Propagation and field assessment of West Virginia native species for roadside revegetation

Road construction removes vegetation from roadside slopes, making them susceptible to erosion and non-native plant introductions. Erosion Sediment Control (ESC) practices can reduce the annual loss of soil by as much as 100 cubic yards per acre and be cost beneficial. ESC includes using plant species that can germinate and survive on reconstructed areas. In West Virginia, the Division of Highways (WVDoH), uses plant species that are not native, and sometimes invasive, to revegetate newly constructed roadsides. This activity contributes to the growing problem of increased non-native and invasive species in our landscape.;This research investigated the potential use of five WV native species for roadside revegetation: Sabatia angularis, Baptisia tinctoria, Rhus aromatica, Vitis riparia, and Parthenocissus quinquefolia . Chapter two describes seed propagation and field planting studies of S. angularis and B. tinctoria. Temperature and chemical pretreatment effects on germination were tested in the greenhouse. Gibberellic acid was found to be the chemical pretreatment that resulted in the greatest percent germination for both species. In the field, germination was compared to greenhouse germination and net population change and mean height were monitored. Baptisia tinctoria had an initial germination rate of 22%, continual increase in net population change, and an overall mean height change of 9.55mm. Sabatia angularis had an initial germination rate of 15%, declined in net population change throughout the season, and had an overall mean height change of 6.83 mm. Chapter three describes root propagation studies of R. aromatica and V. riparia. Indole-butyric-acid (IBA) was found to promote root production of cuttings in the greenhouse. In the field, effects of slope aspect and arbuscular mycorrhizae (AM) treatment on survival and mean height change of R. aromatica and P. quinquefolia cuttings within plots above steep roadside slopes were examined. Despite high mortality, P. quinquefolia showed potential for use in roadside revegetation

Complex karyotypes in flow cytometrically DNA-diploid squamous cell carcinomas of the head and neck.

In squamous cell carcinoma of the head and neck (SCCHN), DNA ploidy as determined by flow cytometry (FCM) has been found to yield prognostic information but only for tumours at oral sites. Cytogenetic findings have indicated complex karyotype to be a correlate of poor clinical outcome. In the present study, 73 SCCHN were investigated with the two techniques. Aneuploid cell populations were identified in 49 (67%) cases by FCM but in only 21 (29%) cases by cytogenetic analysis. The chromosome index (CI), calculated as the mean chromosome number divided by 46, was compared with the respective DNA index (DI) obtained by FCM in 15 tumours, non-diploid according to both techniques, DI being systematically 12% higher than CI in this subgroup. Eight (33%) of the 24 tumours diploid according to FCM had complex karyotypes, three of the tumours being cytogenetically hypodiploid, three diploid and two non-diploid. The findings in the present study may partly explain the low prognostic value of ploidy status as assessed by FCM that has been observed in SCCHN. In addition, we conclude that FCM yields information of the genetic changes that is too unspecific, and that cytogenetic analysis shows a high rate of unsuccessful investigations, thus diminishing the value of the two methods as prognostic factors in SCCHN

Effect of stress relieving heat treatment on surface topography and dimensional accuracy of incrementally formed grade 1 titanium sheet parts

The forming of parts with an optimized surface roughness and high dimensional accuracy is important in many applications of incremental sheet forming (ISF). To realize this, the effect of stress relieving heat treatment of grade-1 Ti parts performed before and after forming on the surface finish and dimensional accuracy was studied. It was found that heat treatment at a temperature of 540 °C for 2 h improves the surface finish of formed parts resulting in a surface with little or no visible tool marks. Additionally, it improves the dimensional accuracy of parts after unclamping from the rig used for forming, in particular, that of parts with shallow wall angles (typically <25°). It was also noted that post-forming heat treatment improves the surface finish of parts. The surface topography of formed parts was studied using interferometry to yield areal surface roughness parameters and subsequently using secondary electron imaging. Back-scatter electron microscopy imaging results coupled with energy-dispersive X-ray (EDX) analysis showed that heat treatment prior to forming leads to tool wear as indicated by the presence of Fe in samples. Furthermore, post-forming heat treatment prevents curling up of formed parts due to compressive stresses if the formed part is trimmed

Target Inhibition Networks: Predicting Selective Combinations of Druggable Targets to Block Cancer Survival Pathways

A recent trend in drug development is to identify drug combinations or multi-target agents that effectively modify multiple nodes of disease-associated networks. Such polypharmacological effects may reduce the risk of emerging drug resistance by means of attacking the disease networks through synergistic and synthetic lethal interactions. However, due to the exponentially increasing number of potential drug and target combinations, systematic approaches are needed for prioritizing the most potent multi-target alternatives on a global network level. We took a functional systems pharmacology approach toward the identification of selective target combinations for specific cancer cells by combining large-scale screening data on drug treatment efficacies and drug-target binding affinities. Our model-based prediction approach, named TIMMA, takes advantage of the polypharmacological effects of drugs and infers combinatorial drug efficacies through system-level target inhibition networks. Case studies in MCF-7 and MDA-MB-231 breast cancer and BxPC-3 pancreatic cancer cells demonstrated how the target inhibition modeling allows systematic exploration of functional interactions between drugs and their targets to maximally inhibit multiple survival pathways in a given cancer type. The TIMMA prediction results were experimentally validated by means of systematic siRNA-mediated silencing of the selected targets and their pairwise combinations, showing increased ability to identify not only such druggable kinase targets that are essential for cancer survival either individually or in combination, but also synergistic interactions indicative of nonadditive drug efficacies. These system-level analyses were enabled by a novel model construction method utilizing maximization and minimization rules, as well as a model selection algorithm based on sequential forward floating search. Compared with an existing computational solution, TIMMA showed both enhanced prediction accuracies in cross validation as well as significant reduction in computation times. Such cost-effective computational-experimental design strategies have the potential to greatly speed-up the drug testing efforts by prioritizing those interventions and interactions warranting further study in individual cancer cases

Identification of selective cytotoxic and synthetic lethal drug responses in triple negative breast cancer cells

Background: Triple negative breast cancer (TNBC) is a highly heterogeneous and aggressive type of cancer that lacks effective targeted therapy. Despite detailed molecular profiling, no targeted therapy has been established. Hence, with the aim of gaining deeper understanding of the functional differences of TNBC subtypes and how that may relate to potential novel therapeutic strategies, we studied comprehensive anticancer-agent responses among a panel of TNBC cell lines.Method: The responses of 301 approved and investigational oncology compounds were measured in 16 TNBC cell lines applying a functional profiling approach. To go beyond the standard drug viability effect profiling, which has been used in most chemosensitivity studies, we utilized a multiplexed readout for both cell viability and cytotoxicity, allowing us to differentiate between cytostatic and cytotoxic responses.Results: Our approach revealed that most single-agent anti-cancer compounds that showed activity for the viability readout had no or little cytotoxic effects. Major compound classes that exhibited this type of response included anti-mitotics, mTOR, CDK, and metabolic inhibitors, as well as many agents selectively inhibiting oncogene-activated pathways. However, within the broad viability-acting classes of compounds, there were often subsets of cell lines that responded by cell death, suggesting that these cells are particularly vulnerable to the tested substance. In those cases we could identify differential levels of protein markers associated with cytotoxic responses. For example, PAI-1, MAPK phosphatase and Notch-3 levels associated with cytotoxic responses to mitotic and proteasome inhibitors, suggesting that these might serve as markers of response also in clinical settings. Furthermore, the cytotoxicity readout highlighted selective synergistic and synthetic lethal drug combinations that were missed by the cell viability readouts. For instance, the MEK inhibitor trametinib synergized with PARP inhibitors. Similarly, combination of two non-cytotoxic compounds, the rapamycin analog everolimus and an ATP-competitive mTOR inhibitor dactolisib, showed synthetic lethality in several mTOR-addicted cell lines.Conclusions: Taken together, by studying the combination of cytotoxic and cytostatic drug responses, we identified a deeper spectrum of cellular responses both to single agents and combinations that may be highly relevant for identifying precision medicine approaches in TNBC as well as in other types of cancers

Enhanced sensitivity to glucocorticoids in cytarabine-resistant AML

We sought to identify drugs that could counteract cytarabine resistance in acute myeloid leukemia (AML) by generating eight resistant variants from MOLM-13 and SHI-1 AML cell lines by long-term drug treatment. These cells were compared with 66 ex vivo chemorefractory samples from cytarabine-treated AML patients. The models and patient cells were subjected to genomic and transcriptomic profiling and high-throughput testing with 250 emerging and clinical oncology compounds. Genomic profiling uncovered deletion of the deoxycytidine kinase (DCK) gene in both MOLM-13- and SHI-1-derived cytarabine-resistant variants and in an AML patient sample. Cytarabine-resistant SHI-1 variants and a subset of chemorefractory AML patient samples showed increased sensitivity to glucocorticoids that are often used in treatment of lymphoid leukemia but not AML. Paired samples taken from AML patients before treatment and at relapse also showed acquisition of glucocorticoid sensitivity. Enhanced glucocorticoid sensitivity was only seen in AML patient samples that were negative for the FLT3 mutation (P = 0.0006). Our study shows that development of cytarabine resistance is associated with increased sensitivity to glucocorticoids in a subset of AML, suggesting a new therapeutic strategy that should be explored in a clinical trial of chemorefractory AML patients carrying wild-type FLT3.Peer reviewe

From drug response profiling to target addiction scoring in cancer cell models

Deconvoluting the molecular target signals behind observed drug response phenotypes is an important part of phenotype-based drug discovery and repurposing efforts. We demonstrate here how our network-based deconvolution approach, named target addiction score (TAS), provides insights into the functional importance of druggable protein targets in cell-based drug sensitivity testing experiments. Using cancer cell line profiling data sets, we constructed a functional classification across 107 cancer cell models, based on their common and unique target addiction signatures. The pan-cancer addiction correlations could not be explained by the tissue of origin, and only correlated in part with molecular and genomic signatures of the heterogeneous cancer cells. The TAS-based cancer cell classification was also shown to be robust to drug response data resampling, as well as predictive of the transcriptomic patterns in an independent set of cancer cells that shared similar addiction signatures with the 107 cancers. The critical protein targets identified by the integrated approach were also shown to have clinically relevant mutation frequencies in patients with various cancer subtypes, including not only well-established pan-cancer genes, such as PTEN tumor suppressor, but also a number of targets that are less frequently mutated in specific cancer types, including ABL1 oncoprotein in acute myeloid leukemia. An application to leukemia patient primary cell models demonstrated how the target deconvolution approach offers functional insights into patient-specific addiction patterns, such as those indicative of their receptor-type tyrosine-protein kinase FLT3 internal tandem duplication (FLT3-ITD) status and co-addiction partners, which may lead to clinically actionable, personalized drug treatment developments. To promote its application to the future drug testing studies, we have made available an open-source implementation of the TAS calculation in the form of a stand-alone R package

Computational-experimental approach to drug-target interaction mapping: A case study on kinase inhibitors

Due to relatively high costs and labor required for experimental profiling of the full target space of chemical compounds, various machine learning models have been proposed as cost-effective means to advance this process in terms of predicting the most potent compound-target interactions for subsequent verification. However, most of the model predictions lack direct experimental validation in the laboratory, making their practical benefits for drug discovery or repurposing applications largely unknown. Here, we therefore introduce and carefully test a systematic computational-experimental framework for the prediction and pre-clinical verification of drug-target interactions using a well-established kernel-based regression algorithm as the prediction model. To evaluate its performance, we first predicted unmeasured binding affinities in a large-scale kinase inhibitor profiling study, and then experimentally tested 100 compound-kinase pairs. The relatively high correlation of 0.77 (p < 0.0001) between the predicted and measured bioactivities supports the potential of the model for filling the experimental gaps in existing compound-target interaction maps. Further, we subjected the model to a more challenging task of predicting target interactions for such a new candidate drug compound that lacks prior binding profile information. As a specific case study, we used tivozanib, an investigational VEGF receptor inhibitor with currently unknown off-target profile. Among 7 kinases with high predicted affinity, we experimentally validated 4 new off-targets of tivozanib, namely the Src-family kinases FRK and FYN A, the non-receptor tyrosine kinase ABL1, and the serine/threonine kinase SLK. Our sub-sequent experimental validation protocol effectively avoids any possible information leakage between the training and validation data, and therefore enables rigorous model validation for practical applications. These results demonstrate that the kernel-based modeling approach offers practical benefits for probing novel insights into the mode of action of investigational compounds, and for the identification of new target selectivities for drug repurposing applications

Structure-activity relationships of the sustained effects of adenosine A2A receptor agonists driven by slow dissociation kinetics

The duration of action of adenosine A2A receptor (A2A) agonists is critical for their clinical efficacy, and we sought to better understand how this can be optimized. The in vitro temporal response profiles of a panel of A2A agonists were studied using cAMP assays in recombinantly (CHO) and endogenously (SH-SY5Y) expressing cells. Some agonists (e.g., 3cd; UK-432,097) but not others (e.g., 3ac; CGS-21680) demonstrated sustained wash-resistant agonism, where residual receptor activation continued after washout. The ability of an antagonist to reverse pre-established agonist responses was used as a surrogate read-out for agonist dissociation kinetics, and together with radioligand binding studies suggested a role for slow off-rate in driving sustained effects. One compound, 3ch, showed particularly marked sustained effects, with a reversal t1/2 > 6 hours and close to maximal effects that remained for at least 5 hours after washing. Based on the structure-activity relationship of these compounds, we suggest that lipophilic N6 and bulky C2 substituents can promote stable and long-lived binding events leading to sustained agonist responses, although a high compound logD is not necessary. This provides new insight into the binding interactions of these ligands and we anticipate that this information could facilitate the rational design of novel long-acting A2A agonists with improved clinical efficacy