The correct identification of differentially abundant microbial taxa between experimental conditions is a methodological and computational challenge. Recent work has produced methods to deal with the ...high sparsity and compositionality characteristic of microbiome data, but independent benchmarks comparing these to alternatives developed for RNA-seq data analysis are lacking.
We compare methods developed for single-cell and bulk RNA-seq, and specifically for microbiome data, in terms of suitability of distributional assumptions, ability to control false discoveries, concordance, power, and correct identification of differentially abundant genera. We benchmark these methods using 100 manually curated datasets from 16S and whole metagenome shotgun sequencing.
The multivariate and compositional methods developed specifically for microbiome analysis did not outperform univariate methods developed for differential expression analysis of RNA-seq data. We recommend a careful exploratory data analysis prior to application of any inferential model and we present a framework to help scientists make an informed choice of analysis methods in a dataset-specific manner.
Gene set analysis is moving towards considering pathway topology as a crucial feature. Pathway elements are complex entities such as protein complexes, gene family members and chemical compounds. The ...conversion of pathway topology to a gene/protein networks (where nodes are a simple element like a gene/protein) is a critical and challenging task that enables topology-based gene set analyses.Unfortunately, currently available R/Bioconductor packages provide pathway networks only from single databases. They do not propagate signals through chemical compounds and do not differentiate between complexes and gene families.
Here we present graphite, a Bioconductor package addressing these issues. Pathway information from four different databases is interpreted following specific biologically-driven rules that allow the reconstruction of gene-gene networks taking into account protein complexes, gene families and sensibly removing chemical compounds from the final graphs. The resulting networks represent a uniform resource for pathway analyses. Indeed, graphite provides easy access to three recently proposed topological methods. The graphite package is available as part of the Bioconductor software suite.
graphite is an innovative package able to gather and make easily available the contents of the four major pathway databases. In the field of topological analysis graphite acts as a provider of biological information by reducing the pathway complexity considering the biological meaning of the pathway elements.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Background:
Multiple sclerosis (MS) is a chronic immune-mediated disease of the central nervous system (CNS). Although cognitive impairment (CI) affects a large proportion of MS patients, only few ...data are available about its prognostic value associated with advanced magnetic resonance imaging (MRI) metrics.
Objectives:
We aimed at investigating the relationship between the early CI and the disease progression over 8-year follow-up in MS patients.
Methods:
We conducted a retrospective 8-year longitudinal study involving 78 patients with relapsing-remitting MS, who completed neuropsychological examination and structural MRI at the time of diagnosis. Each patient was clinically evaluated every 6 months, and cortical thickness was quantified at baseline and at the end of the follow-up. Patients were classified as having normal cognition and mild or severe CI.
Results:
The results show that CI at the time of diagnosis is a good predictor of conversion to definite MS (p < 0.001), disability progression (p < 0.001), as well as of transition to secondary progressive phase (p < 0.001) and of cortical thinning (p < 0.001).
Conclusion:
We confirmed and extended the evidence that early CI might be helpful in the identification of MS patients at high risk of disability progression and poor clinical outcome and should be considered as a marker of most aggressive pathology.
Objective
Gray matter (GM) damage and meningeal inflammation have been associated with early disease onset and a more aggressive disease course in multiple sclerosis (MS), but can these changes be ...identified in the patient early in the disease course?
Methods
To identify possible biomarkers linking meningeal inflammation, GM damage, and disease severity, gene and protein expression were analyzed in meninges and cerebrospinal fluid (CSF) from 27 postmortem secondary progressive MS and 14 control cases. Combined cytokine/chemokine CSF profiling and 3T magnetic resonance imaging (MRI) were performed at diagnosis in 2 independent cohorts of MS patients (35 and 38 subjects) and in 26 non‐MS patients.
Results
Increased expression of proinflammatory cytokines (IFNγ, TNF, IL2, and IL22) and molecules related to sustained B‐cell activity and lymphoid‐neogenesis (CXCL13, CXCL10, LTα, IL6, and IL10) was detected in the meninges and CSF of postmortem MS cases with high levels of meningeal inflammation and GM demyelination. Similar proinflammatory patterns, including increased levels of CXCL13, TNF, IFNγ, CXCL12, IL6, IL8, and IL10, together with high levels of BAFF, APRIL, LIGHT, TWEAK, sTNFR1, sCD163, MMP2, and pentraxin III, were detected in the CSF of MS patients with higher levels of GM damage at diagnosis.
Interpretation
A common pattern of intrathecal (meninges and CSF) inflammatory profile strongly correlates with increased cortical pathology, both at the time of diagnosis and at death. These results suggest a role for detailed CSF analysis combined with MRI as a prognostic marker for more aggressive MS. Ann Neurol 2018 Ann Neurol 2018;83:739–755
Abstract
Long non-coding RNAs (lncRNAs) are emerging as important players in the regulation of several aspects of cellular biology. For a better comprehension of their function, it is fundamental to ...determine their tissue or cell specificity and to identify their subcellular localization. In fact, the activity of lncRNAs may vary according to cell and tissue specificity and subcellular compartmentalization. Myofibers are the smallest complete contractile system of skeletal muscle influencing its contraction velocity and metabolism. How lncRNAs are expressed in different myofibers, participate in metabolism regulation and muscle atrophy or how they are compartmentalized within a single myofiber is still unknown. We compiled a comprehensive catalog of lncRNAs expressed in skeletal muscle, associating the fiber-type specificity and subcellular location to each of them, and demonstrating that many lncRNAs can be involved in the biological processes de-regulated during muscle atrophy. We demonstrated that the lncRNA Pvt1, activated early during muscle atrophy, impacts mitochondrial respiration and morphology and affects mito/autophagy, apoptosis and myofiber size in vivo. This work corroborates the importance of lncRNAs in the regulation of metabolism and neuromuscular pathologies and offers a valuable resource to study the metabolism in single cells characterized by pronounced plasticity.
Ovarian cancer is a leading cause of cancer deaths among women. Effective targets to treat advanced epithelial ovarian cancer (EOC) and biomarkers to predict treatment response are still lacking ...because of the complexity of pathways involved in ovarian cancer progression. Here we show that miR-181a promotes TGF-β-mediated epithelial-to-mesenchymal transition via repression of its functional target, Smad7. miR-181a and phosphorylated Smad2 are enriched in recurrent compared with matched-primary ovarian tumours and their expression is associated with shorter time to recurrence and poor outcome in patients with EOC. Furthermore, ectopic expression of miR-181a results in increased cellular survival, migration, invasion, drug resistance and in vivo tumour burden and dissemination. In contrast, miR-181a inhibition via decoy vector suppression and Smad7 re-expression results in significant reversion of these phenotypes. Combined, our findings highlight an unappreciated role for miR-181a, Smad7, and the TGF-β signalling pathway in high-grade serous ovarian cancer.
Gastroenteropancreatic (GEP) neuroendocrine carcinomas (NECs) and mixed adenoneuroendocrine carcinomas (MANECs) are heterogeneous neoplasms characterized by poor outcome. Microsatellite instability ...(MSI) has recently been found in colorectal NECs showing a better prognosis than expected. However, the frequency of MSI in a large series of GEP-NEC/MANECs is still unknown. In this work, we investigated the incidence of MSI in GEP-NEC/MANECs and characterized their clinicopathologic and molecular features. MSI analysis and immunohistochemistry for mismatch repair proteins (MLH1, MSH2, MSH6 and PMS2) were performed in 89 GEP-NEC/MANECs (six esophageal, 77 gastrointestinal, three pancreatic, and three of the gallbladder). Methylation of 34 genes was studied by methylation-specific multiplex ligation probe amplification. Mutation analysis of BRAF and KRAS was assessed by PCR-pyrosequencing analysis. MSI was observed in 11 NEC/MANECs (12.4%): seven intestinal and four gastric. All but two MSI-cases showed MLH1 methylation and loss of MLH1 protein. The remaining two MSI-cancers showed lack of MSH2 or PMS2 immunohistochemical expression. MSI-NEC/MANECs showed higher methylation levels than microsatellite stable NEC/MANECs (40.6% vs 20.2% methylated genes respectively, P<0.001). BRAF mutation was detected in six out of 88 cases (7%) and KRAS mutation was identified in 15 cases (17%). BRAF mutation was associated with MSI (P<0.0008), while KRAS status did not correlate with any clinicopathologic or molecular feature. Vascular invasion (P=0.0003) and MSI (P=0.0084) were identified as the only independent prognostic factors in multivariate analysis. We conclude that MSI identifies a subset of gastric and intestinal NEC/MANECs with distinct biology and better prognosis. MSI-NEC/MANECs resemble MSI-gastrointestinal adenocarcinomas for frequency, molecular profile and pathogenetic mechanisms.
Stage I epithelial ovarian cancer (EOC) represents about 10% of all EOCs and is characterized by good prognosis with fewer than 20% of patients relapsing. As it occurs less frequently than ...advanced-stage EOC, its molecular features have not been thoroughly investigated. We have demonstrated that in stage I EOC
can predict patients' outcome. In the present study, we analyzed the expression of long non-coding RNAs (lncRNA) to enable potential definition of a non-coding transcriptional signature with prognostic relevance for stage I EOC.
202 snap-frozen stage I EOC tumor biopsies, 47 of which relapsed, were gathered together from three independent tumor tissue collections and subdivided into a training set (
= 73) and a validation set (
= 129). Median follow up was 9 years. LncRNAs' expression profiles were correlated in univariate and multivariate analysis with overall survival (OS) and progression-free survival (PFS).
The expression of
-
, and
was associated in univariate and multivariate analyses with relapse and poor outcome in both training and validation sets (
< 0.001). Using the expression profiles of
-
, and
simultaneously, it was possible to stratify patients into high and low risk. The OS for high- and low-risk individuals are 36 and 123 months, respectively (OR, 15.55; 95% confidence interval, 3.81-63.36).
We have identified a non-coding transcriptional signature predictor of survival and biomarker of relapse for stage I EOC.
.
Survival rates of oral squamous cell carcinoma (OSCC) remained substantially unchanged over the last decades; thus, additional prognostic tools are strongly needed. Salivary miRNAs have emerged as ...excellent non-invasive cancer biomarker candidates, but their association with OSCC prognosis has not been investigated yet. In this study, we analyzed global salivary miRNA expression in OSCC patients and healthy controls, with the aim to define its diagnostic and prognostic potential.
Saliva was collected from patients with newly diagnosed untreated primary OSCC and healthy controls. Global profiling of salivary miRNAs was carried out through a microarray approach, while signature validation was performed by quantitative real-time PCR (RT-qPCR). A stringent statistical approach for microarray and RT-qPCR data normalization was applied. The diagnostic performance of miRNAs and their correlation with OSCC prognosis were comprehensively analyzed.
In total, 25 miRNAs emerged as differentially expressed between OSCC patients and healthy controls and, among them, seven were significantly associated with disease-free survival (DFS). miR-106b-5p, miR-423-5p and miR-193b-3p were expressed at high levels in saliva of OSCC patients and their combination displays the best diagnostic performance (ROC - AUC = 0.98). Moreover, high expression of miR-423-5p was an independent predictor of poor DFS, when included in multivariate survival analysis with the number of positive lymph nodes - the only significant clinical prognosticator. Finally, we observed a significant decrease in miR-423-5p expression in matched post-operative saliva samples, suggesting its potential cancer-specific origin.
Salivary miRNAs identified in our cohort of patients show to be accurate in OSCC detection and to effectively stratify patients according to their likelihood of relapse. These results, if validated in an independent set of patients, could be particularly promising for screening/follow-up of high-risk populations and useful for preoperative prognostic assessment.
Topological gene-set analysis has emerged as a powerful means for omic data interpretation. Although numerous methods for identifying dysregulated genes have been proposed, few of them aim to ...distinguish genes that are the real source of perturbation from those that merely respond to the signal dysregulation. Here, we propose a new method, called SourceSet, able to distinguish between the primary and the secondary dysregulation within a Gaussian graphical model context. The proposed method compares gene expression profiles in the control and in the perturbed condition and detects the differences in both the mean and the covariance parameters with a series of likelihood ratio tests. The resulting evidence is used to infer the primary and the secondary set, i.e. the genes responsible for the primary dysregulation, and the genes affected by the perturbation through network propagation. The proposed method demonstrates high specificity and sensitivity in different simulated scenarios and on several real biological case studies. In order to fit into the more traditional pathway analysis framework, SourceSet R package also extends the analysis from a single to multiple pathways and provides several graphical outputs, including Cytoscape visualization to browse the results.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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