NiPTUNE: an automated pipeline for noninvasive prenatal testing in an accurate, integrative and flexible framework.

Noninvasive prenatal testing (NIPT) consists of determining fetal aneuploidies by quantifying copy number alteration from the sequencing of cell-free DNA (cfDNA) from maternal blood. Due to the presence of cfDNA of fetal origin in maternal blood, in silico approaches have been developed to accurately predict fetal aneuploidies. Although NIPT is becoming a new standard in prenatal screening of chromosomal abnormalities, there are no integrated pipelines available to allow rapid, accurate and standardized data analysis in any clinical setting. Several tools have been developed, however often optimized only for research purposes or requiring enormous amount of retrospective data, making hard their implementation in a clinical context. Furthermore, no guidelines have been provided on how to accomplish each step of the data analysis to achieve reliable results. Finally, there is no integrated pipeline to perform all steps of NIPT analysis. To address these needs, we tested several tools for performing NIPT data analysis. We provide extensive benchmark of tools performances but also guidelines for running them. We selected the best performing tools that we benchmarked and gathered them in a computational pipeline. NiPTUNE is an open source python package that includes methods for fetal fraction estimation, a novel method for accurate gender prediction, a principal component analysis based strategy for quality control and fetal aneuploidies prediction. NiPTUNE is constituted by seven modules allowing the user to run the entire pipeline or each module independently. Using two cohorts composed by 1439 samples with 31 confirmed aneuploidies, we demonstrated that NiPTUNE is a valuable resource for NIPT analysis.

ABEILLE: a novel method for ABerrant Expression Identification empLoying machine LEarning from RNA-sequencing data.

MOTIVATION: Current advances in omics technologies are paving the diagnosis of rare diseases proposing a complementary assay to identify the responsible gene. The use of transcriptomic data to identify aberrant gene expression (AGE) has demonstrated to yield potential pathogenic events. However, popular approaches for AGE identification are limited by the use of statistical tests that imply the choice of arbitrary cut-off for significance assessment and the availability of several replicates not always possible in clinical contexts. RESULTS: Hence, we developed ABerrant Expression Identification empLoying machine LEarning from sequencing data (ABEILLE) a variational autoencoder (VAE)-based method for the identification of AGEs from the analysis of RNA-seq data without the need for replicates or a control group. ABEILLE combines the use of a VAE, able to model any data without specific assumptions on their distribution, and a decision tree to classify genes as AGE or non-AGE. An anomaly score is associated with each gene in order to stratify AGE by the severity of aberration. We tested ABEILLE on a semi-synthetic and an experimental dataset demonstrating the importance of the flexibility of the VAE configuration to identify potential pathogenic candidates. AVAILABILITY AND IMPLEMENTATION: ABEILLE source code is freely available at: https://github.com/UCA-MSI/ABEILLE. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Systemic CLIP-seq analysis and game theory approach to model microRNA mode of binding.

microRNAs (miRNAs) associate with Ago proteins to post-transcriptionally silence gene expression by targeting mRNAs. To characterize the modes of miRNA-binding, we developed a novel computational framework, called optiCLIP, which considers the reproducibility of the identified peaks among replicates based on the peak overlap. We identified 98 999 binding sites for mouse and human miRNAs, from eleven Ago2 CLIP-seq datasets. Clustering the binding preferences, we found heterogeneity of the mode of binding for different miRNAs. Finally, we set up a quantitative model, named miRgame, based on an adaptation of the game theory. We have developed a new algorithm to translate the miRgame into a score that corresponds to a miRNA degree of occupancy for each Ago2 peak. The degree of occupancy summarizes the number of miRNA-binding sites and miRNAs targeting each binding site, and binding energy of each miRNA::RNA heteroduplex in each peak. Ago peaks were stratified accordingly to the degree of occupancy. Target repression correlates with higher score of degree of occupancy and number of miRNA-binding sites within each Ago peak. We validated the biological performance of our new method on miR-155-5p. In conclusion, our data demonstrate that miRNA-binding sites within each Ago2 CLIP-seq peak synergistically interplay to enhance target repression.

A Survey of Autoencoder Algorithms to Pave the Diagnosis of Rare Diseases.

Rare diseases (RDs) concern a broad range of disorders and can result from various origins. For a long time, the scientific community was unaware of RDs. Impressive progress has already been made for certain RDs; however, due to the lack of sufficient knowledge, many patients are not diagnosed. Nowadays, the advances in high-throughput sequencing technologies such as whole genome sequencing, single-cell and others, have boosted the understanding of RDs. To extract biological meaning using the data generated by these methods, different analysis techniques have been proposed, including machine learning algorithms. These methods have recently proven to be valuable in the medical field. Among such approaches, unsupervised learning methods via neural networks including autoencoders (AEs) or variational autoencoders (VAEs) have shown promising performances with applications on various type of data and in different contexts, from cancer to healthy patient tissues. In this review, we discuss how AEs and VAEs have been used in biomedical settings. Specifically, we discuss their current applications and the improvements achieved in diagnostic and survival of patients. We focus on the applications in the field of RDs, and we discuss how the employment of AEs and VAEs would enhance RD understanding and diagnosis.

Recent computational developments on CLIP-seq data analysis and microRNA targeting implications.

Cross-Linking Immunoprecipitation associated to high-throughput sequencing (CLIP-seq) is a technique used to identify RNA directly bound to RNA-binding proteins across the entire transcriptome in cell or tissue samples. Recent technological and computational advances permit the analysis of many CLIP-seq samples simultaneously, allowing us to reveal the comprehensive network of RNA-protein interaction and to integrate it to other genome-wide analyses. Therefore, the design and quality management of the CLIP-seq analyses are of critical importance to extract clean and biological meaningful information from CLIP-seq experiments. The application of CLIP-seq technique to Argonaute 2 (Ago2) protein, the main component of the microRNA (miRNA)-induced silencing complex, reveals the direct binding sites of miRNAs, thus providing insightful information about the role played by miRNA(s). In this review, we summarize and discuss the most recent computational methods for CLIP-seq analysis, and discuss their impact on Ago2/miRNA-binding site identification and prediction with a regard toward human pathologies.

From benchmarking HITS-CLIP peak detection programs to a new method for identification of miRNA-binding sites from Ago2-CLIP data.

Experimental evidence indicates that about 60% of miRNA-binding activity does not follow the canonical rule about the seed matching between miRNA and target mRNAs, but rather a non-canonical miRNA targeting activity outside the seed or with a seed-like motifs. Here, we propose a new unbiased method to identify canonical and non-canonical miRNA-binding sites from peaks identified by Ago2 Cross-Linked ImmunoPrecipitation associated to high-throughput sequencing (CLIP-seq). Since the quality of peaks is of pivotal importance for the final output of the proposed method, we provide a comprehensive benchmarking of four peak detection programs, namely CIMS, PIPE-CLIP, Piranha and Pyicoclip, on four publicly available Ago2-HITS-CLIP datasets and one unpublished in-house Ago2-dataset in stem cells. We measured the sensitivity, the specificity and the position accuracy toward miRNA binding sites identification, and the agreement with TargetScan. Secondly, we developed a new pipeline, called miRBShunter, to identify canonical and non-canonical miRNA-binding sites based on de novo motif identification from Ago2 peaks and prediction of miRNA::RNA heteroduplexes. miRBShunter was tested and experimentally validated on the in-house Ago2-dataset and on an Ago2-PAR-CLIP dataset in human stem cells. Overall, we provide guidelines to choose a suitable peak detection program and a new method for miRNA-target identification.

Benchmarking feature selection and feature extraction methods to improve the performances of machine-learning algorithms for patient classification using metabolomics biomedical data.

Objective: Classification tasks are an open challenge in the field of biomedicine. While several machine-learning techniques exist to accomplish this objective, several peculiarities associated with biomedical data, especially when it comes to omics measurements, prevent their use or good performance achievements. Omics approaches aim to understand a complex biological system through systematic analysis of its content at the molecular level. On the other hand, omics data are heterogeneous, sparse and affected by the classical "curse of dimensionality" problem, i.e. having much fewer observation, samples (n) than omics features (p). Furthermore, a major problem with multi-omics data is the imbalance either at the class or feature level. The objective of this work is to study whether feature extraction and/or feature selection techniques can improve the performances of classification machine-learning algorithms on omics measurements. Methods: Among all omics, metabolomics has emerged as a powerful tool in cancer research, facilitating a deeper understanding of the complex metabolic landscape associated with tumorigenesis and tumor progression. Thus, we selected three publicly available metabolomics datasets, and we applied several feature extraction techniques both linear and non-linear, coupled or not with feature selection methods, and evaluated the performances regarding patient classification in the different configurations for the three datasets. Results: We provide general workflow and guidelines on when to use those techniques depending on the characteristics of the data available. To further test the extension of our approach to other omics data, we have included a transcriptomics and a proteomics data. Overall, for all datasets, we showed that applying supervised feature selection improves the performances of feature extraction methods for classification purposes. Scripts used to perform all analyses are available at: https://github.com/Plant-Net/Metabolomic_project/.

Definition of the effector landscape across 13 phytoplasma proteomes with LEAPH and EffectorComb.

'Candidatus Phytoplasma' genus, a group of fastidious phloem-restricted bacteria, can infect a wide variety of both ornamental and agro-economically important plants. Phytoplasmas secrete effector proteins responsible for the symptoms associated with the disease. Identifying and characterizing these proteins is of prime importance for expanding our knowledge of the molecular bases of the disease. We faced the challenge of identifying phytoplasma's effectors by developing LEAPH, a machine learning ensemble predictor composed of four models. LEAPH was trained on 479 proteins from 53 phytoplasma species, described by 30 features. LEAPH achieved 97.49% accuracy, 95.26% precision and 98.37% recall, ensuring a low false-positive rate and outperforming available state-of-the-art methods. The application of LEAPH to 13 phytoplasma proteomes yields a comprehensive landscape of 2089 putative pathogenicity proteins. We identified three classes according to different secretion models: 'classical', 'classical-like' and 'non-classical'. Importantly, LEAPH identified 15 out of 17 known experimentally validated effectors belonging to the three classes. Furthermore, to help the selection of novel candidates for biological validation, we applied the Self-Organizing Maps algorithm and developed a Shiny app called EffectorComb. LEAPH and the EffectorComb app can be used to boost the characterization of putative effectors at both computational and experimental levels, and can be employed in other phytopathological models.

Identification and characterization of specific motifs in effector proteins of plant parasites using MOnSTER.

Plant pathogens cause billions of dollars of crop loss every year and are a major threat to global food security. Identifying and characterizing pathogens effectors is crucial towards their improved control. Because of their poor sequence conservation, effector identification is challenging, and current methods generate too many candidates without indication for prioritizing experimental studies. In most phyla, effectors contain specific sequence motifs which influence their localization and targets in the plant. Therefore, there is an urgent need to develop bioinformatics tools tailored for pathogen effectors. To circumvent these limitations, we have developed MOnSTER a specific tool that identifies clusters of motifs of protein sequences (CLUMPs). MOnSTER can be fed with motifs identified by de novo tools or from databases such as Pfam and InterProScan. The advantage of MOnSTER is the reduction of motif redundancy by clustering them and associating a score. This score encompasses the physicochemical properties of AAs and the motif occurrences. We built up our method to identify discriminant CLUMPs in oomycetes effectors. Consequently, we applied MOnSTER on plant parasitic nematodes and identified six CLUMPs in about 60% of the known nematode candidate parasitism proteins. Furthermore, we found co-occurrences of CLUMPs with protein domains important for invasion and pathogenicity. The potentiality of this tool goes beyond the effector characterization and can be used to easily cluster motifs and calculate the CLUMP-score on any set of protein sequences.

Analysis of the regulated transcriptome of Neisseria meningitidis in human blood using a tiling array.

Neisseria meningitidis is the major cause of septicemia and meningococcal meningitis. During the course of infection, the bacterium must adapt to different host environments as a crucial factor for survival and dissemination; in particular, one of the crucial factors in N. meningitidis pathogenesis is the ability to grow and survive in human blood. We recently showed that N. meningitidis alters the expression of 30% of the open reading frames (ORFs) of the genome during incubation in human whole blood and suggested the presence of fine regulation at the gene expression level in order to control this step of pathogenesis. In this work, we used a customized tiling oligonucleotide microarray to define the changes in the whole transcriptional profile of N. meningitidis in a time course experiment of ex vivo bacteremia by incubating bacteria in human whole blood and then recovering RNA at different time points. The application of a newly developed bioinformatic tool to the tiling array data set allowed the identification of new transcripts--small intergenic RNAs, cis-encoded antisense RNAs, mRNAs with extended 5' and 3' untranslated regions (UTRs), and operons--differentially expressed in human blood. Here, we report a panel of expressed small RNAs, some of which can potentially regulate genes involved in bacterial metabolism, and we show, for the first time in N. meningitidis, extensive antisense transcription activity. This analysis suggests the presence of a circuit of regulatory RNA elements used by N. meningitidis to adapt to proliferate in human blood that is worthy of further investigation.

GenomeMixer and TRUST: Novel bioinformatics tools to improve reliability of Non-Invasive Prenatal Testing (NIPT) for fetal aneuploidies.

Non-invasive prenatal testing (NIPT) screens for common fetal chromosomal abnormalities through analysis of circulating cell-free DNA in maternal blood by massive parallel sequencing. NIPT reliability relies on both the estimation of the fetal fraction (ff) and on the sequencing depth (sd) but how these parameters are linked is unknown. Several bioinformatics tools have been developed to determine the ff but there is no universal ff threshold applicable across diagnostics laboratories. Thus, we developed two tools allowing the implementation of a strategy for NIPT results validation in clinical practice: GenomeMixer, a semi-supervised approach to create synthetic sequences and to estimate confidence intervals for NIPT validation and TRUST to estimate the reliability of NIPT results based on confidence intervals found in this study. We retrospectively validated these new tools on 2 cohorts for a total of 1439 samples with 31 confirmed aneuploidies. Through the analysis of the interrelationship between ff, sd and chromosomal aberration detection, we demonstrate that these parameters are profoundly connected and cannot be considered independently. Our tools take in account this critical relationship to improve NIPT reliability and facilitate cross laboratory standardization of this screening test.

Cardiac dyspnea risk zones in the South of France identified by geo-pollution trends study.

The incidence of cardiac dyspnea (CD) and the distribution of pollution in the south of France suggests that environmental pollution may have a role in disease triggering. CD is a hallmark symptom of heart failure leading to reduced ability to function and engage in activities of daily living. To show the impact of short-term pollution exposure on the increment of CD emergency room visits, we collected pollutants and climate measurements on a daily basis and 43,400 events of CD in the Région Sud from 2013 to 2018. We used a distributed lag non-linear model (DLNM) to assess the association between air pollution and CD events. We divided the region in 357 zones to reconciliate environmental and emergency room visits data. We applied the DLNM on the entire region, on zones grouped by pollution trends and on singular zones. Each pollutant has a significant effect on triggering CD. Depending on the pollutant, we identified four shapes of exposure curves to describe the impact of pollution on CD events: early and late effect for NO2; U-shape and rainbow-shape (or inverted U) for O3; all the four shapes for PM10. In the biggest cities, O3 has the most significant association along with the PM10. In the west side, a delayed effect triggered by PM10 was found. Zones along the main highway are mostly affected by NO2 pollution with an increase of the association for a period up to 9 days after the pollution peak. Our results can be used by local authorities to set up specific prevention policies, public alerts that adapt to the different zones and support public health prediction-making. We developed a user-friendly web application called Health, Environment in PACA Region Tool (HEART) to collect our results. HEART will allow citizens, researchers and local authorities to monitor the impact of pollution trends on local public health.

Environmental Contaminants Modulate Breast Cancer Development and Outcome in TP53 p.R337H Carriers and Noncarriers.

Two major concerns associated with cancer development in Paraná state, South Brazil, are environmental pollution and the germline TP53 p.R337H variant found in 0.27−0.30% of the population. We assessed breast cancer (BC) risk in rural (C1 and C2) and industrialized (C3) subregions, previously classified by geochemistry, agricultural productivity, and population density. C2 presents lower organochloride levels in rivers and lower agricultural outputs than C1, and lower levels of chlorine anions in rivers and lower industrial activities than C3. TP53 p.R337H status was assessed in 4658 women aged >30 years from C1, C2, and C3, subsequent to a genetic screening (Group 1, longitudinal study). BC risk in this group was 4.58 times higher among TP53 p.R337H carriers. BC prevalence and risk were significantly lower in C2 compared to that in C3. Mortality rate and risk associated with BC in women aged >30 years (n = 8181 deceased women; Group 2) were also lower in C2 than those in C3 and C1. These results suggest that environmental factors modulate BC risk and outcome in carriers and noncarriers.

Tropical ulcers: the first imported cases and review of the literature.

BACKGROUND: A tropical ulcer is a bacterial necrotizing disease of the skin, with an acute or chronic clinical course, caused by anaerobic bacteria, notably Fusobacteria spp. OBJECTIVES: We present six Italian tourists who acquired tropical ulcers in tropical and subtropical countries. MATERIALS & METHODS: Four males and two females acquired a skin ulcer during trips to Brazil, Malaysia, Fiji Islands, Zambia, Tanzania and India. In all patients, medical history, physical and dermatological examination, laboratory tests, bacteriological examinations and biopsy were carried out. RESULTS: All patients were in good general health. All patients stated that the ulcer was caused by a trauma. No fever was reported. Neither lymphangitis nor lymphadenopathy were detected. The ulcer was located on a forearm in one patient, on a leg in two and on an ankle in three patients. All ulcers were malodorous and painful. Laboratory tests revealed mild leucocytosis and a mild increase in erythrocyte sedimentation rate and C-reactive protein. Results of bacteriological examinations revealed the presence of Fusobacterium spp. in five patients. Other bacteria were identified in all patients. Histopathological examination showed: necrosis of the epidermis and dermis; vascular dilatation; oedema in the dermis; massive infiltration with neutrophils, lymphocytes and histiocytes; and fragmented collagen bundles. No signs of vasculitis were observed. All patients were successfully treated with oral metronidazole (1 g/day for two weeks) and, according to antibiograms, with different systemic antibiotics. CONCLUSION: To our knowledge, these are the first cases of tropical ulcers reported in Western tourists.

Multi-Omics Approaches to Improve Mitochondrial Disease Diagnosis: Challenges, Advances, and Perspectives.

Mitochondrial diseases (MD) are rare disorders caused by deficiency of the mitochondrial respiratory chain, which provides energy in each cell. They are characterized by a high clinical and genetic heterogeneity and in most patients, the responsible gene is unknown. Diagnosis is based on the identification of the causative gene that allows genetic counseling, prenatal diagnosis, understanding of pathological mechanisms, and personalized therapeutic approaches. Despite the emergence of Next Generation Sequencing (NGS), to date, more than one out of two patients has no diagnosis in the absence of identification of the responsible gene. Technologies currently used for detecting causal variants (genetic alterations) is far from complete, leading many variants of unknown significance (VUS) and mainly based on the use of whole exome sequencing thus neglecting the identification of non-coding variants. The complexity of human genome and its regulation at multiple levels has led biologists to develop several assays to interrogate the different aspects of biological processes. While one-dimension single omics investigation offers a peek of this complex system, the combination of different omics data allows the discovery of coherent signatures. The community of computational biologists and bioinformaticians, in order to integrate data from different omics, has developed several approaches and tools. However, it is difficult to understand which suits the best to predict diverse phenotypic outcome. First attempts to use multi-omics approaches showed an improvement of the diagnostic power. However, we are far from a complete understanding of MD and their diagnosis. After reviewing multi-omics algorithms developed in the latest years, we are proposing here a novel data-driven classification and we will discuss how multi-omics will change and improve the diagnosis of MD. Due to the growing use of multi-omics approaches in MD, we foresee that this work will contribute to set up good practices to perform multi-omics data integration to improve the prediction of phenotypic outcomes and the diagnostic power of MD.

RNY (YRNA)-derived small RNAs regulate cell death and inflammation in monocytes/macrophages.

The recent discovery of new classes of small RNAs has opened unknown territories to explore new regulations of physiopathological events. We have recently demonstrated that RNY (or Y RNA)-derived small RNAs (referred to as s-RNYs) are an independent class of clinical biomarkers to detect coronary artery lesions and are associated with atherosclerosis burden. Here, we have studied the role of s-RNYs in human and mouse monocytes/macrophages and have shown that in lipid-laden monocytes/macrophages s-RNY expression is timely correlated to the activation of both NF-κB and caspase 3-dependent cell death pathways. Loss- or gain-of-function experiments demonstrated that s-RNYs activate caspase 3 and NF-κB signaling pathways ultimately promoting cell death and inflammatory responses. As, in atherosclerosis, Ro60-associated s-RNYs generated by apoptotic macrophages are released in the blood of patients, we have investigated the extracellular function of the s-RNY/Ro60 complex. Our data demonstrated that s-RNY/Ro60 complex induces caspase 3-dependent cell death and NF-κB-dependent inflammation, when added to the medium of cultured monocytes/macrophages. Finally, we have shown that s-RNY function is mediated by Toll-like receptor 7 (TLR7). Indeed using chloroquine, which disrupts signaling of endosome-localized TLRs 3, 7, 8 and 9 or the more specific TLR7/9 antagonist, the phosphorothioated oligonucleotide IRS954, we blocked the effect of either intracellular or extracellular s-RNYs. These results position s-RNYs as relevant novel functional molecules that impacts on macrophage physiopathology, indicating their potential role as mediators of inflammatory diseases, such as atherosclerosis.

Paternal obesity: how bad is it for sperm quality and progeny health?

There is substantial evidence that paternal obesity is associated not only with an increased incidence of infertility, but also with an increased risk of metabolic disturbance in adult offspring. Apparently, several mechanisms may contribute to the sperm quality alterations associated with paternal obesity, such as physiological/hormonal alterations, oxidative stress, and epigenetic alterations. Along these lines, modifications of hormonal profiles namely reduced androgen levels and elevated estrogen levels, were found associated with lower sperm concentration and seminal volume. Additionally, oxidative stress in testis may induce an increase of the percentage of sperm with DNA fragmentation. The latter, relate to other peculiarities such as alteration of the embryonic development, increased risk of miscarriage, and development of chronic morbidity in the offspring, including childhood cancers. Undoubtedly, epigenetic alterations (ie, DNA methylation, chromatin modifications, and small RNA deregulation) of sperm related to paternal obesity and their consequences on the progeny are poorly understood determinants of paternal obesity-induced transmission. In this review, we summarize and discuss the data available in the literature regarding the biological, physiological, and molecular consequences of paternal obesity on male fertility potential and ultimately progeny health.

De plus en plus de données tendent à montrer que l'obésité paternelle a non seulement des effets néfastes sur la santé métabolique et reproductive de l'individu mais également sur celle de sa descendance. Les mécanismes mis en jeu dans ce processus incluraient des altérations physiologiques et hormonales des fonctions reproductives de l'homme obèse ainsi que des altérations épigénétiques au niveau du génome spermatique. Les modifications hormonales associées à l'obésité et qui se caractérisent principalement par une réduction du taux d'androgènes et une augmentation du niveau d'estrogène induiraient une altération des paramètres spermatiques, une diminution de la concentration ou de la numération totale en spermatozoïde et du volume séminal. Le stress oxydatif dans le testicule induirait une augmentation de la fragmentation de l'ADN spermatique et pourrait rendre compte de l'augmentation des risques de fausses-couches, des problèmes de développement embryonnaire ainsi que de l'augmentation des risques de mortalité chez la descendance, problèmes fréquemment rencontrés lorsque le père est. obèse. Les modifications épigénétiques (altérations des profils de méthylation de l'ADN, de la structure de la chromatine ou/et des profils d'expression des ARN spermatiques) induites par l'obésité sont, quant à elles, loin d'être comprises, même si elles sont, surement, les vecteurs clés de la transmission épigénétique paternelle des maladies métaboliques. L'objet de cette revue est. de résumer puis de discuter les différentes études expérimentales et épidémiologiques publiés à ce jour sur les conséquences physiologiques et moléculaire de l'obésité paternelle sur la santé de l'individu et sur celle de sa descendance.

Viruses and miRNAs: More Friends than Foes.

There is evidence that eukaryotic miRNAs (hereafter called host miRNAs) play a role in the replication and propagation of viruses. Expression or targeting of host miRNAs can be involved in cellular antiviral responses. Most times host miRNAs play a role in viral life-cycles and promote infection through complex regulatory pathways. miRNAs can also be encoded by a viral genome and be expressed in the host cell. Viral miRNAs can share common sequences with host miRNAs or have totally different sequences. They can regulate a variety of biological processes involved in viral infection, including apoptosis, evasion of the immune response, or modulation of viral life-cycle phases. Overall, virus/miRNA pathway interaction is defined by a plethora of complex mechanisms, though not yet fully understood. This article review summarizes recent advances and novel biological concepts related to the understanding of miRNA expression, control and function during viral infections. The article also discusses potential therapeutic applications of this particular host-pathogen interaction.