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Kind S, Castillo CP, Schlichter R, Gorbokon N, Lennartz M, Hornsteiner LS, Dwertmann Rico S, Reiswich V, Viehweger F, Kluth M, Hube-Magg C, Bernreuther C, Büscheck F, Clauditz TS, Fraune C, Hinsch A, Krech T, Lebok P, Steurer S, Burandt E, Minner S, Marx AH, Simon R, Wilczak W, Sauter G, Menz A, Jacobsen F. KLK7 expression in human tumors: a tissue microarray study on 13,447 tumors. BMC Cancer 2024; 24:794. [PMID: 38961454 PMCID: PMC11221178 DOI: 10.1186/s12885-024-12552-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Accepted: 06/23/2024] [Indexed: 07/05/2024] Open
Abstract
BACKGROUND Kallikrein-related peptidase 7 (KLK7) is a chymotrypsin-like serine protease which is essential for the desquamation of corneocytes and thus plays a pivotal role in maintaining skin homeostasis. In cancer, KLK7 overexpression was suggested to represent a route for metastasis through cleavage of cell junction and extracellular matrix proteins of cancer cells. METHODS To comprehensively determine KLK7 protein expression in normal and neoplastic tissues, a tissue microarray containing 13,447 samples from 147 different tumor types and subtypes as well as 608 samples of 76 different normal tissue types was analyzed by immunohistochemistry. RESULTS KLK7 positivity was found in 64 of 147 tumor categories, including 17 tumor categories with at least one strongly positive case. The highest rate of KLK7 positivity was found in squamous cell carcinomas from various sites of origin (positive in 18.1%-63.8%), ovarian and endometrium cancers (4.8%-56.2%), salivary gland tumors (4.8%-13.7%), bilio-pancreatic adenocarcinomas (20.0%-40.4%), and adenocarcinomas of the upper gastrointestinal tract (3.3%-12.5%). KLK7 positivity was linked to nodal metastasis (p = 0.0005), blood vessel infiltration (p = 0.0037), and lymph vessel infiltration (p < 0.0001) in colorectal adenocarcinoma, nodal metastasis in hepatocellular carcinoma (p = 0.0382), advanced pathological tumor stage in papillary thyroid cancer (p = 0.0132), and low grade of malignancy in a cohort of 719 squamous cell carcinomas from 11 different sites of origin (p < 0.0001). CONCLUSIONS These data provide a comprehensive overview on KLK7 expression in normal and neoplastic human tissues. The prognostic relevance of KLK7 expression and the possible role of KLK7 as a drug target need to be further investigated.
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Affiliation(s)
- Simon Kind
- Institute of Pathology, University Medical Center Hamburg-Eppendorf, Martinistr. 52, Hamburg, 20246, Germany
| | - Carolina Palacios Castillo
- Institute of Pathology, University Medical Center Hamburg-Eppendorf, Martinistr. 52, Hamburg, 20246, Germany
| | - Ria Schlichter
- Institute of Pathology, University Medical Center Hamburg-Eppendorf, Martinistr. 52, Hamburg, 20246, Germany
| | - Natalia Gorbokon
- Institute of Pathology, University Medical Center Hamburg-Eppendorf, Martinistr. 52, Hamburg, 20246, Germany
| | - Maximilian Lennartz
- Institute of Pathology, University Medical Center Hamburg-Eppendorf, Martinistr. 52, Hamburg, 20246, Germany
| | - Lisa S Hornsteiner
- Institute of Pathology, University Medical Center Hamburg-Eppendorf, Martinistr. 52, Hamburg, 20246, Germany
| | - Sebastian Dwertmann Rico
- Institute of Pathology, University Medical Center Hamburg-Eppendorf, Martinistr. 52, Hamburg, 20246, Germany
| | - Viktor Reiswich
- Institute of Pathology, University Medical Center Hamburg-Eppendorf, Martinistr. 52, Hamburg, 20246, Germany
| | - Florian Viehweger
- Institute of Pathology, University Medical Center Hamburg-Eppendorf, Martinistr. 52, Hamburg, 20246, Germany
| | - Martina Kluth
- Institute of Pathology, University Medical Center Hamburg-Eppendorf, Martinistr. 52, Hamburg, 20246, Germany
| | - Claudia Hube-Magg
- Institute of Pathology, University Medical Center Hamburg-Eppendorf, Martinistr. 52, Hamburg, 20246, Germany
| | - Christian Bernreuther
- Institute of Pathology, University Medical Center Hamburg-Eppendorf, Martinistr. 52, Hamburg, 20246, Germany
| | - Franziska Büscheck
- Institute of Pathology, University Medical Center Hamburg-Eppendorf, Martinistr. 52, Hamburg, 20246, Germany
| | - Till S Clauditz
- Institute of Pathology, University Medical Center Hamburg-Eppendorf, Martinistr. 52, Hamburg, 20246, Germany
| | - Christoph Fraune
- Institute of Pathology, University Medical Center Hamburg-Eppendorf, Martinistr. 52, Hamburg, 20246, Germany
| | - Andrea Hinsch
- Institute of Pathology, University Medical Center Hamburg-Eppendorf, Martinistr. 52, Hamburg, 20246, Germany
| | - Till Krech
- Institute of Pathology, University Medical Center Hamburg-Eppendorf, Martinistr. 52, Hamburg, 20246, Germany
- Institute of Pathology, Clinical Center Osnabrueck, Osnabrueck, Germany
| | - Patrick Lebok
- Institute of Pathology, University Medical Center Hamburg-Eppendorf, Martinistr. 52, Hamburg, 20246, Germany
- Institute of Pathology, Clinical Center Osnabrueck, Osnabrueck, Germany
| | - Stefan Steurer
- Institute of Pathology, University Medical Center Hamburg-Eppendorf, Martinistr. 52, Hamburg, 20246, Germany
| | - Eike Burandt
- Institute of Pathology, University Medical Center Hamburg-Eppendorf, Martinistr. 52, Hamburg, 20246, Germany
| | - Sarah Minner
- Institute of Pathology, University Medical Center Hamburg-Eppendorf, Martinistr. 52, Hamburg, 20246, Germany
| | - Andreas H Marx
- Department of Pathology, Academic Hospital Fuerth, Fuerth, Germany
| | - Ronald Simon
- Institute of Pathology, University Medical Center Hamburg-Eppendorf, Martinistr. 52, Hamburg, 20246, Germany.
| | - Waldemar Wilczak
- Institute of Pathology, University Medical Center Hamburg-Eppendorf, Martinistr. 52, Hamburg, 20246, Germany
| | - Guido Sauter
- Institute of Pathology, University Medical Center Hamburg-Eppendorf, Martinistr. 52, Hamburg, 20246, Germany
| | - Anne Menz
- Institute of Pathology, University Medical Center Hamburg-Eppendorf, Martinistr. 52, Hamburg, 20246, Germany
| | - Frank Jacobsen
- Institute of Pathology, University Medical Center Hamburg-Eppendorf, Martinistr. 52, Hamburg, 20246, Germany
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Menz A, Lony N, Lennartz M, Dwertmann Rico S, Schlichter R, Kind S, Reiswich V, Viehweger F, Dum D, Luebke AM, Kluth M, Gorbokon N, Hube-Magg C, Bernreuther C, Simon R, Clauditz TS, Sauter G, Hinsch A, Jacobsen F, Marx AH, Steurer S, Minner S, Burandt E, Krech T, Lebok P, Weidemann S. Epithelial Cell Adhesion Molecule (EpCAM) Expression in Human Tumors: A Comparison with Pan-Cytokeratin and TROP2 in 14,832 Tumors. Diagnostics (Basel) 2024; 14:1044. [PMID: 38786342 PMCID: PMC11120328 DOI: 10.3390/diagnostics14101044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2024] [Revised: 04/26/2024] [Accepted: 04/30/2024] [Indexed: 05/25/2024] Open
Abstract
EpCAM is expressed in many epithelial tumors and is used for the distinction of malignant mesotheliomas from adenocarcinomas and as a surrogate pan-epithelial marker. A tissue microarray containing 14,832 samples from 120 different tumor categories was analyzed by immunohistochemistry. EpCAM staining was compared with TROP2 and CKpan. EpCAM staining was detectable in 99 tumor categories. Among 78 epithelial tumor types, the EpCAM positivity rate was ≥90% in 60 categories-including adenocarcinomas, neuroendocrine neoplasms, and germ cell tumors. EpCAM staining was the lowest in hepatocellular carcinomas, adrenocortical tumors, renal cell neoplasms, and in poorly differentiated carcinomas. A comparison of EpCAM and CKpan staining identified a high concordance but EpCAM was higher in testicular seminomas and neuroendocrine neoplasms and CKpan in hepatocellular carcinomas, mesotheliomas, and poorly differentiated non-neuroendocrine tumors. A comparison of EpCAM and TROP2 revealed a higher rate of TROP2 positivity in squamous cell carcinomas and lower rates in many gastrointestinal adenocarcinomas, testicular germ cell tumors, neuroendocrine neoplasms, and renal cell tumors. These data confirm EpCAM as a surrogate epithelial marker for adenocarcinomas and its diagnostic utility for the distinction of malignant mesotheliomas. In comparison to CKpan and TROP2 antibodies, EpCAM staining is particularly common in seminomas and in neuroendocrine neoplasms.
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Affiliation(s)
- Anne Menz
- Institute of Pathology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany; (A.M.); (N.L.); (M.L.); (S.D.R.); (R.S.); (S.K.); (V.R.); (F.V.); (D.D.); (A.M.L.); (M.K.); (N.G.); (C.H.-M.); (C.B.); (T.S.C.); (G.S.); (A.H.); (F.J.); (S.S.); (S.M.); (E.B.); (T.K.); (P.L.); (S.W.)
| | - Nora Lony
- Institute of Pathology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany; (A.M.); (N.L.); (M.L.); (S.D.R.); (R.S.); (S.K.); (V.R.); (F.V.); (D.D.); (A.M.L.); (M.K.); (N.G.); (C.H.-M.); (C.B.); (T.S.C.); (G.S.); (A.H.); (F.J.); (S.S.); (S.M.); (E.B.); (T.K.); (P.L.); (S.W.)
| | - Maximilian Lennartz
- Institute of Pathology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany; (A.M.); (N.L.); (M.L.); (S.D.R.); (R.S.); (S.K.); (V.R.); (F.V.); (D.D.); (A.M.L.); (M.K.); (N.G.); (C.H.-M.); (C.B.); (T.S.C.); (G.S.); (A.H.); (F.J.); (S.S.); (S.M.); (E.B.); (T.K.); (P.L.); (S.W.)
| | - Sebastian Dwertmann Rico
- Institute of Pathology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany; (A.M.); (N.L.); (M.L.); (S.D.R.); (R.S.); (S.K.); (V.R.); (F.V.); (D.D.); (A.M.L.); (M.K.); (N.G.); (C.H.-M.); (C.B.); (T.S.C.); (G.S.); (A.H.); (F.J.); (S.S.); (S.M.); (E.B.); (T.K.); (P.L.); (S.W.)
| | - Ria Schlichter
- Institute of Pathology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany; (A.M.); (N.L.); (M.L.); (S.D.R.); (R.S.); (S.K.); (V.R.); (F.V.); (D.D.); (A.M.L.); (M.K.); (N.G.); (C.H.-M.); (C.B.); (T.S.C.); (G.S.); (A.H.); (F.J.); (S.S.); (S.M.); (E.B.); (T.K.); (P.L.); (S.W.)
| | - Simon Kind
- Institute of Pathology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany; (A.M.); (N.L.); (M.L.); (S.D.R.); (R.S.); (S.K.); (V.R.); (F.V.); (D.D.); (A.M.L.); (M.K.); (N.G.); (C.H.-M.); (C.B.); (T.S.C.); (G.S.); (A.H.); (F.J.); (S.S.); (S.M.); (E.B.); (T.K.); (P.L.); (S.W.)
| | - Viktor Reiswich
- Institute of Pathology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany; (A.M.); (N.L.); (M.L.); (S.D.R.); (R.S.); (S.K.); (V.R.); (F.V.); (D.D.); (A.M.L.); (M.K.); (N.G.); (C.H.-M.); (C.B.); (T.S.C.); (G.S.); (A.H.); (F.J.); (S.S.); (S.M.); (E.B.); (T.K.); (P.L.); (S.W.)
| | - Florian Viehweger
- Institute of Pathology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany; (A.M.); (N.L.); (M.L.); (S.D.R.); (R.S.); (S.K.); (V.R.); (F.V.); (D.D.); (A.M.L.); (M.K.); (N.G.); (C.H.-M.); (C.B.); (T.S.C.); (G.S.); (A.H.); (F.J.); (S.S.); (S.M.); (E.B.); (T.K.); (P.L.); (S.W.)
| | - David Dum
- Institute of Pathology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany; (A.M.); (N.L.); (M.L.); (S.D.R.); (R.S.); (S.K.); (V.R.); (F.V.); (D.D.); (A.M.L.); (M.K.); (N.G.); (C.H.-M.); (C.B.); (T.S.C.); (G.S.); (A.H.); (F.J.); (S.S.); (S.M.); (E.B.); (T.K.); (P.L.); (S.W.)
| | - Andreas M. Luebke
- Institute of Pathology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany; (A.M.); (N.L.); (M.L.); (S.D.R.); (R.S.); (S.K.); (V.R.); (F.V.); (D.D.); (A.M.L.); (M.K.); (N.G.); (C.H.-M.); (C.B.); (T.S.C.); (G.S.); (A.H.); (F.J.); (S.S.); (S.M.); (E.B.); (T.K.); (P.L.); (S.W.)
| | - Martina Kluth
- Institute of Pathology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany; (A.M.); (N.L.); (M.L.); (S.D.R.); (R.S.); (S.K.); (V.R.); (F.V.); (D.D.); (A.M.L.); (M.K.); (N.G.); (C.H.-M.); (C.B.); (T.S.C.); (G.S.); (A.H.); (F.J.); (S.S.); (S.M.); (E.B.); (T.K.); (P.L.); (S.W.)
| | - Natalia Gorbokon
- Institute of Pathology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany; (A.M.); (N.L.); (M.L.); (S.D.R.); (R.S.); (S.K.); (V.R.); (F.V.); (D.D.); (A.M.L.); (M.K.); (N.G.); (C.H.-M.); (C.B.); (T.S.C.); (G.S.); (A.H.); (F.J.); (S.S.); (S.M.); (E.B.); (T.K.); (P.L.); (S.W.)
| | - Claudia Hube-Magg
- Institute of Pathology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany; (A.M.); (N.L.); (M.L.); (S.D.R.); (R.S.); (S.K.); (V.R.); (F.V.); (D.D.); (A.M.L.); (M.K.); (N.G.); (C.H.-M.); (C.B.); (T.S.C.); (G.S.); (A.H.); (F.J.); (S.S.); (S.M.); (E.B.); (T.K.); (P.L.); (S.W.)
| | - Christian Bernreuther
- Institute of Pathology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany; (A.M.); (N.L.); (M.L.); (S.D.R.); (R.S.); (S.K.); (V.R.); (F.V.); (D.D.); (A.M.L.); (M.K.); (N.G.); (C.H.-M.); (C.B.); (T.S.C.); (G.S.); (A.H.); (F.J.); (S.S.); (S.M.); (E.B.); (T.K.); (P.L.); (S.W.)
| | - Ronald Simon
- Institute of Pathology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany; (A.M.); (N.L.); (M.L.); (S.D.R.); (R.S.); (S.K.); (V.R.); (F.V.); (D.D.); (A.M.L.); (M.K.); (N.G.); (C.H.-M.); (C.B.); (T.S.C.); (G.S.); (A.H.); (F.J.); (S.S.); (S.M.); (E.B.); (T.K.); (P.L.); (S.W.)
| | - Till S. Clauditz
- Institute of Pathology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany; (A.M.); (N.L.); (M.L.); (S.D.R.); (R.S.); (S.K.); (V.R.); (F.V.); (D.D.); (A.M.L.); (M.K.); (N.G.); (C.H.-M.); (C.B.); (T.S.C.); (G.S.); (A.H.); (F.J.); (S.S.); (S.M.); (E.B.); (T.K.); (P.L.); (S.W.)
| | - Guido Sauter
- Institute of Pathology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany; (A.M.); (N.L.); (M.L.); (S.D.R.); (R.S.); (S.K.); (V.R.); (F.V.); (D.D.); (A.M.L.); (M.K.); (N.G.); (C.H.-M.); (C.B.); (T.S.C.); (G.S.); (A.H.); (F.J.); (S.S.); (S.M.); (E.B.); (T.K.); (P.L.); (S.W.)
| | - Andrea Hinsch
- Institute of Pathology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany; (A.M.); (N.L.); (M.L.); (S.D.R.); (R.S.); (S.K.); (V.R.); (F.V.); (D.D.); (A.M.L.); (M.K.); (N.G.); (C.H.-M.); (C.B.); (T.S.C.); (G.S.); (A.H.); (F.J.); (S.S.); (S.M.); (E.B.); (T.K.); (P.L.); (S.W.)
| | - Frank Jacobsen
- Institute of Pathology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany; (A.M.); (N.L.); (M.L.); (S.D.R.); (R.S.); (S.K.); (V.R.); (F.V.); (D.D.); (A.M.L.); (M.K.); (N.G.); (C.H.-M.); (C.B.); (T.S.C.); (G.S.); (A.H.); (F.J.); (S.S.); (S.M.); (E.B.); (T.K.); (P.L.); (S.W.)
| | - Andreas H. Marx
- Department of Pathology, Academic Hospital Fuerth, 90766 Fuerth, Germany;
| | - Stefan Steurer
- Institute of Pathology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany; (A.M.); (N.L.); (M.L.); (S.D.R.); (R.S.); (S.K.); (V.R.); (F.V.); (D.D.); (A.M.L.); (M.K.); (N.G.); (C.H.-M.); (C.B.); (T.S.C.); (G.S.); (A.H.); (F.J.); (S.S.); (S.M.); (E.B.); (T.K.); (P.L.); (S.W.)
| | - Sarah Minner
- Institute of Pathology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany; (A.M.); (N.L.); (M.L.); (S.D.R.); (R.S.); (S.K.); (V.R.); (F.V.); (D.D.); (A.M.L.); (M.K.); (N.G.); (C.H.-M.); (C.B.); (T.S.C.); (G.S.); (A.H.); (F.J.); (S.S.); (S.M.); (E.B.); (T.K.); (P.L.); (S.W.)
| | - Eike Burandt
- Institute of Pathology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany; (A.M.); (N.L.); (M.L.); (S.D.R.); (R.S.); (S.K.); (V.R.); (F.V.); (D.D.); (A.M.L.); (M.K.); (N.G.); (C.H.-M.); (C.B.); (T.S.C.); (G.S.); (A.H.); (F.J.); (S.S.); (S.M.); (E.B.); (T.K.); (P.L.); (S.W.)
| | - Till Krech
- Institute of Pathology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany; (A.M.); (N.L.); (M.L.); (S.D.R.); (R.S.); (S.K.); (V.R.); (F.V.); (D.D.); (A.M.L.); (M.K.); (N.G.); (C.H.-M.); (C.B.); (T.S.C.); (G.S.); (A.H.); (F.J.); (S.S.); (S.M.); (E.B.); (T.K.); (P.L.); (S.W.)
- Institute of Pathology, Clinical Center Osnabrueck, 49076 Osnabrueck, Germany
| | - Patrick Lebok
- Institute of Pathology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany; (A.M.); (N.L.); (M.L.); (S.D.R.); (R.S.); (S.K.); (V.R.); (F.V.); (D.D.); (A.M.L.); (M.K.); (N.G.); (C.H.-M.); (C.B.); (T.S.C.); (G.S.); (A.H.); (F.J.); (S.S.); (S.M.); (E.B.); (T.K.); (P.L.); (S.W.)
- Institute of Pathology, Clinical Center Osnabrueck, 49076 Osnabrueck, Germany
| | - Sören Weidemann
- Institute of Pathology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany; (A.M.); (N.L.); (M.L.); (S.D.R.); (R.S.); (S.K.); (V.R.); (F.V.); (D.D.); (A.M.L.); (M.K.); (N.G.); (C.H.-M.); (C.B.); (T.S.C.); (G.S.); (A.H.); (F.J.); (S.S.); (S.M.); (E.B.); (T.K.); (P.L.); (S.W.)
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Zhao S, Liu Q, Su L, Meng L, Tan C, Wei C, Zhang H, Luo T, Zhang Q, Tan YQ, Tu C, Chen H, Gao X. Identification of novel homozygous asthenoteratospermia-causing ARMC2 mutations associated with multiple morphological abnormalities of the sperm flagella. J Assist Reprod Genet 2024; 41:1297-1306. [PMID: 38492154 PMCID: PMC11143164 DOI: 10.1007/s10815-024-03087-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Accepted: 03/07/2024] [Indexed: 03/18/2024] Open
Abstract
PURPOSE To identify the genetic causes of multiple morphological abnormalities in sperm flagella (MMAF) and male infertility in patients from two unrelated Han Chinese families. METHODS Whole-exome sequencing was conducted using blood samples from the two individuals with MMAF and male infertility. Hematoxylin and eosin staining and scanning electron microscopy were performed to evaluate sperm morphology. Ultrastructural and immunostaining analyses of the spermatozoa were performed. The HEK293T cells were used to confirm the pathogenicity of the variants. RESULTS We identified two novel homozygous missense ARMC2 variants: c.314C > T: p.P105L and c.2227A > G: p.N743D. Both variants are absent or rare in the human population genome data and are predicted to be deleterious. In vitro experiments indicated that both ARMC2 variants caused a slightly increased protein expression. ARMC2-mutant spermatozoa showed multiple morphological abnormalities (bent, short, coiled, absent, and irregular) in the flagella. In addition, the spermatozoa of the patients revealed a frequent absence of the central pair complex and disrupted axonemal ultrastructure. CONCLUSION We identified two novel ARMC2 variants that caused male infertility and MMAF in Han Chinese patients. These findings expand the mutational spectrum of ARMC2 and provide insights into the complex causes and pathogenesis of MMAF.
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Affiliation(s)
- Siyi Zhao
- Department of Urology, The First Clinical College of Guangzhou Medical University, Guangzhou, China
| | - Qiong Liu
- Reproductive Medical Center, Jiangxi Maternal and Child Health Hospital, Affiliated Maternal and Child Health Hospital of Nanchang University, Nanchang, China
- Nanchang Medical College, Nanchang, China
| | - Lilan Su
- National Engineering and Research Center of Human Stem Cells and Institute of Reproductive and Stem Cell Engineering, Key Laboratory of Human Stem Cell and Reproductive Engineering, School of Basic Medical Science, Central South University, Changsha, China
| | - Lanlan Meng
- National Engineering and Research Center of Human Stem Cells and Institute of Reproductive and Stem Cell Engineering, Key Laboratory of Human Stem Cell and Reproductive Engineering, School of Basic Medical Science, Central South University, Changsha, China
- Reproductive and Genetic Hospital of CITIC-Xiangya and Clinical Research Center For Reproduction and Genetics in Hunan Province, Changsha, China
| | - Chen Tan
- Reproductive and Genetic Hospital of CITIC-Xiangya and Clinical Research Center For Reproduction and Genetics in Hunan Province, Changsha, China
| | - Chunjia Wei
- National Engineering and Research Center of Human Stem Cells and Institute of Reproductive and Stem Cell Engineering, Key Laboratory of Human Stem Cell and Reproductive Engineering, School of Basic Medical Science, Central South University, Changsha, China
| | - Huan Zhang
- National Engineering and Research Center of Human Stem Cells and Institute of Reproductive and Stem Cell Engineering, Key Laboratory of Human Stem Cell and Reproductive Engineering, School of Basic Medical Science, Central South University, Changsha, China
- Reproductive and Genetic Hospital of CITIC-Xiangya and Clinical Research Center For Reproduction and Genetics in Hunan Province, Changsha, China
| | - Tao Luo
- Institute of Biomedical Innovation, Jiangxi Medical College, Nanchang University, Nanchang, China
| | - Qianjun Zhang
- National Engineering and Research Center of Human Stem Cells and Institute of Reproductive and Stem Cell Engineering, Key Laboratory of Human Stem Cell and Reproductive Engineering, School of Basic Medical Science, Central South University, Changsha, China
- Reproductive and Genetic Hospital of CITIC-Xiangya and Clinical Research Center For Reproduction and Genetics in Hunan Province, Changsha, China
| | - Yue-Qiu Tan
- National Engineering and Research Center of Human Stem Cells and Institute of Reproductive and Stem Cell Engineering, Key Laboratory of Human Stem Cell and Reproductive Engineering, School of Basic Medical Science, Central South University, Changsha, China
- Reproductive and Genetic Hospital of CITIC-Xiangya and Clinical Research Center For Reproduction and Genetics in Hunan Province, Changsha, China
| | - Chaofeng Tu
- National Engineering and Research Center of Human Stem Cells and Institute of Reproductive and Stem Cell Engineering, Key Laboratory of Human Stem Cell and Reproductive Engineering, School of Basic Medical Science, Central South University, Changsha, China.
- Reproductive and Genetic Hospital of CITIC-Xiangya and Clinical Research Center For Reproduction and Genetics in Hunan Province, Changsha, China.
| | - Houyang Chen
- Reproductive Medical Center, Jiangxi Maternal and Child Health Hospital, Affiliated Maternal and Child Health Hospital of Nanchang University, Nanchang, China.
| | - Xingcheng Gao
- Department of Urology, The First Clinical College of Guangzhou Medical University, Guangzhou, China.
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Wang J, Chew BLA, Lai Y, Dong H, Xu L, Liu Y, Fu XY, Lin Z, Shi PY, Lu TK, Luo D, Jaffrey SR, Dedon PC. A systems-level mass spectrometry-based technique for accurate and sensitive quantification of the RNA cap epitranscriptome. Nat Protoc 2023; 18:2671-2698. [PMID: 37567932 DOI: 10.1038/s41596-023-00857-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Accepted: 04/26/2023] [Indexed: 08/13/2023]
Abstract
Chemical modifications of transcripts with a 5' cap occur in all organisms and function in many aspects of RNA metabolism. To facilitate analysis of RNA caps, we developed a systems-level mass spectrometry-based technique, CapQuant, for accurate and sensitive quantification of the cap epitranscriptome. The protocol includes the addition of stable isotope-labeled cap nucleotides (CNs) to RNA, enzymatic hydrolysis of endogenous RNA to release CNs, and off-line enrichment of CNs by ion-pairing high-pressure liquid chromatography, followed by a 17 min chromatography-coupled tandem quadrupole mass spectrometry run for the identification and quantification of individual CNs. The total time required for the protocol can be up to 7 d. In this approach, 26 CNs can be quantified in eukaryotic poly(A)-tailed RNA, bacterial total RNA and viral RNA. This protocol can be modified to analyze other types of RNA and RNA from in vitro sources. CapQuant stands out from other methods in terms of superior specificity, sensitivity and accuracy, and it is not limited to individual caps nor does it require radiolabeling. Thanks to its unique capability of accurately and sensitively quantifying RNA caps on a systems level, CapQuant can reveal both the RNA cap landscape and the transcription start site distribution of capped RNA in a broad range of settings.
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Affiliation(s)
- Jin Wang
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, School of Life Sciences, Inner Mongolia University, Hohhot, People's Republic of China.
- Antimicrobial Resistance Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology, Singapore, Singapore.
- Institute of Biomedical Sciences, Inner Mongolia University, Hohhot, China.
| | - Bing Liang Alvin Chew
- Antimicrobial Resistance Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology, Singapore, Singapore
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
- NTU Institute of Health Technologies, Interdisciplinary Graduate Programme, Nanyang Technological University, Singapore, Singapore
| | - Yong Lai
- Antimicrobial Resistance Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology, Singapore, Singapore
- Department of Chemical and Biological Engineering, Hong Kong University of Science and Technology, Hong Kong, China
| | | | - Luang Xu
- Cancer Science Institute of Singapore, Singapore, Singapore
- School of Life Science and Technology, Shanghai Institute of Biochemistry and Cell Biology, Shanghai, China
| | - Yu Liu
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, School of Life Sciences, Inner Mongolia University, Hohhot, People's Republic of China
| | - Xin-Yuan Fu
- Cancer Science Institute of Singapore, Singapore, Singapore
- Generos Pharmaceutical Co. Ltd, Hangzhou, China
| | - Zhenguo Lin
- Department of Biology, Saint Louis University, St. Louis, MO, USA
| | - Pei-Yong Shi
- Departments of Biochemistry & Molecular Biology and Pharmacology & Toxicology, and Sealy Center for Structural Biology & Molecular Biophysics, University of Texas Medical Branch, Galveston, TX, USA
- GlaxoSmithKline, Rockville, MD, USA
| | - Timothy K Lu
- Antimicrobial Resistance Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology, Singapore, Singapore
- Synthetic Biology Center, Departments of Biological Engineering and Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
- Senti Bio, San Francisco, CA, USA
| | - Dahai Luo
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Samie R Jaffrey
- Department of Pharmacology, Weill Medical College, Cornell University, New York, NY, USA
| | - Peter C Dedon
- Antimicrobial Resistance Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology, Singapore, Singapore.
- Dept. of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
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5
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Mawla AM, van der Meulen T, Huising MO. Chromatin accessibility differences between alpha, beta, and delta cells identifies common and cell type-specific enhancers. BMC Genomics 2023; 24:202. [PMID: 37069576 PMCID: PMC10108528 DOI: 10.1186/s12864-023-09293-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 04/03/2023] [Indexed: 04/19/2023] Open
Abstract
BACKGROUND High throughput sequencing has enabled the interrogation of the transcriptomic landscape of glucagon-secreting alpha cells, insulin-secreting beta cells, and somatostatin-secreting delta cells. These approaches have furthered our understanding of expression patterns that define healthy or diseased islet cell types and helped explicate some of the intricacies between major islet cell crosstalk and glucose regulation. All three endocrine cell types derive from a common pancreatic progenitor, yet alpha and beta cells have partially opposing functions, and delta cells modulate and control insulin and glucagon release. While gene expression signatures that define and maintain cellular identity have been widely explored, the underlying epigenetic components are incompletely characterized and understood. However, chromatin accessibility and remodeling is a dynamic attribute that plays a critical role to determine and maintain cellular identity. RESULTS Here, we compare and contrast the chromatin landscape between mouse alpha, beta, and delta cells using ATAC-Seq to evaluate the significant differences in chromatin accessibility. The similarities and differences in chromatin accessibility between these related islet endocrine cells help define their fate in support of their distinct functional roles. We identify patterns that suggest that both alpha and delta cells are poised, but repressed, from becoming beta-like. We also identify patterns in differentially enriched chromatin that have transcription factor motifs preferentially associated with different regions of the genome. Finally, we not only confirm and visualize previously discovered common endocrine- and cell specific- enhancer regions across differentially enriched chromatin, but identify novel regions as well. We compiled our chromatin accessibility data in a freely accessible database of common endocrine- and cell specific-enhancer regions that can be navigated with minimal bioinformatics expertise. CONCLUSIONS Both alpha and delta cells appear poised, but repressed, from becoming beta cells in murine pancreatic islets. These data broadly support earlier findings on the plasticity in identity of non-beta cells under certain circumstances. Furthermore, differential chromatin accessibility shows preferentially enriched distal-intergenic regions in beta cells, when compared to either alpha or delta cells.
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Affiliation(s)
- Alex M Mawla
- Department of Neurobiology, Physiology & Behavior, College of Biological Sciences, University of California, One Shields Avenue, Davis, CA, 95616, USA
| | - Talitha van der Meulen
- Department of Neurobiology, Physiology & Behavior, College of Biological Sciences, University of California, One Shields Avenue, Davis, CA, 95616, USA
| | - Mark O Huising
- Department of Neurobiology, Physiology & Behavior, College of Biological Sciences, University of California, One Shields Avenue, Davis, CA, 95616, USA.
- Department of Physiology and Membrane Biology, School of Medicine, University of California, One Shields Avenue, Davis, CA, 95616, USA.
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6
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Buki G, Szalai R, Pinter A, Hadzsiev K, Melegh B, Rauch T, Bene J. Correlation between large FBN1 deletions and severe cardiovascular phenotype in Marfan syndrome: Analysis of two novel cases and analytical review of the literature. Mol Genet Genomic Med 2023:e2166. [PMID: 36945115 DOI: 10.1002/mgg3.2166] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 01/26/2023] [Accepted: 03/01/2023] [Indexed: 03/23/2023] Open
Abstract
BACKGROUND Marfan syndrome (MFS) is a clinically heterogeneous hereditary connective tissue disorder. Severe cardiovascular manifestations (i.e., aortic aneurysm and dissection) are the most life-threatening complications. Most of the cases are caused by mutations, a minor group of which are copy number variations (CNV), in the FBN1 gene. METHODS Multiplex ligation-dependent probe amplification test was performed to detect CNVs in 41 MFS patients not carrying disease-causing mutations in FBN1 gene. Moreover, the association was analyzed between the localization of CNVs, the affected regulatory elements and the cardiovascular phenotypes among all cases known from the literature. RESULTS A large two-exon deletion (exon 46 and 47) was identified in two related patients, which was associated with a mild form of cardiovascular phenotype. Severe cardiovascular symptoms were found significantly more frequent in patients with FBN1 large deletion compared to our patients with intragenic small scale FBN1 mutation. Bioinformatic data analyses of regulatory elements located within the FBN1 gene revealed an association between the deletion of STAT3 transcription factor-binding site and cardiovascular symptoms in five out of 25 patients. CONCLUSION Our study demonstrated that large CNVs are often associated with severe cardiovascular manifestations in MFS and the localization of these CNVs affect the phenotype severity.
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Affiliation(s)
- Gergely Buki
- Department of Medical Genetics, Clinical Center, Medical School, University of Pécs, Pécs, Hungary
| | - Renata Szalai
- Department of Medical Genetics, Clinical Center, Medical School, University of Pécs, Pécs, Hungary
| | - Adrienn Pinter
- Department of Medical Genetics, Clinical Center, Medical School, University of Pécs, Pécs, Hungary
| | - Kinga Hadzsiev
- Department of Medical Genetics, Clinical Center, Medical School, University of Pécs, Pécs, Hungary
| | - Bela Melegh
- Department of Medical Genetics, Clinical Center, Medical School, University of Pécs, Pécs, Hungary
| | - Tibor Rauch
- Department of Biochemistry and Medical Chemistry, Medical School, University of Pécs, Pécs, Hungary
| | - Judit Bene
- Department of Medical Genetics, Clinical Center, Medical School, University of Pécs, Pécs, Hungary
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7
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Li F, Li H, Shang J, Liu JX, Dai L, Liu X, Li Y. A network-based method for identifying cancer driver genes based on node control centrality. Exp Biol Med (Maywood) 2022; 248:232-241. [PMID: 36573462 PMCID: PMC10107394 DOI: 10.1177/15353702221139201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
Cancer is one of the major contributors to human mortality and has a serious influence on human survival and health. In biomedical research, the identification of cancer driver genes (cancer drivers for short) is an important task; cancer drivers can promote the progression and generation of cancer. To identify cancer drivers, many methods have been developed. These computational models only identify coding cancer drivers; however, non-coding drivers likewise play significant roles in the progression of cancer. Hence, we propose a Network-based Method for identifying cancer Driver Genes based on node Control Centrality (NMDGCC), which can identify coding and non-coding cancer driver genes. The process of NMDGCC for identifying driver genes mainly includes the following two steps. In the first step, we construct a gene interaction network by using mRNAs and miRNAs expression data in the cancer state. In the second step, the control centrality of the node is used to identify cancer drivers in the constructed network. We use the breast cancer dataset from The Cancer Genome Atlas (TCGA) to verify the effectiveness of NMDGCC. Compared with the existing methods of cancer driver genes identification, NMDGCC has a better performance. NMDGCC also identifies 295 miRNAs as non-coding cancer drivers, of which 158 are related to tumorigenesis of BRCA. We also apply NMDGCC to identify driver genes related to the different breast cancer subtypes. The result shows that NMDGCC detects many cancer drivers of specific cancer subtypes.
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Affiliation(s)
- Feng Li
- School of Computer Science, Qufu Normal University, Rizhao 276826, China
| | - Han Li
- School of Computer Science, Qufu Normal University, Rizhao 276826, China
| | - Junliang Shang
- School of Computer Science, Qufu Normal University, Rizhao 276826, China
| | - Jin-Xing Liu
- School of Computer Science, Qufu Normal University, Rizhao 276826, China
| | - Lingyun Dai
- School of Computer Science, Qufu Normal University, Rizhao 276826, China
| | - Xikui Liu
- Department of Electrical Engineering and Information Technology, Shandong University of Science and Technology, Jinan 250031, China
| | - Yan Li
- Department of Electrical Engineering and Information Technology, Shandong University of Science and Technology, Jinan 250031, China
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8
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Viehweger F, Azem A, Gorbokon N, Uhlig R, Lennartz M, Rico SD, Kind S, Reiswich V, Kluth M, Hube-Magg C, Bernreuther C, Büscheck F, Clauditz TS, Fraune C, Jacobsen F, Krech T, Lebok P, Steurer S, Burandt E, Minner S, Marx AH, Simon R, Sauter G, Menz A, Hinsch A. Desmoglein 3 (Dsg3) Expression in Cancer: A Tissue Microarray Study on 15,869 Tumors. Pathol Res Pract 2022; 240:154200. [DOI: 10.1016/j.prp.2022.154200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 11/03/2022] [Accepted: 11/04/2022] [Indexed: 11/07/2022]
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9
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Lennartz M, Atug D, Dwertmann Rico S, Reiswich V, Viehweger F, Büscheck F, Kluth M, Hube-Magg C, Hinsch A, Bernreuther C, Sauter G, Burandt E, Marx AH, Krech T, Simon R, Minner S, Clauditz TS, Jacobsen F, Lebok P, Gorbokon N, Möller K, Steurer S, Fraune C. Analysis of More than 16,000 Human Tumor and Normal Tissues Identifies Uroplakin 3B as a Useful Diagnostic Marker for Mesothelioma and Normal Mesothelial Cells. Diagnostics (Basel) 2022; 12:2516. [PMID: 36292206 PMCID: PMC9600073 DOI: 10.3390/diagnostics12102516] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 09/20/2022] [Accepted: 10/10/2022] [Indexed: 10/02/2023] Open
Abstract
Uroplakin 3B (Upk3b) is involved in stabilizing and strengthening the urothelial cell layer of the bladder. Based on RNA expression studies, Upk3b is expressed in a limited number of normal and tumor tissues. The potential use of Upk3b as a diagnostic or prognostic marker in tumor diagnosis has not yet been extensively investigated. A tissue microarray containing 17,693 samples from 151 different tumor types/subtypes and 608 samples of 76 different normal tissue types was analyzed by immunohistochemistry. In normal tissues, Upk3b expression was largely limited to mesothelial cells, urothelial umbrella cells, and amnion cells. In tumor tissues, Upk3b was detectable in only 17 of 151 (11.3%) of tumor types. Upk3b expression was most frequent in mesotheliomas (82.1% of epithelioid and 30.8% of biphasic) and in urothelial tumors of the urinary bladder, where the positivity rate decreased from 61.9% in pTaG2 (low grade) to 58.0% in pTaG3 (high grade) and 14.6% in pT2-4 cancers. Among pT2-4 urothelial carcinomas, Upk3b staining was unrelated to tumor stage, lymph node status, and patient prognosis. Less commonly, Upk3b expression was also seen in Brenner tumors of the ovary (10.8%), as well as in four other subtypes of ovarian cancer (0.9-10.6%). Four additional tumor entities showed a weak to moderate Upk3b positivity in less than 5% of cases. In summary, Upk3b immunohistochemistry is a useful diagnostic tool for the distinction of mesotheliomas from other thoracic tumors and the visualization of normal mesothelial and umbrella cells.
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Affiliation(s)
- Maximilian Lennartz
- Department of Pathology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| | - Dennis Atug
- Department of Pathology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| | | | - Viktor Reiswich
- Department of Pathology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| | - Florian Viehweger
- Department of Pathology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| | - Franziska Büscheck
- Department of Pathology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| | - Martina Kluth
- Department of Pathology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| | - Claudia Hube-Magg
- Department of Pathology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| | - Andrea Hinsch
- Department of Pathology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| | - Christian Bernreuther
- Department of Pathology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| | - Guido Sauter
- Department of Pathology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| | - Eike Burandt
- Department of Pathology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| | - Andreas H. Marx
- Department of Pathology, Academic Hospital Fuerth, 90766 Fuerth, Germany
| | - Till Krech
- Department of Pathology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
- Department of Pathology, Clinical Center Osnabrueck, 49076 Osnabrueck, Germany
| | - Ronald Simon
- Department of Pathology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| | - Sarah Minner
- Department of Pathology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| | - Till S. Clauditz
- Department of Pathology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| | - Frank Jacobsen
- Department of Pathology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| | - Patrick Lebok
- Department of Pathology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
- Department of Pathology, Clinical Center Osnabrueck, 49076 Osnabrueck, Germany
| | - Natalia Gorbokon
- Department of Pathology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| | - Katharina Möller
- Department of Pathology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| | - Stefan Steurer
- Department of Pathology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| | - Christoph Fraune
- Department of Pathology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
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Cytokeratin 10 (CK10) expression in cancer: A tissue microarray study on 11,021 tumors. Ann Diagn Pathol 2022; 60:152029. [PMID: 36029589 DOI: 10.1016/j.anndiagpath.2022.152029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Accepted: 08/18/2022] [Indexed: 11/30/2022]
Abstract
Cytokeratin 10 (CK10) is a type I acidic low molecular weight cytokeratin which is mainly expressed in keratinizing squamous epithelium of the skin. Variable levels of CK10 protein have been described in squamous carcinomas of different sites and in some other epithelial neoplasms. To comprehensively determine the prevalence of CK10 expression in normal and neoplastic tissues, a tissue microarray containing 11,021 samples from 131 different tumor types and subtypes was analyzed by immunohistochemistry. CK10 immunostaining was detectable in 41 (31.3 %) of 131 tumor categories, including 18 (13.7 %) tumor types with at least one strongly positive case. The highest rate of positive staining was found in squamous cell carcinomas from various sites of origin (positive in 18.6 %-66.1 %) and in Warthin tumors of salivary glands (47.8 %), followed by various tumor entities known to potentially exhibit areas with squamous cell differentiation such as teratomas (33.3 %), basal cell carcinomas of the skin (14.3 %), adenosquamous carcinomas of the cervix (11.1 %), and several categories of urothelial neoplasms (3.1 %-16.8 %). In a combined analysis of 956 squamous cell carcinomas from 11 different sites of origin, reduced CK10 staining was linked to high grade (p < 0.0001) and advanced stage (p = 0.0015) but unrelated to HPV infection. However, CK10 staining was not statistically related to grade (p = 0.1509) and recurrence-free (p = 0.5247) or overall survival (p = 0.5082) in 176 cervical squamous cell carcinomas. In the urinary bladder, CK10 staining occurred more commonly in muscle-invasive (17.7 %) than in non-invasive urothelial carcinomas (4.0 %-6.0 %; p < 0.0001). In summary, our data corroborate a role of CK10 as a suitable marker for mature, keratinizing squamous cell differentiation in epithelial tissues. CK10 immunohistochemistry may thus be instrumental for a more objective evaluation of the clinical significance of focal squamous differentiation in cancer.
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11
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The dynamic, combinatorial cis-regulatory lexicon of epidermal differentiation. Nat Genet 2021; 53:1564-1576. [PMID: 34650237 PMCID: PMC8763320 DOI: 10.1038/s41588-021-00947-3] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Accepted: 09/01/2021] [Indexed: 01/24/2023]
Abstract
Transcription factors bind DNA sequence motif vocabularies in cis-regulatory elements (CREs) to modulate chromatin state and gene expression during cell state transitions. A quantitative understanding of how motif lexicons influence dynamic regulatory activity has been elusive due to the combinatorial nature of the cis-regulatory code. To address this, we undertook multiomic data profiling of chromatin and expression dynamics across epidermal differentiation to identify 40,103 dynamic CREs associated with 3,609 dynamically expressed genes, then applied an interpretable deep-learning framework to model the cis-regulatory logic of chromatin accessibility. This analysis framework identified cooperative DNA sequence rules in dynamic CREs regulating synchronous gene modules with diverse roles in skin differentiation. Massively parallel reporter assay analysis validated temporal dynamics and cooperative cis-regulatory logic. Variants linked to human polygenic skin disease were enriched in these time-dependent combinatorial motif rules. This integrative approach shows the combinatorial cis-regulatory lexicon of epidermal differentiation and represents a general framework for deciphering the organizational principles of the cis-regulatory code of dynamic gene regulation.
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12
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Smith MD, Pillman K, Jankovic-Karasoulos T, McAninch D, Wan Q, Bogias KJ, McCullough D, Bianco-Miotto T, Breen J, Roberts CT. Large-scale transcriptome-wide profiling of microRNAs in human placenta and maternal plasma at early to mid gestation. RNA Biol 2021; 18:507-520. [PMID: 34412547 PMCID: PMC8677031 DOI: 10.1080/15476286.2021.1963105] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
MicroRNAs (miRNAs) are increasingly seen as important regulators of placental development and opportunistic biomarker targets. Given the difficulty in obtaining samples from early gestation and subsequent paucity of the same, investigation of the role of miRNAs in early gestation human placenta has been limited. To address this, we generated miRNA profiles using 96 placentas from presumed normal pregnancies, across early gestation, in combination with matched profiles from maternal plasma. Placenta samples range from 6 to 23 weeks' gestation, a time period that includes placenta from the early, relatively low but physiological (6-10 weeks' gestation) oxygen environment, and later, physiologically normal oxygen environment (11-23 weeks' gestation).We identified 637 miRNAs with expression in 86 samples (after removing poor quality samples), showing a clear gestational age gradient from 6 to 23 weeks' gestation. We identified 374 differentially expressed (DE) miRNAs between placentas from 6-10 weeks' versus 11-23 weeks' gestation. We see a clear gestational age group bias in miRNA clusters C19MC, C14MC, miR-17 ~ 92 and paralogs, regions that also include many DE miRNAs. Proportional change in expression of placenta-specific miRNA clusters was reflected in maternal plasma.The presumed introduction of oxygenated maternal blood into the placenta (between ~10 and 12 weeks' gestation) changes the miRNA profile of the chorionic villus, particularly in placenta-specific miRNA clusters. Data presented here comprise a clinically important reference set for studying early placenta development and may underpin the generation of minimally invasive methods for monitoring placental health.
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Affiliation(s)
- Melanie D Smith
- Robinson Research Institute, University of Adelaide, Adelaide, SA, Australia.,Adelaide Medical School, University of Adelaide, Adelaide, SA, Australia.,Flinders Health and Medical Research Institute, Flinders University, Bedford Park, SA, Australia
| | - Katherine Pillman
- Centre for Cancer Biology, University of South Australia/SA Pathology, Adelaide, SA, Australia
| | - Tanja Jankovic-Karasoulos
- Robinson Research Institute, University of Adelaide, Adelaide, SA, Australia.,Adelaide Medical School, University of Adelaide, Adelaide, SA, Australia.,Flinders Health and Medical Research Institute, Flinders University, Bedford Park, SA, Australia
| | - Dale McAninch
- Adelaide Medical School, University of Adelaide, Adelaide, SA, Australia
| | - Qianhui Wan
- Flinders Health and Medical Research Institute, Flinders University, Bedford Park, SA, Australia
| | - K Justinian Bogias
- Robinson Research Institute, University of Adelaide, Adelaide, SA, Australia.,Adelaide Medical School, University of Adelaide, Adelaide, SA, Australia
| | - Dylan McCullough
- Flinders Health and Medical Research Institute, Flinders University, Bedford Park, SA, Australia
| | - Tina Bianco-Miotto
- Robinson Research Institute, University of Adelaide, Adelaide, SA, Australia.,School of Agriculture Food and Wine, Waite Research Institute, University of Adelaide, Adelaide, SA, Australia
| | - James Breen
- Robinson Research Institute, University of Adelaide, Adelaide, SA, Australia.,Adelaide Medical School, University of Adelaide, Adelaide, SA, Australia.,South Australian Genomics Centre, South Australian Health & Medical Research Institute, Adelaide, SA, Australia
| | - Claire T Roberts
- Robinson Research Institute, University of Adelaide, Adelaide, SA, Australia.,Adelaide Medical School, University of Adelaide, Adelaide, SA, Australia.,Flinders Health and Medical Research Institute, Flinders University, Bedford Park, SA, Australia
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13
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Li Y, Xu F, Chen F, Chen Y, Ge D, Zhang S, Lu C. Transcriptomics based multi-dimensional characterization and drug screen in esophageal squamous cell carcinoma. EBioMedicine 2021; 70:103510. [PMID: 34365093 PMCID: PMC8353400 DOI: 10.1016/j.ebiom.2021.103510] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2021] [Revised: 07/13/2021] [Accepted: 07/14/2021] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND Esophageal squamous cell carcinoma (ESCC) remains one of the deadly cancer types. Comprehensively dissecting the molecular characterization and the heterogeneity of ESCC paves the way for developing more promising therapeutics. METHODS Expression profiles of multiple ESCC datasets were integrated. ATAC-seq and RNA-seq were combined to reveal the chromatin accessibility features. A prognosis-related subtype classifier (PrSC) was constructed, and its association with the tumor microenvironment (TME) and immunotherapy was assessed. The key gene signature was validated in clinical samples. Based on the TME heterogeneity of ESCC patients, potential subtype-specific therapeutic agents were screened. FINDINGS The common differentially expressed genes (cDEGs) in ESCC were identified. Up-regulated genes (HEATR1, TIMELESS, DTL, GINS1, RUVBL1, and ECT2) were found highly important in ESCC cell survival. The expression alterations of PRIM2, HPGD, NELL2, and TFAP2B were associated with chromatin accessibility changes. PrSC was a robust scoring tool that was not only associated with the prognosis of ESCC patients, but also could reflect the TME heterogeneity. TNS1high fibroblasts were associated with immune exclusion. TG-101348 and Vinorelbine were identified as potential subtype-specific therapeutic agents. Besides, the application of PrSC into two immunotherapy cohorts indicated its potential value in assessing treatment response to immunotherapy. INTERPRETATION Our study depicted the multi-dimensional characterization of ESCC, established a robust scoring tool for the prognosis assessment, highlighted the role of TNS1high fibroblasts in TME, and identified potential drugs for clinical use. FUNDING A full list of funding bodies that contributed to this study can be found in the Acknowledgements section.
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Affiliation(s)
- Yin Li
- Department of Thoracic Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Fengkai Xu
- Department of Thoracic Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Fanghua Chen
- Liver Cancer Institute, Zhongshan Hospital, and Key Laboratory of Carcinogenesis and Cancer Invasion (Ministry of Education), Fudan University, Shanghai, China
| | - Yiwei Chen
- Department of Thoracic Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Di Ge
- Department of Thoracic Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Shu Zhang
- Liver Cancer Institute, Zhongshan Hospital, and Key Laboratory of Carcinogenesis and Cancer Invasion (Ministry of Education), Fudan University, Shanghai, China; Institutes of Biomedical Sciences, Fudan University, Shanghai, China.
| | - Chunlai Lu
- Department of Thoracic Surgery, Zhongshan Hospital, Fudan University, Shanghai, China.
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14
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Dong X, Liu C, Dozmorov M. Review of multi-omics data resources and integrative analysis for human brain disorders. Brief Funct Genomics 2021; 20:223-234. [PMID: 33969380 PMCID: PMC8287916 DOI: 10.1093/bfgp/elab024] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Revised: 03/05/2021] [Accepted: 04/12/2021] [Indexed: 12/20/2022] Open
Abstract
In the last decade, massive omics datasets have been generated for human brain research. It is evolving so fast that a timely update is urgently needed. In this review, we summarize the main multi-omics data resources for the human brains of both healthy controls and neuropsychiatric disorders, including schizophrenia, autism, bipolar disorder, Alzheimer's disease, Parkinson's disease, progressive supranuclear palsy, etc. We also review the recent development of single-cell omics in brain research, such as single-nucleus RNA-seq, single-cell ATAC-seq and spatial transcriptomics. We further investigate the integrative multi-omics analysis methods for both tissue and single-cell data. Finally, we discuss the limitations and future directions of the multi-omics study of human brain disorders.
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Affiliation(s)
- Xianjun Dong
- Harvard Medical School, head of the Genomics and Bioinformatics Hub at Brigham and Women’s Hospital
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15
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Asselta R, Paraboschi EM, Gerussi A, Cordell HJ, Mells GF, Sandford RN, Jones DE, Nakamura M, Ueno K, Hitomi Y, Kawashima M, Nishida N, Tokunaga K, Nagasaki M, Tanaka A, Tang R, Li Z, Shi Y, Liu X, Xiong M, Hirschfield G, Siminovitch KA, Carbone M, Cardamone G, Duga S, Gershwin ME, Seldin MF, Invernizzi P. X Chromosome Contribution to the Genetic Architecture of Primary Biliary Cholangitis. Gastroenterology 2021; 160:2483-2495.e26. [PMID: 33675743 PMCID: PMC8169555 DOI: 10.1053/j.gastro.2021.02.061] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/29/2020] [Revised: 02/15/2021] [Accepted: 02/25/2021] [Indexed: 02/07/2023]
Abstract
BACKGROUND & AIMS Genome-wide association studies in primary biliary cholangitis (PBC) have failed to find X chromosome (chrX) variants associated with the disease. Here, we specifically explore the chrX contribution to PBC, a sexually dimorphic complex autoimmune disease. METHODS We performed a chrX-wide association study, including genotype data from 5 genome-wide association studies (from Italy, United Kingdom, Canada, China, and Japan; 5244 case patients and 11,875 control individuals). RESULTS Single-marker association analyses found approximately 100 loci displaying P < 5 × 10-4, with the most significant being a signal within the OTUD5 gene (rs3027490; P = 4.80 × 10-6; odds ratio [OR], 1.39; 95% confidence interval [CI], 1.028-1.88; Japanese cohort). Although the transethnic meta-analysis evidenced only a suggestive signal (rs2239452, mapping within the PIM2 gene; OR, 1.17; 95% CI, 1.09-1.26; P = 9.93 × 10-8), the population-specific meta-analysis showed a genome-wide significant locus in East Asian individuals pointing to the same region (rs7059064, mapping within the GRIPAP1 gene; P = 6.2 × 10-9; OR, 1.33; 95% CI, 1.21-1.46). Indeed, rs7059064 tags a unique linkage disequilibrium block including 7 genes: TIMM17B, PQBP1, PIM2, SLC35A2, OTUD5, KCND1, and GRIPAP1, as well as a superenhancer (GH0XJ048933 within OTUD5) targeting all these genes. GH0XJ048933 is also predicted to target FOXP3, the main T-regulatory cell lineage specification factor. Consistently, OTUD5 and FOXP3 RNA levels were up-regulated in PBC case patients (1.75- and 1.64-fold, respectively). CONCLUSIONS This work represents the first comprehensive study, to our knowledge, of the chrX contribution to the genetics of an autoimmune liver disease and shows a novel PBC-related genome-wide significant locus.
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Affiliation(s)
- Rosanna Asselta
- Department of Biomedical Sciences, Humanitas University, Milan, Italy; IRCCS Humanitas Research Hospital, Milan, Italy
| | - Elvezia M Paraboschi
- Department of Biomedical Sciences, Humanitas University, Milan, Italy; IRCCS Humanitas Research Hospital, Milan, Italy
| | - Alessio Gerussi
- Division of Gastroenterology and Center for Autoimmune Liver Diseases, Department of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy; European Reference Network on Hepatological Diseases, San Gerardo Hospital, Monza, Italy
| | - Heather J Cordell
- Population Health Sciences Institute, Faculty of Medical Sciences, Newcastle University, United Kingdom
| | - George F Mells
- Academic Department of Medical Genetics, Cambridge University, Cambridge, United Kingdom
| | - Richard N Sandford
- Academic Department of Medical Genetics, Cambridge University, Cambridge, United Kingdom
| | - David E Jones
- Faculty of Medical Sciences, Newcastle University, Newcastle, United Kingdom
| | - Minoru Nakamura
- Clinical Research Center, National Hospital Organization, Nagasaki Medical Center, Nagasaki, Japan; Department of Hepatology, Nagasaki University Graduate School of Biomedical Sciences, Omura, Nagasaki, Japan
| | - Kazuko Ueno
- Genome Medical Science Project, National Center for Global Health and Medicine, Tokyo, Japan
| | - Yuki Hitomi
- Department of Human Genetics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Minae Kawashima
- Department of Human Genetics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Nao Nishida
- Department of Human Genetics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Katsushi Tokunaga
- Genome Medical Science Project, National Center for Global Health and Medicine, Tokyo, Japan; Department of Human Genetics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Masao Nagasaki
- Human Biosciences Unit for the Top Global Course Center for the Promotion of Interdisciplinary Education and Research, Kyoto University, Kyoto, Japan; Center for Genomic Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Atsushi Tanaka
- Department of Medicine, Teikyo University School of Medicine, Tokyo, Japan
| | - Ruqi Tang
- Division of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, State Key Laboratory for Oncogenes and Related Genes, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai Institute of Digestive Disease, Shanghai, China
| | - Zhiqiang Li
- Affiliated Hospital of Qingdao University and Biomedical Sciences Institute of Qingdao University (Qingdao Branch of SJTU Bio-X Institutes), Qingdao University, Qingdao, China; Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Collaborative Innovation Center for Brain Science, Shanghai Jiao Tong University, Shanghai, China
| | - Yongyong Shi
- Affiliated Hospital of Qingdao University and Biomedical Sciences Institute of Qingdao University (Qingdao Branch of SJTU Bio-X Institutes), Qingdao University, Qingdao, China; Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Collaborative Innovation Center for Brain Science, Shanghai Jiao Tong University, Shanghai, China
| | - Xiangdong Liu
- Key Laboratory of Developmental Genes and Human Diseases, Institute of Life Sciences, Southeast University, Nanjing, Jiangsu, China
| | - Ma Xiong
- Division of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, State Key Laboratory for Oncogenes and Related Genes, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai Institute of Digestive Disease, Shanghai, China
| | - Gideon Hirschfield
- Toronto General Hospital Research Institute, Toronto, Ontario, Canada; Department of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Katherine A Siminovitch
- Department of Medicine, University of Toronto, Toronto, Ontario, Canada; Mount Sinai Hospital, Lunenfeld Tanenbaum Research Institute and Toronto General Research Institute, Toronto, Canada; Department of Immunology, University of Toronto, Toronto, Ontario, Canada; Institute of Medical Sciences, University of Toronto, Toronto, Ontario, Canada
| | - Marco Carbone
- Division of Gastroenterology and Center for Autoimmune Liver Diseases, Department of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy; European Reference Network on Hepatological Diseases, San Gerardo Hospital, Monza, Italy
| | - Giulia Cardamone
- Department of Biomedical Sciences, Humanitas University, Milan, Italy; IRCCS Humanitas Research Hospital, Milan, Italy
| | - Stefano Duga
- Department of Biomedical Sciences, Humanitas University, Milan, Italy; IRCCS Humanitas Research Hospital, Milan, Italy
| | | | | | - Pietro Invernizzi
- Division of Gastroenterology and Center for Autoimmune Liver Diseases, Department of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy; European Reference Network on Hepatological Diseases, San Gerardo Hospital, Monza, Italy.
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16
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Pham VVH, Liu L, Bracken CP, Nguyen T, Goodall GJ, Li J, Le TD. pDriver : A novel method for unravelling personalised coding and miRNA cancer drivers. Bioinformatics 2021; 37:3285-3292. [PMID: 33904576 DOI: 10.1093/bioinformatics/btab262] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Revised: 03/19/2021] [Accepted: 04/22/2021] [Indexed: 02/07/2023] Open
Abstract
MOTIVATION Unravelling cancer driver genes is important in cancer research. Although computational methods have been developed to identify cancer drivers, most of them detect cancer drivers at population level. However, two patients who have the same cancer type and receive the same treatment may have different outcomes because each patient has a different genome and their disease might be driven by different driver genes. Therefore new methods are being developed for discovering cancer drivers at individual level, but existing personalised methods only focus on coding drivers while microRNAs (miRNAs) have been shown to drive cancer progression as well. Thus, novel methods are required to discover both coding and miRNA cancer drivers at individual level. RESULTS We propose the novel method, pDriver, to discover personalised cancer drivers. pDriver includes two stages: (1) Constructing gene networks for each cancer patient and (2) Discovering cancer drivers for each patient based on the constructed gene networks. To demonstrate the effectiveness of pDriver, we have applied it to five TCGA cancer datasets and compared it with the state-of-the-art methods. The result indicates that pDriver is more effective than other methods. Furthermore, pDriver can also detect miRNA cancer drivers and most of them have been confirmed to be associated with cancer by literature. We further analyse the predicted personalised drivers for breast cancer patients and the result shows that they are significantly enriched in many GO processes and KEGG pathways involved in breast cancer. AVAILABILITY AND IMPLEMENTATION pDriver is available at https://github.com/pvvhoang/pDriver. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Vu V H Pham
- UniSA STEM, University of South Australia, Mawson Lakes, SA 5095, Australia
| | - Lin Liu
- UniSA STEM, University of South Australia, Mawson Lakes, SA 5095, Australia
| | - Cameron P Bracken
- Centre for Cancer Biology, an alliance of SA Pathology and University of South Australia, Adelaide, SA 5000, Australia.,Department of Medicine, The University of Adelaide, Adelaide, SA 5005, Australia
| | - Thin Nguyen
- Applied Artificial Intelligence Institute, Deakin University, Australia
| | - Gregory J Goodall
- Centre for Cancer Biology, an alliance of SA Pathology and University of South Australia, Adelaide, SA 5000, Australia.,Department of Medicine, The University of Adelaide, Adelaide, SA 5005, Australia
| | - Jiuyong Li
- UniSA STEM, University of South Australia, Mawson Lakes, SA 5095, Australia
| | - Thuc D Le
- UniSA STEM, University of South Australia, Mawson Lakes, SA 5095, Australia
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17
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Pham VVH, Liu L, Bracken CP, Goodall GJ, Li J, Le TD. DriverGroup: a novel method for identifying driver gene groups. Bioinformatics 2021; 36:i583-i591. [PMID: 33381812 DOI: 10.1093/bioinformatics/btaa797] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Abstract
MOTIVATION Identifying cancer driver genes is a key task in cancer informatics. Most existing methods are focused on individual cancer drivers which regulate biological processes leading to cancer. However, the effect of a single gene may not be sufficient to drive cancer progression. Here, we hypothesize that there are driver gene groups that work in concert to regulate cancer, and we develop a novel computational method to detect those driver gene groups. RESULTS We develop a novel method named DriverGroup to detect driver gene groups by using gene expression and gene interaction data. The proposed method has three stages: (i) constructing the gene network, (ii) discovering critical nodes of the constructed network and (iii) identifying driver gene groups based on the discovered critical nodes. Before evaluating the performance of DriverGroup in detecting cancer driver groups, we firstly assess its performance in detecting the influence of gene groups, a key step of DriverGroup. The application of DriverGroup to DREAM4 data demonstrates that it is more effective than other methods in detecting the regulation of gene groups. We then apply DriverGroup to the BRCA dataset to identify driver groups for breast cancer. The identified driver groups are promising as several group members are confirmed to be related to cancer in literature. We further use the predicted driver groups in survival analysis and the results show that the survival curves of patient subpopulations classified using the predicted driver groups are significantly differentiated, indicating the usefulness of DriverGroup. AVAILABILITY AND IMPLEMENTATION DriverGroup is available at https://github.com/pvvhoang/DriverGroup. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Vu V H Pham
- UniSA STEM, University of South Australia, Mawson Lakes, SA, 5095, Australia
| | - Lin Liu
- UniSA STEM, University of South Australia, Mawson Lakes, SA, 5095, Australia
| | - Cameron P Bracken
- Centre for Cancer Biology, an alliance of SA Pathology and University of South Australia, Adelaide, SA, 5000, Australia.,Department of Medicine, The University of Adelaide, Adelaide, SA 5005, Australia
| | - Gregory J Goodall
- Centre for Cancer Biology, an alliance of SA Pathology and University of South Australia, Adelaide, SA, 5000, Australia.,Department of Medicine, The University of Adelaide, Adelaide, SA 5005, Australia
| | - Jiuyong Li
- UniSA STEM, University of South Australia, Mawson Lakes, SA, 5095, Australia
| | - Thuc D Le
- UniSA STEM, University of South Australia, Mawson Lakes, SA, 5095, Australia
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18
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Chen X, Zhou J, Zhang R, Wong AK, Park CY, Theesfeld CL, Troyanskaya OG. Tissue-specific enhancer functional networks for associating distal regulatory regions to disease. Cell Syst 2021; 12:353-362.e6. [PMID: 33689683 DOI: 10.1016/j.cels.2021.02.002] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Revised: 11/13/2020] [Accepted: 02/08/2021] [Indexed: 12/22/2022]
Abstract
Systematic study of tissue-specific function of enhancers and their disease associations is a major challenge. We present an integrative machine-learning framework, FENRIR, that integrates thousands of disparate epigenetic and functional genomics datasets to infer tissue-specific functional relationships between enhancers for 140 diverse human tissues and cell types, providing a regulatory-region-centric approach to systematically identify disease-associated enhancers. We demonstrated its power to accurately prioritize enhancers associated with 25 complex diseases. In a case study on autism, FENRIR-prioritized enhancers showed a significant proband-specific de novo mutation enrichment in a large, sibling-controlled cohort, indicating pathogenic signal. We experimentally validated transcriptional regulatory activities of eight enhancers, including enhancers not previously reported with autism, and demonstrated their differential regulatory potential between proband and sibling alleles. Thus, FENRIR is an accurate and effective framework for the study of tissue-specific enhancers and their role in disease. FENRIR can be accessed at fenrir.flatironinstitute.org/.
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Affiliation(s)
- Xi Chen
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY 10010, USA
| | - Jian Zhou
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY 10010, USA; Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA
| | - Ran Zhang
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA
| | - Aaron K Wong
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY 10010, USA
| | - Christopher Y Park
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY 10010, USA
| | - Chandra L Theesfeld
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA
| | - Olga G Troyanskaya
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY 10010, USA; Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA; Department of Computer Science, Princeton University, Princeton, NJ 08544, USA.
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19
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Chaudhary MS, Pham VVH, Le TD. NIBNA: A network-based node importance approach for identifying breast cancer drivers. Bioinformatics 2021; 37:2521-2528. [PMID: 33677485 DOI: 10.1093/bioinformatics/btab145] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Revised: 01/21/2021] [Accepted: 02/28/2021] [Indexed: 12/11/2022] Open
Abstract
MOTIVATION Identifying meaningful cancer driver genes in a cohort of tumors is a challenging task in cancer genomics. Although existing studies have identified known cancer drivers, most of them focus on detecting coding drivers with mutations. It is acknowledged that non-coding drivers can regulate driver mutations to promote cancer growth. In this work, we propose a novel node importance based network analysis (NIBNA) framework to detect coding and non-coding cancer drivers. We hypothesize that cancer drivers are crucial to the formation of community structures in cancer network, and removing them from the network greatly perturbs the network structure thereby critically affecting the functioning of the network. NIBNA detects cancer drivers using a three-step process; first, a condition-specific network is built by incorporating gene expression data and gene networks, second, the community structures in the network are estimated and third, a centrality-based metric is applied to compute node importance. RESULTS We apply NIBNA to the BRCA dataset and it outperforms existing state-of-art methods in detecting coding cancer drivers. NIBNA also predicts 265 miRNA drivers and majority of these drivers have been validated in literature. Further we apply NIBNA to detect cancer subtype-specific drivers and several predicted drivers have been validated to be associated with cancer subtypes. Lastly, we evaluate NIBNA's performance in detecting epithelial-mesenchymal transition (EMT) drivers, and we confirmed 8 coding and 13 miRNA drivers in the list of known genes. AVAILABILITY The source code can be accessed at: https://github.com/mandarsc/NIBNA. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | - Vu V H Pham
- UniSA STEM, University of South Australia, Mawson Lakes, SA 5095, Australia
| | - Thuc D Le
- UniSA STEM, University of South Australia, Mawson Lakes, SA 5095, Australia
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20
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Nishimichi N, Tsujino K, Kanno K, Sentani K, Kobayashi T, Chayama K, Sheppard D, Yokosaki Y. Induced hepatic stellate cell integrin, α8β1, enhances cellular contractility and TGFβ activity in liver fibrosis. J Pathol 2021; 253:366-373. [PMID: 33433924 PMCID: PMC7986747 DOI: 10.1002/path.5618] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2020] [Revised: 12/23/2020] [Accepted: 01/08/2021] [Indexed: 02/04/2023]
Abstract
No effective therapy exists for fatal fibrosis. New therapeutic targets are needed for hepatic fibrosis because the incidence keeps increasing. The activation and differentiation of fibroblasts into myofibroblasts that causes excessive matrix deposition is central to fibrosis. Here, we investigated whether (and which) integrin receptors for matrix proteins activate hepatic stellate cells (HSCs). First, integrin α‐subunits were investigated systematically for their expression over the course of HSC activation and their distribution on fibroblasts and other systemic primary cells. The most upregulated in plate culture‐activated HSCs and specifically expressed across fibroblast linages was the α8 subunit. An anti‐α8 neutralizing mAb was evaluated in three different murine fibrosis models: for cytotoxic (CCl4 treatment), non‐alcoholic steatohepatitis‐associated and cholestatic fibrosis. In all models, pathology and fibrosis markers (hydroxyproline and α‐smooth muscle actin) were improved following the mAb injection. We also CCl4‐treated mice with inducible Itga8−/−; these mice were protected from increased hydroxyproline levels. Furthermore, ITGA8 was upregulated in specimens from 90 patients with liver fibrosis, indicating the relevance of our findings to liver fibrosis in people. Mechanistically, inhibition or ligand engagement of HSC α8 suppressed and enhanced myofibroblast differentiation, respectively, and HSC/fibroblast α8 activated latent TGFβ. Finally, integrin α8β1 potentially fulfils the growing need for anti‐fibrotic drugs and is an integrin not to be ignored. © 2021 The Authors. The Journal of Pathology published by John Wiley & Sons, Ltd. on behalf of The Pathological Society of Great Britain and Ireland.
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Affiliation(s)
- Norihisa Nishimichi
- Integrin-Matrix Biomedical Science, Translational Research Center, Hiroshima University, Hiroshima, Japan
| | - Kazuyuki Tsujino
- Lung Biology Center, Department of Medicine, Cardiovascular Research Institute, University of California San Francisco (UCSF), San Francisco, CA, USA
| | - Keishi Kanno
- General Internal Medicine, Hiroshima University Hospital, Hiroshima University, Hiroshima, Japan
| | - Kazuhiro Sentani
- Molecular Pathology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Tsuyoshi Kobayashi
- Gastroenterological and Transplant Surgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Kazuaki Chayama
- Gastroenterology and Metabolism, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Dean Sheppard
- Lung Biology Center, Department of Medicine, Cardiovascular Research Institute, University of California San Francisco (UCSF), San Francisco, CA, USA
| | - Yasuyuki Yokosaki
- Integrin-Matrix Biomedical Science, Translational Research Center, Hiroshima University, Hiroshima, Japan
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Abugessaisa I, Ramilowski JA, Lizio M, Severin J, Hasegawa A, Harshbarger J, Kondo A, Noguchi S, Yip CW, Ooi J, Tagami M, Hori F, Agrawal S, Hon C, Cardon M, Ikeda S, Ono H, Bono H, Kato M, Hashimoto K, Bonetti A, Kato M, Kobayashi N, Shin J, de Hoon M, Hayashizaki Y, Carninci P, Kawaji H, Kasukawa T. FANTOM enters 20th year: expansion of transcriptomic atlases and functional annotation of non-coding RNAs. Nucleic Acids Res 2021; 49:D892-D898. [PMID: 33211864 PMCID: PMC7779024 DOI: 10.1093/nar/gkaa1054] [Citation(s) in RCA: 50] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Revised: 10/16/2020] [Accepted: 10/21/2020] [Indexed: 11/15/2022] Open
Abstract
The Functional ANnoTation Of the Mammalian genome (FANTOM) Consortium has continued to provide extensive resources in the pursuit of understanding the transcriptome, and transcriptional regulation, of mammalian genomes for the last 20 years. To share these resources with the research community, the FANTOM web-interfaces and databases are being regularly updated, enhanced and expanded with new data types. In recent years, the FANTOM Consortium's efforts have been mainly focused on creating new non-coding RNA datasets and resources. The existing FANTOM5 human and mouse miRNA atlas was supplemented with rat, dog, and chicken datasets. The sixth (latest) edition of the FANTOM project was launched to assess the function of human long non-coding RNAs (lncRNAs). From its creation until 2020, FANTOM6 has contributed to the research community a large dataset generated from the knock-down of 285 lncRNAs in human dermal fibroblasts; this is followed with extensive expression profiling and cellular phenotyping. Other updates to the FANTOM resource includes the reprocessing of the miRNA and promoter atlases of human, mouse and chicken with the latest reference genome assemblies. To facilitate the use and accessibility of all above resources we further enhanced FANTOM data viewers and web interfaces. The updated FANTOM web resource is publicly available at https://fantom.gsc.riken.jp/.
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Affiliation(s)
- Imad Abugessaisa
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan
| | - Jordan A Ramilowski
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan
- Advanced Medical Research Center, Yokohama City University, Kanagawa, Japan
| | - Marina Lizio
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan
| | - Jesicca Severin
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan
| | - Akira Hasegawa
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan
| | - Jayson Harshbarger
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan
| | - Atsushi Kondo
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan
| | - Shuhei Noguchi
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan
| | - Chi Wai Yip
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan
| | | | - Michihira Tagami
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan
| | - Fumi Hori
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan
| | - Saumya Agrawal
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan
| | - Chung Chau Hon
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan
| | - Melissa Cardon
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan
| | - Shuya Ikeda
- Database Center for Life Science, Research Organization of Information and Systems, Mishima, Shizuoka, Japan
| | - Hiromasa Ono
- Database Center for Life Science, Research Organization of Information and Systems, Mishima, Shizuoka, Japan
| | - Hidemasa Bono
- Database Center for Life Science, Research Organization of Information and Systems, Mishima, Shizuoka, Japan
- Program of Biomedical Science, Graduate School of Integrated Sciences for Life, Hiroshima University
| | - Masaki Kato
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan
| | - Kosuke Hashimoto
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan
| | - Alessandro Bonetti
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan
- Karolinska Institutet, Stockholm, Sweden
| | - Masaki Kato
- RIKEN Head Office for Information Systems and Cybersecurity, Wako, Saitama, Japan
| | - Norio Kobayashi
- RIKEN Head Office for Information Systems and Cybersecurity, Wako, Saitama, Japan
| | - Jay Shin
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan
| | - Michiel de Hoon
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan
| | | | - Piero Carninci
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan
| | - Hideya Kawaji
- Correspondence may also be addressed to Hideya Kawaji.
| | - Takeya Kasukawa
- To whom correspondence should be addressed. Tel: +81 45 503 9222; Fax: +81 45 503 9219;
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22
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Pasquini G, Rojo Arias JE, Schäfer P, Busskamp V. Automated methods for cell type annotation on scRNA-seq data. Comput Struct Biotechnol J 2021; 19:961-969. [PMID: 33613863 PMCID: PMC7873570 DOI: 10.1016/j.csbj.2021.01.015] [Citation(s) in RCA: 84] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Revised: 01/13/2021] [Accepted: 01/13/2021] [Indexed: 12/22/2022] Open
Abstract
The advent of single-cell sequencing started a new era of transcriptomic and genomic research, advancing our knowledge of the cellular heterogeneity and dynamics. Cell type annotation is a crucial step in analyzing single-cell RNA sequencing data, yet manual annotation is time-consuming and partially subjective. As an alternative, tools have been developed for automatic cell type identification. Different strategies have emerged to ultimately associate gene expression profiles of single cells with a cell type either by using curated marker gene databases, correlating reference expression data, or transferring labels by supervised classification. In this review, we present an overview of the available tools and the underlying approaches to perform automated cell type annotations on scRNA-seq data.
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Affiliation(s)
- Giovanni Pasquini
- Technische Universität Dresden, Center for Molecular and Cellular Bioengineering (CMCB), Center for Regenerative Therapies Dresden (CRTD), Dresden 01307, Germany
- Universitäts-Augenklinik Bonn, University of Bonn, Department of Ophthalmology, Bonn 53127, Germany
| | - Jesus Eduardo Rojo Arias
- Wellcome-MRC Cambridge Stem Cell Institute, Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus, University of Cambridge, Cambridge, UK
| | - Patrick Schäfer
- Technische Universität Dresden, Center for Molecular and Cellular Bioengineering (CMCB), Center for Regenerative Therapies Dresden (CRTD), Dresden 01307, Germany
| | - Volker Busskamp
- Technische Universität Dresden, Center for Molecular and Cellular Bioengineering (CMCB), Center for Regenerative Therapies Dresden (CRTD), Dresden 01307, Germany
- Universitäts-Augenklinik Bonn, University of Bonn, Department of Ophthalmology, Bonn 53127, Germany
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23
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Bernstein MN, Ma Z, Gleicher M, Dewey CN. CellO: comprehensive and hierarchical cell type classification of human cells with the Cell Ontology. iScience 2020; 24:101913. [PMID: 33364592 PMCID: PMC7753962 DOI: 10.1016/j.isci.2020.101913] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Revised: 10/28/2020] [Accepted: 12/02/2020] [Indexed: 12/15/2022] Open
Abstract
Cell type annotation is a fundamental task in the analysis of single-cell RNA-sequencing data. In this work, we present CellO, a machine learning-based tool for annotating human RNA-seq data with the Cell Ontology. CellO enables accurate and standardized cell type classification of cell clusters by considering the rich hierarchical structure of known cell types. Furthermore, CellO comes pre-trained on a comprehensive data set of human, healthy, untreated primary samples in the Sequence Read Archive. CellO's comprehensive training set enables it to run out of the box on diverse cell types and achieves competitive or even superior performance when compared to existing state-of-the-art methods. Lastly, CellO's linear models are easily interpreted, thereby enabling exploration of cell-type-specific expression signatures across the ontology. To this end, we also present the CellO Viewer: a web application for exploring CellO's models across the ontology.
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Affiliation(s)
| | - Zhongjie Ma
- Department of Computer Sciences, University of Wisconsin - Madison, Madison, WI 53706, USA
| | - Michael Gleicher
- Department of Computer Sciences, University of Wisconsin - Madison, Madison, WI 53706, USA
| | - Colin N Dewey
- Department of Computer Sciences, University of Wisconsin - Madison, Madison, WI 53706, USA.,Department of Biostatistics and Medical Informatics, University of Wisconsin - Madison, Madison, WI 53792, USA
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24
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Fang J, Pieper AA, Nussinov R, Lee G, Bekris L, Leverenz JB, Cummings J, Cheng F. Harnessing endophenotypes and network medicine for Alzheimer's drug repurposing. Med Res Rev 2020; 40:2386-2426. [PMID: 32656864 PMCID: PMC7561446 DOI: 10.1002/med.21709] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2019] [Revised: 06/23/2020] [Accepted: 06/27/2020] [Indexed: 12/16/2022]
Abstract
Following two decades of more than 400 clinical trials centered on the "one drug, one target, one disease" paradigm, there is still no effective disease-modifying therapy for Alzheimer's disease (AD). The inherent complexity of AD may challenge this reductionist strategy. Recent observations and advances in network medicine further indicate that AD likely shares common underlying mechanisms and intermediate pathophenotypes, or endophenotypes, with other diseases. In this review, we consider AD pathobiology, disease comorbidity, pleiotropy, and therapeutic development, and construct relevant endophenotype networks to guide future therapeutic development. Specifically, we discuss six main endophenotype hypotheses in AD: amyloidosis, tauopathy, neuroinflammation, mitochondrial dysfunction, vascular dysfunction, and lysosomal dysfunction. We further consider how this endophenotype network framework can provide advances in computational and experimental strategies for drug-repurposing and identification of new candidate therapeutic strategies for patients suffering from or at risk for AD. We highlight new opportunities for endophenotype-informed, drug discovery in AD, by exploiting multi-omics data. Integration of genomics, transcriptomics, radiomics, pharmacogenomics, and interactomics (protein-protein interactions) are essential for successful drug discovery. We describe experimental technologies for AD drug discovery including human induced pluripotent stem cells, transgenic mouse/rat models, and population-based retrospective case-control studies that may be integrated with multi-omics in a network medicine methodology. In summary, endophenotype-based network medicine methodologies will promote AD therapeutic development that will optimize the usefulness of available data and support deep phenotyping of the patient heterogeneity for personalized medicine in AD.
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Affiliation(s)
- Jiansong Fang
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong 510006, China
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Andrew A Pieper
- Harrington Discovery Institute, University Hospital Case Medical Center; Department of Psychiatry, Case Western Reserve University, Geriatric Research Education and Clinical Centers, Louis Stokes Cleveland VAMC, Cleveland, OH 44106, USA
| | - Ruth Nussinov
- Cancer and Inflammation Program, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, National Cancer Institute at Frederick, Frederick, MD 21702, USA
- Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel
| | - Garam Lee
- Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV 89106, USA
| | - Lynn Bekris
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA
| | - James B. Leverenz
- Lou Ruvo Center for Brain Health, Neurological Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Jeffrey Cummings
- Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV 89106, USA
- Department of Brain Health, School of Integrated Health Sciences, UNLV, Las Vegas, NV 89154, USA
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, Ohio 44106, USA
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25
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Wu Y, Yang Y, Gu H, Tao B, Zhang E, Wei J, Wang Z, Liu A, Sun R, Chen M, Fan Y, Mao R. Multi-omics analysis reveals the functional transcription and potential translation of enhancers. Int J Cancer 2020; 147:2210-2224. [PMID: 32573785 DOI: 10.1002/ijc.33132] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2020] [Revised: 04/22/2020] [Accepted: 05/19/2020] [Indexed: 12/23/2022]
Abstract
Enhancer can transcribe RNAs, however, most of them were neglected in traditional RNA-seq analysis workflow. Here, we developed a Pipeline for Enhancer Transcription (PET, http://fun-science.club/PET) for quantifying enhancer RNAs (eRNAs) from RNA-seq. By applying this pipeline on lung cancer samples and cell lines, we showed that the transcribed enhancers are enriched with histone marks and transcription factor motifs (JUNB, Hand1-Tcf3 and GATA4). By training a machine learning model, we demonstrate that enhancers can predict prognosis better than their nearby genes. Integrating the Hi-C, ChIP-seq and RNA-seq data, we observe that transcribed enhancers associate with cancer hallmarks or oncogenes, among which LcsMYC-1 (Lung cancer-specific MYC eRNA-1) potentially supports MYC expression. Surprisingly, a significant proportion of transcribed enhancers contain small protein-coding open reading frames (sORFs) and can be translated into microproteins. Our study provides a computational method for eRNA quantification and deepens our understandings of the DNA, RNA and protein nature of enhancers.
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Affiliation(s)
- Yingcheng Wu
- Laboratory of Medical Science, School of Medicine, Nantong University, Nantong, Jiangsu, China.,Department of Pathophysiology, School of Medicine, Nantong University, Nantong, Jiangsu, China
| | - Yang Yang
- Department of Thoracic Surgery, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Hongyan Gu
- Department of Respiratory Medicine, Nantong Sixth People's Hospital, Nantong, Jiangsu, China
| | - Baorui Tao
- Laboratory of Medical Science, School of Medicine, Nantong University, Nantong, Jiangsu, China
| | - Erhao Zhang
- Laboratory of Medical Science, School of Medicine, Nantong University, Nantong, Jiangsu, China
| | - Jinhuan Wei
- Laboratory of Medical Science, School of Medicine, Nantong University, Nantong, Jiangsu, China
| | - Zhou Wang
- School of Life Sciences, Nantong University, Nantong, Jiangsu, China
| | - Aifen Liu
- Laboratory of Medical Science, School of Medicine, Nantong University, Nantong, Jiangsu, China
| | - Rong Sun
- Laboratory of Medical Science, School of Medicine, Nantong University, Nantong, Jiangsu, China
| | - Miaomiao Chen
- Laboratory of Medical Science, School of Medicine, Nantong University, Nantong, Jiangsu, China
| | - Yihui Fan
- Laboratory of Medical Science, School of Medicine, Nantong University, Nantong, Jiangsu, China.,Department of Pathogenic Biology, School of Medicine, Nantong University, Nantong, Jiangsu, China
| | - Renfang Mao
- Laboratory of Medical Science, School of Medicine, Nantong University, Nantong, Jiangsu, China.,Department of Pathophysiology, School of Medicine, Nantong University, Nantong, Jiangsu, China
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26
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Evaluating the informativeness of deep learning annotations for human complex diseases. Nat Commun 2020; 11:4703. [PMID: 32943643 PMCID: PMC7499261 DOI: 10.1038/s41467-020-18515-4] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Accepted: 08/25/2020] [Indexed: 12/12/2022] Open
Abstract
Deep learning models have shown great promise in predicting regulatory effects from DNA sequence, but their informativeness for human complex diseases is not fully understood. Here, we evaluate genome-wide SNP annotations from two previous deep learning models, DeepSEA and Basenji, by applying stratified LD score regression to 41 diseases and traits (average N = 320K), conditioning on a broad set of coding, conserved and regulatory annotations. We aggregated annotations across all (respectively blood or brain) tissues/cell-types in meta-analyses across all (respectively 11 blood or 8 brain) traits. The annotations were highly enriched for disease heritability, but produced only limited conditionally significant results: non-tissue-specific and brain-specific Basenji-H3K4me3 for all traits and brain traits respectively. We conclude that deep learning models have yet to achieve their full potential to provide considerable unique information for complex disease, and that their conditional informativeness for disease cannot be inferred from their accuracy in predicting regulatory annotations. Deep learning models have shown great promise in predicting regulatory effects from DNA sequence. Here the authors evaluate sequence-based epigenomic deep learning models and conclude that these models are not yet ready to inform our knowledge of human disease.
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27
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Angel PW, Rajab N, Deng Y, Pacheco CM, Chen T, Lê Cao KA, Choi J, Wells CA. A simple, scalable approach to building a cross-platform transcriptome atlas. PLoS Comput Biol 2020; 16:e1008219. [PMID: 32986694 PMCID: PMC7544119 DOI: 10.1371/journal.pcbi.1008219] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2020] [Revised: 10/08/2020] [Accepted: 08/04/2020] [Indexed: 12/21/2022] Open
Abstract
Gene expression atlases have transformed our understanding of the development, composition and function of human tissues. New technologies promise improved cellular or molecular resolution, and have led to the identification of new cell types, or better defined cell states. But as new technologies emerge, information derived on old platforms becomes obsolete. We demonstrate that it is possible to combine a large number of different profiling experiments summarised from dozens of laboratories and representing hundreds of donors, to create an integrated molecular map of human tissue. As an example, we combine 850 samples from 38 platforms to build an integrated atlas of human blood cells. We achieve robust and unbiased cell type clustering using a variance partitioning method, selecting genes with low platform bias relative to biological variation. Other than an initial rescaling, no other transformation to the primary data is applied through batch correction or renormalisation. Additional data, including single-cell datasets, can be projected for comparison, classification and annotation. The resulting atlas provides a multi-scaled approach to visualise and analyse the relationships between sets of genes and blood cell lineages, including the maturation and activation of leukocytes in vivo and in vitro. In allowing for data integration across hundreds of studies, we address a key reproduciblity challenge which is faced by any new technology. This allows us to draw on the deep phenotypes and functional annotations that accompany traditional profiling methods, and provide important context to the high cellular resolution of single cell profiling. Here, we have implemented the blood atlas in the open access Stemformatics.org platform, drawing on its extensive collection of curated transcriptome data. The method is simple, scalable and amenable for rapid deployment in other biological systems or computational workflows.
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Affiliation(s)
- Paul W. Angel
- Centre for Stem Cell Systems, The University of Melbourne, Melbourne, Victoria, Australia
| | - Nadia Rajab
- Centre for Stem Cell Systems, The University of Melbourne, Melbourne, Victoria, Australia
| | - Yidi Deng
- Melbourne Integrative Genomics, School of Mathematics and Statistics, The University of Melbourne, Melbourne, Victoria, Australia
| | - Chris M. Pacheco
- Centre for Stem Cell Systems, The University of Melbourne, Melbourne, Victoria, Australia
| | - Tyrone Chen
- Centre for Stem Cell Systems, The University of Melbourne, Melbourne, Victoria, Australia
| | - Kim-Anh Lê Cao
- Melbourne Integrative Genomics, School of Mathematics and Statistics, The University of Melbourne, Melbourne, Victoria, Australia
| | - Jarny Choi
- Centre for Stem Cell Systems, The University of Melbourne, Melbourne, Victoria, Australia
| | - Christine A. Wells
- Centre for Stem Cell Systems, The University of Melbourne, Melbourne, Victoria, Australia
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28
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Mistry BV, Alanazi M, Fitwi H, Al-Harazi O, Rajab M, Altorbag A, Almohanna F, Colak D, Assiri AM. Expression profiling of WD40 family genes including DDB1- and CUL4- associated factor (DCAF) genes in mice and human suggests important regulatory roles in testicular development and spermatogenesis. BMC Genomics 2020; 21:602. [PMID: 32867693 PMCID: PMC7457511 DOI: 10.1186/s12864-020-07016-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2020] [Accepted: 08/20/2020] [Indexed: 12/25/2022] Open
Abstract
BACKGROUND The WD40-repeat containing proteins, including DDB1-CUL4-associated factors (DCAFs), are abundant and conserved proteins that play important roles in different cellular processes including spermatogenesis. DCAFs are subset of WD40 family proteins that contain WDxR motif and have been proposed to function as substrate receptor for Cullin4-RING-based E3 ubiquitin ligase complexes to recruit diverse proteins for ubiquitination, a vital process in spermatogenesis. Large number of WD40 genes has been identified in different species including mouse and human. However, a systematic expression profiling of WD40 genes in different tissues of mouse and human has not been investigated. We hypothesize that large number of WD40 genes may express highly or specifically in the testis, where their expression is uniquely regulated during testis development and spermatogenesis. Therefore, the objective of this study is to mine and characterize expression patterns of WD40 genes in different tissues of mouse and human with particular emphasis on DCAF genes expressions during mouse testicular development. RESULTS Publically available RNA sequencing (RNA seq) data mining identified 347 and 349 WD40 genes in mouse and human, respectively. Hierarchical clustering and heat map analyses of RNA seq datasets revealed differential expression patterns of WD40 genes with around 60-73% of the genes were highly or specifically expressed in testis. Similarly, around 74-83% of DCAF genes were predominantly or specifically expressed in testis. Moreover, WD40 genes showed distinct expression patterns during embryonic and postnatal testis development in mice. Finally, different germ cell populations of testis showed specific patterns of WD40 genes expression. Predicted gene ontology analyses revealed more than 80% of these proteins are implicated in cellular, metabolic, biological regulation and cell localization processes. CONCLUSIONS We have identified large number of WD40 family genes that are highly or specifically expressed in the testes of mouse and human. Moreover, WD40 genes have distinct expression patterns during embryonic and postnatal development of the testis in mice. Further, different germ cell populations within the testis showed specific patterns of WD40 genes expression. These results provide foundation for further research towards understanding the functional genomics and molecular mechanisms of mammalian testis development and spermatogenesis.
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Affiliation(s)
- Bhavesh V Mistry
- Department of Comparative Medicine, King Faisal Specialist Hospital & Research Centre, Riyadh, Saudi Arabia
| | - Maha Alanazi
- Department of Comparative Medicine, King Faisal Specialist Hospital & Research Centre, Riyadh, Saudi Arabia
| | - Hanae Fitwi
- Department of Comparative Medicine, King Faisal Specialist Hospital & Research Centre, Riyadh, Saudi Arabia.,College of Medicine, Alfaisal University, Riyadh, Saudi Arabia
| | - Olfat Al-Harazi
- Biostatistics, Epidemiology and Scientific Computing Department, King Faisal Specialist Hospital & Research Centre, Riyadh, Saudi Arabia
| | - Mohamed Rajab
- Department of Comparative Medicine, King Faisal Specialist Hospital & Research Centre, Riyadh, Saudi Arabia
| | - Abdullah Altorbag
- Department of Comparative Medicine, King Faisal Specialist Hospital & Research Centre, Riyadh, Saudi Arabia
| | - Falah Almohanna
- Department of Comparative Medicine, King Faisal Specialist Hospital & Research Centre, Riyadh, Saudi Arabia
| | - Dilek Colak
- College of Medicine, Alfaisal University, Riyadh, Saudi Arabia
| | - Abdullah M Assiri
- Department of Comparative Medicine, King Faisal Specialist Hospital & Research Centre, Riyadh, Saudi Arabia. .,Biostatistics, Epidemiology and Scientific Computing Department, King Faisal Specialist Hospital & Research Centre, Riyadh, Saudi Arabia. .,Institute for Research and Medical Consultations, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia.
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29
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Hu FF, Liu CJ, Liu LL, Zhang Q, Guo AY. Expression profile of immune checkpoint genes and their roles in predicting immunotherapy response. Brief Bioinform 2020; 22:5894466. [PMID: 32814346 DOI: 10.1093/bib/bbaa176] [Citation(s) in RCA: 155] [Impact Index Per Article: 38.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Revised: 06/28/2020] [Accepted: 07/12/2020] [Indexed: 01/06/2023] Open
Abstract
Immune checkpoint genes (ICGs) play critical roles in circumventing self-reactivity and represent a novel target to develop treatments for cancers. However, a comprehensive analysis for the expression profile of ICGs at a pan-cancer level and their correlation with patient response to immune checkpoint blockade (ICB) based therapy is still lacking. In this study, we defined three expression patterns of ICGs using a comprehensive survey of RNA-seq data of tumor and immune cells from the functional annotation of the mammalian genome (FANTOM5) project. The correlation between the expression patterns of ICGs and patients survival and response to ICB therapy was investigated. The expression patterns of ICGs were robust across cancers, and upregulation of ICGs was positively correlated with high lymphocyte infiltration and good prognosis. Furthermore, we built a model (ICGe) to predict the response of patients to ICB therapy using five features of ICG expression. A validation scenario of six independent datasets containing data of 261 patients with CTLA-4 and PD-1 blockade immunotherapies demonstrated that ICGe achieved area under the curves of 0.64-0.82 and showed a robust performance and outperformed other mRNA-based predictors. In conclusion, this work revealed expression patterns of ICGs and underlying correlations between ICGs and response to ICB, which helps to understand the mechanisms of ICGs in ICB signal pathways and other anticancer treatments.
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Affiliation(s)
- Fei-Fei Hu
- Wuhan University of Science and Technology
| | | | - Lan-Lan Liu
- Huazhong University of Science and Technology
| | - Qiong Zhang
- Huazhong University of Science and Technology
| | - An-Yuan Guo
- Huazhong University of Science and Technology
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30
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Capaci V, Bascetta L, Fantuz M, Beznoussenko GV, Sommaggio R, Cancila V, Bisso A, Campaner E, Mironov AA, Wiśniewski JR, Ulloa Severino L, Scaini D, Bossi F, Lees J, Alon N, Brunga L, Malkin D, Piazza S, Collavin L, Rosato A, Bicciato S, Tripodo C, Mantovani F, Del Sal G. Mutant p53 induces Golgi tubulo-vesiculation driving a prometastatic secretome. Nat Commun 2020; 11:3945. [PMID: 32770028 PMCID: PMC7414119 DOI: 10.1038/s41467-020-17596-5] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2019] [Accepted: 07/03/2020] [Indexed: 12/16/2022] Open
Abstract
TP53 missense mutations leading to the expression of mutant p53 oncoproteins are frequent driver events during tumorigenesis. p53 mutants promote tumor growth, metastasis and chemoresistance by affecting fundamental cellular pathways and functions. Here, we demonstrate that p53 mutants modify structure and function of the Golgi apparatus, culminating in the increased release of a pro-malignant secretome by tumor cells and primary fibroblasts from patients with Li-Fraumeni cancer predisposition syndrome. Mechanistically, interacting with the hypoxia responsive factor HIF1α, mutant p53 induces the expression of miR-30d, which in turn causes tubulo-vesiculation of the Golgi apparatus, leading to enhanced vesicular trafficking and secretion. The mut-p53/HIF1α/miR-30d axis potentiates the release of soluble factors and the deposition and remodeling of the ECM, affecting mechano-signaling and stromal cells activation within the tumor microenvironment, thereby enhancing tumor growth and metastatic colonization.
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Affiliation(s)
- Valeria Capaci
- Laboratorio Nazionale CIB (LNCIB), 34149, Trieste, Italy
| | - Lorenzo Bascetta
- Laboratorio Nazionale CIB (LNCIB), 34149, Trieste, Italy
- International School for Advanced Studies (SISSA), 34146, Trieste, Italy
| | - Marco Fantuz
- Laboratorio Nazionale CIB (LNCIB), 34149, Trieste, Italy
- International School for Advanced Studies (SISSA), 34146, Trieste, Italy
| | | | | | - Valeria Cancila
- Tumor Immunology Unit, Department of Health Science, Human Pathology Section, University of Palermo, School of Medicine, 90133, Palermo, Italy
| | - Andrea Bisso
- Laboratorio Nazionale CIB (LNCIB), 34149, Trieste, Italy
- Department of Experimental Oncology, IEO, European Institute of Oncology IRCCS, 20141, Milan, Italy
| | - Elena Campaner
- Laboratorio Nazionale CIB (LNCIB), 34149, Trieste, Italy
- Dipartimento di Scienze della Vita, Università degli Studi di Trieste, 34127, Trieste, Italy
| | - Alexander A Mironov
- Fondazione Istituto FIRC di Oncologia Molecolare (IFOM), 20139, Milan, Italy
| | - Jacek R Wiśniewski
- Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, 85152, Martinsried, Germany
| | - Luisa Ulloa Severino
- Dipartimento di Scienze della Vita, Università degli Studi di Trieste, 34127, Trieste, Italy
| | - Denis Scaini
- International School for Advanced Studies (SISSA), 34146, Trieste, Italy
| | - Fleur Bossi
- Dipartimento di Scienze della Vita, Università degli Studi di Trieste, 34127, Trieste, Italy
| | - Jodi Lees
- Genetics and Genome Biology Program, The Hospital for Sick Children, Toronto, ON, Canada
| | - Noa Alon
- Genetics and Genome Biology Program, The Hospital for Sick Children, Toronto, ON, Canada
| | - Ledia Brunga
- Genetics and Genome Biology Program, The Hospital for Sick Children, Toronto, ON, Canada
| | - David Malkin
- Genetics and Genome Biology Program, The Hospital for Sick Children, Toronto, ON, Canada
- Department of Pediatrics, University of Toronto, Toronto, ON, Canada
| | - Silvano Piazza
- Laboratorio Nazionale CIB (LNCIB), 34149, Trieste, Italy
| | - Licio Collavin
- Laboratorio Nazionale CIB (LNCIB), 34149, Trieste, Italy
- Dipartimento di Scienze della Vita, Università degli Studi di Trieste, 34127, Trieste, Italy
| | - Antonio Rosato
- Veneto Institute of Oncology IOV-IRCCS, 35128, Padua, Italy
- Department of Surgery, Oncology and Gastroenterology, University of Padova, 35128, Padova, Italy
| | - Silvio Bicciato
- Center for Genome Research, University of Modena and Reggio Emilia, 41125, Modena, Italy
| | - Claudio Tripodo
- Tumor Immunology Unit, Department of Health Science, Human Pathology Section, University of Palermo, School of Medicine, 90133, Palermo, Italy
| | - Fiamma Mantovani
- Laboratorio Nazionale CIB (LNCIB), 34149, Trieste, Italy
- Dipartimento di Scienze della Vita, Università degli Studi di Trieste, 34127, Trieste, Italy
| | - Giannino Del Sal
- Laboratorio Nazionale CIB (LNCIB), 34149, Trieste, Italy.
- Fondazione Istituto FIRC di Oncologia Molecolare (IFOM), 20139, Milan, Italy.
- Dipartimento di Scienze della Vita, Università degli Studi di Trieste, 34127, Trieste, Italy.
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31
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The multifaceted functional role of DNA methylation in immune-mediated rheumatic diseases. Clin Rheumatol 2020; 40:459-476. [PMID: 32613397 DOI: 10.1007/s10067-020-05255-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Revised: 06/15/2020] [Accepted: 06/22/2020] [Indexed: 12/22/2022]
Abstract
Genomic predisposition cannot explain the onset of complex diseases, as well illustrated by the largely incomplete concordance among monozygotic twins. Epigenetic mechanisms, including DNA methylation, chromatin remodelling and non-coding RNA, are considered to be the link between environmental stimuli and disease onset on a permissive genetic background in autoimmune and chronic inflammatory diseases. The paradigmatic cases of rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), systemic sclerosis (SSc), Sjogren's syndrome (SjS) and type-1 diabetes (T1D) share the loss of immunological tolerance to self-antigen influenced by several factors, with a largely incomplete role of individual genomic susceptibility. The most widely investigated epigenetic mechanism is DNA methylation which is associated with gene silencing and is due to the binding of methyl-CpG binding domain (MBD)-containing proteins, such as MECP2, to 5-methylcytosine (5mC). Indeed, a causal relationship occurs between DNA methylation and transcription factors occupancy and recruitment at specific genomic locus. In most cases, the results obtained in different studies are controversial in terms of DNA methylation comparison while fascinating evidence comes from the comparison of the epigenome in clinically discordant monozygotic twins. In this manuscript, we will review the mechanisms of epigenetics and DNA methylation changes in specific immune-mediated rheumatic diseases to highlight remaining unmet needs and to identify possible shared mechanisms beyond different tissue involvements with common therapeutic opportunities. Key Points • DNA methylation has a crucial role in regulating and tuning the immune system. • Evidences suggest that dysregulation of DNA methylation is pivotal in the context of immune-mediated rheumatic diseases. • DNA methylation dysregulation in FOXP3 and interferons-related genes is shared within several autoimmune diseases. • DNA methylation is an attractive marker for diagnosis and therapy.
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32
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Hou R, Denisenko E, Forrest ARR. scMatch: a single-cell gene expression profile annotation tool using reference datasets. Bioinformatics 2020; 35:4688-4695. [PMID: 31028376 PMCID: PMC6853649 DOI: 10.1093/bioinformatics/btz292] [Citation(s) in RCA: 57] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2018] [Revised: 03/28/2019] [Accepted: 04/21/2019] [Indexed: 12/17/2022] Open
Abstract
MOTIVATION Single-cell RNA sequencing (scRNA-seq) measures gene expression at the resolution of individual cells. Massively multiplexed single-cell profiling has enabled large-scale transcriptional analyses of thousands of cells in complex tissues. In most cases, the true identity of individual cells is unknown and needs to be inferred from the transcriptomic data. Existing methods typically cluster (group) cells based on similarities of their gene expression profiles and assign the same identity to all cells within each cluster using the averaged expression levels. However, scRNA-seq experiments typically produce low-coverage sequencing data for each cell, which hinders the clustering process. RESULTS We introduce scMatch, which directly annotates single cells by identifying their closest match in large reference datasets. We used this strategy to annotate various single-cell datasets and evaluated the impacts of sequencing depth, similarity metric and reference datasets. We found that scMatch can rapidly and robustly annotate single cells with comparable accuracy to another recent cell annotation tool (SingleR), but that it is quicker and can handle larger reference datasets. We demonstrate how scMatch can handle large customized reference gene expression profiles that combine data from multiple sources, thus empowering researchers to identify cell populations in any complex tissue with the desired precision. AVAILABILITY AND IMPLEMENTATION scMatch (Python code) and the FANTOM5 reference dataset are freely available to the research community here https://github.com/forrest-lab/scMatch. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Rui Hou
- Harry Perkins Institute of Medical Research, QEII Medical Centre and Centre for Medical Research, The University of Western Australia, Nedlands, Perth, WA 6009, Australia
| | - Elena Denisenko
- Harry Perkins Institute of Medical Research, QEII Medical Centre and Centre for Medical Research, The University of Western Australia, Nedlands, Perth, WA 6009, Australia
| | - Alistair R R Forrest
- Harry Perkins Institute of Medical Research, QEII Medical Centre and Centre for Medical Research, The University of Western Australia, Nedlands, Perth, WA 6009, Australia
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33
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Davis JE, Insigne KD, Jones EM, Hastings QA, Boldridge WC, Kosuri S. Dissection of c-AMP Response Element Architecture by Using Genomic and Episomal Massively Parallel Reporter Assays. Cell Syst 2020; 11:75-85.e7. [PMID: 32603702 DOI: 10.1016/j.cels.2020.05.011] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2019] [Revised: 02/16/2020] [Accepted: 05/26/2020] [Indexed: 11/15/2022]
Abstract
In eukaryotes, transcription factors (TFs) orchestrate gene expression by binding to TF-binding sites (TFBSs) and localizing transcriptional co-regulators and RNA polymerase II to cis-regulatory elements. However, we lack a basic understanding of the relationship between TFBS composition and their quantitative transcriptional responses. Here, we measured expression driven by 17,406 synthetic cis-regulatory elements with varied compositions of a model TFBS, the c-AMP response element (CRE) by using massively parallel reporter assays (MPRAs). We find CRE number, affinity, and promoter proximity largely determines expression. In addition, we observe expression modulation based on the spacing between CREs and CRE distance to the promoter, where expression follows a helical periodicity. Finally, we compare library expression between an episomal MPRA and a genomically integrated MPRA, where a single cis-regulatory element is assayed per cell at a defined locus. These assays largely recapitulate each other, although weaker, non-canonical CREs exhibit greater activity in a genomic context.
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Affiliation(s)
- Jessica E Davis
- Department of Chemistry and Biochemistry, UCLA-DOE Institute for Genomics and Proteomics, Molecular Biology Institute, Quantitative and Computational Biology Institute, Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, and Jonsson Comprehensive Cancer Center, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Kimberly D Insigne
- Department of Chemistry and Biochemistry, UCLA-DOE Institute for Genomics and Proteomics, Molecular Biology Institute, Quantitative and Computational Biology Institute, Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, and Jonsson Comprehensive Cancer Center, University of California, Los Angeles, Los Angeles, CA 90095, USA; Bioinformatics Interdepartmental Graduate Program, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Eric M Jones
- Department of Chemistry and Biochemistry, UCLA-DOE Institute for Genomics and Proteomics, Molecular Biology Institute, Quantitative and Computational Biology Institute, Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, and Jonsson Comprehensive Cancer Center, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Quinn A Hastings
- Department of Chemistry and Biochemistry, UCLA-DOE Institute for Genomics and Proteomics, Molecular Biology Institute, Quantitative and Computational Biology Institute, Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, and Jonsson Comprehensive Cancer Center, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - W Clifford Boldridge
- Department of Chemistry and Biochemistry, UCLA-DOE Institute for Genomics and Proteomics, Molecular Biology Institute, Quantitative and Computational Biology Institute, Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, and Jonsson Comprehensive Cancer Center, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Sriram Kosuri
- Department of Chemistry and Biochemistry, UCLA-DOE Institute for Genomics and Proteomics, Molecular Biology Institute, Quantitative and Computational Biology Institute, Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, and Jonsson Comprehensive Cancer Center, University of California, Los Angeles, Los Angeles, CA 90095, USA.
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Demircioğlu D, Cukuroglu E, Kindermans M, Nandi T, Calabrese C, Fonseca NA, Kahles A, Lehmann KV, Stegle O, Brazma A, Brooks AN, Rätsch G, Tan P, Göke J. A Pan-cancer Transcriptome Analysis Reveals Pervasive Regulation through Alternative Promoters. Cell 2020; 178:1465-1477.e17. [PMID: 31491388 DOI: 10.1016/j.cell.2019.08.018] [Citation(s) in RCA: 104] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2018] [Revised: 12/13/2018] [Accepted: 08/07/2019] [Indexed: 02/08/2023]
Abstract
Most human protein-coding genes are regulated by multiple, distinct promoters, suggesting that the choice of promoter is as important as its level of transcriptional activity. However, while a global change in transcription is recognized as a defining feature of cancer, the contribution of alternative promoters still remains largely unexplored. Here, we infer active promoters using RNA-seq data from 18,468 cancer and normal samples, demonstrating that alternative promoters are a major contributor to context-specific regulation of transcription. We find that promoters are deregulated across tissues, cancer types, and patients, affecting known cancer genes and novel candidates. For genes with independently regulated promoters, we demonstrate that promoter activity provides a more accurate predictor of patient survival than gene expression. Our study suggests that a dynamic landscape of active promoters shapes the cancer transcriptome, opening new diagnostic avenues and opportunities to further explore the interplay of regulatory mechanisms with transcriptional aberrations in cancer.
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Affiliation(s)
- Deniz Demircioğlu
- Computational and Systems Biology, Genome Institute of Singapore, Singapore 138672, Singapore; School of Computing, National University of Singapore, Singapore 117417, Singapore
| | - Engin Cukuroglu
- Computational and Systems Biology, Genome Institute of Singapore, Singapore 138672, Singapore
| | - Martin Kindermans
- Computational and Systems Biology, Genome Institute of Singapore, Singapore 138672, Singapore
| | - Tannistha Nandi
- Computational and Systems Biology, Genome Institute of Singapore, Singapore 138672, Singapore
| | - Claudia Calabrese
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK; Genome Biology Unit, EMBL, Heidelberg, 69117, Germany
| | - Nuno A Fonseca
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK; CIBIO/InBIO - Research Center in Biodiversity and Genetic Resources, Universidade do Porto, Vairão 4485-601, Portugal
| | - André Kahles
- Department of Computer Science, ETH Zurich, Zurich 8092, Switzerland; Department of Biology, ETH Zurich, Zurich 8093, Switzerland; Computational Biology Center, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; SIB Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland; Biomedical Informatics Research, University Hospital Zurich, Zurich 8091, Switzerland
| | - Kjong-Van Lehmann
- Department of Computer Science, ETH Zurich, Zurich 8092, Switzerland; Computational Biology Center, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; SIB Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland; Biomedical Informatics Research, University Hospital Zurich, Zurich 8091, Switzerland
| | - Oliver Stegle
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK; Genome Biology Unit, EMBL, Heidelberg, 69117, Germany; Division of Computational Genomics and Systems Genetics, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany
| | - Alvis Brazma
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Angela N Brooks
- Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA 95064, USA
| | - Gunnar Rätsch
- Department of Computer Science, ETH Zurich, Zurich 8092, Switzerland; Department of Biology, ETH Zurich, Zurich 8093, Switzerland; Computational Biology Center, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; SIB Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland; Biomedical Informatics Research, University Hospital Zurich, Zurich 8091, Switzerland; Weill Cornell Medical College, New York, NY 10065, USA
| | - Patrick Tan
- Program in Cancer and Stem Cell Biology, Duke-NUS Medical School, Singapore 169857, Singapore; Cancer Science Institute of Singapore, National University of Singapore, Singapore 117599, Singapore; Cancer Therapeutics and Stratified Oncology, Genome Institute of Singapore, Singapore 138672, Singapore; SingHealth/Duke-NUS Institute of Precision Medicine, National Heart Centre Singapore, Singapore 169856, Singapore; Cellular and Molecular Research, National Cancer Centre, Singapore 169610, Singapore; Singapore Gastric Cancer Consortium, Singapore 119074, Singapore
| | - Jonathan Göke
- Computational and Systems Biology, Genome Institute of Singapore, Singapore 138672, Singapore; Cellular and Molecular Research, National Cancer Centre, Singapore 169610, Singapore.
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35
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Kerr CH, Skinnider MA, Andrews DDT, Madero AM, Chan QWT, Stacey RG, Stoynov N, Jan E, Foster LJ. Dynamic rewiring of the human interactome by interferon signaling. Genome Biol 2020; 21:140. [PMID: 32539747 PMCID: PMC7294662 DOI: 10.1186/s13059-020-02050-y] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2019] [Accepted: 05/20/2020] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND The type I interferon (IFN) response is an ancient pathway that protects cells against viral pathogens by inducing the transcription of hundreds of IFN-stimulated genes. Comprehensive catalogs of IFN-stimulated genes have been established across species and cell types by transcriptomic and biochemical approaches, but their antiviral mechanisms remain incompletely characterized. Here, we apply a combination of quantitative proteomic approaches to describe the effects of IFN signaling on the human proteome, and apply protein correlation profiling to map IFN-induced rearrangements in the human protein-protein interaction network. RESULTS We identify > 26,000 protein interactions in IFN-stimulated and unstimulated cells, many of which involve proteins associated with human disease and are observed exclusively within the IFN-stimulated network. Differential network analysis reveals interaction rewiring across a surprisingly broad spectrum of cellular pathways in the antiviral response. We identify IFN-dependent protein-protein interactions mediating novel regulatory mechanisms at the transcriptional and translational levels, with one such interaction modulating the transcriptional activity of STAT1. Moreover, we reveal IFN-dependent changes in ribosomal composition that act to buffer IFN-stimulated gene protein synthesis. CONCLUSIONS Our map of the IFN interactome provides a global view of the complex cellular networks activated during the antiviral response, placing IFN-stimulated genes in a functional context, and serves as a framework to understand how these networks are dysregulated in autoimmune or inflammatory disease.
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Affiliation(s)
- Craig H Kerr
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada
- Department of Biochemistry and Molecular Biology, University of British Columbia, Vancouver, BC, V6T 1Z3, Canada
- Current Address: Department of Genetics, Stanford University, Stanford, CA, 94305, USA
| | - Michael A Skinnider
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada
| | - Daniel D T Andrews
- Department of Biochemistry and Molecular Biology, University of British Columbia, Vancouver, BC, V6T 1Z3, Canada
| | - Angel M Madero
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada
| | - Queenie W T Chan
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada
| | - R Greg Stacey
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada
| | - Nikolay Stoynov
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada
| | - Eric Jan
- Department of Biochemistry and Molecular Biology, University of British Columbia, Vancouver, BC, V6T 1Z3, Canada
| | - Leonard J Foster
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada.
- Department of Biochemistry and Molecular Biology, University of British Columbia, Vancouver, BC, V6T 1Z3, Canada.
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36
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Cao Y, Wang X, Peng G. SCSA: A Cell Type Annotation Tool for Single-Cell RNA-seq Data. Front Genet 2020; 11:490. [PMID: 32477414 PMCID: PMC7235421 DOI: 10.3389/fgene.2020.00490] [Citation(s) in RCA: 60] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Accepted: 04/20/2020] [Indexed: 11/13/2022] Open
Abstract
Currently most methods take manual strategies to annotate cell types after clustering the single-cell RNA sequencing (scRNA-seq) data. Such methods are labor-intensive and heavily rely on user expertise, which may lead to inconsistent results. We present SCSA, an automatic tool to annotate cell types from scRNA-seq data, based on a score annotation model combining differentially expressed genes (DEGs) and confidence levels of cell markers from both known and user-defined information. Evaluation on real scRNA-seq datasets from different sources with other methods shows that SCSA is able to assign the cells into the correct types at a fully automated mode with a desirable precision.
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Affiliation(s)
| | - Xiaoyue Wang
- Center for Bioinformatics, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & School of Basic Medicine, Peking Union Medical College, Beijing, China
| | - Gongxin Peng
- Center for Bioinformatics, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & School of Basic Medicine, Peking Union Medical College, Beijing, China
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37
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Treviño LS, Dong J, Kaushal A, Katz TA, Jangid RK, Robertson MJ, Grimm SL, Ambati CSR, Putluri V, Cox AR, Kim KH, May TD, Gallo MR, Moore DD, Hartig SM, Foulds CE, Putluri N, Coarfa C, Walker CL. Epigenome environment interactions accelerate epigenomic aging and unlock metabolically restricted epigenetic reprogramming in adulthood. Nat Commun 2020; 11:2316. [PMID: 32385268 PMCID: PMC7210260 DOI: 10.1038/s41467-020-15847-z] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2019] [Accepted: 03/19/2020] [Indexed: 12/13/2022] Open
Abstract
Our early-life environment has a profound influence on developing organs that impacts metabolic function and determines disease susceptibility across the life-course. Using a rat model for exposure to an endocrine disrupting chemical (EDC), we show that early-life chemical exposure causes metabolic dysfunction in adulthood and reprograms histone marks in the developing liver to accelerate acquisition of an adult epigenomic signature. This epigenomic reprogramming persists long after the initial exposure, but many reprogrammed genes remain transcriptionally silent with their impact on metabolism not revealed until a later life exposure to a Western-style diet. Diet-dependent metabolic disruption was largely driven by reprogramming of the Early Growth Response 1 (EGR1) transcriptome and production of metabolites in pathways linked to cholesterol, lipid and one-carbon metabolism. These findings demonstrate the importance of epigenome:environment interactions, which early in life accelerate epigenomic aging, and later in adulthood unlock metabolically restricted epigenetic reprogramming to drive metabolic dysfunction. Early life exposure to environmental stressors, including endocrine disrupting chemicals (EDCs), can impact health later in life. Here, the authors show that neonatal EDC exposure in rats causes epigenetic reprogramming in the liver, which is transcriptionally silent until animals are placed on a Western-style diet.
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Affiliation(s)
- Lindsey S Treviño
- Center for Precision Environmental Health, Baylor College of Medicine, Houston, TX, 77030, USA.,Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, 77030, USA.,Division of Health Equities, Department of Population Sciences, City of Hope, Duarte, CA, 91010, USA
| | - Jianrong Dong
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Ahkilesh Kaushal
- Center for Precision Environmental Health, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Tiffany A Katz
- Center for Precision Environmental Health, Baylor College of Medicine, Houston, TX, 77030, USA.,Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Rahul Kumar Jangid
- Center for Precision Environmental Health, Baylor College of Medicine, Houston, TX, 77030, USA.,Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Matthew J Robertson
- Advanced Technology Cores, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Sandra L Grimm
- Advanced Technology Cores, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Chandra Shekar R Ambati
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Vasanta Putluri
- Advanced Technology Cores, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Aaron R Cox
- Department of Medicine, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Kang Ho Kim
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Thaddeus D May
- Department of Pediatrics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Morgan R Gallo
- Center for Precision Environmental Health, Baylor College of Medicine, Houston, TX, 77030, USA
| | - David D Moore
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Sean M Hartig
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, 77030, USA.,Department of Medicine, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Charles E Foulds
- Center for Precision Environmental Health, Baylor College of Medicine, Houston, TX, 77030, USA.,Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, 77030, USA.,Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Nagireddy Putluri
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Cristian Coarfa
- Center for Precision Environmental Health, Baylor College of Medicine, Houston, TX, 77030, USA. .,Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, 77030, USA. .,Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, 77030, USA.
| | - Cheryl Lyn Walker
- Center for Precision Environmental Health, Baylor College of Medicine, Houston, TX, 77030, USA. .,Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, 77030, USA. .,Department of Medicine, Baylor College of Medicine, Houston, TX, 77030, USA. .,Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, 77030, USA. .,Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA.
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38
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Wang Y, Dai G, Gu Z, Liu G, Tang K, Pan YH, Chen Y, Lin X, Wu N, Chen H, Feng S, Qiu S, Sun H, Li Q, Xu C, Mao Y, Zhang YE, Khaitovich P, Wang YL, Liu Q, Han JDJ, Shao Z, Wei G, Xu C, Jing N, Li H. Accelerated evolution of an Lhx2 enhancer shapes mammalian social hierarchies. Cell Res 2020; 30:408-420. [PMID: 32238901 PMCID: PMC7196073 DOI: 10.1038/s41422-020-0308-7] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2019] [Accepted: 03/12/2020] [Indexed: 12/26/2022] Open
Abstract
Social hierarchies emerged during evolution, and social rank influences behavior and health of individuals. However, the evolutionary mechanisms of social hierarchy are still unknown in amniotes. Here we developed a new method and performed a genome-wide screening for identifying regions with accelerated evolution in the ancestral lineage of placental mammals, where mammalian social hierarchies might have initially evolved. Then functional analyses were conducted for the most accelerated region designated as placental-accelerated sequence 1 (PAS1, P = 3.15 × 10-18). Multiple pieces of evidence show that PAS1 is an enhancer of the transcription factor gene Lhx2 involved in brain development. PAS1s isolated from various amniotes showed different cis-regulatory activity in vitro, and affected the expression of Lhx2 differently in the nervous system of mouse embryos. PAS1 knock-out mice lack social stratification. PAS1 knock-in mouse models demonstrate that PAS1s determine the social dominance and subordinate of adult mice, and that social ranks could even be turned over by mutated PAS1. All homozygous mutant mice had normal huddled sleeping behavior, motor coordination and strength. Therefore, PAS1-Lhx2 modulates social hierarchies and is essential for establishing social stratification in amniotes, and positive Darwinian selection on PAS1 plays pivotal roles in the occurrence of mammalian social hierarchies.
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Affiliation(s)
- Yuting Wang
- CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Yueyang Road 320, 200031, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Guangyi Dai
- CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Yueyang Road 320, 200031, Shanghai, China
| | - Zhili Gu
- CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Yueyang Road 320, 200031, Shanghai, China
| | - Guopeng Liu
- CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Yueyang Road 320, 200031, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Ke Tang
- Precise Genome Engineering Center, School of Life Sciences, Guangzhou University, Guangzhou, 510405, Guangdong, China
| | - Yi-Hsuan Pan
- Key Laboratory of Brain Functional Genomics of Ministry of Education, School of Life Science, East China Normal University, 200062, Shanghai, China
| | - Yujie Chen
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Yueyang Road 320, 200031, Shanghai, China
| | - Xin Lin
- CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Yueyang Road 320, 200031, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Nan Wu
- Key Laboratory of Brain Functional Genomics of Ministry of Education, School of Life Science, East China Normal University, 200062, Shanghai, China
| | - Haoshan Chen
- University of Chinese Academy of Sciences, Beijing, China
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Yueyang Road 320, 200031, Shanghai, China
| | - Su Feng
- University of Chinese Academy of Sciences, Beijing, China
- State Key Laboratory of Cell Biology, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Yueyang Road 320, 200031, Shanghai, China
| | - Shou Qiu
- University of Chinese Academy of Sciences, Beijing, China
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Yueyang Road 320, 200031, Shanghai, China
| | - Hongduo Sun
- CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Yueyang Road 320, 200031, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Qian Li
- CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Yueyang Road 320, 200031, Shanghai, China
| | - Chuan Xu
- CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Yueyang Road 320, 200031, Shanghai, China
| | - Yanan Mao
- University of Chinese Academy of Sciences, Beijing, China
- Key Laboratory of Zoological Systematics and Evolution & State Key Laboratory of Integrated Management of Pest Insects and Rodents, Institute of Zoology, Chinese Academy of Sciences, 100101, Beijing, China
| | - Yong Edward Zhang
- University of Chinese Academy of Sciences, Beijing, China
- Key Laboratory of Zoological Systematics and Evolution & State Key Laboratory of Integrated Management of Pest Insects and Rodents, Institute of Zoology, Chinese Academy of Sciences, 100101, Beijing, China
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, 650223, Kunming, China
| | - Philipp Khaitovich
- CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Yueyang Road 320, 200031, Shanghai, China
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, 650223, Kunming, China
| | - Yan-Ling Wang
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, 100101, Beijing, China
| | - Qunxiu Liu
- Shanghai Zoological Park, 200335, Shanghai, China
| | - Jing-Dong Jackie Han
- CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Yueyang Road 320, 200031, Shanghai, China
| | - Zhen Shao
- CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Yueyang Road 320, 200031, Shanghai, China
| | - Gang Wei
- CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Yueyang Road 320, 200031, Shanghai, China
| | - Chun Xu
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Yueyang Road 320, 200031, Shanghai, China
| | - Naihe Jing
- State Key Laboratory of Cell Biology, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Yueyang Road 320, 200031, Shanghai, China
| | - Haipeng Li
- CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Yueyang Road 320, 200031, Shanghai, China.
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, 650223, Kunming, China.
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39
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Bedi K, Paulsen MT, Wilson TE, Ljungman M. Characterization of novel primary miRNA transcription units in human cells using Bru-seq nascent RNA sequencing. NAR Genom Bioinform 2020; 2:lqz014. [PMID: 31709421 PMCID: PMC6824518 DOI: 10.1093/nargab/lqz014] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2019] [Revised: 10/07/2019] [Accepted: 10/26/2019] [Indexed: 12/12/2022] Open
Abstract
MicroRNAs (miRNAs) are key contributors to gene regulatory networks. Because miRNAs are processed from RNA polymerase II transcripts, insight into miRNA regulation requires a comprehensive understanding of the regulation of primary miRNA transcripts. We used Bru-seq nascent RNA sequencing and hidden Markov model segmentation to map primary miRNA transcription units (TUs) across 32 human cell lines, allowing us to describe TUs encompassing 1443 miRNAs from miRBase and 438 from MirGeneDB. We identified TUs for 61 miRNAs with an unknown CAGE TSS signal for MirGeneDB miRNAs. Many primary transcripts containing miRNA sequences failed to generate mature miRNAs, suggesting that miRNA biosynthesis is under both transcriptional and post-transcriptional control. In addition to constitutive and cell-type specific TU expression regulated by differential promoter usage, miRNA synthesis can be regulated by transcription past polyadenylation sites (transcriptional read through) and promoter divergent transcription (PROMPTs). We identified 197 miRNA TUs with novel promoters, 97 with transcriptional read-throughs and 3 miRNA TUs that resemble PROMPTs in at least one cell line. The miRNA TU annotation data resource described here reveals a greater complexity in miRNA regulation than previously known and provides a framework for identifying cell-type specific differences in miRNA transcription in cancer and cell transition states.
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Affiliation(s)
- Karan Bedi
- Department of Radiation Oncology, Rogel Cancer Center and Center for RNA Biomedicine, University of Michigan, Ann Arbor, MI 48109, USA
| | - Michelle T Paulsen
- Department of Radiation Oncology, Rogel Cancer Center and Center for RNA Biomedicine, University of Michigan, Ann Arbor, MI 48109, USA
| | - Thomas E Wilson
- Department of Pathology and Department of Human Genetics, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - Mats Ljungman
- Department of Radiation Oncology, Rogel Cancer Center and Center for RNA Biomedicine, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
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40
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Yu F, Qiu C, Xu C, Tian Q, Zhao LJ, Wu L, Deng HW, Shen H. Mendelian Randomization Identifies CpG Methylation Sites With Mediation Effects for Genetic Influences on BMD in Peripheral Blood Monocytes. Front Genet 2020; 11:60. [PMID: 32180791 PMCID: PMC7059767 DOI: 10.3389/fgene.2020.00060] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2019] [Accepted: 01/17/2020] [Indexed: 12/18/2022] Open
Abstract
Osteoporosis is mainly characterized by low bone mineral density (BMD) and is an increasingly serious public health concern. DNA methylation is a major epigenetic mechanism that may contribute to the variation in BMD and may mediate the effects of genetic and environmental factors of osteoporosis. In this study, we performed an epigenome-wide DNA methylation analysis in peripheral blood monocytes of 118 Caucasian women with extreme BMD values. Further, we developed and implemented a novel analytical framework that integrates Mendelian randomization with genetic fine mapping and colocalization to evaluate the causal relationships between DNA methylation and BMD phenotype. We identified 2,188 differentially methylated CpGs (DMCs) between the low and high BMD groups and distinguished 30 DMCs that may mediate the genetic effects on BMD. The causal relationship was further confirmed by eliminating the possibility of horizontal pleiotropy, linkage effect and reverse causality. The fine-mapping analysis determined 25 causal variants that are most likely to affect the methylation levels at these mediator DMCs. The majority of the causal methylation quantitative loci and DMCs reside within cell type-specific histone mark peaks, enhancers, promoters, promoter flanking regions and CTCF binding sites, supporting the regulatory potentials of these loci. The established causal pathways from genetic variant to BMD phenotype mediated by DNA methylation provide a gene list to aid in designing future functional studies and lead to a better understanding of the genetic and epigenetic mechanisms underlying the variation of BMD.
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Affiliation(s)
- Fangtang Yu
- Center for Bioinformatics and Genomics, Department of Global Biostatistics and Data Science, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, United States
| | - Chuan Qiu
- Center for Bioinformatics and Genomics, Department of Global Biostatistics and Data Science, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, United States
| | - Chao Xu
- Center for Bioinformatics and Genomics, Department of Global Biostatistics and Data Science, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, United States
| | - Qing Tian
- Center for Bioinformatics and Genomics, Department of Global Biostatistics and Data Science, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, United States
| | - Lan-Juan Zhao
- Center for Bioinformatics and Genomics, Department of Global Biostatistics and Data Science, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, United States
| | - Li Wu
- Center for Bioinformatics and Genomics, Department of Global Biostatistics and Data Science, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, United States
| | - Hong-Wen Deng
- Center for Bioinformatics and Genomics, Department of Global Biostatistics and Data Science, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, United States.,School of Basic Medical Science, Central South University, Changsha, China
| | - Hui Shen
- Center for Bioinformatics and Genomics, Department of Global Biostatistics and Data Science, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, United States
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Characterizing chromatin landscape from aggregate and single-cell genomic assays using flexible duration modeling. Nat Commun 2020; 11:747. [PMID: 32029740 PMCID: PMC7004981 DOI: 10.1038/s41467-020-14497-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2019] [Accepted: 01/13/2020] [Indexed: 11/09/2022] Open
Abstract
ATAC-seq has become a leading technology for probing the chromatin landscape of single and aggregated cells. Distilling functional regions from ATAC-seq presents diverse analysis challenges. Methods commonly used to analyze chromatin accessibility datasets are adapted from algorithms designed to process different experimental technologies, disregarding the statistical and biological differences intrinsic to the ATAC-seq technology. Here, we present a Bayesian statistical approach that uses latent space models to better model accessible regions, termed ChromA. ChromA annotates chromatin landscape by integrating information from replicates, producing a consensus de-noised annotation of chromatin accessibility. ChromA can analyze single cell ATAC-seq data, correcting many biases generated by the sparse sampling inherent in single cell technologies. We validate ChromA on multiple technologies and biological systems, including mouse and human immune cells, establishing ChromA as a top performing general platform for mapping the chromatin landscape in different cellular populations from diverse experimental designs. Most currently available statistical tools for the analysis of ATAC-seq data were repurposed from tools developed for other functional genomics data (e.g. ChIP-seq). Here, Gabitto et al develop ChromA, a Bayesian statistical approach for the analysis of both bulk and single-cell ATAC-seq data.
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Hekselman I, Yeger-Lotem E. Mechanisms of tissue and cell-type specificity in heritable traits and diseases. Nat Rev Genet 2020; 21:137-150. [DOI: 10.1038/s41576-019-0200-9] [Citation(s) in RCA: 67] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/12/2019] [Indexed: 02/07/2023]
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Pham VVH, Liu L, Bracken CP, Goodall GJ, Long Q, Li J, Le TD. CBNA: A control theory based method for identifying coding and non-coding cancer drivers. PLoS Comput Biol 2019; 15:e1007538. [PMID: 31790386 PMCID: PMC6907873 DOI: 10.1371/journal.pcbi.1007538] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2019] [Revised: 12/12/2019] [Accepted: 11/12/2019] [Indexed: 02/06/2023] Open
Abstract
A key task in cancer genomics research is to identify cancer driver genes. As these genes initialise and progress cancer, understanding them is critical in designing effective cancer interventions. Although there are several methods developed to discover cancer drivers, most of them only identify coding drivers. However, non-coding RNAs can regulate driver mutations to develop cancer. Hence, novel methods are required to reveal both coding and non-coding cancer drivers. In this paper, we develop a novel framework named Controllability based Biological Network Analysis (CBNA) to uncover coding and non-coding cancer drivers (i.e. miRNA cancer drivers). CBNA integrates different genomic data types, including gene expression, gene network, mutation data, and contains a two-stage process: (1) Building a network for a condition (e.g. cancer condition) and (2) Identifying drivers. The application of CBNA to the BRCA dataset demonstrates that it is more effective than the existing methods in detecting coding cancer drivers. In addition, CBNA also predicts 17 miRNA drivers for breast cancer. Some of these predicted miRNA drivers have been validated by literature and the rest can be good candidates for wet-lab validation. We further use CBNA to detect subtype-specific cancer drivers and several predicted drivers have been confirmed to be related to breast cancer subtypes. Another application of CBNA is to discover epithelial-mesenchymal transition (EMT) drivers. Of the predicted EMT drivers, 7 coding and 6 miRNA drivers are in the known EMT gene lists. Cancer is a disease of cells in human body and it causes a high rate of deaths worldwide. There has been evidence that coding and non-coding RNAs are key players in the initialisation and progression of cancer. These coding and non-coding RNAs are considered as cancer drivers. To design better diagnostic and therapeutic plans for cancer patients, we need to know the roles of cancer drivers in cancer development as well as their regulatory mechanisms in the human body. In this study, we propose a novel framework to identify coding and non-coding cancer drivers (i.e. miRNA cancer drivers). The proposed framework is applied to the breast cancer dataset for identifying drivers of breast cancer. Comparing our method with existing methods in predicting coding cancer drivers, our method shows a better performance. Several miRNA cancer drivers predicted by our method have already been validated by literature. The predicted cancer drivers by our method could be a potential source for further wet-lab experiments to discover the causes of cancer. In addition, the proposed method can be used to detect drivers of cancer subtypes and drivers of the epithelial-mesenchymal transition in cancer.
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Affiliation(s)
- Vu V. H. Pham
- School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, Australia
| | - Lin Liu
- School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, Australia
| | - Cameron P. Bracken
- Centre for Cancer Biology, an alliance of SA Pathology and University of South Australia, Adelaide, Australia
- Department of Medicine, The University of Adelaide, Adelaide, Australia
| | - Gregory J. Goodall
- Centre for Cancer Biology, an alliance of SA Pathology and University of South Australia, Adelaide, Australia
- Department of Medicine, The University of Adelaide, Adelaide, Australia
| | - Qi Long
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Jiuyong Li
- School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, Australia
- * E-mail: (JL); (TL)
| | - Thuc D. Le
- School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, Australia
- * E-mail: (JL); (TL)
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Wang J, Alvin Chew BL, Lai Y, Dong H, Xu L, Balamkundu S, Cai WM, Cui L, Liu CF, Fu XY, Lin Z, Shi PY, Lu TK, Luo D, Jaffrey SR, Dedon PC. Quantifying the RNA cap epitranscriptome reveals novel caps in cellular and viral RNA. Nucleic Acids Res 2019; 47:e130. [PMID: 31504804 PMCID: PMC6847653 DOI: 10.1093/nar/gkz751] [Citation(s) in RCA: 105] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2019] [Revised: 07/16/2019] [Accepted: 08/23/2019] [Indexed: 12/13/2022] Open
Abstract
Chemical modification of transcripts with 5' caps occurs in all organisms. Here, we report a systems-level mass spectrometry-based technique, CapQuant, for quantitative analysis of an organism's cap epitranscriptome. The method was piloted with 21 canonical caps-m7GpppN, m7GpppNm, GpppN, GpppNm, and m2,2,7GpppG-and 5 'metabolite' caps-NAD, FAD, UDP-Glc, UDP-GlcNAc, and dpCoA. Applying CapQuant to RNA from purified dengue virus, Escherichia coli, yeast, mouse tissues, and human cells, we discovered new cap structures in humans and mice (FAD, UDP-Glc, UDP-GlcNAc, and m7Gpppm6A), cell- and tissue-specific variations in cap methylation, and high proportions of caps lacking 2'-O-methylation (m7Gpppm6A in mammals, m7GpppA in dengue virus). While substantial Dimroth-induced loss of m1A and m1Am arose with specific RNA processing conditions, human lymphoblast cells showed no detectable m1A or m1Am in caps. CapQuant accurately captured the preference for purine nucleotides at eukaryotic transcription start sites and the correlation between metabolite levels and metabolite caps.
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Affiliation(s)
- Jin Wang
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, Inner Mongolia University, Hohhot, People's Republic of China
- School of Life Sciences, Inner Mongolia University, Hohhot, People's Republic of China
- Antimicrobial Resistance Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology, Singapore
| | - Bing Liang Alvin Chew
- Antimicrobial Resistance Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology, Singapore
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
- NTU Institute of Health Technologies, Interdisciplinary Graduate Programme, Nanyang Technological University, Singapore
| | - Yong Lai
- Antimicrobial Resistance Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology, Singapore
| | - Hongping Dong
- Shanghai Blueray Biopharma, Shanghai, People's Republic of China
| | - Luang Xu
- Cancer Science Institute of Singapore, Singapore
| | - Seetharamsingh Balamkundu
- Antimicrobial Resistance Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology, Singapore
- School of Biological Sciences, Nanyang Technological University, Singapore
| | - Weiling Maggie Cai
- Antimicrobial Resistance Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology, Singapore
- Department of Microbiology, National University of Singapore, Singapore
| | - Liang Cui
- Antimicrobial Resistance Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology, Singapore
| | - Chuan Fa Liu
- School of Biological Sciences, Nanyang Technological University, Singapore
| | - Xin-Yuan Fu
- Cancer Science Institute of Singapore, Singapore
| | - Zhenguo Lin
- Department of Biology, Saint Louis University, St. Louis, MO, USA
| | - Pei-Yong Shi
- Departments of Biochemistry & Molecular Biology and Pharmacology & Toxicology, and Sealy Center for Structural Biology & Molecular Biophysics, University of Texas Medical Branch, Galveston, TX, USA
| | - Timothy K Lu
- Antimicrobial Resistance Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology, Singapore
- Synthetic Biology Center, Departments of Biological Engineering and Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Dahai Luo
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
| | - Samie R Jaffrey
- Department of Pharmacology, Weill Medical College, Cornell University, New York, NY, USA
| | - Peter C Dedon
- Antimicrobial Resistance Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology, Singapore
- Dept. of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
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45
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Biological Network Approaches and Applications in Rare Disease Studies. Genes (Basel) 2019; 10:genes10100797. [PMID: 31614842 PMCID: PMC6827097 DOI: 10.3390/genes10100797] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2019] [Revised: 09/30/2019] [Accepted: 10/10/2019] [Indexed: 12/26/2022] Open
Abstract
Network biology has the capability to integrate, represent, interpret, and model complex biological systems by collectively accommodating biological omics data, biological interactions and associations, graph theory, statistical measures, and visualizations. Biological networks have recently been shown to be very useful for studies that decipher biological mechanisms and disease etiologies and for studies that predict therapeutic responses, at both the molecular and system levels. In this review, we briefly summarize the general framework of biological network studies, including data resources, network construction methods, statistical measures, network topological properties, and visualization tools. We also introduce several recent biological network applications and methods for the studies of rare diseases.
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Breen MS, Dobbyn A, Li Q, Roussos P, Hoffman GE, Stahl E, Chess A, Sklar P, Li JB, Devlin B, Buxbaum JD. Global landscape and genetic regulation of RNA editing in cortical samples from individuals with schizophrenia. Nat Neurosci 2019; 22:1402-1412. [PMID: 31455887 PMCID: PMC6791127 DOI: 10.1038/s41593-019-0463-7] [Citation(s) in RCA: 54] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2018] [Accepted: 07/09/2019] [Indexed: 12/28/2022]
Abstract
RNA editing critically regulates neurodevelopment and normal neuronal function. The global landscape of RNA editing was surveyed across 364 schizophrenia cases and 383 control postmortem brain samples from the CommonMind Consortium, comprising two regions: dorsolateral prefrontal cortex and anterior cingulate cortex. In schizophrenia, RNA editing sites in genes encoding AMPA-type glutamate receptors and postsynaptic density proteins were less edited, whereas those encoding translation initiation machinery were edited more. These sites replicate between brain regions, map to 3'-untranslated regions and intronic regions, share common sequence motifs and overlap with binding sites for RNA-binding proteins crucial for neurodevelopment. These findings cross-validate in hundreds of non-overlapping dorsolateral prefrontal cortex samples. Furthermore, ~30% of RNA editing sites associate with cis-regulatory variants (editing quantitative trait loci or edQTLs). Fine-mapping edQTLs with schizophrenia risk loci revealed co-localization of eleven edQTLs with six loci. The findings demonstrate widespread altered RNA editing in schizophrenia and its genetic regulation, and suggest a causal and mechanistic role of RNA editing in schizophrenia neuropathology.
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Affiliation(s)
- Michael S Breen
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Seaver Autism Center for Research and Treatment, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Amanda Dobbyn
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Qin Li
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Panos Roussos
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mental Illness Research, Education, and Clinical Center (VISN 2 South), James J. Peters VA Medical Center, Bronx, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Gabriel E Hoffman
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technologies, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Eli Stahl
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technologies, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Andrew Chess
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technologies, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Cell Developmental and Regenerative Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Pamela Sklar
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technologies, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jin Billy Li
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Bernie Devlin
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Joseph D Buxbaum
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Seaver Autism Center for Research and Treatment, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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Sapkota Y, Cheung YT, Moon W, Shelton K, Wilson CL, Wang Z, Mulrooney DA, Zhang J, Armstrong GT, Hudson MM, Robison LL, Krull KR, Yasui Y. Whole-Genome Sequencing of Childhood Cancer Survivors Treated with Cranial Radiation Therapy Identifies 5p15.33 Locus for Stroke: A Report from the St. Jude Lifetime Cohort Study. Clin Cancer Res 2019; 25:6700-6708. [PMID: 31462438 DOI: 10.1158/1078-0432.ccr-19-1231] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2019] [Revised: 06/10/2019] [Accepted: 08/21/2019] [Indexed: 02/07/2023]
Abstract
PURPOSE To identify genetic factors associated with risk of stroke among survivors of childhood cancer treated with cranial radiotherapy (CRT). EXPERIMENTAL DESIGN We analyzed whole-genome sequencing (36.8-fold) data of 686 childhood cancer survivors of European ancestry [median (range), 40.4 (12.4-64.7) years old; 54% male] from the St. Jude Lifetime Cohort study treated with CRT, of whom 116 (17%) had clinically diagnosed stroke. Association analyses (single-variant and Burden/SKAT tests) were performed, adjusting for demographic characteristics and childhood cancer treatment exposures. RESULTS We identified a genome-wide significant association between 5p15.33 locus and stroke [rs112896372: HR = 2.55; P = 1.42 × 10-8], with a stronger association (HR = 3.68) among survivors treated with CRT dose 25-50 Gray (Gy) and weaker associations among those treated with CRT doses <20 or 20-25 or >50 Gy (HRs = 2.14, 2.40, and 2.28). The association was replicated in 90 CRT-exposed African survivors (HR = 3.05; P = 0.034). In CRT-exposed Europeans, rs112896372 significantly (P < 0.001) improved predictive ability (AUC = 0.717) for determining stroke risk than nongenetic factors alone (AUC = 0.663) at 30 years since diagnosis, with significant improvement among African survivors (P = 0.047). SNP rs112896372 was further evaluated in three independent datasets including 1,641 European (HR = 1.54; P = 0.055) and 316 African survivors (HR = 1.88; P = 0.283) not treated with CRT, and 166,988 males in the UK Biobank (OR = 1.0012; P = 0.042). CONCLUSIONS A novel locus 5p15.33 is associated with stroke risk among childhood cancer survivors, with a possible CRT dose-specific effect. The locus is of potential clinical utility in characterizing individuals who may benefit from surveillance and intervention strategies.
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Affiliation(s)
- Yadav Sapkota
- Department of Epidemiology and Cancer Control, St. Jude Children's Research Hospital, Memphis, Tennessee.
| | - Yin Ting Cheung
- School of Pharmacy, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, N.T. Hong Kong
| | - Wonjong Moon
- Department of Epidemiology and Cancer Control, St. Jude Children's Research Hospital, Memphis, Tennessee
| | - Kyla Shelton
- Department of Epidemiology and Cancer Control, St. Jude Children's Research Hospital, Memphis, Tennessee
| | - Carmen L Wilson
- Department of Epidemiology and Cancer Control, St. Jude Children's Research Hospital, Memphis, Tennessee
| | - Zhaoming Wang
- Department of Epidemiology and Cancer Control, St. Jude Children's Research Hospital, Memphis, Tennessee.,Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, Tennessee
| | - Daniel A Mulrooney
- Department of Epidemiology and Cancer Control, St. Jude Children's Research Hospital, Memphis, Tennessee.,Department of Oncology, St. Jude Children's Research Hospital, Memphis, Tennessee
| | - Jinghui Zhang
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, Tennessee
| | - Gregory T Armstrong
- Department of Epidemiology and Cancer Control, St. Jude Children's Research Hospital, Memphis, Tennessee
| | - Melissa M Hudson
- Department of Epidemiology and Cancer Control, St. Jude Children's Research Hospital, Memphis, Tennessee.,Department of Oncology, St. Jude Children's Research Hospital, Memphis, Tennessee
| | - Leslie L Robison
- Department of Epidemiology and Cancer Control, St. Jude Children's Research Hospital, Memphis, Tennessee
| | - Kevin R Krull
- Department of Epidemiology and Cancer Control, St. Jude Children's Research Hospital, Memphis, Tennessee
| | - Yutaka Yasui
- Department of Epidemiology and Cancer Control, St. Jude Children's Research Hospital, Memphis, Tennessee
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Palmieri V, Backes C, Ludwig N, Fehlmann T, Kern F, Meese E, Keller A. IMOTA: an interactive multi-omics tissue atlas for the analysis of human miRNA-target interactions. Nucleic Acids Res 2019; 46:D770-D775. [PMID: 28977416 PMCID: PMC5753248 DOI: 10.1093/nar/gkx701] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2017] [Accepted: 07/28/2017] [Indexed: 12/12/2022] Open
Abstract
Web repositories for almost all 'omics' types have been generated-detailing the repertoire of representatives across different tissues or cell types. A logical next step is the combination of these valuable sources. With IMOTA (interactive multi omics tissue atlas), we developed a database that includes 23 725 relations between miRNAs and 23 tissues, 310 932 relations between mRNAs and the same tissues as well as 63 043 relations between proteins and the 23 tissues in Homo sapiens. IMOTA also contains data on tissue-specific interactions, e.g. information on 331 413 miRNAs and target gene pairs that are jointly expressed in the considered tissues. By using intuitive filter and visualization techniques, it is with minimal effort possible to answer various questions. These include rather general questions but also requests specific for genes, miRNAs or proteins. An example for a general task could be 'identify all miRNAs, genes and proteins in the lung that are highly expressed and where experimental evidence proves that the miRNAs target the genes'. An example for a specific request for a gene and a miRNA could for example be 'In which tissues is miR-34c and its target gene BCL2 expressed?'. The IMOTA repository is freely available online at https://ccb-web.cs.uni-saarland.de/imota/.
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Affiliation(s)
- Valeria Palmieri
- Chair for Clinical Bioinformatics, Saarland University, 66123 Saarbrücken, Germany
| | - Christina Backes
- Chair for Clinical Bioinformatics, Saarland University, 66123 Saarbrücken, Germany
| | - Nicole Ludwig
- Department for Human Genetics, Saarland University, 66424 Homburg, Germany
| | - Tobias Fehlmann
- Chair for Clinical Bioinformatics, Saarland University, 66123 Saarbrücken, Germany
| | - Fabian Kern
- Chair for Clinical Bioinformatics, Saarland University, 66123 Saarbrücken, Germany
| | - Eckart Meese
- Department for Human Genetics, Saarland University, 66424 Homburg, Germany
| | - Andreas Keller
- Chair for Clinical Bioinformatics, Saarland University, 66123 Saarbrücken, Germany
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Sharma S, Petsalaki E. Large-scale datasets uncovering cell signalling networks in cancer: context matters. Curr Opin Genet Dev 2019; 54:118-124. [PMID: 31200172 DOI: 10.1016/j.gde.2019.05.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2019] [Revised: 04/09/2019] [Accepted: 05/09/2019] [Indexed: 12/28/2022]
Abstract
Cell signaling pathways control the responses of cells to external perturbations. Depending on the cell's internal state, genetic background and environmental context, signaling pathways rewire to elicit the appropriate response. Such rewiring also can lead to cancer development and progression or cause resistance to therapies. While there exist static maps of annotated pathways, they do not capture these rewired networks. As large-scale datasets across multiple contexts and patients are becoming available the doors to infer and study context-specific signaling network have also opened. In this review, we will highlight the most recent approaches to study context-specific signaling networks using large-scale omics and genetic perturbation datasets, with a focus on studies of cancer and cancer-related pathways.
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Affiliation(s)
- Sumana Sharma
- EMBL-EBI, Wellcome Genome Campus, CB10 1SD, Hinxton, Cambridgeshire, UK
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Ahlberg J, Giragossian C, Li H, Myzithras M, Raymond E, Caviness G, Grimaldi C, Brown SE, Perez R, Yang D, Kroe-Barrett R, Joseph D, Pamulapati C, Coble K, Ruus P, Woska JR, Ganesan R, Hansel S, Mbow ML. Retrospective analysis of model-based predictivity of human pharmacokinetics for anti-IL-36R monoclonal antibody MAB92 using a rat anti-mouse IL-36R monoclonal antibody and RNA expression data (FANTOM5). MAbs 2019; 11:956-964. [PMID: 31068073 PMCID: PMC6601564 DOI: 10.1080/19420862.2019.1615345] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
Abstract
Accurate prediction of the human pharmacokinetics (PK) of a candidate monoclonal antibody from nonclinical data is critical to maximize the success of clinical trials. However, for monoclonal antibodies exhibiting nonlinear clearance due to target-mediated drug disposition, PK predictions are particularly challenging. That challenge is further compounded for molecules lacking cross-reactivity in a nonhuman primate, in which case a surrogate antibody selective for the target in rodent may be required. For these cases, prediction of human PK must account for any interspecies differences in binding kinetics, target expression, target turnover, and potentially epitope. We present here a model-based method for predicting the human PK of MAB92 (also known as BI 655130), a humanized IgG1 κ monoclonal antibody directed against human IL-36R. Preclinical PK was generated in the mouse with a chimeric rat anti-mouse IgG2a surrogate antibody cross-reactive against mouse IL-36R. Target-specific parameters such as antibody binding affinity (KD), internalization rate of the drug target complex (kint), target degradation rate (kdeg), and target abundance (R0) were integrated into the model. Two different methods of assigning human R0 were evaluated: the first assumed comparable expression between human and mouse and the second used high-resolution mRNA transcriptome data (FANTOM5) as a surrogate for expression. Utilizing the mouse R0 to predict human PK, AUC0-∞ was substantially underpredicted for nonsaturating doses; however, after correcting for differences in RNA transcriptome between species, AUC0-∞ was predicted largely within 1.5-fold of observations in first-in-human studies, demonstrating the validity of the modeling approach. Our results suggest that semi-mechanistic models incorporating RNA transcriptome data and target-specific parameters may improve the predictivity of first-in-human PK.
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Affiliation(s)
- Jennifer Ahlberg
- a Biotherapeutics Disccovery Research , Boehringer Ingelheim Pharmaceuticals, Inc , Ridgefield , CT , USA
| | - Craig Giragossian
- a Biotherapeutics Disccovery Research , Boehringer Ingelheim Pharmaceuticals, Inc , Ridgefield , CT , USA
| | - Hua Li
- a Biotherapeutics Disccovery Research , Boehringer Ingelheim Pharmaceuticals, Inc , Ridgefield , CT , USA
| | - Maria Myzithras
- a Biotherapeutics Disccovery Research , Boehringer Ingelheim Pharmaceuticals, Inc , Ridgefield , CT , USA
| | - Ernie Raymond
- a Biotherapeutics Disccovery Research , Boehringer Ingelheim Pharmaceuticals, Inc , Ridgefield , CT , USA
| | - Gary Caviness
- a Biotherapeutics Disccovery Research , Boehringer Ingelheim Pharmaceuticals, Inc , Ridgefield , CT , USA
| | - Christine Grimaldi
- a Biotherapeutics Disccovery Research , Boehringer Ingelheim Pharmaceuticals, Inc , Ridgefield , CT , USA
| | - Su-Ellen Brown
- a Biotherapeutics Disccovery Research , Boehringer Ingelheim Pharmaceuticals, Inc , Ridgefield , CT , USA
| | - Rocio Perez
- a Biotherapeutics Disccovery Research , Boehringer Ingelheim Pharmaceuticals, Inc , Ridgefield , CT , USA
| | - Danlin Yang
- a Biotherapeutics Disccovery Research , Boehringer Ingelheim Pharmaceuticals, Inc , Ridgefield , CT , USA
| | - Rachel Kroe-Barrett
- a Biotherapeutics Disccovery Research , Boehringer Ingelheim Pharmaceuticals, Inc , Ridgefield , CT , USA
| | - David Joseph
- a Biotherapeutics Disccovery Research , Boehringer Ingelheim Pharmaceuticals, Inc , Ridgefield , CT , USA
| | - Chandrasena Pamulapati
- a Biotherapeutics Disccovery Research , Boehringer Ingelheim Pharmaceuticals, Inc , Ridgefield , CT , USA
| | - Kelly Coble
- a Biotherapeutics Disccovery Research , Boehringer Ingelheim Pharmaceuticals, Inc , Ridgefield , CT , USA
| | - Peter Ruus
- b Translational Medicine, Clinical Pharmacology , Boehringer Ingelheim Pharma GmbH & Co. KG , Ingelheim am Rein , Germany
| | - Joseph R Woska
- a Biotherapeutics Disccovery Research , Boehringer Ingelheim Pharmaceuticals, Inc , Ridgefield , CT , USA
| | - Rajkumar Ganesan
- a Biotherapeutics Disccovery Research , Boehringer Ingelheim Pharmaceuticals, Inc , Ridgefield , CT , USA
| | - Steven Hansel
- a Biotherapeutics Disccovery Research , Boehringer Ingelheim Pharmaceuticals, Inc , Ridgefield , CT , USA
| | - M Lamine Mbow
- a Biotherapeutics Disccovery Research , Boehringer Ingelheim Pharmaceuticals, Inc , Ridgefield , CT , USA
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