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Brooks MD, Szeto RC. Biological nitrogen fixation maintains carbon/nitrogen balance and photosynthesis at elevated CO 2. Plant Cell Environ 2024; 47:2178-2191. [PMID: 38481026 DOI: 10.1111/pce.14873] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/04/2023] [Revised: 01/17/2024] [Accepted: 02/22/2024] [Indexed: 04/30/2024]
Abstract
Understanding crop responses to elevated CO2 is necessary to meet increasing agricultural demands. Crops may not achieve maximum potential yields at high CO2 due to photosynthetic downregulation, often associated with nitrogen limitation. Legumes have been proposed to have an advantage at elevated CO2 due to their ability to exchange carbon for nitrogen. Here, the effects of biological nitrogen fixation (BNF) on the physiological and gene expression responses to elevated CO2 were examined at multiple nitrogen levels by comparing alfalfa mutants incapable of nitrogen fixation to wild-type. Elemental analysis revealed a role for BNF in maintaining shoot carbon/nitrogen (C/N) balance under all nitrogen treatments at elevated CO2, whereas the effect of BNF on biomass was only observed at elevated CO2 and the lowest nitrogen dose. Lower photosynthetic rates at were associated with the imbalance in shoot C/N. Genome-wide transcriptional responses were used to identify carbon and nitrogen metabolism genes underlying the traits. Transcription factors important to C/N signalling were identified from inferred regulatory networks. This work supports the hypothesis that maintenance of C/N homoeostasis at elevated CO2 can be achieved in plants capable of BNF and revealed important regulators in the underlying networks including an alfalfa (Golden2-like) GLK ortholog.
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Affiliation(s)
- Matthew D Brooks
- Global Change and Photosynthesis Research Unit, USDA ARS, Urbana, Illinois, USA
| | - Ronnia C Szeto
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
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2
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Broman MT, Nadadur RD, Perez-Cervantes C, Burnicka-Turek O, Lazarevic S, Gams A, Laforest B, Steimle JD, Iddir S, Wang Z, Smith L, Mazurek SR, Olivey HE, Zhou P, Gadek M, Shen KM, Khan Z, Theisen JWM, Yang XH, Ikegami K, Efimov IR, Pu WT, Weber CR, McNally EM, Svensson EC, Moskowitz IP. A Genomic Link From Heart Failure to Atrial Fibrillation Risk: FOG2 Modulates a TBX5/GATA4-Dependent Atrial Gene Regulatory Network. Circulation 2024; 149:1205-1230. [PMID: 38189150 DOI: 10.1161/circulationaha.123.066804] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 12/11/2023] [Indexed: 01/09/2024]
Abstract
BACKGROUND The relationship between heart failure (HF) and atrial fibrillation (AF) is clear, with up to half of patients with HF progressing to AF. The pathophysiological basis of AF in the context of HF is presumed to result from atrial remodeling. Upregulation of the transcription factor FOG2 (friend of GATA2; encoded by ZFPM2) is observed in human ventricles during HF and causes HF in mice. METHODS FOG2 expression was assessed in human atria. The effect of adult-specific FOG2 overexpression in the mouse heart was evaluated by whole animal electrophysiology, in vivo organ electrophysiology, cellular electrophysiology, calcium flux, mouse genetic interactions, gene expression, and genomic function, including a novel approach for defining functional transcription factor interactions based on overlapping effects on enhancer noncoding transcription. RESULTS FOG2 is significantly upregulated in the human atria during HF. Adult cardiomyocyte-specific FOG2 overexpression in mice caused primary spontaneous AF before the development of HF or atrial remodeling. FOG2 overexpression generated arrhythmia substrate and trigger in cardiomyocytes, including calcium cycling defects. We found that FOG2 repressed atrial gene expression promoted by TBX5. FOG2 bound a subset of GATA4 and TBX5 co-bound genomic locations, defining a shared atrial gene regulatory network. FOG2 repressed TBX5-dependent transcription from a subset of co-bound enhancers, including a conserved enhancer at the Atp2a2 locus. Atrial rhythm abnormalities in mice caused by Tbx5 haploinsufficiency were rescued by Zfpm2 haploinsufficiency. CONCLUSIONS Transcriptional changes in the atria observed in human HF directly antagonize the atrial rhythm gene regulatory network, providing a genomic link between HF and AF risk independent of atrial remodeling.
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Affiliation(s)
- Michael T Broman
- Department of Medicine, Section of Cardiology (M.T.B., B.L., S.R.M.), University of Chicago, IL
| | - Rangarajan D Nadadur
- Departments of Pediatrics (R.D.N., C.P.-C., O.B.-T., S.L., J.D.S., S.I., Z.W., L.S., M.G., K.M.S., Z.K., J.W.M.T., X.H.Y., I.P.M.), University of Chicago, IL
- Pathology (R.D.N., C.P.-C., O.B.-T., S.L., J.D.S., S.I., Z.W., L.S., M.G., K.M.S., Z.K., J.W.M.T., X.H.Y., C.R.W., I.P.M.), University of Chicago, IL
- Human Genetics (R.D.N., C.P.-C., O.B.-T., S.L., J.D.S., S.I., Z.W., L.S., M.G., K.M.S., Z.K., J.W.M.T., X.H.Y., I.P.M.), University of Chicago, IL
| | - Carlos Perez-Cervantes
- Departments of Pediatrics (R.D.N., C.P.-C., O.B.-T., S.L., J.D.S., S.I., Z.W., L.S., M.G., K.M.S., Z.K., J.W.M.T., X.H.Y., I.P.M.), University of Chicago, IL
- Pathology (R.D.N., C.P.-C., O.B.-T., S.L., J.D.S., S.I., Z.W., L.S., M.G., K.M.S., Z.K., J.W.M.T., X.H.Y., C.R.W., I.P.M.), University of Chicago, IL
- Human Genetics (R.D.N., C.P.-C., O.B.-T., S.L., J.D.S., S.I., Z.W., L.S., M.G., K.M.S., Z.K., J.W.M.T., X.H.Y., I.P.M.), University of Chicago, IL
| | - Ozanna Burnicka-Turek
- Departments of Pediatrics (R.D.N., C.P.-C., O.B.-T., S.L., J.D.S., S.I., Z.W., L.S., M.G., K.M.S., Z.K., J.W.M.T., X.H.Y., I.P.M.), University of Chicago, IL
- Pathology (R.D.N., C.P.-C., O.B.-T., S.L., J.D.S., S.I., Z.W., L.S., M.G., K.M.S., Z.K., J.W.M.T., X.H.Y., C.R.W., I.P.M.), University of Chicago, IL
- Human Genetics (R.D.N., C.P.-C., O.B.-T., S.L., J.D.S., S.I., Z.W., L.S., M.G., K.M.S., Z.K., J.W.M.T., X.H.Y., I.P.M.), University of Chicago, IL
| | - Sonja Lazarevic
- Departments of Pediatrics (R.D.N., C.P.-C., O.B.-T., S.L., J.D.S., S.I., Z.W., L.S., M.G., K.M.S., Z.K., J.W.M.T., X.H.Y., I.P.M.), University of Chicago, IL
- Pathology (R.D.N., C.P.-C., O.B.-T., S.L., J.D.S., S.I., Z.W., L.S., M.G., K.M.S., Z.K., J.W.M.T., X.H.Y., C.R.W., I.P.M.), University of Chicago, IL
- Human Genetics (R.D.N., C.P.-C., O.B.-T., S.L., J.D.S., S.I., Z.W., L.S., M.G., K.M.S., Z.K., J.W.M.T., X.H.Y., I.P.M.), University of Chicago, IL
| | - Anna Gams
- Department of Biomedical Engineering, George Washington University (A.G., I.R.E.), Washington, DC
| | - Brigitte Laforest
- Department of Medicine, Section of Cardiology (M.T.B., B.L., S.R.M.), University of Chicago, IL
| | - Jeffrey D Steimle
- Pathology (R.D.N., C.P.-C., O.B.-T., S.L., J.D.S., S.I., Z.W., L.S., M.G., K.M.S., Z.K., J.W.M.T., X.H.Y., C.R.W., I.P.M.), University of Chicago, IL
- Human Genetics (R.D.N., C.P.-C., O.B.-T., S.L., J.D.S., S.I., Z.W., L.S., M.G., K.M.S., Z.K., J.W.M.T., X.H.Y., I.P.M.), University of Chicago, IL
| | - Sabrina Iddir
- Pathology (R.D.N., C.P.-C., O.B.-T., S.L., J.D.S., S.I., Z.W., L.S., M.G., K.M.S., Z.K., J.W.M.T., X.H.Y., C.R.W., I.P.M.), University of Chicago, IL
- Human Genetics (R.D.N., C.P.-C., O.B.-T., S.L., J.D.S., S.I., Z.W., L.S., M.G., K.M.S., Z.K., J.W.M.T., X.H.Y., I.P.M.), University of Chicago, IL
| | - Zhezhen Wang
- Departments of Pediatrics (R.D.N., C.P.-C., O.B.-T., S.L., J.D.S., S.I., Z.W., L.S., M.G., K.M.S., Z.K., J.W.M.T., X.H.Y., I.P.M.), University of Chicago, IL
- Pathology (R.D.N., C.P.-C., O.B.-T., S.L., J.D.S., S.I., Z.W., L.S., M.G., K.M.S., Z.K., J.W.M.T., X.H.Y., C.R.W., I.P.M.), University of Chicago, IL
- Human Genetics (R.D.N., C.P.-C., O.B.-T., S.L., J.D.S., S.I., Z.W., L.S., M.G., K.M.S., Z.K., J.W.M.T., X.H.Y., I.P.M.), University of Chicago, IL
| | - Linsin Smith
- Departments of Pediatrics (R.D.N., C.P.-C., O.B.-T., S.L., J.D.S., S.I., Z.W., L.S., M.G., K.M.S., Z.K., J.W.M.T., X.H.Y., I.P.M.), University of Chicago, IL
- Pathology (R.D.N., C.P.-C., O.B.-T., S.L., J.D.S., S.I., Z.W., L.S., M.G., K.M.S., Z.K., J.W.M.T., X.H.Y., C.R.W., I.P.M.), University of Chicago, IL
- Human Genetics (R.D.N., C.P.-C., O.B.-T., S.L., J.D.S., S.I., Z.W., L.S., M.G., K.M.S., Z.K., J.W.M.T., X.H.Y., I.P.M.), University of Chicago, IL
| | - Stefan R Mazurek
- Department of Medicine, Section of Cardiology (M.T.B., B.L., S.R.M.), University of Chicago, IL
| | - Harold E Olivey
- Department of Biology, Indiana University Northwest, Gary (H.E.O.)
| | - Pingzhu Zhou
- School of Medicine, Shanghai University, China (P.Z.)
| | - Margaret Gadek
- Departments of Pediatrics (R.D.N., C.P.-C., O.B.-T., S.L., J.D.S., S.I., Z.W., L.S., M.G., K.M.S., Z.K., J.W.M.T., X.H.Y., I.P.M.), University of Chicago, IL
- Pathology (R.D.N., C.P.-C., O.B.-T., S.L., J.D.S., S.I., Z.W., L.S., M.G., K.M.S., Z.K., J.W.M.T., X.H.Y., C.R.W., I.P.M.), University of Chicago, IL
- Human Genetics (R.D.N., C.P.-C., O.B.-T., S.L., J.D.S., S.I., Z.W., L.S., M.G., K.M.S., Z.K., J.W.M.T., X.H.Y., I.P.M.), University of Chicago, IL
| | - Kaitlyn M Shen
- Departments of Pediatrics (R.D.N., C.P.-C., O.B.-T., S.L., J.D.S., S.I., Z.W., L.S., M.G., K.M.S., Z.K., J.W.M.T., X.H.Y., I.P.M.), University of Chicago, IL
- Pathology (R.D.N., C.P.-C., O.B.-T., S.L., J.D.S., S.I., Z.W., L.S., M.G., K.M.S., Z.K., J.W.M.T., X.H.Y., C.R.W., I.P.M.), University of Chicago, IL
- Human Genetics (R.D.N., C.P.-C., O.B.-T., S.L., J.D.S., S.I., Z.W., L.S., M.G., K.M.S., Z.K., J.W.M.T., X.H.Y., I.P.M.), University of Chicago, IL
| | - Zoheb Khan
- Departments of Pediatrics (R.D.N., C.P.-C., O.B.-T., S.L., J.D.S., S.I., Z.W., L.S., M.G., K.M.S., Z.K., J.W.M.T., X.H.Y., I.P.M.), University of Chicago, IL
- Pathology (R.D.N., C.P.-C., O.B.-T., S.L., J.D.S., S.I., Z.W., L.S., M.G., K.M.S., Z.K., J.W.M.T., X.H.Y., C.R.W., I.P.M.), University of Chicago, IL
- Human Genetics (R.D.N., C.P.-C., O.B.-T., S.L., J.D.S., S.I., Z.W., L.S., M.G., K.M.S., Z.K., J.W.M.T., X.H.Y., I.P.M.), University of Chicago, IL
| | - Joshua W M Theisen
- Departments of Pediatrics (R.D.N., C.P.-C., O.B.-T., S.L., J.D.S., S.I., Z.W., L.S., M.G., K.M.S., Z.K., J.W.M.T., X.H.Y., I.P.M.), University of Chicago, IL
- Pathology (R.D.N., C.P.-C., O.B.-T., S.L., J.D.S., S.I., Z.W., L.S., M.G., K.M.S., Z.K., J.W.M.T., X.H.Y., C.R.W., I.P.M.), University of Chicago, IL
- Human Genetics (R.D.N., C.P.-C., O.B.-T., S.L., J.D.S., S.I., Z.W., L.S., M.G., K.M.S., Z.K., J.W.M.T., X.H.Y., I.P.M.), University of Chicago, IL
| | - Xinan H Yang
- Departments of Pediatrics (R.D.N., C.P.-C., O.B.-T., S.L., J.D.S., S.I., Z.W., L.S., M.G., K.M.S., Z.K., J.W.M.T., X.H.Y., I.P.M.), University of Chicago, IL
- Pathology (R.D.N., C.P.-C., O.B.-T., S.L., J.D.S., S.I., Z.W., L.S., M.G., K.M.S., Z.K., J.W.M.T., X.H.Y., C.R.W., I.P.M.), University of Chicago, IL
- Human Genetics (R.D.N., C.P.-C., O.B.-T., S.L., J.D.S., S.I., Z.W., L.S., M.G., K.M.S., Z.K., J.W.M.T., X.H.Y., I.P.M.), University of Chicago, IL
| | - Kohta Ikegami
- Division of Molecular and Cardiovascular Biology, Cincinnati Children's Hospital Medical Center, OH (K.I.)
| | - Igor R Efimov
- Department of Biomedical Engineering, George Washington University (A.G., I.R.E.), Washington, DC
| | - William T Pu
- Harvard Stem Cell Institute, Harvard University, Cambridge, MA (W.T.P.)
- Department of Cardiology, Boston Children's Hospital, MA (W.T.P.)
| | - Christopher R Weber
- Pathology (R.D.N., C.P.-C., O.B.-T., S.L., J.D.S., S.I., Z.W., L.S., M.G., K.M.S., Z.K., J.W.M.T., X.H.Y., C.R.W., I.P.M.), University of Chicago, IL
| | | | | | - Ivan P Moskowitz
- Departments of Pediatrics (R.D.N., C.P.-C., O.B.-T., S.L., J.D.S., S.I., Z.W., L.S., M.G., K.M.S., Z.K., J.W.M.T., X.H.Y., I.P.M.), University of Chicago, IL
- Pathology (R.D.N., C.P.-C., O.B.-T., S.L., J.D.S., S.I., Z.W., L.S., M.G., K.M.S., Z.K., J.W.M.T., X.H.Y., C.R.W., I.P.M.), University of Chicago, IL
- Human Genetics (R.D.N., C.P.-C., O.B.-T., S.L., J.D.S., S.I., Z.W., L.S., M.G., K.M.S., Z.K., J.W.M.T., X.H.Y., I.P.M.), University of Chicago, IL
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3
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Roehrig A, Hirsch TZ, Pire A, Morcrette G, Gupta B, Marcaillou C, Imbeaud S, Chardot C, Gonzales E, Jacquemin E, Sekiguchi M, Takita J, Nagae G, Hiyama E, Guérin F, Fabre M, Aerts I, Taque S, Laithier V, Branchereau S, Guettier C, Brugières L, Fresneau B, Zucman-Rossi J, Letouzé E. Single-cell multiomics reveals the interplay of clonal evolution and cellular plasticity in hepatoblastoma. Nat Commun 2024; 15:3031. [PMID: 38589411 PMCID: PMC11001886 DOI: 10.1038/s41467-024-47280-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 03/21/2024] [Indexed: 04/10/2024] Open
Abstract
Hepatoblastomas (HB) display heterogeneous cellular phenotypes that influence the clinical outcome, but the underlying mechanisms are poorly understood. Here, we use a single-cell multiomic strategy to unravel the molecular determinants of this plasticity. We identify a continuum of HB cell states between hepatocytic (scH), liver progenitor (scLP) and mesenchymal (scM) differentiation poles, with an intermediate scH/LP population bordering scLP and scH areas in spatial transcriptomics. Chromatin accessibility landscapes reveal the gene regulatory networks of each differentiation pole, and the sequence of transcription factor activations underlying cell state transitions. Single-cell mapping of somatic alterations reveals the clonal architecture of each tumor, showing that each genetic subclone displays its own range of cellular plasticity across differentiation states. The most scLP subclones, overexpressing stem cell and DNA repair genes, proliferate faster after neo-adjuvant chemotherapy. These results highlight how the interplay of clonal evolution and epigenetic plasticity shapes the potential of HB subclones to respond to chemotherapy.
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Affiliation(s)
- Amélie Roehrig
- Centre de Recherche des Cordeliers, Université Paris Cité, Sorbonne Université, INSERM, Paris, France
| | - Theo Z Hirsch
- Centre de Recherche des Cordeliers, Université Paris Cité, Sorbonne Université, INSERM, Paris, France
| | - Aurore Pire
- Centre de Recherche des Cordeliers, Université Paris Cité, Sorbonne Université, INSERM, Paris, France
| | - Guillaume Morcrette
- Centre de Recherche des Cordeliers, Université Paris Cité, Sorbonne Université, INSERM, Paris, France
- Department of Pathology, Robert Debré and Necker-Enfants Malades Hospitals, APHP, Paris, France
| | - Barkha Gupta
- Centre de Recherche des Cordeliers, Université Paris Cité, Sorbonne Université, INSERM, Paris, France
| | | | - Sandrine Imbeaud
- Centre de Recherche des Cordeliers, Université Paris Cité, Sorbonne Université, INSERM, Paris, France
| | | | - Emmanuel Gonzales
- Pediatric Hepatology and Liver Transplantation Unit, National Reference Centre for Rare Pediatric Liver Diseases, FILFOIE, ERN RARE LIVER, APHP, Bicêtre University Hospital, University of Paris-Saclay, Le Kremlin Bicêtre, and INSERM UMR_S 1193, Hepatinov, University of Paris-Saclay, Orsay, France
| | - Emmanuel Jacquemin
- Pediatric Hepatology and Liver Transplantation Unit, National Reference Centre for Rare Pediatric Liver Diseases, FILFOIE, ERN RARE LIVER, APHP, Bicêtre University Hospital, University of Paris-Saclay, Le Kremlin Bicêtre, and INSERM UMR_S 1193, Hepatinov, University of Paris-Saclay, Orsay, France
| | - Masahiro Sekiguchi
- Department of Pediatrics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Junko Takita
- Department of Pediatrics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- Department of Pediatrics, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Genta Nagae
- Genome Science Laboratory, Research Center for Advanced Science and Technology (RCAST), the University of Tokyo, Tokyo, Japan
| | - Eiso Hiyama
- Department of Pediatric Surgery, Hiroshima University Hospital, Hiroshima, Japan
- Department of Biomedical Science, Natural Science Center for Basic Research and Development, Hiroshima University, Hiroshima, Japan
| | - Florent Guérin
- Department of Pediatric Surgery, Bicêtre Hospital, APHP, Paris-Saclay University, Orsay, France
| | - Monique Fabre
- Department of Pathology, Hôpital Universitaire Necker-Enfants malades, AP-HP, Paris, France
| | - Isabelle Aerts
- Oncology Center SIREDO, Institut Curie, PSL Research University, Paris, France
| | - Sophie Taque
- Département de Pédiatrie, CHU Fontenoy, Rennes, France
| | - Véronique Laithier
- Department of Children Oncology, Centre Hospitalier Universitaire Besançon, Besançon, France
| | - Sophie Branchereau
- Department of Pediatric Surgery, Bicêtre Hospital, APHP, Paris-Saclay University, Orsay, France
| | - Catherine Guettier
- Department of Pathology Hôpital Bicêtre-AP-HP, INSERM U1193, Paris-Saclay University, Orsay, France
| | - Laurence Brugières
- Gustave Roussy, Université Paris-Saclay, Department of Children and Adolescents Oncology, Villejuif, France
| | - Brice Fresneau
- Gustave Roussy, Université Paris-Saclay, Department of Children and Adolescents Oncology, Villejuif, France
| | - Jessica Zucman-Rossi
- Centre de Recherche des Cordeliers, Université Paris Cité, Sorbonne Université, INSERM, Paris, France.
- Hôpital Européen Georges Pompidou, Assistance Publique Hôpitaux de Paris, Paris, France.
| | - Eric Letouzé
- Centre de Recherche des Cordeliers, Université Paris Cité, Sorbonne Université, INSERM, Paris, France.
- CRCI2NA, Nantes Université, INSERM, CNRS, Nantes, France.
- University Hospital Hôtel-Dieu, Nantes, France.
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4
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Ranek JS, Stallaert W, Milner JJ, Redick M, Wolff SC, Beltran AS, Stanley N, Purvis JE. DELVE: feature selection for preserving biological trajectories in single-cell data. Nat Commun 2024; 15:2765. [PMID: 38553455 PMCID: PMC10980758 DOI: 10.1038/s41467-024-46773-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 03/07/2024] [Indexed: 04/02/2024] Open
Abstract
Single-cell technologies can measure the expression of thousands of molecular features in individual cells undergoing dynamic biological processes. While examining cells along a computationally-ordered pseudotime trajectory can reveal how changes in gene or protein expression impact cell fate, identifying such dynamic features is challenging due to the inherent noise in single-cell data. Here, we present DELVE, an unsupervised feature selection method for identifying a representative subset of molecular features which robustly recapitulate cellular trajectories. In contrast to previous work, DELVE uses a bottom-up approach to mitigate the effects of confounding sources of variation, and instead models cell states from dynamic gene or protein modules based on core regulatory complexes. Using simulations, single-cell RNA sequencing, and iterative immunofluorescence imaging data in the context of cell cycle and cellular differentiation, we demonstrate how DELVE selects features that better define cell-types and cell-type transitions. DELVE is available as an open-source python package: https://github.com/jranek/delve .
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Affiliation(s)
- Jolene S Ranek
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Computational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Wayne Stallaert
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA
| | - J Justin Milner
- Department of Microbiology and Immunology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, USA
| | - Margaret Redick
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Computational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Samuel C Wolff
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Computational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Adriana S Beltran
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Human Pluripotent Cell Core, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, USA
| | - Natalie Stanley
- Computational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
| | - Jeremy E Purvis
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- Computational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
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5
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Liu Y, Wu Y. Single-Cell Nuclear Sequencing Atlas Revealed the Induction of Parkinson's Disease by RELN+ Neuron 3 and the Gene Regulatory Network of MSRA. Curr Med Chem 2024; 31:CMC-EPUB-139467. [PMID: 38561620 DOI: 10.2174/0109298673289286240322041842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 02/27/2024] [Accepted: 03/12/2024] [Indexed: 04/04/2024]
Abstract
AIMS To determine the cell types that promoted the progression of Parkinson's disease (PD) using the substantia nigra in the brain tissues derived from patients with PD and normal controls. BACKGROUND PD is an incurable neurodegenerative disease that threatens the physical activity of the aging population, and the complex molecular mechanisms remain be comprehensively elucidated. OBJECTIVE To describe potential disease-promoting cell types in PD and to provide a theoretical basis. METHODS Single-cell nuclear sequencing data of nine PD samples and control samples from Gene Expression Omnibus (GEO) were included, and heterogeneous cell subpopulations in the substantia nigra were identified by annotation analysis. Potential pathogenic cell subpopulations of PD were determined based on the expression data of marker genes. Cell differentiation trajectories and communication networks were generated by Pseudotime trajectory analysis and cell communication analysis. Furthermore, single-- cell regulatory network inference and clustering (SCENIC) analysis was conducted to determine the regulatory network of transcription factor-target genes in PD. RESULTS Among the nine cell subpopulations classified, RELN+neuron 3 showed reduced abundance and dopamine secretion capacity in PD and was therefore considered as a promoter of PD pathogenesis and progression. The regulatory network of MSRA action was involved in the developmental process of cells in the central nervous system, indicating that MSRA and its targets might serve as potential therapeutic targets for PD. RELN+neuron 3 had two directions of differentiation, specifically, branch 1 exhibited a high apoptotic profile and branch 2 exhibited a high cell death profile. In addition, the intensity of EPHA and EPHB signaling was attenuated between RELN+neuron 3 and other cell subpopulations. CONCLUSION To conclude, this study identified a subpopulation of RELN+neuron 3 cells with markedly reduced abundance in the brain substantia nigra in PD. The MSRA-involved gene regulatory networks was considered as a novel therapeutic network for PD.
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Affiliation(s)
- Yin Liu
- Rehabilitation Medicine Department of the Fourth Inpatient Department, Harbin Medical University Affiliated Second Hospital, Harbin, 150010, China
| | - Yun Wu
- Neurology Department of the Eighth Inpatient Department, Harbin Medical University Affiliated Second Hospital, Harbin, 150010, China
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6
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Venhuizen J, van Bergen MGJM, Bergevoet SM, Gilissen D, Spruijt CG, Wingens L, van den Akker E, Vermeulen M, Jansen JH, Martens JHA, van der Reijden BA. GFI1B and LSD1 repress myeloid traits during megakaryocyte differentiation. Commun Biol 2024; 7:374. [PMID: 38548886 PMCID: PMC10978956 DOI: 10.1038/s42003-024-06090-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Accepted: 03/21/2024] [Indexed: 04/01/2024] Open
Abstract
The transcription factor Growth Factor Independence 1B (GFI1B) recruits Lysine Specific Demethylase 1 A (LSD1/KDM1A) to stimulate gene programs relevant for megakaryocyte and platelet biology. Inherited pathogenic GFI1B variants result in thrombocytopenia and bleeding propensities with varying intensity. Whether these affect similar gene programs is unknow. Here we studied transcriptomic effects of four patient-derived GFI1B variants (GFI1BT174N,H181Y,R184P,Q287*) in MEG01 megakaryoblasts. Compared to normal GFI1B, each variant affected different gene programs with GFI1BQ287* uniquely failing to repress myeloid traits. In line with this, single cell RNA-sequencing of induced pluripotent stem cell (iPSC)-derived megakaryocytes revealed a 4.5-fold decrease in the megakaryocyte/myeloid cell ratio in GFI1BQ287* versus normal conditions. Inhibiting the GFI1B-LSD1 interaction with small molecule GSK-LSD1 resulted in activation of myeloid genes in normal iPSC-derived megakaryocytes similar to what was observed for GFI1BQ287* iPSC-derived megakaryocytes. Thus, GFI1B and LSD1 facilitate gene programs relevant for megakaryopoiesis while simultaneously repressing programs that induce myeloid differentiation.
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Affiliation(s)
- Jeron Venhuizen
- Department of Laboratory Medicine, Laboratory of Hematology, Radboud University Medical Center, Research Institute for Medical Innovation, Nijmegen, The Netherlands
| | - Maaike G J M van Bergen
- Department of Laboratory Medicine, Laboratory of Hematology, Radboud University Medical Center, Research Institute for Medical Innovation, Nijmegen, The Netherlands
| | - Saskia M Bergevoet
- Department of Laboratory Medicine, Laboratory of Hematology, Radboud University Medical Center, Research Institute for Medical Innovation, Nijmegen, The Netherlands
| | - Daan Gilissen
- Department of Laboratory Medicine, Laboratory of Hematology, Radboud University Medical Center, Research Institute for Medical Innovation, Nijmegen, The Netherlands
| | - Cornelia G Spruijt
- Department of Molecular Biology, Faculty of Science, Oncode Institute, Radboud University Nijmegen, Nijmegen, The Netherlands
| | - Laura Wingens
- Department of Molecular Developmental Biology, Faculty of Science, Radboud University Nijmegen, Nijmegen, The Netherlands
| | - Emile van den Akker
- Department of Hematopoiesis, Sanquin Research and Landsteiner Laboratory, Amsterdam, Amsterdam, The Netherlands
| | - Michiel Vermeulen
- Department of Molecular Biology, Faculty of Science, Oncode Institute, Radboud University Nijmegen, Nijmegen, The Netherlands
- Division of Molecular Genetics, The Netherlands Cancer Institute, Plesmanlaan 121, Amsterdam, The Netherlands
| | - Joop H Jansen
- Department of Laboratory Medicine, Laboratory of Hematology, Radboud University Medical Center, Research Institute for Medical Innovation, Nijmegen, The Netherlands
| | - Joost H A Martens
- Department of Molecular Biology, Faculty of Science, Oncode Institute, Radboud University Nijmegen, Nijmegen, The Netherlands.
| | - Bert A van der Reijden
- Department of Laboratory Medicine, Laboratory of Hematology, Radboud University Medical Center, Research Institute for Medical Innovation, Nijmegen, The Netherlands.
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7
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Sil P, Subbaroyan A, Kulkarni S, Martin OC, Samal A. Biologically meaningful regulatory logic enhances the convergence rate in Boolean networks and bushiness of their state transition graph. Brief Bioinform 2024; 25:bbae150. [PMID: 38581421 PMCID: PMC10998641 DOI: 10.1093/bib/bbae150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 02/14/2024] [Accepted: 03/19/2024] [Indexed: 04/08/2024] Open
Abstract
Boolean models of gene regulatory networks (GRNs) have gained widespread traction as they can easily recapitulate cellular phenotypes via their attractor states. Their overall dynamics are embodied in a state transition graph (STG). Indeed, two Boolean networks (BNs) with the same network structure and attractors can have drastically different STGs depending on the type of Boolean functions (BFs) employed. Our objective here is to systematically delineate the effects of different classes of BFs on the structural features of the STG of reconstructed Boolean GRNs while keeping network structure and biological attractors fixed, and explore the characteristics of BFs that drive those features. Using $10$ reconstructed Boolean GRNs, we generate ensembles that differ in BFs and compute from their STGs the dynamics' rate of contraction or 'bushiness' and rate of 'convergence', quantified with measures inspired from cellular automata (CA) that are based on the garden-of-Eden (GoE) states. We find that biologically meaningful BFs lead to higher STG 'bushiness' and 'convergence' than random ones. Obtaining such 'global' measures gets computationally expensive with larger network sizes, stressing the need for feasible proxies. So we adapt Wuensche's $Z$-parameter in CA to BFs in BNs and provide four natural variants, which, along with the average sensitivity of BFs computed at the network level, comprise our descriptors of local dynamics and we find some of them to be good proxies for bushiness. Finally, we provide an excellent proxy for the 'convergence' based on computing transient lengths originating at random states rather than GoE states.
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Affiliation(s)
- Priyotosh Sil
- The Institute of Mathematical Sciences (IMSc), Chennai, 600113, India
- Homi Bhabha National Institute (HBNI), Mumbai, 400094, India
| | - Ajay Subbaroyan
- The Institute of Mathematical Sciences (IMSc), Chennai, 600113, India
- Homi Bhabha National Institute (HBNI), Mumbai, 400094, India
| | - Saumitra Kulkarni
- The Institute of Mathematical Sciences (IMSc), Chennai, 600113, India
| | - Olivier C Martin
- Université Paris-Saclay, CNRS, INRAE, Univ Evry, Institute of Plant Sciences Paris-Saclay (IPS2), 91405, Orsay, France
- Université de Paris, CNRS, INRAE, Institute of Plant Sciences Paris-Saclay (IPS2), 91405, Orsay, France
| | - Areejit Samal
- The Institute of Mathematical Sciences (IMSc), Chennai, 600113, India
- Homi Bhabha National Institute (HBNI), Mumbai, 400094, India
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8
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Niederauer GF, de Oliveira GL, Aono AH, da Silva Graciano D, Carmello-Guerreiro SM, Moura MF, de Souza AP. Uncovering the molecular mechanisms of russet skin formation in Niagara grapevine (Vitis vinifera × Vitis labrusca). Sci Rep 2024; 14:6600. [PMID: 38504117 PMCID: PMC10950848 DOI: 10.1038/s41598-024-55745-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 02/27/2024] [Indexed: 03/21/2024] Open
Abstract
Grape breeding programs are mostly focused on developing new varieties with high production volume, sugar contents, and phenolic compound diversity combined with resistance and tolerance to the main pathogens under culture and adverse environmental conditions. The 'Niagara' variety (Vitis labrusca × Vitis vinifera) is one of the most widely produced and commercialized table grapes in Brazil. In this work, we selected three Niagara somatic variants with contrasting berry phenotypes and performed morphological and transcriptomic analyses of their berries. Histological sections of the berries were also performed to understand anatomical and chemical composition differences of the berry skin between the genotypes. An RNA-Seq pipeline was implemented, followed by global coexpression network modeling. 'Niagara Steck', an intensified russet mutant with the most extreme phenotype, showed the largest difference in expression and showed selection of coexpressed network modules involved in the development of its russet-like characteristics. Enrichment analysis of differently expressed genes and hub network modules revealed differences in transcription regulation, auxin signaling and cell wall and plasmatic membrane biogenesis. Cutin- and suberin-related genes were also differently expressed, supporting the anatomical differences observed with microscopy.
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Affiliation(s)
- Guilherme Francio Niederauer
- Molecular Biology and Genetic Engineering Center (CBMEG), University of Campinas (UNICAMP), Campinas, SP, Brazil
| | - Geovani Luciano de Oliveira
- Molecular Biology and Genetic Engineering Center (CBMEG), University of Campinas (UNICAMP), Campinas, SP, Brazil
| | - Alexandre Hild Aono
- Molecular Biology and Genetic Engineering Center (CBMEG), University of Campinas (UNICAMP), Campinas, SP, Brazil
| | - Diego da Silva Graciano
- Department of Plant Biology, Biology Institute, State University of Campinas (UNICAMP), Campinas, SP, Brazil
| | | | | | - Anete Pereira de Souza
- Molecular Biology and Genetic Engineering Center (CBMEG), University of Campinas (UNICAMP), Campinas, SP, Brazil.
- Department of Plant Biology, Biology Institute, State University of Campinas (UNICAMP), Campinas, SP, Brazil.
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9
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Paczkó M, Vörös D, Szabó P, Jékely G, Szathmáry E, Szilágyi A. A neural network-based model framework for cell-fate decisions and development. Commun Biol 2024; 7:323. [PMID: 38486083 PMCID: PMC10940658 DOI: 10.1038/s42003-024-05985-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Accepted: 02/28/2024] [Indexed: 03/18/2024] Open
Abstract
Gene regulatory networks (GRNs) fulfill the essential function of maintaining the stability of cellular differentiation states by sustaining lineage-specific gene expression, while driving the progression of development. However, accounting for the relative stability of intermediate differentiation stages and their divergent trajectories remains a major challenge for models of developmental biology. Here, we develop an empirical data-based associative GRN model (AGRN) in which regulatory networks store multilineage stage-specific gene expression profiles as associative memory patterns. These networks are capable of responding to multiple instructive signals and, depending on signal timing and identity, can dynamically drive the differentiation of multipotent cells toward different cell state attractors. The AGRN dynamics can thus generate diverse lineage-committed cell populations in a robust yet flexible manner, providing an attractor-based explanation for signal-driven cell fate decisions during differentiation and offering a readily generalizable modelling tool that can be applied to a wide variety of cell specification systems.
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Affiliation(s)
- Mátyás Paczkó
- Institute of Evolution, HUN-REN Centre for Ecological Research, Konkoly-Thege M. út 29-33, 1121, Budapest, Hungary
- Doctoral School of Biology, Institute of Biology, ELTE Eötvös Loránd University, Pázmány Péter sétány 1/C, 1117, Budapest, Hungary
| | - Dániel Vörös
- Institute of Evolution, HUN-REN Centre for Ecological Research, Konkoly-Thege M. út 29-33, 1121, Budapest, Hungary
- Doctoral School of Biology, Institute of Biology, ELTE Eötvös Loránd University, Pázmány Péter sétány 1/C, 1117, Budapest, Hungary
| | - Péter Szabó
- Institute of Evolution, HUN-REN Centre for Ecological Research, Konkoly-Thege M. út 29-33, 1121, Budapest, Hungary
| | - Gáspár Jékely
- Living Systems Institute, University of Exeter, Stocker Road 4QD, EX4, Exeter, UK
| | - Eörs Szathmáry
- Institute of Evolution, HUN-REN Centre for Ecological Research, Konkoly-Thege M. út 29-33, 1121, Budapest, Hungary.
- Center for the Conceptual Foundations of Science, Parmenides Foundation, Hindenburgstr. 15, 82343, Pöcking, Germany.
- Department of Plant Systematics, Ecology and Theoretical Biology, Eötvös Loránd University, Pázmány Péter sétány 1/C, 1117, Budapest, Hungary.
| | - András Szilágyi
- Institute of Evolution, HUN-REN Centre for Ecological Research, Konkoly-Thege M. út 29-33, 1121, Budapest, Hungary
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10
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Demko V, Belova T, Messerer M, Hvidsten TR, Perroud PF, Ako AE, Johansen W, Mayer KFX, Olsen OA, Lang D. Regulation of developmental gatekeeping and cell fate transition by the calpain protease DEK1 in Physcomitrium patens. Commun Biol 2024; 7:261. [PMID: 38438476 PMCID: PMC10912778 DOI: 10.1038/s42003-024-05933-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 02/19/2024] [Indexed: 03/06/2024] Open
Abstract
Calpains are cysteine proteases that control cell fate transitions whose loss of function causes severe, pleiotropic phenotypes in eukaryotes. Although mainly considered as modulatory proteases, human calpain targets are directed to the N-end rule degradation pathway. Several such targets are transcription factors, hinting at a gene-regulatory role. Here, we analyze the gene-regulatory networks of the moss Physcomitrium patens and characterize the regulons that are misregulated in mutants of the calpain DEFECTIVE KERNEL1 (DEK1). Predicted cleavage patterns of the regulatory hierarchies in five DEK1-controlled subnetworks are consistent with a pleiotropic and regulatory role during cell fate transitions targeting multiple functions. Network structure suggests DEK1-gated sequential transitions between cell fates in 2D-to-3D development. Our method combines comprehensive phenotyping, transcriptomics and data science to dissect phenotypic traits, and our model explains the protease function as a switch gatekeeping cell fate transitions potentially also beyond plant development.
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Affiliation(s)
- Viktor Demko
- Department of Plant Sciences, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432, Ås, Norway
- Department of Plant Physiology, Faculty of Natural Sciences, Comenius University in Bratislava, Ilkovicova 6, 84104, Bratislava, Slovakia
- Plant Science and Biodiversity Center, Slovak Academy of Sciences, Dubravska cesta 9, 84104, Bratislava, Slovakia
| | - Tatiana Belova
- Department of Plant Sciences, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432, Ås, Norway
- Centre for Molecular Medicine Norway, University of Oslo, Oslo, Norway
| | - Maxim Messerer
- Plant Genome and Systems Biology, Helmholtz Center Munich-Research Center for Environmental Health, 85764, Neuherberg, Germany
| | - Torgeir R Hvidsten
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences, Ås, Norway
| | - Pierre-François Perroud
- Institut Jean-Pierre Bourgin, INRAE, AgroParisTech, Université Paris-Saclay, 78000, Versailles, France
| | - Ako Eugene Ako
- Department of Biotechnology, Inland Norway University of Applied Sciences, Holsetgata 31, 2318, Hamar, Norway
- School of Animal, Rural and Environmental Sciences, Nottingham Trent University, Brackenhurst Campus, Southwell, Nottinghamshire, NG25 0QF, UK
| | - Wenche Johansen
- Department of Biotechnology, Inland Norway University of Applied Sciences, Holsetgata 31, 2318, Hamar, Norway
| | - Klaus F X Mayer
- Plant Genome and Systems Biology, Helmholtz Center Munich-Research Center for Environmental Health, 85764, Neuherberg, Germany
- School of Life Sciences, Technical University Munich, 85354, Freising, Germany
| | - Odd-Arne Olsen
- Department of Plant Sciences, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432, Ås, Norway
| | - Daniel Lang
- Plant Genome and Systems Biology, Helmholtz Center Munich-Research Center for Environmental Health, 85764, Neuherberg, Germany.
- Bundeswehr Institute of Microbiology, Microbial Genomics and Bioforensics, 80937, Munich, Germany.
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11
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Barretto LAF, Van PKT, Fowler CC. Conserved patterns of sequence diversification provide insight into the evolution of two-component systems in Enterobacteriaceae. Microb Genom 2024; 10:001215. [PMID: 38502064 PMCID: PMC11004495 DOI: 10.1099/mgen.0.001215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 02/29/2024] [Indexed: 03/20/2024] Open
Abstract
Two-component regulatory systems (TCSs) are a major mechanism used by bacteria to sense and respond to their environments. Many of the same TCSs are used by biologically diverse organisms with different regulatory needs, suggesting that the functions of TCS must evolve. To explore this topic, we analysed the amino acid sequence divergence patterns of a large set of broadly conserved TCS across different branches of Enterobacteriaceae, a family of Gram-negative bacteria that includes biomedically important genera such as Salmonella, Escherichia, Klebsiella and others. Our analysis revealed trends in how TCS sequences change across different proteins or functional domains of the TCS, and across different lineages. Based on these trends, we identified individual TCS that exhibit atypical evolutionary patterns. We observed that the relative extent to which the sequence of a given TCS varies across different lineages is generally well conserved, unveiling a hierarchy of TCS sequence conservation with EnvZ/OmpR as the most conserved TCS. We provide evidence that, for the most divergent of the TCS analysed, PmrA/PmrB, different alleles were horizontally acquired by different branches of this family, and that different PmrA/PmrB sequence variants have highly divergent signal-sensing domains. Collectively, this study sheds light on how TCS evolve, and serves as a compendium for how the sequences of the TCS in this family have diverged over the course of evolution.
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Affiliation(s)
- Luke A. F. Barretto
- Department of Biological Sciences, University of Alberta, Edmonton, AB, T6G2E9, Canada
| | - Patryc-Khang T. Van
- Department of Biological Sciences, University of Alberta, Edmonton, AB, T6G2E9, Canada
| | - Casey C. Fowler
- Department of Biological Sciences, University of Alberta, Edmonton, AB, T6G2E9, Canada
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12
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Guerrero-Peña L, Suarez-Bregua P, Sánchez-Ruiloba L, Méndez-Martínez L, García-Fernández P, Tur R, Tena JJ, Rotllant J. Unraveling the transcriptomic landscape of eye migration and visual adaptations during flatfish metamorphosis. Commun Biol 2024; 7:253. [PMID: 38429383 PMCID: PMC10907633 DOI: 10.1038/s42003-024-05951-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Accepted: 02/21/2024] [Indexed: 03/03/2024] Open
Abstract
Flatfish undergo a remarkable metamorphosis from symmetrical pelagic larvae to fully asymmetrical benthic juveniles. The most distinctive features of this transformation is the migration of one eye. The molecular role of thyroid hormone in the metamorphosis process in flatfishes is well established. However, the regulatory network that facilitates eye movement remains enigmatic. This paper presents a morphological investigation of the metamorphic process in turbot eyes, using advanced imaging techniques and a global view of gene expression. The study covers migrant and non-migrant eyes and aims to identify the genes that are active during ocular migration. Our transcriptomic analysis shows a significant up-regulation of immune-related genes. The analysis of eye-specific genes reveals distinct patterns during the metamorphic process. Myosin is highlighted in the non-migrant eye, while ependymin is highlighted in the migrant eye, possibly involved in optic nerve regeneration. Furthermore, a potential association between the alx3 gene and cranial restructuring has been identified. Additionally, it confirmed simultaneous adaptation to low light in both eyes, as described by changes in opsins expression during the metamorphic process. The study also revealed that ocular migration activates systems asynchronously in both eyes, providing insight into multifaceted reorganization processes during metamorphosis of flatfish.
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Affiliation(s)
- Laura Guerrero-Peña
- Aquatic Biotechnology Lab., Institute of Marine Research, Spanish National Research Council (IIM-CSIC), 36208, Vigo, Spain
| | - Paula Suarez-Bregua
- Aquatic Biotechnology Lab., Institute of Marine Research, Spanish National Research Council (IIM-CSIC), 36208, Vigo, Spain
| | - Lucía Sánchez-Ruiloba
- Institute of Marine Research, Spanish National Research Council (IIM-CSIC), 36208, Vigo, Spain
| | - Luis Méndez-Martínez
- Aquatic Biotechnology Lab., Institute of Marine Research, Spanish National Research Council (IIM-CSIC), 36208, Vigo, Spain
| | | | - Ricardo Tur
- Nueva Pescanova Biomarine Center, S.L., 36980, O Grove, Spain
| | - Juan J Tena
- Centro Andaluz de Biología del Desarrollo (CABD), CSIC-Universidad Pablo de Olavide, 41013, Sevilla, Spain
| | - Josep Rotllant
- Aquatic Biotechnology Lab., Institute of Marine Research, Spanish National Research Council (IIM-CSIC), 36208, Vigo, Spain.
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13
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Ilker E, Hinczewski M. Bioenergetic costs and the evolution of noise regulation by microRNAs. Proc Natl Acad Sci U S A 2024; 121:e2308796121. [PMID: 38386708 PMCID: PMC10907262 DOI: 10.1073/pnas.2308796121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2023] [Accepted: 01/14/2024] [Indexed: 02/24/2024] Open
Abstract
Noise control, together with other regulatory functions facilitated by microRNAs (miRNAs), is believed to have played important roles in the evolution of multicellular eukaryotic organisms. miRNAs can dampen protein fluctuations via enhanced degradation of messenger RNA (mRNA), but this requires compensation by increased mRNA transcription to maintain the same expression levels. The overall mechanism is metabolically expensive, leading to questions about how it might have evolved in the first place. We develop a stochastic model of miRNA noise regulation, coupled with a detailed analysis of the associated metabolic costs. Additionally, we calculate binding free energies for a range of miRNA seeds, the short sequences which govern target recognition. We argue that natural selection may have fine-tuned the Michaelis-Menten constant [Formula: see text] describing miRNA-mRNA affinity and show supporting evidence from analysis of experimental data. [Formula: see text] is constrained by seed length, and optimal noise control (minimum protein variance at a given energy cost) is achievable for seeds of 6 to 7 nucleotides in length, the most commonly observed types. Moreover, at optimality, the degree of noise reduction approaches the theoretical bound set by the Wiener-Kolmogorov linear filter. The results illustrate how selective pressure toward energy efficiency has potentially shaped a crucial regulatory pathway in eukaryotes.
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Affiliation(s)
- Efe Ilker
- Max Planck Institute for the Physics of Complex Systems, Dresden01187, Germany
| | - Michael Hinczewski
- Department of Physics, Case Western Reserve University, Cleveland, OH44106
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14
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Arthur TD, Nguyen JP, D'Antonio-Chronowska A, Matsui H, Silva NS, Joshua IN, Luchessi AD, Greenwald WWY, D'Antonio M, Pera MF, Frazer KA. Complex regulatory networks influence pluripotent cell state transitions in human iPSCs. Nat Commun 2024; 15:1664. [PMID: 38395976 PMCID: PMC10891157 DOI: 10.1038/s41467-024-45506-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Accepted: 01/26/2024] [Indexed: 02/25/2024] Open
Abstract
Stem cells exist in vitro in a spectrum of interconvertible pluripotent states. Analyzing hundreds of hiPSCs derived from different individuals, we show the proportions of these pluripotent states vary considerably across lines. We discover 13 gene network modules (GNMs) and 13 regulatory network modules (RNMs), which are highly correlated with each other suggesting that the coordinated co-accessibility of regulatory elements in the RNMs likely underlie the coordinated expression of genes in the GNMs. Epigenetic analyses reveal that regulatory networks underlying self-renewal and pluripotency are more complex than previously realized. Genetic analyses identify thousands of regulatory variants that overlapped predicted transcription factor binding sites and are associated with chromatin accessibility in the hiPSCs. We show that the master regulator of pluripotency, the NANOG-OCT4 Complex, and its associated network are significantly enriched for regulatory variants with large effects, suggesting that they play a role in the varying cellular proportions of pluripotency states between hiPSCs. Our work bins tens of thousands of regulatory elements in hiPSCs into discrete regulatory networks, shows that pluripotency and self-renewal processes have a surprising level of regulatory complexity, and suggests that genetic factors may contribute to cell state transitions in human iPSC lines.
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Affiliation(s)
- Timothy D Arthur
- Biomedical Sciences Graduate Program, University of California, San Diego, La Jolla, CA, 92093, USA
- Division of Biomedical Informatics, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Jennifer P Nguyen
- Division of Biomedical Informatics, University of California, San Diego, La Jolla, CA, 92093, USA
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA, 92093, USA
| | | | - Hiroko Matsui
- Institute of Genomic Medicine, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, 92093, USA
| | - Nayara S Silva
- Northeast Biotechnology Network (RENORBIO), Graduate Program in Biotechnology, Federal University of Rio Grande do Norte, Natal, Brazil
| | - Isaac N Joshua
- Institute of Genomic Medicine, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, 92093, USA
| | - André D Luchessi
- Northeast Biotechnology Network (RENORBIO), Graduate Program in Biotechnology, Federal University of Rio Grande do Norte, Natal, Brazil
- Department of Clinical and Toxicological Analysis, Federal University of Rio Grande do Norte, Natal, Brazil
| | - William W Young Greenwald
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Matteo D'Antonio
- Division of Biomedical Informatics, University of California, San Diego, La Jolla, CA, 92093, USA
- Institute of Genomic Medicine, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, 92093, USA
| | | | - Kelly A Frazer
- Department of Pediatrics, University of California San Diego, La Jolla, CA, 92093, USA.
- Institute of Genomic Medicine, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, 92093, USA.
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15
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Shukla N, Harshini V, Raval I, Patel AK, Joshi CG. lncRNA-miRNA-mRNA network in kidney transcriptome of Labeo rohita under hypersaline environment. Sci Data 2024; 11:226. [PMID: 38388642 PMCID: PMC10883911 DOI: 10.1038/s41597-024-03056-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Accepted: 02/08/2024] [Indexed: 02/24/2024] Open
Abstract
The present study describes the kidney transcriptome of Labeo rohita, a freshwater fish, exposed to gradually increased salinity concentrations (2, 4, 6 and 8ppt). A total of 10.25 Gbps data was generated, and a suite of bioinformatics tools, including FEELnc, CPC2 and BLASTn were employed for identification of long non-coding RNAs (lncRNAs) and micro RNAs (miRNAs). Our analysis revealed a total of 170, 118, 99, and 269 differentially expressed lncRNA and 120, 118, 99, and 124 differentially expressed miRNAs in 2, 4, 6 and 8 ppt treatment groups respectively. Two competing endogenous RNA (ceRNA) networks were constructed i.e. A* ceRNA network with up-regulated lncRNAs and mRNAs, down-regulated miRNAs; and B* ceRNA network vice versa. 2ppt group had 131 and 83 lncRNA-miRNA-mRNA pairs in A* and B* networks, respectively. 4ppt group featured 163 pairs in A* network and 191 in B* network, while the 6ppt had 103 and 105 pairs. 8ppt group included 192 and 174 pairs. These networks illuminate the intricate RNA interactions in freshwater fish to varying salinity conditions.
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Affiliation(s)
- Nitin Shukla
- Gujarat Biotechnology Research Centre, Sector 11, Gandhinagar, Gujarat, India
| | - Vemula Harshini
- Gujarat Biotechnology Research Centre, Sector 11, Gandhinagar, Gujarat, India
| | - Ishan Raval
- Gujarat Biotechnology Research Centre, Sector 11, Gandhinagar, Gujarat, India
| | - Amrutlal K Patel
- Gujarat Biotechnology Research Centre, Sector 11, Gandhinagar, Gujarat, India.
| | - Chaitanya G Joshi
- Gujarat Biotechnology Research Centre, Sector 11, Gandhinagar, Gujarat, India.
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16
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Naidu A, Lulu S. S. Systems and computational analysis of gene expression datasets reveals GRB-2 suppression as an acute immunomodulatory response against enteric infections in endemic settings. Front Immunol 2024; 15:1285785. [PMID: 38433833 PMCID: PMC10906661 DOI: 10.3389/fimmu.2024.1285785] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 01/05/2024] [Indexed: 03/05/2024] Open
Abstract
Introduction Enteric infections are a major cause of under-5 (age) mortality in low/middle-income countries. Although vaccines against these infections have already been licensed, unwavering efforts are required to boost suboptimalefficacy and effectiveness in regions that are highly endemic to enteric pathogens. The role of baseline immunological profiles in influencing vaccine-induced immune responses is increasingly becoming clearer for several vaccines. Hence, for the development of advanced and region-specific enteric vaccines, insights into differences in immune responses to perturbations in endemic and non-endemic settings become crucial. Materials and methods For this reason, we employed a two-tiered system and computational pipeline (i) to study the variations in differentially expressed genes (DEGs) associated with immune responses to enteric infections in endemic and non-endemic study groups, and (ii) to derive features (genes) of importance that keenly distinguish between these two groups using unsupervised machine learning algorithms on an aggregated gene expression dataset. The derived genes were further curated using topological analysis of the constructed STRING networks. The findings from these two tiers are validated using multilayer perceptron classifier and were further explored using correlation and regression analysis for the retrieval of associated gene regulatory modules. Results Our analysis reveals aggressive suppression of GRB-2, an adaptor molecule integral for TCR signaling, as a primary immunomodulatory response against S. typhi infection in endemic settings. Moreover, using retrieved correlation modules and multivariant regression models, we found a positive association between regulators of activated T cells and mediators of Hedgehog signaling in the endemic population, which indicates the initiation of an effector (involving differentiation and homing) rather than an inductive response upon infection. On further exploration, we found STAT3 to be instrumental in designating T-cell functions upon early responses to enteric infections in endemic settings. Conclusion Overall, through a systems and computational biology approach, we characterized distinct molecular players involved in immune responses to enteric infections in endemic settings in the process, contributing to the mounting evidence of endemicity being a major determiner of pathogen/vaccine-induced immune responses. The gained insights will have important implications in the design and development of region/endemicity-specific vaccines.
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Affiliation(s)
- Akshayata Naidu
- Integrative Multi-omics Lab, Department of Biotechnology, Vellore Institute of Technology, Vellore, Tamil Nadu, India
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17
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Ledru N, Wilson PC, Muto Y, Yoshimura Y, Wu H, Li D, Asthana A, Tullius SG, Waikar SS, Orlando G, Humphreys BD. Predicting proximal tubule failed repair drivers through regularized regression analysis of single cell multiomic sequencing. Nat Commun 2024; 15:1291. [PMID: 38347009 PMCID: PMC10861555 DOI: 10.1038/s41467-024-45706-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 01/31/2024] [Indexed: 02/15/2024] Open
Abstract
Renal proximal tubule epithelial cells have considerable intrinsic repair capacity following injury. However, a fraction of injured proximal tubule cells fails to undergo normal repair and assumes a proinflammatory and profibrotic phenotype that may promote fibrosis and chronic kidney disease. The healthy to failed repair change is marked by cell state-specific transcriptomic and epigenomic changes. Single nucleus joint RNA- and ATAC-seq sequencing offers an opportunity to study the gene regulatory networks underpinning these changes in order to identify key regulatory drivers. We develop a regularized regression approach to construct genome-wide parametric gene regulatory networks using multiomic datasets. We generate a single nucleus multiomic dataset from seven adult human kidney samples and apply our method to study drivers of a failed injury response associated with kidney disease. We demonstrate that our approach is a highly effective tool for predicting key cis- and trans-regulatory elements underpinning the healthy to failed repair transition and use it to identify NFAT5 as a driver of the maladaptive proximal tubule state.
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Affiliation(s)
- Nicolas Ledru
- Division of Nephrology, Department of Medicine, Washington University in St. Louis School of Medicine, St. Louis, MO, USA
| | - Parker C Wilson
- Division of Anatomic and Molecular Pathology, Department of Pathology and Immunology, Washington University in St. Louis, St. Louis, MO, USA
| | - Yoshiharu Muto
- Division of Nephrology, Department of Medicine, Washington University in St. Louis School of Medicine, St. Louis, MO, USA
| | - Yasuhiro Yoshimura
- Division of Nephrology, Department of Medicine, Washington University in St. Louis School of Medicine, St. Louis, MO, USA
| | - Haojia Wu
- Division of Nephrology, Department of Medicine, Washington University in St. Louis School of Medicine, St. Louis, MO, USA
| | - Dian Li
- Division of Nephrology, Department of Medicine, Washington University in St. Louis School of Medicine, St. Louis, MO, USA
| | - Amish Asthana
- Department of Surgery, Wake Forest Baptist Medical Center; Wake Forest Institute for Regenerative Medicine, Wake Forest School of Medicine, Winston Salem, NC, USA
| | - Stefan G Tullius
- Division of Transplant Surgery and Transplant Surgery Research Laboratory, Department of Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Sushrut S Waikar
- Section of Nephrology, Department of Medicine, Boston University Chobanian and Avedisian School of Medicine, Boston Medical Center, Boston, MA, USA
| | - Giuseppe Orlando
- Department of Surgery, Wake Forest Baptist Medical Center; Wake Forest Institute for Regenerative Medicine, Wake Forest School of Medicine, Winston Salem, NC, USA
| | - Benjamin D Humphreys
- Division of Nephrology, Department of Medicine, Washington University in St. Louis School of Medicine, St. Louis, MO, USA.
- Department of Developmental Biology, Washington University in St. Louis School of Medicine, St. Louis, MO, USA.
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18
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He Z, Lan Y, Zhou X, Yu B, Zhu T, Yang F, Fu LY, Chao H, Wang J, Feng RX, Zuo S, Lan W, Chen C, Chen M, Zhao X, Hu K, Chen D. Single-cell transcriptome analysis dissects lncRNA-associated gene networks in Arabidopsis. Plant Commun 2024; 5:100717. [PMID: 37715446 PMCID: PMC10873878 DOI: 10.1016/j.xplc.2023.100717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Revised: 08/14/2023] [Accepted: 09/12/2023] [Indexed: 09/17/2023]
Abstract
The plant genome produces an extremely large collection of long noncoding RNAs (lncRNAs) that are generally expressed in a context-specific manner and have pivotal roles in regulation of diverse biological processes. Here, we mapped the transcriptional heterogeneity of lncRNAs and their associated gene regulatory networks at single-cell resolution. We generated a comprehensive cell atlas at the whole-organism level by integrative analysis of 28 published single-cell RNA sequencing (scRNA-seq) datasets from juvenile Arabidopsis seedlings. We then provided an in-depth analysis of cell-type-related lncRNA signatures that show expression patterns consistent with canonical protein-coding gene markers. We further demonstrated that the cell-type-specific expression of lncRNAs largely explains their tissue specificity. In addition, we predicted gene regulatory networks on the basis of motif enrichment and co-expression analysis of lncRNAs and mRNAs, and we identified putative transcription factors orchestrating cell-type-specific expression of lncRNAs. The analysis results are available at the single-cell-based plant lncRNA atlas database (scPLAD; https://biobigdata.nju.edu.cn/scPLAD/). Overall, this work demonstrates the power of integrative single-cell data analysis applied to plant lncRNA biology and provides fundamental insights into lncRNA expression specificity and associated gene regulation.
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Affiliation(s)
- Zhaohui He
- State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing 210023, China
| | - Yangming Lan
- State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing 210023, China
| | - Xinkai Zhou
- State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing 210023, China
| | - Bianjiong Yu
- State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing 210023, China
| | - Tao Zhu
- State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing 210023, China
| | - Fa Yang
- State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing 210023, China
| | - Liang-Yu Fu
- State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing 210023, China
| | - Haoyu Chao
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou 310058, China
| | - Jiahao Wang
- Jiangsu Key Laboratory of Crop Genomics and Molecular Breeding/Key Laboratory of Plant Functional Genomics of the Ministry of Education, College of Agriculture, Yangzhou University, Yangzhou 225009, China; Co-Innovation Center for Modern Production Technology of Grain Crops of Jiangsu Province/Key Laboratory of Crop Genetics and Physiology of Jiangsu Province, Yangzhou University, Yangzhou 225009, China
| | - Rong-Xu Feng
- Zhejiang Zhoushan High School, Zhoushan 316099, China
| | - Shimin Zuo
- Jiangsu Key Laboratory of Crop Genomics and Molecular Breeding/Key Laboratory of Plant Functional Genomics of the Ministry of Education, College of Agriculture, Yangzhou University, Yangzhou 225009, China; Co-Innovation Center for Modern Production Technology of Grain Crops of Jiangsu Province/Key Laboratory of Crop Genetics and Physiology of Jiangsu Province, Yangzhou University, Yangzhou 225009, China
| | - Wenzhi Lan
- State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing 210023, China
| | - Chunli Chen
- National Key Laboratory for Germplasm Innovation and Utilization for Fruit and Vegetable Horticultural Crops, Hubei Hongshan Laboratory, Wuhan 430070, China
| | - Ming Chen
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou 310058, China.
| | - Xue Zhao
- State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing 210023, China.
| | - Keming Hu
- Jiangsu Key Laboratory of Crop Genomics and Molecular Breeding/Key Laboratory of Plant Functional Genomics of the Ministry of Education, College of Agriculture, Yangzhou University, Yangzhou 225009, China; Co-Innovation Center for Modern Production Technology of Grain Crops of Jiangsu Province/Key Laboratory of Crop Genetics and Physiology of Jiangsu Province, Yangzhou University, Yangzhou 225009, China.
| | - Dijun Chen
- State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing 210023, China.
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19
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Weinberger M, Simões FC, Gungoosingh T, Sauka-Spengler T, Riley PR. Distinct epicardial gene regulatory programs drive development and regeneration of the zebrafish heart. Dev Cell 2024; 59:351-367.e6. [PMID: 38237592 DOI: 10.1016/j.devcel.2023.12.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 08/12/2023] [Accepted: 12/20/2023] [Indexed: 02/08/2024]
Abstract
Unlike the adult mammalian heart, which has limited regenerative capacity, the zebrafish heart fully regenerates following injury. Reactivation of cardiac developmental programs is considered key to successfully regenerating the heart, yet the regulation underlying the response to injury remains elusive. Here, we compared the transcriptome and epigenome of the developing and regenerating zebrafish epicardia. We identified epicardial enhancer elements with specific activity during development or during adult heart regeneration. By generating gene regulatory networks associated with epicardial development and regeneration, we inferred genetic programs driving each of these processes, which were largely distinct. Loss of Hif1ab, Nrf1, Tbx2b, and Zbtb7a, central regulators of the regenerating epicardial network, in injured hearts resulted in elevated epicardial cell numbers infiltrating the wound and excess fibrosis after cryoinjury. Our work identifies differences between the regulatory blueprint deployed during epicardial development and regeneration, underlining that heart regeneration goes beyond the reactivation of developmental programs.
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Affiliation(s)
- Michael Weinberger
- Department of Physiology, Anatomy and Genetics, Institute of Developmental & Regenerative Medicine, University of Oxford, Oxford OX3 7TY, Oxfordshire, UK; Radcliffe Department of Medicine, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford OX3 9DS, Oxfordshire, UK
| | - Filipa C Simões
- Department of Physiology, Anatomy and Genetics, Institute of Developmental & Regenerative Medicine, University of Oxford, Oxford OX3 7TY, Oxfordshire, UK
| | - Trishalee Gungoosingh
- Department of Physiology, Anatomy and Genetics, Institute of Developmental & Regenerative Medicine, University of Oxford, Oxford OX3 7TY, Oxfordshire, UK
| | - Tatjana Sauka-Spengler
- Radcliffe Department of Medicine, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford OX3 9DS, Oxfordshire, UK; Stowers Institute for Medical Research, Kansas City, MO, USA.
| | - Paul R Riley
- Department of Physiology, Anatomy and Genetics, Institute of Developmental & Regenerative Medicine, University of Oxford, Oxford OX3 7TY, Oxfordshire, UK.
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20
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Huang J, Chen J, Wang C, Lai L, Mi H, Chen S. Deciphering the molecular classification of pediatric sepsis: integrating WGCNA and machine learning-based classification with immune signatures for the development of an advanced diagnostic model. Front Genet 2024; 15:1294381. [PMID: 38348451 PMCID: PMC10859440 DOI: 10.3389/fgene.2024.1294381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Accepted: 01/16/2024] [Indexed: 02/15/2024] Open
Abstract
Introduction: Pediatric sepsis (PS) is a life-threatening infection associated with high mortality rates, necessitating a deeper understanding of its underlying pathological mechanisms. Recently discovered programmed cell death induced by copper has been implicated in various medical conditions, but its potential involvement in PS remains largely unexplored. Methods: We first analyzed the expression patterns of cuproptosis-related genes (CRGs) and assessed the immune landscape of PS using the GSE66099 dataset. Subsequently, PS samples were isolated from the same dataset, and consensus clustering was performed based on differentially expressed CRGs. We applied weighted gene co-expression network analysis to identify hub genes associated with PS and cuproptosis. Results: We observed aberrant expression of 27 CRGs and a specific immune landscape in PS samples. Our findings revealed that patients in the GSE66099 dataset could be categorized into two cuproptosis clusters, each characterized by unique immune landscapes and varying functional classifications or enriched pathways. Among the machine learning approaches, Extreme Gradient Boosting demonstrated optimal performance as a diagnostic model for PS. Discussion: Our study provides valuable insights into the molecular mechanisms underlying PS, highlighting the involvement of cuproptosis-related genes and immune cell infiltration.
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Affiliation(s)
- Junming Huang
- Department of Urology, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, China
- Department of Urology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Jinji Chen
- Department of Urology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Chengbang Wang
- Department of Urology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Lichuan Lai
- Department of Laboratory, The People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, China
| | - Hua Mi
- Department of Urology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Shaohua Chen
- Department of Urology, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, China
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21
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Sun Z, Zhang L, Wang R, Wang Z, Liang X, Gao J. Identification of shared pathogenetic mechanisms between COVID-19 and IC through bioinformatics and system biology. Sci Rep 2024; 14:2114. [PMID: 38267482 PMCID: PMC10808107 DOI: 10.1038/s41598-024-52625-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 01/22/2024] [Indexed: 01/26/2024] Open
Abstract
COVID-19 increased global mortality in 2019. Cystitis became a contributing factor in SARS-CoV-2 and COVID-19 complications. The complex molecular links between cystitis and COVID-19 are unclear. This study investigates COVID-19-associated cystitis (CAC) molecular mechanisms and drug candidates using bioinformatics and systems biology. Obtain the gene expression profiles of IC (GSE11783) and COVID-19 (GSE147507) from the Gene Expression Omnibus (GEO) database. Identified the common differentially expressed genes (DEGs) in both IC and COVID-19, and extracted a number of key genes from this group. Subsequently, conduct Gene Ontology (GO) functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis on the DEGs. Additionally, design a protein-protein interaction (PPI) network, a transcription factor gene regulatory network, a TF miRNA regulatory network, and a gene disease association network using the DEGs. Identify and extract hub genes from the PPI network. Then construct Nomogram diagnostic prediction models based on the hub genes. The DSigDB database was used to forecast many potential molecular medicines that are associated with common DEGs. Assess the precision of hub genes and Nomogram models in diagnosing IC and COVID-19 by employing Receiver Operating Characteristic (ROC) curves. The IC dataset (GSE57560) and the COVID-19 dataset (GSE171110) were selected to validate the models' diagnostic accuracy. A grand total of 198 DEGs that overlapped were found and chosen for further research. FCER1G, ITGAM, LCP2, LILRB2, MNDA, SPI1, and TYROBP were screened as the hub genes. The Nomogram model, built using the seven hub genes, demonstrates significant utility as a diagnostic prediction model for both IC and COVID-19. Multiple potential molecular medicines associated with common DEGs have been discovered. These pathways, hub genes, and models may provide new perspectives for future research into mechanisms and guide personalised and effective therapeutics for IC patients infected with COVID-19.
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Affiliation(s)
- Zhenpeng Sun
- Department of Urology, Qingdao Municipal Hospital, No.5, Donghai Middle Road, Shinan District, Qingdao, 266001, Shandong, China
- Qingdao Medical College, Qingdao University, Qingdao, China
| | - Li Zhang
- Institute of Systems Medicine, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
- Suzhou Institute of Systems Medicine, Suzhou, China
| | - Ruihong Wang
- Department of Outpatient, Qingdao Central Hospital, Qingdao University, Qingdao, China
| | - Zheng Wang
- Zhucheng People's Hospital, Zhucheng, China
| | - Xin Liang
- Department of Urology, Qingdao Municipal Hospital, No.5, Donghai Middle Road, Shinan District, Qingdao, 266001, Shandong, China
| | - Jiangang Gao
- Department of Urology, Qingdao Municipal Hospital, No.5, Donghai Middle Road, Shinan District, Qingdao, 266001, Shandong, China.
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22
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Murrugarra D, Veliz-Cuba A, Dimitrova E, Kadelka C, Wheeler M, Laubenbacher R. Modular Control of Biological Networks. ArXiv 2024:arXiv:2401.12477v1. [PMID: 38344220 PMCID: PMC10854280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/17/2024]
Abstract
The concept of control is central to understanding and applications of biological network models. Some of their key structural features relate to control functions, through gene regulation, signaling, or metabolic mechanisms, and computational models need to encode these. Applications of models often focus on model-based control, such as in biomedicine or metabolic engineering. This paper presents an approach to model-based control that exploits two common features of biological networks, namely their modular structure and canalizing features of their regulatory mechanisms. The paper focuses on intracellular regulatory networks, represented by Boolean network models. A main result of this paper is that control strategies can be identified by focusing on one module at a time. This paper also presents a criterion based on canalizing features of the regulatory rules to identify modules that do not contribute to network control and can be excluded. For even moderately sized networks, finding global control inputs is computationally very challenging. The modular approach presented here leads to a highly efficient approach to solving this problem. This approach is applied to a published Boolean network model of blood cancer large granular lymphocyte (T-LGL) leukemia to identify a minimal control set that achieves a desired control objective.
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Affiliation(s)
- David Murrugarra
- Department of Mathematics, University of Kentucky, Lexington, KY 40506, USA
| | - Alan Veliz-Cuba
- Department of Mathematics, University of Dayton, Dayton, Ohio 45469, USA
| | - Elena Dimitrova
- Mathematics Department, California Polytechnic State University, San Luis Obispo, CA 93407, USA
| | - Claus Kadelka
- Department of Mathematics, Iowa State University, Ames, IA 50011, USA
| | - Matthew Wheeler
- Department of Medicine, University of Florida, Gainesville, FL 32610, USA
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23
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Whittle BJ, Izuogu OG, Lowes H, Deen D, Pyle A, Coxhead J, Lawson RA, Yarnall AJ, Jackson MS, Santibanez-Koref M, Hudson G. Early-stage idiopathic Parkinson's disease is associated with reduced circular RNA expression. NPJ Parkinsons Dis 2024; 10:25. [PMID: 38245550 PMCID: PMC10799891 DOI: 10.1038/s41531-024-00636-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 01/08/2024] [Indexed: 01/22/2024] Open
Abstract
Neurodegeneration in Parkinson's disease (PD) precedes diagnosis by years. Early neurodegeneration may be reflected in RNA levels and measurable as a biomarker. Here, we present the largest quantification of whole blood linear and circular RNAs (circRNA) in early-stage idiopathic PD, using RNA sequencing data from two cohorts (PPMI = 259 PD, 161 Controls; ICICLE-PD = 48 PD, 48 Controls). We identified a replicable increase in TMEM252 and LMNB1 gene expression in PD. We identified novel differences in the expression of circRNAs from ESYT2, BMS1P1 and CCDC9, and replicated trends of previously reported circRNAs. Overall, using circRNA as a diagnostic biomarker in PD did not show any clear improvement over linear RNA, minimising its potential clinical utility. More interestingly, we observed a general reduction in circRNA expression in both PD cohorts, accompanied by an increase in RNASEL expression. This imbalance implicates the activation of an innate antiviral immune response and suggests a previously unknown aspect of circRNA regulation in PD.
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Affiliation(s)
- Benjamin J Whittle
- Wellcome Centre for Mitochondrial Research, Biosciences Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Osagie G Izuogu
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | - Hannah Lowes
- Wellcome Centre for Mitochondrial Research, Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Dasha Deen
- Wellcome Centre for Mitochondrial Research, Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Angela Pyle
- Wellcome Centre for Mitochondrial Research, Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Jon Coxhead
- Biosciences Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Rachael A Lawson
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Alison J Yarnall
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Michael S Jackson
- Biosciences Institute, Newcastle University, Newcastle upon Tyne, UK
| | | | - Gavin Hudson
- Wellcome Centre for Mitochondrial Research, Biosciences Institute, Newcastle University, Newcastle upon Tyne, UK.
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24
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Ehsan N, Kotis BM, Castel SE, Song EJ, Mancuso N, Mohammadi P. Haplotype-aware modeling of cis-regulatory effects highlights the gaps remaining in eQTL data. Nat Commun 2024; 15:522. [PMID: 38225224 PMCID: PMC10789818 DOI: 10.1038/s41467-024-44710-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 12/30/2023] [Indexed: 01/17/2024] Open
Abstract
Expression Quantitative Trait Loci (eQTLs) are critical to understanding the mechanisms underlying disease-associated genomic loci. Nearly all protein-coding genes in the human genome have been associated with one or more eQTLs. Here we introduce a multi-variant generalization of allelic Fold Change (aFC), aFC-n, to enable quantification of the cis-regulatory effects in multi-eQTL genes under the assumption that all eQTLs are known and conditionally independent. Applying aFC-n to 458,465 eQTLs in the Genotype-Tissue Expression (GTEx) project data, we demonstrate significant improvements in accuracy over the original model in estimating the eQTL effect sizes and in predicting genetically regulated gene expression over the current tools. We characterize some of the empirical properties of the eQTL data and use this framework to assess the current state of eQTL data in terms of characterizing cis-regulatory landscape in individual genomes. Notably, we show that 77.4% of the genes with an allelic imbalance in a sample show 0.5 log2 fold or more of residual imbalance after accounting for the eQTL data underlining the remaining gap in characterizing regulatory landscape in individual genomes. We further contrast this gap across tissue types, and ancestry backgrounds to identify its correlates and guide future studies.
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Affiliation(s)
- Nava Ehsan
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA
| | - Bence M Kotis
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA
| | - Stephane E Castel
- Department of Systems Biology, Columbia University, New York, NY, USA
- New York Genome Center, New York, NY, USA
| | - Eric J Song
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA
| | - Nicholas Mancuso
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern, California, CA, USA
| | - Pejman Mohammadi
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA.
- Center for Immunity and Immunotherapies, Seattle Children's Research Institute, Seattle, WA, USA.
- Department of Pediatrics, University of Washington School of Medicine, Seattle, WA, USA.
- Department of Genome Sciences, University of Washington, Seattle, WA, USA.
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25
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Wang X, Duan M, Li J, Ma A, Xin G, Xu D, Li Z, Liu B, Ma Q. MarsGT: Multi-omics analysis for rare population inference using single-cell graph transformer. Nat Commun 2024; 15:338. [PMID: 38184630 PMCID: PMC10771517 DOI: 10.1038/s41467-023-44570-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Accepted: 12/14/2023] [Indexed: 01/08/2024] Open
Abstract
Rare cell populations are key in neoplastic progression and therapeutic response, offering potential intervention targets. However, their computational identification and analysis often lag behind major cell types. To fill this gap, we introduce MarsGT: Multi-omics Analysis for Rare population inference using a Single-cell Graph Transformer. It identifies rare cell populations using a probability-based heterogeneous graph transformer on single-cell multi-omics data. MarsGT outperforms existing tools in identifying rare cells across 550 simulated and four real human datasets. In mouse retina data, it reveals unique subpopulations of rare bipolar cells and a Müller glia cell subpopulation. In human lymph node data, MarsGT detects an intermediate B cell population potentially acting as lymphoma precursors. In human melanoma data, it identifies a rare MAIT-like population impacted by a high IFN-I response and reveals the mechanism of immunotherapy. Hence, MarsGT offers biological insights and suggests potential strategies for early detection and therapeutic intervention of disease.
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Affiliation(s)
- Xiaoying Wang
- School of Mathematics, Shandong University, Jinan, Shandong, 250100, China
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, 43210, USA
- Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH, 43210, USA
| | - Maoteng Duan
- School of Mathematics, Shandong University, Jinan, Shandong, 250100, China
| | - Jingxian Li
- School of Mathematics, Shandong University, Jinan, Shandong, 250100, China
| | - Anjun Ma
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, 43210, USA
- Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH, 43210, USA
| | - Gang Xin
- Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH, 43210, USA
| | - Dong Xu
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, 65211, USA
- Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, 65211, USA
| | - Zihai Li
- Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH, 43210, USA
| | - Bingqiang Liu
- School of Mathematics, Shandong University, Jinan, Shandong, 250100, China.
| | - Qin Ma
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, 43210, USA.
- Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH, 43210, USA.
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26
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Harripaul R, Morini E, Salani M, Logan E, Kirchner E, Bolduc J, Chekuri A, Currall B, Yadav R, Erdin S, Talkowski ME, Gao D, Slaugenhaupt S. Transcriptome analysis in a humanized mouse model of familial dysautonomia reveals tissue-specific gene expression disruption in the peripheral nervous system. Sci Rep 2024; 14:570. [PMID: 38177237 PMCID: PMC10766950 DOI: 10.1038/s41598-023-51137-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 12/31/2023] [Indexed: 01/06/2024] Open
Abstract
Familial dysautonomia (FD) is a rare recessive neurodevelopmental disease caused by a splice mutation in the Elongator acetyltransferase complex subunit 1 (ELP1) gene. This mutation results in a tissue-specific reduction of ELP1 protein, with the lowest levels in the central and peripheral nervous systems (CNS and PNS, respectively). FD patients exhibit complex neurological phenotypes due to the loss of sensory and autonomic neurons. Disease symptoms include decreased pain and temperature perception, impaired or absent myotatic reflexes, proprioceptive ataxia, and progressive retinal degeneration. While the involvement of the PNS in FD pathogenesis has been clearly recognized, the underlying mechanisms responsible for the preferential neuronal loss remain unknown. In this study, we aimed to elucidate the molecular mechanisms underlying FD by conducting a comprehensive transcriptome analysis of neuronal tissues from the phenotypic mouse model TgFD9; Elp1Δ20/flox. This mouse recapitulates the same tissue-specific ELP1 mis-splicing observed in patients while modeling many of the disease manifestations. Comparison of FD and control transcriptomes from dorsal root ganglion (DRG), trigeminal ganglion (TG), medulla (MED), cortex, and spinal cord (SC) showed significantly more differentially expressed genes (DEGs) in the PNS than the CNS. We then identified genes that were tightly co-expressed and functionally dependent on the level of full-length ELP1 transcript. These genes, defined as ELP1 dose-responsive genes, were combined with the DEGs to generate tissue-specific dysregulated FD signature genes and networks. Within the PNS networks, we observed direct connections between Elp1 and genes involved in tRNA synthesis and genes related to amine metabolism and synaptic signaling. Importantly, transcriptomic dysregulation in PNS tissues exhibited enrichment for neuronal subtype markers associated with peptidergic nociceptors and myelinated sensory neurons, which are known to be affected in FD. In summary, this study has identified critical tissue-specific gene networks underlying the etiology of FD and provides new insights into the molecular basis of the disease.
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Affiliation(s)
- Ricardo Harripaul
- Center for Genomic Medicine, Massachusetts General Hospital Research Institute, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics and Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Elisabetta Morini
- Center for Genomic Medicine, Massachusetts General Hospital Research Institute, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Monica Salani
- Center for Genomic Medicine, Massachusetts General Hospital Research Institute, Boston, MA, USA
| | - Emily Logan
- Center for Genomic Medicine, Massachusetts General Hospital Research Institute, Boston, MA, USA
| | - Emily Kirchner
- Center for Genomic Medicine, Massachusetts General Hospital Research Institute, Boston, MA, USA
| | - Jessica Bolduc
- Center for Genomic Medicine, Massachusetts General Hospital Research Institute, Boston, MA, USA
| | - Anil Chekuri
- Center for Genomic Medicine, Massachusetts General Hospital Research Institute, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Benjamin Currall
- Center for Genomic Medicine, Massachusetts General Hospital Research Institute, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics and Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Rachita Yadav
- Center for Genomic Medicine, Massachusetts General Hospital Research Institute, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics and Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Serkan Erdin
- Center for Genomic Medicine, Massachusetts General Hospital Research Institute, Boston, MA, USA
- Program in Medical and Population Genetics and Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Michael E Talkowski
- Center for Genomic Medicine, Massachusetts General Hospital Research Institute, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics and Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Dadi Gao
- Center for Genomic Medicine, Massachusetts General Hospital Research Institute, Boston, MA, USA.
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
| | - Susan Slaugenhaupt
- Center for Genomic Medicine, Massachusetts General Hospital Research Institute, Boston, MA, USA.
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
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27
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Mollanoori H, Ghelmani Y, Hassani B, Dehghani M. Integrated whole transcriptome profiling revealed a convoluted circular RNA-based competing endogenous RNAs regulatory network in colorectal cancer. Sci Rep 2024; 14:91. [PMID: 38167453 PMCID: PMC10761719 DOI: 10.1038/s41598-023-50230-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 12/17/2023] [Indexed: 01/05/2024] Open
Abstract
Recently, it has been identified that circRNAs can act as miRNA sponge to regulate gene expression in various types of cancers, associating them with cancer initiation and progression. The present study aims to identify colorectal cancer-related circRNAs and the underpinning mechanisms of circRNA/miRNA/mRNA networks in the development and progress of Colorectal Cancer. Differentially expressed circRNAs, miRNAs, and mRNAs were identified in GEO microarray datasets using the Limma package of R. The analysis of differentially expressed circRNAs resulted in 23 upregulated and 31 downregulated circRNAs. CeRNAs networks were constructed by intersecting the results of predicted and experimentally validated databases, circbank and miRWalk, and by performing DEMs and DEGs analysis using Cytoscape. Next, functional enrichment analysis was performed for DEGs included in ceRNA networks. Followed by survival analysis, expression profile assessment using TCGA and GEO data, and ROC curve analysis we identified a ceRNA sub-networks that revealed the potential regulatory effect of hsa_circ_0001955 and hsa_circ_0071681 on survival-related genes, namely KLF4, MYC, CCNA2, RACGAP1, and CD44. Overall, we constructed a convoluted regulatory network and outlined its likely mechanisms of action in CRC, which may contribute to the development of more effective approaches for early diagnosis, prognosis, and treatment of CRC.
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Affiliation(s)
- Hasan Mollanoori
- Medical Genetics Research Center, Shahid Sadoughi University of Medical Sciences, Yazd, Iran
| | - Yaser Ghelmani
- Clinical Research Development Center, Shahid Sadoughi Hospital, Shahid Sadoughi University of Medical Sciences, Yazd, Iran
| | - Bita Hassani
- Sarem Gynecology, Obstertrics and Infertility Research Center, Sarem Women's Hospital, Iran University of Medical Sciences (IUMS), Tehran, Iran
| | - Mohammadreza Dehghani
- Medical Genetics Research Center, Shahid Sadoughi University of Medical Sciences, Yazd, Iran.
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28
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Bravo González-Blas C, Matetovici I, Hillen H, Taskiran II, Vandepoel R, Christiaens V, Sansores-García L, Verboven E, Hulselmans G, Poovathingal S, Demeulemeester J, Psatha N, Mauduit D, Halder G, Aerts S. Single-cell spatial multi-omics and deep learning dissect enhancer-driven gene regulatory networks in liver zonation. Nat Cell Biol 2024; 26:153-167. [PMID: 38182825 PMCID: PMC10791584 DOI: 10.1038/s41556-023-01316-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 11/15/2023] [Indexed: 01/07/2024]
Abstract
In the mammalian liver, hepatocytes exhibit diverse metabolic and functional profiles based on their location within the liver lobule. However, it is unclear whether this spatial variation, called zonation, is governed by a well-defined gene regulatory code. Here, using a combination of single-cell multiomics, spatial omics, massively parallel reporter assays and deep learning, we mapped enhancer-gene regulatory networks across mouse liver cell types. We found that zonation affects gene expression and chromatin accessibility in hepatocytes, among other cell types. These states are driven by the repressors TCF7L1 and TBX3, alongside other core hepatocyte transcription factors, such as HNF4A, CEBPA, FOXA1 and ONECUT1. To examine the architecture of the enhancers driving these cell states, we trained a hierarchical deep learning model called DeepLiver. Our study provides a multimodal understanding of the regulatory code underlying hepatocyte identity and their zonation state that can be used to engineer enhancers with specific activity levels and zonation patterns.
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Affiliation(s)
- Carmen Bravo González-Blas
- VIB Center for Brain & Disease Research, Leuven, Belgium
- Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Irina Matetovici
- VIB Center for Brain & Disease Research, Leuven, Belgium
- VIB Center for AI and Computational Biology (VIB.AI), Leuven, Belgium
- VIB Tech Watch, VIB Headquarters, Ghent, Belgium
| | - Hanne Hillen
- VIB Center for Cancer Biology, Leuven, Belgium
- Department of Oncology, KU Leuven, Leuven, Belgium
| | - Ibrahim Ihsan Taskiran
- VIB Center for Brain & Disease Research, Leuven, Belgium
- Department of Human Genetics, KU Leuven, Leuven, Belgium
- VIB Center for AI and Computational Biology (VIB.AI), Leuven, Belgium
| | - Roel Vandepoel
- VIB Center for Brain & Disease Research, Leuven, Belgium
- Department of Human Genetics, KU Leuven, Leuven, Belgium
- VIB Center for AI and Computational Biology (VIB.AI), Leuven, Belgium
| | - Valerie Christiaens
- VIB Center for Brain & Disease Research, Leuven, Belgium
- Department of Human Genetics, KU Leuven, Leuven, Belgium
- VIB Center for AI and Computational Biology (VIB.AI), Leuven, Belgium
| | - Leticia Sansores-García
- VIB Center for Cancer Biology, Leuven, Belgium
- Department of Oncology, KU Leuven, Leuven, Belgium
| | - Elisabeth Verboven
- VIB Center for Cancer Biology, Leuven, Belgium
- Department of Oncology, KU Leuven, Leuven, Belgium
| | - Gert Hulselmans
- VIB Center for Brain & Disease Research, Leuven, Belgium
- Department of Human Genetics, KU Leuven, Leuven, Belgium
- VIB Center for AI and Computational Biology (VIB.AI), Leuven, Belgium
| | | | - Jonas Demeulemeester
- VIB Center for Brain & Disease Research, Leuven, Belgium
- Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Nikoleta Psatha
- VIB Center for Brain & Disease Research, Leuven, Belgium
- Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - David Mauduit
- VIB Center for Brain & Disease Research, Leuven, Belgium
- Department of Human Genetics, KU Leuven, Leuven, Belgium
- VIB Center for AI and Computational Biology (VIB.AI), Leuven, Belgium
| | - Georg Halder
- VIB Center for Cancer Biology, Leuven, Belgium
- Department of Oncology, KU Leuven, Leuven, Belgium
| | - Stein Aerts
- VIB Center for Brain & Disease Research, Leuven, Belgium.
- Department of Human Genetics, KU Leuven, Leuven, Belgium.
- VIB Center for AI and Computational Biology (VIB.AI), Leuven, Belgium.
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29
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Ishikawa M, Sugino S, Masuda Y, Tarumoto Y, Seto Y, Taniyama N, Wagai F, Yamauchi Y, Kojima Y, Kiryu H, Yusa K, Eiraku M, Mochizuki A. RENGE infers gene regulatory networks using time-series single-cell RNA-seq data with CRISPR perturbations. Commun Biol 2023; 6:1290. [PMID: 38155269 PMCID: PMC10754834 DOI: 10.1038/s42003-023-05594-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Accepted: 11/15/2023] [Indexed: 12/30/2023] Open
Abstract
Single-cell RNA-seq analysis coupled with CRISPR-based perturbation has enabled the inference of gene regulatory networks with causal relationships. However, a snapshot of single-cell CRISPR data may not lead to an accurate inference, since a gene knockout can influence multi-layered downstream over time. Here, we developed RENGE, a computational method that infers gene regulatory networks using a time-series single-cell CRISPR dataset. RENGE models the propagation process of the effects elicited by a gene knockout on its regulatory network. It can distinguish between direct and indirect regulations, which allows for the inference of regulations by genes that are not knocked out. RENGE therefore outperforms current methods in the accuracy of inferring gene regulatory networks. When used on a dataset we derived from human-induced pluripotent stem cells, RENGE yielded a network consistent with multiple databases and literature. Accurate inference of gene regulatory networks by RENGE would enable the identification of key factors for various biological systems.
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Affiliation(s)
- Masato Ishikawa
- Institute for Life and Medical Sciences, Kyoto University, Kyoto, 606-8507, Japan.
| | - Seiichi Sugino
- Institute for Life and Medical Sciences, Kyoto University, Kyoto, 606-8507, Japan
| | - Yoshie Masuda
- Institute for Life and Medical Sciences, Kyoto University, Kyoto, 606-8507, Japan
| | - Yusuke Tarumoto
- Institute for Life and Medical Sciences, Kyoto University, Kyoto, 606-8507, Japan
| | - Yusuke Seto
- Institute for Life and Medical Sciences, Kyoto University, Kyoto, 606-8507, Japan
| | - Nobuko Taniyama
- Institute for Life and Medical Sciences, Kyoto University, Kyoto, 606-8507, Japan
| | - Fumi Wagai
- Institute for Life and Medical Sciences, Kyoto University, Kyoto, 606-8507, Japan
| | - Yuhei Yamauchi
- Institute for Life and Medical Sciences, Kyoto University, Kyoto, 606-8507, Japan
| | - Yasuhiro Kojima
- Laboratory of Computational Life Science, National Cancer Center Research Institute, Tokyo, 104-0045, Japan
| | - Hisanori Kiryu
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, 277-8561, Japan
| | - Kosuke Yusa
- Institute for Life and Medical Sciences, Kyoto University, Kyoto, 606-8507, Japan
| | - Mototsugu Eiraku
- Institute for Life and Medical Sciences, Kyoto University, Kyoto, 606-8507, Japan
- Institute for the Advanced Study of Human Biology (WPI-ASHBi), Kyoto University, Kyoto, 606-8507, Japan
| | - Atsushi Mochizuki
- Institute for Life and Medical Sciences, Kyoto University, Kyoto, 606-8507, Japan
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30
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Coleman DJL, Keane P, Luque-Martin R, Chin PS, Blair H, Ames L, Kellaway SG, Griffin J, Holmes E, Potluri S, Assi SA, Bushweller J, Heidenreich O, Cockerill PN, Bonifer C. Gene regulatory network analysis predicts cooperating transcription factor regulons required for FLT3-ITD+ AML growth. Cell Rep 2023; 42:113568. [PMID: 38104314 PMCID: PMC10874628 DOI: 10.1016/j.celrep.2023.113568] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 11/03/2023] [Accepted: 11/27/2023] [Indexed: 12/19/2023] Open
Abstract
Acute myeloid leukemia (AML) is a heterogeneous disease caused by different mutations. Previously, we showed that each mutational subtype develops its specific gene regulatory network (GRN) with transcription factors interacting within multiple gene modules, many of which are transcription factor genes themselves. Here, we hypothesize that highly connected nodes within such networks comprise crucial regulators of AML maintenance. We test this hypothesis using FLT3-ITD-mutated AML as a model and conduct an shRNA drop-out screen informed by this analysis. We show that AML-specific GRNs predict crucial regulatory modules required for AML growth. Furthermore, our work shows that all modules are highly connected and regulate each other. The careful multi-omic analysis of the role of one (RUNX1) module by shRNA and chemical inhibition shows that this transcription factor and its target genes stabilize the GRN of FLT3-ITD+ AML and that its removal leads to GRN collapse and cell death.
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Affiliation(s)
- Daniel J L Coleman
- Institute of Cancer and Genomic Sciences, College of Medicine and Dentistry, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
| | - Peter Keane
- Institute of Cancer and Genomic Sciences, College of Medicine and Dentistry, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK; School of Biosciences, University of Birmingham, Birmingham B15 2TT, U.K
| | - Rosario Luque-Martin
- Wolfson Childhood Cancer Research Centre, Translational and Clinical Research Institute, Newcastle University, Herschel Building, Level 6, Brewery Lane, Newcastle upon Tyne NE1 7RU, UK
| | - Paulynn S Chin
- Institute of Cancer and Genomic Sciences, College of Medicine and Dentistry, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
| | - Helen Blair
- Wolfson Childhood Cancer Research Centre, Translational and Clinical Research Institute, Newcastle University, Herschel Building, Level 6, Brewery Lane, Newcastle upon Tyne NE1 7RU, UK
| | - Luke Ames
- Institute of Cancer and Genomic Sciences, College of Medicine and Dentistry, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
| | - Sophie G Kellaway
- Institute of Cancer and Genomic Sciences, College of Medicine and Dentistry, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
| | - James Griffin
- Institute of Cancer and Genomic Sciences, College of Medicine and Dentistry, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
| | - Elizabeth Holmes
- Institute of Cancer and Genomic Sciences, College of Medicine and Dentistry, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
| | - Sandeep Potluri
- Institute of Cancer and Genomic Sciences, College of Medicine and Dentistry, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
| | - Salam A Assi
- Institute of Cancer and Genomic Sciences, College of Medicine and Dentistry, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
| | - John Bushweller
- University of Virginia, 1340 Jefferson Park Avenue, Charlottesville, VA 22908, USA
| | - Olaf Heidenreich
- Wolfson Childhood Cancer Research Centre, Translational and Clinical Research Institute, Newcastle University, Herschel Building, Level 6, Brewery Lane, Newcastle upon Tyne NE1 7RU, UK; Prinses Máxima Centrum for Pediatric Oncology, Postbus 113, 3720 AC Bilthoven, Heidelberglaan 25, 3584CS Utrecht, the Netherlands.
| | - Peter N Cockerill
- Institute of Cancer and Genomic Sciences, College of Medicine and Dentistry, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK.
| | - Constanze Bonifer
- Institute of Cancer and Genomic Sciences, College of Medicine and Dentistry, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK.
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31
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Biswas A, Sahoo S, Riedlinger GM, Ghodoussipour S, Jolly MK, De S. Transcriptional state dynamics lead to heterogeneity and adaptive tumor evolution in urothelial bladder carcinoma. Commun Biol 2023; 6:1292. [PMID: 38129585 PMCID: PMC10739805 DOI: 10.1038/s42003-023-05668-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Accepted: 12/04/2023] [Indexed: 12/23/2023] Open
Abstract
Intra-tumor heterogeneity contributes to treatment failure and poor survival in urothelial bladder carcinoma (UBC). Analyzing transcriptome from a UBC cohort, we report that intra-tumor transcriptomic heterogeneity indicates co-existence of tumor cells in epithelial and mesenchymal-like transcriptional states and bi-directional transition between them occurs within and between tumor subclones. We model spontaneous and reversible transition between these partially heritable states in cell lines and characterize their population dynamics. SMAD3, KLF4 and PPARG emerge as key regulatory markers of the transcriptional dynamics. Nutrient limitation, as in the core of large tumors, and radiation treatment perturb the dynamics, initially selecting for a transiently resistant phenotype and then reconstituting heterogeneity and growth potential, driving adaptive evolution. Dominance of transcriptional states with low PPARG expression indicates an aggressive phenotype in UBC patients. We propose that phenotypic plasticity and dynamic, non-genetic intra-tumor heterogeneity modulate both the trajectory of disease progression and adaptive treatment response in UBC.
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Affiliation(s)
- Antara Biswas
- Rutgers Cancer Institute of New Jersey, Rutgers the State University of New Jersey, New Brunswick, NJ, USA.
| | | | - Gregory M Riedlinger
- Rutgers Cancer Institute of New Jersey, Rutgers the State University of New Jersey, New Brunswick, NJ, USA
| | - Saum Ghodoussipour
- Rutgers Cancer Institute of New Jersey, Rutgers the State University of New Jersey, New Brunswick, NJ, USA
| | | | - Subhajyoti De
- Rutgers Cancer Institute of New Jersey, Rutgers the State University of New Jersey, New Brunswick, NJ, USA.
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32
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Tutino M, Hankinson J, Murray C, Lowe L, Kerry G, Rattray M, Custovic A, Johnston SL, Shi C, Orozco G, Eyre S, Martin P, Simpson A, Curtin JA. Identification of differences in CD4 + T-cell gene expression between people with asthma and healthy controls. Sci Rep 2023; 13:22796. [PMID: 38129444 PMCID: PMC10739740 DOI: 10.1038/s41598-023-49135-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Accepted: 12/04/2023] [Indexed: 12/23/2023] Open
Abstract
Functional enrichment analysis of genome-wide association study (GWAS)-summary statistics has suggested that CD4+ T-cells play an important role in asthma pathogenesis. Despite this, CD4+ T-cells are under-represented in asthma transcriptome studies. To fill the gap, 3'-RNA-Seq was used to generate gene expression data on CD4+ T-cells (isolated within 2 h from collection) from peripheral blood from participants with well-controlled asthma (n = 32) and healthy controls (n = 11). Weighted Gene Co-expression Network Analysis (WGCNA) was used to identify sets of co-expressed genes (modules) associated with the asthma phenotype. We identified three modules associated with asthma, which are strongly enriched for GWAS-identified asthma genes, antigen processing/presentation and immune response to viral infections. Through integration of publicly available eQTL and GWAS summary statistics (colocalisation), and protein-protein interaction (PPI) data, we identified PTPRC, a potential druggable target, as a putative master regulator of the asthma gene-expression profiles. Using a co-expression network approach, with integration of external genetic and PPI data, we showed that CD4+ T-cells from peripheral blood from asthmatics have different expression profiles, albeit small in magnitude, compared to healthy controls, for sets of genes involved in immune response to viral infections (upregulated) and antigen processing/presentation (downregulated).
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Affiliation(s)
- Mauro Tutino
- Division of Infection, Immunity and Respiratory Medicine, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, M13 9PL, UK.
| | - Jenny Hankinson
- Division of Infection, Immunity and Respiratory Medicine, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, M13 9PL, UK
| | - Clare Murray
- Division of Infection, Immunity and Respiratory Medicine, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, M13 9PL, UK
- Manchester Biomedical Research Centre, Manchester University NHS Foundation Trust, Manchester, UK
| | - Lesley Lowe
- Division of Infection, Immunity and Respiratory Medicine, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, M13 9PL, UK
- Manchester Biomedical Research Centre, Manchester University NHS Foundation Trust, Manchester, UK
| | - Gina Kerry
- Division of Infection, Immunity and Respiratory Medicine, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, M13 9PL, UK
- Manchester Biomedical Research Centre, Manchester University NHS Foundation Trust, Manchester, UK
| | - Magnus Rattray
- Division of Informatics, Imaging and Data Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Adnan Custovic
- National Heart and Lung Institute, Asthma UK Centre in Allergic Mechanisms of Asthma, Imperial College London, London, UK
| | - Sebastian L Johnston
- National Heart and Lung Institute, Asthma UK Centre in Allergic Mechanisms of Asthma, Imperial College London, London, UK
| | - Chenfu Shi
- Centre for Genetics and Genomics Versus Arthritis, Division of Musculoskeletal and Dermatological Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
| | - Gisela Orozco
- Manchester Biomedical Research Centre, Manchester University NHS Foundation Trust, Manchester, UK
- Centre for Genetics and Genomics Versus Arthritis, Division of Musculoskeletal and Dermatological Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
| | - Stephen Eyre
- Manchester Biomedical Research Centre, Manchester University NHS Foundation Trust, Manchester, UK
- Centre for Genetics and Genomics Versus Arthritis, Division of Musculoskeletal and Dermatological Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
| | - Paul Martin
- Centre for Genetics and Genomics Versus Arthritis, Division of Musculoskeletal and Dermatological Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
- The Lydia Becker Institute of Immunology and Inflammation, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Angela Simpson
- Division of Infection, Immunity and Respiratory Medicine, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, M13 9PL, UK
| | - John A Curtin
- Division of Infection, Immunity and Respiratory Medicine, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, M13 9PL, UK
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Trujillo-Ortíz R, Espinal-Enríquez J, Hernández-Lemus E. The Role of Transcription Factors in the Loss of Inter-Chromosomal Co-Expression for Breast Cancer Subtypes. Int J Mol Sci 2023; 24:17564. [PMID: 38139393 PMCID: PMC10743684 DOI: 10.3390/ijms242417564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 12/05/2023] [Accepted: 12/06/2023] [Indexed: 12/24/2023] Open
Abstract
Breast cancer encompasses a diverse array of subtypes, each exhibiting distinct clinical characteristics and treatment responses. Unraveling the underlying regulatory mechanisms that govern gene expression patterns in these subtypes is essential for advancing our understanding of breast cancer biology. Gene co-expression networks (GCNs) help us identify groups of genes that work in coordination. Previous research has revealed a marked reduction in the interaction of genes located on different chromosomes within GCNs for breast cancer, as well as for lung, kidney, and hematopoietic cancers. However, the reasons behind why genes on the same chromosome often co-express remain unclear. In this study, we investigate the role of transcription factors in shaping gene co-expression networks within the four main breast cancer subtypes: Luminal A, Luminal B, HER2+, and Basal, along with normal breast tissue. We identify communities within each GCN and calculate the transcription factors that may regulate these communities, comparing the results across different phenotypes. Our findings indicate that, in general, regulatory behavior is to a large extent similar among breast cancer molecular subtypes and even in healthy networks. This suggests that transcription factor motif usage does not fully determine long-range co-expression patterns. Specific transcription factor motifs, such as CCGGAAG, appear frequently across all phenotypes, even involving multiple highly connected transcription factors. Additionally, certain transcription factors exhibit unique actions in specific subtypes but with limited influence. Our research demonstrates that the loss of inter-chromosomal co-expression is not solely attributable to transcription factor regulation. Although the exact mechanism responsible for this phenomenon remains elusive, this work contributes to a better understanding of gene expression regulatory programs in breast cancer.
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Affiliation(s)
- Rodrigo Trujillo-Ortíz
- Computational Genomics Division, Instituto Nacional de Medicina Genómica, Mexico City 14610, Mexico;
| | - Jesús Espinal-Enríquez
- Computational Genomics Division, Instituto Nacional de Medicina Genómica, Mexico City 14610, Mexico;
- Center for Complexity Sciences, Universidad Nacional Autónoma de México, Mexico City 01010, Mexico
| | - Enrique Hernández-Lemus
- Computational Genomics Division, Instituto Nacional de Medicina Genómica, Mexico City 14610, Mexico;
- Center for Complexity Sciences, Universidad Nacional Autónoma de México, Mexico City 01010, Mexico
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Vannier N, Mesny F, Getzke F, Chesneau G, Dethier L, Ordon J, Thiergart T, Hacquard S. Genome-resolved metatranscriptomics reveals conserved root colonization determinants in a synthetic microbiota. Nat Commun 2023; 14:8274. [PMID: 38092730 PMCID: PMC10719396 DOI: 10.1038/s41467-023-43688-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 11/16/2023] [Indexed: 12/17/2023] Open
Abstract
The identification of processes activated by specific microbes during microbiota colonization of plant roots has been hampered by technical constraints in metatranscriptomics. These include lack of reference genomes, high representation of host or microbial rRNA sequences in datasets, or difficulty to experimentally validate gene functions. Here, we recolonized germ-free Arabidopsis thaliana with a synthetic, yet representative root microbiota comprising 106 genome-sequenced bacterial and fungal isolates. We used multi-kingdom rRNA depletion, deep RNA-sequencing and read mapping against reference microbial genomes to analyse the in planta metatranscriptome of abundant colonizers. We identified over 3,000 microbial genes that were differentially regulated at the soil-root interface. Translation and energy production processes were consistently activated in planta, and their induction correlated with bacterial strains' abundance in roots. Finally, we used targeted mutagenesis to show that several genes consistently induced by multiple bacteria are required for root colonization in one of the abundant bacterial strains (a genetically tractable Rhodanobacter). Our results indicate that microbiota members activate strain-specific processes but also common gene sets to colonize plant roots.
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Affiliation(s)
- Nathan Vannier
- Department of Plant Microbe Interactions, Max Planck Institute for Plant Breeding Research, 50829, Cologne, Germany
- IGEPP, INRAE, Institut Agro, Univ Rennes, 35653, Le Rheu, France
| | - Fantin Mesny
- Department of Plant Microbe Interactions, Max Planck Institute for Plant Breeding Research, 50829, Cologne, Germany
- Institute for Plant Sciences, University of Cologne, 50923, Cologne, Germany
| | - Felix Getzke
- Department of Plant Microbe Interactions, Max Planck Institute for Plant Breeding Research, 50829, Cologne, Germany
| | - Guillaume Chesneau
- Department of Plant Microbe Interactions, Max Planck Institute for Plant Breeding Research, 50829, Cologne, Germany
| | - Laura Dethier
- Department of Plant Microbe Interactions, Max Planck Institute for Plant Breeding Research, 50829, Cologne, Germany
| | - Jana Ordon
- Department of Plant Microbe Interactions, Max Planck Institute for Plant Breeding Research, 50829, Cologne, Germany
| | - Thorsten Thiergart
- Department of Plant Microbe Interactions, Max Planck Institute for Plant Breeding Research, 50829, Cologne, Germany
| | - Stéphane Hacquard
- Department of Plant Microbe Interactions, Max Planck Institute for Plant Breeding Research, 50829, Cologne, Germany.
- Cluster of Excellence on Plant Sciences, Max Planck Institute for Plant Breeding Research, 50829, Cologne, Germany.
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35
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Ram A, Murphy D, DeCuzzi N, Patankar M, Hu J, Pargett M, Albeck JG. A guide to ERK dynamics, part 2: downstream decoding. Biochem J 2023; 480:1909-1928. [PMID: 38038975 PMCID: PMC10754290 DOI: 10.1042/bcj20230277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Revised: 11/03/2023] [Accepted: 11/09/2023] [Indexed: 12/02/2023]
Abstract
Signaling by the extracellular signal-regulated kinase (ERK) pathway controls many cellular processes, including cell division, death, and differentiation. In this second installment of a two-part review, we address the question of how the ERK pathway exerts distinct and context-specific effects on multiple processes. We discuss how the dynamics of ERK activity induce selective changes in gene expression programs, with insights from both experiments and computational models. With a focus on single-cell biosensor-based studies, we summarize four major functional modes for ERK signaling in tissues: adjusting the size of cell populations, gradient-based patterning, wave propagation of morphological changes, and diversification of cellular gene expression states. These modes of operation are disrupted in cancer and other related diseases and represent potential targets for therapeutic intervention. By understanding the dynamic mechanisms involved in ERK signaling, there is potential for pharmacological strategies that not only simply inhibit ERK, but also restore functional activity patterns and improve disease outcomes.
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Affiliation(s)
- Abhineet Ram
- Department of Molecular and Cellular Biology, University of California, Davis, CA, U.S.A
| | - Devan Murphy
- Department of Molecular and Cellular Biology, University of California, Davis, CA, U.S.A
| | - Nicholaus DeCuzzi
- Department of Molecular and Cellular Biology, University of California, Davis, CA, U.S.A
| | - Madhura Patankar
- Department of Molecular and Cellular Biology, University of California, Davis, CA, U.S.A
| | - Jason Hu
- Department of Molecular and Cellular Biology, University of California, Davis, CA, U.S.A
| | - Michael Pargett
- Department of Molecular and Cellular Biology, University of California, Davis, CA, U.S.A
| | - John G. Albeck
- Department of Molecular and Cellular Biology, University of California, Davis, CA, U.S.A
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36
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Kurt Z, Cheng J, Barrere-Cain R, McQuillen CN, Saleem Z, Hsu N, Jiang N, Pan C, Franzén O, Koplev S, Wang S, Björkegren J, Lusis AJ, Blencowe M, Yang X. Shared and distinct pathways and networks genetically linked to coronary artery disease between human and mouse. eLife 2023; 12:RP88266. [PMID: 38060277 PMCID: PMC10703441 DOI: 10.7554/elife.88266] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/08/2023] Open
Abstract
Mouse models have been used extensively to study human coronary artery disease (CAD) or atherosclerosis and to test therapeutic targets. However, whether mouse and human share similar genetic factors and pathogenic mechanisms of atherosclerosis has not been thoroughly investigated in a data-driven manner. We conducted a cross-species comparison study to better understand atherosclerosis pathogenesis between species by leveraging multiomics data. Specifically, we compared genetically driven and thus CAD-causal gene networks and pathways, by using human GWAS of CAD from the CARDIoGRAMplusC4D consortium and mouse GWAS of atherosclerosis from the Hybrid Mouse Diversity Panel (HMDP) followed by integration with functional multiomics human (STARNET and GTEx) and mouse (HMDP) databases. We found that mouse and human shared >75% of CAD causal pathways. Based on network topology, we then predicted key regulatory genes for both the shared pathways and species-specific pathways, which were further validated through the use of single cell data and the latest CAD GWAS. In sum, our results should serve as a much-needed guidance for which human CAD-causal pathways can or cannot be further evaluated for novel CAD therapies using mouse models.
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Affiliation(s)
- Zeyneb Kurt
- Department of Integrative Biology and Physiology, University of California, Los AngelesLos AngelesUnited States
- The Information School at the University of SheffieldSheffieldUnited Kingdom
| | - Jenny Cheng
- Department of Integrative Biology and Physiology, University of California, Los AngelesLos AngelesUnited States
- Interdepartmental Program of Molecular, Cellular and Integrative Physiology, University of California, Los AngelesLos AngelesUnited States
| | - Rio Barrere-Cain
- Department of Integrative Biology and Physiology, University of California, Los AngelesLos AngelesUnited States
| | - Caden N McQuillen
- Department of Integrative Biology and Physiology, University of California, Los AngelesLos AngelesUnited States
| | - Zara Saleem
- Department of Integrative Biology and Physiology, University of California, Los AngelesLos AngelesUnited States
| | - Neil Hsu
- Department of Integrative Biology and Physiology, University of California, Los AngelesLos AngelesUnited States
| | - Nuoya Jiang
- Department of Integrative Biology and Physiology, University of California, Los AngelesLos AngelesUnited States
| | - Calvin Pan
- Department of Medicine, Division of Cardiology, University of California, Los AngelesLos AngelesUnited States
| | - Oscar Franzén
- Department of Genetics & Genomic Sciences, Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount SinaiNew YorkUnited States
| | - Simon Koplev
- Department of Genetics & Genomic Sciences, Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount SinaiNew YorkUnited States
| | - Susanna Wang
- Department of Integrative Biology and Physiology, University of California, Los AngelesLos AngelesUnited States
| | - Johan Björkegren
- Department of Genetics & Genomic Sciences, Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount SinaiNew YorkUnited States
- Department of Medicine, (Huddinge), Karolinska InstitutetHuddingeSweden
| | - Aldons J Lusis
- Department of Medicine, Division of Cardiology, University of California, Los AngelesLos AngelesUnited States
- Departments of Human Genetics & Microbiology, Immunology, and Molecular Genetics, UCLALos AngelesUnited States
- Cardiovascular Research Laboratory, David Geffen School of Medicine, UCLALos AngelesUnited States
| | - Montgomery Blencowe
- Department of Integrative Biology and Physiology, University of California, Los AngelesLos AngelesUnited States
- Interdepartmental Program of Molecular, Cellular and Integrative Physiology, University of California, Los AngelesLos AngelesUnited States
| | - Xia Yang
- Department of Integrative Biology and Physiology, University of California, Los AngelesLos AngelesUnited States
- Interdepartmental Program of Molecular, Cellular and Integrative Physiology, University of California, Los AngelesLos AngelesUnited States
- Interdepartmental Program of Bioinformatics, University of California, Los AngelesLos AngelesUnited States
- Department of Molecular and Medical Pharmacology, University of California, Los AngelesLos AngelesUnited States
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37
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Abstract
A deep analysis of multiple genomic datasets reveals which genetic pathways associated with atherosclerosis and coronary artery disease are shared between mice and humans.
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Affiliation(s)
- Arya Mani
- Department of Internal Medicine and Genetics, Yale University School of MedicineNew HavenUnited States
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38
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Persad S, Choo ZN, Dien C, Sohail N, Masilionis I, Chaligné R, Nawy T, Brown CC, Sharma R, Pe'er I, Setty M, Pe'er D. SEACells infers transcriptional and epigenomic cellular states from single-cell genomics data. Nat Biotechnol 2023; 41:1746-1757. [PMID: 36973557 PMCID: PMC10713451 DOI: 10.1038/s41587-023-01716-9] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2022] [Accepted: 02/20/2023] [Indexed: 03/29/2023]
Abstract
Metacells are cell groupings derived from single-cell sequencing data that represent highly granular, distinct cell states. Here we present single-cell aggregation of cell states (SEACells), an algorithm for identifying metacells that overcome the sparsity of single-cell data while retaining heterogeneity obscured by traditional cell clustering. SEACells outperforms existing algorithms in identifying comprehensive, compact and well-separated metacells in both RNA and assay for transposase-accessible chromatin (ATAC) modalities across datasets with discrete cell types and continuous trajectories. We demonstrate the use of SEACells to improve gene-peak associations, compute ATAC gene scores and infer the activities of critical regulators during differentiation. Metacell-level analysis scales to large datasets and is particularly well suited for patient cohorts, where per-patient aggregation provides more robust units for data integration. We use our metacells to reveal expression dynamics and gradual reconfiguration of the chromatin landscape during hematopoietic differentiation and to uniquely identify CD4 T cell differentiation and activation states associated with disease onset and severity in a Coronavirus Disease 2019 (COVID-19) patient cohort.
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Affiliation(s)
- Sitara Persad
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Computer Science, Fu Foundation School of Engineering & Applied Science, Columbia University, New York, NY, USA
| | - Zi-Ning Choo
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Christine Dien
- Basic Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Computational Biology Program, Public Health Sciences Division and Translational Data Science IRC, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Noor Sohail
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ignas Masilionis
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ronan Chaligné
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Tal Nawy
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Chrysothemis C Brown
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Roshan Sharma
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Itsik Pe'er
- Department of Computer Science, Fu Foundation School of Engineering & Applied Science, Columbia University, New York, NY, USA
| | - Manu Setty
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
- Basic Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA.
- Computational Biology Program, Public Health Sciences Division and Translational Data Science IRC, Fred Hutchinson Cancer Center, Seattle, WA, USA.
| | - Dana Pe'er
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
- Howard Hughes Medical Institute, New York, NY, USA.
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39
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Noack F, Vangelisti S, Ditzer N, Chong F, Albert M, Bonev B. Joint epigenome profiling reveals cell-type-specific gene regulatory programmes in human cortical organoids. Nat Cell Biol 2023; 25:1873-1883. [PMID: 37996647 PMCID: PMC10709149 DOI: 10.1038/s41556-023-01296-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Accepted: 10/17/2023] [Indexed: 11/25/2023]
Abstract
Gene expression is regulated by multiple epigenetic mechanisms, which are coordinated in development and disease. However, current multiomics methods are frequently limited to one or two modalities at a time, making it challenging to obtain a comprehensive gene regulatory signature. Here, we describe a method-3D genome, RNA, accessibility and methylation sequencing (3DRAM-seq)-that simultaneously interrogates spatial genome organization, chromatin accessibility and DNA methylation genome-wide and at high resolution. We combine 3DRAM-seq with immunoFACS and RNA sequencing in cortical organoids to map the cell-type-specific regulatory landscape of human neural development across multiple epigenetic layers. Finally, we apply a massively parallel reporter assay to profile cell-type-specific enhancer activity in organoids and to functionally assess the role of key transcription factors for human enhancer activation and function. More broadly, 3DRAM-seq can be used to profile the multimodal epigenetic landscape in rare cell types and different tissues.
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Affiliation(s)
- Florian Noack
- Helmholtz Pioneer Campus, Helmholtz Zentrum München, Neuherberg, Germany
| | - Silvia Vangelisti
- Helmholtz Pioneer Campus, Helmholtz Zentrum München, Neuherberg, Germany
| | - Nora Ditzer
- Center for Regenerative Therapies Dresden, Technische Universität Dresden, Dresden, Germany
| | - Faye Chong
- Helmholtz Pioneer Campus, Helmholtz Zentrum München, Neuherberg, Germany
| | - Mareike Albert
- Center for Regenerative Therapies Dresden, Technische Universität Dresden, Dresden, Germany
| | - Boyan Bonev
- Helmholtz Pioneer Campus, Helmholtz Zentrum München, Neuherberg, Germany.
- Physiological Genomics, Biomedical Center, Ludwig-Maximilians-Universität München, Munich, Germany.
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40
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Ang MY, Takeuchi F, Kato N. Deciphering the genetic landscape of obesity: a data-driven approach to identifying plausible causal genes and therapeutic targets. J Hum Genet 2023; 68:823-833. [PMID: 37620670 PMCID: PMC10678330 DOI: 10.1038/s10038-023-01189-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 08/08/2023] [Accepted: 08/15/2023] [Indexed: 08/26/2023]
Abstract
OBJECTIVES Genome-wide association studies (GWAS) have successfully revealed numerous susceptibility loci for obesity. However, identifying the causal genes, pathways, and tissues/cell types responsible for these associations remains a challenge, and standardized analysis workflows are lacking. Additionally, due to limited treatment options for obesity, there is a need for the development of new pharmacological therapies. This study aimed to address these issues by performing step-wise utilization of knowledgebase for gene prioritization and assessing the potential relevance of key obesity genes as therapeutic targets. METHODS AND RESULTS First, we generated a list of 28,787 obesity-associated SNPs from the publicly available GWAS dataset (approximately 800,000 individuals in the GIANT meta-analysis). Then, we prioritized 1372 genes with significant in silico evidence against genomic and transcriptomic data, including transcriptionally regulated genes in the brain from transcriptome-wide association studies. In further narrowing down the gene list, we selected key genes, which we found to be useful for the discovery of potential drug seeds as demonstrated in lipid GWAS separately. We thus identified 74 key genes for obesity, which are highly interconnected and enriched in several biological processes that contribute to obesity, including energy expenditure and homeostasis. Of 74 key genes, 37 had not been reported for the pathophysiology of obesity. Finally, by drug-gene interaction analysis, we detected 23 (of 74) key genes that are potential targets for 78 approved and marketed drugs. CONCLUSIONS Our results provide valuable insights into new treatment options for obesity through a data-driven approach that integrates multiple up-to-date knowledgebases.
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Affiliation(s)
- Mia Yang Ang
- Department of Clinical Genome Informatics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
- Department of Gene Diagnostics and Therapeutics, Medical Genomics Center, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan.
| | - Fumihiko Takeuchi
- Department of Gene Diagnostics and Therapeutics, Medical Genomics Center, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
| | - Norihiro Kato
- Department of Clinical Genome Informatics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- Department of Gene Diagnostics and Therapeutics, Medical Genomics Center, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
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41
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Hecker D, Lauber M, Behjati Ardakani F, Ashrafiyan S, Manz Q, Kersting J, Hoffmann M, Schulz MH, List M. Computational tools for inferring transcription factor activity. Proteomics 2023; 23:e2200462. [PMID: 37706624 DOI: 10.1002/pmic.202200462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 08/11/2023] [Accepted: 08/22/2023] [Indexed: 09/15/2023]
Abstract
Transcription factors (TFs) are essential players in orchestrating the regulatory landscape in cells. Still, their exact modes of action and dependencies on other regulatory aspects remain elusive. Since TFs act cell type-specific and each TF has its own characteristics, untangling their regulatory interactions from an experimental point of view is laborious and convoluted. Thus, there is an ongoing development of computational tools that estimate transcription factor activity (TFA) from a variety of data modalities, either based on a mapping of TFs to their putative target genes or in a genome-wide, gene-unspecific fashion. These tools can help to gain insights into TF regulation and to prioritize candidates for experimental validation. We want to give an overview of available computational tools that estimate TFA, illustrate examples of their application, debate common result validation strategies, and discuss assumptions and concomitant limitations.
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Affiliation(s)
- Dennis Hecker
- Goethe University Frankfurt, Frankfurt am Main, Germany
- German Center for Cardiovascular Research, Partner site Rhein-Main, Frankfurt am Main, Germany
- Cardio-Pulmonary Institute, Goethe University Hospital, Frankfurt am Main, Germany
| | - Michael Lauber
- Big Data in BioMedicine Group, Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Fatemeh Behjati Ardakani
- Goethe University Frankfurt, Frankfurt am Main, Germany
- German Center for Cardiovascular Research, Partner site Rhein-Main, Frankfurt am Main, Germany
- Cardio-Pulmonary Institute, Goethe University Hospital, Frankfurt am Main, Germany
| | - Shamim Ashrafiyan
- Goethe University Frankfurt, Frankfurt am Main, Germany
- German Center for Cardiovascular Research, Partner site Rhein-Main, Frankfurt am Main, Germany
- Cardio-Pulmonary Institute, Goethe University Hospital, Frankfurt am Main, Germany
| | - Quirin Manz
- Big Data in BioMedicine Group, Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Johannes Kersting
- Big Data in BioMedicine Group, Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
- GeneSurge GmbH, München, Germany
| | - Markus Hoffmann
- Big Data in BioMedicine Group, Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
- Institute for Advanced Study, Technical University of Munich, Garching, Germany
- National Institute of Diabetes, Digestive, and Kidney Diseases, National Institutes of Health, Bethesda, Maryland, USA
| | - Marcel H Schulz
- Goethe University Frankfurt, Frankfurt am Main, Germany
- German Center for Cardiovascular Research, Partner site Rhein-Main, Frankfurt am Main, Germany
- Cardio-Pulmonary Institute, Goethe University Hospital, Frankfurt am Main, Germany
| | - Markus List
- Big Data in BioMedicine Group, Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
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Vinchhi R, Yelpure C, Balachandran M, Matange N. Pervasive gene deregulation underlies adaptation and maladaptation in trimethoprim-resistant E. coli. mBio 2023; 14:e0211923. [PMID: 38032208 PMCID: PMC10746255 DOI: 10.1128/mbio.02119-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Accepted: 10/17/2023] [Indexed: 12/01/2023] Open
Abstract
IMPORTANCE Bacteria employ a number of mechanisms to adapt to antibiotics. Mutations in transcriptional regulators alter the expression levels of genes that can change the susceptibility of bacteria to antibiotics. Two-component signaling proteins are a major class of signaling molecule used by bacteria to regulate transcription. In previous work, we found that mutations in MgrB, a feedback regulator of the PhoQP two-component system, conferred trimethoprim tolerance to Escherichia coli. Here, we elucidate how mutations in MgrB have a domino-like effect on the gene regulatory network of E. coli. As a result, pervasive perturbation of gene regulation ensues. Depending on the environmental context, this pervasive deregulation is either adaptive or maladaptive. Our study sheds light on how deregulation of gene expression can be beneficial for bacteria when challenged with antibiotics, and why regulators like MgrB may have evolved in the first place.
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Affiliation(s)
- Rhea Vinchhi
- Department of Biology, Indian Institute of Science Education and Research, Pashan, Pune, India
| | - Chetna Yelpure
- Department of Biology, Indian Institute of Science Education and Research, Pashan, Pune, India
| | - Manasvi Balachandran
- Department of Biology, Indian Institute of Science Education and Research, Pashan, Pune, India
| | - Nishad Matange
- Department of Biology, Indian Institute of Science Education and Research, Pashan, Pune, India
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Chomthong M, Griffiths H. Prospects and perspectives: inferring physiological and regulatory targets for CAM from molecular and modelling approaches. Ann Bot 2023; 132:583-596. [PMID: 37742290 PMCID: PMC10799989 DOI: 10.1093/aob/mcad142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/26/2023] [Revised: 08/26/2023] [Accepted: 09/21/2023] [Indexed: 09/26/2023]
Abstract
BACKGROUND AND SCOPE This review summarizes recent advances in our understanding of Crassulacean Acid Metabolism (CAM) by integrating evolutionary, ecological, physiological, metabolic and molecular perspectives. A number of key control loops which moderate the expression of CAM phases, and their metabolic and molecular control, are explored. These include nocturnal stomatal opening, activation of phosphoenolpyruvate carboxylase by a specific protein kinase, interactions with circadian clock control, as well as daytime decarboxylation and activation of Rubisco. The vacuolar storage and release of malic acid and the interplay between the supply and demand for carbohydrate reserves are also key metabolic control points. FUTURE OPPORTUNITIES We identify open questions and opportunities, with experimentation informed by top-down molecular modelling approaches allied with bottom-up mechanistic modelling systems. For example, mining transcriptomic datasets using high-speed systems approaches will help to identify targets for future genetic manipulation experiments to define the regulation of CAM (whether circadian or metabolic control). We emphasize that inferences arising from computational approaches or advanced nuclear sequencing techniques can identify potential genes and transcription factors as regulatory targets. However, these outputs then require systematic evaluation, using genetic manipulation in key model organisms over a developmental progression, combining gene silencing and metabolic flux analysis and modelling to define functionality across the CAM day-night cycle. From an evolutionary perspective, the origins and function of CAM succulents and responses to water deficits are set against the mesophyll and hydraulic limitations imposed by cell and tissue succulence in contrasting morphological lineages. We highlight the interplay between traits across shoots (3D vein density, mesophyll conductance and cell shrinkage) and roots (xylem embolism and segmentation). Thus, molecular, biophysical and biochemical processes help to curtail water losses and exploit rapid rehydration during restorative rain events. In the face of a changing climate, we hope such approaches will stimulate opportunities for future research.
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Affiliation(s)
- Methawi Chomthong
- Department of Plant Sciences, University of Cambridge, Cambridge, CB2 3EA, UK
| | - Howard Griffiths
- Department of Plant Sciences, University of Cambridge, Cambridge, CB2 3EA, UK
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Yamaguchi N, Chang EW, Lin Z, Shekhar A, Bu L, Khodadadi-Jamayran A, Tsirigos A, Cen Y, Phoon CKL, Moskowitz IP, Park DS. An Anterior Second Heart Field Enhancer Regulates the Gene Regulatory Network of the Cardiac Outflow Tract. Circulation 2023; 148:1705-1722. [PMID: 37772400 PMCID: PMC10905423 DOI: 10.1161/circulationaha.123.065700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 08/28/2023] [Indexed: 09/30/2023]
Abstract
BACKGROUND Conotruncal defects due to developmental abnormalities of the outflow tract (OFT) are an important cause of cyanotic congenital heart disease. Dysregulation of transcriptional programs tuned by NKX2-5 (NK2 homeobox 5), GATA6 (GATA binding protein 6), and TBX1 (T-box transcription factor 1) have been implicated in abnormal OFT morphogenesis. However, there remains no consensus on how these transcriptional programs function in a unified gene regulatory network within the OFT. METHODS We generated mice harboring a 226-nucleotide deletion of a highly conserved cardiac enhancer containing 2 GATA-binding sites located ≈9.4 kb upstream of the transcription start site of Nkx2-5 (Nkx2-5∆enh) using CRISPR-Cas9 gene editing and assessed phenotypes. Cardiac defects in Nkx2-5∆enh/∆enh mice were structurally characterized using histology and scanning electron microscopy, and physiologically assessed using electrocardiography, echocardiography, and optical mapping. Transcriptome analyses were performed using RNA sequencing and single-cell RNA sequencing data sets. Endogenous GATA6 interaction with and activity on the NKX2-5 enhancer was studied using chromatin immunoprecipitation sequencing and transposase-accessible chromatin sequencing in human induced pluripotent stem cell-derived cardiomyocytes. RESULTS Nkx2-5∆enh/∆enh mice recapitulated cyanotic conotruncal defects seen in patients with NKX2-5, GATA6, and TBX1 mutations. Nkx2-5∆enh/∆enh mice also exhibited defects in right Purkinje fiber network formation, resulting in right bundle-branch block. Enhancer deletion reduced embryonic Nkx2-5 expression selectively in the right ventricle and OFT of mutant hearts, indicating that enhancer activity is localized to the anterior second heart field. Transcriptional profiling of the mutant OFT revealed downregulation of important genes involved in OFT rotation and septation, such as Tbx1, Pitx2, and Sema3c. Endogenous GATA6 interacted with the highly conserved enhancer in human induced pluripotent stem cell-derived cardiomyocytes and in wild-type mouse hearts. We found critical dose dependency of cardiac enhancer accessibility on GATA6 gene dosage in human induced pluripotent stem cell-derived cardiomyocytes. CONCLUSIONS Our results using human and mouse models reveal an essential gene regulatory network of the OFT that requires an anterior second heart field enhancer to link GATA6 with NKX2-5-dependent rotation and septation gene programs.
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Affiliation(s)
- Naoko Yamaguchi
- The Leon H. Charney Division of Cardiology, New York University Grossman School of Medicine, 435 East 30th Street, Science Building 723, New York, NY, 10016, USA
| | - Ernest W. Chang
- The Leon H. Charney Division of Cardiology, New York University Grossman School of Medicine, 435 East 30th Street, Science Building 723, New York, NY, 10016, USA
| | - Ziyan Lin
- NYU Applied Bioinformatics Labs, New York University Grossman School of Medicine, 227 East 30th Street, TRB, New York, NY,10016, USA
| | - Akshay Shekhar
- Regeneron Pharmaceuticals, Inc. Biotechnology, 777 Old Saw Mill River Road, Tarrytown, NY, 10591, USA
| | - Lei Bu
- The Leon H. Charney Division of Cardiology, New York University Grossman School of Medicine, 435 East 30th Street, Science Building 723, New York, NY, 10016, USA
| | - Alireza Khodadadi-Jamayran
- NYU Applied Bioinformatics Labs, New York University Grossman School of Medicine, 227 East 30th Street, TRB, New York, NY,10016, USA
| | - Aristotelis Tsirigos
- NYU Applied Bioinformatics Labs, New York University Grossman School of Medicine, 227 East 30th Street, TRB, New York, NY,10016, USA
| | - Yiyun Cen
- The Leon H. Charney Division of Cardiology, New York University Grossman School of Medicine, 435 East 30th Street, Science Building 723, New York, NY, 10016, USA
| | - Colin K. L. Phoon
- Division of Pediatric Cardiology, Hassenfeld Children’s Hospital at NYU Langone, New York University Grossman School of Medicine, Fink Children’s Center, 160 East 32nd Street, 2nd floor/L-3, New York, NY, 10016, USA
| | - Ivan P. Moskowitz
- Department of Pediatrics, Pathology, and Human Genetics, The University of Chicago, 900 East 57th Street, KCBD Room 5102, Chicago, IL, 60637, USA
| | - David S. Park
- The Leon H. Charney Division of Cardiology, New York University Grossman School of Medicine, 435 East 30th Street, Science Building 723, New York, NY, 10016, USA
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Tan K, Zhang C, He Z, Zeng P. Construction of an anoikis-associated lncRNA-miRNA-mRNA network reveals the prognostic role of β-elemene in non-small cell lung cancer. Sci Rep 2023; 13:20185. [PMID: 37980372 PMCID: PMC10657389 DOI: 10.1038/s41598-023-46480-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 11/01/2023] [Indexed: 11/20/2023] Open
Abstract
β-Elemene is the main active ingredient in Curcumae Rhizoma that exerts antitumour effects. Anoikis affects tumour development through various biological pathways in non-small cell lung cancer (NSCLC), but the regulation between β-elemene and anoikis remains to be explored. First, we explored the molecular expression patterns of anoikis-associated genes (AAGs) using consensus clustering and characterized the impact of AAGs on patient prognosis, clinical characteristics, and genomic instability. In addition, we revealed that AAG regulatory genes have rich interactions with β-elemene targets, and established a lncRNA-miRNA-mRNA network to explain the effect of β-elemene on anoikis. Finally, to reveal the prognostic effect of their correlation, the prognostic scoring model and clinical nomogram of β-elemene and anoikis were successfully established by least absolute shrinkage and selection operator (LASSO) and random forest algorithms. This prognostic scoring model containing noncoding RNA (ncRNA) can indicate the immunotherapy and mutational landscape, providing a novel theoretical basis and direction for the study of the antitumour mechanism of β-elemene in NSCLC patients.
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Affiliation(s)
- Kai Tan
- Hunan University of Chinese Medicine, Changsha, 410208, Hunan, People's Republic of China
| | - Changhui Zhang
- Hunan University of Chinese Medicine, Changsha, 410208, Hunan, People's Republic of China
| | - Zuomei He
- Cancer Research Institute of Hunan Academy of Traditional Chinese Medicine, Changsha, 410006, Hunan, People's Republic of China
- Hunan Academy of Traditional Chinese Medicine Affiliated Hospital, Changsha, 410006, Hunan, People's Republic of China
| | - Puhua Zeng
- Cancer Research Institute of Hunan Academy of Traditional Chinese Medicine, Changsha, 410006, Hunan, People's Republic of China.
- Hunan Academy of Traditional Chinese Medicine Affiliated Hospital, Changsha, 410006, Hunan, People's Republic of China.
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Sleight VA. Cell type and gene regulatory network approaches in the evolution of spiralian biomineralisation. Brief Funct Genomics 2023; 22:509-516. [PMID: 37592885 PMCID: PMC10658180 DOI: 10.1093/bfgp/elad033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Revised: 07/10/2023] [Accepted: 07/20/2023] [Indexed: 08/19/2023] Open
Abstract
Biomineralisation is the process by which living organisms produce hard structures such as shells and bone. There are multiple independent origins of biomineralised skeletons across the tree of life. This review gives a glimpse into the diversity of spiralian biominerals and what they can teach us about the evolution of novelty. It discusses different levels of biological organisation that may be informative to understand the evolution of biomineralisation and considers the relationship between skeletal and non-skeletal biominerals. More specifically, this review explores if cell type and gene regulatory network approaches could enhance our understanding of the evolutionary origins of biomineralisation.
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Affiliation(s)
- Victoria A Sleight
- School of Biological Sciences, University of Aberdeen, Aberdeen, United Kingdom
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Dastidar SG, De Kumar B, Lauckner B, Parrello D, Perley D, Vlasenok M, Tyagi A, Koney NKK, Abbas A, Nechaev S. Transcriptional responses of cancer cells to heat shock-inducing stimuli involve amplification of robust HSF1 binding. Nat Commun 2023; 14:7420. [PMID: 37973875 PMCID: PMC10654513 DOI: 10.1038/s41467-023-43157-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Accepted: 11/01/2023] [Indexed: 11/19/2023] Open
Abstract
Responses of cells to stimuli are increasingly discovered to involve the binding of sequence-specific transcription factors outside of known target genes. We wanted to determine to what extent the genome-wide binding and function of a transcription factor are shaped by the cell type versus the stimulus. To do so, we induced the Heat Shock Response pathway in two different cancer cell lines with two different stimuli and related the binding of its master regulator HSF1 to nascent RNA and chromatin accessibility. Here, we show that HSF1 binding patterns retain their identity between basal conditions and under different magnitudes of activation, so that common HSF1 binding is globally associated with distinct transcription outcomes. HSF1-induced increase in DNA accessibility was modest in scale, but occurred predominantly at remote genomic sites. Apart from regulating transcription at existing elements including promoters and enhancers, HSF1 binding amplified during responses to stimuli may engage inactive chromatin.
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Affiliation(s)
- Sayantani Ghosh Dastidar
- Department of Biomedical Sciences, University of North Dakota School of Medicine, Grand Forks, ND, 58202, USA
- Illumina, Inc., San Diego, CA, 92122, USA
| | - Bony De Kumar
- Department of Biomedical Sciences, University of North Dakota School of Medicine, Grand Forks, ND, 58202, USA
- Yale Center for Genome Analysis, Department of Genetics, Yale University School of Medicine, New Haven, CT, 06510, USA
| | - Bo Lauckner
- Department of Biomedical Sciences, University of North Dakota School of Medicine, Grand Forks, ND, 58202, USA
| | - Damien Parrello
- Department of Biomedical Sciences, University of North Dakota School of Medicine, Grand Forks, ND, 58202, USA
| | - Danielle Perley
- Department of Biomedical Sciences, University of North Dakota School of Medicine, Grand Forks, ND, 58202, USA
- Canadian Centre for Computational Genomics, McGill Genome Centre, Montreal, QC, H3A0G1, Canada
| | - Maria Vlasenok
- Department of Biomedical Sciences, University of North Dakota School of Medicine, Grand Forks, ND, 58202, USA
- Center for Molecular and Cellular Biology, Skolkovo Institute of Science and Technology, Moscow, 121205, Russia
| | - Antariksh Tyagi
- Department of Biomedical Sciences, University of North Dakota School of Medicine, Grand Forks, ND, 58202, USA
- Yale Center for Genome Analysis, Department of Genetics, Yale University School of Medicine, New Haven, CT, 06510, USA
| | - Nii Koney-Kwaku Koney
- Department of Biomedical Sciences, University of North Dakota School of Medicine, Grand Forks, ND, 58202, USA
- University of Ghana Medical School, University of Ghana, Accra, Ghana
| | - Ata Abbas
- Department of Biomedical Sciences, University of North Dakota School of Medicine, Grand Forks, ND, 58202, USA
- Department of Biochemistry, Case Western Reserve University, Cleveland, OH, 44106, USA
| | - Sergei Nechaev
- Department of Biomedical Sciences, University of North Dakota School of Medicine, Grand Forks, ND, 58202, USA.
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Sukko N, Kalapanulak S, Saithong T. Trehalose metabolism coordinates transcriptional regulatory control and metabolic requirements to trigger the onset of cassava storage root initiation. Sci Rep 2023; 13:19973. [PMID: 37968317 PMCID: PMC10651926 DOI: 10.1038/s41598-023-47095-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Accepted: 11/09/2023] [Indexed: 11/17/2023] Open
Abstract
Cassava storage roots (SR) are an important source of food energy and raw material for a wide range of applications. Understanding SR initiation and the associated regulation is critical to boosting tuber yield in cassava. Decades of transcriptome studies have identified key regulators relevant to SR formation, transcriptional regulation and sugar metabolism. However, there remain uncertainties over the roles of the regulators in modulating the onset of SR development owing to the limitation of the widely applied differential gene expression analysis. Here, we aimed to investigate the regulation underlying the transition from fibrous (FR) to SR based on Dynamic Network Biomarker (DNB) analysis. Gene expression analysis during cassava root initiation showed the transition period to SR happened in FR during 8 weeks after planting (FR8). Ninety-nine DNB genes associated with SR initiation and development were identified. Interestingly, the role of trehalose metabolism, especially trehalase1 (TRE1), in modulating metabolites abundance and coordinating regulatory signaling and carbon substrate availability via the connection of transcriptional regulation and sugar metabolism was highlighted. The results agree with the associated DNB characters of TRE1 reported in other transcriptome studies of cassava SR initiation and Attre1 loss of function in literature. The findings help fill the knowledge gap regarding the regulation underlying cassava SR initiation.
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Affiliation(s)
- Nattavat Sukko
- Bioinformatics and Systems Biology Program, School of Bioresources and Technology and School of Information Technology, King Mongkut's University of Technology Thonburi (Bang Khun Thian), Bangkok, 10150, Thailand
| | - Saowalak Kalapanulak
- Bioinformatics and Systems Biology Program, School of Bioresources and Technology and School of Information Technology, King Mongkut's University of Technology Thonburi (Bang Khun Thian), Bangkok, 10150, Thailand.
- School of Bioresources and Technology, King Mongkut's University of Technology Thonburi (Bang Khun Thian), Bangkok, 10150, Thailand.
- Center for Agricultural Systems Biology, Systems Biology and Bioinformatics Research Group, Pilot Plant Development and Training Institute, King Mongkut's University of Technology Thonburi (Bang Khun Thian), Bangkok, 10150, Thailand.
| | - Treenut Saithong
- Bioinformatics and Systems Biology Program, School of Bioresources and Technology and School of Information Technology, King Mongkut's University of Technology Thonburi (Bang Khun Thian), Bangkok, 10150, Thailand.
- School of Bioresources and Technology, King Mongkut's University of Technology Thonburi (Bang Khun Thian), Bangkok, 10150, Thailand.
- Center for Agricultural Systems Biology, Systems Biology and Bioinformatics Research Group, Pilot Plant Development and Training Institute, King Mongkut's University of Technology Thonburi (Bang Khun Thian), Bangkok, 10150, Thailand.
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Ng ETH, Kinjo AR. Plasticity-led evolution as an intrinsic property of developmental gene regulatory networks. Sci Rep 2023; 13:19830. [PMID: 37963964 PMCID: PMC10645858 DOI: 10.1038/s41598-023-47165-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 11/09/2023] [Indexed: 11/16/2023] Open
Abstract
The modern evolutionary synthesis seemingly fails to explain how a population can survive a large environmental change: the pre-existence of heritable variants adapted to the novel environment is too opportunistic, whereas the search for new adaptive mutations after the environmental change is so slow that the population may go extinct. Plasticity-led evolution, the initial environmental induction of a novel adaptive phenotype followed by genetic accommodation, has been proposed to solve this problem. However, the mechanism enabling plasticity-led evolution remains unclear. Here, we present computational models that exhibit behaviors compatible with plasticity-led evolution by extending the Wagner model of gene regulatory networks. The models show adaptive plastic response and the uncovering of cryptic mutations under large environmental changes, followed by genetic accommodation. Moreover, these behaviors are consistently observed over distinct novel environments. We further show that environmental cues, developmental processes, and hierarchical regulation cooperatively amplify the above behaviors and accelerate evolution. These observations suggest plasticity-led evolution is a universal property of complex developmental systems independent of particular mutations.
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Affiliation(s)
- Eden Tian Hwa Ng
- Department of Mathematics, Faculty of Science, Universiti Brunei Darussalam, Jalan Tungku Link, Gadong, BE1410, Brunei Darussalam
| | - Akira R Kinjo
- Department of Mathematics, Faculty of Science, Universiti Brunei Darussalam, Jalan Tungku Link, Gadong, BE1410, Brunei Darussalam.
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50
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Hou R, Hon CC, Huang Y. CamoTSS: analysis of alternative transcription start sites for cellular phenotypes and regulatory patterns from 5' scRNA-seq data. Nat Commun 2023; 14:7240. [PMID: 37945584 PMCID: PMC10636040 DOI: 10.1038/s41467-023-42636-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Accepted: 10/16/2023] [Indexed: 11/12/2023] Open
Abstract
Five-prime single-cell RNA-seq (scRNA-seq) has been widely employed to profile cellular transcriptomes, however, its power of analysing transcription start sites (TSS) has not been fully utilised. Here, we present a computational method suite, CamoTSS, to precisely identify TSS and quantify its expression by leveraging the cDNA on read 1, which enables effective detection of alternative TSS usage. With various experimental data sets, we have demonstrated that CamoTSS can accurately identify TSS and the detected alternative TSS usages showed strong specificity in different biological processes, including cell types across human organs, the development of human thymus, and cancer conditions. As evidenced in nasopharyngeal cancer, alternative TSS usage can also reveal regulatory patterns including systematic TSS dysregulations.
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Affiliation(s)
- Ruiyan Hou
- School of Biomedical Sciences, University of Hong Kong, Hong Kong, SAR, China
| | - Chung-Chau Hon
- RIKEN Center for Integrative Medical Sciences, Yokohama City, Kanagawa, 230-0045, Japan
- Graduate School of Integrated Sciences for Life, Hiroshima University, Higashi-Hiroshima, Japan
| | - Yuanhua Huang
- School of Biomedical Sciences, University of Hong Kong, Hong Kong, SAR, China.
- Department of Statistics and Actuarial Science, University of Hong Kong, Hong Kong, AR, China.
- Center for Translational Stem Cell Biology, Hong Kong Science and Technology Park, Hong Kong, SAR, China.
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