Interrogating Plant Cell Culture Library for Novel Antimicrobial Agents

The Plant Cell Culture Library (PCCL) at UMass Amherst contains more than 2,200 live plant cell cultures, representing diverse plant species from around the world. The availability of this collection offers a rich resource for us to discover bioactive phytochemicals and uncover their mechanisms of action. Using data-mining surveys of bioactive plant extracts, I have organized subsets of PCCL cell lines that are likely to possess antifungal, antibacterial, antiviral, anthelmintic, anti-trypanosomal, or anticancer properties, which prove to be useful when deciding which species to screen first against a specific pathogen. Another distinct advantage of using the live plant cells in this research is the ability to stimulate the biosynthesis of pathogen-specific phytochemicals upon simulation of an attack (elicitation) by the microorganism in question. This could be accomplished by pathogen homogenates or plant hormones responsible for mounting defenses to infection. Over the past six months, I have been working to optimize elicitation, lysis, and extraction conditions for obtaining high-throughput screening materials to be used against variable pathogens. Equipped with crude extracts from appropriately elicited cells, I am collaborating with a multidisciplinary team of UMass scientists to develop and implement high-throughput screening protocols for profiling a large number of plant-derived materials against various pathogens. Recently, I have screened a small pool (40) of extracts derived from cell lines with predicted anti-fungal properties against the highly resistant strain of fungus Fusarium oxysporum, one of the causal agents of an opportunistic infection often seen in immunocompromised patients known as fusariosis. Gratifyingly, I have found several plant species that produced specialized metabolites with better antifungal activity than the leading antibiotic against F. oxysporum, Amphotericin B, validating this line of antimicrobial research. We are also actively reaching out to other academic labs partners to form partnerships in diverse antimicrobial research venues

Clinical signs and symptoms of Wilson disease in a real-world cohort of patients in the United States: a medical chart review study

IntroductionThere are limited data from the United States regarding the real-world signs and symptoms of Wilson disease (WD). This retrospective, observational medical chart review was conducted to identify real-world characteristics of patients with WD in the United States, as well as WD signs and symptoms at diagnosis and over time.MethodsDe-identified clinical data were abstracted from medical charts of US patients diagnosed with WD between January 1, 2012, and June 30, 2017. Hepatic, neurologic, and psychiatric biochemical findings, signs, and symptoms were characterized at diagnosis and follow-up/during treatment.ResultsIn total, 225 WD patients were included in the study. The mean (SD) age at diagnosis was 24.7 (9.8) years, and 65.3% were male. Median (Q1–Q3) follow-up after diagnosis was 39.5 (33.8–60.4) months. The most common disease presentation at WD diagnosis was combined neurologic/psychiatric and hepatic (52.9%), followed by neurologic/psychiatric (20.0%), hepatic (16.9%), and asymptomatic (10.2%). Common clinical characteristics at diagnosis were Kayser-Fleischer rings (77.2%), low ceruloplasmin levels (95.2%), high hepatic copper (97.8%), elevated 24-hour urinary copper excretion (90.2%), and abnormal liver function tests (38.7%–85.1%). At diagnosis, the most common biochemical findings or hepatic sign/symptoms were abnormal liver enzymes (50.7%), abdominal pain (16.6%), and fatigue (15.7%). The most common neurologic signs/symptoms were headache (18.3%), dysarthria (17.4%), and ataxia (17.0%). Common psychiatric signs/symptoms included anxiety/depression/other mood changes (36.2%), emotional lability (12.8%), and increased irritability/anger outbursts (9.2%). Prevalence of biochemical abnormalities or signs/symptoms among patients at diagnosis and after ~1-year follow-up were neurologic (60.1% and 44.0%), hepatic (69.6% and 37.8%), and psychiatric (53.7% and 37.6%), respectively. Common new onset symptoms at ~1-year post-WD diagnosis were abnormal liver enzymes (5.6%), headache (6.2%), and anxiety/depression/other mood changes (7.2%).ConclusionsThese real-world, descriptive data highlight the clinical complexity and heterogeneity of WD and the need for better education about diagnostic testing and multidisciplinary support. Although rare, the neurologic, psychiatric, and hepatic signs/symptoms of WD have a substantial clinical impact

Model for end-stage liver disease (MELD) exception guidelines: Results and recommendations from the MELD exception study group and conference (MESSAGE) for the approval of patients who need liver transplantation with diseases not considered by the standard MELD formula

No abstract.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/55914/1/20979_ftp.pd

Behavioral modifications by a large-northern herbivore to mitigate warming conditions

Background: Temperatures in arctic-boreal regions are increasing rapidly and pose significant challenges to moose (Alces alces), a heat-sensitive large-bodied mammal. Moose act as ecosystem engineers, by regulating forest carbon and structure, below ground nitrogen cycling processes, and predator-prey dynamics. Previous studies showed that during hotter periods, moose displayed stronger selection for wetland habitats, taller and denser forest canopies, and minimized exposure to solar radiation. However, previous studies regarding moose behavioral thermoregulation occurred in Europe or southern moose range in North America. Understanding whether ambient temperature elicits a behavioral response in high-northern latitude moose populations in North America may be increasingly important as these arctic-boreal systems have been warming at a rate two to three times the global mean. Methods: We assessed how Alaska moose habitat selection changed as a function of ambient temperature using a step-selection function approach to identify habitat features important for behavioral thermoregulation in summer (June–August). We used Global Positioning System telemetry locations from four populations of Alaska moose (n = 169) from 2008 to 2016. We assessed model fit using the quasi-likelihood under independence criterion and conduction a leave-one-out cross validation. Results: Both male and female moose in all populations increasingly, and nonlinearly, selected for denser canopy cover as ambient temperature increased during summer, where initial increases in the conditional probability of selection were initially sharper then leveled out as canopy density increased above ~ 50%. However, the magnitude of selection response varied by population and sex. In two of the three populations containing both sexes, females demonstrated a stronger selection response for denser canopy at higher temperatures than males. We also observed a stronger selection response in the most southerly and northerly populations compared to populations in the west and central Alaska. Conclusions: The impacts of climate change in arctic-boreal regions increase landscape heterogeneity through processes such as increased wildfire intensity and annual area burned, which may significantly alter the thermal environment available to an animal. Understanding habitat selection related to behavioral thermoregulation is a first step toward identifying areas capable of providing thermal relief for moose and other species impacted by climate change in arctic-boreal regions.publishedVersio

Integrating snow science and wildlife ecology in Arctic-boreal North America

Snow covers Arctic and boreal regions (ABRs) for approximately 9 months of the year, thus snowscapes dominate the form and function of tundra and boreal ecosystems. In recent decades, Arctic warming has changed the snowcover\u27s spatial extent and distribution, as well as its seasonal timing and duration, while also altering the physical characteristics of the snowpack. Understanding the little studied effects of changing snowscapes on its wildlife communities is critical. The goal of this paper is to demonstrate the urgent need for, and suggest an approach for developing, an improved suite of temporally evolving, spatially distributed snow products to help understand how dynamics in snowscape properties impact wildlife, with a specific focus on Alaska and northwestern Canada. Via consideration of existing knowledge of wildlife-snow interactions, currently available snow products for focus region, and results of three case studies, we conclude that improving snow science in the ABR will be best achieved by focusing efforts on developing data-model fusion approaches to produce fit-for-purpose snow products that include, but are not limited to, wildlife ecology. The relative wealth of coordinated in situ measurements, airborne and satellite remote sensing data, and modeling tools being collected and developed as part of NASA\u27s Arctic Boreal Vulnerability Experiment and SnowEx campaigns, for example, provide a data rich environment for developing and testing new remote sensing algorithms and retrievals of snowscape properties