Septoria Leaf Speck, a New Disease of Sunflower, Caused by Septoria helianthina sp. nov.

Many strains of a pathogenic fungus were isolated from diseased sunflower leaf tissue. According to their characteristics they belonged to the genus Septoria . Leaf speck symptoms, characteristic for spontaneous infection, were also reproduced by artificial inoculation of sunflower plants in the greenhouse.
Comparing the morphological, cultural and pathogenic properties of the isolated fungus with other sunflower pathogens, it was indicated that the strains, originating from diseased sunflower leaves represented a new, so far not described species. Therefore, for the causal agent of this disease, the name Septoria helianthina Petrov & Arsenijevic was suggested.     

Polarization-resolved SHG imaging as a fast screening method for collagen alterations during aging: Comparison with light and electron microscopy

Our previous study on rat skin showed that cumulative oxidative pressure induces profound structural and ultrastructural alterations in both rat skin epidermis and dermis during aging. Here, we aimed to investigate the biophotonic properties of collagen as a main dermal component in the function of chronological aging. We used second harmonic generation (SHG) and two-photon excited fluorescence (TPEF) on 5 μm thick skin paraffin sections from 15-day-, 1-month- and 21-month-old rats, respectively, to analyze collagen alterations, in comparison to conventional light and electron microscopy methods. Obtained results show that polarization-resolved SHG (PSHG) images can detect collagen fiber alterations in line with chronological aging and that this method is consistent with light and electron microscopy. Moreover, the β coefficient calculated from PSHG images points out that delicate alterations lead to a more ordered structure of collagen molecules due to oxidative damage. The results of this study also open the possibility of successfully applying this fast and label-free method to previously fixed samples.     

Function of membrane domains in rho-of-plant signaling

In a crowded environment, establishing interactions between different molecular partners can take a long time. Biological membranes have solved this issue, as they simultaneously are fluid and possess compartmentalized domains. This nanoscale organization of the membrane is often based on weak, local, and multivalent interactions between lipids and proteins. However, from local interactions at the nanoscale, different functional properties emerge at the higher scale, and these are critical to regulate and integrate cellular signaling. Rho of Plant (ROP) proteins are small guanosine triphosphate hydrolase enzymes (GTPases) involved in hormonal, biotic, and abiotic signaling, as well as fundamental cell biological properties such as polarity, vesicular trafficking, and cytoskeleton dynamics. Association with the membrane is essential for ROP function, as well as their precise targeting within micrometer-sized polar domains (i.e. microdomains) and nanometer-sized clusters (i.e. nanodomains). Here, we review our current knowledge about the formation and the maintenance of the ROP domains in membranes. Furthermore, we propose a model for ROP membrane targeting and discuss how the nanoscale organization of ROPs in membranes could determine signaling parameters like signal specificity, amplification, and integration.

The nanoscale organization of Rho of Plant proteins creates emergent properties that determine cellular signaling.     

A combination of urinary biomarker panel and PancRISK score for earlier detection of pancreatic cancer: A case–control study

BackgroundPancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancers, with around 9% of patients surviving >5 years. Asymptomatic in its initial stages, PDAC is mostly diagnosed late, when already a locally advanced or metastatic disease, as there are no useful biomarkers for detection in its early stages, when surgery can be curative. We have previously described a promising biomarker panel (LYVE1, REG1A, and TFF1) for earlier detection of PDAC in urine. Here, we aimed to establish the accuracy of an improved panel, including REG1B instead of REG1A, and an algorithm for data interpretation, the PancRISK score, in additional retrospectively collected urine specimens. We also assessed the complementarity of this panel with CA19-9 and explored the daily variation and stability of the biomarkers and their performance in common urinary tract cancers.Methods and findingsClinical specimens were obtained from multiple centres: Barts Pancreas Tissue Bank, University College London, University of Liverpool, Spanish National Cancer Research Center, Cambridge University Hospital, and University of Belgrade. The biomarker panel was assayed on 590 urine specimens: 183 control samples, 208 benign hepatobiliary disease samples (of which 119 were chronic pancreatitis), and 199 PDAC samples (102 stage I–II and 97 stage III–IV); 50.7% were from female individuals. PDAC samples were collected from patients before treatment. The samples were assayed using commercially available ELISAs. Statistical analyses were performed using non-parametric Kruskal–Wallis tests adjusted for multiple comparisons, and multiple logistic regression. Training and validation datasets for controls and PDAC samples were obtained after random division of the whole available dataset in a 1:1 ratio. The substitution of REG1A with REG1B enhanced the performance of the panel to detect resectable PDAC. In a comparison of controls and PDAC stage I–II samples, the areas under the receiver operating characteristic curve (AUCs) increased from 0.900 (95% CI 0.843–0.957) and 0.926 (95% CI 0.843–1.000) in the training (50% of the dataset) and validation sets, respectively, to 0.936 in both the training (95% CI 0.903–0.969) and the validation (95% CI 0.888–0.984) datasets for the new panel including REG1B. This improved panel showed both sensitivity (SN) and specificity (SP) to be >85%. Plasma CA19-9 enhanced the performance of this panel in discriminating PDAC I–II patients from controls, with AUC = 0.992 (95% CI 0.983–1.000), SN = 0.963 (95% CI 0.913–1.000), and SP = 0.967 (95% CI 0.924–1.000). We demonstrate that the biomarkers do not show significant daily variation, and that they are stable for up to 5 days at room temperature. The main limitation of our study is the low number of stage I–IIA PDAC samples (n = 27) and lack of samples from individuals with hereditary predisposition to PDAC, for which specimens collected from control individuals were used as a proxy.ConclusionsWe have successfully validated our urinary biomarker panel, which was improved by substituting REG1A with REG1B. At a pre-selected cutoff of >80% SN and SP for the affiliated PancRISK score, we demonstrate a clinically applicable risk stratification tool with a binary output for risk of developing PDAC (‘elevated’ or ‘normal’). PancRISK provides a step towards precision surveillance for PDAC patients, which we will test in a prospective clinical study, UroPanc.

Silvana Debernardi and colleagues establish a clinical risk score and a biomarker panel for early detection of pancreatic cancer.