Human isotype‐dependent inhibitory antibody responses against Mycobacterium tuberculosis

Accumulating evidence from experimental animal models suggests that antibodies play a protective role against tuberculosis (TB). However, little is known about the antibodies generated upon Mycobacterium tuberculosis (MTB) exposure in humans. Here, we performed a molecular and functional characterization of the human B‐cell response to MTB by generating recombinant monoclonal antibodies from single isolated B cells of untreated adult patients with acute pulmonary TB and from MTB‐exposed healthcare workers. The data suggest that the acute plasmablast response to MTB originates from reactivated memory B cells and indicates a mucosal origin. Through functional analyses, we identified MTB inhibitory antibodies against mycobacterial antigens including virulence factors that play important roles in host cell infection. The inhibitory activity of anti‐MTB antibodies was directly linked to their isotype. Monoclonal as well as purified serum IgA antibodies showed MTB blocking activity independently of Fc alpha receptor expression, whereas IgG antibodies promoted the host cell infection. Together, the data provide molecular insights into the human antibody response to MTB and may thereby facilitate the design of protective vaccination strategies

Visceral fat obesity is the key risk factor for the development of reflux erosive esophagitis in 40–69-years subjects

[Background] Visceral fat obesity can be defined quantitatively by abdominal computed tomography, however, the usefulness of measuring visceral fat area to assess the etiology of gastrointestinal reflux disease has not been fully elucidated. [Methods] A total of 433 healthy subjects aged 40–69 years (234 men, 199 women) were included in the study. The relationship between obesity-related factors (total fat area, visceral fat area, subcutaneous fat area, waist circumference, and body mass index) and the incidence of reflux erosive esophagitis was investigated. Lifestyle factors and stomach conditions relevant to the onset of erosive esophagitis were also analyzed. [Results] The prevalence of reflux erosive esophagitis was 27.2% (118/433; 106 men, 12 women). Visceral fat area was higher in subjects with erosive esophagitis than in those without (116.6 cm2 vs. 64.9 cm2, respectively). The incidence of erosive esophagitis was higher in subjects with visceral fat obesity (visceral fat area ≥ 100 cm2) than in those without (61.2% vs. 12.8%, respectively). Visceral fat obesity had the highest odds ratio (OR) among obesity-related factors. Multivariate analysis showed that visceral fat area was associated with the incidence of erosive esophagitis (OR = 2.18), indicating that it is an independent risk factor for erosive esophagitis. In addition, daily alcohol intake (OR = 1.54), gastric atrophy open type (OR = 0.29), and never-smoking history (OR = 0.49) were also independently associated with the development of erosive esophagitis. [Conclusions] Visceral fat obesity is the key risk factor for the development of reflux erosive esophagitis in subjects aged 40–69 years

Pneumolysin induced mitochondrial dysfunction leads to release of mitochondrial DNA

Abstract Streptococcus pneumoniae (S.pn.) is the most common bacterial pathogen causing community acquired pneumonia. The pore-forming toxin pneumolysin (PLY) is the major virulence factor of S.pn. and supposed to affect alveolar epithelial cells thereby activating the immune system by liberation of danger-associated molecular patterns (DAMP). To test this hypothesis, we established a novel live-cell imaging based assay to analyse mitochondrial function and associated release of mitochondrial DNA (mtDNA) as DAMP in real-time. We first revealed that bacterially released PLY caused significant changes of the cellular ATP homeostasis and led to morphologic alterations of mitochondria in human alveolar epithelial cells in vitro and, by use of spectral live-tissue imaging, in human alveoli. This was accompanied by strong mitochondrial calcium influx and loss of mitochondrial membrane potential resulting in opening of the mitochondrial permeability transition pore and mtDNA release without activation of intrinsic apoptosis. Moreover, our data indicate cellular mtDNA liberation via microvesicles, which may contribute to S.pn. related pro-inflammatory immune activation in the human alveolar compartment

InFusion: Advancing Discovery of Fusion Genes and Chimeric Transcripts from Deep RNA-Sequencing Data

<div><p>Analysis of fusion transcripts has become increasingly important due to their link with cancer development. Since high-throughput sequencing approaches survey fusion events exhaustively, several computational methods for the detection of gene fusions from RNA-seq data have been developed. This kind of analysis, however, is complicated by native trans-splicing events, the splicing-induced complexity of the transcriptome and biases and artefacts introduced in experiments and data analysis. There are a number of tools available for the detection of fusions from RNA-seq data; however, certain differences in specificity and sensitivity between commonly used approaches have been found. The ability to detect gene fusions of different types, including isoform fusions and fusions involving non-coding regions, has not been thoroughly studied yet. Here, we propose a novel computational toolkit called InFusion for fusion gene detection from RNA-seq data. InFusion introduces several unique features, such as discovery of fusions involving intergenic regions, and detection of anti-sense transcription in chimeric RNAs based on strand-specificity. Our approach demonstrates superior detection accuracy on simulated data and several public RNA-seq datasets. This improved performance was also evident when evaluating data from RNA deep-sequencing of two well-established prostate cancer cell lines. InFusion identified 26 novel fusion events that were validated in vitro, including alternatively spliced gene fusion isoforms and chimeric transcripts that include intergenic regions. The toolkit is freely available to download from <a href="http:/bitbucket.org/kokonech/infusion" target="_blank">http:/bitbucket.org/kokonech/infusion</a>.</p></div

Clustering of breakpoint candidates.

<p>The arrows of the SPLIT alignments and the dot lines of BRIDGE alignments demonstrate the direction to the breakpoint position. (A) Initial clusters are created from intersecting SPLIT and BRIDGE alignments. (B) Cluster 4 is separated from cluster 1 based on the directionality, which is inferred from the alignment strand and order. (C) Cluster 5 is separated from cluster 2 based on the putative breakpoint position. Alignments belonging to the same breakpoint candidate have the same color. BRIDGE reads are marked with b, SPLIT reads are marked with s. A SPLIT read assumes an exact breakpoint, while a BRIDGE read assumes an approximate breakpoint within allowed insert size distance.</p

Fusion events detected in public RNA-seq datasets.

<p>Fusion events detected in public RNA-seq datasets.</p

TMPRSS2-ERG fusion isoforms.

<p>(A) Genomic structure of the TMPRSS2–ERG fusion transcripts discovered from deep sequencing data by InFusion. Isoform 3 is a known transcript, while isoforms 1 and 2 are novel. Transcript names are taken from the Ensembl v.68 database. (B) RT-PCR validation of isoforms in VCaP, LNCaP, RWPE-1 and PrEC cell lines; NTC = no template control. The PCR primer design was based on the output from the InFusion pipeline. In order to detect only one product, one PCR primer specific for Isoform 3 was designed to cover the fusion junction site. A 50 bp DNA ladder was co-run as size marker; bright bands indicate 250 bp and 500 bp. (C) Relative expression levels of the fusion isoforms as measured by qRT-PCR. All measurements were performed in triplicate, mean expression values were computed relative to GAPDH. Plotted values are normalized to the computed expression of isoform 3. (D) Expression levels of isoforms estimated in RPKM under the assumption of uniform coverage.</p

Comparison of fusion detection tools on simulated data.

<p>Comparison of fusion detection tools on simulated data.</p

Example of fusion detection from RNA-seq data.

<p>The fusion consists of exons 1 and 2 from gene A and exons 3 and 4 from gene B. SPLIT reads cover the junction point, while BRIDGE reads span the junction point within the insert region, which is not sequenced.</p

RNA-Seq sample details.

<p>RNA-Seq sample details.</p