THE DUAL ROLE OF YAP IN DRIVING TGFß-MEDIATED ENDMT

Endothelial-to-mesenchymal transition (EndMT) is a biological process that allows the transdifferentiation of endothelial cells into mesenchymal cells, thus originating cells capable of novel functions necessary for the surrounding environment. EndMT regulates endocardial cushion formation during embryo development, and it is stimulated by the TGF\u3b2/BMP family of ligands. In adults, EndMT is activated upon an injury event or during pathological conditions like organ fibrosis, cerebral cavernous malformation, cancer-associated fibroblast generation, and others. Hence, it is necessary to better characterize the molecular regulators cooperating with TGF\u3b2 signaling in driving EndMT, to possibly provide novel therapeutic targets to treat these pathological conditions. Here we studied YAP, a co-transcriptional regulator involved in several cell biology processes, among which epithelial-to-mesenchymal transition (EMT). Since EndMT is considered a \u201cspecialized\u201d form of EMT, and since YAP and TGF\u3b2 signaling were shown to cross-talk in other contexts, we hypothesized that YAP contributes to EndMT by modulating TGF\u3b2 signaling, and characterized the underlying molecular mechanism. Results here presented demonstrate that YAP is required for a complete TGF\u3b2-mediated EndMT response in vitro, and that YAP contributes specifically to SMAD3-, but not SMAD1-, driven EndMT gene transcription. We provide novel evidence that YAP positively regulates EndMT playing the twofold role of acting as SMAD3 co-transcriptional factor on the promoter of EndMT target genes and, in parallel, preventing SMAD3 from degradation. YAP is therefore emerging as a possible candidate target to inhibit pathological TGF\u3b2-driven EndMT

Tracing the identity of Parmigiano Reggiano “Prodotto di Montagna - Progetto Territorio” cheese using NMR spectroscopy and multivariate data analysis

Background Nuclear magnetic resonance (NMR) spectroscopy is one of the well-established tools for food metabolomic analysis, as it proved to be very effective in authenticity and quality control of dairy products, as well as to follow product evolution during processing and storage. The analytical assessment of the EU mountain denomination label, specifically for Parmigiano Reggiano "Prodotto di Montagna - Progetto Territorio" (Mountain-CQ) cheese, has received limited attention. Although it was established in 2012 the EU mountain denomination label has not been much studied from an analytical point of view. Nonetheless, tracing a specific profile for the mountain products is essential to support the value chain of this specialty. Results The aim of the study was to produce an identity profile for Parmigiano Reggiano “Prodotto di Montagna - Progetto Territorio” (Mountain-CQ) cheese, and to differentiate it from Parmigiano Reggiano PDO samples (conventional-PDO) using 1H NMR spectroscopy coupled with multivariate data analysis. Three different approaches were applied and compared. First, the spectra-as-such were analysed after proper preprocessing. For the other two approaches, Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) was used for signals resolution and features extraction, either individually on manually-defined spectral intervals or by reapplying MCR-ALS on the whole spectra with selectivity constraints using the reconstructed “pure profiles” as initial estimates and targets. All approaches provided comparable information regarding the samples’ distribution, as in all three cases the separation between the two product categories conventional-PDO and Mountain-CQ could be highlighted. Moreover, a novel MATLAB toolbox for features extraction via MCR-ALS was developed and used in synergy with the Chenomx library, allowing for a putative identification of the selected features. Significance A first identity profile for Parmigiano Reggiano “Prodotto di Montagna - Progetto Territorio” obtained by interpreting the metabolites signals in NMR spectroscopy was obtained. Our workflow and toolbox for generating the features dataset allows a more straightforward interpretation of the results, to overcome the limitations due to dimensionality and to peaks overlapping, but also to include the signals assignment and matching since the early stages of the data processing and analysis

Optimization of Supercritical Carbon Dioxide Extraction of Rice Bran Oil and γ-Oryzanol Using Multi-Factorial Design of Experiment

After rice harvesting, the milling processes generate many by-products including husk, bran, germs, and broken rice representing around 40% of the total grain. Bran, one of the external cereal layers, contains proteins, dietary fibers, minerals, and lipids. One of the most common rice bran utilization is the extraction of rice bran oil (RBO). Among all vegetable oils, RBO presents a unique chemical composition rich in antioxidant compounds such as γ-oryzanol that provide several beneficial properties. RBO is generally extracted by exploiting hexane, a solvent toxic to the environment and human health. The growing demand for this oil has led researchers to look for more sustainable extraction techniques. Supercritical carbon dioxide (SC-CO2) has been successfully applied to extract oil and functional compounds from several matrices. In this work, the SC-CO2 extraction of RBO was optimized using a Design of Experiment (DoE) on a pilot scale. "The DoE approach involving multilinear regression allowed modelling the yield in RBO and gamma oryzanol as a function of temperature and pressure, keeping the extraction time constant, as decided by the company. This approach made it possible to optimize the extraction yield and to identify the best temperature (40 °C), while also highlighting that pressure did not play any influential role in the process, at least concerning the analyzed experimental domain on this industrial plant. A model for computing the extraction yield as a function of temperature and pressure was obtained. This study shows that it is possible to obtain good quality RBO, rich in γ-oryzanol and essential fatty acids, using low temperatures and pressures, starting from a rice milling by-product. Graphical Abstract: [Figure not available: see fulltext.

An integrated workflow for robust alignment and simplified quantitative analysis of NMR spectrometry data

<p>Abstract</p> <p>Background</p> <p>Nuclear magnetic resonance spectroscopy (NMR) is a powerful technique to reveal and compare quantitative metabolic profiles of biological tissues. However, chemical and physical sample variations make the analysis of the data challenging, and typically require the application of a number of preprocessing steps prior to data interpretation. For example, noise reduction, normalization, baseline correction, peak picking, spectrum alignment and statistical analysis are indispensable components in any NMR analysis pipeline.</p> <p>Results</p> <p>We introduce a novel suite of informatics tools for the quantitative analysis of NMR metabolomic profile data. The core of the processing cascade is a novel peak alignment algorithm, called hierarchical Cluster-based Peak Alignment (CluPA). The algorithm aligns a target spectrum to the reference spectrum in a top-down fashion by building a hierarchical cluster tree from peak lists of reference and target spectra and then dividing the spectra into smaller segments based on the most distant clusters of the tree. To reduce the computational time to estimate the spectral misalignment, the method makes use of Fast Fourier Transformation (FFT) cross-correlation. Since the method returns a high-quality alignment, we can propose a simple methodology to study the variability of the NMR spectra. For each aligned NMR data point the ratio of the between-group and within-group sum of squares (BW-ratio) is calculated to quantify the difference in variability between and within predefined groups of NMR spectra. This differential analysis is related to the calculation of the F-statistic or a one-way ANOVA, but without distributional assumptions. Statistical inference based on the BW-ratio is achieved by bootstrapping the null distribution from the experimental data.</p> <p>Conclusions</p> <p>The workflow performance was evaluated using a previously published dataset. Correlation maps, spectral and grey scale plots show clear improvements in comparison to other methods, and the down-to-earth quantitative analysis works well for the CluPA-aligned spectra. The whole workflow is embedded into a modular and statistically sound framework that is implemented as an R package called "speaq" ("spectrum alignment and quantitation"), which is freely available from <url>http://code.google.com/p/speaq/</url>.</p

Diabetes mellitus, maternal adiposity, and insulin-dependent gestational diabetes are associated with COVID-19 in pregnancy: the INTERCOVID study

BACKGROUND: Among nonpregnant individuals, diabetes mellitus and high body mass index increase the risk of COVID-19 and its severity.OBJECTIVE: This study aimed to determine whether diabetes mellitus and high body mass index are risk factors for COVID-19 in pregnancy and whether gestational diabetes mellitus is associated with COVID-19 diagnosis.STUDY DESIGN: INTERCOVID was a multinational study conducted between March 2020 and February 2021 in 43 institutions from 18 countries, enrolling 2184 pregnant women aged >= 18 years; a total of 2071 women were included in the analyses. For each woman diagnosed with COVID-19, 2 nondiagnosed women delivering or initiating antenatal care at the same institution were also enrolled. The main exposures were preexisting diabetes mellitus, high body mass index (overweight or obesity was defined as a body mass index >= 25 kg/m(2)), and gestational diabetes mellitus in pregnancy. The main outcome was a confirmed diagnosis of COVID-19 based on a real-time polymerase chain reaction test, antigen test, antibody test, radiological pulmonary findings, or >= 2 predefined COVID-19 symptoms at any time during pregnancy or delivery. Relationships of exposures and COVID-19 diagnosis were assessed using generalized linear models with a Poisson distribution and log link function, with robust standard errors to account for model misspecification. Furthermore, we conducted sensitivity analyses: (1) restricted to those with a real-time polymerase chain reaction test or an antigen test in the last week of pregnancy, (2) restricted to those with a real-time polymerase chain reaction test or an antigen test during the entire pregnancy, (3) generating values for missing data using multiple imputation, and (4) analyses controlling for month of enrollment. In addition, among women who were diagnosed with COVID-19, we examined whether having gestational diabetes mellitus, diabetes mellitus, or high body mass index increased the risk of having symptomatic vs asymptomatic COVID-19.RESULTS: COVID-19 was associated with preexisting diabetes mellitus (risk ratio, 1.94; 95% confidence interval, 1.55-2.42), overweight or obesity (risk ratio, 1.20; 95% confidence interval, 1.06-1.37), and gestational diabetes mellitus (risk ratio, 1.21; 95% confidence interval, 0.99-1.46). The gestational diabetes mellitus association was specifically among women requiring insulin, whether they were of normal weight (risk ratio, 1.79; 95% confidence interval, 1.06-3.01) or overweight or obese (risk ratio, 1.77; 95% confidence interval, 1.28-2.45). A somewhat stronger association with COVID-19 diagnosis was observed among women with preexisting diabetes mellitus, whether they were of normal weight (risk ratio, 1.93; 95% confidence interval, 1.18-3.17) or overweight or obese (risk ratio, 2.32; 95% confidence interval, 1.82-2.97). When the sample was restricted to those with a real-time polymerase chain reaction test or an antigen test in the week before delivery or during the entire pregnancy, including missing variables using imputation or controlling for month of enrollment, the observed associations were comparable.CONCLUSION: Diabetes mellitus and overweight or obesity were risk factors for COVID-19 diagnosis in pregnancy, and insulin-dependent gestational diabetes mellitus was associated with the disease. Therefore, it is essential that women with these comorbidities are vaccinated

Prognostic value of metabolic response in breast cancer patients receiving neoadjuvant chemotherapy

<p>Abstract</p> <p>Background</p> <p>Today's clinical diagnostic tools are insufficient for giving accurate prognosis to breast cancer patients. The aim of our study was to examine the tumor metabolic changes in patients with locally advanced breast cancer caused by neoadjuvant chemotherapy (NAC), relating these changes to clinical treatment response and long-term survival.</p> <p>Methods</p> <p>Patients (n = 89) participating in a randomized open-label multicenter study were allocated to receive either NAC as epirubicin or paclitaxel monotherapy. Biopsies were excised pre- and post-treatment, and analyzed by high resolution magic angle spinning magnetic resonance spectroscopy (HR MAS MRS). The metabolite profiles were examined by paired and unpaired multivariate methods and findings of important metabolites were confirmed by spectral integration of the metabolite peaks.</p> <p>Results</p> <p>All patients had a significant metabolic response to NAC, and pre- and post-treatment spectra could be discriminated with 87.9%/68.9% classification accuracy by paired/unpaired partial least squares discriminant analysis (PLS-DA) (<it>p </it>< 0.001). Similar metabolic responses were observed for the two chemotherapeutic agents. The metabolic responses were related to patient outcome. Non-survivors (< 5 years) had increased tumor levels of lactate (<it>p </it>= 0.004) after treatment, while survivors (≥ 5 years) experienced a decrease in the levels of glycine (<it>p </it>= 0.047) and choline-containing compounds (<it>p </it>≤ 0.013) and an increase in glucose (<it>p </it>= 0.002) levels. The metabolic responses were not related to clinical treatment response.</p> <p>Conclusions</p> <p>The differences in tumor metabolic response to NAC were associated with breast cancer survival, but not to clinical response. Monitoring metabolic responses to NAC by HR MAS MRS may provide information about tumor biology related to individual prognosis.</p

Maternal and Neonatal Morbidity and Mortality Among Pregnant Women With and Without COVID-19 Infection: The INTERCOVID Multinational Cohort Study.

Importance: Detailed information about the association of COVID-19 with outcomes in pregnant individuals compared with not-infected pregnant individuals is much needed. Objective: To evaluate the risks associated with COVID-19 in pregnancy on maternal and neonatal outcomes compared with not-infected, concomitant pregnant individuals. Design, Setting, and Participants: In this cohort study that took place from March to October 2020, involving 43 institutions in 18 countries, 2 unmatched, consecutive, not-infected women were concomitantly enrolled immediately after each infected woman was identified, at any stage of pregnancy or delivery, and at the same level of care to minimize bias. Women and neonates were followed up until hospital discharge. Exposures: COVID-19 in pregnancy determined by laboratory confirmation of COVID-19 and/or radiological pulmonary findings or 2 or more predefined COVID-19 symptoms. Main Outcomes and Measures: The primary outcome measures were indices of (maternal and severe neonatal/perinatal) morbidity and mortality; the individual components of these indices were secondary outcomes. Models for these outcomes were adjusted for country, month entering study, maternal age, and history of morbidity. Results: A total of 706 pregnant women with COVID-19 diagnosis and 1424 pregnant women without COVID-19 diagnosis were enrolled, all with broadly similar demographic characteristics (mean [SD] age, 30.2 [6.1] years). Overweight early in pregnancy occurred in 323 women (48.6%) with COVID-19 diagnosis and 554 women (40.2%) without. Women with COVID-19 diagnosis were at higher risk for preeclampsia/eclampsia (relative risk [RR], 1.76; 95% CI, 1.27-2.43), severe infections (RR, 3.38; 95% CI, 1.63-7.01), intensive care unit admission (RR, 5.04; 95% CI, 3.13-8.10), maternal mortality (RR, 22.3; 95% CI, 2.88-172), preterm birth (RR, 1.59; 95% CI, 1.30-1.94), medically indicated preterm birth (RR, 1.97; 95% CI, 1.56-2.51), severe neonatal morbidity index (RR, 2.66; 95% CI, 1.69-4.18), and severe perinatal morbidity and mortality index (RR, 2.14; 95% CI, 1.66-2.75). Fever and shortness of breath for any duration was associated with increased risk of severe maternal complications (RR, 2.56; 95% CI, 1.92-3.40) and neonatal complications (RR, 4.97; 95% CI, 2.11-11.69). Asymptomatic women with COVID-19 diagnosis remained at higher risk only for maternal morbidity (RR, 1.24; 95% CI, 1.00-1.54) and preeclampsia (RR, 1.63; 95% CI, 1.01-2.63). Among women who tested positive (98.1% by real-time polymerase chain reaction), 54 (13%) of their neonates tested positive. Cesarean delivery (RR, 2.15; 95% CI, 1.18-3.91) but not breastfeeding (RR, 1.10; 95% CI, 0.66-1.85) was associated with increased risk for neonatal test positivity. Conclusions and Relevance: In this multinational cohort study, COVID-19 in pregnancy was associated with consistent and substantial increases in severe maternal morbidity and mortality and neonatal complications when pregnant women with and without COVID-19 diagnosis were compared. The findings should alert pregnant individuals and clinicians to implement strictly all the recommended COVID-19 preventive measures

Preeclampsia and COVID-19: results from the INTERCOVID prospective longitudinal study.

BACKGROUND: It is unclear whether the suggested link between COVID-19 during pregnancy and preeclampsia is an independent association or if these are caused by common risk factors. OBJECTIVE: This study aimed to quantify any independent association between COVID-19 during pregnancy and preeclampsia and to determine the effect of these variables on maternal and neonatal morbidity and mortality. STUDY DESIGN: This was a large, longitudinal, prospective, unmatched diagnosed and not-diagnosed observational study assessing the effect of COVID-19 during pregnancy on mothers and neonates. Two consecutive not-diagnosed women were concomitantly enrolled immediately after each diagnosed woman was identified, at any stage during pregnancy or delivery, and at the same level of care to minimize bias. Women and neonates were followed until hospital discharge using the standardized INTERGROWTH-21st protocols and electronic data management system. A total of 43 institutions in 18 countries contributed to the study sample. The independent association between the 2 entities was quantified with the risk factors known to be associated with preeclampsia analyzed in each group. The outcomes were compared among women with COVID-19 alone, preeclampsia alone, both conditions, and those without either of the 2 conditions. RESULTS: We enrolled 2184 pregnant women; of these, 725 (33.2%) were enrolled in the COVID-19 diagnosed and 1459 (66.8%) in the COVID-19 not-diagnosed groups. Of these women, 123 had preeclampsia of which 59 of 725 (8.1%) were in the COVID-19 diagnosed group and 64 of 1459 (4.4%) were in the not-diagnosed group (risk ratio, 1.86; 95% confidence interval, 1.32-2.61). After adjustment for sociodemographic factors and conditions associated with both COVID-19 and preeclampsia, the risk ratio for preeclampsia remained significant among all women (risk ratio, 1.77; 95% confidence interval, 1.25-2.52) and nulliparous women specifically (risk ratio, 1.89; 95% confidence interval, 1.17-3.05). There was a trend but no statistical significance among parous women (risk ratio, 1.64; 95% confidence interval, 0.99-2.73). The risk ratio for preterm birth for all women diagnosed with COVID-19 and preeclampsia was 4.05 (95% confidence interval, 2.99-5.49) and 6.26 (95% confidence interval, 4.35-9.00) for nulliparous women. Compared with women with neither condition diagnosed, the composite adverse perinatal outcome showed a stepwise increase in the risk ratio for COVID-19 without preeclampsia, preeclampsia without COVID-19, and COVID-19 with preeclampsia (risk ratio, 2.16; 95% confidence interval, 1.63-2.86; risk ratio, 2.53; 95% confidence interval, 1.44-4.45; and risk ratio, 2.84; 95% confidence interval, 1.67-4.82, respectively). Similar findings were found for the composite adverse maternal outcome with risk ratios of 1.76 (95% confidence interval, 1.32-2.35), 2.07 (95% confidence interval, 1.20-3.57), and 2.77 (95% confidence interval, 1.66-4.63). The association between COVID-19 and gestational hypertension and the direction of the effects on preterm birth and adverse perinatal and maternal outcomes, were similar to preeclampsia, but confined to nulliparous women with lower risk ratios. CONCLUSION: COVID-19 during pregnancy is strongly associated with preeclampsia, especially among nulliparous women. This association is independent of any risk factors and preexisting conditions. COVID-19 severity does not seem to be a factor in this association. Both conditions are associated independently of and in an additive fashion with preterm birth, severe perinatal morbidity and mortality, and adverse maternal outcomes. Women with preeclampsia should be considered a particularly vulnerable group with regard to the risks posed by COVID-19