Sensitive detection of EBV microRNAs across cancer spectrum reveals association with decreased survival in adult acute myelocytic leukemia

Epstein Barr virus (EBV) is the etiologic agent involved in numerous human cancers. After infecting the host, EBV establishes a latent infection, with low levels of messenger RNA (mRNA) and protein expression, evolved to evade immune recognition. Conversely, EBV microRNAs (miRNA) are expressed ubiquitously and abundantly within infected cells. Their role in tumor biology and clinical outcomes across the spectrum of cancer is not fully explained. Here, we applied our bioinformatics pipeline for quantitative EBV miRNA detection to examine sequencing data of 8,955 individual tumor samples across 27 tumor types representing the breadth of cancer. We uncover an association of intermediate levels of viral miRNA with decreased survival in adult acute myeloid leukemia (AML) patients (P = 0.00013). Prognostic modeling of this association suggests that increased EBV miRNA levels represent an independent risk factor for poor patient outcomes. Furthermore, we explore differences in expression between elevated and absent viral miRNA loads in adult AML tumors finding that EBV positivity was associated with proinflammatory signals. Together, given no associations were found for pediatric AML, our analyses suggests EBV positivity has the potential for being a prognostic biomarker and might represent a surrogate measure related to immune impairment in adult patients

Minimal Assumptions for Optimal Serology Classification: Theory and Implications for Multidimensional Settings and Impure Training Data

Minimizing error in prevalence estimates and diagnostic classifiers remains a challenging task in serology. In theory, these problems can be reduced to modeling class-conditional probability densities (PDFs) of measurement outcomes, which control all downstream analyses. However, this task quickly succumbs to the curse of dimensionality, even for assay outputs with only a few dimensions (e.g. target antigens). To address this problem, we propose a technique that uses empirical training data to classify samples and estimate prevalence in arbitrary dimension without direct access to the conditional PDFs. We motivate this method via a lemma that relates relative conditional probabilities to minimum-error classification boundaries. This leads us to formulate an optimization problem that: (i) embeds the data in a parameterized, curved space; (ii) classifies samples based on their position relative to a coordinate axis; and (iii) subsequently optimizes the space by minimizing the empirical classification error of pure training data, for which the classes are known. Interestingly, the solution to this problem requires use of a homotopy-type method to stabilize the optimization. We then extend the analysis to the case of impure training data, for which the classes are unknown. We find that two impure datasets suffice for both prevalence estimation and classification, provided they satisfy a linear independence property. Lastly, we discuss how our analysis unifies discriminative and generative learning techniques in a common framework based on ideas from set and measure theory. Throughout, we validate our methods in the context of synthetic data and a research-use SARS-CoV-2 enzyme-linked immunosorbent (ELISA) assay

IoT sensors for modern structural health monitoring. A new frontier

The problem of determining the structural safety level of buildings and civil engineering infrastructures (CEIs) is raising growing concern worldwide. Most of the reinforced concrete constructions have a design life not greater than 100 years, and today it is necessary to face the problem of assessing their level of safety and structural integrity. Such problem is even more pressing when a construction is subjected to extreme environmental conditions. The long-term goal of this study is the realization of wireless low- cost devices, and a data management software, for the structural health monitoring of buildings and CEIs, with remotely controlled sensors embedded in, or installed on, the structural elements, to measure stresses together with accelerations. Once equipped with such system, each construction can become part of the Internet of Things, permitting users and authorities to be alerted in case structural safety is diminished or compromised. A crucial aspect is the unaltered preservation of measurement data over time, which cannot just rely on third parties, and for which it is necessary the exploitation of suitable data-protection technologies. This study have been carried out by experimental testing and validation, both in lab and on site, of the monitoring devices designed and realized. Results show that it is possible to realize low-cost monitoring systems, and related installation techniques, for integration in every new or existing buildings and CEIs

Permanent monitoring of thin structures with low-cost devices

Recently, structural monitoring technology invested in methodologies that give direct information on structures' stress state. Optic fibers, strain gauges, pressure cells give real-time data on the stress condition of a structural element, often determining the area where peak stresses have been reached, with a clear advantage over other less direct monitoring methodologies, such as, e.g., the use of accelerometers and inverse analysis to estimate internal forces. In addition, stresses can be recorded in a data log for analysis after a loading event, as well as for taking into account the lifelong stress state of the structure. Beams and columns of a reinforced concrete frame can be effectively monitored for flexural loads. Differently, thin shells are most of their lifespan under membrane regime, and, when properly designed, they rarely move to the bending regime. Our proposal is to monitor the stress in thin structures by small-sized low- cost devices able to record the stress history at key locations, sending alerts when necessary, with the aim of ensuring safety against the risk of collapse, or simply to perform maintenance/repairing activities. Such devices are realized with cheap off-the-shelf electronics and traditional strain gauges. The application examples are given as laboratory tests performed on a reinforced concrete plate, a masonry panel, and a steel beam. Results shows that the permanent monitoring control of stresses can be conveniently carried out on new structures using low-cost devices of the type we designed and realized in-house

Towards a reliable calculation of relic radiation from primordial gravitational waves

Inflationary gravitational waves, behaving as additional radiation in the Early Universe, can increase the effective number of relativistic species (Neff) by a further correction that depends on the integrated energy-density in gravitational waves over all scales. This effect is typically used to constrain (blue-tilted) models of inflation in light of the bounds resulting from the big bang nucleosynthesis. In this paper, we recompute this contribution, discussing some caveats of the state-of-the-art analyses. Through a parametric investigation, we first demonstrate that the calculation is dominated by the ultraviolet frequencies of the integral and therefore by the behaviour of the tensor spectrum on scales corresponding to modes that cross the horizon very close to the end of inflation, when the slow-roll dynamics breaks down and the production of gravitational waves becomes strongly model dependent. Motivated by these results, we realize a theoretical Monte Carlo and, working within the framework of the Effective Field Theory of inflation, we investigate the observable predictions of a very broad class of models. For each model, we solve a system of coupled differential equations whose solution completely specifies the evolution of the spectrum up to the end of inflation. We prove the calculation of ΔNGWeff to be remarkably model dependent and therefore conclude that accurate analyses are needed to infer reliable information on the inflationary Universe

Correcting pervasive errors in RNA crystallography through enumerative structure prediction

Three-dimensional RNA models fitted into crystallographic density maps exhibit pervasive conformational ambiguities, geometric errors and steric clashes. To address these problems, we present enumerative real-space refinement assisted by electron density under Rosetta (ERRASER), coupled to Python-based hierarchical environment for integrated 'xtallography' (PHENIX) diffraction-based refinement. On 24 data sets, ERRASER automatically corrects the majority of MolProbity-assessed errors, improves the average Rfree factor, resolves functionally important discrepancies in noncanonical structure and refines low-resolution models to better match higher-resolution models

Large genomic aberrations detected by SNP array are independent prognosticators of a shorter time to first treatment in chronic lymphocytic leukemia patients with normal FISH

Background Genomic complexity can predict the clinical course of patients affected by chronic lymphocytic leukemia (CLL) with a normal FISH. However, large studies are still lacking. Here, we analyzed a large series of CLL patients and also carried out the so far largest comparison of FISH versus single-nucleotide polymorphism (SNP) array in this disease. Patients and methods SNP-array data were derived from a previously reported dataset. Results Seventy-seven of 329 CLL patients (23%) presented with a normal FISH. At least one large (>5 Mb) genomic aberration was detected by SNP array in 17 of 77 patients (22%); this finding significantly affected TTT. There was no correlation with the presence of TP53 mutations. In multivariate analysis, including age, Binet stage, IGHV genes mutational status and large genomic lesion, the latter three factors emerged as independent prognosticators. The concordance between FISH and SNP array varied between 84 and 97%, depending on the specific genomic locus investigated. Conclusions SNP array detected additional large genomic aberrations not covered by the standard FISH panel predicting the outcome of CLL patient

Interplay between IL-10, IFN-γ, IL-17A and PD-1 Expressing EBNA1-Specific CD4+ and CD8+ T Cell Responses in the Etiologic Pathway to Endemic Burkitt Lymphoma

Children diagnosed with endemic Burkitt lymphoma (eBL) are deficient in interferon-γ (IFN-γ) responses to Epstein–Barr Nuclear Antigen1 (EBNA1), the viral protein that defines the latency I pattern in this B cell tumor. However, the contributions of immune-regulatory cytokines and phenotypes of the EBNA1-specific T cells have not been characterized for eBL. Using a bespoke flow cytometry assay we measured intracellular IFN-γ, IL-10, IL-17A expression and phenotyped CD4+ and CD8+ T cell effector memory subsets specific to EBNA1 for eBL patients compared to two groups of healthy children with divergent malaria exposures. In response to EBNA1 and a malaria antigen (PfSEA-1A), the three study groups exhibited strikingly different cytokine expression and T cell memory profiles. EBNA1-specific IFN-γ-producing CD4+ T cell response rates were lowest in eBL (40%) compared to children with high malaria (84%) and low malaria (66%) exposures (p < 0.0001 and p = 0.0004, respectively). However, eBL patients did not differ in CD8+ T cell response rates or the magnitude of IFN-γ expression. In contrast, eBL children were more likely to have EBNA1-specific CD4+ T cells expressing IL-10, and less likely to have polyfunctional IFN-γ+IL-10+ CD4+ T cells (p = 0.02). They were also more likely to have IFN-γ+IL-17A+, IFN-γ+ and IL-17A+ CD8+ T cell subsets compared to healthy children. Cytokine-producing T cell subsets were predominantly CD45RA+CCR7+ TNAIVE-LIKE cells, yet PD-1, a marker of persistent activation/exhaustion, was more highly expressed by the central memory (TCM) and effector memory (TEM) T cell subsets. In summary, our study suggests that IL-10 mediated immune regulation and depletion of IFN-γ+ EBNA1-specific CD4+ T cells are complementary mechanisms that contribute to impaired T cell cytotoxicity in eBL pathogenesis

The SF3B1 inhibitor spliceostatin A (SSA) elicits apoptosis in chronic lymphocytic leukemia cells through downregulation of Mcl-1

The pro-survival Bcl-2 family member Mcl-1 is expressed in chronic lymphocytic leukemia (CLL), with high expression correlated with progressive disease. The spliceosome inhibitor spliceostatin A (SSA), is known to regulate Mcl-1 and so here we assessed the ability of SSA to elicit apoptosis in CLL. SSA induced apoptosis of CLL cells at low nanomolar concentrations in a dose- and time-dependent manner, but independently of SF3B1 mutational status, IGHV status and CD38 or ZAP70 expression. However, normal B and T cells were less sensitive than CLL cells (P=0.006 and P&lt;0.001, respectively). SSA altered the splicing of anti-apoptotic MCL-1L to MCL-1s in CLL cells coincident with induction of apoptosis. Overexpression studies in Ramos cells suggested Mcl-1 was important for SSA-induced killing since its expression inversely correlated with apoptosis (P=0.001). IL4 and CD40L, present in patient lymph nodes, are known to protect tumor cells from apoptosis and significantly inhibited SSA, ABT-263 and ABT-199 induced killing following administration to CLL cells (P=0.008). However, by combining SSA with the Bcl-2/Bcl-xL antagonists ABT-263 or ABT-199, we were able to overcome this pro-survival effect. We conclude that SSA combined with Bcl-2/Bcl-xL antagonists may have therapeutic utility for CL

Endemic Burkitt lymphoma avatar mouse models for exploring inter-patient tumor variation and testing targeted therapies

Endemic Burkitt lymphoma (BL) is a childhood cancer in sub-Saharan Africa characterized by Epstein-Barr virus and malaria-associated aberrant B-cell activation and MYC chromosomal translocation. Survival rates hover at 50% after conventional chemotherapies; therefore, clinically relevant models are necessary to test additional therapies. Hence, we established five patient-derived BL tumor cell lines and corresponding NSG-BL avatar mouse models. Transcriptomics confirmed that our BL lines maintained fidelity from patient tumors to NSG-BL tumors. However, we found significant variation in tumor growth and survival among NSG-BL avatars and in Epstein-Barr virus protein expression patterns. We tested rituximab responsiveness and found one NSG-BL model exhibiting direct sensitivity, characterized by apoptotic gene expression counterbalanced by unfolded protein response and mTOR pro-survival pathways. In rituximab-unresponsive tumors, we observed an IFN-α signature confirmed by the expression of IRF7 and ISG15. Our results demonstrate significant inter-patient tumor variation and heterogeneity, and that contemporary patient-derived BL cell lines and NSG-BL avatars are feasible tools to guide new therapeutic strategies and improve outcomes for these children