An acidic microenvironment in Tuberculosis increases extracellular matrix degradation by regulating macrophage inflammatory responses.

Mycobacterium tuberculosis (M.tb) infection causes marked tissue inflammation leading to lung destruction and morbidity. The inflammatory extracellular microenvironment is acidic, however the effect of this acidosis on the immune response to M.tb is unknown. Using RNA-seq we show that acidosis produces system level transcriptional change in M.tb infected human macrophages regulating almost 4000 genes. Acidosis specifically upregulated extracellular matrix (ECM) degradation pathways with increased expression of Matrix metalloproteinases (MMPs) which mediate lung destruction in Tuberculosis. Macrophage MMP-1 and -3 secretion was increased by acidosis in a cellular model. Acidosis markedly suppresses several cytokines central to control of M.tb infection including TNF-α and IFN-γ. Murine studies demonstrated expression of known acidosis signaling G-protein coupled receptors OGR-1 and TDAG-8 in Tuberculosis which are shown to mediate the immune effects of decreased pH. Receptors were then demonstrated to be expressed in patients with TB lymphadenitis. Collectively, our findings show that an acidic microenvironment modulates immune function to reduce protective inflammatory responses and increase extracellular matrix degradation in Tuberculosis. Acidosis receptors are therefore potential targets for host directed therapy in patients.

Assessment of insert sizes and adapter content in fastq data from NexteraXT libraries.

The Illumina NexteraXT transposon protocol is a cost effective way to generate paired end libraries. However, the resulting insert size is highly sensitive to the concentration of DNA used, and the variation of insert sizes is often large. One consequence of this is some fragments may have an insert shorter than the length of a single read, particularly where the library is designed to produce overlapping paired end reads in order to produce longer continuous sequences. Such small insert sizes mean fewer longer reads, and also result in the presence of adapter at the end of the read. Here is presented a protocol to use publicly available tools to identify read pairs with small insert sizes and so likely to contain adapter, to check the sequence of the adapter, and remove adapter sequence from the reads. This protocol does not require a reference genome or prior knowledge of the sequence to be trimmed. Whilst the presence of fragments with small insert sizes may be a particular problem for NexteraXT libraries, the principle can be applied to any Illumina dataset in which the presence of such small inserts is suspected.

POCUS: mining genomic sequence annotation to predict disease genes.

Here we present POCUS (prioritization of candidate genes using statistics), a novel computational approach to prioritize candidate disease genes that is based on over-representation of functional annotation between loci for the same disease. We show that POCUS can provide high (up to 81-fold) enrichment of real disease genes in the candidate-gene shortlists it produces compared with the original large sets of positional candidates. In contrast to existing methods, POCUS can also suggest counterintuitive candidates.