A single-cell time-lapse of mouse prenatal development from gastrula to birth.

The house mouse (Mus musculus) is an exceptional model system, combining genetic tractability with close evolutionary affinity to humans1,2. Mouse gestation lasts only 3 weeks, during which the genome orchestrates the astonishing transformation of a single-cell zygote into a free-living pup composed of more than 500 million cells. Here, to establish a global framework for exploring mammalian development, we applied optimized single-cell combinatorial indexing3 to profile the transcriptional states of 12.4 million nuclei from 83 embryos, precisely staged at 2- to 6-hour intervals spanning late gastrulation (embryonic day 8) to birth (postnatal day 0). From these data, we annotate hundreds of cell types and explore the ontogenesis of the posterior embryo during somitogenesis and of kidney, mesenchyme, retina and early neurons. We leverage the temporal resolution and sampling depth of these whole-embryo snapshots, together with published data4-8 from earlier timepoints, to construct a rooted tree of cell-type relationships that spans the entirety of prenatal development, from zygote to birth. Throughout this tree, we systematically nominate genes encoding transcription factors and other proteins as candidate drivers of the in vivo differentiation of hundreds of cell types. Remarkably, the most marked temporal shifts in cell states are observed within one hour of birth and presumably underlie the massive physiological adaptations that must accompany the successful transition of a mammalian fetus to life outside the womb.

Single-cell, whole-embryo phenotyping of mammalian developmental disorders.

Mouse models are a critical tool for studying human diseases, particularly developmental disorders1. However, conventional approaches for phenotyping may fail to detect subtle defects throughout the developing mouse2. Here we set out to establish single-cell RNA sequencing of the whole embryo as a scalable platform for the systematic phenotyping of mouse genetic models. We applied combinatorial indexing-based single-cell RNA sequencing3 to profile 101 embryos of 22 mutant and 4 wild-type genotypes at embryonic day 13.5, altogether profiling more than 1.6 million nuclei. The 22 mutants represent a range of anticipated phenotypic severities, from established multisystem disorders to deletions of individual regulatory regions4,5. We developed and applied several analytical frameworks for detecting differences in composition and/or gene expression across 52 cell types or trajectories. Some mutants exhibit changes in dozens of trajectories whereas others exhibit changes in only a few cell types. We also identify differences between widely used wild-type strains, compare phenotyping of gain- versus loss-of-function mutants and characterize deletions of topological associating domain boundaries. Notably, some changes are shared among mutants, suggesting that developmental pleiotropy might be 'decomposable' through further scaling of this approach. Overall, our findings show how single-cell profiling of whole embryos can enable the systematic molecular and cellular phenotypic characterization of mouse mutants with unprecedented breadth and resolution.

Embryo model completes gastrulation to neurulation and organogenesis.

Embryonic stem (ES) cells can undergo many aspects of mammalian embryogenesis in vitro1-5, but their developmental potential is substantially extended by interactions with extraembryonic stem cells, including trophoblast stem (TS) cells, extraembryonic endoderm stem (XEN) cells and inducible XEN (iXEN) cells6-11. Here we assembled stem cell-derived embryos in vitro from mouse ES cells, TS cells and iXEN cells and showed that they recapitulate the development of whole natural mouse embryo in utero up to day 8.5 post-fertilization. Our embryo model displays headfolds with defined forebrain and midbrain regions and develops a beating heart-like structure, a trunk comprising a neural tube and somites, a tail bud containing neuromesodermal progenitors, a gut tube, and primordial germ cells. This complete embryo model develops within an extraembryonic yolk sac that initiates blood island development. Notably, we demonstrate that the neurulating embryo model assembled from Pax6-knockout ES cells aggregated with wild-type TS cells and iXEN cells recapitulates the ventral domain expansion of the neural tube that occurs in natural, ubiquitous Pax6-knockout embryos. Thus, these complete embryoids are a powerful in vitro model for dissecting the roles of diverse cell lineages and genes in development. Our results demonstrate the self-organization ability of ES cells and two types of extraembryonic stem cells to reconstitute mammalian development through and beyond gastrulation to neurulation and early organogenesis.

Systematic integrated analysis of genetic and epigenetic variation in diabetic kidney disease.

Poor metabolic control and host genetic predisposition are critical for diabetic kidney disease (DKD) development. The epigenome integrates information from sequence variations and metabolic alterations. Here, we performed a genome-wide methylome association analysis in 500 subjects with DKD from the Chronic Renal Insufficiency Cohort for DKD phenotypes, including glycemic control, albuminuria, kidney function, and kidney function decline. We show distinct methylation patterns associated with each phenotype. We define methylation variations that are associated with underlying nucleotide variations (methylation quantitative trait loci) and show that underlying genetic variations are important drivers of methylation changes. We implemented Bayesian multitrait colocalization analysis (moloc) and summary data-based Mendelian randomization to systematically annotate genomic regions that show association with kidney function, methylation, and gene expression. We prioritized 40 loci, where methylation and gene-expression changes likely mediate the genotype effect on kidney disease development. Functional annotation suggested the role of inflammation, specifically, apoptotic cell clearance and complement activation in kidney disease development. Our study defines methylation changes associated with DKD phenotypes, the key role of underlying genetic variations driving methylation variations, and prioritizes methylome and gene-expression changes that likely mediate the genotype effect on kidney disease pathogenesis.

Loss of IL-27Rα Results in Enhanced Tubulointerstitial Fibrosis Associated with Elevated Th17 Responses.

Clinical and experimental studies have established that immune cells such as alternatively activated (M2) macrophages and Th17 cells play a role in the progression of chronic kidney disease, but the endogenous pathways that limit these processes are not well understood. The cytokine IL-27 has been shown to limit immune-mediated pathology in other systems by effects on these cell types, but this has not been thoroughly investigated in the kidney. Unilateral ureteral obstruction was performed on wild-type and IL-27Rα-/- mice. After 2 wk, kidneys were extracted, and the degree of injury was measured by hydroxyproline assay and quantification of neutrophil gelatinase-associated lipocalin mRNA. Immune cell infiltrate was evaluated by immunohistochemistry and flow cytometry. An anti-IL-17A mAb was subsequently administered to IL-27Rα-/- mice every 2 d from day of surgery with evaluation as described after 2 wk. After unilateral ureteral obstruction, IL-27 deficiency resulted in increased tissue injury and collagen deposition associated with higher levels of chemokine mRNA and increased numbers of M2 macrophages. Loss of the IL-27Rα led to increased infiltration of activated CD4+ T cells that coproduced IL-17A and TNF-α, and blockade of IL-17A partially ameliorated kidney injury. Patients with chronic kidney disease had elevated serum levels of IL-27 and IL-17A, whereas expression of transcripts for the IL-27RA and the IL-17RA in the tubular epithelial cells of patients with renal fibrosis correlated with disease severity. These data suggest that endogenous IL-27 acts at several points in the inflammatory cascade to limit the magnitude of immune-mediated damage to the kidney.

Jagged1/Notch2 controls kidney fibrosis via Tfam-mediated metabolic reprogramming.

While Notch signaling has been proposed to play a key role in fibrosis, the direct molecular pathways targeted by Notch signaling and the precise ligand and receptor pair that are responsible for kidney disease remain poorly defined. In this study, we found that JAG1 and NOTCH2 showed the strongest correlation with the degree of interstitial fibrosis in a genome-wide expression analysis of a large cohort of human kidney samples. Transcript analysis of mouse kidney disease models, including folic-acid (FA)-induced nephropathy, unilateral ureteral obstruction (UUO), or apolipoprotein L1 (APOL1)-associated kidney disease, indicated that Jag1 and Notch2 levels were higher in all analyzed kidney fibrosis models. Mice with tubule-specific deletion of Jag1 or Notch2 (Kspcre/Jag1flox/flox and Kspcre/Notch2flox/flox) had no kidney-specific alterations at baseline but showed protection from FA-induced kidney fibrosis. Tubule-specific genetic deletion of Notch1 and global knockout of Notch3 had no effect on fibrosis. In vitro chromatin immunoprecipitation experiments and genome-wide expression studies identified the mitochondrial transcription factor A (Tfam) as a direct Notch target. Re-expression of Tfam in tubule cells prevented Notch-induced metabolic and profibrotic reprogramming. Tubule-specific deletion of Tfam resulted in fibrosis. In summary, Jag1 and Notch2 play a key role in kidney fibrosis development by regulating Tfam expression and metabolic reprogramming.

Genomic integration of ERRγ-HNF1ß regulates renal bioenergetics and prevents chronic kidney disease.

Mitochondrial dysfunction is increasingly recognized as a critical determinant of both hereditary and acquired kidney diseases. However, it remains poorly understood how mitochondrial metabolism is regulated to support normal kidney function and how its dysregulation contributes to kidney disease. Here, we show that the nuclear receptor estrogen-related receptor gamma (ERRγ) and hepatocyte nuclear factor 1 beta (HNF1ß) link renal mitochondrial and reabsorptive functions through coordinated epigenomic programs. ERRγ directly regulates mitochondrial metabolism but cooperatively controls renal reabsorption via convergent binding with HNF1ß. Deletion of ERRγ in renal epithelial cells (RECs), in which it is highly and specifically expressed, results in severe renal energetic and reabsorptive dysfunction and progressive renal failure that recapitulates phenotypes of animals and patients with HNF1ß loss-of-function gene mutations. Moreover, ERRγ expression positively correlates with renal function and is decreased in patients with chronic kidney disease (CKD). REC-ERRγ KO mice share highly overlapping renal transcriptional signatures with human patients with CKD. Together these findings reveal a role for ERRγ in directing independent and HNF1ß-integrated programs for energy production and use essential for normal renal function and the prevention of kidney disease.

Phenome-wide association analysis suggests the APOL1 linked disease spectrum primarily drives kidney-specific pathways.

The relationship between commonly occurring genetic variants (G1 and G2) in the APOL1 gene in African Americans and different disease traits, such as kidney disease, cardiovascular disease, and pre-eclampsia, remains the subject of controversy. Here we took a genotype-first approach, a phenome-wide association study, to define the spectrum of phenotypes associated with APOL1 high-risk variants in 1,837 African American participants of Penn Medicine Biobank and 4,742 African American participants of Vanderbilt BioVU. In the Penn Medicine Biobank, outpatient creatinine measurement-based estimated glomerular filtration rate and multivariable regression models were used to evaluate the association between high-risk APOL1 status and renal outcomes. In meta-analysis of both cohorts, the strongest phenome-wide association study associations were for the high-risk APOL1 variants and diagnoses codes were highly significant for "kidney dialysis" (odds ratio 3.75) and "end stage kidney disease" (odds ratio 3.42). A number of phenotypes were associated with APOL1 high-risk genotypes in an analysis adjusted only for demographic variables. However, no associations were detected with non-renal phenotypes after controlling for chronic/end stage kidney disease status. Using calculated estimated glomerular filtration rate -based phenotype analysis in the Penn Medicine Biobank, APOL1 high-risk status was associated with prevalent chronic/end stage kidney disease /kidney transplant (odds ratio 2.27, 95% confidence interval 1.67-3.08). In high-risk participants, the estimated glomerular filtration rate was 15.4 mL/min/1.73m2; significantly lower than in low-risk participants. Thus, although APOL1 high-risk variants are associated with a range of phenotypes, the risks for other associated phenotypes appear much lower and in our dataset are driven by a primary effect on renal disease.

Genetic-Variation-Driven Gene-Expression Changes Highlight Genes with Important Functions for Kidney Disease.

Chronic kidney disease (CKD) is a complex gene-environmental disease affecting close to 10% of the US population. Genome-wide association studies (GWASs) have identified sequence variants, localized to non-coding genomic regions, associated with kidney function. Despite these robust observations, the mechanism by which variants lead to CKD remains a critical unanswered question. Expression quantitative trait loci (eQTL) analysis is a method to identify genetic variation associated with gene expression changes in specific tissue types. We hypothesized that an integrative analysis combining CKD GWAS and kidney eQTL results can identify candidate genes for CKD. We performed eQTL analysis by correlating genotype with RNA-seq-based gene expression levels in 96 human kidney samples. Applying stringent statistical criteria, we detected 1,886 genes whose expression differs with the sequence variants. Using direct overlap and Bayesian methods, we identified new potential target genes for CKD. With respect to one of the target genes, lysosomal beta A mannosidase (MANBA), we observed that genetic variants associated with MANBA expression in the kidney showed statistically significant colocalization with variants identified in CKD GWASs, indicating that MANBA is a potential target gene for CKD. The expression of MANBA was significantly lower in kidneys of subjects with risk alleles. Suppressing manba expression in zebrafish resulted in renal tubule defects and pericardial edema, phenotypes typically induced by kidney dysfunction. Our analysis shows that gene-expression changes driven by genetic variation in the kidney can highlight potential new target genes for CKD development.

Genome-Wide Association Study of Diabetic Kidney Disease Highlights Biology Involved in Glomerular Basement Membrane Collagen.

BACKGROUND: Although diabetic kidney disease demonstrates both familial clustering and single nucleotide polymorphism heritability, the specific genetic factors influencing risk remain largely unknown. METHODS: To identify genetic variants predisposing to diabetic kidney disease, we performed genome-wide association study (GWAS) analyses. Through collaboration with the Diabetes Nephropathy Collaborative Research Initiative, we assembled a large collection of type 1 diabetes cohorts with harmonized diabetic kidney disease phenotypes. We used a spectrum of ten diabetic kidney disease definitions based on albuminuria and renal function. RESULTS: Our GWAS meta-analysis included association results for up to 19,406 individuals of European descent with type 1 diabetes. We identified 16 genome-wide significant risk loci. The variant with the strongest association (rs55703767) is a common missense mutation in the collagen type IV alpha 3 chain (COL4A3) gene, which encodes a major structural component of the glomerular basement membrane (GBM). Mutations in COL4A3 are implicated in heritable nephropathies, including the progressive inherited nephropathy Alport syndrome. The rs55703767 minor allele (Asp326Tyr) is protective against several definitions of diabetic kidney disease, including albuminuria and ESKD, and demonstrated a significant association with GBM width; protective allele carriers had thinner GBM before any signs of kidney disease, and its effect was dependent on glycemia. Three other loci are in or near genes with known or suggestive involvement in this condition (BMP7) or renal biology (COLEC11 and DDR1). CONCLUSIONS: The 16 diabetic kidney disease-associated loci may provide novel insights into the pathogenesis of this condition and help identify potential biologic targets for prevention and treatment.

Cytosine methylation predicts renal function decline in American Indians.

Diabetic nephropathy accounts for most of the excess mortality in individuals with diabetes, but the molecular mechanisms by which nephropathy develops are largely unknown. Here we tested cytosine methylation levels at 397,063 genomic CpG sites for association with decline in the estimated glomerular filtration rate (eGFR) over a six year period in 181 diabetic Pima Indians. Methylation levels at 77 sites showed significant association with eGFR decline after correction for multiple comparisons. A model including methylation level at two probes (cg25799291 and cg22253401) improved prediction of eGFR decline in addition to baseline eGFR and the albumin to creatinine ratio with the percent of variance explained significantly improving from 23.1% to 42.2%. Cg22253401 was also significantly associated with eGFR decline in a case-control study derived from the Chronic Renal Insufficiency Cohort. Probes at which methylation significantly associated with eGFR decline were localized to gene regulatory regions and enriched for genes with metabolic functions and apoptosis. Three of the 77 probes that were associated with eGFR decline in blood samples showed directionally consistent and significant association with fibrosis in microdissected human kidney tissue, after correction for multiple comparisons. Thus, cytosine methylation levels may provide biomarkers of disease progression in diabetic nephropathy and epigenetic variations contribute to the development of diabetic kidney disease.

The rice TAL effector-dependent resistance protein XA10 triggers cell death and calcium depletion in the endoplasmic reticulum.

The recognition between disease resistance (R) genes in plants and their cognate avirulence (Avr) genes in pathogens can produce a hypersensitive response of localized programmed cell death. However, our knowledge of the early signaling events of the R gene-mediated hypersensitive response in plants remains limited. Here, we report the cloning and characterization of Xa10, a transcription activator-like (TAL) effector-dependent R gene for resistance to bacterial blight in rice (Oryza sativa). Xa10 contains a binding element for the TAL effector AvrXa10 (EBEAvrXa10) in its promoter, and AvrXa10 specifically induces Xa10 expression. Expression of Xa10 induces programmed cell death in rice, Nicotiana benthamiana, and mammalian HeLa cells. The Xa10 gene product XA10 localizes as hexamers in the endoplasmic reticulum (ER) and is associated with ER Ca(2+) depletion in plant and HeLa cells. XA10 variants that abolish programmed cell death and ER Ca(2+) depletion in N. benthamiana and HeLa cells also abolish disease resistance in rice. We propose that XA10 is an inducible, intrinsic terminator protein that triggers programmed cell death by a conserved mechanism involving disruption of the ER and cellular Ca(2+) homeostasis.

Genetic Effects on Fine-Grained Human Cortical Regionalization.

Various brain structural and functional features such as cytoarchitecture, topographic mapping, gyral/sulcal anatomy, and anatomical and functional connectivity have been used in human brain parcellation. However, the fine-grained intrinsic genetic architecture of the cortex remains unknown. In the present study, we parcellated specific regions of the cortex into subregions based on genetic correlations (i.e., shared genetic influences) between the surface area of each pair of cortical locations within the seed region. The genetic correlations were estimated by comparing the correlations of the surface area between monozygotic and dizygotic twins using bivariate twin models. Our genetic subdivisions of diverse brain regions were reproducible across 2 independent datasets and corresponded closely to fine-grained functional specializations. Furthermore, subregional genetic correlation profiles were generally consistent with functional connectivity patterns. Our findings indicate that the magnitude of the genetic covariance in brain anatomy could be used to delineate the boundaries of functional subregions of the brain and may be of value in the next generation human brain atlas.

Engineering low phorbol ester Jatropha curcas seed by intercepting casbene biosynthesis.

KEY MESSAGE: Casbene is a precursor to phorbol esters and down-regulating casbene synthase effectively reduces phorbol ester biosynthesis. Seed-specific reduction of phorbol ester (PE) helps develop Jatropha seed cake for animal nutrition. Phorbol esters (PEs) are diterpenoids present in some Euphorbiaceae family members like Jatropha curcas L. (Jatropha), a tropical shrub yielding high-quality oil suitable as feedstock for biodiesel and bio jet fuel. Jatropha seed contains up to 40 % of oil and can produce oil together with cake containing high-quality proteins. However, skin-irritating and cancer-promoting PEs make Jatropha cake meal unsuitable for animal nutrition and also raise some safety and environmental concerns on its planting and processing. Two casbene synthase gene (JcCASA163 and JcCASD168) homologues were cloned from Jatropha genome and both genes were highly expressed during seed development. In vitro functional analysis proved casbene synthase activity of JcCASA163 in converting geranylgeranyl diphosphate into casbene which has been speculated to be the precursor to PEs. A seed-specific promoter driving inverted repeats for RNAi interference targeting at either JcCASA163 or both genes could effectively down-regulate casbene synthase gene expression with concurrent marked reduction of PE level (by as much as 85 %) in seeds with no pleiotropic effects observed. Such engineered low PE in seed was heritable and co-segregated with the transgene. Our work implicated casbene synthase in Jatropha PE biosynthesis and provided evidence for casbene being the precursor for PEs. The success in reducing seed PE content through down-regulation of casbene synthase demonstrates the feasibility of intercepting PE biosynthesis in Jatropha seed to help address safety concerns on Jatropha plantation and seed processing and facilitate use of its seed protein for animal nutrition.

HMDD v2.0: a database for experimentally supported human microRNA and disease associations.

The Human microRNA Disease Database (HMDD; available via the Web site at http://cmbi.bjmu.edu.cn/hmdd and http://202.38.126.151/hmdd/tools/hmdd2.html) is a collection of experimentally supported human microRNA (miRNA) and disease associations. Here, we describe the HMDD v2.0 update that presented several novel options for users to facilitate exploration of the data in the database. In the updated database, miRNA-disease association data were annotated in more details. For example, miRNA-disease association data from genetics, epigenetics, circulating miRNAs and miRNA-target interactions were integrated into the database. In addition, HMDD v2.0 presented more data that were generated based on concepts derived from the miRNA-disease association data, including disease spectrum width of miRNAs and miRNA spectrum width of human diseases. Moreover, we provided users a link to download all the data in the HMDD v2.0 and a link to submit novel data into the database. Meanwhile, we also maintained the old version of HMDD. By keeping data sets up-to-date, HMDD should continue to serve as a valuable resource for investigating the roles of miRNAs in human disease.

LncRNADisease: a database for long-non-coding RNA-associated diseases.

In this article, we describe a long-non-coding RNA (lncRNA) and disease association database (LncRNADisease), which is publicly accessible at http://cmbi.bjmu.edu.cn/lncrnadisease. In recent years, a large number of lncRNAs have been identified and increasing evidence shows that lncRNAs play critical roles in various biological processes. Therefore, the dysfunctions of lncRNAs are associated with a wide range of diseases. It thus becomes important to understand lncRNAs' roles in diseases and to identify candidate lncRNAs for disease diagnosis, treatment and prognosis. For this purpose, a high-quality lncRNA-disease association database would be extremely beneficial. Here, we describe the LncRNADisease database that collected and curated approximately 480 entries of experimentally supported lncRNA-disease associations, including 166 diseases. LncRNADisease also curated 478 entries of lncRNA interacting partners at various molecular levels, including protein, RNA, miRNA and DNA. Moreover, we annotated lncRNA-disease associations with genomic information, sequences, references and species. We normalized the disease name and the type of lncRNA dysfunction and provided a detailed description for each entry. Finally, we developed a bioinformatic method to predict novel lncRNA-disease associations and integrated the method and the predicted associated diseases of 1564 human lncRNAs into the database.

A Large-Scale Analysis of the Relationship of Synonymous SNPs Changing MicroRNA Regulation with Functionality and Disease.

Historically, owing to not changing amino acid composition of protein sequences, synonymous mutations are commonly assumed to be neutral during evolution and therefore have no effect on the phenotype and disease. Here, based on observations from large-scale analysis of genomic data, we predicted the putative synonymous SNPs that could result in functional consequences and disease risk through changing the microRNA-mediated gene regulation. We found that nearly half of the synonymous SNPs could affect protein expression by changing microRNA regulation in human genome and these SNPs significantly prefer to be associated with human diseases and traits. The synonymous SNPs changing microRNA-mediated gene regulation tend to be more under recent positive selection, prefer to affect gene expression, and implicate in human disease. We conclude that the miRNA-mediated regulation changes could be a potential mechanism for the contributions of synonymous SNPs to protein functions and disease risks.

The relationship between rational drug design and drug side effects.

Previous analysis of systems pharmacology has revealed a tendency of rational drug design in the pharmaceutical industry. The targets of new drugs tend to be close with the corresponding disease genes in the biological networks. However, it remains unclear whether the rational drug design introduces disadvantages, i.e. side effects. Therefore, it is important to dissect the relationship between rational drug design and drug side effects. Based on a recently released drug side effect database, SIDER, here we analyzed the relationship between drug side effects and the rational drug design. We revealed that the incidence drug side effect is significantly associated with the network distance of drug targets and diseases genes. Drugs with the distances of three or four have the smallest incidence of side effects, whereas drugs with the distances of more than four or smaller than three show significantly greater incidence of side effects. Furthermore, protein drugs and small molecule drugs show significant differences. Drugs hitting membrane targets and drugs hitting cytoplasm targets also show differences. Failure drugs because of severe side effects show smaller network distances than approved drugs. These results suggest that researchers should be prudent on rationalizing the drug design. Too small distances between drug targets and diseases genes may not always be advantageous for rational design for drug discovery.

Multi-condition and multi-modal temporal profile inference during mouse embryonic development.

The emergence of single-cell time-series datasets enables modeling of changes in various types of cellular profiles over time. However, due to the disruptive nature of single-cell measurements, it is impossible to capture the full temporal trajectory of a particular cell. Furthermore, single-cell profiles can be collected at mismatched time points across different conditions (e.g., sex, batch, disease) and data modalities (e.g., scRNA-seq, scATAC-seq), which makes modeling challenging. Here we propose a joint modeling framework, Sunbear, for integrating multi-condition and multi-modal single-cell profiles across time. Sunbear can be used to impute single-cell temporal profile changes, align multi-dataset and multi-modal profiles across time, and extrapolate single-cell profiles in a missing modality. We applied Sunbear to reveal sex-biased transcription during mouse embryonic development and predict dynamic relationships between epigenetic priming and transcription for cells in which multi-modal profiles are unavailable. Sunbear thus enables the projection of single-cell time-series snapshots to multi-modal and multi-condition views of cellular trajectories.