ℓ 1-Penalized censored Gaussian graphical model.

Graphical lasso is one of the most used estimators for inferring genetic networks. Despite its diffusion, there are several fields in applied research where the limits of detection of modern measurement technologies make the use of this estimator theoretically unfounded, even when the assumption of a multivariate Gaussian distribution is satisfied. Typical examples are data generated by polymerase chain reactions and flow cytometer. The combination of censoring and high-dimensionality make inference of the underlying genetic networks from these data very challenging. In this article, we propose an $\ell_1$-penalized Gaussian graphical model for censored data and derive two EM-like algorithms for inference. We evaluate the computational efficiency of the proposed algorithms by an extensive simulation study and show that, when censored data are available, our proposal is superior to existing competitors both in terms of network recovery and parameter estimation. We apply the proposed method to gene expression data generated by microfluidic Reverse Transcription quantitative Polymerase Chain Reaction technology in order to make inference on the regulatory mechanisms of blood development. A software implementation of our method is available on github (https://github.com/LuigiAugugliaro/cglasso).

Sparse relative risk regression models.

Clinical studies where patients are routinely screened for many genomic features are becoming more routine. In principle, this holds the promise of being able to find genomic signatures for a particular disease. In particular, cancer survival is thought to be closely linked to the genomic constitution of the tumor. Discovering such signatures will be useful in the diagnosis of the patient, may be used for treatment decisions and, perhaps, even the development of new treatments. However, genomic data are typically noisy and high-dimensional, not rarely outstripping the number of patients included in the study. Regularized survival models have been proposed to deal with such scenarios. These methods typically induce sparsity by means of a coincidental match of the geometry of the convex likelihood and a (near) non-convex regularizer. The disadvantages of such methods are that they are typically non-invariant to scale changes of the covariates, they struggle with highly correlated covariates, and they have a practical problem of determining the amount of regularization. In this article, we propose an extension of the differential geometric least angle regression method for sparse inference in relative risk regression models. A software implementation of our method is available on github (https://github.com/LuigiAugugliaro/dgcox).

Model selection for factorial Gaussian graphical models with an application to dynamic regulatory networks.

Factorial Gaussian graphical Models (fGGMs) have recently been proposed for inferring dynamic gene regulatory networks from genomic high-throughput data. In the search for true regulatory relationships amongst the vast space of possible networks, these models allow the imposition of certain restrictions on the dynamic nature of these relationships, such as Markov dependencies of low order - some entries of the precision matrix are a priori zeros - or equal dependency strengths across time lags - some entries of the precision matrix are assumed to be equal. The precision matrix is then estimated by l1-penalized maximum likelihood, imposing a further constraint on the absolute value of its entries, which results in sparse networks. Selecting the optimal sparsity level is a major challenge for this type of approaches. In this paper, we evaluate the performance of a number of model selection criteria for fGGMs by means of two simulated regulatory networks from realistic biological processes. The analysis reveals a good performance of fGGMs in comparison with other methods for inferring dynamic networks and of the KLCV criterion in particular for model selection. Finally, we present an application on a high-resolution time-course microarray data from the Neisseria meningitidis bacterium, a causative agent of life-threatening infections such as meningitis. The methodology described in this paper is implemented in the R package sglasso, freely available at CRAN, http://CRAN.R-project.org/package=sglasso.

Differential expression of specific microRNA and their targets in acute myeloid leukemia.

Acute myeloid leukemia (AML) the most common acute leukemia in adults is characterized by various cytogenetic and molecular abnormalities. However, the genetic etiology of the disease is not yet fully understood. MicroRNAs (miRNA) are small noncoding RNAs which regulate the expression of target mRNAs both at transcriptional and translational level. In recent years, miRNAs have been identified as a novel mechanism in gene regulation, which show variable expression during myeloid differentiation. We studied miRNA expression of leukemic blasts of 29 cases of newly diagnosed and genetically defined AML using quantitative reverse transcription polymerase chain reaction (RT-PCR) for 365 human miRNA. We showed that miRNA expression profiling reveals distinctive miRNA signatures that correlate with cytogenetic and molecular subtypes of AML. Specific miRNAs with consolidated role on cell proliferation and differentiation such as miR-155, miR-221, let-7, miR-126 and miR-196b appear to be associated with particular subtypes. We observed a significant differentially expressed miRNA profile that characterizes two subgroups of AML with different mechanism of leukemogenesis: core binding factor (CBF) and cytogenetically normal AML with mutations in the genes of NPM1 and FLT3-ITD. We demonstrated, for the first time, the inverse correlation of expression levels between miRNA and their targets in specific AML genetic groups. We suggest that miRNA deregulation may act as complementary hit in the multisteps mechanism of leukemogenesis offering new therapeutic strategies.

miR-155 regulative network in FLT3 mutated acute myeloid leukemia.

BACKGROUND: Acute myeloid leukemia (AML) represents a heterogeneous disorder with recurrent chromosomal alterations and molecular abnormalities. Among AML with normal karyotype (NK-AML) FLT3 activating mutation, internal tandem duplication (FLT3-ITD), is present in about 30% of patients, conferring unfavorable outcome. Our previous data demonstrated specific up-regulation of miR-155 in FLT3-ITD+ AML. miR-155 is known to be directly implicated in normal hematopoiesis and in some pathologies such as myeloid hyperplasia and acute lymphoblastic leukemia. METHODS AND RESULTS: To investigate about the potential influence of miR-155 de-regulation in FLT3-mutated AML we generated a transcription factors regulatory network and combined this with data from multiple sources that predict miR-155 interactions. From these analyses, we derived a sub-network, called "miR-155 module" that describes functional relationship among miR-155 and transcription factors in FLT3-mutated AML. We found that "miR-155 module" is characterized by the presence of six transcription factors as central hubs: four miR-155 regulators (JUN, RUNX1, FOSb, JUNB) and two targets of miR-155 (SPI1, CEBPB) all known to be "master" genes of myelopoiesis. We found, in FLT3-mutated AML, a significant down-regulation of miR-155 target genes CEBPB and SPI1 and up-regulation of miR-155 regulator genes JUN and RUNX1. We also showed that PKC412-related FLT3 inhibition, in MV4-11 cell line, causes down-regulation of miR-155 and increased level of mRNA and protein of miR-155 target SPI1. We showed in experiments of miR-155 mimic in K562 cell line, a high increase of miR-155 and an inverse correlation with the mRNA levels of its targets SPI1 and CEBPB. Moreover silencing of miR-155 in primary AMLs causes mRNA up-regulation of its target SPI1 and CEBPB. CONCLUSION: Our results suggest that activating mutation of FLT3 in AML can lead, through the induction of JUN, to an increased expression of miR-155, which then causes down-regulation of SPI1 and CEBPB and consequently may causes block of myeloid differentiation.