Transcriptional regulatory control of mammalian nephron progenitors revealed by multi-factor cistromic analysis and genetic studies

Nephron progenitor number determines nephron endowment; a reduced nephron count is linked to the onset of kidney disease. Several transcriptional regulators including Six2, Wt1, Osr1, Sall1, Eya1, Pax2, and Hox11 paralogues are required for specification and/or maintenance of nephron progenitors. However, little is known about the regulatory intersection of these players. Here, we have mapped nephron progenitor-specific transcriptional networks of Six2, Hoxd11, Osr1, and Wt1. We identified 373 multi-factor associated ‘regulatory hotspots’ around genes closely associated with progenitor programs. To examine their functional significance, we deleted ‘hotspot’ enhancer elements for Six2 and Wnt4. Removal of the distal enhancer for Six2 leads to a ~40% reduction in Six2 expression. When combined with a Six2 null allele, progeny display a premature depletion of nephron progenitors. Loss of the Wnt4 enhancer led to a significant reduction of Wnt4 expression in renal vesicles and a mildly hypoplastic kidney, a phenotype also enhanced in combination with a Wnt4 null mutation. To explore the regulatory landscape that supports proper target gene expression, we performed CTCF ChIP-seq to identify insulator-boundary regions. One such putative boundary lies between the Six2 and Six3 loci. Evidence for the functional significance of this boundary was obtained by deep sequencing of the radiation-induced Brachyrrhine (Br) mutant allele. We identified an inversion of the Six2/Six3 locus around the CTCF-bound boundary, removing Six2 from its distal enhancer regulation, but placed next to Six3 enhancer elements which support ectopic Six2 expression in the lens where Six3 is normally expressed. Six3 is now predicted to fall under control of the Six2 distal enhancer. Consistent with this view, we observed ectopic Six3 in nephron progenitors. 4C-seq supports the model for Six2 distal enhancer interactions in wild-type and Br/+ mouse kidneys. Together, these data expand our view of the regulatory genome and regulatory landscape underpinning mammalian nephrogenesis

Transcriptional regulatory control of mammalian nephron progenitors revealed by multi-factor cistromic analysis and genetic studies.

Nephron progenitor number determines nephron endowment; a reduced nephron count is linked to the onset of kidney disease. Several transcriptional regulators including Six2, Wt1, Osr1, Sall1, Eya1, Pax2, and Hox11 paralogues are required for specification and/or maintenance of nephron progenitors. However, little is known about the regulatory intersection of these players. Here, we have mapped nephron progenitor-specific transcriptional networks of Six2, Hoxd11, Osr1, and Wt1. We identified 373 multi-factor associated \u27regulatory hotspots\u27 around genes closely associated with progenitor programs. To examine their functional significance, we deleted \u27hotspot\u27 enhancer elements for Six2 and Wnt4. Removal of the distal enhancer for Six2 leads to a ~40% reduction in Six2 expression. When combined with a Six2 null allele, progeny display a premature depletion of nephron progenitors. Loss of the Wnt4 enhancer led to a significant reduction of Wnt4 expression in renal vesicles and a mildly hypoplastic kidney, a phenotype also enhanced in combination with a Wnt4 null mutation. To explore the regulatory landscape that supports proper target gene expression, we performed CTCF ChIP-seq to identify insulator-boundary regions. One such putative boundary lies between the Six2 and Six3 loci. Evidence for the functional significance of this boundary was obtained by deep sequencing of the radiation-induced Brachyrrhine (Br) mutant allele. We identified an inversion of the Six2/Six3 locus around the CTCF-bound boundary, removing Six2 from its distal enhancer regulation, but placed next to Six3 enhancer elements which support ectopic Six2 expression in the lens where Six3 is normally expressed. Six3 is now predicted to fall under control of the Six2 distal enhancer. Consistent with this view, we observed ectopic Six3 in nephron progenitors. 4C-seq supports the model for Six2 distal enhancer interactions in wild-type and Br/+ mouse kidneys. Together, these data expand our view of the regulatory genome and regulatory landscape underpinning mammalian nephrogenesis

Quality of dietary fat and genetic risk of type 2 diabetes: individual participant data meta-analysis.

OBJECTIVE: To investigate whether the genetic burden of type 2 diabetes modifies the association between the quality of dietary fat and the incidence of type 2 diabetes. DESIGN: Individual participant data meta-analysis. DATA SOURCES: Eligible prospective cohort studies were systematically sourced from studies published between January 1970 and February 2017 through electronic searches in major medical databases (Medline, Embase, and Scopus) and discussion with investigators. REVIEW METHODS: Data from cohort studies or multicohort consortia with available genome-wide genetic data and information about the quality of dietary fat and the incidence of type 2 diabetes in participants of European descent was sought. Prospective cohorts that had accrued five or more years of follow-up were included. The type 2 diabetes genetic risk profile was characterized by a 68-variant polygenic risk score weighted by published effect sizes. Diet was recorded by using validated cohort-specific dietary assessment tools. Outcome measures were summary adjusted hazard ratios of incident type 2 diabetes for polygenic risk score, isocaloric replacement of carbohydrate (refined starch and sugars) with types of fat, and the interaction of types of fat with polygenic risk score. RESULTS: Of 102 305 participants from 15 prospective cohort studies, 20 015 type 2 diabetes cases were documented after a median follow-up of 12 years (interquartile range 9.4-14.2). The hazard ratio of type 2 diabetes per increment of 10 risk alleles in the polygenic risk score was 1.64 (95% confidence interval 1.54 to 1.75, I2=7.1%, τ2=0.003). The increase of polyunsaturated fat and total omega 6 polyunsaturated fat intake in place of carbohydrate was associated with a lower risk of type 2 diabetes, with hazard ratios of 0.90 (0.82 to 0.98, I2=18.0%, τ2=0.006; per 5% of energy) and 0.99 (0.97 to 1.00, I2=58.8%, τ2=0.001; per increment of 1 g/d), respectively. Increasing monounsaturated fat in place of carbohydrate was associated with a higher risk of type 2 diabetes (hazard ratio 1.10, 95% confidence interval 1.01 to 1.19, I2=25.9%, τ2=0.006; per 5% of energy). Evidence of small study effects was detected for the overall association of polyunsaturated fat with the risk of type 2 diabetes, but not for the omega 6 polyunsaturated fat and monounsaturated fat associations. Significant interactions between dietary fat and polygenic risk score on the risk of type 2 diabetes (P>0.05 for interaction) were not observed. CONCLUSIONS: These data indicate that genetic burden and the quality of dietary fat are each associated with the incidence of type 2 diabetes. The findings do not support tailoring recommendations on the quality of dietary fat to individual type 2 diabetes genetic risk profiles for the primary prevention of type 2 diabetes, and suggest that dietary fat is associated with the risk of type 2 diabetes across the spectrum of type 2 diabetes genetic risk.The EPIC-InterAct study received funding from the European Union (Integrated Project LSHM-CT-2006-037197 in the Framework Programme 6 of the European Community). We thank all EPIC participants and staff for their contribution to the study. We thank Nicola Kerrison (MRC Epidemiology Unit, University of Cambridge, Cambridge, UK) for managing the data for the InterAct Project. In addition, InterAct investigators acknowledge funding from the following agencies: MT: Health Research Fund (FIS) of the Spanish Ministry of Health; the CIBER en Epidemiología y Salud Pública (CIBERESP), Spain; Murcia Regional Government (N° 6236); JS: JS was supported by a Heisenberg-Professorship (SP716/2-1), a Clinical Research Group (KFO218/1) and a research group (Molecular Nutrition to JS) of the Bundesministerium für Bildung und Forschung (BMBF); YTvdS, JWJB, PHP, IS: Verification of diabetes cases was additionally funded by NL Agency grant IGE05012 and an Incentive Grant from the Board of the UMC Utrecht; HBBdM: Dutch Ministry of Public Health, Welfare and Sports (VWS), Netherlands Cancer Registry (NKR), LK Research Funds, Dutch Prevention Funds, Dutch ZON (Zorg Onderzoek Nederland), World Cancer Research Fund (WCRF), Statistics Netherlands (The Netherlands); MDCL: Health Research Fund (FIS) of the Spanish Ministry of Health; Murcia Regional Government (N° 6236); FLC: Cancer Research UK; PD: Wellcome Trust; LG: Swedish Research Council; GH: The county of Västerbotten; RK: Deutsche Krebshilfe; TJK: Cancer Research UK; KK: Medical Research Council UK, Cancer Research UK; AK: Medical Research Council (Cambridge Lipidomics Biomarker Research Initiative); CN: Health Research Fund (FIS) of the Spanish Ministry of Health; Murcia Regional Government (N° 6236); KO: Danish Cancer Society; OP: Faculty of Health Science, 47 University of Aarhus, Denmark; JRQ: Asturias Regional Government; LRS: Asturias Regional Government; AT: Danish Cancer Society; RT: AIRE-ONLUS Ragusa, AVIS-Ragusa, Sicilian Regional Government; DLvdA, WMMV: Dutch Ministry of Public Health, Welfare and Sports (VWS), Netherlands Cancer Registry (NKR), LK Research Funds, Dutch Prevention Funds, Dutch ZON (Zorg Onderzoek Nederland), World Cancer Research Fund (WCRF), Statistics Netherlands (The Netherlands); MMC: Wellcome Trust (083270/Z/07/Z), MRC (G0601261)

Transcriptional regulatory control of mammalian nephron progenitors revealed by multi-factor cistromic analysis and genetic studies.

Nephron progenitor number determines nephron endowment; a reduced nephron count is linked to the onset of kidney disease. Several transcriptional regulators including Six2, Wt1, Osr1, Sall1, Eya1, Pax2, and Hox11 paralogues are required for specification and/or maintenance of nephron progenitors. However, little is known about the regulatory intersection of these players. Here, we have mapped nephron progenitor-specific transcriptional networks of Six2, Hoxd11, Osr1, and Wt1. We identified 373 multi-factor associated 'regulatory hotspots' around genes closely associated with progenitor programs. To examine their functional significance, we deleted 'hotspot' enhancer elements for Six2 and Wnt4. Removal of the distal enhancer for Six2 leads to a ~40% reduction in Six2 expression. When combined with a Six2 null allele, progeny display a premature depletion of nephron progenitors. Loss of the Wnt4 enhancer led to a significant reduction of Wnt4 expression in renal vesicles and a mildly hypoplastic kidney, a phenotype also enhanced in combination with a Wnt4 null mutation. To explore the regulatory landscape that supports proper target gene expression, we performed CTCF ChIP-seq to identify insulator-boundary regions. One such putative boundary lies between the Six2 and Six3 loci. Evidence for the functional significance of this boundary was obtained by deep sequencing of the radiation-induced Brachyrrhine (Br) mutant allele. We identified an inversion of the Six2/Six3 locus around the CTCF-bound boundary, removing Six2 from its distal enhancer regulation, but placed next to Six3 enhancer elements which support ectopic Six2 expression in the lens where Six3 is normally expressed. Six3 is now predicted to fall under control of the Six2 distal enhancer. Consistent with this view, we observed ectopic Six3 in nephron progenitors. 4C-seq supports the model for Six2 distal enhancer interactions in wild-type and Br/+ mouse kidneys. Together, these data expand our view of the regulatory genome and regulatory landscape underpinning mammalian nephrogenesis

Predicting neurological outcome in comatose patients after cardiac arrest with multiscale deep neural networks

Objective: Electroencephalography (EEG) is an important tool for neurological outcome prediction after cardiac arrest. However, the complexity of continuous EEG data limits timely and accurate interpretation by clinicians. We develop a deep neural network (DNN) model to leverage complex EEG trends for early and accurate assessment of cardiac arrest coma recovery likelihood. Methods: We developed a multiscale DNN combining convolutional neural networks (CNN) and recurrent neural networks (long short-term memory [LSTM]) using EEG and demographic information (age, gender, shockable rhythm) from a multicenter cohort of 1,038 cardiac arrest patients. The CNN learns EEG feature representations while the multiscale LSTM captures short-term and long-term EEG dynamics on multiple time scales. Poor outcome is defined as a Cerebral Performance Category (CPC) score of 3-5 and good outcome as CPC score 1-2 at 3-6 months after cardiac arrest. Performance is evaluated using area under the receiver operating characteristic curve (AUC) and calibration error. Results: Model performance increased with EEG duration, with AUC increasing from 0.83 (95% Confidence Interval [CI] 0.79-0.87 at 12h to 0.91 (95%CI 0.88-0.93) at 66h. Sensitivity of good and poor outcome prediction was 77% and 75% at a specificity of 90%, respectively. Sensitivity of poor outcome was 50% at a specificity of 99%. Predicted probability was well matched to the observation frequency of poor outcomes, with a calibration error of 0.11 [0.09-0.14]. Conclusions: These results demonstrate that incorporating EEG evolution over time improves the accuracy of neurologic outcome prediction for patients with coma after cardiac arrest

Quality of dietary fat and genetic risk of type 2 diabetes: individual participant data meta-analysis.

OBJECTIVE: To investigate whether the genetic burden of type 2 diabetes modifies the association between the quality of dietary fat and the incidence of type 2 diabetes. DESIGN: Individual participant data meta-analysis. DATA SOURCES: Eligible prospective cohort studies were systematically sourced from studies published between January 1970 and February 2017 through electronic searches in major medical databases (Medline, Embase, and Scopus) and discussion with investigators. REVIEW METHODS: Data from cohort studies or multicohort consortia with available genome-wide genetic data and information about the quality of dietary fat and the incidence of type 2 diabetes in participants of European descent was sought. Prospective cohorts that had accrued five or more years of follow-up were included. The type 2 diabetes genetic risk profile was characterized by a 68-variant polygenic risk score weighted by published effect sizes. Diet was recorded by using validated cohort-specific dietary assessment tools. Outcome measures were summary adjusted hazard ratios of incident type 2 diabetes for polygenic risk score, isocaloric replacement of carbohydrate (refined starch and sugars) with types of fat, and the interaction of types of fat with polygenic risk score. RESULTS: Of 102 305 participants from 15 prospective cohort studies, 20 015 type 2 diabetes cases were documented after a median follow-up of 12 years (interquartile range 9.4-14.2). The hazard ratio of type 2 diabetes per increment of 10 risk alleles in the polygenic risk score was 1.64 (95% confidence interval 1.54 to 1.75, I2=7.1%, τ2=0.003). The increase of polyunsaturated fat and total omega 6 polyunsaturated fat intake in place of carbohydrate was associated with a lower risk of type 2 diabetes, with hazard ratios of 0.90 (0.82 to 0.98, I2=18.0%, τ2=0.006; per 5% of energy) and 0.99 (0.97 to 1.00, I2=58.8%, τ2=0.001; per increment of 1 g/d), respectively. Increasing monounsaturated fat in place of carbohydrate was associated with a higher risk of type 2 diabetes (hazard ratio 1.10, 95% confidence interval 1.01 to 1.19, I2=25.9%, τ2=0.006; per 5% of energy). Evidence of small study effects was detected for the overall association of polyunsaturated fat with the risk of type 2 diabetes, but not for the omega 6 polyunsaturated fat and monounsaturated fat associations. Significant interactions between dietary fat and polygenic risk score on the risk of type 2 diabetes (P>0.05 for interaction) were not observed. CONCLUSIONS: These data indicate that genetic burden and the quality of dietary fat are each associated with the incidence of type 2 diabetes. The findings do not support tailoring recommendations on the quality of dietary fat to individual type 2 diabetes genetic risk profiles for the primary prevention of type 2 diabetes, and suggest that dietary fat is associated with the risk of type 2 diabetes across the spectrum of type 2 diabetes genetic risk.The EPIC-InterAct study received funding from the European Union (Integrated Project LSHM-CT-2006-037197 in the Framework Programme 6 of the European Community). We thank all EPIC participants and staff for their contribution to the study. We thank Nicola Kerrison (MRC Epidemiology Unit, University of Cambridge, Cambridge, UK) for managing the data for the InterAct Project. In addition, InterAct investigators acknowledge funding from the following agencies: MT: Health Research Fund (FIS) of the Spanish Ministry of Health; the CIBER en Epidemiología y Salud Pública (CIBERESP), Spain; Murcia Regional Government (N° 6236); JS: JS was supported by a Heisenberg-Professorship (SP716/2-1), a Clinical Research Group (KFO218/1) and a research group (Molecular Nutrition to JS) of the Bundesministerium für Bildung und Forschung (BMBF); YTvdS, JWJB, PHP, IS: Verification of diabetes cases was additionally funded by NL Agency grant IGE05012 and an Incentive Grant from the Board of the UMC Utrecht; HBBdM: Dutch Ministry of Public Health, Welfare and Sports (VWS), Netherlands Cancer Registry (NKR), LK Research Funds, Dutch Prevention Funds, Dutch ZON (Zorg Onderzoek Nederland), World Cancer Research Fund (WCRF), Statistics Netherlands (The Netherlands); MDCL: Health Research Fund (FIS) of the Spanish Ministry of Health; Murcia Regional Government (N° 6236); FLC: Cancer Research UK; PD: Wellcome Trust; LG: Swedish Research Council; GH: The county of Västerbotten; RK: Deutsche Krebshilfe; TJK: Cancer Research UK; KK: Medical Research Council UK, Cancer Research UK; AK: Medical Research Council (Cambridge Lipidomics Biomarker Research Initiative); CN: Health Research Fund (FIS) of the Spanish Ministry of Health; Murcia Regional Government (N° 6236); KO: Danish Cancer Society; OP: Faculty of Health Science, 47 University of Aarhus, Denmark; JRQ: Asturias Regional Government; LRS: Asturias Regional Government; AT: Danish Cancer Society; RT: AIRE-ONLUS Ragusa, AVIS-Ragusa, Sicilian Regional Government; DLvdA, WMMV: Dutch Ministry of Public Health, Welfare and Sports (VWS), Netherlands Cancer Registry (NKR), LK Research Funds, Dutch Prevention Funds, Dutch ZON (Zorg Onderzoek Nederland), World Cancer Research Fund (WCRF), Statistics Netherlands (The Netherlands); MMC: Wellcome Trust (083270/Z/07/Z), MRC (G0601261)

The <i>Six2</i> regulatory landscape is altered in the <i>Br</i> mouse leading to reduced Six2 expression and ectopic Six3 expression in the kidney.

<p>(A) Schematic showing the X-irradiation induced breakpoints and subsequent deletion with inversion that resulted in the <i>Br</i> allele. LE = Lens enhancer (putative), PE = proximal enhancer, DE = distal enhancer. Black box between the <i>Six2</i> and <i>Six3</i> loci represents the predicted boundary. (B) Interaction matrix (top) generated by Hi-C data (Hardison lab hESC Hi-C data, <a href="http://promoter.bx.psu.edu/hi-c/view.php" target="_blank">http://promoter.bx.psu.edu/hi-c/view.php</a>). Genomic view showing Six2 ChIP-seq, CTCF-NP ChIP-seq, and 4C-seq data (bottom). The region inverted in the <i>Br</i> allele is highlighted. Dashed square indicates a predicted TAD boundary element that lies between <i>Six3</i> and <i>Six2</i> loci [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1007181#pgen.1007181.ref070" target="_blank">70</a>]. (C) qPCR showing the relative expression levels of <i>Six2</i> and <i>Six3</i> in E13.5 kidneys of the indicated genotype. (D) E13.5 kidneys of the indicated genotype were sectioned and immunostained for Six2 and Six3. Arrow points to the low level Six3 expression in nephron progenitors. (E) Immunostaining for Six2, Six3, cytokeratin (CK), and DAPI in E11.5 kidneys of the indicated genotype.</p

Deletion of the <i>Six2</i> distal enhancer leads to reduction in <i>Six2</i> levels and concomitant loss of a <i>Six2</i> allele results in severe renal hypoplasia.

<p>(A) Schematic of the <i>Six2</i> locus showing the location of the proximal (PE) and distal (DE) enhancer elements. The <i>DE</i> was targeted for deletion using CRISPR/Cas9 and the resulting Cas9-mediated deletion of the <i>Six2-DE</i> is shown. (B) Brightfield images of whole urogenital systems from E16.5 embryos resulting from <i>Six2</i>^{<i>∆DE/+</i>} matings or <i>Six2</i>^<i>CE/+</i> x <i>Six2</i>^{<i>∆DE/+</i>} crosses. (C) Immunostaining for Wt1 to identify nephron progenitors and podocytes, LTL (<i>Lotus tetragonolobus</i> lectin) to mark proximal tubules, and Cdh1 to show the collecting duct network of kidneys associated with (B). (D) Immunostaining for Six2 to identify nephron progenitors in kidneys associated with (B). (E) Box plots showing results of qPCR for <i>Six2</i> and <i>Pax2</i> (normalized to <i>GAPDH</i>) from nephron progenitors (NP) and nephron progenitor-depleted cortex. Genotypes and number of samples analyzed are shown. (F) Samples from <i>Six2</i>^{<i>GCE/GCE</i>} were compared to <i>Six2</i>^{<i>∆DE/GCE</i>} collected at early stages of kidney development and immunostained with Six2 to mark the nephron progenitors, Pax8 to identify differentiating structures (Pax8 antibody appears to cross react with Pax2 as seen by expression in Ecad+ collecting duct), and Ecad to mark epithelial structures.</p

Six2, Hoxd11, Osr1, and Wt1 binding sites are enriched near nephron progenitor specific genes and those associated with differentiation programs.

<p>(A) Scatter plots show gene expression profiles and correlation of the Six2GFP+ versus the Six2GFP- RNA-seq from E16.5 mouse kidney cortex. Specific genes for each category are highlighted in orange (Six2+) or blue (Six2-). TPM = Transcripts Per Kilobase Million. (B) Scatter plots show gene expression profiles and correlation of RNA-seq from E16.5 Cited1RFP+ cells versus P2 Six2GFP+ cells. Genes specific to each population are highlighted in red (Cited1RFP+) or green (Six2GFP+). Examples of specific genes are listed and highlighted on the plot. (C) Barplots show p-values indicating enrichment of Six2, Hoxd11, Osr1, and Wt1 binding sites, as well as regulatory hotspots (Six2/Hoxd11/Osr1/Wt1 overlapping sites) in genes that are specific to the Six2+ cortex fraction, specific to the Six2- cortex, enriched in self-renewing nephron progenitors, or enriched in differentiating nephron progenitors, respectively. The regulatory domain was defined as +/-500 kb from transcription start site. TFBS = transcription factor binding site. ‘Obs.’, number of peaks associated with genes annotated with corresponding term; ‘Exp.’, number of peaks expected to be associated with genes annotated with corresponding term by chance. Fold represents the fold enrichment or expected values. (D) Bar plots showing the percentage of total genes for each condition (x-axis) that falls into each category of 1) nephron progenitor (NP) enriched, 2) enriched in self-renewing nephron progenitors, or 3) enriched in differentiating progenitors.</p

Regulatory hotspots in nephron progenitors defined by co-binding of Six2, Hoxd11, Osr1 and Wt1.

<p>(A) Heatmap shows significance of pairwise overlap between transcription factor binding sites (left, represented by binomial -log10 p-value) or between assigned target genes (right, represented by ratio). TFBS = transcription factor binding site. (B) Venn diagram shows the overlap of Six2, Hoxd11, Osr1, and Wt1 binding sites (left) and target genes (right). The 4-way overlapping sites were defined as the ‘regulatory hotspots’. The 4-way overlapping target genes were defined as ‘core targets’. (C) Barplots show result of gene ontology (GO) analysis on the ‘regulatory hotspots’ (left). Examples of ‘core targets’ known to have roles in the nephron progenitors and their differentiation are listed (right). (D) Genome browser view of Six2, Hoxd11, Osr1, and Wt1 ChIP-seq signals at the ‘regulatory hotspots’ (shadow area) near <i>Six2</i> and <i>Wnt4</i>. (E) Six2 immunoprecipitation from E16.5 kidney nuclear extracts. Western blot was probed with antibodies to Six2, Hoxd11, and Wt1 to identify protein complexes.</p