Re-appraising the role of T-cell derived interferon gamma in restriction of Mycobacterium tuberculosis in the murine lung: T-cell derived IFNγ is required to restrict pulmonary Mtb.

T cells producing interferon gamma (IFNγ) have long been considered a stalwart for immune protection against Mycobacterium tuberculosis (Mtb), but their relative importance to pulmonary immunity has been challenged by murine studies which achieved protection by adoptively transferred Mtb-specific IFNγ-/- T cells. Using IFNγ-/- T cell chimeric mice and adoptive transfer of IFNγ-/- T cells into TCRß-/-δ-/- mice, we demonstrate that control of lung Mtb burden is in fact dependent on T cell-derived IFNγ, and furthermore, mice selectively deficient in T cell-derived IFNγ develop exacerbated disease compared to T cell-deficient controls despite equivalent lung bacterial burdens. Deficiency in T cell-derived IFNγ skews infected and bystander monocyte-derived macrophages (MDMs) to an alternative M2 phenotype, and promotes neutrophil and eosinophil influx. Our studies support an important role for T cell-derived IFNγ in pulmonary immunity against TB.

IRF3 inhibits IFN-γ-mediated restriction of intracellular pathogens in macrophages independently of IFNAR.

Macrophages use an array of innate immune sensors to detect intracellular pathogens and to tailor effective antimicrobial responses. In addition, extrinsic activation with the cytokine IFN-γ is often required as well to tip the scales of the host-pathogen balance toward pathogen restriction. However, little is known about how host-pathogen sensing impacts the antimicrobial IFN-γ-activated state. It was observed that in the absence of IRF3, a key downstream component of pathogen sensing pathways, IFN-γ-primed macrophages more efficiently restricted the intracellular bacterium Legionella pneumophila and the intracellular protozoan parasite Trypanosoma cruzi. This effect did not require IFNAR, the receptor for Type I IFNs known to be induced by IRF3, nor the sensing adaptors MyD88/TRIF, MAVS, or STING. This effect also did not involve differential activation of STAT1, the major signaling protein downstream of both Type 1 and Type 2 IFN receptors. IRF3-deficient macrophages displayed a significantly altered IFN-γ-induced gene expression program, with up-regulation of microbial restriction factors such as Nos2. Finally, we found that IFN-γ-primed but not unprimed macrophages largely excluded the activated form of IRF3 from the nucleus following bacterial infection. These data are consistent with a relationship of mutual inhibition between IRF3 and IFN-γ-activated programs, possibly as a component of a partially reversible mechanism for modulating the activity of potent innate immune effectors (such as Nos2) in the context of intracellular infection.

Perturbation of m6A writers reveals two distinct classes of mRNA methylation at internal and 5' sites.

N6-methyladenosine (m6A) is a common modification of mRNA with potential roles in fine-tuning the RNA life cycle. Here, we identify a dense network of proteins interacting with METTL3, a component of the methyltransferase complex, and show that three of them (WTAP, METTL14, and KIAA1429) are required for methylation. Monitoring m6A levels upon WTAP depletion allowed the definition of accurate and near single-nucleotide resolution methylation maps and their classification into WTAP-dependent and -independent sites. WTAP-dependent sites are located at internal positions in transcripts, topologically static across a variety of systems we surveyed, and inversely correlated with mRNA stability, consistent with a role in establishing "basal" degradation rates. WTAP-independent sites form at the first transcribed base as part of the cap structure and are present at thousands of sites, forming a previously unappreciated layer of transcriptome complexity. Our data shed light on the proteomic and transcriptional underpinnings of this RNA modification.

Inhibition of KRAS-driven tumorigenicity by interruption of an autocrine cytokine circuit.

Although the roles of mitogen-activated protein kinase (MAPK) and phosphoinositide 3-kinase (PI3K) signaling in KRAS-driven tumorigenesis are well established, KRAS activates additional pathways required for tumor maintenance, the inhibition of which are likely to be necessary for effective KRAS-directed therapy. Here, we show that the IκB kinase (IKK)-related kinases Tank-binding kinase-1 (TBK1) and IKKε promote KRAS-driven tumorigenesis by regulating autocrine CCL5 and interleukin (IL)-6 and identify CYT387 as a potent JAK/TBK1/IKKε inhibitor. CYT387 treatment ablates RAS-associated cytokine signaling and impairs Kras-driven murine lung cancer growth. Combined CYT387 treatment and MAPK pathway inhibition induces regression of aggressive murine lung adenocarcinomas driven by Kras mutation and p53 loss. These observations reveal that TBK1/IKKε promote tumor survival by activating CCL5 and IL-6 and identify concurrent inhibition of TBK1/IKKε, Janus-activated kinase (JAK), and MEK signaling as an effective approach to inhibit the actions of oncogenic KRAS.

First Universities Allied for Essential Medicines (UAEM) Neglected Diseases and Innovation Symposium.

Universities Allied for Essential Medicines organized its first Neglected Diseases and Innovation Symposium to address expanding roles of public sector research institutions in innovation in research and development of biomedical technologies for treatment of diseases, particularly neglected tropical diseases. Universities and other public research institutions are increasingly integrated into the pharmaceutical innovation system. Academic entities now routinely undertake robust high-throughput screening and medicinal chemistry research programs to identify lead compounds for small molecule drugs and novel drug targets. Furthermore, product development partnerships are emerging between academic institutions, non-profit entities, and biotechnology and pharmaceutical companies to create diagnostics, therapies, and vaccines for diseases of the poor. With not for profit mission statements, open access publishing standards, open source platforms for data sharing and collaboration, and a shift in focus to more translational research, universities and other public research institutions are well-placed to accelerate development of medical technologies, particularly for neglected tropical diseases.

Eu.Gene Analyzer a tool for integrating gene expression data with pathway databases.

MOTIVATION: Eu.Gene Analyzer is an easy-to-use, stand-alone application that allows rapid and powerful microarray data analysis in the context of biological pathways. Its intuitive graphical user interface makes it an easy and flexible tool, even for the first-time user. Eu.Gene supports a variety of array platforms, organisms and pathway ontologies, transparently deals with multiple nomenclature systems and seamlessly integrates data from different sources. Two different statistical methods, the Fisher Exact Test and the Gene Set Enrichment Analysis (GSEA), are implemented to identify biological pathways transcriptionally affected under experimental conditions. A suite of tools is offered to define, visualize and share custom non-redundant pathway sets. In conclusion, Eu.Gene Analyzer is a new software application that takes advantage of information from multiple pathway databases to build a comprehensive interpretation of experimental results in a simple, intuitive environment.

Analysis of gene expression profiles reveals novel correlations with the clinical course of colorectal cancer.

In order to discover potential markers of prognosis in colorectal cancer (CRC) we have determined gene expression profiles, using cDNA microarrays in CRC samples obtained from 19 patients in Dukes stages C and D, with favorable clinical course (Dukes C patients, survival >5 years after surgery, group A, n=7) or unfavorable clinical course (Dukes stage C and D patients, survival <5 years after surgery, group B, n=12). Gene expression was measured in RNA from each tumor, using a pool of equal amounts of RNA from all tumors as a reference. To identify and rank differentially expressed genes we used three different analytical methods: (i) Significance Analysis of Microarrays (SAM), (ii) Cox's Proportional Hazard Model, and (iii) Trend Filter (a mathematical method for the assessment of numerical trends). The level of expression of a gene in an individual tumor was regarded as of interest when that gene was identified as differentially expressed by at least two of these three methods. By these stringent criteria we identified eight genes (ITGB2, MRPS11, NPR1, TXNL2, PHF10, PRSS8, KCNK3, JAK3) that were correlated with prolonged survival after surgery. Pathway analysis showed that patients with favorable prognosis had several activated metabolic pathways (carbon metabolism, transcription, amino acid and nitrogen metabolism, signaling and fibroblast growth factor receptor pathways). To further validate individual gene expression findings, the RNA level of each gene identified as a marker with microarrays was measured by real-time RT-PCR in CRC samples from an independent group of 55 patients. In this set of patients the Cox Proportional Hazard Model analysis demonstrated a significant association between increased patient survival and low expression of ITGB2 (p = 0.011) and NPR1 (p = 0.023) genes.

Systems-level analyses identify extensive coupling among gene expression machines.

Here, we develop computational methods to assess and consolidate large, diverse protein interaction data sets, with the objective of identifying proteins involved in the coupling of multicomponent complexes within the yeast gene expression pathway. From among approximately 43 000 total interactions and 2100 proteins, our methods identify known structural complexes, such as the spliceosome and SAGA, and functional modules, such as the DEAD-box helicases, within the interaction network of proteins involved in gene expression. Our process identifies and ranks instances of three distinct, biologically motivated motifs, or patterns of coupling among distinct machineries involved in different subprocesses of gene expression. Our results confirm known coupling among transcription, RNA processing, and export, and predict further coupling with translation and nonsense-mediated decay. We systematically corroborate our analysis with two independent, comprehensive experimental data sets. The methods presented here may be generalized to other biological processes and organisms to generate principled, systems-level network models that provide experimentally testable hypotheses for coupling among biological machines.