antGLasso: An Efficient Tensor Graphical Lasso Algorithm

The class of bigraphical lasso algorithms (and, more broadly, 'tensor'-graphical lasso algorithms) has been used to estimate dependency structures within matrix and tensor data. However, all current methods to do so take prohibitively long on modestly sized datasets. We present a novel tensor-graphical lasso algorithm that analytically estimates the dependency structure, unlike its iterative predecessors. This provides a speedup of multiple orders of magnitude, allowing this class of algorithms to be used on large, real-world datasets.Comment: 9 pages (21 including supplementary material), 8 figures, submitted to the GLFrontiers workshop at NeurIPS 202

Tbx1 is a negative modulator of Mef2c

The developmental role of the T-box transcription factor Tbx1 is exquisitely dosage-sensitive. In this study, we performed a microarray-based transcriptome analysis of E9.5 embryo tissues across a previously generated Tbx1 mouse allelic series. This analysis identified several genes whose expression was affected by Tbx1 dosage. Interestingly, we found that the expression of the gene encoding the cardiogenic transcription factor Mef2c was negatively correlated to Tbx1 dosage. In vivo data revealed Mef2c up-regulation in the second heart field (SHF) of Tbx1 null mutant embryos compared with wild-type littermates at E9.5. Conversely, Mef2c expression was decreased in the SHF and in somites of Tbx1 gain-of-function mutants. These results are consistent with the described role of Tbx1 in suppressing cardiac progenitor cell differentiation and indicate also a negative effect of Tbx1 on Mef2c during skeletal muscle differentiation. We show that Tbx1 occupies conserved regulatory regions of the Mef2c locus, suggesting a direct effect on Mef2c transcription. However, we also show that Tbx1 interferes with the Gata4→ Mef2c regulatory pathway. Overall, our study uncovered a target of Tbx1 with critical developmental roles, so highlighting the power of the dosage gradient approach that we used

Mantra 2.0: an online collaborative resource for drug mode of action and repurposing by network analysis

Abstract Summary: Elucidation of molecular targets of a compound [mode of action (MoA)] and its off-targets is a crucial step in drug development. We developed an online collaborative resource (MANTRA 2.0) that supports this process by exploiting similarities between drug-induced transcriptional profiles. Drugs are organized in a network of nodes (drugs) and edges (similarities) highlighting 'communities' of drugs sharing a similar MoA. A user can upload gene expression profiles before and after drug treatment in one or multiple cell types. An automated processing pipeline transforms the gene expression profiles into a unique drug 'node' embedded in the drug-network. Visual inspection of the neighbouring drugs and communities helps in revealing its MoA and to suggest new applications of known drugs (drug repurposing). MANTRA 2.0 allows storing and sharing user-generated network nodes, thus making MANTRA 2.0 a collaborative ever-growing resource. Availability and implementation: The web tool is freely available for academic use at http://mantra.tigem.it. Contact: [email protected]

Evidence of key role of Cdk2 overexpression in pemphigus vulgaris

The pathogenesis of pemphigus vulgaris (PV) is still poorly understood. Autoantibodies present in PV patients can promote detrimental effects by triggering altered transduction of signals, which results in a final acantholysis. To investigate mechanisms involved in PV, cultured keratinocytes were treated with PV serum. PV sera were able to promote the cell cycle progression, inducing the accumulation of cyclin-dependent kinase 2 (Cdk2). Microarray analysis on keratinocytes detected that PV serum induced important changes in genes coding for one and the same proteins with known biological functions involved in PV disease (560 differentially expressed genes were identified). Then, we used two different approaches to investigate the role of Cdk2. First, small interfering RNA depletion of Cdk2 prevented cell-cell detachment induced by PV sera. Second, pharmacological inhibition of Cdk2 activity through roscovitine prevented blister formation and acantholysis in the mouse model of the disease. In vivo PV serum was found to alter multiple different pathways by microarray analysis (1463 differentially expressed genes were identified). Major changes in gene expression induced by roscovitine were studied through comparison of effects of PV serum alone and in association with roscovitine. The most significantly enriched pathways were cell communication, gap junction, focal adhesion, adherens junction, and tight junction. Our data indicate that major Cdk2-dependent multiple gene regulatory events are present in PV. This alteration may influence the evolution of PV and its therapy. © 2008 by The American Society for Biochemistry and Molecular Biology, Inc

SRSF1-dependent inhibition of C9ORF72-repeat RNA nuclear export: genome-wide mechanisms for neuroprotection in amyotrophic lateral sclerosis.

BACKGROUND: Loss of motor neurons in amyotrophic lateral sclerosis (ALS) leads to progressive paralysis and death. Dysregulation of thousands of RNA molecules with roles in multiple cellular pathways hinders the identification of ALS-causing alterations over downstream changes secondary to the neurodegenerative process. How many and which of these pathological gene expression changes require therapeutic normalisation remains a fundamental question. METHODS: Here, we investigated genome-wide RNA changes in C9ORF72-ALS patient-derived neurons and Drosophila, as well as upon neuroprotection taking advantage of our gene therapy approach which specifically inhibits the SRSF1-dependent nuclear export of pathological C9ORF72-repeat transcripts. This is a critical study to evaluate (i) the overall safety and efficacy of the partial depletion of SRSF1, a member of a protein family involved itself in gene expression, and (ii) a unique opportunity to identify neuroprotective RNA changes. RESULTS: Our study shows that manipulation of 362 transcripts out of 2257 pathological changes, in addition to inhibiting the nuclear export of repeat transcripts, is sufficient to confer neuroprotection in C9ORF72-ALS patient-derived neurons. In particular, expression of 90 disease-altered transcripts is fully reverted upon neuroprotection leading to the characterisation of a human C9ORF72-ALS disease-modifying gene expression signature. These findings were further investigated in vivo in diseased and neuroprotected Drosophila transcriptomes, highlighting a list of 21 neuroprotective changes conserved with 16 human orthologues in patient-derived neurons. We also functionally validated the high neuroprotective potential of one of these disease-modifying transcripts, demonstrating that inhibition of ALS-upregulated human KCNN1-3 (Drosophila SK) voltage-gated potassium channel orthologs mitigates degeneration of human motor neurons and Drosophila motor deficits. CONCLUSIONS: Strikingly, the partial depletion of SRSF1 leads to expression changes in only a small proportion of disease-altered transcripts, indicating that not all RNA alterations need normalization and that the gene therapeutic approach is safe in the above preclinical models as it does not disrupt globally gene expression. The efficacy of this intervention is also validated at genome-wide level with transcripts modulated in the vast majority of biological processes affected in C9ORF72-ALS. Finally, the identification of a characteristic signature with key RNA changes modified in both the disease state and upon neuroprotection also provides potential new therapeutic targets and biomarkers.This work was initiated with the Medical Research Council (MRC) grant MR/M010864/1 (KN, GMH, PJS) and the MND Association grant Hautbergue/Apr16/846–791 (GMH, LF, AJW, PJS, LMC). This research was further supported by the MRC New Investigator research grant MR/R024162/1 (GMH) and the Biotechnology and Biological Sciences Research Council (BBSRC) grant BB/S005277/1 (GMH). LC was supported by H2020-EU EU Marie Curie fellowship CONTESSA (ID: 660388). CDSS is funded by an AstraZeneca Post-Doctoral award. LF was funded by the Thierry Latran Foundation (FTLAAP2016/ Astrocyte secretome) and is currently supported by the MND Association grant Apr16/848–791 and the Academy of Medical Sciences Springboard Award. AJW was supported by MRC core funding (MC_UU_00015/6) and ERC Starting grant (DYNAMITO; 309742). GMH also reports grants Apr17/854–791 from the MND Association, Thierry Latran FTLAAP2016/ Astrocyte secretome and Royal Society International Exchanges grant IEC\R3\17010 during the course of this study. MA acknowledge grants from Alzheimer’s Research UK (ARUK-PG2018B-005), European Research Council (ERC Advanced Award 294745) and MRC DPFS (129016). PJS is supported as an NIHR Senior Investigator Investigator (NF-SI-0617–10077) and by the MND Association (AMBRoSIA 972–797) and MRC grant MR/S004920/1

BATS: a Bayesian user-friendly software for Analyzing Time Series microarray experiments

BATS is a user-friendly software for Bayesian Analysis of Time Series microarray experiments based on the novel, truly functional and fully Bayesian approach proposed in Angelini et at. (2006). The software is specifically designed for time series data. It allows an user to automatically identify and rank differentially expressed genes and to estimate their expression profiles. BATS successfully manages various technical difficulties which arise in microarray time-course experiments, such as a small number of observations, non-uniform sampling intervals, and presence of missing or multiple data. BATS can carry out analysis with both simulated and real experimental data. It also handles data from different platforms. 1 Availability: BATS is written in Matlab and executable in Windows (Macintosh and Linux version are currently under development). It is freely available upon request from the authors.

Disease Rescue and Increased Lifespan in a Model of Cardiomyopathy and Muscular Dystrophy by Combined AAV Treatments

The BIO14.6 hamster is an excellent animal model for inherited cardiomyopathy, because of its lethal and well-documented course, due to a spontaneous deletion of delta-sarcoglycan gene promoter and first exon. The muscle disease is progressive and average lifespan is 11 months, because heart slowly dilates towards heart failure.Based on the ability of adeno-associated viral (AAV) vectors to transduce heart together with skeletal muscle following systemic administration, we delivered human delta-sarcoglycan cDNA into male BIO14.6 hamsters by testing different ages of injection, routes of administration and AAV serotypes. Body-wide restoration of delta-SG expression was associated with functional reconstitution of the sarcoglycan complex and with significant lowering of centralized nuclei and fibrosis in skeletal muscle. Motor ability and cardiac functions were completely rescued. However, BIO14.6 hamsters having less than 70% of fibers recovering sarcoglycan developed cardiomyopathy, even if the total rescued protein was normal. When we used serotype 2/8 in combination with serotype 2/1, lifespan was extended up to 22 months with sustained heart function improvement.Our data support multiple systemic administrations of AAV as a general therapeutic strategy for clinical trials in cardiomyopathies and muscle disorders

Modeling the European Central Bank official rate: a stochastic approach

Following its main task of price stability in the euro area, the European Central Bank (ECB) increases or decreases interest rates in order to cool inflation or respectively to support economic growth. Monetary policy shows delayed effects on inflation and thus the ECB modifies interest rates on the basis of forecasts about the state of economy over the coming quarters. Aim of our contribution is to provide a stochastic model for the ECB official rate taking into account the expectations on the future state of economy. We propose a non homogeneous Poisson process to describe the intervention times of the ECB. In particular the jump process parameters depend on the evolution of the economic cycle as modeled by a MS-AR model. We show an application on suitably aggregated European data

Classification, Multiple Hypothesis Testing And Wavelet Thresholding Procedures With Applications

[ITALIANO] Lo sviluppo delle metodologie statitistiche per l’analisi dati è generalmente collegato a progressi ottenuti in altri campi scientifici. Da un lato l’analisi statistica è spesso indirizzata a problemi reali, di conseguenza, il miglioramento delle metodologie nasce dall’esigenza di fornire una soluzione sempre pi`u accurata ed efficiente a problemi specifici. D’altro canto accade anche che le procedure statistiche siano prima esplorate in ambito teorico e successivamente testate prima in simulazione e quindi su dati reali. In quest’ottica, lo scopo di questo lavoro è quello di mostrare sia come problemi reali possano essere efficientemente risolti mediante tecniche statistiche, sia come modelli statistici teorici possano essere adatti a descrivere problemi reali. La tesi è organizzata come segue. Nel Capitolo 1 viene affrontato il problema della classificazione supervisionata con lo scopo di risolvere il problema della classificazione di immagini. Vengono passati in rassegna alcuni metodi standard ed in particolare è descritto il problema della classificazione di immagini mediante tecniche locali. I risultati dell’applicazione delle metodologie proposte a dati reali e simulati verranno poi presentati nel Capitolo 4. Nel Capitolo 2 viene introdotto il problema dei test di ipotesi multipla con l’obiettivo di fornire uno strumento di analisi di dati da cDNA microarray. Viene fornita una prospettiva critica dell’impostazione Bayesiana e frequentista del problema e sono descritti punti di forza , di debolezza e di contatto tra le due filosofie. L’applicazione a dati 4 reali da cDNA microarray delle metodologie discusse sar`a presentata nel Capitolo 6. Nel Capitolo 3 sono analizzate nel dominio wavelet alcune regole di thresholding indotte da una variazione del principio bayesiano del Maximum A Posteriori (MAP). Le regole MAP sono azioni Bayesiane che massimizzano la probabilit`a a posteriori. La metodologia proposta risulta essere di tipo thersholding ed è caratterizzata dalla propriet`a di selezionare la moda della probabilit`a a posteriori che risulta essere più grande in valore assoluto, da cui il nome Larger Posterior Mode (LPM). Forniamo un’analisi del rischio associato alla regola LPM e mostriamo come le sue prestazioni della regola LPM sono competitive con quelle di tecniche di letteratura. Il Capitolo 6 presenta infine una discussone sulla scelta degli iperparametri, uno studio in simulazione della rregola LPM ed una sua applicazione ad un problema reale. Questo lavoro è stato svolto durante la mia attività di ricerca presso l’Istituto per le Applicazioni del Calcolo Mauro Picone (IAC) , sezione di Napoli. L’interesse all’analisi dei dati da DNA microarray `e nato da una collaborazione con il Telethon Institute of Genetic and Medicine (TIGEM) e con il Policlinico di Napoli, dove sono stati fisicamente effettuati gli esperimenti sui DNA microarray. La parte finale della tesi è stata svolta durante il mio periodo di ricerca presso il Georgia Institute of Technology, Atlanta, Georgia. / [ENGLISH] The aim of this work is to show how different real world problems can be solved efficiently by statistical technics, and simultaneously to show how theoretical statistical models can fit real data problems. The present thesis is organized as follows. In Chapter 1 we deal with the problem of supervised classification having in mind the problem of image classification. We review some of the classical statistical methods for pattern recognition, introduce the problem of localized classification of images and propose new localized discriminant analysis methods. Applications of the proposed methodology to simulated and real data, will be provided in Chapter 4. In Chapter 2 we introduce the statistical problem of multiple hypothesis testing with the target of analyzing cDNA microarray data. We review the guiding lines of frequentist and Bayesian approach to multiple hypothesis testing, describing strength and weakness of the two philosophies andtrying to find some connections between them. The application of the described methods to a genetic microarray data experiment is provided in Chapter 6. In Chapter 3 we explore the thresholding rules in the wavelet domain induced by a variation of the Bayesian Maximum A Posteriori (MAP) principle. The MAP rules are Bayes actions that maximize the posterior. The proposed rule is thresholding and always picks the mode of the posterior larger in absolute value, thus the name Larger Posterior Mode (LPM). We show that the introduced shrinkage performs comparably to several popular shrinkage techniques. The exact risk properties of the thresholding rule are explored. Comprehensive simulations and comparisons are provided in Chapter 6 which also contains discussion on the selection of hyperparameters and a real-life application of the introduced shrinkage. The present work was done during my research activity at the Istituto per le Applicazioni del Calcolo "Mauro Picone" (IAC) in Naples. The interest on microarrays data was motivated by a collaboration with the Teleton Institute of Genetic and Medicine (TIGEM) and the Policlinico of Naples, where the biological experiments were carried out. The last part of this work was done during a visiting period at the Georgia Institute of Technology (GATECH), in Atlanta, U.S.A

Combining experimental evidences from replicates and nearby species data for annotating novel genomes

For several years now, there has been an exponential growth of the amount of life science data (e.g., sequenced complete genomes, 3D structures, DNA chips, Mass spectroscopy data) generated by high throughput experiments. Carrying out analyses of complex, voluminous, and heterogeneous data and guiding the analysis of data using a statistical and mathematical sound methodology is thus of paramount importance. Here we make and justify the observation that experimental replicates and phylogenetic data may be combined to strength the evidences on identifying transcriptional motifs, which seems to be quite difficult using other currently used methods. We present a case study considering sequences and microarray data from fungi species. Although we show that our methodology can result of immediate practical utility to bioinformaticians and biologists for annotating new genomes, here the focus is also on discussing the dependent interesting mathematical problems that high throughput data integration poses