scholarly journals Accurate prediction of cis-regulatory modules reveals a prevalent regulatory genome of humans

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Pengyu Ni ◽  
Zhengchang Su
Keyword(s):  
Binding Sites ◽  
State Of The Art ◽  
Cell Types ◽  
Evolutionary Constraints ◽  
Specific Cell ◽  
Cell Type ◽  
Phase 3 ◽  
Regulatory Modules ◽  
Regulatory Genome ◽  
Encode Phase

Abstract cis-regulatory modules(CRMs) formed by clusters of transcription factor (TF) binding sites (TFBSs) are as important as coding sequences in specifying phenotypes of humans. It is essential to categorize all CRMs and constituent TFBSs in the genome. In contrast to most existing methods that predict CRMs in specific cell types using epigenetic marks, we predict a largely cell type agonistic but more comprehensive map of CRMs and constituent TFBSs in the gnome by integrating all available TF ChIP-seq datasets. Our method is able to partition 77.47% of genome regions covered by available 6092 datasets into a CRM candidate (CRMC) set (56.84%) and a non-CRMC set (43.16%). Intriguingly, the predicted CRMCs are under strong evolutionary constraints, while the non-CRMCs are largely selectively neutral, strongly suggesting that the CRMCs are likely cis-regulatory, while the non-CRMCs are not. Our predicted CRMs are under stronger evolutionary constraints than three state-of-the-art predictions (GeneHancer, EnhancerAtlas and ENCODE phase 3) and substantially outperform them for recalling VISTA enhancers and non-coding ClinVar variants. We estimated that the human genome might encode about 1.47M CRMs and 68M TFBSs, comprising about 55% and 22% of the genome, respectively; for both of which, we predicted 80%. Therefore, the cis-regulatory genome appears to be more prevalent than originally thought.

scholarly journals Accurate prediction of cis-regulatory modules reveals a prevalent regulatory genome of humans

2020 ◽  
Author(s):  
Pengyu Ni ◽  
Zhengchang Su
Keyword(s):  
Transcription Factor ◽  
Human Genome ◽  
Binding Sites ◽  
State Of The Art ◽  
Accurate Prediction ◽  
Evolutionary Constraints ◽  
Phase 3 ◽  
Regulatory Modules ◽  
Regulatory Genome ◽  
Encode Phase

AbstractAnnotating all cis-regulatory modules (CRMs) and transcription factor (TF) binding sites(TFBSs) in genomes remains challenging. We tackled the task by integrating putative TFBSs motifs found in available 6,092 datasets covering 77.47% of the human genome. This approach enabled us to partition the covered genome regions into a CRM candidate (CRMC) set (56.84%) and a non-CRMC set (43.16%). Intriguingly, like known enhancers, the predicted 1,404,973 CRMCs are under strong evolutionary constraints, suggesting that they might be cis-regulator. In contrast, the non-CRMCs are largely selectively neutral, suggesting that they might not be cis-regulatory. Our method substantially outperforms three state-of-the-art methods (GeneHancers, EnhancerAtlas and ENCODE phase 3) for recalling VISTA enhancers and ClinVar variants, as well as by measurements of evolutionary constraints. We estimated that the human genome might encode about 1.46 million CRMs and 67 million TFBSs, comprising about 55% and 22% of the genome, respectively; for both of which, we predicted 80%. Therefore, the cis-regulatory genome appears to be more prevalent than originally thought.

scholarly journals Epigenetic reprogramming of cell identity: lessons from development for regenerative medicine

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Amitava Basu ◽  
Vijay K. Tiwari
Keyword(s):  
Regenerative Medicine ◽  
Cell Types ◽  
Direct Conversion ◽  
Cellular Reprogramming ◽  
Specific Cell ◽  
Cell Type ◽  
Pathological Conditions ◽  
Gene Regulatory ◽  
Induced Pluripotent ◽  
And Function

AbstractEpigenetic mechanisms are known to define cell-type identity and function. Hence, reprogramming of one cell type into another essentially requires a rewiring of the underlying epigenome. Cellular reprogramming can convert somatic cells to induced pluripotent stem cells (iPSCs) that can be directed to differentiate to specific cell types. Trans-differentiation or direct reprogramming, on the other hand, involves the direct conversion of one cell type into another. In this review, we highlight how gene regulatory mechanisms identified to be critical for developmental processes were successfully used for cellular reprogramming of various cell types. We also discuss how the therapeutic use of the reprogrammed cells is beginning to revolutionize the field of regenerative medicine particularly in the repair and regeneration of damaged tissue and organs arising from pathological conditions or accidents. Lastly, we highlight some key challenges hindering the application of cellular reprogramming for therapeutic purposes.

scholarly journals Shisa6 mediates cell-type specific regulation of depression in the nucleus accumbens

2021 ◽  
Author(s):  
Hee-Dae Kim ◽  
Jing Wei ◽  
Tanessa Call ◽  
Nicole Teru Quintus ◽  
Alexander J. Summers ◽  
...  
Keyword(s):  
Nucleus Accumbens ◽  
Molecular Mechanisms ◽  
Cell Types ◽  
Current Treatment ◽  
Specific Cell ◽  
Excitatory Synapses ◽  
Cell Type ◽  
Cell Type Specific ◽  
Specific Regulation ◽  
Circuit Function

AbstractDepression is the leading cause of disability and produces enormous health and economic burdens. Current treatment approaches for depression are largely ineffective and leave more than 50% of patients symptomatic, mainly because of non-selective and broad action of antidepressants. Thus, there is an urgent need to design and develop novel therapeutics to treat depression. Given the heterogeneity and complexity of the brain, identification of molecular mechanisms within specific cell-types responsible for producing depression-like behaviors will advance development of therapies. In the reward circuitry, the nucleus accumbens (NAc) is a key brain region of depression pathophysiology, possibly based on differential activity of D1- or D2- medium spiny neurons (MSNs). Here we report a circuit- and cell-type specific molecular target for depression, Shisa6, recently defined as an AMPAR component, which is increased only in D1-MSNs in the NAc of susceptible mice. Using the Ribotag approach, we dissected the transcriptional profile of D1- and D2-MSNs by RNA sequencing following a mouse model of depression, chronic social defeat stress (CSDS). Bioinformatic analyses identified cell-type specific genes that may contribute to the pathogenesis of depression, including Shisa6. We found selective optogenetic activation of the ventral tegmental area (VTA) to NAc circuit increases Shisa6 expression in D1-MSNs. Shisa6 is specifically located in excitatory synapses of D1-MSNs and increases excitability of neurons, which promotes anxiety- and depression-like behaviors in mice. Cell-type and circuit-specific action of Shisa6, which directly modulates excitatory synapses that convey aversive information, identifies the protein as a potential rapid-antidepressant target for aberrant circuit function in depression.

scholarly journals Cell type-specific biogenesis of novel vesicles containing viral products in human cytomegalovirus infection

2020 ◽  
Author(s):  
Samina Momtaz ◽  
Belen Molina ◽  
Luwanika Mlera ◽  
Felicia Goodrum ◽  
Jean M. Wilson
Keyword(s):  
Endothelial Cells ◽  
Human Cytomegalovirus ◽  
Biosynthetic Pathway ◽  
Cell Types ◽  
Endocytic Pathway ◽  
Specific Cell ◽  
Cell Type ◽  
Subcellular Compartment ◽  
Increased Risk ◽  
Cell Type Specific

AbstractHuman cytomegalovirus (HCMV), while highly restricted for the human species, infects an unlimited array of cell types in the host. Patterns of infection are dictated by the cell type infected, but cell type-specific factors and how they impact tropism for specific cell types is poorly understood. Previous studies in primary endothelial cells showed that HCMV infection induces large multivesicular-like bodies that incorporate viral products including dense bodies and virions. Here we define the nature of these large vesicles using a recombinant virus where UL32, encoding the pp150 tegument protein, is fused in frame with green fluorescent protein (GFP, TB40/E-UL32-GFP). Cells were fixed and labeled with antibodies against subcellular compartment markers and imaged using confocal and super-resolution microscopy. In fibroblasts, UL32-GFP-positive vesicles were marked with classical markers of MVBs, including CD63 and lysobisphosphatidic acid (LBPA), both classical MVB markers, as well as the clathrin and LAMP1. Unexpectedly, UL32-GFP-positive vesicles in endothelial cells were not labeled by CD63, and LBPA was completely lost from infected cells. We defined these UL32-positive vesicles in endothelial cells using markers for the cis-Golgi (GM130), lysosome (LAMP1), and autophagy (LC3B). These findings suggest that virus-containing MVBs in fibroblasts are derived from the canonical endocytic pathway and takeover classical exosomal release pathway. Virus containing MVBs in HMVECs are derived from the early biosynthetic pathway and exploit a less characterized early Golgi-LAMP1-associated non-canonical secretory autophagy pathway. These results reveal striking cell-type specific membrane trafficking differences in host pathways that are exploited by HCMV.ImportanceHuman cytomegalovirus (HCMV) is a herpesvirus that, like all herpesvirus, that establishes a life long infection. HCMV remains a significant cause of morbidity and mortality in the immunocompromised and HCMV seropositivity is associated with increased risk vascular disease. HCMV infects many cells in the human and the biology underlying the different patterns of infection in different cell types is poorly understood. Endothelial cells are important target of infection that contribute to hematogenous spread of the virus to tissues. Here we define striking differences in the biogenesis of large vesicles that incorporate virions in fibroblasts and endothelial cells. In fibroblasts, HCMV is incorporated into canonical MVBs derived from an endocytic pathway, whereas HCMV matures through vesicles derived from the biosynthetic pathway in endothelial cells. This work defines basic biological differences between these cell types that may impact the outcome of infection.

scholarly journals Detection of Cell Types Contributing to Cancer From Circulating, Cell-Free Methylated DNA

2021 ◽  
Vol 12 ◽  
Author(s):  
Megan E. Barefoot ◽  
Netanel Loyfer ◽  
Amber J. Kiliti ◽  
A. Patrick McDeed ◽  
Tommy Kaplan ◽  
...  
Keyword(s):  
Cancer Cells ◽  
Cell Types ◽  
Circulating Tumor Dna ◽  
Cancer Diagnostics ◽  
Tissue Homeostasis ◽  
Specific Cell ◽  
Cell Type ◽  
Liquid Biopsies ◽  
Methylated Dna ◽  
Cell Type Specific

Detection of cellular changes in tissue biopsies has been the basis for cancer diagnostics. However, tissue biopsies are invasive and limited by inaccuracies due to sampling locations, restricted sampling frequency, and poor representation of tissue heterogeneity. Liquid biopsies are emerging as a complementary approach to traditional tissue biopsies to detect dynamic changes in specific cell populations. Cell-free DNA (cfDNA) fragments released into the circulation from dying cells can be traced back to the tissues and cell types they originated from using DNA methylation, an epigenetic regulatory mechanism that is highly cell-type specific. Decoding changes in the cellular origins of cfDNA over time can reveal altered host tissue homeostasis due to local cancer invasion and metastatic spread to distant organs as well as treatment responses. In addition to host-derived cfDNA, changes in cancer cells can be detected from cell-free, circulating tumor DNA (ctDNA) by monitoring DNA mutations carried by cancer cells. Here, we will discuss computational approaches to identify and validate robust biomarkers of changed tissue homeostasis using cell-free, methylated DNA in the circulation. We highlight studies performing genome-wide profiling of cfDNA methylation and those that combine genetic and epigenetic markers to further identify cell-type specific signatures. Finally, we discuss opportunities and current limitations of these approaches for implementation in clinical oncology.

Development of a multiomics and functional drug sensitivity platform to investigate cell type specific drug effects in AML.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e19013-e19013
Author(s):  
Marianne T. Santaguida ◽  
Ryosuke Kita ◽  
Steven A. Schaffert ◽  
Erica K. Anderson ◽  
Kamran A Ali ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Drug Response ◽  
Drug Sensitivity ◽  
Ex Vivo ◽  
Cell Types ◽  
Specific Cell ◽  
Rna Seq ◽  
Cell Type ◽  
Drug Sensitivity Assay ◽  
Cell Type Specific

e19013 Background: Understanding the heterogeneity of AML is necessary for developing targeted drugs and diagnostics. A key measure of heterogeneity is the variance in response to treatments. Previously, we developed an ex vivo flow cytometry drug sensitivity assay (DSA) that predicted response to treatments in myelodysplastic syndrome. Unlike bulk cell viability measures of other drug sensitivity assays, our flow cytometry assay provides single cell resolution. The assay measures a drug’s effect on the viability or functional state of specific cell types. Here we present the development of this technology for AML, with additional measurements of DNA-Seq and RNA-Seq. Using the data from this assay, we aim to characterize the heterogeneity in AML drug sensitivity and the molecular mechanisms that drive it. Methods: As an initial feasibility analysis, we assayed 1 bone marrow and 3 peripheral blood AML patient samples. For the DSA, the samples were cultured with six AML standard of care (SOC) compounds across seven doses, in addition to two combinations. The cells were stained to detect multiple cell types including tumor blasts, and drug response was measured by flow cytometry. For the multi-omics, the cells were magnetically sorted to enrich for blasts and then assayed using a targeted 400 gene DNA-Seq panel and whole bulk transcriptome RNA-Seq. For comparison with BeatAML, Pearson correlations between gene expression and venetoclax sensitivity were investigated. Results: In our drug sensitivity assay, we measured dose response curves for the six SOC compounds, for each different cell type across each sample. The dose responses had cell type specific effects, including differences in drug response between CD11b+ blasts, CD11b- blasts, and other non-blast populations. Integrating with the DNA-Seq and RNA-Seq data, known associations between ex vivo drug response and gene expression were identified with additional cell type specificity. For example, BCL2A1 expression was negatively correlated with venetoclax sensitivity in CD11b- blasts but not in CD11b+ blasts. To further corroborate, among the top 1000 genes associated with venetoclax sensitivity in BeatAML, 93.7% had concordant directionality in effect. Conclusions: Here we describe the development of an integrated ex vivo drug sensitivity assay and multi-omics dataset. The data demonstrated that ex vivo responses to compounds differ between cell types, highlighting the importance of measuring drug response in specific cell types. In addition, we demonstrated that integrating these data will provide unique insights on molecular mechanisms that affect cell type specific drug response. As we continue to expand the number of patient samples evaluated with our multi-dimensional platform, this dataset will provide insights for novel drug target discovery, biomarker development, and, in the future, informing treatment decisions.

scholarly journals Whole genome DNA sequencing provides an atlas of somatic mutagenesis in healthy human cells and identifies a tumor-prone cell type

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Irene Franco ◽  
Hafdis T. Helgadottir ◽  
Aldo Moggio ◽  
Malin Larsson ◽  
Peter Vrtačnik ◽  
...  
Keyword(s):  
Dna Sequencing ◽  
Somatic Mutation ◽  
Somatic Mutations ◽  
Adult Life ◽  
Cell Types ◽  
Human Cells ◽  
Specific Cell ◽  
Whole Genome ◽  
Cell Type ◽  
Healthy Human

Abstract Background The lifelong accumulation of somatic mutations underlies age-related phenotypes and cancer. Mutagenic forces are thought to shape the genome of aging cells in a tissue-specific way. Whole genome analyses of somatic mutation patterns, based on both types and genomic distribution of variants, can shed light on specific processes active in different human tissues and their effect on the transition to cancer. Results To analyze somatic mutation patterns, we compile a comprehensive genetic atlas of somatic mutations in healthy human cells. High-confidence variants are obtained from newly generated and publicly available whole genome DNA sequencing data from single non-cancer cells, clonally expanded in vitro. To enable a well-controlled comparison of different cell types, we obtain single genome data (92% mean coverage) from multi-organ biopsies from the same donors. These data show multiple cell types that are protected from mutagens and display a stereotyped mutation profile, despite their origin from different tissues. Conversely, the same tissue harbors cells with distinct mutation profiles associated to different differentiation states. Analyses of mutation rate in the coding and non-coding portions of the genome identify a cell type bearing a unique mutation pattern characterized by mutation enrichment in active chromatin, regulatory, and transcribed regions. Conclusions Our analysis of normal cells from healthy donors identifies a somatic mutation landscape that enhances the risk of tumor transformation in a specific cell population from the kidney proximal tubule. This unique pattern is characterized by high rate of mutation accumulation during adult life and specific targeting of expressed genes and regulatory regions.

scholarly journals SSMD: a semi-supervised approach for a robust cell type identification and deconvolution of mouse transcriptomics data

2020 ◽  
Author(s):  
Xiaoyu Lu ◽  
Szu-Wei Tu ◽  
Wennan Chang ◽  
Changlin Wan ◽  
Jiashi Wang ◽  
...  
Keyword(s):  
Small Sample Size ◽  
Cell Types ◽  
Small Sample ◽  
Training Data ◽  
Mouse Tissue ◽  
Marker Genes ◽  
Specific Cell ◽  
Cell Type ◽  
Data Set ◽  
Transcriptomics Data

Abstract Deconvolution of mouse transcriptomic data is challenged by the fact that mouse models carry various genetic and physiological perturbations, making it questionable to assume fixed cell types and cell type marker genes for different data set scenarios. We developed a Semi-Supervised Mouse data Deconvolution (SSMD) method to study the mouse tissue microenvironment. SSMD is featured by (i) a novel nonparametric method to discover data set-specific cell type signature genes; (ii) a community detection approach for fixing cell types and their marker genes; (iii) a constrained matrix decomposition method to solve cell type relative proportions that is robust to diverse experimental platforms. In summary, SSMD addressed several key challenges in the deconvolution of mouse tissue data, including: (i) varied cell types and marker genes caused by highly divergent genotypic and phenotypic conditions of mouse experiment; (ii) diverse experimental platforms of mouse transcriptomics data; (iii) small sample size and limited training data source and (iv) capable to estimate the proportion of 35 cell types in blood, inflammatory, central nervous or hematopoietic systems. In silico and experimental validation of SSMD demonstrated its high sensitivity and accuracy in identifying (sub) cell types and predicting cell proportions comparing with state-of-the-arts methods. A user-friendly R package and a web server of SSMD are released via https://github.com/xiaoyulu95/SSMD.

Prenet: Predictive network from ATAC-SEQ data

2020 ◽  
Vol 18 (01) ◽  
pp. 2040003 ◽  
Author(s):  
Nazmus Salehin ◽  
Patrick P. L. Tam ◽  
Pierre Osteil
Keyword(s):  
Regulatory Network ◽  
State Of The Art ◽  
Cell Types ◽  
Cell Type ◽  
Regulatory Pathways ◽  
Novel Approach ◽  
Multiple Cell ◽  
Bona Fide ◽  
Gene Regulatory ◽  
Accessible Chromatin

Assays for transposase-accessible chromatin sequencing (ATAC-seq) provides an innovative approach to study chromatin status in multiple cell types. Moreover, it is also possible to efficiently extract differentially accessible chromatin (DACs) regions by using state-of-the-art algorithms (e.g. DESeq2) to predict gene activity in specific samples. Furthermore, it has recently been shown that small dips in sequencing peaks can be attributed to the binding of transcription factors. These dips, also known as footprints, can be used to identify trans-regulating interactions leading to gene expression. Current protocols used to identify footprints (e.g. pyDNAse and HINT-ATAC) have shown limitations resulting in the discovery of many false positive footprints. We generated a novel approach to identify genuine footprints within any given ATAC-seq dataset. Herein, we developed a new pipeline embedding DACs together with bona fide footprints resulting in the generation of a Predictive gene regulatory Network (PreNet) simply from ATAC-seq data. We further demonstrated that PreNet can be used to unveil meaningful molecular regulatory pathways in a given cell type.

scholarly journals Complex Cell Type-Specific Roles of Autophagy in Liver Fibrosis and Cirrhosis

Pathogens ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 225
Author(s):  
Tzu-Min Hung ◽  
Chih-Chiang Hsiao ◽  
Chih-Wen Lin ◽  
Po-Huang Lee
Keyword(s):  
Liver Fibrosis ◽  
Cell Types ◽  
Degradation Pathway ◽  
Cellular Tissue ◽  
Future Research ◽  
Specific Cell ◽  
Cell Type ◽  
Stage Disease ◽  
Multiple Cell ◽  
End Stage

The lysosomal degradation pathway, or autophagy, plays a fundamental role in cellular, tissue, and organismal homeostasis. A correlation between dysregulated autophagy and liver fibrosis (including end-stage disease, cirrhosis) is well-established. However, both the up and downregulation of autophagy have been implicated in fibrogenesis. For example, the inhibition of autophagy in hepatocytes and macrophages can enhance liver fibrosis, whereas autophagic activity in hepatic stellate cells and reactive ductular cells is permissive towards fibrogenesis. In this review, the contributions of specific cell types to liver fibrosis as well as the mechanisms underlying the effects of autophagy are summarized. In view of the functional effects of multiple cell types on the complex process of hepatic fibrogenesis, integrated approaches that consider the role of autophagy in each liver cell type should be a focus of future research.

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