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Serum Pooling is the Answer to the Reproducibility Crisis

Protein Phosphatase Protocols

The effects of knocking down DUSP10 at 0 hr differed from those of the other five phosphatases. Taken together, their effects on keratinocyte self-renewal and differentiation suggest that all six are commitment-associated protein phosphatases, with DUSP10 differing from the other phosphatases in potentially antagonising commitment.

To examine the effect of knocking down the pro-commitment phosphatases on the ability of keratinocytes to reconstitute a multi-layered epithelium, we seeded cells on de-epidermised human dermis and cultured them at the air-medium interface for three weeks Figure 2g ; Figure 2—figure supplement 2f,g. However, the number of TPpositive cells was increased, whether measured as a proportion of the total number of cells Figure 2g or per length of basement membrane Figure 2—figure supplement 2g. Conversely, PTPN13 knockdown led to an increase in the percentage of Kipositive cells and a slight reduction in epidermal thickness Figure 2g , which could reflect an increased rate of transit through the epidermal layers.

In addition, Ki67 and TPpositive cells were no longer confined to the basal cell layer of epidermis reconstituted following phosphatase knockdown but were also present throughout the viable suprabasal layers. To complement our studies with siRNAs, which achieve transient knockdown, we performed stable knockdown of the pro-commitment phosphatases with shRNA lentiviral vectors two different shRNAs per target; Figure 2—figure supplement 3a and performed epidermal reconstitution experiments Figure 2—figure supplement 3b,c.

We also developed a tool for automated, unbiased measurements of epidermal thickness Figure 2—figure supplement 4 , Source code 1. No apoptotic cells were observed in reconstituted epidermis, as evaluated by lack of Caspase-3 labelling. Thus, on both transient and stable knockdown, the transition from the stem to the differentiated cell compartment was disturbed Figure 2—figure supplement 3f. To identify the signalling networks affected by upregulation of phosphatases during commitment, we performed GO analysis of ranked peptides that were dephosphorylated at 4 hr.

Several of the proteins we identified are components of more than one pathway Figure 3b and all have been reported previously to regulate epidermal differentiation Connelly et al. In particular, constitutive activation of ERK delays suspension-induced differentiation Haase et al. Cyclophilin B: loading control. We next ranked protein phosphorylation sites according to the log 2 -fold decrease at 4 hr, plotting the ratio between the change in phosphorylation sites and the change in total protein Supplementary file 5. To specifically identify dephosphorylation events, we excluded from the ranking phosphorylations that remained constant while total protien abundance increased by more than 0.

Transcriptional regulation of epidermal differentiation is mediated by the Activator Protein 1 AP1 family of transcription factors Eckert et al. Quantification of the levels of AP1 transcripts during suspension-induced terminal differentiation revealed that different AP1 factors changed with different kinetics, as reported previously Gandarillas and Watt, Figure 3f.

In line with these observations, knockdown of individual pro-commitment phosphatases reduced the induction of MAF AP1 factors in suspension Figure 3f ; Supplementary file 6. These experiments are consistent with a model whereby induction of phosphatases in committed keratinocytes causes dephosphorylation of ERK MAPK and prevents the increase in expression of AP1 transcription factors that execute the terminal differentiation program.

Consistent with its known regulation of p38 MAPK Caunt and Keyse, , DUSP10 knockdown increased phospho-p38 at 0 hr; however, this effect was not observed at later times in suspension Figure 3—figure supplement 1c,d. We next examined whether the six phosphatases we identified experimentally could form a single phosphatase-phosphatase interaction network governing commitment and differentiation. To this end, we employed the automated reasoning approach encapsulated in the tool RE:IN, as described by Dunn et al.

We encoded a set of experimental constraints comprising the different time points analysed during suspension-induced differentiation Figure 1a. These constraints define whether each gene should be on or off at each time step along the differentiation trajectory Materials and methods. Using this approach, we formally deduced that a single Boolean network was unable to recapitulate the measured gene expression dynamics Figure 4—figure supplement 1a.

To obtain the necessary experimental data to identify critical network interactions, we performed individual knockdowns of the phosphatases after 0, 4, 8 or 12 hr in suspension. Next, we measured by RT-qPCR the effect that each individual knockdown had on the mRNA level of any of the other phosphatases Figure 4—figure supplement 2a at each time point.

This led to the inferred mechanistic networks depicted in Figure 4a , where the node colours show fold-change in each phosphatase with time in suspension, relative to the 0 hr time-point. Arrows indicate positive effects on expression and T-bars show inhibitory effects. These networks reveal different interactions at play at each of the time points, suggesting that the network topology reconfigures during commitment and differentiation.

We defined three experimental constraints gene expression changes during suspension-induced differentiation in the absence of pharmacological inhibitors, as well as under TSA and PKCi treatments.

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Each constraint encodes discrete gene expression states at the indicated time steps. For cells in the absence of drugs we imposed a switching scheme, whereby the system must change the representative network in order to achieve the expression constraints. Yellow boxes indicate the network that represents the system at that step; blank boxes for a given step column indicate that we did not impose a specific network, so the system can remain in the current network or switch forwards.

The discretisation is available in Supplementary file 9. Solid lines show interactions already calculated in a , while dashed lines are possible interactions inferred from Figure 4—figure supplement 1. See also Supplementary file 8 and 9. In the control both stem and differentiated cell states are stable attractors , while commitment is an unstable state.

On TSA treatment there is a mandatory switch from the 0 hr network but the 12 hr network cannot be reached at any time point; we therefore hypothesise that commitment becomes a stable state while the stem and differentiated cell states are unstable. In light of these inferred mechanistic interactions, we again employed a Boolean network abstraction to explore the dynamics of epidermal stem cell commitment. Using the discretised gene expression levels defined above Supplementary file 8 , we sought to test whether the network topologies derived for each time point Figure 4a,b were consistent with the data.

RE:SIN allowed us to determine whether the inferred networks could recapitulate the dynamic changes in gene expression by similarly encoding these as expected states on the commitment trajectory Figure 4—figure supplement 1b. We first found that the networks inferred by the genetic knockdown experiments could not satisfy the experimental constraints.

This suggested that there were additional interactions between the phosphatases. To identify such interactions, we analysed the effect that each individual knockdown had on the protein levels of the other phosphatases Figure 4—figure supplement 2d ; Supplementary file 10 and the effect of DUSP6 and DUSP10 overexpression on phosphatase mRNA levels Figure 4—figure supplement 2e.

We included those as possible interactions, again using the concept of an ABN at each time step Figure 4—figure supplement 1c ; Supplementary file 8. By defining an ABN at each time point that incorporates the possible phosphatase interactions deduced from the knockdown experiments, we found that the experimental constraints could be satisfied.

Furthermore, our analysis revealed which of the possible interactions were required to meet these constraints. These could therefore be considered as definite interactions Figure 4c , compare with Figure 4—figure supplement 1c. We thus used the approach encapsulated in RE:SIN to uncover the networks governing differentiation at each time point. The Boolean network analysis indicates a key role for DUSP6 at commitment 4 hr , when DUSP6 expression is positively regulated by all the other phosphatases in the network and by a self-activating loop Figure 4a.

In contrast, at all other time points the interactions between individual phosphatases were predominantly negative.

It is also notable that DUSP10 was predominantly negatively regulated by other phosphatases, except at 4 hr, when it was positively regulated by PTPN1 and by an autoregulatory loop Figure 4a. However, cells treated with TSA still underwent commitment, as evaluated by loss of colony-forming ability and downregulation of TP63, whereas those treated with the PKC inhibitor did not. We next used our modelling approach to test whether network switching still occurs under PKCi and TSA treatment using the constrained ABNs that we derived for each stage.

Under both treatments, we found that we could not transit through the networks while respecting the expected expression states, corroborating the experimental findings that terminal differentiation is blocked in these conditions. The PKCi phosphatase expression pattern at 12 hr was compatible with the phosphatase interaction network derived at 0 hr, supporting the conclusion that PKCi arrests cells in the stem cell compartment.

In TSA-treated cells the network must switch from that derived at 0 hr to that derived at 4 hr, and subsequently to that derived at 8 hr, or straight from 0 hr to 8 hr. Neither PKCi nor TSA treatment resulted in an expression pattern compatible with the network derived at 12 hr for untreated cells, consistent with the inhibition of differentiation.

The above findings can be summarised using dynamical systems terminology. Epidermal differentiation can be described by two saddle-node bifurcations Figure 4e,f. We start with a single minimum that corresponds to a stable state: the stem cell. Then we pass through a first saddle-node bifurcation where we have two minima, so the system can switch from one state to the other and this corresponds to commitment.

Next the global minimum changes and we finally have a second saddle-node bifurcation that leads again to a single steady state, which corresponds to the differentiated cells Zhang et al. From here, the stem cell state and the terminally differentiated state emerge as stable states, while commitment is inherently unstable and serves as a biological switch.

By wholemount labelling the basal layer of sheets of human epidermis Jensen et al. The patterned distribution of phosphatases could be recreated in vitro by culturing keratinocytes on collagen-coated PDMS elastomer substrates that mimic the topographical features of the human epidermal-dermal interface Viswanathan et al. These results indicate that the phosphatases are subject to spatial regulation that is independent of signals from cells in the underlying dermis. We have presented evidence that epidermal commitment is a biological switch controlled by a network of protein phosphatases that are regulated spatially and temporally.

Applying Boolean network analysis to our experimental data, we were unable to recapitulate the observed changes in phosphatase gene expression using a single ABN, but we could do so by implementing a scheme of network switching.

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This led us to conclude that the interactions among the commitment phosphatases change in the course of differentiation. The negative feedback loops predominating at 0, 8 and 12 hr are known to result in stable phenotypes because the network is able to counteract additional inputs Zeigler et al. However, at 4 hr all but one of the interactions were positive. Positive feedback loops lead to instability, because the network amplifies any inputs it receives. This supports the concept of commitment as an unstable state, which aligns with the experimental evidence of that happening within a defined temporal window.

The key role of DUPS6 at commitment fits well with its upregulation by Serum Response Factor, which is known to control keratinocyte differentiation Connelly et al. However, the involvement of multiple phosphatases in commitment may protect cells from undergoing premature terminal differentiation.

Protein Phosphatase Protocols

Furthermore, their patterned distribution within the epidermal basal layer would be consistent with different phosphatases regulating commitment in different positions along the basement membrane, and with the ability of different external stimuli to trigger differentiation via different intracellular pathways Watt, The upregulation of basal layer markers in the suprabasal epidermal layers on knockdown of pro-commitment phosphatases mimics features of psoriatic lesions in which ERK is known to be upregulated Haase et al. These observations lead us to speculate that commitment is abnormally stabilised in psoriasis, as observed in TSA treated keratinocytes in suspension.

Given that protein phosphatases within the commitment network are known to regulate expression of inflammatory cytokines Caunt and Keyse, , deregulated phosphatase expression could potentially contribute to the pathophysiology of psoriasis. In conclusion, the simple experimental model of suspension-induced keratinocyte differentiation has enabled us to capture some of the complex transcriptional and post-translational events that control the transition from the stem to the differentiated cell compartment.

We now have the opportunity to explore, systematically, the key upstream and downstream regulators of protein phosphatases during epidermal homeostasis, injury and disease. For knockdown or overexpression experiments, keratinocytes were grown in Keratinocyte-SFM medium Gibco supplemented with 0. PDMS substrates that mimic the topography of the epidermal-dermal junction were generated as described previously Viswanathan et al. Genome-wide expression profiling was performed using the Illumina BeadArray platform and standard protocols.

Microarray initial processing and normalisation were performed with BeadStudio software. BeadChip internal p -values technical bead replicates were used to identify genes significantly expressed above the background noise. In normal prostate epithelial cells, androgen withdrawal led to decreased cell proliferation, increased apoptosis, and atrophy, whereas tumor prostate epithelial cells overcame these deficiencies by shifting to an androgen-independent state 40 , When androgen-responsive prostate cancer cell lines were treated with synthetic androgens, various MKPs become overexpressed, including VHR.

Androgen-sensitive grafts expressing VHR showed increased resistance to castration-induced apoptosis, and tumor regression was inversely correlated with VHR expression One molecular mechanism that has been suggested for inhibiting prostate cancer progression is based on the finding that VHR knockdown led to JNK activation and apoptosis, suggesting potential therapeutic applications aimed at targeting this phosphatase.

Finally, VHR is also overexpressed in dysplastic nevi DN , which are benign melanocytic tumors with a hyper-proliferative phenotype The enhanced motility phenotype observed in these cells supports the hypothesis that the VHR phosphatase plays important roles in the development of metastatic tumors. It seems that with regard for the mechanisms underlying tumorigenesis, this dual phosphatase plays two opposing functions.

However, in some cellular contexts, VHR downregulation triggers hyper-proliferation, migration and invasiveness in these cells. Thus, it can also be considered a tumor suppressor Another VHR function associated with tumor onset, establishment, growth, and maintenance is the regulation of angiogenesis, which is a crucial step during tumorigenesis.

Genomic instability is characteristic of a broad spectrum of cancers, and genomic alterations can occur during any cell division. These alterations or instabilities are minimized by four major mechanisms: high-fidelity DNA replication during S phase, precise chromosome segregation in M phase, accurate and error-free repair of DNA damage, and a cell cycle progression that is coordinated by cell cycle checkpoints Therefore, a disruption in any step in one or more of these four mechanisms can lead to genomic instability and contribute to cancer development.

The role of this phosphatase in the formation of multipolar spindles in cancer cells was recently investigated Interestingly, VHR is highly enriched in the nucleus of various cell lines 17 , 26 , 38 , 52 , especially after genotoxic stress This may indicate that this phosphatase has other substrates or that it has additional roles in maintaining genomic stability that could be directly or indirectly related to MAPK functions or other substrates.

In fact, recent studies using bioinformatics approaches and validation analyses have suggested that novel VHR substrates are involved in genomic stability They applied a bioinformatics analysis approach to identify human nuclear proteins that could be putative VHR substrates. Previous authors have also employed mass spectrometry to identify novel VHR substrates under cellular genotoxic stress conditions These proteins are tyrosine-phosphorylated in vivo and in vitro and could therefore be potential targets of dephosphorylation by VHR, especially because they are involved in cell cycle regulation and genomic instability DNA damage response and repair processes 53 - Thus, the phosphatase activity of VHR against these three proteins should be further investigated.

VHR also mediates other signaling events in circulatory system cells and blood-related diseases The first such studies were performed in resting T cells, which constitutively express VHR. During resting, activating TCR did not induce the expression of this enzyme via positive-feedback mechanisms. These mechanisms regulated TCR and induced signaling Another study showed that silencing VHR in Staphylococcus aureus -infected human and murine macrophages increased the production of pro-inflammatory cytokines via NF-kB signaling.

The phosphatase activity of VHR seemed to affect this inflammatory response via a negative feedback component, and the observed increase in cytokine levels may have been responsible for the hyper-responsiveness observed in the host immune system However, VHR knockout did not significantly affect T and B lymphocytes, neutrophils, monocytes, or platelet counts Additional studies have shown that VHR involvement in the immune response to septic shock is dependent on sex hormones produced by the host animals.

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Another very recent and important report provided mechanistic clues about the roles that VHR plays in platelet aggregation 61 , VHR knock-out mice exhibited deficiencies in thrombus formation, suggesting that VHR contributes to arterial thrombosis but is unnecessary for primary hemostasis 61 , The drugs currently used in clinics to treat thrombosis usually cause unwanted side effects, such as increasing the risk of gastrointestinal toxicity, neutropenia, thrombocytopenia, and bleeding, in addition to increasing the incidence of arterial thrombosis.

Altogether, these novel functions of VHR strongly support the notion that this enzyme is a potential target for the development of new therapies for platelet aggregation in circulatory system cells and diseases It has been shown that VHR gene expression and protein levels are constitutively high in many different cell lines and diverse tissues 38 , The VHR protein is highly stable even under high-stress conditions, such as cellular genotoxic or oxidative pressure 26 , VHR gene and protein expression levels are not dependent on feedback regulation, such as that associated with many other MKPs 1 , 5 , 8 , However, most VHR-inhibited cell lines exhibit phenotypes that are clearly associated with cell cycle arrest, senescence, and reduced cell survival even though VHR knock-out mice display a normal phenotype and are free of apparent pathological features 17 , 19 , 60 , However, one of the key remaining questions is that of how VHR expression varies among patients carrying different types of cancer.

By mining freely available databases containing gene expression information related to human cancers, it is possible to analyze, for example, correlations between patient survival times and VHR expression. VHR-containing gene sets are available for 13 different cancer types, including approximately 50 cancer subtypes or clinical variables, in this web tool. To illustrate the potential of this toolkit and because these topics are very tightly correlated with previously published cellular experiments , we selected VHR gene expression and patient survival for our analysis 64 , As shown in Figure 1 , positive survival curves were observed when VHR was overexpressed in lung, kidney, sarcoma, lymphoma, and breast cancer.

The data showed that low VHR expression had a significantly negative effect on patient survival. These results, despite the fact that they are associative and predictive, when analyzed in primary and immortalized cell lines derived from the same tumor tissues, overall survival then was substantially lower in cells deficient in VHR than in control cells 17 , 19 , 52 , However, as shown in Figure 2 , VHR overexpression resulted in negative survival curves in lung, brain, and breast cancers.

Although these cancers have different GEO dataset IDs, are associated with different clinical variables, and originated from a different numbers of samples ranging from to , high VHR expression had a statistically significant negative effect on survival in all three cancer types. These findings are in accordance with those presented in other reports in the literature showing that in some cell lines and tissues, VHR deficiency increases proliferation and survival 24 , 35 , while VHR overexpression leads to cell death usually by apoptosis.

An analysis of the correlations among the expression profiles of the VHR gene in various human cancers, patient survival times, and the cellular phenotypes related to VHR loss or gain of function LOF or GOF experiments showed that the biological functions of VHR seem to be tissue-specific. Additionally, VHR behaves both positively and negatively to regulate or mediate different types of human diseases.

Therefore, the dual-specificity phosphatase VHR, similar to many other PTPs, has emerged as an attractive drug target for future clinical interventions 11 , 12 , 62 , Although these have been shown to be very potent inhibitory drugs, their low selectivity makes them poor candidates for clinical trials. Because of the diversity of substrates and biological functions attributed to VHR, which, as discussed in the previous sections, can be easily extended to animal studies and possibly even to a clinical setting, a great deal of effort has been made to identify specific VHR inhibitors.

Once obtained, these compounds should be tested in animal models and humans. It is especially important to facilitate the development of inhibitors that do not affect cellular processes in surrounding healthy tissues. These include compounds that are usually obtained identified, isolated and purified from natural sources, such as plants, fungi, bacteria and seaweeds, and have high specificity for particular phosphatases Additionally, very recently, an alternative to targeting DUSP enzymes at the transcriptional level was presented by research into omega 3 fatty acids ingestion.

For example, docosahexaenoic acid DHA exerts a protective effect on mice brain development, and when it was added to the diet in mice as DHA-enriched fish-oil, it induced astroglial hyperactivation in a dose-dependent manner that was mediated by glial fibrillary acidic protein GFAP and dependent on increased MKP3 activity. The authors thank Prof. Protein tyrosine phosphatases in the human genome. The protein kinase complement of the human genome.

Mustelin T. In: Moorhead G, editor. Protein Phosphatase Protocols. Totowa, NJ: Springer; Farooq A, Zhou MM. Structure and regulation of MAPK phosphatases. Cell Signal. Atypical DUSPs: 19 phosphatases in search of a role. In: Lazo PA editor. Emerging Signaling Pathways in Tumor Biology. Kerala, India: Transworld Research Network; A catalytic mechanism for the dual-specific phosphatases. Protein tyrosine phosphatases: mechanisms of catalysis and regulation.


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