Rationally designed dendritic silica nanoparticles for oral delivery of exenatide

Type 2 diabetes makes up approximately 85% of all diabetic cases and it is linked to approximately one-third of all hospitalisations. Newer therapies with long-acting biologics such as glucagon-like peptide-1 (GLP-1) analogues have been promising in managing the disease, but they cannot reverse the pathology of the disease. Additionally, their parenteral administration is often associated with high healthcare costs, risk of infections, and poor patient adherence associated with phobia of needles. Oral delivery of these compounds would significantly improve patient compliance; however, poor enzymatic stability and low permeability across the gastrointestinal tract makes this task challenging. In the present work, large pore dendritic silica nanoparticles (DSNPs) with a pore size of ~10 nm were prepared, functionalized, and optimized in order to achieve high peptide loading and improve intestinal permeation of exenatide, a GLP-1 analogue. Compared to the loading capacity of the most popular, Mobil Composition of Matter No. 41 (MCM-41) with small pores, DSNPs showed significantly high loading owing to their large and dendritic pore structure. Among the tested DSNPs, pristine and phosphonate-modified DSNPs (PDSNPs) displayed remarkable loading of 40 and 35% w/w, respectively. Furthermore, particles successfully coated with positively charged chitosan reduced the burst release of exenatide at both pH 1.2 and 6.8. Compared with free exenatide, both chitosan-coated and uncoated PDSNPs enhanced exenatide transport through the Caco-2 monolayer by 1.7 fold. Interestingly, when a triple co-culture model of intestinal permeation was used, chitosan-coated PDSNPs performed better compared to both PDSNPs and free exenatide, which corroborated our hypothesis behind using chitosan to interact with mucus and improve permeation. These results indicate the emerging role of large pore silica nanoparticles as promising platforms for oral delivery of biologics such as exenatide.We thank the National Health and Medical Research Council’s Project Grant GNT1107836 and Early Career Fellowship and Career Development Fellowship to A.P. We also thank NHMRC for EC Fellowship to T.K. We would also like to thank the Centre of Microscopy and Microanalysis at The University of Queensland for providing facilities to conduct TEM. This article was, in part, a result of the project NORTE-01-0145-FEDER-000012, supported by the Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). This work was financed by FEDER—Fundo Europeu de Desenvolvimento Regional funds—through the COMPETE 2020–Operational Programme for Competitiveness and Internationalization (POCI), Portugal 2020, and by Portuguese funds through FCT—Fundação para a Ciência e a Tecnologia/Ministério da Ciência, Tecnologia e Ensino Superior in the framework of the project “Institute for Research and Innovation in Health Sciences” (POCI-01-0145-FEDER-007274)

A miRNA-Target Prediction Case Study

Giansanti, V., Castelli, M., Beretta, S., & Merelli, I. (2019). Comparing Deep and Machine Learning Approaches in Bioinformatics: A miRNA-Target Prediction Case Study. In V. V. Krzhizhanovskaya, M. H. Lees, P. M. A. Sloot, J. J. Dongarra, J. M. F. Rodrigues, P. J. S. Cardoso, J. Monteiro, ... R. Lam (Eds.), Computational Science – ICCS 2019: 19th International Conference, Faro, Portugal, June 12–14, 2019, Proceedings, Part III (pp. 31-44). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11538 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-22744-9_3MicroRNAs (miRNAs) are small non-coding RNAs with a key role in the post-transcriptional gene expression regularization, thanks to their ability to link with the target mRNA through the complementary base pairing mechanism. Given their role, it is important to identify their targets and, to this purpose, different tools were proposed to solve this problem. However, their results can be very different, so the community is now moving toward the deployment of integration tools, which should be able to perform better than the single ones. As Machine and Deep Learning algorithms are now in their popular years, we developed different classifiers from both areas to verify their ability to recognize possible miRNA-mRNA interactions and evaluated their performance, showing the potentialities and the limits that those algorithms have in this field. Here, we apply two deep learning classifiers and three different machine learning models to two different miRNA-mRNA datasets, of predictions from 3 different tools: TargetScan, miRanda, and RNAhybrid. Although an experimental validation of the results is needed to better confirm the predictions, deep learning techniques achieved the best performance when the evaluation scores are taken into account.authorsversionpublishe

Early rheumatoid arthritis is characterized by a distinct and transient synovial fluid cytokine profile of T cell and stromal cell origin

Pathological processes involved in the initiation of rheumatoid synovitis remain unclear. We undertook the present study to identify immune and stromal processes that are present soon after the clinical onset of rheumatoid arthritis ( RA) by assessing a panel of T cell, macrophage, and stromal cell related cytokines and chemokines in the synovial fluid of patients with early synovitis. Synovial fluid was aspirated from inflamed joints of patients with inflammatory arthritis of duration 3 months or less, whose outcomes were subsequently determined by follow up. For comparison, synovial fluid was aspirated from patients with acute crystal arthritis, established RA and osteoarthritis. Rheumatoid factor activity was blocked in the synovial fluid samples, and a panel of 23 cytokines and chemokines measured using a multiplex based system. Patients with early inflammatory arthritis who subsequently developed RA had a distinct but transient synovial fluid cytokine profile. The levels of a range of T cell, macrophage and stromal cell related cytokines ( e. g. IL-2, IL-4, IL-13, IL-17, IL-15, basic fibroblast growth factor and epidermal growth factor) were significantly elevated in these patients within 3 months after symptom onset, as compared with early arthritis patients who did not develop RA. In addition, this profile was no longer present in established RA. In contrast, patients with non-rheumatoid persistent synovitis exhibited elevated levels of interferon-gamma at initiation. Early synovitis destined to develop into RA is thus characterized by a distinct and transient synovial fluid cytokine profile. The cytokines present in the early rheumatoid lesion suggest that this response is likely to influence the microenvironment required for persistent RA

Transcription profiling reveals potential mechanisms of dysbiosis in the oral microbiome of rhesus macaques with chronic untreated SIV infection.

A majority of individuals infected with human immunodeficiency virus (HIV) have inadequate access to antiretroviral therapy and ultimately develop debilitating oral infections that often correlate with disease progression. Due to the impracticalities of conducting host-microbe systems-based studies in HIV infected patients, we have evaluated the potential of simian immunodeficiency virus (SIV) infected rhesus macaques to serve as a non-human primate model for oral manifestations of HIV disease. We present the first description of the rhesus macaque oral microbiota and show that a mixture of human commensal bacteria and "macaque versions" of human commensals colonize the tongue dorsum and dental plaque. Our findings indicate that SIV infection results in chronic activation of antiviral and inflammatory responses in the tongue mucosa that may collectively lead to repression of epithelial development and impact the microbiome. In addition, we show that dysbiosis of the lingual microbiome in SIV infection is characterized by outgrowth of Gemella morbillorum that may result from impaired macrophage function. Finally, we provide evidence that the increased capacity of opportunistic pathogens (e.g. E. coli) to colonize the microbiome is associated with reduced production of antimicrobial peptides

The interplay of intrinsic and extrinsic bounded noises in genetic networks

After being considered as a nuisance to be filtered out, it became recently clear that biochemical noise plays a complex role, often fully functional, for a genetic network. The influence of intrinsic and extrinsic noises on genetic networks has intensively been investigated in last ten years, though contributions on the co-presence of both are sparse. Extrinsic noise is usually modeled as an unbounded white or colored gaussian stochastic process, even though realistic stochastic perturbations are clearly bounded. In this paper we consider Gillespie-like stochastic models of nonlinear networks, i.e. the intrinsic noise, where the model jump rates are affected by colored bounded extrinsic noises synthesized by a suitable biochemical state-dependent Langevin system. These systems are described by a master equation, and a simulation algorithm to analyze them is derived. This new modeling paradigm should enlarge the class of systems amenable at modeling. We investigated the influence of both amplitude and autocorrelation time of a extrinsic Sine-Wiener noise on: $(i)$ the Michaelis-Menten approximation of noisy enzymatic reactions, which we show to be applicable also in co-presence of both intrinsic and extrinsic noise, $(ii)$ a model of enzymatic futile cycle and $(iii)$ a genetic toggle switch. In $(ii)$ and $(iii)$ we show that the presence of a bounded extrinsic noise induces qualitative modifications in the probability densities of the involved chemicals, where new modes emerge, thus suggesting the possibile functional role of bounded noises

Na+ imaging reveals little difference in action potential–evoked Na+ influx between axon and soma

Author Posting. © The Authors, 2010. This is the author's version of the work. It is posted here by permission of Nature Publishing Group for personal use, not for redistribution. The definitive version was published in Nature Neuroscience 13 (2010): 852-860, doi:10.1038/nn.2574.In cortical pyramidal neurons, the axon initial segment (AIS) plays a pivotal role in synaptic integration. It has been asserted that this property reflects a high density of Na+ channels in AIS. However, we here report that AP–associated Na+ flux, as measured by high–speed fluorescence Na+ imaging, is about 3 times larger in the rat AIS than in the soma. Spike evoked Na+ flux in the AIS and the first node of Ranvier is about the same, and in the basal dendrites it is about 8 times lower. At near threshold voltages persistent Na+ conductance is almost entirely axonal. Finally, we report that on a time scale of seconds, passive diffusion and not pumping is responsible for maintaining transmembrane Na+ gradients in thin axons during high frequency AP firing. In computer simulations, these data were consistent with the known features of AP generation in these neurons.Supported by US– Israel BSF Grant (2003082), Grass Faculty Grant from the MBL, NIH Grant (NS16295), Multiple Sclerosis Society Grant (PP1367), and a fellowship from the Gruss Lipper Foundation

A New Era in the Quest for Dark Matter

There is a growing sense of `crisis' in the dark matter community, due to the absence of evidence for the most popular candidates such as weakly interacting massive particles, axions, and sterile neutrinos, despite the enormous effort that has gone into searching for these particles. Here, we discuss what we have learned about the nature of dark matter from past experiments, and the implications for planned dark matter searches in the next decade. We argue that diversifying the experimental effort, incorporating astronomical surveys and gravitational wave observations, is our best hope to make progress on the dark matter problem.Comment: Published in Nature, online on 04 Oct 2018. 13 pages, 1 figur

iQuantitator: A tool for protein expression inference using iTRAQ

<p>Abstract</p> <p>Background</p> <p>Isobaric Tags for Relative and Absolute Quantitation (iTRAQ™) [Applied Biosystems] have seen increased application in differential protein expression analysis. To facilitate the growing need to analyze iTRAQ data, especially for cases involving multiple iTRAQ experiments, we have developed a modeling approach, statistical methods, and tools for estimating the relative changes in protein expression under various treatments and experimental conditions.</p> <p>Results</p> <p>This modeling approach provides a unified analysis of data from multiple iTRAQ experiments and links the observed quantity (reporter ion peak area) to the experiment design and the calculated quantity of interest (treatment-dependent protein and peptide fold change) through an additive model under log transformation. Others have demonstrated, through a case study, this modeling approach and noted the computational challenges of parameter inference in the unbalanced data set typical of multiple iTRAQ experiments. Here we present the development of an inference approach, based on hierarchical regression with batching of regression coefficients and Markov Chain Monte Carlo (MCMC) methods that overcomes some of these challenges. In addition to our discussion of the underlying method, we also present our implementation of the software, simulation results, experimental results, and sample output from the resulting analysis report.</p> <p>Conclusion</p> <p>iQuantitator's process-based modeling approach overcomes limitations in current methods and allows for application in a variety of experimental designs. Additionally, hypertext-linked documents produced by the tool aid in the interpretation and exploration of results.</p

Analytic philosophy for biomedical research: the imperative of applying yesterday's timeless messages to today's impasses

The mantra that "the best way to predict the future is to invent it" (attributed to the computer scientist Alan Kay) exemplifies some of the expectations from the technical and innovative sides of biomedical research at present. However, for technical advancements to make real impacts both on patient health and genuine scientific understanding, quite a number of lingering challenges facing the entire spectrum from protein biology all the way to randomized controlled trials should start to be overcome. The proposal in this chapter is that philosophy is essential in this process. By reviewing select examples from the history of science and philosophy, disciplines which were indistinguishable until the mid-nineteenth century, I argue that progress toward the many impasses in biomedicine can be achieved by emphasizing theoretical work (in the true sense of the word 'theory') as a vital foundation for experimental biology. Furthermore, a philosophical biology program that could provide a framework for theoretical investigations is outlined

Targeted antitumour therapy – future perspectives

The advent of targeted therapy presents an unprecedented opportunity for advances in the treatment of cancer. A key challenge will be to translate the undoubted promise of targeted agents into tangible clinical benefits. Achieving this goal is likely to be dependent upon a number of factors. These include continued research to improve our understanding of the heterogeneity and complexity of the tumour microenvironment; refinement of clinical trial design to incorporate nontraditional end points such as the optimum biological dose and health-related quality of life; and the use of technological advancements in proteomics, genomics and biomarker development to better predict tumour types and patient subsets that may be particularly responsive to treatment, as well as enable a more accurate assessment of drug effect at the molecular level. In summary, the future success of targeted agents will require an integrated multidisciplinary approach involving all stakeholders