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E Quantification of the number of epithelial cells per mm 2 of the kidney cortex that express Ki Fold expression changes were normalised against the expression level of the 18S ribosomal RNA.
Defects in function or structure of the primary cilium cause kidney cyst formation reviewed in Nauli and Zhou, ; Pan et al , and cystic epithelial cells in VHL patients frequently lack a primary cilium Thoma et al , a. Combined with our previous findings Thoma et al , a , these data suggest that loss of Vhlh and Pten leads to failure of kidney epithelial cells to maintain their primary cilia, resulting in uncontrolled proliferation and cyst formation.
Arrowheads in D depict cells that lack primary cilia. To investigate whether there is a cell-autonomous requirement for pVHL and PTEN in cilia formation or cilia maintenance, we performed cilia assays using cultured cells.
Consistent with our previous findings in other cell systems Thoma et al , a , knockdown of VHL sensitised RPE-1 cells to lose their already established primary cilia in response to serum re-addition for 4 h Figure 6A.
Knockdown of AKT1 rescued the effect of serum on VHL knockdown cells Figure 6A , demonstrating genetically that AKT1 activity contributes to cilia loss and supporting earlier data that suggested the requirement of a wortmannin-sensitive signalling pathway for serum-induced cilia loss in VHL -negative cells Thoma et al , a. A Quantitation of frequency of ciliated RPE-1 cells following short hairpin-mediated gene knockdown using vectors expressing non-silencing n. B Western analysis of RPE-1 cells after control knockdown n.
C Western analysis of RPE-1 cells after control knockdown n. Subconfluent cells were subjected to serum starvation for the indicated times. E Western analysis of cells from D that were grown for 3 days in the absence of serum prior to harvesting. Although AKT activation is a necessary but not sufficient component of a serum-induced signalling pathway that causes loss of cilia in pVHL-deficient cells, we reasoned that additional signalling pathways might be required for this response and therefore attempted to pharmacologically define the molecular requirements for cilia loss.
Western blotting confirmed the specific effects of the remaining inhibitors upon their respective signalling pathways Figure 7B. Multiple pathways control cilia loss in pVHL-deficient cells. A Quantitation of frequency of ciliated immortalised MEFs following short hairpin-mediated gene knockdown using vectors expressing non-silencing n.
B Western analysis of cells from A. Given the requirement for both AKT and ERK signalling in causing cilia loss in pVHL-deficient cells in culture, we investigated the activation status of these signalling pathways in kidney cysts in vivo.
Thus, in two different physiological settings of VHL-associated cyst formation there is activation of two distinct signalling pathways that are necessary for cilia loss in pVHL-deficient cells. We suggest that, at the minimum, the combination of loss of pVHL function and activation of PI3K and ERK signalling is required to induce loss of cilia and subsequent cyst formation in vivo.
Despite intense investigations, the molecular genetics underlying renal pathology in VHL disease remains incompletely understood. Studies of kidneys from VHL patients revealed a low frequency of multicellular, cystic lesions compared with single cell lesions, implying that the early proliferative advantage conveyed by biallelic VHL inactivation may be relatively modest Mandriota et al , Consistent with this view, conditional inactivation of Vhlh in the mouse liver and kidney elicits renal cysts only after long latency with low penetrance Rankin et al , Moreover, although renal cysts of VHL patients display a reduced frequency of primary cilia, consistent with the role of this sensory organelle as a suppressor of uncontrolled proliferation and cyst formation, cell biological evidence suggests that pVHL is dispensable for cilia formation in primary cells and that additional pathway s need to be inactivated for cells to lose their primary cilia Frew and Krek, ; Thoma et al , a , b.
Here, we identify the PTEN tumour suppressor as at least one potential critical suppressor of the conversion of pVHL-deficient renal tubular epithelial cells to cysts, an initial step of kidney cancer progression. We show that VHL mutant cystic lesions in VHL patients display hyperactivation of the PI3K signalling pathway and that mimicking these conditions by combined mutation of Vhlh and Pten causes kidney cysts in mice.
Notably, deletion of Kif3a under the control of the Ksp1. Further elucidation of the signalling events that cooperate with pVHL deficiency would be aided by the identification of the component s in serum that induces cilia loss. The primary cilium-based model of cyst formation that we propose here needs to be viewed in light of the fact that pVHL has multiple biochemical activities.
Such cooperative effects of Vhlh and Pten mutation could be independent of cilia but may nonetheless contribute to dysregulated cell proliferation and cyst formation. Partial or complete blockage of urinary flow due to experimental manipulation or congenital abnormality can lead to a variety of kidney abnormalities, including cysts, in humans and in other mammals Peters, If cysts arose simply due to changes in pressure within the kidney it would be expected that cysts would also occur in tubules in which Cre was not active.
These mice were subsequently intercrossed to provide genetic background-matched sets of parents from which Ksp1. Littermate mice that lacked the Ksp1. MEFs were derived from E PCR-mediated detection of Cre-mediated recombination at the Vhlh and Pten loci was performed as described Haase et al , ; Lesche et al , Production of lentivirus expressing Cre, MEF infections, generation of Vhlh knockdown immortalised MEFs and cilia assays were performed as described Thoma et al , a. Expression of a second hairpin was achieved by infection with lentiviruses pCMV-nz expressing a non-silencing control hairpin n.
After separation of the fragments on an agarose gel the corresponding fragment was purified and religated to form pCMV-nz. Cloning of n. After infection, cells were selected with 0. We thank all members of our laboratory for discussions. EMBO J. Published online May Author information Article notes Copyright and License information Disclaimer.
Received Feb 6; Accepted Apr Abstract In patients with von Hippelï¿½Lindau VHL disease, renal cysts and clear cell renal cell carcinoma ccRCC arise from renal tubular epithelial cells containing biallelic inactivation of the VHL tumour suppressor gene. Introduction Inheritance of a mutant allele of the von Hippelï¿½Lindau tumour suppressor gene VHL predisposes affected individuals to develop diverse tumours, including renal cysts and clear cell renal cell carcinoma ccRCC Lonser et al , ; Kim and Kaelin, Open in a separate window.
Figure 1. This would allow researchers to perform a single experiment to obtain information about which proteins are involved and the manner in which they participate in specific biological processes and their quantitative behavior. Specifically, proteomics could be used to study proteinï¿½protein interaction networks in their native and perturbed states and reveal how complex diseases such as cancer or diabetes rewire these networks Lage et al.
Furthermore, improved proteomic profiling could facilitate the search for new protein biomarkers in tissue and blood, since more samples and a larger number of proteins could be quantitatively compared across many patients Liu et al.
Applying proteomics techniques to signaling networks would require dense temporal sampling and accurate quantification of posttranslational modifications to capture fast-acting changes in, for example, phosphorylation states Bodenmiller et al.
Furthermore, accurate data matrices would allow a multitude of tools from statistics and machine learning to draw inferences about causal interactions among different proteomic compounds Swan et al. Applying such data-driven methods to biological problems might uncover important regulatory mechanisms and implicate novel proteins in well-studied biological processes, which could help researchers to better determine the behavior of the system. Finally, such matrices could foster integration with high-throughput data from other fields such as genomics and other sequencing-based fields in which comprehensive data matrices are already a standard experimental output.
However, obtaining high-quality data matrices from proteomics data has historically been highly challenging. Therefore most proteomics approaches rely on extensive biochemical fractionation methods that produce a mostly pure form of the analyte and then subsequently use highly sensitive analysis techniques to determine the nature of and quantify the analyte.
Initially, fractionation was achieved on whole proteins using two-dimensional biochemical separation 2D-PAGE by isoelectric focusing and apparent molecular mass separation, and subsequent identification of separated species was performed by Edman sequencing or mass spectrometry MS.
This approach was supplanted by a number of strategies based on online chromatographic peptide separation and subsequent gas-phase separation or isolation of selected peptide ions precursor ions in the gas phase. When applied to whole-cell lysates, shotgun proteomics provides fast enumeration of the most abundant protein species present in the sample, which enables exploratory data analysis and identification of previously unknown peptides.
However, whereas shotgun proteomics allows discovery-driven research and offers high throughput, its sensitivity is strongly sample dependent, and it suffers from inconsistent identification reproducibility across samples.
This is mainly due to the fact that for complex samples, the number of peptides by far exceeds the number of sequencing cycles provided by the mass spectrometer, leading to an undersampling of the proteome Figure 1b ; Michalski et al. These challenges are substantially influenced by different sample preparation and quantification strategies. Furthermore, each quantification strategy comes with its own challenges and provides different quantitative accuracy and throughput. Isotopic labeling approaches such as Isotope-coded affinity tag ICAT , stable isotope labeling with amino acids in cell culture SILAC , or dimethyl N-terminal labeling deliver high quantitative accuracy but increase sample complexity and further exacerbate the undersampling problem.
On the other hand, isobaric labeling approaches like iTRAQ and TMT can increase multiplexing and decrease cross-sample variability on the MS1 level but at the cost of coupling quantification to fragmentation and thus accepting missing values for cases for which no fragmentation was triggered. Even though isotopic and isobaric labeling methods support multiplexing, the capacity is limited to a few two to 10 channels per MS run, which still poses a substantial challenge in large-scale analyses, in which hundreds of samples may be analyzed.
Finally, label-free approaches do not increase sample complexity but still suffer from undersampling, as well as from reduced quantitative accuracy due to the lack of an internal standard. In the context of the systems biology data matrix, the data produced by shotgun proteomics thus pose significant challenges, since measurements are performed with high throughput and coverage but generally low comprehensiveness. In addition, the more samples are analyzed and the more biologically diverse the samples are, the lower is the number of complete rows; due to the intensity dependence of the sampling and undersampling issues for complex samples, the missing values will generally not be missing completely at random Bruderer et al.
Specifically, proteins that are variable across the experimental conditions will likely contain more missing values with those conditions not quantified where abundance is low , whereas highly abundant, invariant proteins are faithfully sampled by the approach.
It is therefore the efficiency of shotgun proteomics that produces maximal information on a single sample that is detrimental to the production of highly informative data matrices on multiple samples , since sampling more often occurs at noninformative positions, whereas information-rich processes with high variance are sparsely sampled.
In SRM mode, the mass spectrometer is programmed to deterministically record the signal at fixed coordinates across the chromatographic retention time. These coordinates the assay are specific to a peptide analyte and will reliably detect the analyte signal if present, similarly to a classical biochemical assay such as an antibody-based method.
The acquisition of signal for multiple fragment ions transitions ensures high specificity Sherman et al. This deterministic acquisition strategy increases reproducibility and quantification consistency compared to shotgun approaches, where sampling is semistochastic and data acquisition for each single peptide depends on a multitude of factors. However, SRM is limited by throughput and can only monitor dozens to hundreds of peptides per run, since the deterministic sampling strategy implies acquiring signal even at time points at which no analyte elutes in order to collect complete chromatographic traces Picotti et al.
Thus the data matrices obtained from SRM are much more complete than those produced by shotgun proteomics but generally contain one to two orders of magnitude fewer proteins Figure 1c.
Because the proteins to be measured have to be preselected, the measurements tend to be biased by prior hypotheses and may not cover all biologically relevant cellular processes and pathways. Therefore SRM has mostly been used in studies in which large sample numbers are required and only few proteins are under investigation such as clinical biomarker studies; Cima et al.
For systems biology investigations, neither SRM nor shotgun approaches are fully satisfactory to generate the desired complete data matrix. Whereas shotgun proteomics places heavy emphasis on the analyte dimensions and successfully identifies many protein species, it is often challenging to trace analytes across the sample dimension Figure 1b. Conversely, SRM is well able to quantify analytes across many MS runs but suffers from low throughput in the analyte dimension Figure 1c.
To allow proteomics to become a true systems science, efforts should be directed toward improving proteomics measurement with regard to both dimensions of the data matrix, which means that future improvements in measurement technology and analysis strategy should be evaluated by the quality of the data matrices they are able to produce. Although the field was highly successful in compiling extensive protein inventories in the past, future efforts should turn toward the generation of fully quantitative, high-quality data matrices.
This challenge has been recognized by the field, and multiple efforts toward this aim have been presented recently or are under way. In particular, recent advances in acquiring and analyzing data-independent acquisition mass-spectrometric data, such as SWATH-MS data, constitute a promising advance toward this goal Gillet et al.
In SWATH-MS, the mass spectrometer performs deterministic acquisition of fragment ion spectra but does not aim to target specific peptides explicitly by their intensity as shotgun does or by prior hypothesis as SRM does.
Instead, SWATH-MS records the complete fragment ion signal in a single experiment, essentially creating a complete digital representation of all fragment ion signals in a biological sample. This digitized sample can then be used to extract quantitative information for individual peptides after data acquisition. However, one of the main limitations of SWATH-MS is the complexity of the resulting data, which consists of highly multiplexed fragment ion spectra that require novel algorithmic approaches for deconvolution.
To assign signal to individual peptides and quantify analytes, multiple open-source tools using complementary algorithms are available, but further research is required to improve the underlying analysis approaches and fully exploit the potential of SWATH-MS. This table compares three major techniques used in mass spectrometryï¿½based proteomics according to different performance criteria: shotgun proteomics, targeted proteomics or SRM, and data-independent acquisition or SWATH-MS.
All three techniques have unique benefits and disadvantages; therefore different techniques need to be applied for different tasks. Thus, SWATH-MS is a technology that addresses both dimensions of the data matrix at the same time and allows true systems analysis on protein measurements. It provides a valuable addition to the set of tools available to proteomics researchers and strikes a balance between throughput and reproducibility, making it an interesting option next to shotgun and targeted proteomics.
SWATH-MS is thus a promising technology that could help to provide the proteomics field with complete and accurate data matrices and may play a key role in investigating systems biology questions on the protein level.
When evaluating proteomics techniques from the viewpoint of the quantitative proteotype data matrix, we can obtain a much clearer picture of data utility for systems biology studies.
It becomes apparent that neither patchy matrices littered with missing values nor highly consistent measurements of a few proteins are sufficient for systems approaches to biology. Although shotgun and SRM are valuable for a multitude of purposes, new paradigms need to be developed in order to be able to apply unbiased, data-driven systems approaches in proteomics.
The field should embrace this realization and increase efforts to establish novel experimental and computational methods able to produce data matrices with extensive proteome coverage and high comprehensiveness suitable for quantitative biology approaches. Current technology and analysis software has matured enough by now to tackle the next major challenge in proteomics, namely the proteotype data matrix.
They combine the strength of SRM high reproducibility and quantitative accuracy with the high throughput of shotgun proteomics, thus focusing on both analyte and sample dimension of the data matrix at the same time. Using SWATH-MS, proteomics technology can produce quantitatively accurate and qualitatively complete data matrices, allowing researchers to track protein quantities across many samples. These advances in the field will allow proteomics researchers to ask novel questions about ensembles of proteins and their behavior across many experimental conditions, time points, and individuals.
Thus proteomics is expected to contribute significantly to the emerging fields of precision and personalized medicine, high-throughput screening, and analysis, as well as to systems biology and systems medicine. DOI: Mol Biol Cell. Hannes L. Author information Article notes Copyright and License information Disclaimer. This article is distributed by The American Society for Cell Biology under license from the author s. Two months after publication it is available to the public under an Attributionï¿½Noncommercialï¿½Share Alike 3.
Abstract Historically, many mass spectrometryï¿½based proteomic studies have aimed at compiling an inventory of protein compounds present in a biological sample, with the long-term objective of creating a proteome map of a species. Open in a separate window. Footnotes DOI: Mass spectrometry-based proteomics. Phosphoproteomic analysis reveals interconnected system-wide responses to perturbations of kinases and phosphatases in yeast. Sci Signal. Extending the limits of quantitative proteome profiling with data-independent acquisition and application to acetaminophen-treated three-dimensional liver microtissues.
Mol Cell Proteomics. Cancer genetics-guided discovery of serum biomarker signatures for diagnosis and prognosis of prostate cancer.
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Several additional formulas and extensions towards semimartingales are provided in Aspects of Signatures. Lecture 6 Deep Calibration : a deep calibration implementation for the Heston model and deep calibration for local stochastic volatility models.
We distinguish three sorts of deep calibration: learning directly the map from market data to model parameters, learning the map from model parameters to market data and inverting it by inverse problem methodology, and parmetrizing infinite dimensional parmeters by neural networks.
Notice that the Heston calibration code runs safely under Python 3. Lecture 7 Deep Reinforcement Learning : a short theoretical introduction to concepts of reinforcement learning as iPython notebook.
A typical exam for this lecture. Ricky T. Ilya Chevyrev, Andrej Kromilitzin, A primer on the signature method in machine learning, arxiv.
Coifman: Provable approximation properties of deep neural networks, arxiv. Weinan E, Jiequn Han, Arnulf Jentzen: Deep learning-based numerical methods for high-dimensional parabolic partial differential equations and backward stochastic differential equations, arxiv.
Catherine F. Higham, Desmond J. Terry Lyons, Rough paths, Signatures and the modelling of functions on streams, arxiv. Terry Lyons, Harald Oberhauser, Sketching the order of events, arxiv. Josef Teichmann, A recent talk in Konstanz on randomness in training algorithms, Josef Teichmann, A recent talk in Oslo on stationary versions of discrete signature in the spirit of reservoir computing,
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