scholarly journals DeepG4: A deep learning approach to predict cell-type specific active G-quadruplex regions

2021 ◽  
Vol 17 (8) ◽  
pp. e1009308
Author(s):  
Vincent Rocher ◽  
Matthieu Genais ◽  
Elissar Nassereddine ◽  
Raphael Mourad
Keyword(s):  
Deep Learning ◽  
Double Helix ◽  
Complex Molecule ◽  
Cell Types ◽  
Sequence Motif ◽  
Cell Type ◽  
G Quadruplex ◽  
Cell Type Specific

DNA is a complex molecule carrying the instructions an organism needs to develop, live and reproduce. In 1953, Watson and Crick discovered that DNA is composed of two chains forming a double-helix. Later on, other structures of DNA were discovered and shown to play important roles in the cell, in particular G-quadruplex (G4). Following genome sequencing, several bioinformatic algorithms were developed to map G4s in vitro based on a canonical sequence motif, G-richness and G-skewness or alternatively sequence features including k-mers, and more recently machine/deep learning. Recently, new sequencing techniques were developed to map G4s in vitro (G4-seq) and G4s in vivo (G4 ChIP-seq) at few hundred base resolution. Here, we propose a novel convolutional neural network (DeepG4) to map cell-type specific active G4 regions (e.g. regions within which G4s form both in vitro and in vivo). DeepG4 is very accurate to predict active G4 regions in different cell types. Moreover, DeepG4 identifies key DNA motifs that are predictive of G4 region activity. We found that such motifs do not follow a very flexible sequence pattern as current algorithms seek for. Instead, active G4 regions are determined by numerous specific motifs. Moreover, among those motifs, we identified known transcription factors (TFs) which could play important roles in G4 activity by contributing either directly to G4 structures themselves or indirectly by participating in G4 formation in the vicinity. In addition, we used DeepG4 to predict active G4 regions in a large number of tissues and cancers, thereby providing a comprehensive resource for researchers. Availability: https://github.com/morphos30/DeepG4.

scholarly journals DeepG4 : A deep learning approach to predict active G-quadruplexes from DNA

2020 ◽  
Author(s):  
Vincent Rocher ◽  
Matthieu Genais ◽  
Elissar Nassereddine ◽  
Raphael Mourad
Keyword(s):  
Deep Learning ◽  
Double Helix ◽  
Complex Molecule ◽  
Genome Project ◽  
Sequence Motif ◽  
Cell Type ◽  
G Quadruplex ◽  
The Impact

AbstractDNA is a complex molecule carrying the instructions an organism needs to develop, live and reproduce. In 1953, Watson and Crick discovered that DNA is composed of two chains forming a double-helix. Later on, other structures of DNA were discovered and shown to play important roles in the cell, in particular G-quadruplex (G4). Following genome sequencing, several bioinformatic algorithms were developed to map G4s in vitro based on a canonical sequence motif, G-richness and G-skewness or alternatively sequence features including k-mers, and more recently machine/deep learning. Here, we propose a novel convolutional neural network (DeepG4) to map active G4s (forming both in vitro and in vivo). DeepG4 is very accurate to predict active G4s, while most state-of-the-art algorithms fail. Moreover, DeepG4 identifies key DNA motifs that are predictive of G4 activity. We found that active G4 motifs do not follow a very flexible sequence pattern as current algorithms seek for. Instead, active G4s are determined by numerous specific motifs. Moreover, among those motifs, we identified known transcription factors (TFs) which could play important roles in G4 activity by contributing either directly to G4 structures themselves or indirectly by participating in G4 formation in the vicinity. Moreover, we showed that specific TFs might explain G4 activity depending on cell type. Lastly, variant analysis suggests that SNPs altering predicted G4 activity could affect transcription and chromatin, e.g. gene expression, H3K4me3 mark and DNA methylation. Thus, DeepG4 paves the way for future studies assessing the impact of known disease-associated variants on DNA secondary structure by providing a mechanistic interpretation of SNP impact on transcription and chromatin.Availability: https://github.com/morphos30/DeepG4.Author summaryDNA is a molecule carrying genetic information and found in all living cells. In 1953, Watson and Crick found that DNA has a double helix structure. However, other DNA structures were later identified, and most notably, G-quadruplex (G4). In 2000, the Human Genome Project revealed the widespread presence of G4s in the genome using algorithms. To date, all G4 mapping algorithms were developed to map G4s on naked DNA, without knowing if they could be formed in the cell. Here, we designed a novel artificial intelligence algorithm that could map G4s active in the cell from the DNA sequence. We showed its better accuracy compared to existing algorithms. Moreover, we identified key transcriptional factor motifs that could explain G4 activity depending on cell type. Lastly, we demonstrated the existence of mutations that could alter G4 activity and therefore impact molecular processes, such as transcription, in the cell. Such results could provide a novel mechanistic interpretation of known disease-associated mutations.

scholarly journals Revisiting the role of IRF3 in inflammation and immunity by conditional and specifically targeted gene ablation in mice

2018 ◽  
Vol 115 (20) ◽  
pp. 5253-5258 ◽  
Author(s):  
Hideyuki Yanai ◽  
Shiho Chiba ◽  
Sho Hangai ◽  
Kohei Kometani ◽  
Asuka Inoue ◽  
...  
Keyword(s):  
Immune Cell ◽  
Cell Types ◽  
Type I ◽  
Type I Ifn ◽  
Cell Type ◽  
Gene Ablation ◽  
Cell Type Specific

IFN regulatory factor 3 (IRF3) is a transcription regulator of cellular responses in many cell types that is known to be essential for innate immunity. To confirm IRF3’s broad role in immunity and to more fully discern its role in various cellular subsets, we engineered Irf3-floxed mice to allow for the cell type-specific ablation of Irf3. Analysis of these mice confirmed the general requirement of IRF3 for the evocation of type I IFN responses in vitro and in vivo. Furthermore, immune cell ontogeny and frequencies of immune cell types were unaffected when Irf3 was selectively inactivated in either T cells or B cells in the mice. Interestingly, in a model of lipopolysaccharide-induced septic shock, selective Irf3 deficiency in myeloid cells led to reduced levels of type I IFN in the sera and increased survival of these mice, indicating the myeloid-specific, pathogenic role of the Toll-like receptor 4–IRF3 type I IFN axis in this model of sepsis. Thus, Irf3-floxed mice can serve as useful tool for further exploring the cell type-specific functions of this transcription factor.

scholarly journals Adult Human Multipotent Neural Cells Could Be Distinguished from Other Cell Types by Proangiogenic Paracrine Effects via MCP-1 and GRO

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Sung Soo Kim ◽  
Hee-Jang Pyeon ◽  
Yoon Kyung Bae ◽  
Hyun Nam ◽  
Chung Kwon Kim ◽  
...  
Keyword(s):  
Stem Cells ◽  
Cell Types ◽  
Neural Cells ◽  
Cell Type ◽  
Paracrine Factors ◽  
Adult Human ◽  
Cell Type Specific ◽  
Functional Features

Adult human multipotent neural cells (ahMNCs) are unique cells derived from adult human temporal lobes. They show multipotent differentiation potentials into neurons and astrocytes. In addition, they possess proangiogenic capacities. The objective of this study was to characterize ahMNCs in terms of expression of cell type-specific markers, in vitro differentiation potentials, and paracrine factors compared with several other cell types including fetal neural stem cells (fNSCs) to provide detailed molecular and functional features of ahMNCs. Interestingly, the expression of cell type-specific markers of ahMNCs could not be differentiated from those of pericytes, mesenchymal stem cells (MSCs), or fNSCs. In contrast, differentiation potentials of ahMNCs and fNSCs into neural cells were higher than those of other cell types. Compared with MSCs, ahMNCs showed lower differentiation capacities into osteogenic and adipogenic cells. Moreover, ahMNCs uniquely expressed higher levels of MCP-1 and GRO family paracrine factors than fNSCs and MSCs. These high levels of MCP-1 and GRO family mediated in vivo proangiogenic effects of ahMNCs. These results indicate that ahMNCs have their own distinct characteristics that could distinguish ahMNCs from other cell types. Characteristics of ahMNCs could be utilized further in the preclinical and clinical development of ahMNCs for regenerative medicine. They could also be used as experimental references for other cell types including fNSCs.

scholarly journals Cell type-dependent effects of ellagic acid on cellular metabolism

2018 ◽  
Author(s):  
Alexandra L. Boehning ◽  
Safia A. Essien ◽  
Erica L. Underwood ◽  
Pramod K. Dash ◽  
Darren Boehning
Keyword(s):  
Mitochondrial Function ◽  
Ellagic Acid ◽  
Cell Types ◽  
Cellular Respiration ◽  
Cell Type ◽  
Specific Effects ◽  
Cell Type Specific ◽  
High Doses

AbstractEllagic acid is a botanical polyphenol which has been shown to have numerous effects on cellular function. Ellagic acid can induce apoptosis and inhibit the proliferation of various cancer cell types in vitro and in vivo. As such, ellagic acid has attracted significant interest as a potential chemotherapeutic compound. One mechanism by which ellagic acid has been proposed to affect cellular physiology is by regulating metabolic pathways. Here we show the dose-dependent effects of ellagic acid on cellular energy production and downstream induction of the apoptotic program in HEK293, HeLa, MCF7, and HepG2 cells. At physiologically relevant doses, eallgic acid has pleiotropic and cell-type specific effects on mitochondrial function. At high doses ellagic acid can also influence glycolytic pathways and induce cell death. Our results demonstrate that ellagic acid can influence mitochondrial function at therapeutically relevant concentrations. The observed effects of ellagic acid on cellular respiration are complex and cell type-specific, which may limit the chemotherapeutic utility of this compound.

scholarly journals Cell-type specific analysis of physiological action of estrogen in mouse oviducts

2020 ◽  
Author(s):  
Emily A. McGlade ◽  
Gerardo G. Herrera ◽  
Kalli K. Stephens ◽  
Sierra L. W. Olsen ◽  
Sarayut Winuthayanon ◽  
...  
Keyword(s):  
Hormone Replacement ◽  
Cell Types ◽  
Normal Embryo ◽  
Developmental Defect ◽  
Cell Type ◽  
Specific Analysis ◽  
Cell Type Specific ◽  
17Β Estradiol ◽  
Oviductal Cell

AbstractOne of the endogenous estrogens, 17β-estradiol (E2) is a female steroid hormone secreted from the ovary. It is well established that E2 causes biochemical and histological changes in the uterus. The oviduct response to E2 is virtually unknown in an in vivo environment. In this study, we assessed the effect of E2 on each oviductal cell type, using an ovariectomized-hormone-replacement mouse model, single cell RNA-sequencing (scRNA-seq), in situ hybridization, and cell-type-specific deletion in mice. We found that each cell type in the oviduct responded to E2 distinctively, especially ciliated and secretory epithelial cells. The treatment of exogenous E2 did not drastically alter the transcriptomic profile from that of endogenous E2 produced during estrus. Moreover, we have identified and validated genes of interest in our datasets that may be used as cell- and region-specific markers in the oviduct. Insulin-like growth factor 1 (Igf1) was characterized as an E2-target gene in the mouse oviduct and was also expressed in human Fallopian tubes. Deletion of Igf1 in progesterone receptor (Pgr)-expressing cells resulted in female subfertility, partially due to an embryo developmental defect and embryo retention within the oviduct. In summary, we have shown that oviductal cell types are differentially regulated by E2 and support gene expression changes that are required for normal embryo development and transport in mouse models.

scholarly journals Btbd11 is an inhibitory interneuron specific synaptic scaffolding protein that supports excitatory synapse structure and function

2021 ◽  
Author(s):  
Alexei M. Bygrave ◽  
Ayesha Sengupta ◽  
Ella P. Jackert ◽  
Mehroz Ahmed ◽  
Beloved Adenuga ◽  
...  
Keyword(s):  
Inhibitory Interneuron ◽  
Nmda Receptor Antagonist ◽  
Specific Protein ◽  
Network Activity ◽  
Scaffolding Protein ◽  
Inhibitory Interneurons ◽  
Cell Type ◽  
Cell Type Specific

Synapses in the brain exhibit cell–type–specific differences in basal synaptic transmission and plasticity. Here, we evaluated cell–type–specific differences in the composition of glutamatergic synapses, identifying Btbd11, as an inhibitory interneuron–specific synapse–enriched protein. Btbd11 is highly conserved across species and binds to core postsynaptic proteins including Psd–95. Intriguingly, we show that Btbd11 can undergo liquid–liquid phase separation when expressed with Psd–95, supporting the idea that the glutamatergic post synaptic density in synapses in inhibitory and excitatory neurons exist in a phase separated state. Knockout of Btbd11 from inhibitory interneurons decreased glutamatergic signaling onto parvalbumin–positive interneurons. Further, both in vitro and in vivo, we find that Btbd11 knockout disrupts network activity. At the behavioral level, Btbd11 knockout from interneurons sensitizes mice to pharmacologically induced hyperactivity following NMDA receptor antagonist challenge. Our findings identify a cell–type–specific protein that supports glutamatergic synapse function in inhibitory interneurons–with implication for circuit function and animal behavior.

scholarly journals Profound functional and molecular diversity of mitochondria revealed by cell type-specific profiling in vivo

2018 ◽  
Author(s):  
Caroline Fecher ◽  
Laura Trovò ◽  
Stephan A. Müller ◽  
Nicolas Snaidero ◽  
Jennifer Wettmarshausen ◽  
...  
Keyword(s):  
Molecular Diversity ◽  
Molecular Level ◽  
Cell Types ◽  
Long Chain Fatty Acids ◽  
Cell Type ◽  
Mitochondrial Proteome ◽  
Novel Approach ◽  
Cell Type Specific ◽  
And Function

AbstractMitochondria vary in morphology and function in different tissues, however little is known about their molecular diversity among cell types. To investigate mitochondrial diversity in vivo, we developed an efficient protocol to isolate cell type-specific mitochondria based on a new MitoTag mouse. We profiled the mitochondrial proteome of three major neural cell types in cerebellum and identified a substantial number of differential mitochondrial markers for these cell types in mice and humans. Based on predictions from these proteomes, we demonstrate that astrocytic mitochondria metabolize long-chain fatty acids more efficiently than neurons. Moreover, we identified Rmdn3 as a major determinant of ER-mitochondria proximity in Purkinje cells. Our novel approach enables exploring mitochondrial diversity on the functional and molecular level in many in vivo contexts.

scholarly journals SeqEnhDL: sequence-based classification of cell type-specific enhancers using deep learning models

2020 ◽  
Author(s):  
Yupeng Wang ◽  
Rosario B. Jaime-Lara ◽  
Abhrarup Roy ◽  
Ying Sun ◽  
Xinyue Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Dna Sequences ◽  
Cell Types ◽  
Learning Models ◽  
Cell Type ◽  
Coding Sequences ◽  
Sequence Features ◽  
Cell Type Specific ◽  
Different Cell Types

AbstractWe propose SeqEnhDL, a deep learning framework for classifying cell type-specific enhancers based on sequence features. DNA sequences of “strong enhancer” chromatin states in nine cell types from the ENCODE project were retrieved to build and test enhancer classifiers. For any DNA sequence, sequential k-mer (k=5, 7, 9 and 11) fold changes relative to randomly selected non-coding sequences were used as features for deep learning models. Three deep learning models were implemented, including multi-layer perceptron (MLP), Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). All models in SeqEnhDL outperform state-of-the-art enhancer classifiers including gkm-SVM and DanQ, with regard to distinguishing cell type-specific enhancers from randomly selected non-coding sequences. Moreover, SeqEnhDL is able to directly discriminate enhancers from different cell types, which has not been achieved by other enhancer classifiers. Our analysis suggests that both enhancers and their tissue-specificity can be accurately identified according to their sequence features. SeqEnhDL is publicly available at https://github.com/wyp1125/SeqEnhDL.

Chagas Disease Chemotherapy: What Do We Know So Far?

2021 ◽  
Vol 27 ◽  
Author(s):  
Aline Araujo Zuma ◽  
Wanderley de Souza
Keyword(s):  
Host Cell ◽  
Chagas Disease ◽  
Chronic Phase ◽  
Cell Types ◽  
Neglected Tropical Disease ◽  
Cell Type ◽  
Cure Rate ◽  
New Compounds

: Chagas disease is a Neglected Tropical Disease (NTD), and although endemic in Latin America, affects around 6-7 million people infected worldwide. The treatment of Chagas disease is based on benznidazole and nifurtimox, which are the only available drugs. However, they are not effective during the chronic phase and cause several side effects. Furthermore, BZ promotes cure in 80% of the patients in the acute phase, but the cure rate drops to 20% in adults in the chronic phase of the disease. In this review, we present several studies published in the last six years, which describes the antiparasitic potential of distinct drugs, from the synthesis of new compounds aiming to target the parasite, as well as the repositioning and the combination of drugs. We highlight several compounds for having shown results that are equivalent or superior to BZ, which means that they should be further studied, either in vitro or in vivo. Furthermore, we stand out the differences in the effects of BZ on the same strain of T. cruzi, which might be related to methodological differences such as parasite and cell ratios, host cell type and the time of adding the drug. In addition, we discuss the wide variety of strains and also the cell types used as a host cell, which makes it difficult to compare the trypanocidal effect of the compounds.

scholarly journals Oligodendrocytes and Microglia Are Selectively Vulnerable to Combined Hypoxia and Hypoglycemia Injury in Vitro

1998 ◽  
Vol 18 (5) ◽  
pp. 521-530 ◽  
Author(s):  
Susan A. Lyons ◽  
Helmut Kettenmann
Keyword(s):  
Lactate Dehydrogenase ◽  
Glial Cell ◽  
Glial Cells ◽  
Cell Types ◽  
Neonatal Rat ◽  
Radical Scavenger ◽  
Cell Type ◽  
Hypoxic Conditions ◽  
Cell Type Specific

The major classes of glial cells, namely astrocytes, oligodendrocytes, and microglial cells were compared in parallel for their susceptibility to damage after combined hypoxia and hypoglycemia or hypoxia alone. The three glial cell types were isolated from neonatal rat brains, separated, and incubated in N2/CO2-gassed buffer-containing glucose or glucose substitutes, 2-deoxyglucose or mannitol (both nonmetabolizable sugars). The damage to the cells after 6 hours' exposure was determined at 0, 1, 3, 7 days based on release of lactate dehydrogenase and counting of ethidium bromide–stained dead cells, double-stained with cell-type specific markers. When 2-deoxyglucose replaced glucose during 6 hours of hypoxia, both oligodendrocytes and microglia rarely survived (18% and 12%, respectively). Astroglia initially increased the release of lactate dehydrogenase but maintained 98% to 99% viability. When mannitol, a radical scavenger and osmolarity stabilizer, replaced glucose during 6 hours of hypoxia, oligodendrocytes rarely survived (10%), astroglia survival remained at 99%, but microglia survival increased to 50%. After exposure to 6 and 42 hours, respectively, of hypoxic conditions alone, oligodendrocytes exhibited 10% survival whereas microglia and astroglia were only temporarily stressed and subsequently survived. In conclusion, oligodendrocytes, then microglia, are the most vulnerable glial cell types in response to hypoxia or hypoglycemia conditions, whereas astrocytes from the same preparations recover.

Sign in / Sign up

Export Citation Format

Share Document