Giulio Pavesi

Polyribonucleotide phosphorylase (PNPase) is a phosphorolytic RNA exonuclease highly conserved throughout evolution. In Escherichia coli, PNPase controls complex phenotypic traits like biofilm formation and growth at low temperature. In human cells, PNPase is located in mitochondria, where it is implicated in the RNA import from the cytoplasm, the mitochondrial RNA degradation and the processing of R-loops, namely stable RNA–DNA hybrids displacing a DNA strand. In this work, we show that the human PNPase (hPNPase) expressed in E. coli causes oxidative stress, SOS response activation and R-loops accumulation. Hundreds of E. coli RNAs are stabilized in presence of hPNPase, whereas only few transcripts are destabilized. Moreover, phenotypic traits typical of E. coli strains lacking PNPase are strengthened in presence of the human enzyme. We discuss the hypothesis that hPNPase expressed in E. coli may bind, but not degrade, the RNA, in agreement with previous in vitro data showing that phosphate concentrations in the range of those found in the bacterial cytoplasm and, more relevant, in the mitochondria, inhibit its activity.

The transcription factor SOX2 is important for brain development and for neural stem cells (NSC) maintenance. Sox2-deleted (Sox2-del) NSC from neonatal mouse brain are lost after few passages in culture. Two highly expressed genes, Fos and Socs3, are strongly downregulated in Sox2-del NSC; we previously showed that Fos or Socs3 overexpression by lentiviral transduction fully rescues NSC’s long-term maintenance in culture. Sox2-del NSC are severely defective in neuronal production when induced to differentiate. NSC rescued by Sox2 reintroduction correctly differentiate into neurons. Similarly, Fos transduction rescues normal or even increased numbers of immature neurons expressing beta-tubulinIII, but not more differentiated markers (MAP2). Additionally, many cells with both beta-tubulinIII and GFAP expression appear, indicating that FOS stimulates the initial differentiation of a “mixed” neuronal/glial progenitor. The unexpected rescue by FOS suggested that FOS, a SOX2 transcriptional target, might act on neuronal genes, together with SOX2. CUT&RUN analysis to detect genome-wide binding of SOX2, FOS, and JUN (the AP1 complex) revealed that a high proportion of genes expressed in NSC are bound by both SOX2 and AP1. Downregulated genes in Sox2-del NSC are highly enriched in genes that are also expressed in neurons, and a high proportion of the “neuronal” genes are bound by both SOX2 and AP1.

Transcriptomics studies have been facilitated by the development of microarray and RNA-Seq technologies, with thousands of expression datasets available for many species. However, the quality of data can be highly variable, making the combined analysis of different datasets difficult and unreliable. Most of the microarray data for Medicago truncatula, the barrel medic, have been stored and made publicly accessible on the web database Medicago truncatula Gene Expression atlas (MtGEA). The aim of this work is to ameliorate the quality of the MtGEA database through a general method based on logical and statistical relationships among parameters and conditions. The initial 716 columns available in the dataset were reduced to 607 by evaluating the quality of data through the sum of the expression levels over the entire transcriptome probes and Pearson correlation among hybridizations. The reduced dataset shows great improvements in the consistency of the data, with a reduction in both false positives and false negatives resulting from Pearson correlation and GO enrichment analysis among genes. The approach we used is of general validity and our intent is to extend the analysis to other plant microarray databases.

RNA-Seq has become the de facto standard technique for characterization and quantification of transcriptomes, and a large number of methods and tools have been proposed to model and detect differential gene expression based on the comparison of transcript abundances across different samples. However, state-of-the-art methods for this task are usually designed for pairwise comparisons, that is, can identify significant variation of expression only between two conditions or samples. We describe the use of RNentropy, a methodology based on information theory, devised to overcome this limitation. RNentropy can thus detect significant variations of gene expression in RNA-Seq data across any number of samples and conditions, and can be applied downstream of any analysis pipeline for the quantification of gene expression from raw sequencing data. RNentropy takes as input gene (or transcript) expression values, defined with any measure suitable for the comparison of transcript levels across samples and conditions. The output consists of genes (or transcripts) exhibiting significant variation of expression across the conditions studied, together with the samples in which they result to be over- or underexpressed. RNentropy is implemented as an R package and freely available from the CRAN repository. We provide a detailed guide to the functions and parameters of the package and usage examples to demonstrate the software capabilities, also showing how it can be applied to the analysis of single-cell RNA sequencing data.

Various next generation sequencing (NGS) based strategies have been successfully used in the recent past for tracing origins and understanding the evolution of infectious agents, investigating the spread and transmission chains of outbreaks, as well as facilitating the development of effective and rapid molecular diagnostic tests and contributing to the hunt for treatments and vaccines. The ongoing COVID-19 pandemic poses one of the greatest global threats in modern history and has already caused severe social and economic costs. The development of efficient and rapid sequencing methods to reconstruct the genomic sequence of SARS-CoV-2, the etiological agent of COVID-19, has been fundamental for the design of diagnostic molecular tests and to devise effective measures and strategies to mitigate the diffusion of the pandemic. Diverse approaches and sequencing methods can, as testified by the number of available sequences, be applied to SARS-CoV-2 genomes. However, each technology and sequencing approach has its own advantages and limitations. In the current review, we will provide a brief, but hopefully comprehensive, account of currently available platforms and methodological approaches for the sequencing of SARS-CoV-2 genomes. We also present an outline of current repositories and databases that provide access to SARS-CoV-2 genomic data and associated metadata. Finally, we offer general advice and guidelines for the appropriate sharing and deposition of SARS-CoV-2 data and metadata, and suggest that more efficient and standardized integration of current and future SARS-CoV-2-related data would greatly facilitate the struggle against this new pathogen. We hope that our ‘vademecum’ for the production and handling of SARS-CoV-2-related sequencing data, will contribute to this objective.

Chromatin immunoprecipitation followed by next-generation sequencing (ChIP-Seq) has opened new avenues of research in the genome-wide characterization of regulatory DNA-protein interactions at the genetic and epigenetic level. As a consequence, it has become the de facto standard for studies on the regulation of transcription, and literally thousands of data sets for transcription factors and cofactors in different conditions and species are now available to the scientific community. However, while pipelines and best practices have been established for the analysis of a single experiment, there is still no consensus on the best way to perform an integrated analysis of multiple datasets in the same condition, in order to identify the most relevant and widespread regulatory modules composed by different transcription factors and cofactors. We present here a computational pipeline for this task, that integrates peak summit colocalization, a novel statistical framework for the evaluation of its significance, and motif enrichment analysis. We show examples of its application to ENCODE data, that led to the identification of relevant regulatory modules composed of different factors, as well as the organization on DNA of the binding motifs responsible for their recruitment.

Heavy methyl Stable Isotope Labeling with Amino acids in Cell culture (hmSILAC) is a metabolic labeling strategy employed in proteomics to increase the confidence of global identification of methylated peptides by MS. However, to this day, the automatic and robust identification of heavy and light peak doublets from MS‐raw data of hmSILAC experiments is a challenging task, for which the choice of computational methods is very limited. Here, hmSEEKER, a software designed to work downstream of a MaxQuant analysis for in‐depth search of MS peak pairs that correspond to light and heavy methyl‐peptide within MaxQuant‐generated tables is described with good sensitivity and specificity. The software is written in Perl, and its code and user manual are freely available at Bitbucket (https://bit.ly/2scCT9u).

RNA sequencing (RNA-Seq) has become the experimental standard in transcriptome studies. While most of the bioinformatic pipelines for the analysis of RNA-Seq data and the identification of significant changes in transcript abundance are based on the comparison of two conditions, it is common practice to perform several experiments in parallel (e.g. from different individuals, developmental stages, tissues), for the identification of genes showing a significant variation of expression across all the conditions studied. In this work we present RNentropy, a methodology based on information theory devised for this task, which given expression estimates from any number of RNA-Seq samples and conditions identifies genes or transcripts with a significant variation of expression across all the conditions studied, together with the samples in which they are over- or under-expressed. To show the capabilities offered by our methodology, we applied it to different RNA-Seq datasets: 48 biological replicates of two different yeast conditions; samples extracted from six human tissues of three individuals; seven different mouse brain cell types; human liver samples from six individuals. Results, and their comparison to different state of the art bioinformatic methods, show that RNentropy can provide a quick and in depth analysis of significant changes in gene expression profiles over any number of conditions.

Large-scale initiatives aiming to recover the complete sequence of thousands of human genomes are currently being undertaken worldwide, concurring to the generation of a comprehensive catalog of human genetic variation. The ultimate and most ambitious goal of human population scale genomics is the characterization of the so-called human “variome”, through the identification of causal mutations or haplotypes. Several research institutions worldwide currently use genotyping assays based on Next Generation Sequencing (NGS) for diagnostics and clinical screenings, and the widespread application of such technologies promises major revolutions in medical science. Bioinformatic analysis of human resequencing data is one of the main factors limiting the effectiveness and general applicability of NGS for clinical studies. The requirement for multiple tools, to be combined in dedicated protocols in order to accommodate different types of data (gene panels, exomes, or whole genomes) and the high variability of the data makes difficult the establishment of a ultimate strategy of general use. While there already exist several studies comparing sensitivity and accuracy of bioinformatic pipelines for the identification of single nucleotide variants (SNV) from resequencing data, little is known about the impact of quality assessment and reads preprocessing strategies. In this work we discuss major strengths and limitations of the various genome resequencing protocols are currently used in molecular diagnostics and for the discovery of novel disease causing mutations. By taking advantage of publicly available data we devise and suggest a series of best practices for the preprocessing of the data that consistently improve the outcome of genotyping with minimal impacts on computational costs.