Effects of Adrenal Medulla and Sciatic Nerve Co-Grafts in Rats with Unilateral Substantia Nigra Lesions

Major limitations of adrenal medulla transplantation in animal models of Parkinson's disease have been the relatively small behavioral effects and the poor or inconsistent graft survival. Transplantation of fragments of sural nerve in combination with adrenal medulla has been reported to increase the survival of chromaffin cells in adrenal medulla grafts in primates. In the present study, the possibility was tested that peripheral nerve co-grafts would increase the functional effects of adrenal medulla grafts in a 6-hydroxydopamine-lesioned rat model. Animals received unilateral substantia nigra lesions, and subsequently received intraventricular grafts of adrenal medulla, sciatic nerve, adrenal medulla plus sciatic nerve, or sham grafts consisting of medium only. Functional effects of the grafts were tested using apomorphine-induced rotational behavior. The sciatic nerve co-grafts did not increase the survival of TH-immunoreactive chromaffin cells. The co-grafting treatment also did not augment the overall effect of adrenal medulla grafts on rotational behavior. In the animals with substantial numbers of surviving chromaffin cells, however, the animals with sciatic nerve co-grafts showed greater decreases in rotational behavior as compared to the animals with adrenal medulla grafts alone, even though the number of surviving cells was not increased

Near infra-red photoimmunotherapy with anti-CEA-IR700 results in extensive tumor lysis and a significant decrease in tumor burden in orthotopic mouse models of pancreatic cancer.

Photoimmunotherapy (PIT) of cancer utilizes tumor-specific monoclonal antibodies conjugated to a photosensitizer phthalocyanine dye IR700 which becomes cytotoxic upon irradiation with near infrared light. In this study, we aimed to evaluate the efficacy of PIT on human pancreatic cancer cells in vitro and in vivo in an orthotopic nude mouse model. The binding capacity of anti-CEA antibody to BxPC-3 human pancreatic cancer cells was determined by FACS analysis. An in vitro cytotoxicity assay was used to determine cell death following treatment with PIT. For in vivo determination of PIT efficacy, nude mice were orthotopically implanted with BxPC-3 pancreatic tumors expressing green fluorescent protein (GFP). After tumor engraftment, the mice were divided into two groups: (1) treatment with anti-CEA-IR700 + 690 nm laser and (2) treatment with 690 nm laser only. Anti-CEA-IR700 (100 μg) was administered to group (1) via tail vein injection 24 hours prior to therapy. Tumors were then surgically exposed and treated with phototherapy at an intensity of 150 mW/cm2 for 30 minutes. Whole body imaging was done subsequently for 5 weeks using an OV-100 small animal imaging system. Anti-CEA-IR700 antibody bound to the BxPC3 cells to a high degree as shown by FACS analysis. Anti-CEA-IR700 caused extensive cancer cell killing after light activation compared to control cells in cytotoxicity assays. In the orthotopic models of pancreatic cancer, the anti-CEA-IR700 group had significantly smaller tumors than the control after 5 weeks (p<0.001). There was no significant difference in the body weights of mice in the anti-CEA-IR700 and control groups indicating that PIT was well tolerated by the mice

Computational analysis reveals histotype-dependent molecular profile and actionable mutation effects across cancers

Background: Comprehensive mutational profiling data now available on all major cancers have led to proposals of novel molecular tumor classifications that modify or replace the established organ- and tissue-based tumor typing. The rationale behind such molecular reclassifications is that genetic alterations underlying cancer pathology predict response to therapy and may therefore offer a more precise view on cancer than histology. The use of individual actionable mutations to select cancers for treatment across histotypes is already being tested in the so-called basket trials with variable success rates. Here, we present a computational approach that facilitates the systematic analysis of the histological context dependency of mutational effects by integrating genomic and proteomic tumor profiles across cancers. Methods: To determine effects of oncogenic mutations onprotein profiles, we usedtheenergy distance, which comparesthe Euclidean distancesof protein profiles in tumors with an oncogenic mutation (inner distance) to that in tumors without the mutation (outer distance) and performed Monte Carlo simulations for the significance analysis. Finally, the proteins were ranked by their contribution to profile differences to identify proteins characteristic of oncogenic mutation effects across cancers. Results: We apply our approach to four current proposals of molecular tumor classifications and major therapeutically relevant actionable genes. All 12 actionable genes evaluated show effects on the protein level in the corresponding tumor type and showed additional mutation-related protein profiles in 21 tumor types. Moreover, our analysis identifies consistent cross-cancer effects for 4 genes (FGFR1, ERRB2, IDH1, KRAS/NRAS) in 14 tumor types. We further use cell line drug response data to validate our findings. Conclusions: This computational approach can be used to identify mutational signatures that have protein-level effects and can therefore contribute to preclinical in silico tests of the efficacy of molecular classifications as well as the druggability of individual mutations. It thus supports the identification of novel targeted therapies effective across cancers and guides efficient basket trial designs

Patient-level proteomic network prediction by explainable artificial intelligence

Understanding the pathological properties of dysregulated protein networks in individual patients’ tumors is the basis for precision therapy. Functional experiments are commonly used, but cover only parts of the oncogenic signaling networks, whereas methods that reconstruct networks from omics data usually only predict average network features across tumors. Here, we show that the explainable AI method layer-wise relevance propagation (LRP) can infer protein interaction networks for individual patients from proteomic profiling data. LRP reconstructs average and individual interaction networks with an AUC of 0.99 and 0.93, respectively, and outperforms state-of-the-art network prediction methods for individual tumors. Using data from The Cancer Proteome Atlas, we identify known and potentially novel oncogenic network features, among which some are cancer-type specific and show only minor variation among patients, while others are present across certain tumor types but differ among individual patients. Our approach may therefore support predictive diagnostics in precision oncology by inferring “patient-level” oncogenic mechanisms

Camera collars reveal macronutrient balancing in free-rangingmale moose during summer

Understanding how the nutritional properties of food resources drive foraging choicesis important for the management and conservation of wildlife populations. For moose(Alces alces), recent experimental and observational studies during the winter haveshown macronutrient balancing between available protein (AP) and highly metaboliz-able macronutrients (total non-structural carbohydrates [TNC] and lipids). Here, wecombined the use of continuous-recording camera collars with plant nutrient analysesand forage availability measurements to obtain a detailed insight into the food andnutritional choices of three wild moose in Norway over a 5-day period in summer. Wefound that moose derived their macronutrient energy primarily from carbohydrates(74.2%), followed by protein (13.1%), and lipids (12.7%). Diets were dominated bydeciduous tree browse (71%). Willows (Salix spp.) were selected for and constituted51% of the average diet. Moose consumed 25 different food items during the studyperiod of which 9 comprised 95% of the diet. Moose tightly regulated their intake ofprotein to highly metabolizable macronutrients (AP:TNC + lipids) to a ratio of 1:2.7(0.37 ± 0.002SD). They did this by feeding on foods that most closely matched thetarget macronutrient ratio such as Salix spp., or by combining nutritionally imbalancedfoods (complementary feeding) in a non-random manner that minimized deviationsfrom the intake target. The observed patterns of macronutrient balancing alignedwell with the findings of winter studies. Differential feeding on nutritionally balanceddowny birch (Betula pubescens) leaves versus imbalanced twigs+leaves across mooseindividuals indicated that macronutrient balancing may occur on as fine a scale asforaging bites on a single plant species. Utilized forages generally met the suggestedrequirement thresholds for the minerals calcium, phosphorus, copper, molybdenum,and magnesium but tended to be low in sodium. Our findings offer new insights intothe foraging behavior of a model species in ungulate nutritional ecology and contrib-ute to informed decision-making in wildlife and forest management. cervid, deer, large herbivore, macronutrient balancing, nutritional ecologypublishedVersio

Total- and Monomethyl-Mercury and Major Ions in Coastal California Fog Water: Results from Two Years of Sampling on Land and at Sea

Marine fog water samples were collected over two summers (2014–2015) with active strand collectors (CASCC) at eight coastal sites from Humboldt to Monterey counties in California, USA, and on four ocean cruises along the California coastline in order to investigate mercury (Hg) cycling at the ocean-atmosphere-land interface. The mean concentration of monomethylmercury (MMHg) in fog water across terrestrial sites for both years was 1.6 ± 1.9 ng L-1 (\u3c0.01–10.4 ng L-1, N = 149), which corresponds to 5.7% (2.0–10.8%) of total Hg (HgT) in fog. Rain water samples from three sites had mean MMHg concentrations of 0.20 ± 0.12 ng L-1 (N = 5) corresponding to 1.4% of HgT. Fog water samples collected at sea had MMHg concentrations of 0.08 ± 0.15 ng L-1 (N = 14) corresponding to 0.4% of HgT. Significantly higher MMHg concentrations in fog were observed at terrestrial sites next to the ocean relative to a site 40 kilometers inland, and the mean difference was 1.6 ng L-1. Using a rate constant for photo-demethylation of MMHg of -0.022 h-1 based on previous demethylation experiments and a coastal-inland fog transport time of 12 hours, a mean difference of only 0.5 ng L-1 of MMHg was predicted between coastal and inland sites, indicating other unknown source and/or sink pathways are important for MMHg in fog. Fog water deposition to a standard passive 1.00 m2 fog collector at six terrestrial sites averaged 0.10 ± 0.07 L m-2 d-1, which was ∼2% of typical rainwater deposition in this area. Mean air-surface fog water fluxes of MMHg and HgT were then calculated to be 34 ± 40 ng m-2 y-1 and 546 ± 581 ng m-2 y-1, respectively. These correspond to 33% and 13% of the rain fluxes, respectively