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Recombination activating genes (RAGs) are tightly regulated during lymphoid differentiation, and their mutations cause a spectrum of severe immunological disorders. Hematopoietic stem and progenitor cell (HSPC) transplantation is the treatment of choice but is limited by donor availability and toxicity. To overcome these issues, we developed gene editing strategies targeting a corrective sequence into the human RAG1 gene by homology-directed repair (HDR) and validated them by tailored two-dimensional, three-dimensional, and in vivo xenotransplant platforms to assess rescue of expression and function. Whereas integration into intron 1 of RAG1 achieved suboptimal correction, in-frame insertion into exon 2 drove physiologic human RAG1 expression and activity, allowing disruption of the dominant-negative effects of unrepaired hypomorphic alleles. Enhanced HDR-mediated gene editing enabled the correction of human RAG1 in HSPCs from patients with hypomorphic RAG1 mutations to overcome T and B cell differentiation blocks. Gene correction efficiency exceeded the minimal proportion of functional HSPCs required to rescue immunodeficiency in Rag1(-/- )mice, supporting the clinical translation of HSPC gene editing for the treatment of RAG1 deficiency.

Complete elimination of B-cell acute lymphoblastic leukemia (B-ALL) by a risk-adapted primary treatment approach remains a clinical key objective, which fails in up to a third of patients. Recent evidence has implicated subpopulations of B-ALL cells with stem-like features in disease persistence. We hypothesized that microRNA-126, a core regulator of hematopoietic and leukemic stem cells, may resolve intratumor heterogeneity in B-ALL and uncover therapy-resistant subpopulations. We exploited patient-derived xenograft (PDX) models with B-ALL cells transduced with a miR-126 reporter allowing the prospective isolation of miR-126(high) cells for their functional and transcriptional characterization. Discrete miR-126(high) populations, often characterized by MIR126 locus demethylation, were identified in 8/9 PDX models and showed increased repopulation potential, in vivo chemotherapy resistance and hallmarks of quiescence, inflammation and stress-response pathway activation. Cells with a miR-126(high) transcriptional profile were identified as distinct disease subpopulations by single-cell RNA sequencing in diagnosis samples from adult and pediatric B-ALL. Expression of miR-126 and locus methylation were tested in several pediatric and adult B-ALL cohorts, which received standardized treatment. High microRNA-126 levels and locus demethylation at diagnosis associate with suboptimal response to induction chemotherapy (MRD > 0.05% at day +33 or MRD+ at day +78). [Figure not available: see fulltext.].

Immune escape represents a major driver of acute myeloid leukemia (AML) reemergence after allogeneic hematopoietic cell transplantation (allo-HCT), with up to 40% of relapses prompted by nongenomic loss of HLA class II expression in leukemia cells. By integrative analysis of gene expression, DNA methylation, and chromatin accessibility in paired diagnosis/relapse primary samples and in the respective patient-derived xenografts (PDX), we identify the polycomb repressive complex 2 (PRC2) as a key epigenetic driver of this immune escape modality. We report that loss of expression of HLA class II molecules is accompanied by a PRC2-dependent reduction in chromatin accessibility. Pharmacologic inhibition of PRC2 subunits rescues HLA class II expression in AML relapses in vitro and in vivo, with consequent recovery of leukemia recognition by CD4+ T cells. Our results uncover a novel link between epigenetics and leukemia immune escape, which may rapidly translate into innovative strategies to cure or prevent AML posttransplantation relapse.

Within the chromatin, distal elements interact with promoters to regulate specific transcriptional programs. Histone acetylation, interfering with the net charges of the nucleosomes, is a key player in this regulation. Here, we report that the oncoprotein SET is a critical determinant for the levels of histone acetylation within enhancers. We disclose that a condition in which SET is accumulated, the severe Schinzel-Giedion Syndrome (SGS), is characterized by a failure in the usage of the distal regulatory regions typically employed during fate commitment. This is accompanied by the usage of alternative enhancers leading to a massive rewiring of the distal control of the gene transcription. This represents a (mal)adaptive mechanism that, on one side, allows to achieve a certain degree of differentiation, while on the other affects the fine and corrected maturation of the cells. Thus, we propose the differential in cis-regulation as a contributing factor to the pathological basis of SGS and possibly other the SET-related disorders in humans.

Objective: Trained immunity (TI) is a de facto memory program of innate immune cells, characterized by immunometabolic and epigenetic changes sustaining enhanced production of cytokines. TI evolved as a protective mechanism against infections; however, inappropriate activation can cause detrimental inflammation and might be implicated in the pathogenesis of chronic inflammatory diseases. In this study, we investigated the role of TI in the pathogenesis of giant cell arteritis (GCA), a large-vessel vasculitis characterized by aberrant macrophage activation and excess cytokine production. Methods: Monocytes from GCA patients and from age- and sex-matched healthy donors were subjected to polyfunctional studies, including cytokine production assays at baseline and following stimulation, intracellular metabolomics, chromatin immunoprecipitation-qPCR, and combined ATAC/RNA sequencing. Immunometabolic activation (i.e. glycolysis) was assessed in inflamed vessels of GCA patients with FDG-PET and immunohistochemistry (IHC), and the role of this pathway in sustaining cytokine production was confirmed with selective pharmacologic inhibition in GCA monocytes. Results: GCA monocytes exhibited hallmark molecular features of TI. Specifically, these included enhanced IL-6 production upon stimulation, typical immunometabolic changes (e.g. increased glycolysis and glutaminolysis) and epigenetic changes promoting enhanced transcription of genes governing pro-inflammatory activation. Immunometabolic changes of TI (i.e. glycolysis) were a feature of myelomonocytic cells in GCA lesions and were required for enhanced cytokine production. Conclusions: Myelomonocytic cells in GCA activate TI programs sustaining enhanced inflammatory activation with excess cytokine production.

Gene editing by engineered nucleases has revolutionized the field of gene therapy by enabling targeted and precise modification of the genome. However, the limited availability of methods for clonal tracking of edited cells has resulted in a paucity of information on the diversity, abundance and behavior of engineered clones. Here we detail the wet laboratory and bioinformatic BAR-Seq pipeline, a strategy for clonal tracking of cells harboring homology-directed targeted integration of a barcoding cassette. We present the BAR-Seq web application, an online, freely available and easy-to-use software that allows performing clonal tracking analyses on raw sequencing data without any computational resources or advanced bioinformatic skills. BAR-Seq can be applied to most editing strategies, and we describe its use to investigate the clonal dynamics of human edited hematopoietic stem/progenitor cells in xenotransplanted hosts. Notably, BAR-Seq may be applied in both basic and translational research contexts to investigate the biology of edited cells and stringently compare editing protocols at a clonal level. Our BAR-Seq pipeline allows library preparation and validation in a few days and clonal analyses of edited cell populations in 1 week.

Precise correction of the CD40LG gene in T cells and hematopoietic stem/progenitor cells (HSPC) holds promise for treating X-linked hyper-IgM Syndrome (HIGM1), but its actual therapeutic potential remains elusive. Here, we developed a one-size-fits-all editing strategy for effective T-cell correction, selection, and depletion and investigated the therapeutic potential of T-cell and HSPC therapies in the HIGM1 mouse model. Edited patients’ derived CD4 T cells restored physiologically regulated CD40L expression and contact-dependent B-cell helper function. Adoptive transfer of wild-type T cells into conditioned HIGM1 mice rescued antigen-specific IgG responses and protected mice from a disease-relevant pathogen. We then obtained ~ 25% CD40LG editing in long-term repopulating human HSPC. Transplanting such proportion of wild-type HSPC in HIGM1 mice rescued immune functions similarly to T-cell therapy. Overall, our findings suggest that autologous edited T cells can provide immediate and substantial benefits to HIGM1 patients and position T-cell ahead of HSPC gene therapy because of easier translation, lower safety concerns and potentially comparable clinical benefits.

Hematopoietic stem cells (HSCs) are regulated by signals from the bone marrow (BM) niche that tune hematopoiesis at steady state and in hematologic disorders. To understand HSC-niche interactions in altered nonmalignant homeostasis, we selected b-thalassemia, a hemoglobin disorder, as a paradigm. In this severe congenital anemia, alterations secondary to the primary hemoglobin defect have a potential impact on HSC-niche cross talk. We report that HSCs in thalassemic mice (th3) have an impaired function, caused by the interaction with an altered BM niche. The HSC self-renewal defect is rescued after cell transplantation into a normal microenvironment, thus proving the active role of the BM stroma. Consistent with the common finding of osteoporosis in patients, we found reduced bone deposition with decreased levels of parathyroid hormone (PTH), which is a key regulator of bone metabolism but also of HSC activity. In vivo activation of PTH signaling through the reestablished Jagged1 and osteopontin levels correlated with the rescue of the functional pool of th3 HSCs by correcting HSC-niche cross talk. Reduced HSC quiescence was confirmed in thalassemic patients, along with altered features of the BM stromal niche. Our findings reveal a defect in HSCs in b-thalassemia induced by an altered BM microenvironment and provide novel and relevant insight for improving transplantation and gene therapy approaches.

Targeted gene editing in hematopoietic stem cells (HSCs) is a promising treatment for several diseases. However, the limited efficiency of homology-directed repair (HDR) in HSCs and the unknown impact of the procedure on clonal composition and dynamics of transplantation have hampered clinical translation. Here, we apply a barcoding strategy to clonal tracking of edited cells (BAR-Seq) and show that editing activates p53, which substantially shrinks the HSC clonal repertoire in hematochimeric mice, although engrafted edited clones preserve multilineage and self-renewing capacity. Transient p53 inhibition restored polyclonal graft composition. We increased HDR efficiency by forcing cell-cycle progression and upregulating components of the HDR machinery through transient expression of the adenovirus 5 E4orf6/7 protein, which recruits the cell-cycle controller E2F on its target genes. Combined E4orf6/7 expression and p53 inhibition resulted in HDR editing efficiencies of up to 50% in the long-term human graft, without perturbing repopulation and self-renewal of edited HSCs. This enhanced protocol should broaden applicability of HSC gene editing and pave its way to clinical translation.

Precise gene editing in hematopoietic stem and progenitor cells (HSPCs) holds promise for treating genetic diseases. However, responses triggered by programmable nucleases in HSPCs are poorly characterized and may negatively impact HSPC engraftment and long-term repopulation capacity. Here, we induced either one or several DNA double-stranded breaks (DSBs) with optimized zinc-finger and CRISPR/Cas9 nucleases and monitored DNA damage response (DDR) foci induction, cell-cycle progression, and transcriptional responses in HSPC subpopulations, with up to single-cell resolution. p53-mediated DDR pathway activation was the predominant response to even single-nuclease-induced DSBs across all HSPC subtypes analyzed. Excess DSB load and/or adeno-associated virus (AAV)-mediated delivery of DNA repair templates induced cumulative p53 pathway activation, constraining proliferation, yield, and engraftment of edited HSPCs. However, functional impairment was reversible when DDR burden was low and could be overcome by transient p53 inhibition. These findings provide molecular and functional evidence for feasible and seamless gene editing in HSPCs. Precise gene editing has the potential to treat immune and hematological diseases. Genovese, Naldini, Di Micco, and colleagues now show that gene-editing procedures are well tolerated by hematopoietic stem cells and provide molecular evidence of the feasibility of seamless gene editing, strengthening translation of such approaches to humans.

MicroRNAs (miRNAs) are small non-coding RNAs with a key role in the post-transcriptional gene expression regularization, thanks to their ability to link with the target mRNA through the complementary base pairing mechanism. Given their role, it is important to identify their targets and, to this purpose, different tools were proposed to solve this problem. However, their results can be very different, so the community is now moving toward the deployment of integration tools, which should be able to perform better than the single ones. As Machine and Deep Learning algorithms are now in their popular years, we developed different classifiers from both areas to verify their ability to recognize possible miRNA-mRNA interactions and evaluated their performance, showing the potentialities and the limits that those algorithms have in this field. Here, we apply two deep learning classifiers and three different machine learning models to two different miRNA-mRNA datasets, of predictions from 3 different tools: TargetScan, miRanda, and RNAhybrid. Although an experimental validation of the results is needed to better confirm the predictions, deep learning techniques achieved the best performance when the evaluation scores are taken into account.

Item does not contain fulltext, Trained immunity (TI) is a proinflammatory program induced in monocyte/macrophages upon sensing of specific pathogens and is characterized by immunometabolic and epigenetic changes that enhance cytokine production. Maladaptive activation of TI (ie, in the absence of infection) may result in detrimental inflammation and development of disease; however, the exact role and extent of inappropriate activation of TI in the pathogenesis of human diseases is undetermined. In this study, we uncovered the oncogene-induced, maladaptive induction of TI in the pathogenesis of a human inflammatory myeloid neoplasm (Erdheim-Chester disease, [ECD]), characterized by the BRAFV600E oncogenic mutation in monocyte/macrophages and excess cytokine production. Mechanistically, myeloid cells expressing BRAFV600E exhibit all molecular features of TI: activation of the AKT/mammalian target of rapamycin signaling axis; increased glycolysis, glutaminolysis, and cholesterol synthesis; epigenetic changes on promoters of genes encoding cytokines; and enhanced cytokine production leading to hyperinflammatory responses. In patients with ECD, effective therapeutic strategies combat this maladaptive TI phenotype; in addition, pharmacologic inhibition of immunometabolic changes underlying TI (ie, glycolysis) effectively dampens cytokine production by myeloid cells. This study revealed the deleterious potential of inappropriate activation of TI in the pathogenesis of human inflammatory myeloid neoplasms and the opportunity for inhibition of TI in conditions characterized by maladaptive myeloid-driven inflammation.

Allogeneic hematopoietic stem cell transplantation is the treatment of choice for autosomal recessive osteopetrosis caused by defects in the TCIRG1 gene. Despite recent progress in conditioning, a relevant number of patients are not eligible for allogeneic stem cell transplantation because of the severity of the disease and significant transplant-related morbidity. We exploited peripheral CD34+ cells, known to circulate at high frequency in the peripheral blood of TCIRG1-deficient patients, as a novel cell source for autologous transplantation of gene corrected cells. Detailed phenotypical analysis showed that circulating CD34+ cells have a cellular composition that resembles bone marrow, supporting their use in gene therapy protocols. Transcriptomic profile revealed enrichment in genes expressed by hematopoietic stem and progenitor cells (HSPCs). To overcome the limit of bone marrow harvest/ HSPC mobilization and serial blood drawings in TCIRG1 patients, we applied UM171-based ex-vivo expansion of HSPCs coupled with lentiviral gene transfer. Circulating CD34+ cells from TCIRG1-defective patients were transduced with a clinically-optimized lentiviral vector (LV) expressing TCIRG1 under the control of phosphoglycerate promoter and expanded ex vivo. Expanded cells maintained long-term engraftment capacity and multi-lineage repopulating potential when transplanted in vivo both in primary and secondary NSG recipients. Moreover, when CD34+ cells were differentiated in vitro, genetically corrected osteoclasts resorbed the bone efficiently. Overall, we provide evidence that expansion of circulating HSPCs coupled to gene therapy can overcome the limit of stem cell harvest in osteopetrotic patients, thus opening the way to future gene-based treatment of skeletal diseases caused by bone marrow fibrosis.

FBCA (Flux Balance Cellular Automata) has been recently proposed as a new multi-scale modeling framework to represent the spatial dynamics of multi-cellular systems, while simultaneously taking into account the metabolic activity of individual cells. Preliminary results have revealed the potentialities of the framework in enabling to identify and analyze complex emergent properties of cellular populations, such as spatial patterns phenomena and synchronization effects. Here we move a step forward, by exploring the possibility of integrating real-world data into the framework. To this end, we seek to customize the metabolism of individual cells according to single-cell gene expression profiles. We investigate the effect on cell metabolism of the interplay between: (a) the environmental conditions determined by nutrient diffusion dynamics; (b) the activation or deactivation of metabolic pathways determined by gene expression.

Background: In order to fully characterize the genome of an individual, the reconstruction of the two distinct copies of each chromosome, called haplotypes, is essential. The computational problem of inferring the full haplotype of a cell starting from read sequencing data is known as haplotype assembly, and consists in assigning all heterozygous Single Nucleotide Polymorphisms (SNPs) to exactly one of the two chromosomes. Indeed, the knowledge of complete haplotypes is generally more informative than analyzing single SNPs and plays a fundamental role in many medical applications. Results: To reconstruct the two haplotypes, we addressed the weighted Minimum Error Correction (wMEC) problem, which is a successful approach for haplotype assembly. This NP-hard problem consists in computing the two haplotypes that partition the sequencing reads into two disjoint sub-sets, with the least number of corrections to the SNP values. To this aim, we propose here GenHap, a novel computational method for haplotype assembly based on Genetic Algorithms, yielding optimal solutions by means of a global search process. In order to evaluate the effectiveness of our approach, we run GenHap on two synthetic (yet realistic) datasets, based on the Roche/454 and PacBio RS II sequencing technologies. We compared the performance of GenHap against HapCol, an efficient state-of-the-art algorithm for haplotype phasing. Our results show that GenHap always obtains high accuracy solutions (in terms of haplotype error rate), and is up to 4× faster than HapCol in the case of Roche/454 instances and up to 20× faster when compared on the PacBio RS II dataset. Finally, we assessed the performance of GenHap on two different real datasets. Conclusions: Future-generation sequencing technologies, producing longer reads with higher coverage, can highly benefit from GenHap, thanks to its capability of efficiently solving large instances of the haplotype assembly problem. Moreover, the optimization approach pr

The reconstruction of the haplotype pair for each chromosome is a hot topic in Bioinformatics and Genome Analysis. In Haplotype Assembly (HA), all heterozygous Single Nucleotide Polymorphisms (SNPs) have to be assigned to exactly one of the two chromosomes. In this work, we outline the state-of-the-art on HA approaches and present an in-depth analysis of the computational performance of GenHap, a recent method based on Genetic Algorithms. GenHap was designed to tackle the computational complexity of the HA problem by means of a divide-et-impera strategy that effectively leverages multi-core architectures. In order to evaluate GenHap’s performance, we generated different instances of synthetic (yet realistic) data exploiting empirical error models of four different sequencing platforms (namely, Illumina NovaSeq, Roche/454, PacBio RS II and Oxford Nanopore Technologies MinION). Our results show that the processing time generally decreases along with the read length, involving a lower number of sub-problems to be distributed on multiple cores.

In the last two decades deep learning has attracted a lot of attention internationally, solving problems in different application domains and achieving results beyond expectations. For example it has been applied in bioinformatics, game playing, imaging processing, object detection, robotic and drug discovery. One of the main reasons for the incremented use of deep learning algorithms is the need to implement approaches for the analysis of the large amount of data produces in every field, bringing researchers to dedicate their work to deep learning development. One of the main topics discussed up today is the possibility to run the training of deep models in a parallel fashion, so to reduce the time otherwise needed to find the hyperparameters and to make the achievement of the result faster.

There are many molecular biology approaches to the analysis of microRNA (miRNA) and target interactions, but the experiments are complex and expensive. For this reason, in silico computational approaches able to model these molecular interactions are highly desirable. Although several computational methods have been developed for predicting the interactions between miRNA and target genes, there are substantial differences in the results achieved since most algorithms provide a large number of false positives. Accordingly, machine learning approaches are widely used to integrate predictions obtained from different tools. In this work, we adopt a method called multidimensional multiclass GP with multidimensional populations (M3GP), which relies on a genetic programming approach, to integrate and classify results from different miRNA-target prediction tools. The results are compared with those obtained with other classifiers, showing competitive accuracy. Since we aim to provide genome-wide predictions with M3GP and, considering the high number of miRNA-target interactions to test (also in different species), a parallel implementation of this algorithm is recommended. In this paper, we discuss the theoretical aspects of this algorithm and propose three different parallel implementations. We show that M3GP is highly parallelizable, it can be used to achieve genome-wide predictions, and its adoption provides great advantages when handling big datasets.

In complex phenotypes (e.g., psychiatric diseases) single locus tests, commonly performed with genome-wide association studies, have proven to be limited in discovering strong gene associations. A growing body of evidence suggests that epistatic non-linear effects may be responsible for complex phenotypes arising from the interaction of different biological factors. A major issue in epistasis analysis is the computational burden due to the huge number of statistical tests to be performed when considering all the potential genotype combinations. In this work, we developed a computational efficient approach to compute empirical p-values concerning the presence of epistasis at a genome-wide scale in bipolar disorder, which is a typical example of complex phenotype with a relevant but unexplained genetic background. By running our approach we were able to identify 13 epistasis interactions between variants located in genes potentially involved in biological processes associated with the analyzed phenotype.

Expression quantitative trait loci (eQTL) analysis is an emerging method for establishing the impact of genetic variations (such as single nucleotide polymorphisms) on the expression levels of genes. Although different methods for evaluating the impact of these variations are proposed in the literature, the results obtained are mostly in disagreement, entailing a considerable number of false-positive predictions. For this reason, we propose an approach based on Logistic Model Trees that integrates the predictions of different eQTL mapping tools to produce more reliable results. More precisely, we employ a machine learning-based method using logistic functions to perform a linear regression able to classify the predictions of three eQTL analysis tools (namely, R/qtl, MatrixEQTL, and mRMR). Given the lack of a reference dataset and that computational predictions are not so easy to test experimentally, the performance of our approach is assessed using data from the DREAM5 challenge. The results show the quality of the aggregated prediction is better than that obtained by each single tool in terms of both precision and recall. We also performed a test on real data, employing genotypes and microRNA expression profiles from Caenorhabditis elegans, which proved that we were able to correctly classify all the experimentally validated eQTLs. These good results come both from the integration of the different predictions, and from the ability of this machine learning algorithm to find the best cutoff thresholds for each tool. This combination makes our integration approach suitable for improving eQTL predictions for testing in a laboratory, reducing the number of false-positive results.

The reconstruction of the two distinct copies of each chromosome, called haplotypes, is an essential process for the characterization of the genome of an individual. Here we address a successful approach for haplotype assembly, called the weighted Minimum Error Correction (wMEC) problem, which consists in computing the two haplotypes that partition the sequencing reads into two disjoint sub-sets with the least number of corrections to the Single Nucleotide Polymorphisms values. To solve this problem we propose GenHap, a computational method based on Genetic Algorithms, which are able to obtain optimal solutions thanks to a global search process. To evaluate the effectiveness of GenHap, we test it on a synthetic (yet realistic) dataset based on the PacBio RS II sequencing technology. We compare the performance of GenHap against HapCol, an efficient state-of-the-art algorithm for haplotype assembly. We show that GenHap always obtains high accuracy solutions (in terms of haplotype error rate), and is up to 20× faster than HapCol on this synthetic (yet realistic) dataset.

Background: Bioinformatics tools designed to identify lentiviral or retroviral vector insertion sites in the genome of host cells are used to address the safety and long-term efficacy of hematopoietic stem cell gene therapy applications and to study the clonal dynamics of hematopoietic reconstitution. The increasing number of gene therapy clinical trials combined with the increasing amount of Next Generation Sequencing data, aimed at identifying integration sites, require both highly accurate and efficient computational software able to correctly process "big data" in a reasonable computational time. Results: Here we present VISPA2 (Vector Integration Site Parallel Analysis, version 2), the latest optimized computational pipeline for integration site identification and analysis with the following features: (1) the sequence analysis for the integration site processing is fully compliant with paired-end reads and includes a sequence quality filter before and after the alignment on the target genome; (2) an heuristic algorithm to reduce false positive integration sites at nucleotide level to reduce the impact of Polymerase Chain Reaction or trimming/alignment artifacts; (3) a classification and annotation module for integration sites; (4) a user friendly web interface as researcher front-end to perform integration site analyses without computational skills; (5) the time speedup of all steps through parallelization (Hadoop free). Conclusions: We tested VISPA2 performances using simulated and real datasets of lentiviral vector integration sites, previously obtained from patients enrolled in a hematopoietic stem cell gene therapy clinical trial and compared the results with other preexisting tools for integration site analysis. On the computational side, VISPA2 showed a>6-fold speedup and improved precision and recall metrics (1 and 0.97 respectively) compared to previously developed computational pipelines. These performances indicate that VISPA2 is a fast, reliable a

HIV-1 insertions targeting BACH2 or MLK2 are enriched and persist for decades in hematopoietic cells from patients under combination antiretroviral therapy. However, it is unclear how these insertions provide such selective advantage to infected cell clones. Here, we show that in 30/87 (34%) patients under combination antiretroviral therapy, BACH2, and STAT5B are activated by insertions triggering the formation of mRNAs that contain viral sequences fused by splicing to their first protein-coding exon. These chimeric mRNAs, predicted to express full-length proteins, are enriched in T regulatory and T central memory cells, but not in other T lymphocyte subsets or monocytes. Overexpression of BACH2 or STAT5B in primary T regulatory cells increases their proliferation and survival without compromising their function. Hence, we provide evidence that HIV-1-mediated insertional activation of BACH2 and STAT5B favor the persistence of a viral reservoir in T regulatory cells in patients under combination antiretroviral therapy.

MicroRNAs (miRNAs) are small non-coding RNA molecules that have an important role in a wide range of biological processes, since they interact with specific mRNAs affecting the expression of the corresponding proteins. The role of miRNA can be deeply influenced by Single Nucleotide Polymorphisms (SNPs), in particular in their seed sites, since these variations may modify their affinity with particular transcripts, but they may also generate novel binding capabilities for specific miRNA binding sites or destroy them. Several computational tools for miRNA-target site predictions have been developed, but the obtained results are often not in agreement, making the study the binding sites hard, and the analysis of SNP effects even harder. For these reasons, we developed a web application called Rank miRNA, which allows to retrieve and aggregate the results of three prediction tools, but also to process and compare new input miRNA sequences, allowing the analysis of how variations impact on their function. Therefore, our tool is also able to predict the impact of SNPs (and any other kind of variations) on miRNA-mRNA binding capability and also to find the target genes of (potentially new) miRNA sequences. We evaluated the performance of Rank miRNA on specific human SNPs, which are likely to be involved in several mental disorder diseases, showing the potentiality of our tool in helping the study of miRNA-target interactions.

In molecular biology, the interaction mechanisms between microRNAs (miRNAs) with their messenger RNA targets are poorly understood. This is the reason why many miRNA-Target prediction methods are available, but their results are often inconsistent. A lot of efforts focus on the quality of the sequence match between miRNA and target rather than on the role of the mRNA secondary structure in which the target is embedded. Nonetheless, it is known that the miRNA secondary structures contribute to target recognition, because there is an energetic cost to freeing base-pairing interactions within mRNA to make the target accessible for miRNA binding. This approach is implemented by PITA (Probability of Interaction by Target Accessibility), a very computational-intensive tool that is able to provide accurate results even when little is know about the conservation of the miRNA. In this paper we propose a new implementation of PITA, called lPITA, able to exploit a coarse-grained parallelism over low power architectures to reduce both execution times and the power consumption.

Background: Haplotype phasing is an important problem in the analysis of genomics information. Given a set of DNA fragments of an individual, it consists of determining which one of the possible alleles (alternative forms of a gene) each fragment comes from. Haplotype information is relevant to gene regulation, epigenetics, genome-wide association studies, evolutionary and population studies, and the study of mutations. Haplotyping is currently addressed as an optimisation problem aiming at solutions that minimise, for instance, error correction costs, where costs are a measure of the confidence in the accuracy of the information acquired from DNA sequencing. Solutions have typically an exponential computational complexity. WHATSHAP is a recent optimal approach which moves computational complexity from DNA fragment length to fragment overlap, i.e., coverage, and is hence of particular interest when considering sequencing technology's current trends that are producing longer fragments. Results: Given the potential relevance of efficient haplotyping in several analysis pipelines, we have designed and engineered PWHATSHAP, a parallel, high-performance version of WHATSHAP. PWHATSHAP is embedded in a toolkit developed in Python and supports genomics datasets in standard file formats. Building on WHATSHAP, PWHATSHAP exhibits the same complexity exploring a number of possible solutions which is exponential in the coverage of the dataset. The parallel implementation on multi-core architectures allows for a relevant reduction of the execution time for haplotyping, while the provided results enjoy the same high accuracy as that provided by WHATSHAP, which increases with coverage. Conclusions: Due to its structure and management of the large datasets, the parallelisation of WHATSHAP posed demanding technical challenges, which have been addressed exploiting a high-level parallel programming framework. The result, PWHATSHAP, is a freely available toolkit that improves the effici

Gene and protein networks are very important to model complex large-scale systems in molecular biology. Inferring or reverseengineering such networks can be defined as the process of identifying gene/protein interactions from experimental data through computational analysis. However, this task is typically complicated by the enormously large scale of the unknowns in a rather small sample size. Furthermore, when the goal is to study causal relationships within the network, tools capable of overcoming the limitations of correlation networks are required. In this work, we make use of Bayesian Graphical Models to attach this problem and, specifically, we perform a comparative study of different state-of-the-art heuristics, analyzing their performance in inferring the structure of the Bayesian Network from breast cancer data.

Although several computational methods have been developed for predicting interactions between miRNA and target genes, there are substantial differences in the achieved results. For this reason, machine learning approaches are widely used for integrating the predictions obtained from different tools. In this work we adopt a method, called M3GP, which relies on a genetic programming approach, to classify results from three tools: miRanda, TargetScan, and RNAhybrid. Such algorithm is highly parallelizable and its adoption provides great advantages while handling problems involving big datasets, since it is independent from the implementation and from the architecture on which it is executed. More precisely, we apply this technique for the classification of the achieved miRNA target predictions and we compare its results with those obtained with other classifiers.

The interpretation of genome-wide association study is difficult, as it is hard to understand how polymorphisms can affect gene regulation, in particular for trans-regulatory elements located far from their controlling gene. Using RNA or protein expression data as phenotypes, it is possible to correlate their variations with specific genotypes. This technique is usually referred to as expression Quantitative Trait Loci (eQTLs) analysis and only few packages exist for the integration of genotype patterns and expression profiles. In particular, tools are needed for the analysis of next-generation sequencing (NGS) data on a genome-wide scale, which is essential to identify eQTLs able to control a large number of genes (hotspots). Here we present SPIRE (Software for Polymorphism Identification Regulating Expression), a generic, modular and functionally highly flexible pipeline for eQTL processing. SPIRE integrates different univariate and multivariate approaches for eQTL analysis, paying particular attention to the scalability of the procedure in order to support cis- as well as trans-mapping, thus allowing the identification of hotspots in NGS data. In particular, we demonstrated how SPIRE can handle big association study datasets, reproducing published results and improving the identification of trans-eQTLs. Furthermore, we employed the pipeline to analyse novel data concerning the genotypes of two different C. elegans strains (N2 and Hawaii) and related miRNA expression data, obtained using RNA-Seq. A miRNA regulatory hotspot was identified in chromosome 1, overlapping the transcription factor grh-1, known to be involved in the early phases of embryonic development of C. elegans. In a follow-up qPCR experiment we were able to verify most of the predicted eQTLs, as well as to show, for a novel miRNA, a significant difference in the sequences of the two analysed strains of C. elegans. SPIRE is publicly available as open source software at https://bitbucket.org/bereste

Metabolic models are the most widespread type of models in systems biology and are currently used in several applications, including metabolic engineering and investigations of pathological states in which metabolic disorders play a relevant role. Once a model has been defined and corroborated, sensitivity analysis techniques can be used to study the model behavior in relation to perturbations of the model parameters. Here, we describe how it is possible to combine regionalized sensitivity analysis and response surface methodology to screen and quantitatively characterize the relation between metabolic phenotypes and biochemical reactions rates. By means of this approach, we identi ̄ed the most important reactions for the citric acid e®lux from mitochondria, one of the key metabolic traits of cancer cells.

Systems biology is a multidisciplinary research area aimed at investigating biological systems by developing mathematical models that approach the study and the analysis of both the structure and behaviour of a biological phenomenon from a system perspective. The dynamics described by such mathematical models can be deeply affected by many parameters, and an extensive exploration of the parameters space in order to find crucial factors is most of the time prohibitive since it requires the execution of a huge number of computer simulations. Sensitivity analysis techniques can help in understanding how much the uncertainty in the model outcome is determined by the uncertainties, or by the variations, of the model input factors (components, reactions and respective parameters). In this work we exploit the European Grid Infrastructure to manage the calculations required to perform the SA on a stochastic model of bacterial chemotaxis, using an improved version of the first order screening method of Morris. According to the results achieved in our exploratory analysis, the European Grid Infrastructure is a useful solution for distributing the stochastic simulations required to carry out the SA of a stochastic model. Considering that the more intensive the computation the more scalable the infrastructure, grid computing can be a suitable technology for large scale biological models analysis

miRNA target genes prediction represents a crucial step in miRNAs functional characterization. In this context, the challenging issue remains predictions accuracy and recognition of false positive results. In this article myMIR, a web based system for increasing reliability of miRNAs predicted targets lists, is presented. myMIR implements an integrated pipeline for computing ranked miRNA::target lists and provides annotations for narrowing them down. The system relies on knowledge base data, suitably integrated in order to extend the functional characterization of targeted genes to miRNAs, by highlighting the search on over-represented annotation terms. Validation results show a dramatic reduction in the quantity of predictions and an increase in the sensitivity, when compared to other methods. This improves the predictions accuracy and allows the formulation of novel hypotheses on miRNAs functional involvement.

Background: Tissue MicroArray technology aims to perform immunohistochemical staining on hundreds of different tissue samples simultaneously. It allows faster analysis, considerably reducing costs incurred in staining. A time consuming phase of the methodology is the selection of tissue areas within paraffin blocks: no utilities have been developed for the identification of areas to be punched from the donor block and assembled in the recipient block.Results: The presented work supports, in the specific case of a primary subtype of breast cancer (tubular breast cancer), the semi-automatic discrimination and localization between normal and pathological regions within the tissues. The diagnosis is performed by analysing specific morphological features of the sample such as the absence of a double layer of cells around the lumen and the decay of a regular glands-and-lobules structure. These features are analysed using an algorithm which performs the extraction of morphological parameters from images and compares them to experimentally validated threshold values. Results are satisfactory since in most of the cases the automatic diagnosis matches the response of the pathologists. In particular, on a total of 1296 sub-images showing normal and pathological areas of breast specimens, algorithm accuracy, sensitivity and specificity are respectively 89%, 84% and 94%.Conclusions: The proposed work is a first attempt to demonstrate that automation in the Tissue MicroArray field is feasible and it can represent an important tool for scientists to cope with this high-throughput technique.

Background: Tissue MicroArray technology aims to perform immunohistochemical staining on hundreds of different tissue samples simultaneously. It allows faster analysis, considerably reducing costs incurred in staining. A time consuming phase of the methodology is the selection of tissue areas within paraffin blocks: no utilities have been developed for the identification of areas to be punched from the donor block and assembled in the recipient block. Results: The presented work supports, in the specific case of a primary subtype of breast cancer (tubular breast cancer), the semi-automatic discrimination and localization between normal and pathological regions within the tissues. The diagnosis is performed by analysing specific morphological features of the sample such as the absence of a double layer of cells around the lumen and the decay of a regular glands-and-lobules structure. These features are analysed using an algorithm which performs the extraction of morphological parameters from images and compares them to experimentally validated threshold values. Results are satisfactory since in most of the cases the automatic diagnosis matches the response of the pathologists. In particular, on a total of 1296 sub-images showing normal and pathological areas of breast specimens, algorithm accuracy, sensitivity and specificity are respectively 89%, 84% and 94%. Conclusions: The proposed work is a first attempt to demonstrate that automation in the Tissue MicroArray field is feasible and it can represent an important tool for scientists to cope with this high-throughput technique.

Stochastic modelling and simulations play a major role in Systems Biology because, at molecular level, biological systems exhibit noise coming both from within the cell (intrinsic) and from the environment (extrinsic). Stochastic modelling takes into account the effects of noise over the system dynamics, that can strongly affect the behavior of the system in conditions of relatively low amounts of molecular species. Stochastic simulations provide an effective way to describe the system dynamics, and can be applied on systems where specified chemical species are processed by a set of biochemical reactions, each one characterized by a stochastic constant. In the context of stochastic modelling, Parameter Sweep Applications (PSAs) can be a useful way to explore the huge spaces generated by the combinations of variables and parameters values in order to test their effects on systems dynamics. PSAs are common in the scientific community and are structured as sets of instances, each one characterized by a distinct parametrisation. A PSA that aims to sample such large spaces must involve a large number of instances and hence the problem becomes very time consuming. However, the independence of each instance of a particular PSA makes the distributed computing paradigm a very useful solution for large scale PSAs. In this work we present a grid based version of a multi-volume stochastic simulator, tau-DPP, implemented on the EGEE project platform. The aim of the proposed work is to exploit this platform for testing the reliability of PSAs over the grid, pointing out critical factors, bottlenecks and scalability by providing data about our experience in this kind of biological modelling and simulations. As a case study, we present a number of PSAs for a stochastic model of bacterial chemotaxis composed of 59 reactions and 31 chemical species. © 2009 IEEE.