Constraints on cosmological birefringence energy dependence from CMB polarization data

We study the possibility of constraining the energy dependence of cosmological birefringence by using CMB polarization data. We consider four possible behaviors, characteristic of different theoretical scenarios: energy-independent birefringence motivated by Chern-Simons interactions of the electromagnetic field, linear energy dependence motivated by a 'Weyl' interaction of the electromagnetic field, quadratic energy dependence, motivated by quantum gravity modifications of low-energy electrodynamics, and inverse quadratic dependence, motivated by Faraday rotation generated by primordial magnetic fields. We constrain the parameters associated to each kind of dependence and use our results to give constraints on the models mentioned. We forecast the sensitivity that Planck data will be able to achieve in this respect.Comment: 15 pages, 5 figures. v2 matches JCAP published versio

Deciphering the multi-step nuclear transport of viral cargoes

Transport of molecules between the nucleus and the cytoplasm is tightly regulated by the nuclear pore complex (NPC), the "gatekeeper" of the nucleus. NPCs allow passage of small molecules ( 15 nm), such as pathogens, mRNAs and pre-ribosomal subunits. Strikingly, it has been observed that NPCs can accommodate Hepatitis B virus capsids of 36 nm in diameter, almost comparable with the dimensions of the central channel itself. The mechanism for the transport of cargoes with such exceptional size are still unclear, especially the role of multivalent NTR binding when several NLSs are present on the cargo surface. In my PhD thesis I characterized the nuclear import properties of large cargoes in cells. In order to do so, I firstly developed a large cargo model suitable for nuclear transport studies based on virus-like particles. It comprises more than 20 different cargoes with size range 18-36 nm and different amount of NLSs exposed on their surface. Following the development of a semi-automatic kinetic cellular transport assay, I have shown that large cargoes require a minimum number of bound NTRs in order to achieve productive entry into the nucleus, and I further found evidence of a linear relationship between #NLSs and import efficiency. Moreover, the experiments revealed that the minimum threshold for import increases non-linearly with size, reaching up to an unprecedented 100 NTRs per cargo and pointing to a high drop in the transport efficiency for cargoes > 25 nm, which could represent a previously undescribed “second gate” at the NPC. In order to go beyond the global picture from bulk import kinetics, I developed a microscopy setup and LabVIEW software for high-resolution studies of large cargo import in cells. Performing dual-color super-resolution imaging of nucleoporins and cargoes, I have shown that HBV capsids specifically dock on NPCs and interact with both the cytoplasmic component and directly with the permeability barrier. Additional single-particle tracking experiments allow me now to follow the import process with high spatiotemporal resolution, dissecting the multiple steps involved in large cargo import (docking, barrier crossing, release into the nucleus). Integrating this knowledge with the findings from bulk kinetics studies and super-resolution microscopy paves the way for an integrated view of large cargo transport through the NPC

Network-based approaches to explore complex biological systems towards network medicine

Network medicine relies on different types of networks: from the molecular level of protein–protein interactions to gene regulatory network and correlation studies of gene expression. Among network approaches based on the analysis of the topological properties of protein–protein interaction (PPI) networks, we discuss the widespread DIAMOnD (disease module detection) algorithm. Starting from the assumption that PPI networks can be viewed as maps where diseases can be identified with localized perturbation within a specific neighborhood (i.e., disease modules), DIAMOnD performs a systematic analysis of the human PPI network to uncover new disease-associated genes by exploiting the connectivity significance instead of connection density. The past few years have witnessed the increasing interest in understanding the molecular mechanism of post-transcriptional regulation with a special emphasis on non-coding RNAs since they are emerging as key regulators of many cellular processes in both physiological and pathological states. Recent findings show that coding genes are not the only targets that microRNAs interact with. In fact, there is a pool of different RNAs—including long non-coding RNAs (lncRNAs) —competing with each other to attract microRNAs for interactions, thus acting as competing endogenous RNAs (ceRNAs). The framework of regulatory networks provides a powerful tool to gather new insights into ceRNA regulatory mechanisms. Here, we describe a data-driven model recently developed to explore the lncRNA-associated ceRNA activity in breast invasive carcinoma. On the other hand, a very promising example of the co-expression network is the one implemented by the software SWIM (switch miner), which combines topological properties of correlation networks with gene expression data in order to identify a small pool of genes—called switch genes—critically associated with drastic changes in cell phenotype. Here, we describe SWIM tool along with its applications to cancer research and compare its predictions with DIAMOnD disease genes

A new procedure to analyze RNA non-branching structures

RNA structure prediction and structural motifs analysis are challenging tasks in the investigation of RNA function. We propose a novel procedure to detect structural motifs shared between two RNAs (a reference and a target). In particular, we developed two core modules: (i) nbRSSP_extractor, to assign a unique structure to the reference RNA encoded by a set of non-branching structures; (ii) SSD_finder, to detect structural motifs that the target RNA shares with the reference, by means of a new score function that rewards the relative distance of the target non-branching structures compared to the reference ones. We integrated these algorithms with already existing software to reach a coherent pipeline able to perform the following two main tasks: prediction of RNA structures (integration of RNALfold and nbRSSP_extractor) and search for chains of matches (integration of Structator and SSD_finder)

A discipline-enriched dataset for tracking the computational turn of European universities

In recent years, academic research appears to have been going through a methodological turning point. The discussion around the impact that computational methods will have on traditional fields of study has been the focus of many calls for papers and panels at established conferences. However, despite the high prevalence of this topic in the academic debate, it remains very challenging to assess whether academia as a whole has been actually adopting more digital resources and methods during the recent years. We are currently studying this topic by combining hermeneutic and text mining practices while analyzing one of the primary research output of European universities, namely doctoral theses. In this work, we present an enriched dataset we created for addressing this research questions and the first results of the analyses we have conducted so far

SWIM: A computational tool to unveiling crucial nodes in complex biological networks

SWItchMiner (SWIM) is a wizard-like software implementation of a procedure, previously described, able to extract information contained in complex networks. Specifically, SWIM allows unearthing the existence of a new class of hubs, called "fight-club hubs", characterized by a marked negative correlation with their first nearest neighbors. Among them, a special subset of genes, called "switch genes", appears to be characterized by an unusual pattern of intra- and inter-module connections that confers them a crucial topological role, interestingly mirrored by the evidence of their clinic-biological relevance. Here, we applied SWIM to a large panel of cancer datasets from The Cancer Genome Atlas, in order to highlight switch genes that could be critically associated with the drastic changes in the physiological state of cells or tissues induced by the cancer development. We discovered that switch genes are found in all cancers we studied and they encompass protein coding genes and non-coding RNAs, recovering many known key cancer players but also many new potential biomarkers not yet characterized in cancer context. Furthermore, SWIM is amenable to detect switch genes in different organisms and cell conditions, with the potential to uncover important players in biologically relevant scenarios, including but not limited to human cancer

Cargo transport through the nuclear pore complex at a glance

Bidirectional transport of macromolecules across the nuclear envelope is a hallmark of eukaryotic cells, in which the genetic material is compartmentalized inside the nucleus. The nuclear pore complex (NPC) is the major gateway to the nucleus and it regulates nucleocytoplasmic transport, which is key to processes including transcriptional regulation and cell cycle control. Accordingly, components of the nuclear transport machinery are often found to be dysregulated or hijacked in diseases. In this Cell Science at a Glance article and accompanying poster, we provide an overview of our current understanding of cargo transport through the NPC, from the basic transport signals and machinery to more emerging aspects, all from a 'cargo perspective'. Among these, we discuss the transport of large cargoes (>15 nm), as well as the roles of different cargo properties to nuclear transport, from size and number of bound nuclear transport receptors (NTRs), to surface and mechanical properties

SAveRUNNER: a network-based algorithm for drug repurposing and its application to COVID-19

The novelty of new human coronavirus COVID-19/SARS-CoV-2 and the lack of effective drugs and vaccines gave rise to a wide variety of strategies employed to fight this worldwide pandemic. Many of these strategies rely on the repositioning of existing drugs that could shorten the time and reduce the cost compared to de novo drug discovery. In this study, we presented a new network-based algorithm for drug repositioning, called SAveRUNNER (Searching off-lAbel dRUg aNd NEtwoRk), which predicts drug-disease associations by quantifying the interplay between the drug targets and the disease-specific proteins in the human interactome via a novel network-based similarity measure that prioritizes associations between drugs and diseases locating in the same network neighborhoods. Specifically, we applied SAveRUNNER on a panel of 14 selected diseases with a consolidated knowledge about their disease-causing genes and that have been found to be related to COVID-19 for genetic similarity, comorbidity, or for their association to drugs tentatively repurposed to treat COVID-19. Focusing specifically on SARS subnetwork, we identified 282 repurposable drugs, including some the most rumored off-label drugs for COVID-19 treatments, as well as a new combination therapy of 5 drugs, actually used in clinical practice. Furthermore, to maximize the efficiency of putative downstream validation experiments, we prioritized 24 potential anti-SARS-CoV repurposable drugs based on their network-based similarity values. These top-ranked drugs include ACE-inhibitors, monoclonal antibodies, and thrombin inhibitors. Finally, our findings were in-silico validated by performing a gene set enrichment analysis, which confirmed that most of the network-predicted repurposable drugs may have a potential treatment effect against human coronavirus infections.Comment: 42 pages, 9 figure

Characterization of DNA methylation as a function of biological complexity via dinucleotide inter-distances

We perform a statistical study of the distances between successive occurrencies of a given dinucleotide in the DNA sequence for a number of organisms of different complexity. Our analysis highlights peculiar features of the dinucleotide CG distribution in mammalian DNA, pointing towards a connection with the role of such dinucleotide in DNA methylation. While the CG distributions of mammals exhibit exponential tails with comparable parameters, the picture for the other organisms studied (e.g., fish, insects, bacteria and viruses) is more heterogeneous, possibly because in these organisms DNA methylation has different functional roles. Our analysis suggests that the distribution of the distances between dinucleotides CG provides useful insights in characterizing and classifying organisms in terms of methylation functionalities.Comment: 13 pages, 5 figures. To be published in the Philosophical Transactions A theme issue "DNA as information

Ginger (Zingiber officinale Roscoe) powder as dietary supplementation in rabbit: life performances, carcass characteristics and meat quality

The aim of this study was to evaluate the effect of dietary Zingiber officinale Roscoe (ginger) powder on rabbit productive performances, meat quality and shelf-life of raw and cooked meat. Ninety hybrid rabbits of 60 days old were fed three different diets: basal diet (control, C), basal diet supplemented by 4 g of ginger powder on 100 g of feed (G4) and basal diet supplemented by 8 g of ginger powder on 100 g of feed (G8) (3.6 and 7.2 g/100 g of dry matter for G4 and G8, respectively). Live weight, average daily gain and feed intake were recorded. Ten rabbits of each group were slaughtered at 90 days of age and meat quality was assessed during seven days of storage at 4 °C. Live performance and slaughter traits did not show any significant differences. Dietary ginger powder induced modification in pH of raw samples and in colour indexes of both raw and cooked meat. Lipid oxidation of raw samples was delayed in time by ginger feed addition even if no modification was highlighted in antioxidant capacity. Ginger powder could be a potential supplementation in diet of rabbits for increasing meat shelf-life