Increased motility and invasiveness of pancreatic cancer cells are associated with epithelial to mesenchymal transition (EMT). Snai1 and Slug are zinc-finger transcription factors that trigger this ...process by repressing E-cadherin and enhancing vimentin and N-cadherin protein expression. However, the mechanisms that regulate this activation in pancreatic tumors remain elusive. MUC1, a transmembrane mucin glycoprotein, is associated with the most invasive forms of pancreatic ductal adenocarcinomas (PDA). In this study, we show that over expression of MUC1 in pancreatic cancer cells triggers the molecular process of EMT, which translates to increased invasiveness and metastasis. EMT was significantly reduced when MUC1 was genetically deleted in a mouse model of PDA or when all seven tyrosines in the cytoplasmic tail of MUC1 were mutated to phenylalanine (mutated MUC1 CT). Using proteomics, RT-PCR and western blotting, we revealed a significant increase in vimentin, Slug and Snail expression with repression of E-Cadherin in MUC1-expressing cells compared with cells expressing the mutated MUC1 CT. In the cells that carried the mutated MUC1 CT, MUC1 failed to co-immunoprecipitate with β-catenin and translocate to the nucleus, thereby blocking transcription of the genes associated with EMT and metastasis. Thus, functional tyrosines are critical in stimulating the interactions between MUC1 and β-catenin and their nuclear translocation to initiate the process of EMT. This study signifies the oncogenic role of MUC1 CT and is the first to identify a direct role of the MUC1 in initiating EMT during pancreatic cancer. The data may have implications in future design of MUC1-targeted therapies for pancreatic cancer.
Here we used flow cytometry (FCM) and filtration paired with amplicon sequencing to determine the abundance and composition of small low nucleic acid (LNA)-content bacteria in a variety of freshwater ...ecosystems. We found that FCM clusters associated with LNA-content bacteria were ubiquitous across several ecosystems, varying from 50 to 90% of aquatic bacteria. Using filter-size separation, we separated small LNA-content bacteria (passing 0.4 µm filter) from large bacteria (captured on 0.4 µm filter) and characterized communities with 16S amplicon sequencing. Small and large bacteria each represented different sub-communities within the ecosystems' community. Moreover, we were able to identify individual operational taxonomical units (OTUs) that appeared exclusively with small bacteria (434 OTUs) or exclusively with large bacteria (441 OTUs). Surprisingly, these exclusive OTUs clustered at the phylum level, with many OTUs appearing exclusively with small bacteria identified as candidate phyla (i.e. lacking cultured representatives) and symbionts. We propose that LNA-content bacteria observed with FCM encompass several previously characterized categories of bacteria (ultramicrobacteria, ultra-small bacteria, candidate phyla radiation) that share many traits including small size and metabolic dependencies on other microorganisms.
Fluorescent staining coupled with flow cytometry (FCM) is often used for the monitoring, quantification and characterization of bacteria in engineered and environmental aquatic ecosystems including ...seawater, freshwater, drinking water, wastewater, and industrial bioreactors. However, infrequent grab sampling hampers accurate characterization and subsequent understanding of microbial dynamics in all of these ecosystems. A logic technological progression is high throughput and full automation of the sampling, staining, measurement, and data analysis steps. Here we assess the feasibility and applicability of automated FCM by means of actual data sets produced with prototype instrumentation. As proof-of-concept we demonstrate examples of microbial dynamics in (i) flowing tap water from a municipal drinking water supply network and (ii) river water from a small creek subject to two rainfall events. In both cases, automated measurements were done at 15-min intervals during 12-14 consecutive days, yielding more than 1000 individual data points for each ecosystem. The extensive data sets derived from the automated measurements allowed for the establishment of baseline data for each ecosystem, as well as for the recognition of daily variations and specific events that would most likely be missed (or miss-characterized) by infrequent sampling. In addition, the online FCM data from the river water was combined and correlated with online measurements of abiotic parameters, showing considerable potential for a better understanding of cause-and-effect relationships in aquatic ecosystems. Although several challenges remain, the successful operation of an automated online FCM system and the basic interpretation of the resulting data sets represent a breakthrough toward the eventual establishment of fully automated online microbiological monitoring technologies.
Rapid contamination of drinking water in distribution and storage systems can occur due to pressure drop, backflow, cross-connections, accidents, and bio-terrorism. Small volumes of a concentrated ...contaminant (e.g., wastewater) can contaminate large volumes of water in a very short time with potentially severe negative health impacts. The technical limitations of conventional, cultivation-based microbial detection methods neither allow for timely detection of such contaminations, nor for the real-time monitoring of subsequent emergency remediation measures (e.g., shock-chlorination). Here we applied a newly developed continuous, ultra high-frequency flow cytometry approach to track a rapid pollution event and subsequent disinfection of drinking water in an 80-min laboratory scale simulation. We quantified total (TCC) and intact (ICC) cell concentrations as well as flow cytometric fingerprints in parallel in real-time with two different staining methods. The ingress of wastewater was detectable almost immediately (i.e., after 0.6% volume change), significantly changing TCC, ICC, and the flow cytometric fingerprint. Shock chlorination was rapid and detected in real time, causing membrane damage in the vast majority of bacteria (i.e., drop of ICC from more than 380 cells μl
to less than 30 cells μl
within 4 min). Both of these effects as well as the final wash-in of fresh tap water followed calculated predictions well. Detailed and highly quantitative tracking of microbial dynamics at very short time scales and for different characteristics (e.g., concentration, membrane integrity) is feasible. This opens up multiple possibilities for targeted investigation of a myriad of bacterial short-term dynamics (e.g., disinfection, growth, detachment, operational changes) both in laboratory-scale research and full-scale system investigations in practice.
Short-term fluctuations in bacterial concentrations in drinking water systems, occurring on time scales of hours-to-weeks, are essentially unexplored due to a lack of microbial monitoring tools that ...allow high frequency measurements. Here, we applied fully automated online flow cytometry to measure the total cell concentrations (TCC) in both raw water (karstic groundwater) and treated water (flocculation – ultrafiltration (UF) – ozonation – granular active carbon (GAC) filtration) during a period of 70 days at high temporal resolution (n > 4000 for both water types). We detected and characterized in considerable detail aperiodic fluctuations in the raw water following regional precipitation, with TCC increasing up to 50-fold from a dry weather baseline of approximately 120 cells μl−1 to an event peak of > 5000 cells μl−1. Moreover, we observed the buffering of the treatment plant against these fluctuations, but in addition we recorded a completely unexpected periodic fluctuation of TCC in the treated water after GAC filtration. We concluded that the latter was the result of fluctuating water abstraction from the treatment plant reservoir by two connected water utilities, which resulted in variations in water throughput in the plant. This in turn influenced bacterial detachment and dilution in the GAC filter. This study provides strong evidence of multiple different microbial dynamics occurring in a drinking water treatment system. Given numerous possible sources of natural and operational fluctuations in raw water and drinking water treatment plants, such microbial fluctuations should be expected in many systems. The high-frequency monitoring approach presented herein can improve the understanding and eventual mitigation of such fluctuations.
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•More than 8000 flow cytometric measurements of raw and treated groundwater in 70 days.•Raw water total cell concentrations increased up to 50-fold after regional precipitation.•A multi-step treatment train hedged against aperiodic microbial peak loads in raw water.•Periodic variations treated water throughput caused variations in total cell concentrations.
Monitoring of microbial drinking water quality is a key component for ensuring safety and understanding risk, but conventional monitoring strategies are typically based on low sampling frequencies ...(e.g., quarterly or monthly). This is of concern because many drinking water sources, such as karstic springs are often subject to changes in bacterial concentrations on much shorter time scales (e.g., hours to days), for example after precipitation events. Microbial contamination events are crucial from a risk assessment perspective and should therefore be targeted by monitoring strategies to establish both the frequency of their occurrence and the magnitude of bacterial peak concentrations. In this study we used monitoring data from two specific karstic springs. We assessed the performance of conventional monitoring based on historical records and tested a number of alternative strategies based on a high-resolution data set of bacterial concentrations in spring water collected with online flow cytometry (FCM). We quantified the effect of increasing sampling frequency and found that for the specific case studied, at least bi-weekly sampling would be needed to detect precipitation events with a probability of >90%. We then proposed an optimized monitoring strategy with three targeted samples per event, triggered by precipitation measurements. This approach is more effective and efficient than simply increasing overall sampling frequency. It would enable the water utility to (1) analyze any relevant event and (2) limit median underestimation of peak concentrations to approximately 10%. We conclude with a generalized perspective on sampling optimization and argue that the assessment of short-term dynamics causing microbial peak loads initially requires increased sampling/analysis efforts, but can be optimized subsequently to account for limited resources. This offers water utilities and public health authorities systematic ways to evaluate and optimize their current monitoring strategies.
Pancreatic ductal adenocarcinoma (PDA) has one of the worst prognoses of all cancers. Mucin 1 (MUC1), a transmembrane mucin glycoprotein, is a key modulator of several signaling pathways that affect ...oncogenesis, motility and metastasis. Its expression is known to be associated with poor prognosis in patients. However, the precise mechanism remains elusive. We report a novel association of MUC1 with platelet-derived growth factor-A (PDGFA). PDGFA is one of the many drivers of tumor growth, angiogenesis and metastasis in PDA. Using mouse PDA models as well as human samples, we show clear evidence that MUC1 regulates the expression and secretion of PDGFA. This, in turn, influences proliferation and invasion of pancreatic cancer cells leading to higher tumor burden in vivo. In addition, we reveal that MUC1 overexpressing cells are heavily dependent on PDGFA both for proliferation and invasion, whereas MUC1-null cells are not. Moreover, PDGFA and MUC1 are critical for translocation of β catenin to the nucleus for oncogenesis to ensue. Finally, we elucidate the underlying mechanism by which MUC1 regulates PDGFA expression and secretion in pancreatic cancer cells. We show that MUC1 associates with Hif1-α, a known transcription factor involved in controlling PDGFA expression. Furthermore, MUC1 facilitates Hif1-α translocation to the nucleus. In summary, we have demonstrated that MUC1-induced invasion and proliferation occurs via increased exogenous production of PDGFA. Thus, impeding MUC1 regulation of PDGFA signaling may be therapeutically beneficial for patients with PDA.
Cell density is an important factor in all microbiome research, where interactions are of interest. It is also the most important parameter for the operation and control of most biotechnological ...processes. In the past, cell density determination was often performed offline and manually, resulting in a delay between sampling and immediate data processing, preventing quick action. While there are now some online methods for rapid and automated cell density determination, they are unable to distinguish between the different cell types in bacterial communities. To address this gap, an online automated flow cytometry procedure is proposed for real-time high-resolution analysis of bacterial communities. On the one hand, it allows for the online automated calculation of cell concentrations and, on the other, for the differentiation between different cell subsets of a bacterial community. To achieve this, the OC-300 automation device (onCyt Microbiology, Zürich, Switzerland) was coupled with the flow cytometer CytoFLEX (Beckman Coulter, Brea, USA). The OC-300 performs the automatic sampling, dilution, fixation and 4',6-diamidino-2-phenylindole (DAPI) staining of a bacterial sample before sending it to the CytoFLEX for measurement. It is demonstrated that this method can reproducibly measure both cell density and fingerprint-like patterns of bacterial communities, generating suitable data for powerful automated data analysis and interpretation pipelines. In particular, the automated, high-resolution partitioning of clustered data into cell subsets opens up the possibility of correlation analysis to identify the operational or abiotic/biotic causes of community disturbances or state changes, which can influence the interaction potential of organisms in microbiomes or even affect the performance of individual organisms.
•A greywater treatment system was monitored with automated flow cytometry and turbidity.•Stagnation in biological activated carbon filter led to peaks in total cell concentration and ...turbidity.•Strong correlation was observed between TCC and turbidity.•Stagnation did not lead to increase of opportunistic pathogens in the biofilter's effluent.
A key characteristic of decentralized greywater treatment and reuse is high variability in both nutrient concentrations and flow. This variability in flow leads to stagnant water in the system and causes short-term fluctuations in the effluent water quality. Automated monitoring tools provide data to understand the mechanisms underlying the dynamics and to adapt control strategies accordingly. We investigated the fluctuations in a building-scale greywater treatment system comprising a membrane bioreactor followed by a biological activated carbon filter. Short-term dynamics in the effluent of the biological activated carbon filter were monitored with automated flow cytometry and turbidity, and the impact of these fluctuations on various hygiene-relevant parameters in the reuse water was evaluated. Continuous biofilm detachment into the stagnant water in the biological activated carbon filter led to temporarily increased turbidity and cell concentrations in the effluent after periods of stagnation. The fluctuations in cell concentrations were consistent with a model assuming higher detachment rates during flow than during times with stagnant water. For this system, total cell concentration and turbidity were strongly correlated. We also showed that the observed increase in cell concentration was not related to either an increase of organic carbon concentration or the concentration of two opportunistic pathogens, P. aeruginosa and L. pneumophila. Our findings demonstrate that turbidity measurements are sensitive to changes in the effluent water quality and can be used to monitor the fluctuations caused by intermittent flow. Intermittent flow did not lead to an increase in opportunistic pathogens, and this study provides no indications that stagnant water in biological activated carbon filters need be prevented.
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Drinking water utilities and researchers continue to rely on the century-old heterotrophic plate counts (HPC) method for routine assessment of general microbiological water quality. Bacterial cell ...counting with flow cytometry (FCM) is one of a number of alternative methods that challenge this status quo and provide an opportunity for improved water quality monitoring. After more than a decade of application in drinking water research, FCM methodology is optimised and established for routine application, supported by a considerable amount of data from multiple full-scale studies. Bacterial cell concentrations obtained by FCM enable quantification of the entire bacterial community instead of the minute fraction of cultivable bacteria detected with HPC (typically < 1% of all bacteria). FCM measurements are reproducible with relative standard deviations below 3% and can be available within 15 min of samples arriving in the laboratory. High throughput sample processing and complete automation are feasible and FCM analysis is arguably less expensive than HPC when measuring more than 15 water samples per day, depending on the laboratory and selected staining procedure(s). Moreover, many studies have shown FCM total (TCC) and intact (ICC) cell concentrations to be reliable and robust process variables, responsive to changes in the bacterial abundance and relevant for characterising and monitoring drinking water treatment and distribution systems. The purpose of this critical review is to initiate a constructive discussion on whether FCM could replace HPC in routine water quality monitoring. We argue that FCM provides a faster, more descriptive and more representative quantification of bacterial abundance in drinking water.
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•Routine drinking water monitoring still relies on heterotrophic plate counts (HPC).•Flow cytometry (FCM) is proposed as a better method for process monitoring.•No good correlation was found between FCM and HPC data (n = 3,675).•Good correlations were found between FCM and ATP data (n = 1,441).•FCM advantages are: relevance, speed, accuracy, costs and automation potential.
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