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Mammalian organs exhibit distinct physiology, disease susceptibility and injury responses between the sexes. In the mouse kidney, sexually dimorphic gene activity maps predominantly to proximal tubule (PT) segments. Bulk RNA-seq data demonstrated sex differences were established from 4 and 8 weeks after birth under gonadal control. Hormone injection studies and genetic removal of androgen and estrogen receptors demonstrated androgen receptor (AR) mediated regulation of gene activity in PT cells as the regulatory mechanism. Interestingly, caloric restriction feminizes the male kidney. Single-nuclear multiomic analysis identified putative cis-regulatory regions and cooperating factors mediating PT responses to AR activity in the mouse kidney. In the human kidney, a limited set of genes showed conserved sex-linked regulation while analysis of the mouse liver underscored organ-specific differences in the regulation of sexually dimorphic gene expression. These findings raise interesting questions on the evolution, physiological significance, and disease and metabolic linkage, of sexually dimorphic gene activity.

Mammalian organs exhibit distinct physiology, disease susceptibility, and injury responses between the sexes. In the mouse kidney, sexually dimorphic gene activity maps predominantly to proximal tubule (PT) segments. Bulk RNA sequencing (RNA-seq) data demonstrated that sex differences were established from 4 and 8 weeks after birth under gonadal control. Hormone injection studies and genetic removal of androgen and estrogen receptors demonstrated androgen receptor (AR)-mediated regulation of gene activity in PT cells as the regulatory mechanism. Interestingly, caloric restriction feminizes the male kidney. Single-nuclear multiomic analysis identified putative cis-regulatory regions and cooperating factors mediating PT responses to AR activity in the mouse kidney. In the human kidney, a limited set of genes showed conserved sex-linked regulation, whereas analysis of the mouse liver underscored organ-specific differences in the regulation of sexually dimorphic gene expression. These findings raise interesting questions on the evolution, physiological significance, disease, and metabolic linkage of sexually dimorphic gene activity.

Supercharge the value of your machine learning models by building scalable and robust solutions that can serve them in production environmentsKey FeaturesExplore hyperparameter optimization and model management toolsLearn object-oriented programming and functional programming in Python to build your own ML libraries and packagesExplore key ML engineering patterns like microservices and the Extract Transform Machine Learn (ETML) pattern with use casesBook DescriptionMachine learning engineering is a thriving discipline at the interface of software development and machine learning. This book will help developers working with machine learning and Python to put their knowledge to work and create high-quality machine learning products and services. Machine Learning Engineering with Python takes a hands-on approach to help you get to grips with essential technical concepts, implementation patterns, and development methodologies to have you up and running in no time. You'll begin by understanding key steps of the machine learning development life cycle before moving on to practical illustrations and getting to grips with building and deploying robust machine learning solutions. As you advance, you'll explore how to create your own toolsets for training and deployment across all your projects in a consistent way. The book will also help you get hands-on with deployment architectures and discover methods for scaling up your solutions while building a solid understanding of how to use cloud-based tools effectively. Finally, you'll work through examples to help you solve typical business problems. By the end of this book, you'll be able to build end-to-end machine learning services using a variety of techniques and design your own processes for consistently performant machine learning engineering.What you will learnFind out what an effective ML engineering process looks likeUncover options for automating training and deployment and learn how to use themDiscover how to build yo

Supercharge the value of your machine learning models by building scalable and robust solutions that can serve them in production environmentsKey FeaturesExplore hyperparameter optimization and model management toolsLearn object-oriented programming and functional programming in Python to build your own ML libraries and packagesExplore key ML engineering patterns like microservices and the Extract Transform Machine Learn (ETML) pattern with use casesBook DescriptionMachine learning engineering is a thriving discipline at the interface of software development and machine learning. This book will help developers working with machine learning and Python to put their knowledge to work and create high-quality machine learning products and services. Machine Learning Engineering with Python takes a hands-on approach to help you get to grips with essential technical concepts, implementation patterns, and development methodologies to have you up and running in no time. You'll begin by understanding key steps of the machine learning development life cycle before moving on to practical illustrations and getting to grips with building and deploying robust machine learning solutions. As you advance, you'll explore how to create your own toolsets for training and deployment across all your projects in a consistent way. The book will also help you get hands-on with deployment architectures and discover methods for scaling up your solutions while building a solid understanding of how to use cloud-based tools effectively. Finally, you'll work through examples to help you solve typical business problems. By the end of this book, you'll be able to build end-to-end machine learning services using a variety of techniques and design your own processes for consistently performant machine learning engineering.What you will learnFind out what an effective ML engineering process looks likeUncover options for automating training and deployment and learn how to use themDiscover how to build yo

Supercharge the value of your machine learning models by building scalable and robust solutions that can serve them in production environmentsKey FeaturesExplore hyperparameter optimization and model management toolsLearn object-oriented programming and functional programming in Python to build your own ML libraries and packagesExplore key ML engineering patterns like microservices and the Extract Transform Machine Learn (ETML) pattern with use casesBook DescriptionMachine learning engineering is a thriving discipline at the interface of software development and machine learning. This book will help developers working with machine learning and Python to put their knowledge to work and create high-quality machine learning products and services. Machine Learning Engineering with Python takes a hands-on approach to help you get to grips with essential technical concepts, implementation patterns, and development methodologies to have you up and running in no time. You'll begin by understanding key steps of the machine learning development life cycle before moving on to practical illustrations and getting to grips with building and deploying robust machine learning solutions. As you advance, you'll explore how to create your own toolsets for training and deployment across all your projects in a consistent way. The book will also help you get hands-on with deployment architectures and discover methods for scaling up your solutions while building a solid understanding of how to use cloud-based tools effectively. Finally, you'll work through examples to help you solve typical business problems. By the end of this book, you'll be able to build end-to-end machine learning services using a variety of techniques and design your own processes for consistently performant machine learning engineering.What you will learnFind out what an effective ML engineering process looks likeUncover options for automating training and deployment and learn how to use themDiscover how to build yo

Supercharge the value of your machine learning models by building scalable and robust solutions that can serve them in production environmentsKey FeaturesExplore hyperparameter optimization and model management toolsLearn object-oriented programming and functional programming in Python to build your own ML libraries and packagesExplore key ML engineering patterns like microservices and the Extract Transform Machine Learn (ETML) pattern with use casesBook DescriptionMachine learning engineering is a thriving discipline at the interface of software development and machine learning. This book will help developers working with machine learning and Python to put their knowledge to work and create high-quality machine learning products and services. Machine Learning Engineering with Python takes a hands-on approach to help you get to grips with essential technical concepts, implementation patterns, and development methodologies to have you up and running in no time. You'll begin by understanding key steps of the machine learning development life cycle before moving on to practical illustrations and getting to grips with building and deploying robust machine learning solutions. As you advance, you'll explore how to create your own toolsets for training and deployment across all your projects in a consistent way. The book will also help you get hands-on with deployment architectures and discover methods for scaling up your solutions while building a solid understanding of how to use cloud-based tools effectively. Finally, you'll work through examples to help you solve typical business problems. By the end of this book, you'll be able to build end-to-end machine learning services using a variety of techniques and design your own processes for consistently performant machine learning engineering.What you will learnFind out what an effective ML engineering process looks likeUncover options for automating training and deployment and learn how to use themDiscover how to build yo

Analysis of kidney disease-causing genes and pathology resulting from systemic diseases highlight the importance of the kidney's filtering system, the renal corpuscles. To elucidate the developmental processes that establish the renal corpuscle, we performed single-nucleus droplet-based sequencing of the human fetal kidney. This enabled the identification of nephron, interstitial, and vascular cell types that together generate the renal corpuscles. Trajectory analysis identified transient developmental gene expression, predicting precursors or mature podocytes express FBLN2, BMP4, or NTN4, in conjunction with recruitment, differentiation, and modeling of vascular and mesangial cell types into a functional filter. In vitro studies provide evidence that these factors exhibit angiogenic or mesangial recruiting and inductive properties consistent with a key organizing role for podocyte precursors in kidney development. Together these studies define a spatiotemporal developmental program for the primary filtration unit of the human kidney and provide novel insights into cell interactions regulating co-assembly of constituent cell types.

Analysis of kidney disease-causing genes and pathology resulting from systemic diseases highlight the importance of the kidney's filtering system, the renal corpuscles. To elucidate the developmental processes that establish the renal corpuscle, we performed single-nucleus droplet-based sequencing of the human fetal kidney. This enabled the identification of nephron, interstitial, and vascular cell types that together generate the renal corpuscles. Trajectory analysis identified transient developmental gene expression, predicting precursors or mature podocytes express FBLN2, BMP4, or NTN4, in conjunction with recruitment, differentiation, and modeling of vascular and mesangial cell types into a functional filter. In vitro studies provide evidence that these factors exhibit angiogenic or mesangial recruiting and inductive properties consistent with a key organizing role for podocyte precursors in kidney development. Together these studies define a spatiotemporal developmental program for the primary filtration unit of the human kidney and provide novel insights into cell interactions regulating co-assembly of constituent cell types.

BACKGROUND During normal human kidney development, nephrogenesis (the formation of nephrons) is complete by term birth, with the majority of nephrons formed late in gestation. The aim of this study was to morphologically examine nephrogenesis in fetal human kidneys from 20 to 41weeks of gestation. METHODS Kidney samples were obtained at autopsy from 71 infants that died acutely in utero or within 24h after birth. Using image analysis, nephrogenic zone width, the number of glomerular generations, renal corpuscle cross-sectional area and the cellular composition of glomeruli were examined. Kidneys from female and male infants were analysed separately. FINDINGS The number of glomerular generations formed within the fetal kidneys was directly proportional to gestational age, body weight and kidney weight, with variability between individuals in the ultimate number of generations (8 to 12) and in the timing of the cessation of nephrogenesis (still ongoing at 37weeks gestation in one infant). There was a slight but significant (r2=0.30, P=0.001) increase in renal corpuscle cross-sectional area from mid gestation to term in females, but this was not evident in males. The proportions of podocytes, endothelial and non-epithelial cells within mature glomeruli were stable throughout gestation. INTERPRETATION These findings highlight spatial and temporal variability in nephrogenesis in the developing human kidney, whereas the relative cellular composition of glomeruli does not appear to be influenced by gestational age.

Polarization of node cells along the anterior-posterior axis of mouse embryos is responsible for left-right symmetry breaking. How node cells become polarized has remained unknown, however. Wnt5a and Wnt5b are expressed posteriorly relative to the node, whereas genes for Sfrp inhibitors of Wnt signaling are expressed anteriorly. Here we show that polarization of node cells is impaired in Wnt5a-/-Wnt5b-/- and Sfrp mutant embryos, and also in the presence of a uniform distribution of Wnt5a or Sfrp1, suggesting that Wnt5 and Sfrp proteins act as instructive signals in this process. The absence of planar cell polarity (PCP) core proteins Prickle1 and Prickle2 in individual cells or local forced expression of Wnt5a perturbed polarization of neighboring wild-type cells. Our results suggest that opposing gradients of Wnt5a and Wnt5b and of their Sfrp inhibitors, together with intercellular signaling via PCP proteins, polarize node cells along the anterior-posterior axis for breaking of left-right symmetry.

Advancing our understanding of embryonic development is heavily dependent on identification of novel pathways or regulators. Although genome-wide techniques such as RNA sequencing are ideally suited for discovering novel candidate genes, they are unable to yield spatially resolved information in embryos or tissues. Microscopy-based approaches, using in situ hybridization, for example, can provide spatial information about gene expression, but are limited to analyzing one or a few genes at a time. Here, we present a method where we combine traditional histological techniques with low-input RNA sequencing and mathematical image reconstruction to generate a high-resolution genome-wide 3D atlas of gene expression in the zebrafish embryo at three developmental stages. Importantly, our technique enables searching for genes that are expressed in specific spatial patterns without manual image annotation. We envision broad applicability of RNA tomography as an accurate and sensitive approach for spatially resolved transcriptomics in whole embryos and dissected organs.

Hedgehog signaling controls pattern formation in many vertebrate tissues. The downstream effectors of the pathway are the bifunctional Gli transcription factors, which, depending on hedgehog concentration, act as either transcriptional activators or repressors. Quantitatively understanding the interplay between Gli activator and repressor forms for patterning complex tissues is an open challenge. Here, we describe a reductionist mathematical model for how Gli activators and repressors are integrated in space and time to regulate transcriptional outputs of hedgehog signaling, using the pathway readouts Gli1 and Ptch1 as a model system. Spatially resolved measurements of absolute transcript numbers for these genes allow us to infer spatiotemporal variations of Gli activator and repressor levels. We validate our model by successfully predicting expression changes of Gli1 and Ptch1 in mutants at different developmental stages and in different tissues. Our results provide a starting point for understanding gene regulation by bifunctional transcription factors during mammalian development.

Advancing our understanding of embryonic development is heavily dependent on identification of novel pathways or regulators. Although genome-wide techniques such as RNA sequencing are ideally suited for discovering novel candidate genes, they are unable to yield spatially resolved information in embryos or tissues. Microscopy-based approaches, using in situ hybridization, for example, can provide spatial information about gene expression, but are limited to analyzing one or a few genes at a time. Here, we present a method where we combine traditional histological techniques with low-input RNA sequencing and mathematical image reconstruction to generate a high-resolution genome-wide 3D atlas of gene expression in the zebrafish embryo at three developmental stages. Importantly, our technique enables searching for genes that are expressed in specific spatial patterns without manual image annotation. We envision broad applicability of RNA tomography as an accurate and sensitive approach for spatially resolved transcriptomics in whole embryos and dissected organs.

Hedgehog signaling controls pattern formation in many vertebrate tissues. The downstream effectors of the pathway are the bifunctional Gli transcription factors, which, depending on hedgehog concentration, act as either transcriptional activators or repressors. Quantitatively understanding the interplay between Gli activator and repressor forms for patterning complex tissues is an open challenge. Here, we describe a reductionist mathematical model for how Gli activators and repressors are integrated in space and time to regulate transcriptional outputs of hedgehog signaling, using the pathway readouts Gli1 and Ptch1 as a model system. Spatially resolved measurements of absolute transcript numbers for these genes allow us to infer spatiotemporal variations of Gli activator and repressor levels. We validate our model by successfully predicting expression changes of Gli1 and Ptch1 in mutants at different developmental stages and in different tissues. Our results provide a starting point for understanding gene regulation by bifunctional transcription factors during mammalian development.

Lineage-tracing experiments have indicated that some premigratory neural crest cells (NCCs) are pleuripotent, generating sensory and sympathetic neurons and their associated glia. Using an inducible Cre recombinase-based fate mapping system, we have permanently marked a subpopulation of NCCs that expresses Ngn2, a bHLH transcription factor required for sensory neurogenesis, and compared its fate to the bulk NCC population marked by expression of Wnt1. Ngn2+ progenitors were four times more likely than Wnt1+ NCCs to contribute to sensory rather than sympathetic ganglia. Within dorsal root ganglia, however, both Ngn2- and Wnt1-expressing cells were equally likely to generate neurons or glia. These data suggest that Ngn2 marks an NCC subpopulation with a predictable fate bias, early in migration. Taken together with previous work, these data suggest that NCCs become restricted to sensory or autonomic sublineages before becoming committed to neuronal or glial fates.

Lineage-tracing experiments have indicated that some premigratory neural crest cells (NCCs) are pleuripotent, generating sensory and sympathetic neurons and their associated glia. Using an inducible Cre recombinase-based fate mapping system, we have permanently marked a subpopulation of NCCs that expresses Ngn2, a bHLH transcription factor required for sensory neurogenesis, and compared its fate to the bulk NCC population marked by expression of Wnt1. Ngn2+ progenitors were four times more likely than Wnt1+ NCCs to contribute to sensory rather than sympathetic ganglia. Within dorsal root ganglia, however, both Ngn2- and Wnt1-expressing cells were equally likely to generate neurons or glia. These data suggest that Ngn2 marks an NCC subpopulation with a predictable fate bias, early in migration. Taken together with previous work, these data suggest that NCCs become restricted to sensory or autonomic sublineages before becoming committed to neuronal or glial fates.

Wnt genes encode a large family of secreted signaling molecules related to the Drosophila pattern regulating gene wingless. Several members of the Wnt family are implicated in mammary tumorigenesis suggesting that Wnt-activated pathways are a normal component of mammary gland development. Consistent with this hypothesis, we have demonstrated that seven Wnt genes are expressed in the developing mammary gland. One of these, Wnt-7a, appears to be required for epithelial branching in the adult mammary gland. In contrast, Wnt-6, Wnt-lOa and Wnt-lOb, are expressed at the earliest stages suggesting roles in induction and growth of the embryonic mammary gland. Recent evidence indicates that Frizzled members encode Wnt-receptors, and that Lef-l and Lmx-lb are transcriptional mediators of Wnt-signaling. In this regard we have demonstrated that Fz-6 is expressed in the embryonic mammary gland, consistent with transducing Wnt signals, and nmx-lb, like Lef-l, is required for mammary gland development. We are using a combination of transplantation and transgenic procedures to further explore these pathways. Our hope is that a better understanding of normal development of the mammary gland will provide new insights into possible mechanisms underlying tumor formation.

Wnt genes encode a large family of secreted signaling molecules related to the Drosophila pattern regulating gene wingless. Several members of the Wnt family are implicated in mammary tumorigenesis suggesting that Wnt-activated pathways are a normal component of mammary gland development. Consistent with this hypothesis, we have demonstrated that six Wnt genes are expressed in the developing mammary gland. Two of these, Wnt-6 and Wnt-lO, are expressed from the earliest stages suggesting roles in induction and growth of the mammary gland. To address their functions, we are analyzing their expression in detail and have initiated approaches to generate mutants. Further, we are developing strategies which will eventually allow tissue specific activation and mutation of genes in the mammary epithelium. Finally, recent work suggests that frizzleds encode wingless receptors. We have initiated studies examining frizzleds in the mammary gland. Frizzled-6 is expressed quite specifically in the early mammary gland. It seems reasonable to expect that an understanding of Wnt/frizzled interactions will afford new insights into normal development of the mammary gland, and the pathways that lead to tumor formation.

Wnt genesaencode a large family of secreted signaling molecules related to the Drosophila pattern regulating gene wingless. Several members have been implicated in mammary tumorigenesis.

A fusion gene construct, in which the coding sequence for bacterial chloramphenicol acetyltransferase (CAT; acetyl-CoA: chloramphenicol 3-O-acetyltransferase, EC 2.3.1.28) was placed under the control of the regulatory region of the Drosophila gene encoding the 70-kilodalton heat shock protein [Di Nocera, P. P. & Dawid, I. B. (1983) Proc. Natl. Acad. Sci. USA 80, 7095-7098], was microinjected into the cytoplasm of unfertilized sea urchin eggs. Pluteus-stage embryos developing from the injected eggs were exposed to high temperature conditions that we found would elicit an endogenous sea urchin heat shock response. These embryos express the gene for CAT and, after heat treatment, display 8-10 times more CAT enzyme activity than do extracts from control embryos cultured at normal temperatures. The injected DNA is present in high molecular weight concatenates and, during development, is amplified about 100-fold. Amplified sequences are responsible for all or most of the induced CAT enzyme activity.

A fusion gene construct, in which the coding sequence for bacterial chloramphenicol acetyltransferase (CAT; acetyl-CoA: chloramphenicol 3-O-acetyltransferase, EC 2.3.1.28) was placed under the control of the regulatory region of the Drosophila gene encoding the 70-kilodalton heat shock protein [Di Nocera, P. P. & Dawid, I. B. (1983) Proc. Natl. Acad. Sci. USA 80, 7095-7098], was microinjected into the cytoplasm of unfertilized sea urchin eggs. Pluteus-stage embryos developing from the injected eggs were exposed to high temperature conditions that we found would elicit an endogenous sea urchin heat shock response. These embryos express the gene for CAT and, after heat treatment, display 8-10 times more CAT enzyme activity than do extracts from control embryos cultured at normal temperatures. The injected DNA is present in high molecular weight concatenates and, during development, is amplified about 100-fold. Amplified sequences are responsible for all or most of the induced CAT enzyme activity.