The mouse is the most commonly used mammalian model to study disease, including kidney disease. However, close attention needs to be paid to the differences and effects of genetic background. The ...default choice of most investigators is to use C57BL/6 mice, but not all C57BL/6 mice are the same. Ever since the C57BL/6 line was first established, differences in the genetic background have risen between substrains, which have major implications in the phenotypes expressed in kidney disease. Furthermore, considering that C57BL/6 substrains are relatively resistant to kidney damage, there can be major benefits in selecting other mouse inbred strains when studying kidney disease. These strains can show more similar responses regarding kidney damage as in humans, and results may therefore translate better to human application. Genetically diverse mice, such as the Diversity Outbred mice, allow investigators to study kidney phenotypes with comparable levels of genetic diversity as seen in humans, which yield results that more closely reflect the variation in human disease outcomes due to genetic variation. Hence, embracing the genetic diversity that is present in mice can lead to better translational research methods. Investigators need to always take into consideration that genetic background is a variable that can alter results significantly, and optimization of translational research asks for careful strain selection and more rigorous reporting of the genetic background that is being used in experiment.
Current methods of scoring histological kidney samples, specifically glomeruli, do not allow for collection of quantitative data in a high-throughput and consistent manner. Neither untrained ...individuals nor computers are presently capable of identifying glomerular features, so expert pathologists must do the identification and score using a categorical matrix, complicating statistical analysis. Critical information regarding overall health and physiology is encoded in these samples. Rapid comprehensive histological scoring could be used, in combination with other physiological measures, to significantly advance renal research. Therefore, we used machine learning to develop a high-throughput method to automatically identify and collect quantitative data from glomeruli. Our method requires minimal human interaction between steps and provides quantifiable data independent of user bias. The method uses free existing software and is usable without extensive image analysis training. Validation of the classifier and feature scores in mice is highlighted in this work and shows the power of applying this method in murine research. Preliminary results indicate that the method can be applied to data sets from different species after training on relevant data, allowing for fast glomerular identification and quantitative measurements of glomerular features. Validation of the classifier and feature scores are highlighted in this work and show the power of applying this method. The resulting data are free from user bias. Continuous data, such that statistical analysis can be performed, allows for more precise and comprehensive interrogation of samples. These data can then be combined with other physiological data to broaden our overall understanding of renal function.
Unique genetic adaptations are present in bears of every species across the world. From (nearly) shutting down important organs during hibernation to preventing harm from lifestyles that could easily ...cause metabolic diseases in humans, bears may hold the answer to various human ailments. However, only a few of these unique traits are currently being investigated at the molecular level, partly because of the lack of necessary tools. One of these tools is well-annotated genome assemblies from the different, extant bear species. These reference genomes are needed to allow us to identify differences in genetic variants, isoforms, gene expression, and genomic features such as transposons and identify those that are associated with biomedical-relevant traits. In this review we assess the current state of the genome assemblies of the eight different bear species, discuss current gaps, and the future benefits these reference genomes may have in informing human biomedical applications, while at the same time improving bear conservation efforts.
Nephropathologic analyses provide important outcomes-related data in experiments with the animal models that are essential for understanding kidney disease pathophysiology. Precision medicine ...increases the demand for quantitative, unbiased, reproducible, and efficient histopathologic analyses, which will require novel high-throughput tools. A deep learning technique, the convolutional neural network, is increasingly applied in pathology because of its high performance in tasks like histology segmentation.
We investigated use of a convolutional neural network architecture for accurate segmentation of periodic acid-Schiff-stained kidney tissue from healthy mice and five murine disease models and from other species used in preclinical research. We trained the convolutional neural network to segment six major renal structures: glomerular tuft, glomerulus including Bowman's capsule, tubules, arteries, arterial lumina, and veins. To achieve high accuracy, we performed a large number of expert-based annotations, 72,722 in total.
Multiclass segmentation performance was very high in all disease models. The convolutional neural network allowed high-throughput and large-scale, quantitative and comparative analyses of various models. In disease models, computational feature extraction revealed interstitial expansion, tubular dilation and atrophy, and glomerular size variability. Validation showed a high correlation of findings with current standard morphometric analysis. The convolutional neural network also showed high performance in other species used in research-including rats, pigs, bears, and marmosets-as well as in humans, providing a translational bridge between preclinical and clinical studies.
We developed a deep learning algorithm for accurate multiclass segmentation of digital whole-slide images of periodic acid-Schiff-stained kidneys from various species and renal disease models. This enables reproducible quantitative histopathologic analyses in preclinical models that also might be applicable to clinical studies.
Little is known about the molecular changes that take place in the kidney during the aging process. In order to better understand these changes, we measured mRNA and protein levels in genetically ...diverse mice at different ages. We observed distinctive change in mRNA and protein levels as a function of age. Changes in both mRNA and protein are associated with increased immune infiltration and decreases in mitochondrial function. Proteins show a greater extent of change and reveal changes in a wide array of biological processes including unique, organ-specific features of aging in kidney. Most importantly, we observed functionally important age-related changes in protein that occur in the absence of corresponding changes in mRNA. Our findings suggest that mRNA profiling alone provides an incomplete picture of molecular aging in the kidney and that examination of changes in proteins is essential to understand aging processes that are not transcriptionally regulated.
With the advent and increased accessibility of deep neural networks (DNNs), complex properties of histologic images can be rigorously and reproducibly quantified. We used DNN-based transfer learning ...to analyze histologic images of periodic acid-Schiff–stained renal sections from a cohort of mice with different genotypes. We demonstrate that DNN-based machine learning has strong generalization performance on multiple histologic image processing tasks. The neural network extracted quantitative image features and used them as classifiers to look for differences between mice of different genotypes. Excellent performance was observed at segmenting glomeruli from non-glomerular structure and subsequently predicting the genotype of the animal on the basis of glomerular quantitative image features. The DNN-based genotype classifications highly correlate with mesangial matrix expansion scored by a pathologist (R.E.C.), which differed in these animals. In addition, by analyzing non-glomeruli images, the neural network identified novel histologic features that differed by genotype, including the presence of vacuoles, nuclear count, and proximal tubule brush border integrity, which was validated with immunohistologic staining. These features were not identified in systematic pathologic examination. Our study demonstrates the power of DNNs to extract biologically relevant phenotypes and serve as a platform for discovering novel phenotypes. These results highlight the synergistic possibilities for pathologists and DNNs to radically scale up our ability to generate novel mechanistic hypotheses in disease.
Mutations in
are responsible for 80% of cases of X-linked Alport Syndrome (XLAS). Although genes that cause AS are well characterized, people with AS who have similar genetic mutations present with a ...wide variation in the extent of kidney impairment and age of onset, suggesting the activities of modifier genes.
We created a cohort of genetically diverse XLAS male and female mice using the Diversity Outbred mouse resource and measured albuminuria, GFR, and gene expression. Using a quantitative trait locus approach, we mapped modifier genes that can best explain the underlying phenotypic variation measured in our diverse population.
Genetic analysis identified several loci associated with the variation in albuminuria and GFR, including a locus on the X chromosome associated with X inactivation and a locus on chromosome 2 containing
. Subsequent analysis of genetically reduced
expression in
knockout mice showed a decrease in albuminuria, podocyte effacement, and podocyte protrusions in the glomerular basement membrane, which support the candidacy of
as a modifier gene for AS.
With this novel approach, we emulated the variability in the severity of kidney phenotypes found in human patients with Alport Syndrome through albuminuria and GFR measurements. This approach can identify modifier genes in kidney disease that can be used as novel therapeutic targets.
Uromodulin is a zona pellucida-type protein essentially produced in the thick ascending limb (TAL) of the mammalian kidney. It is the most abundant protein in normal urine. Defective uromodulin ...processing is associated with various kidney disorders. The luminal release and subsequent polymerization of uromodulin depend on its cleavage mediated by the serine protease hepsin. The biological relevance of a proper cleavage of uromodulin remains unknown. Here we combined in vivo testing on hepsin-deficient mice, ex vivo analyses on isolated tubules and in vitro studies on TAL cells to demonstrate that hepsin influence on uromodulin processing is an important modulator of salt transport via the sodium cotransporter NKCC2 in the TAL. At baseline, hepsin-deficient mice accumulate uromodulin, along with hyperactivated NKCC2, resulting in a positive sodium balance and a better adaptation to water deprivation. In conditions of high salt intake, defective uromodulin processing predisposes hepsin-deficient mice to a salt-wasting phenotype, with a decreased salt sensitivity. These modifications are associated with intracellular accumulation of uromodulin, endoplasmic reticulum-stress and signs of tubular damage. These studies expand the physiological role of hepsin and uromodulin and highlight the importance of hepsin-mediated processing of uromodulin for kidney tubule homeostasis and salt sensitivity.
The health risks that arise from environmental exposures vary widely within and across human populations, and these differences are largely determined by genetic variation and gene-by-environment ...(gene-environment) interactions. However, risk assessment in laboratory mice typically involves isogenic strains and therefore, does not account for these known genetic effects. In this context, genetically heterogenous cell lines from laboratory mice are promising tools for population-based screening because they provide a way to introduce genetic variation in risk assessment without increasing animal use. Cell lines from genetic reference populations of laboratory mice offer genetic diversity, power for genetic mapping, and potentially, predictive value for in vivo experimentation in genetically matched individuals. To explore this further, we derived a panel of fibroblast lines from a genetic reference population of laboratory mice (the Diversity Outbred, DO). We then used high-content imaging to capture hundreds of cell morphology traits in cells exposed to the oxidative stress-inducing arsenic metabolite monomethylarsonous acid (MMAIII). We employed dose-response modeling to capture latent parameters of response and we then used these parameters to identify several hundred cell morphology quantitative trait loci (cmQTL). Response cmQTL encompass genes with established associations with cellular responses to arsenic exposure, including Abcc4 and Txnrd1, as well as novel gene candidates like Xrcc2. Moreover, baseline trait cmQTL highlight the influence of natural variation on fundamental aspects of nuclear morphology. We show that the natural variants influencing response include both coding and non-coding variation, and that cmQTL haplotypes can be used to predict response in orthogonal cell lines. Our study sheds light on the major molecular initiating events of oxidative stress that are under genetic regulation, including the NRF2-mediated antioxidant response, cellular detoxification pathways, DNA damage repair response, and cell death trajectories.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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