Salivary biomarkers and proteomics: Future diagnostic and clinical utilities = Biomarkers e proteomica salivari: Prospettive future cliniche e diagnostiche

Saliva testing is a non-invasive and inexpensive test that can serve as a source of information useful for diagnosis of disease. As we enter the era of genomic technologies and –omic research, collection of saliva has increased. Recent proteomic platforms have analysed the human salivary proteome and characterised about 3000 differentially expressed proteins and peptides: in saliva, more than 90% of proteins in weight are derived from the secretion of three couples of “major” glands; all the other components are derived from minor glands, gingival crevicular fluid, mucosal exudates and oral microflora. The most common aim of proteomic analysis is to discriminate between physiological and pathological conditions. A proteomic protocol to analyze the whole saliva proteome is not currently available. It is possible distinguish two type of proteomic platforms: top-down proteomics investigates intact naturally-occurring structure of a protein under examination; bottom-up proteomics analyses peptide fragments after pre-digestion (typically with trypsin). Because of this heterogeneity, many different biomarkers may be proposed for the same pathology. The salivary proteome has been characterised in several diseases: oral squamous cell carcinoma and oral leukoplakia, chronic graft-versus-host disease Sjögren’s syndrome and other autoimmune disorders such as SAPHO, schizophrenia and bipolar disorder, and genetic diseases like Down’s Syndrome and Wilson disease. The results of research reported herein suggest that in the near future human saliva will be a relevant diagnostic fluid for clinical diagnosis and prognosis

Article a new epigenetic model to stratify glioma patients according to their immunosuppressive state

Gliomas are the most common primary neoplasm of the central nervous system. A promising frontier in the definition of glioma prognosis and treatment is represented by epigenetics. Further-more, in this study, we developed a machine learning classification model based on epigenetic data (CpG probes) to separate patients according to their state of immunosuppression. We considered 573 cases of low-grade glioma (LGG) and glioblastoma (GBM) from The Cancer Genome Atlas (TCGA). First, from gene expression data, we derived a novel binary indicator to flag patients with a favorable immune state. Then, based on previous studies, we selected the genes related to the immune state of tumor microenvironment. After, we improved the selection with a data-driven procedure, based on Boruta. Finally, we tuned, trained, and evaluated both random forest and neural network classifiers on the resulting dataset. We found that a multi-layer perceptron network fed by the 338 probes selected by applying both expert choice and Boruta results in the best performance, achieving an out-of-sample accuracy of 82.8%, a Matthews correlation coefficient of 0.657, and an area under the ROC curve of 0.9. Based on the proposed model, we provided a method to stratify glioma patients according to their epigenomic state

Salivary biomarkers and proteomics: future diagnostic and clinical utilities

Lo studio della proteomica salivare, test economico e non invasivo, rappresenta una fonte di numerose informazioni, ed è utile per la diagnosi di svariate malattie. Da quando siamo entrati nellera della tecnologia genomica e delle scienze omiche, la raccolta di campioni salivari è aumentata esponenzialmente. Recenti piattaforme proteomiche hanno analizzato il proteoma salivare umano, caratterizzando circa 3000 peptidi e proteine, espressi in maniera differente: più del 90% in peso deriva dalla secrezione delle tre ghiandole salivari maggiori, mentre la restante parte proviene dalle ghiandole salivari minori, dal fluido crevicolare gengivale, da essudati mucosi e dalla microflora orale. Lobiettivo principale dellanalisi proteomica è discriminare tra condizioni fisiologiche e patologiche. Ad oggi, tuttavia, non esiste un preciso protocollo che permetta di analizzare lintero proteoma salivare, pertanto sono state realizzate svariate strategie. Innanzitutto, è possibile distinguere due tipologie di piattaforme proteomiche: lapproccio top-down prevede lanalisi delle proteine sotto esame come entità intatte; nellapproccio bottom-up la caratterizzazione della proteina avviene mediante lo studio dei peptidi ottenuti dopo digestione enzimatica (con tripsina tipicamente). A causa di questa eterogeneità, per una stessa patologia sono stati proposti differenti biomarkers. Il proteoma salivare è stato caratterizzato in numerose malattie: carcinoma squamoso e leucoplachie orali, malattia del trapianto contro lospite (GVHD) cronica, sindrome di Sjögren e altri disordini autoimmuni come la sindrome SAPHO (sinovite, acne, pustolosi, iperostosi e osteite), schizofrenia e disordine bipolare, malattie genetiche come la sindrome di Down o la malattia di Wilson. In conclusione, i risultati delle ricerche riportate in questa review suggeriscono che nel prossimo futuro la saliva diverrà un fluido di indubbia rilevanza diagnostica utile per fini clinici, sia diagnostici, sia prognostici

The Undiscovered Ultradiffuse Galaxies of the Local Group

Ultradiffuse galaxies (UDGs) are attractive candidates to probe cosmological models and test theories of galaxy formation at low masses; however, they are difficult to detect because of their low surface brightness. In the Local Group a handful of UDGs have been found to date, most of which are satellites of the Milky Way and M31, and only two are isolated galaxies. It is unclear whether so few UDGs are expected. We address this by studying the population of UDGs formed in hydrodynamic constrained simulations of the Local Group from the HESTIA suite. For a Local Group with a total enclosed mass M LG( < 2.5 Mpc) = 8 × 1012 M⊙, we predict that there are 12 ± 3 isolated UDGs (68% confidence) with stellar masses 106 ≤ M */M⊙ < 109, and effective radii R e ≥ 1.5 kpc, within 2.5 Mpc of the Local Group, of which 2 − 1 + 2 (68% confidence) are detectable in the footprint of the Sloan Digital Sky Survey (SDSS). Accounting for survey incompleteness, we find that almost the entire population of UDGs in the Local Group field would be observable in a future all-sky survey with a depth similar to the SDSS, the Dark Energy Survey, or the Legacy Survey of Space and Time. Our results suggest that there is a population of UDGs in the Local Group awaiting discovery

A probabilistic deep learning model to distinguish cusps and cores in dwarf galaxies

Numerical simulations within a cold dark matter (DM) cosmology form haloes whose density profiles have a steep inner slope (‘cusp’), yet observations of galaxies often point towards a flat central ‘core’. We develop a convolutional mixture density neural network model to derive a probability density function (PDF) of the inner density slopes of DM haloes. We train the network on simulated dwarf galaxies from the NIHAO and AURIGA projects, which include both DM cusps and cores: line-of-sight velocities and 2D spatial distributions of their stars are used as inputs to obtain a PDF representing the probability of predicting a specific inner slope. The model recovers accurately the expected DM profiles: ∼82 per cent of the galaxies have a derived inner slope within ±0.1 of their true value, while ∼98 per cent within ±0.3. We apply our model to four Local Group dwarf spheroidal galaxies and find results consistent with those obtained with the Jeans modelling based code GRAVSPHERE: the Fornax dSph has a strong indication of possessing a central DM core, Carina and Sextans have cusps (although the latter with large uncertainties), while Sculptor shows a double peaked PDF indicating that a cusp is preferred, but a core cannot be ruled out. Our results show that simulation-based inference with neural networks provide a innovative and complementary method for the determination of the inner matter density profiles in galaxies, which in turn can help constrain the properties of the elusive DM

SubHaloes going Notts: The SubHalo-Finder Comparison Project

We present a detailed comparison of the substructure properties of a single Milky Way sized dark matter halo from the Aquarius suite at five different resolutions, as identified by a variety of different (sub-)halo finders for simulations of cosmic structure formation. These finders span a wide range of techniques and methodologies to extract and quantify substructures within a larger non-homogeneous background density (e.g. a host halo). This includes real-space, phase-space, velocity-space and time- space based finders, as well as finders employing a Voronoi tessellation, friends-of-friends techniques, or refined meshes as the starting point for locating substructure.A common post-processing pipeline was used to uniformly analyse the particle lists provided by each finder. We extract quantitative and comparable measures for the subhaloes, primarily focusing on mass and the peak of the rotation curve for this particular study. We find that all of the finders agree extremely well on the presence and location of substructure and even for properties relating to the inner part part of the subhalo (e.g. the maximum value of the rotation curve). For properties that rely on particles near the outer edge of the subhalo the agreement is at around the 20 per cent level. We find that basic properties (mass, maximum circular velocity) of a subhalo can be reliably recovered if the subhalo contains more than 100 particles although its presence can be reliably inferred for a lower particle number limit of 20. We finally note that the logarithmic slope of the subhalo cumulative number count is remarkably consistent and <1 for all the finders that reached high resolution. If correct, this would indicate that the larger and more massive, respectively, substructures are the most dynamically interesting and that higher levels of the (sub-)subhalo hierarchy become progressively less important.Comment: 16 pages, 7 figures, 2 tables, Accepted for MNRA

IFN-\u3b3 and other serum cytokines in head and neck squamous cell carcinomas

Altered immune responses have been reported in head and neck cancer, and some of these responses have been associated with poor clinical outcomes. A multiple-array technology platform was used to simultaneously evaluate the levels of 25 cytokines. Pre-treatment serum levels were evaluated in 31 HNSCC patients and 6 healthy controls. The levels of 8 cytokines, specifically IL-1ra, IL-2, IL-5, IL-6, IL-8, IL-17, IFN-\u3b3 and IP-10, were significantly higher in patients than in controls. Among cancer patients we observed lower levels of IFN-\u3b3 and IL-7 in cases with nodal metastases compared to those with cN0 disease. We observed increases in the levels of some serum cytokines in HNSCC patients, as well as reductions in selected cytokines associated with regional progression. These findings provide an intriguing perspective on the development and validation of novel markers for follow-up evaluations and predictions of regional spreading in HNSCC patients