The novel membrane enhanced peptide synthesis process

In this research project we investigated the feasibility of incorporating organic solvent nanofiltration techniques with peptide synthesis and developed the Membrane Enhanced Peptide Synthesis process – the MEPS process. Two membranes had been identified to be applicable for the MEPS process to separate the peptide building block from post reaction waste. These are the commercially available Inopor ZrO2/Al2O3 hydrophobic membrane and the cross-linked polyimide membrane that had been fabricated in our laboratory. Two penta-peptides were synthesized on a soluble polymeric support to demonstrate the principle of MEPS process. The purity and yields of these penta-peptides were excellent when compared with one synthesized using the Liquid Phase Peptide Synthesis (LPPS) and Solid Phase Peptide Synthesis (SPPS) processes. To improve the quality and supply of membrane for the MEPS process a number of membrane fabrication parameters were investigated. This investigation demonstrated ways of manipulating the performance of the cross-linked polyimide membrane which gives engineers the opportunity to tailor make polymeric membrane to meet the requirement of the MEPS process. This membrane optimisation provides the MEPS process with a constant supply of reproducible membrane and allows this process to be further developed into a highly repeatable process. Other soluble polymeric support products were also been investigated in an attempt to avoid product contamination by PEGylated waste. Peptide chains were built onto a degradable polymeric support and once the desired peptide sequence had completed, the polymeric support was then completely hydrolysed in acid to obtain a high purity peptide product. Results showed this simple idea was not as straight forward to perform as expected. It demonstrated that the idea was possible and has great potential but further development is required. A number of recommendations have been suggested for further improvement and optimisation of this newly developed MEPS process. Not only these are related to the enhancement of the membrane stability, improvement in peptide crude purity and product yield, but also other potential applications of the MEPS principle

Translation inhibition by rocaglates is independent of eIF4E phosphorylation status

Rocaglates are natural products that inhibit protein synthesis in eukaryotes and exhibit antineoplastic activity. In vitro biochemical assays, affinity chromatography experiments coupled with mass spectrometry analysis, and in vivo genetic screens have identified eukaryotic initiation factor (eIF) 4A as a direct molecular target of rocaglates. eIF4A is the RNA helicase subunit of eIF4F, a complex that mediates cap-dependent ribosome recruitment to mRNA templates. The eIF4F complex has been implicated in tumor initiation and maintenance through elevated levels or increased phosphorylation status of its cap-binding subunit, eIF4E, thus furthering the interest toward developing rocaglates as antineoplastic agents. Recent experiments have indicated that rocaglates also interact with prohibitins 1 and 2, proteins implicated in c-Raf-MEK-ERK signaling. Because increased ERK signaling stimulates eIF4E phosphorylation status, rocaglates are also expected to inhibit eIF4E phosphorylation status, a point that has not been thoroughly investigated. It is currently unknown whether the effects on translation observed with rocaglates are solely through eIF4A inhibition or also a feature of blocking eIF4E phosphorylation. Here, we show that rocaglates inhibit translation through an eIF4E phosphorylation-independent mechanism.P50 GM067041 - NIGMS NIH HHS; R01 GM073855 - NIGMS NIH HHS; GM-067041 - NIGMS NIH HHS; MOP-106530 - Canadian Institutes of Health Researc

Synthesis of aza-rocaglates via ESIPT-mediated (3+2) photocycloaddition

Synthesis of aza-rocaglates, nitrogen-containing analogues of the rocaglate natural products, is reported. The route features ESIPT-mediated (3+2) photocycloaddition of 1-alkyl-2-aryl-3-hydroxyquinolinones with the dipolarophile methyl cinnamate. A continuous photoflow reactor was utilized for photocycloadditions. An array of compounds bearing the hexahydrocyclopenta[b]indole core structure was synthesized and evaluated in translation inhibition assays.R01 CA175744 - NCI NIH HHS; R01 GM073855 - NIGMS NIH HHS; R24 GM111625 - NIGMS NIH HHS; R35 GM118173 - NIGMS NIH HH

Rural Taxation Reforms and Compulsory Education Finance in China

In recent decades, the responsibility for the financing of compulsory education in rural China has rested with townships and villages which, with limited tax authority and uneven revenue capacity, increasingly relied on a plethora of arbitrarily imposed fees for funding. To reduce farmers‘ fiscal burdens, since 2000 the central government has installed a series of rural taxation reforms. Correspondingly, the central government shifted the administrative responsibilities of rural compulsory education to the county level in 2001, and implemented a series of policies to make up for the loss of revenues to education. Using a provincial-level dataset from 1998 to 2006, this study examines whether and how the rural taxation reforms have affected the adequacy and equality of compulsory education finance in China, and addresses related theoretical and policy implications from the perspective of intergovernmental fiscal relations

Fiscal Effects of Local Option Sales Tax on School Facilities Funding: Evidence from North Carolina

Since the 1970s, the North Carolina Legislature has authorized its counties to levy four local option sales taxes (LOST). Proceeds from two of them are partially restricted for school capital needs; two other LOST are used to augment counties’ general revenues that may also affect school capital funding. Experiences from other states have raised concerns that the adoption of LOST may increase inequality in school finance, but the empirical results have been mixed. Using a data set of one hundred North Carolina county school districts from 2004 to 2006, this study examines how public school facilities are funded, and investigates whether the adoption of LOST aggravates or alleviates inequality in public school capital revenues in the state

Generating counterfactual explanations of tumor spatial proteomes to discover effective, combinatorial therapies that enhance cancer immunotherapy

Recent advances in spatial omics methods enable the molecular composition of human tumors to be imaged at micron-scale resolution across hundreds of patients and ten to thousands of molecular imaging channels. Large-scale molecular imaging datasets offer a new opportunity to understand how the spatial organization of proteins and cell types within a tumor modulate the response of a patient to different therapeutic strategies and offer potential insights into the design of novel therapies to increase patient response. However, spatial omics datasets require computational analysis methods that can scale to incorporate hundreds to thousands of imaging channels (ie colors) while enabling the extraction of molecular patterns that correlate with treatment responses across large number of patients with potentially heterogeneous tumors presentations. Here, we have develop a machine learning strategy for the identification and design of signaling molecule combinations that predict the degree of immune system engagement with a specific patient tumors. We specifically train a classifier to predict T cell distribution in patient tumors using the images from 30-40 molecular imaging channels. Second, we apply a gradient descent based counterfactual reasoning strategy to the classifier and discover combinations of signaling molecules predicted to increase T cell infiltration. Applied to spatial proteomics data of melanoma tumor, our model predicts that increasing the level of CXCL9, CXCL10, CXCL12, CCL19 and decreasing the level of CCL8 in melanoma tumor will increase T cell infiltration by 10-fold across a cohort of 69 patients. The model predicts that the combination is many fold more effective than single target perturbations. Our work provides a paradigm for machine learning based prediction and design of cancer therapeutics based on classification of immune system activity in spatial omics data