Optical Trapping with High Forces Reveals Unexpected Behaviors of Prion Fibrils

Amyloid fibrils are important in diverse cellular functions, feature in many human diseases and have potential applications in nanotechnology. Here we describe methods that combine optical trapping and fluorescent imaging to characterize the forces that govern the integrity of amyloid fibrils formed by a yeast prion protein. A crucial advance was to use the self-templating properties of amyloidogenic proteins to tether prion fibrils, enabling their manipulation in the optical trap. At normal pulling forces the fibrils were impervious to disruption. At much higher forces (up to 250 pN), discontinuities occurred in force-extension traces before fibril rupture. Experiments with selective amyloid-disrupting agents and mutations demonstrated that such discontinuities were caused by the unfolding of individual subdomains. Thus, our results reveal unusually strong noncovalent intermolecular contacts that maintain fibril integrity even when individual monomers partially unfold and extend fibril length.National Institutes of Health (U.S.) (Grant GM025874)National Science Foundation (U.S.). CAREER (Award 0643745

Crowdsourced estimation of cognitive decline and resilience in Alzheimer's disease

Identifying accurate biomarkers of cognitive decline is essential for advancing early diagnosis and prevention therapies in Alzheimer's disease. The Alzheimer's disease DREAM Challenge was designed as a computational crowdsourced project to benchmark the current state-of-the-art in predicting cognitive outcomes in Alzheimer's disease based on high dimensional, publicly available genetic and structural imaging data. This meta-analysis failed to identify a meaningful predictor developed from either data modality, suggesting that alternate approaches should be considered for prediction of cognitive performance

K13 blocks KSHV lytic replication and deregulates vIL6 nad hIL6 expression: A model of lytic replication induced clonal selection in viral oncogenesis

Background. Accumulating evidence suggests that dysregulated expression of lytic genes plays an important role in KSHV (Kaposi's sarcoma associated herpesvirus) tumorigenesis. However, the molecular events leading to the dysregulation of KSHV lytic gene expression program are incompletely understood. Methodoloxy/Principal Findings. We have studied the effect of KSHV-encoded latent protein vFLIP K13, a potent activator of the NF-κB pathway, on lytic reactivation of the virus. We demonstrate that K13 antagonizes RTA, the KSHV lytic-regulator, and effectively blocks the expression of lytic proteins, production of infectious virions and death of the infected cells. Induction of lytic replication selects for clones with increased K13 expression and NF-κB activity, while siRNA-mediated silencing of K13 induces the expression of lytic genes. However, the suppressive effect of K13 on RTA-induced lytic genes is not uniform and it falls to block RTA-induced viral IL6 secretion and cooperates with RTA to enhance cellular IL-6 production, thereby dysregulating the lytic gene expression program. Conclusions/Significance. Our results support a model in which ongoing KSHV, lytic replication selects for clones with progressively higher levels of K13 expression and NF-κB activity, which in turn drive KSHV tumorigenesis by not only directly stimulating cellular survival and proliferation, but also indirectly by dysregulating the viral lytic gene program and allowing non-lytic production of growth-promoting viral and cellular genes. Lytic Replication-Induced Clonal Selection (LyRICS) may represent a general mechanism in viral oncogenesis. 2007 Zhao et al

The regulation of IL-10 expression

Interleukin (IL)-10 is an important immunoregulatory cytokine and an understanding of how IL-10 expression is controlled is critical in the design of immune intervention strategies. IL-10 is produced by almost all cell types within the innate (including macrophages, monocytes, dendritic cells (DCs), mast cells, neutrophils, eosinophils and natural killer cells) and adaptive (including CD4(+) T cells, CD8(+) T cells and B cells) immune systems. The mechanisms of IL-10 regulation operate at several stages including chromatin remodelling at the Il10 locus, transcriptional regulation of Il10 expression and post-transcriptional regulation of Il10 mRNA. In addition, whereas some aspects of Il10 gene regulation are conserved between different immune cell types, several are cell type- or stimulus-specific. Here, we outline the complexity of IL-10 production by discussing what is known about its regulation in macrophages, monocytes, DCs and CD4(+) T helper cells

Building ensembles of adaptive nested dichotomies with random-pair selection

A system of nested dichotomies is a method of decomposing a multi-class problem into a collection of binary problems. Such a system recursively applies binary splits to divide the set of classes into two subsets, and trains a binary classifier for each split. Although ensembles of nested dichotomies with random structure have been shown to perform well in practice, using a more sophisticated class subset selection method can be used to improve classification accuracy. We investigate an approach to this problem called random-pair selection, and evaluate its effectiveness compared to other published methods of subset selection. We show that our method outperforms other methods in many cases when forming ensembles of nested dichotomies, and is at least on par in all other cases. The software related to this paper is available at https://svn.cms.waikato.ac.nz/svn/weka/trunk/packages/ internal/ensemblesOfNestedDichotomies/

Small Interfering RNA Targeting M2 Gene Induces Effective and Long Term Inhibition of Influenza A Virus Replication

RNA interference (RNAi) provides a powerful new means to inhibit viral infection specifically. However, the selection of siRNA-resistant viruses is a major concern in the use of RNAi as antiviral therapeutics. In this study, we conducted a lentiviral vector with a H1-short hairpin RNA (shRNA) expression cassette to deliver small interfering RNAs (siRNAs) into mammalian cells. Using this vector that also expresses enhanced green fluorescence protein (EGFP) as surrogate marker, stable shRNA-expressing cell lines were successfully established and the inhibition efficiencies of rationally designed siRNAs targeting to conserved regions of influenza A virus genome were assessed. The results showed that a siRNA targeting influenza M2 gene (siM2) potently inhibited viral replication. The siM2 was not only effective for H1N1 virus but also for highly pathogenic avian influenza virus H5N1. In addition to its M2 inhibition, the siM2 also inhibited NP mRNA accumulation and protein expression. A long term inhibition effect of the siM2 was demonstrated and the emergence of siRNA-resistant mutants in influenza quasispecies was not observed. Taken together, our study suggested that M2 gene might be an optimal RNAi target for antiviral therapy. These findings provide useful information for the development of RNAi-based prophylaxis and therapy for human influenza virus infection

Fetal brain tissue annotation and segmentation challenge results

In-utero fetal MRI is emerging as an important tool in the diagnosis and analysis of the developing human brain. Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in the research and clinical context. However, manual segmentation of cerebral structures is time-consuming and prone to error and inter-observer variability. Therefore, we organized the Fetal Tissue Annotation (FeTA) Challenge in 2021 in order to encourage the development of automatic segmentation algorithms on an international level. The challenge utilized FeTA Dataset, an open dataset of fetal brain MRI reconstructions segmented into seven different tissues (external cerebrospinal fluid, gray matter, white matter, ventricles, cerebellum, brainstem, deep gray matter). 20 international teams participated in this challenge, submitting a total of 21 algorithms for evaluation. In this paper, we provide a detailed analysis of the results from both a technical and clinical perspective. All participants relied on deep learning methods, mainly U-Nets, with some variability present in the network architecture, optimization, and image pre- and post-processing. The majority of teams used existing medical imaging deep learning frameworks. The main differences between the submissions were the fine tuning done during training, and the specific pre- and post-processing steps performed. The challenge results showed that almost all submissions performed similarly. Four of the top five teams used ensemble learning methods. However, one team's algorithm performed significantly superior to the other submissions, and consisted of an asymmetrical U-Net network architecture. This paper provides a first of its kind benchmark for future automatic multi-tissue segmentation algorithms for the developing human brain in utero

The P2Y1 receptor is involved in the maintenance of glucose homeostasis and in insulin secretion in mice

Pancreatic β cells express several P2 receptors including P2Y1 and the modulation of insulin secretion by extracellular nucleotides has suggested that these receptors may contribute to the regulation of glucose homeostasis. To determine whether the P2Y1 receptor is involved in this process, we performed studies in P2Y1 mice. In baseline conditions, P2Y1-mice exhibited a 15% increase in glycemia and a 40% increase in insulinemia, associated with a 10% increase in body weight, pointing to a role of the P2Y1 receptor in the control of glucose metabolism. Dynamic experiments further showed that P2Y1-mice exhibited a tendency to glucose intolerance. These features were associated with a decrease in the plasma levels of free fatty acid and triglycerides. When fed a lipids and sucrose enriched diet for 15 weeks, the two genotypes no longer displayed any significant differences. To determine whether the P2Y1 receptor was directly involved in the control of insulin secretion, experiments were carried out in isolated Langerhans islets. In the presence of high concentrations of glucose, insulin secretion was significantly greater in islets from P2Y1-mice. Altogether, these results show that the P2Y1 receptor plays a physiological role in the maintenance of glucose homeostasis at least in part by regulating insulin secretion

Fetal Brain Tissue Annotation and Segmentation Challenge Results

In-utero fetal MRI is emerging as an important tool in the diagnosis and analysis of the developing human brain. Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in the research and clinical context. However, manual segmentation of cerebral structures is time-consuming and prone to error and inter-observer variability. Therefore, we organized the Fetal Tissue Annotation (FeTA) Challenge in 2021 in order to encourage the development of automatic segmentation algorithms on an international level. The challenge utilized FeTA Dataset, an open dataset of fetal brain MRI reconstructions segmented into seven different tissues (external cerebrospinal fluid, grey matter, white matter, ventricles, cerebellum, brainstem, deep grey matter). 20 international teams participated in this challenge, submitting a total of 21 algorithms for evaluation. In this paper, we provide a detailed analysis of the results from both a technical and clinical perspective. All participants relied on deep learning methods, mainly U-Nets, with some variability present in the network architecture, optimization, and image pre- and post-processing. The majority of teams used existing medical imaging deep learning frameworks. The main differences between the submissions were the fine tuning done during training, and the specific pre- and post-processing steps performed. The challenge results showed that almost all submissions performed similarly. Four of the top five teams used ensemble learning methods. However, one team's algorithm performed significantly superior to the other submissions, and consisted of an asymmetrical U-Net network architecture. This paper provides a first of its kind benchmark for future automatic multi-tissue segmentation algorithms for the developing human brain in utero.Comment: Results from FeTA Challenge 2021, held at MICCAI; Manuscript submitte