Lung cancer, one of the most common causes of cancer-related death worldwide, has been associated with high treatment cost and imposed great burdens. The 5-year postoperative survival rate of lung cancer (13%) is lower than many other leading cancers indicating the urgent needs to dissect its pathogenic mechanisms and discover specific biomarkers. Although several proteins have been proposed to be potential candidates for the diagnosis of lung cancer, they present low accuracy in clinical settings. Metabolomics has thus emerged as a very promising tool for biomarker discovery. To date, many lung cancer-related metabolites have been highlighted in the literature but no database is available for scientists to retrieve this information. Herein, we construct and introduce the first Lung Cancer Metabolome Database (LCMD), a freely available online database depositing 2013 lung cancer-related metabolites identified from 65 mass spectrometry-based lung cancer metabolomics studies. Researchers are able to explore LCMD via two ways. Firstly, by applying various filters in the “Browse Metabolites” mode, users can access a list of lung cancer-related metabolites that satisfy the filter specifications. For each metabolite, users can acquire the value of the fold change (cancer/normal), statistical significance (p-value) of the fold change, and the comparative research designs of all the mass spectrometry-based lung cancer metabolomics studies that identify this metabolite. Secondly, by applying various filters in the “Browse Studies” mode, users can obtain a list of mass spectrometry-based lung cancer metabolomics studies that satisfy the filter specifications. For each study, users can view the type of studied specimen, mass spectrometry (MS) method, MS data processing software, and differential analysis method, as well as all the identified lung cancer-related metabolites. Furthermore, the overview of each study is clearly illustrated by a graphical summary. The LCMD ( http://cosbi7.ee.ncku.edu.tw/LCMD/ ) is the first database that brings together the meaningful information of lung cancer-related metabolites. The development of the LCMD is envisioned to promote the biomarker discovery of lung cancer.

Pregestational diabetes mellitus (PGDM; including type 1 diabetes mellitus [T1DM] and type 2 diabetes mellitus [T2DM]) and gestational diabetes mellitus (GDM) are common metabolic diseases during pregnancy. It was estimated that approximately 2.2% of all births in the USA were to females with PGDM. 1 The prevalence of GDM varies substantially worldwide and occurs in approximately 1% to more than 30% of pregnancies depending on age, geography, ethnicity, lifestyle, and genetic factors. 2 Many epidemiological studies suggest an increased risk of neurodevelopmental disorders (NDDs) in offspring born to mothers with PGDM or GDM, such as autism spectrum disorder (ASD), 3-5 attention-deficit/hyperactivity disorder (ADHD), 5,6 developmental delay, 7 intellectual disability, 5 cerebral palsy (CP), 8 and epilepsy/infantile spasms. 9 Thereby, maternal hyperglycaemia, hyperinsulinaemia, inflammation, epigenetic factors, autoimmune dysfunction, placental malperfusion, or oxidative stress in utero are considered to be the potential causes of childhood NDDs. [10][11][12] Although several studies have reported the association of maternal PGDM or GDM with childhood NDDs, most of them focused only on specific types of NDD (e.g. ASD or ADHD) and many of them did not report the effect of T1DM, T2DM, and GDM on NDDs at the same time. It would be very interesting to comprehensively assess the impact of PGDM and GDM on a wide range of NDDs using data collected in the same cohort; we could then directly compare the associations of T1DM, T2DM, and GDM on childhood NDDs. For instance, although one case-control study suggested that maternal diabetes increases the risk of neonatal seizures in term-born infants, 9 whether T1DM, T2DM, and GDM have the same influence remains unanswered and deserves further investigation.

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Pattern-recognition receptors (PRRs) trigger innate immune defenses against pathogen infection via downstream signaling pathways linking to inflammation and cell-autonomous immunity like phagocytosis and autophagy. IKK family kinases, IKKα and IKKβ, function to relay PRR signals to proinflammatory cytokine production to amplify innate immune responses. TBK1, a non-canonical IKK kinase, links nucleic acid sensors to type I interferon induction against viral infection and also regulates the autophagic clearance of intracellular bacteria. TBK1-Associated Protein in Endolysosomes designated TAPE, also known as CC2D1A, is an innate immune regulator acting upstream of Trif to regulate the TLR3 and TLR4 pathways, or upstream of MAVS to regulate the cytosolic RIG-I-like receptor (RLR) pathways. To our best knowledge, TAPE is the first regulator implicated in both the endosomal TLR and cytosolic RLR pathways at such an early step. We are thus interested in investigating in vivo roles of TAPE in innate immunity and molecular mechanisms by which TAPE regulates TLRs, RLRs, and possibly other PRRs. TAPE conditional knockout (cKO) mice, in which TAPE is selectively disrupted in immune cells, have been generated for our study. Our results showed that upon influenza A virus infection, TAPE cKO mice exhibited a more severe mortality than wild type mice. Further, TAPE cKO mice were shown to be more susceptible to Salmonella Typhimurium infection but more resistant to LPS-induced septic shock. Notably, ex vivo results showed that TAPE was critical for the autophagic clearance of Salmonella Typhimurium. Together, our data support a critical role for TAPE in regulating innate immune defenses through TLRs, RLRs, and autophagy.