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Nanoparticles (NPs) based on biocompatible and biodegradable polymers such as poly(lactic-co-glycolic acid) (PLGA) and polycaprolactone (PCL) represent effective systems for systemic drug delivery. Upon injection into the blood circuit, the NP surface is rapidly modified due to adsorption of proteins that form a 'protein corona' (PC). The PC plays an important role in cellular targeting, uptake and NP bio-distribution. Hence, the study of interactions between NPs and serum proteins appears as key for biomedical applications and safety of NPs. In the present work, we report on the comparative protein fluorescence quenching extent, thermodynamics of protein binding and identification of proteins in the soft and hard corona layers of PLGA and PCL NPs. NPs were prepared via a single emulsionsolvent evaporation technique and characterized with respect to size, zeta potential, surface morphology and hydrophobicity. Protein fluorescence quenching experiments were performed against human serum albumin. The thermodynamics of serum protein binding onto the NPs was studied using isothermal titration calorimetry. Semi-quantitative analysis of proteins in the PC layers was conducted using gel electrophoresis and mass spectrometry using human serum. Our results demonstrated the influence of particle hydrophobicity on the thermodynamics of protein binding. Human serum proteins bind to a greater extent and with greater affinity to PCL NPs than PLGA NPs. Several proteins were detected in the hard and soft corona of the NPs, representing their unique proteome fingerprints. Some proteins were unique to the PCL NPs. We anticipate that our findings will assist with rational design of polymeric NPs for effective drug delivery applications.

Drought severely affects crop yield and yield stability. Maize and sorghum are major crops in Africa and globally, and both are negatively impacted by drought. However, sorghum has a better ability to withstand drought than maize. Consequently, this study identifies differences between maize and sorghum grown in water deficit conditions, and identifies proteins associated with drought tolerance in these plant species. Leaf relative water content and proline content were measured, and label-free proteomics analysis was carried out to identify differences in protein expression in the two species in response to water deficit. Water deficit enhanced the proline accumulation in sorghum roots to a higher degree than in maize, and this higher accumulation was associated with enhanced water retention in sorghum. Proteomic analyses identified proteins with differing expression patterns between the two species, revealing key metabolic pathways that explain the better drought tolerance of sorghum than maize. These proteins include phenylalanine/tyrosine ammonia-lyases, indole-3-acetaldehyde oxidase, sucrose synthase and phenol/catechol oxidase. This study highlights the importance of phenylpropanoids, sucrose, melanin-related metabolites and indole acetic acid (auxin) as determinants of the differences in drought stress tolerance between maize and sorghum. The selection of maize and sorghum genotypes with enhanced expression of the genes encoding these differentially expressed proteins, or genetically engineering maize and sorghum to increase the expression of such genes, can be used as strategies for the production of maize and sorghum varieties with improved drought tolerance.

While proteomics has demonstrated its value for model organisms and for organisms with mature genome sequence annotations, proteomics has been of less value in nonmodel organisms that are unaccompanied by genome sequence annotations. This project sought to determine the value of RNA-Seq experiments as a basis for establishing a set of protein sequences to represent a nonmodel organism, in this case, the pseudocereal chia. Assembling four publicly available chia RNA-Seq datasets produced transcript sequence sets with a high BUSCO completeness, though the number of transcript sequences and Trinity “genes” varied considerably among them. After six-frame translation, ProteinOrtho detected substantial numbers of orthologs among other species within the taxonomic order Lamiales. These protein sequence databases demonstrated a good identification efficiency for three different LC-MS/MS proteomics experiments, though a seed proteome showed considerable variability in the identification of peptides based on seed protein sequence inclusion. If a proteomics experiment emphasizes a particular tissue, an RNA-Seq experiment incorporating that same tissue is more likely to support a database search identification of that proteome.

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