A novel antibiotic class targeting the lipopolysaccharide transporter

Carbapenem-resistant Acinetobacter baumannii (CRAB) has emerged as a major global pathogen with limited treatment options1. No new antibiotic chemical class with activity against A. baumannii has reached patients in over 50 years1. Here we report the identification and optimization of tethered macrocyclic peptide (MCP) antibiotics with potent antibacterial activity against CRAB. The mechanism of action of this molecule class involves blocking the transport of bacterial lipopolysaccharide from the inner membrane to its destination on the outer membrane, through inhibition of the LptB2FGC complex. A clinical candidate derived from the MCP class, zosurabalpin (RG6006), effectively treats highly drug-resistant contemporary isolates of CRAB both in vitro and in mouse models of infection, overcoming existing antibiotic resistance mechanisms. This chemical class represents a promising treatment paradigm for patients with invasive infections due to CRAB, for whom current treatment options are inadequate, and additionally identifies LptB2FGC as a tractable target for antimicrobial drug development.

Qualimap 2: advanced multi-sample quality control for high-throughput sequencing data

MOTIVATION

Detection of random errors and systematic biases is a crucial step of a robust pipeline for processing high-throughput sequencing (HTS) data. Bioinformatics software tools capable of performing this task are available, either for general analysis of HTS data or targeted to a specific sequencing technology. However, most of the existing QC instruments only allow processing of one sample at a time.

RESULTS

Qualimap 2 represents a next step in the QC analysis of HTS data. Along with comprehensive single-sample analysis of alignment data, it includes new modes that allow simultaneous processing and comparison of multiple samples. As with the first version, the new features are available via both graphical and command line interface. Additionally, it includes a large number of improvements proposed by the user community.

AVAILABILITY AND IMPLEMENTATION

The implementation of the software along with documentation is freely available at http://www.qualimap.org.

CONTACT

meyer@mpiib-berlin.mpg.de

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Qualimap: evaluating next-generation sequencing alignment data

MOTIVATION

The sequence alignment/map (SAM) and the binary alignment/map (BAM) formats have become the standard method of representation of nucleotide sequence alignments for next-generation sequencing data. SAM/BAM files usually contain information from tens to hundreds of millions of reads. Often, the sequencing technology, protocol and/or the selected mapping algorithm introduce some unwanted biases in these data. The systematic detection of such biases is a non-trivial task that is crucial to drive appropriate downstream analyses.

RESULTS

We have developed Qualimap, a Java application that supports user-friendly quality control of mapping data, by considering sequence features and their genomic properties. Qualimap takes sequence alignment data and provides graphical and statistical analyses for the evaluation of data. Such quality-control data are vital for highlighting problems in the sequencing and/or mapping processes, which must be addressed prior to further analyses.

AVAILABILITY

Qualimap is freely available from http://www.qualimap.org.

Genome-wide differential genetic profiling characterizes colorectal cancers with genetic instability and specific routes to HLA class I loss and immune escape

AIM

We compared the expression of genes related to inflammatory and cytotoxic functions between MSI and MSS (HLA-class I-negative and HLA-class I-positive) colorectal cancers (CRCs), seeking evidence of differences in inflammatory mediators and cytotoxic T-cell responses. Twenty-two CRCs were divided into three study groups as a function of HLA class I expression and MSI phenotype: 8 MSI tumours, 6 MSS/HLA- tumours and 6 MSS/HLA+ tumours (controls).

FINDINGS

A first comparison between eight MSI and six MSS/HLA-positive (control) cancers, based on microarray analysis on an Affymetrix(®) HG-U133-Plus-PM plate, identified 1974 differentially expressed genes (P < 0.05). We grouped genes in Gene Ontology functional categories: apoptotic programme (72 genes, P = 5.5·10(-3)), leucocyte activation (43 genes, P = 1.8·10(-5)), T-cell activation (24 genes, P = 6.3·10(-4)), inflammatory response (40 genes, 2.3·10(-2)) and cytokine production (10 genes, P = 1.9·10(-2)). Real-time PCR and immunohistochemical evaluation were used to validate the data, finding that increased mRNA levels of pro-inflammatory cytokines and cytotoxic mediators were associated with greater infiltration by CD8+T lymphocytes in the MSI group (P < 0.001). Finally, HLA-class I-negative tumours were not grouped together but rather in accordance with features of the gene expression profile of MSI or MSS tumours. As expected, genes associated with antigen processing machinery and MHC class I molecules (TAP2, B2m) were downregulated in MSS/HLA-class I-negative CRCs (n = 6) in comparison to controls.

CONCLUSIONS

In conclusion, microarray and immunohistochemical data may be useful to comprehensively assess tumour-host interactions and differentiate MSI from MSS cancers. The two types of tumour, MSI/HLA-class I-negative and MSS/HLA-class I-negative, showed marked differences in the composition and intensity of infiltrating leucocytes, suggesting that their immune escape strategies involve distinct pathways.

Genome-wide gene expression analysis in mouse embryonic stem cells

Embryonic stem cell studies have generated great interest, due to their ability to form a wide variety of matured cells. However, there remains a poor understanding of mechanisms regulating the cell state of embryonic stem cells (ESCs) and of the genes they express during early differentiation. Gene expression analysis may be a valuable tool to elucidate either the molecular pathways involved in self-renewal and pluripotency, or early differentiation and to identify potential molecular therapy targets. The aim of this study was to characterize at the molecular level the undifferentiated mouse ESC state and the early development towards embryoid bodies. To attempt this issue, we performed CodeLink Mouse Uniset I 20K bioarrays in a well-characterized mouse ESC line, MES3, 3- and 7 day-old embryoid bodies and we compared our findings with those in adult tissue cells. Gene expression results were subsequently validated in a commercial stem cell line, CGR8 (ATCC). Significance Analysis of Microarrays (SAM) was used to identify statistically significant changes in microarray data. We identified 3664 genes expressed at significantly greater levels in MES3 stem cells than in adult tissue cells, which included 611 with 3-fold higher gene expression levels versus the adult cells. We also investigated the gene expression profile during early embryoid body formation, identifying 2040 and 2243 genes that were up-regulated in 3- and 7- day-old embryoid bodies, respectively. Our gene expression results in MES3 cells were partially confirmed in CGR8 cells, showing numerous genes that are expressed in both mouse stem cells. In conclusion, our results suggest that commonly expressed genes may be strong candidates for involvement in the maintenance of a pluripotent and undifferentiated phenotype and in early development.

Differential expression in RNA-seq: a matter of depth

Next-generation sequencing (NGS) technologies are revolutionizing genome research, and in particular, their application to transcriptomics (RNA-seq) is increasingly being used for gene expression profiling as a replacement for microarrays. However, the properties of RNA-seq data have not been yet fully established, and additional research is needed for understanding how these data respond to differential expression analysis. In this work, we set out to gain insights into the characteristics of RNA-seq data analysis by studying an important parameter of this technology: the sequencing depth. We have analyzed how sequencing depth affects the detection of transcripts and their identification as differentially expressed, looking at aspects such as transcript biotype, length, expression level, and fold-change. We have evaluated different algorithms available for the analysis of RNA-seq and proposed a novel approach--NOISeq--that differs from existing methods in that it is data-adaptive and nonparametric. Our results reveal that most existing methodologies suffer from a strong dependency on sequencing depth for their differential expression calls and that this results in a considerable number of false positives that increases as the number of reads grows. In contrast, our proposed method models the noise distribution from the actual data, can therefore better adapt to the size of the data set, and is more effective in controlling the rate of false discoveries. This work discusses the true potential of RNA-seq for studying regulation at low expression ranges, the noise within RNA-seq data, and the issue of replication.

Differential expression in RNA-seq: a matter of depth

Next-generation sequencing (NGS) technologies are revolutionizing genome research, and in particular, their application to transcriptomics (RNA-seq) is increasingly being used for gene expression profiling as a replacement for microarrays. However, the properties of RNA-seq data have not been yet fully established, and additional research is needed for understanding how these data respond to differential expression analysis. In this work, we set out to gain insights into the characteristics of RNA-seq data analysis by studying an important parameter of this technology: the sequencing depth. We have analyzed how sequencing depth affects the detection of transcripts and their identification as differentially expressed, looking at aspects such as transcript biotype, length, expression level, and fold-change. We have evaluated different algorithms available for the analysis of RNA-seq and proposed a novel approach--NOISeq--that differs from existing methods in that it is data-adaptive and nonparametric. Our results reveal that most existing methodologies suffer from a strong dependency on sequencing depth for their differential expression calls and that this results in a considerable number of false positives that increases as the number of reads grows. In contrast, our proposed method models the noise distribution from the actual data, can therefore better adapt to the size of the data set, and is more effective in controlling the rate of false discoveries. This work discusses the true potential of RNA-seq for studying regulation at low expression ranges, the noise within RNA-seq data, and the issue of replication.

Paintomics: a web based tool for the joint visualization of transcriptomics and metabolomics data

Motivation: The development of the omics technologies such as transcriptomics, proteomics and metabolomics has made possible the realization of systems biology studies where biological systems are interrogated at different levels of biochemical activity (gene expression, protein activity and/or metabolite concentration). An effective approach to the analysis of these complex datasets is the joined visualization of the disparate biomolecular data on the framework of known biological pathways.

Results: We have developed the Paintomics web server as an easy-to-use bioinformatics resource that facilitates the integrated visual analysis of experiments where transcriptomics and metabolomics data have been measured on different conditions for the same samples. Basically, Paintomics takes complete transcriptomics and metabolomics datasets, together with lists of significant gene or metabolite changes, and paints this information on KEGG pathway maps.

Availability: Paintomics is freely available at http://www.paintomics.org.

Contact:aconesa@cipf.es