a significant and specific overrepresentation of gained dileucine motifs in cytosolic tails of transmembrane proteins. Dileucine motif gains thus appear to be a recurrent cause of disease.

biochemistry↗