Optimized protocol for the extraction of RNA and DNA from frozen whole blood sample stored in a single EDTA tube

Cryopreservation of whole blood is useful for DNA collection, and clinical and basic research. Blood samples in ethylenediaminetetraacetic acid disodium salt (EDTA) tubes stored at − 80 °C are suitable for DNA extraction, but not for high-quality RNA extraction. Herein, a new methodology for high-quality RNA extraction from human blood samples is described. Quickly thawing frozen whole blood on aluminum blocks at room temperature could minimize RNA degradation, and improve RNA yield and quality compared with thawing the samples in a 37 °C water bath. Furthermore, the use of the NucleoSpin RNA kit increased RNA yield by fivefold compared with the PAXgene Blood RNA Kit. Thawing blood samples on aluminum blocks significantly increased the DNA yield by ~ 20% compared with thawing in a 37 °C water bath or on ice. Moreover, by thawing on aluminum blocks and using the NucleoSpin RNA and QIAamp DNA Blood kits, the extraction of RNA and DNA of sufficient quality and quantity was achieved from frozen EDTA whole blood samples that were stored for up to 8.5 years. Thus, extracting RNA from frozen whole blood in EDTA tubes after long-term storage is feasible. These findings may help advance gene expression analysis, as well as biomarker research for various diseases

Interferon signaling and hypercytokinemia-related gene expression in the blood of antidepressant non-responders

Only 50% of patients with depression respond to the first antidepressant drug administered. Thus, biomarkers for prediction of antidepressant responses are needed, as predicting which patients will not respond to antidepressants can optimize selection of alternative therapies. We aimed to identify biomarkers that could predict antidepressant responsiveness using a novel data-driven approach based on statistical pattern recognition. We retrospectively divided patients with major depressive disorder into antidepressant responder and non-responder groups. Comprehensive gene expression analysis was performed using peripheral blood without narrowing the genes. We designed a classifier according to our own discrete Bayes decision rule that can handle categorical data. Nineteen genes showed differential expression in the antidepressant non-responder group (n = 15) compared to the antidepressant responder group (n = 15). In the training sample of 30 individuals, eight candidate genes had significantly altered expression according to quantitative real-time polymerase chain reaction. The expression of these genes was examined in an independent test sample of antidepressant responders (n = 22) and non-responders (n = 12). Using the discrete Bayes classifier with the HERC5, IFI6, and IFI44 genes identified in the training set yielded 85% discrimination accuracy for antidepressant responsiveness in the 34 test samples. Pathway analysis of the RNA sequencing data for antidepressant responsiveness identified that hypercytokinemia- and interferon-related genes were increased in non-responders. Disease and biofunction analysis identified changes in genes related to inflammatory and infectious diseases, including coronavirus disease. These results strongly suggest an association between antidepressant responsiveness and inflammation, which may be useful for future treatment strategies for depression

Novel Biomarkers for Personalized Cancer Immunotherapy

Cancer immunotherapy has emerged as a novel and effective treatment strategy for several types of cancer. Immune checkpoint inhibitors (ICIs) have recently demonstrated impressive clinical benefit in some advanced cancers. Nonetheless, in the majority of patients, the successful use of ICIs is limited by a low response rate, high treatment cost, and treatment-related toxicity. Therefore, it is necessary to identify predictive and prognostic biomarkers to select the patients who are most likely to benefit from, and respond well to, these therapies. In this review, we summarize the evidence for candidate biomarkers of response to cancer immunotherapy

The Activator of GntII Genes for Gluconate Metabolism, GntH, Exerts Negative Control of GntR-Regulated GntI Genes in Escherichia coli

Gluconate is one of the preferred carbon sources of Escherichia coli, and two sets of gnt genes (encoding the GntI and GntII systems) are involved in its transport and metabolism. GntR represses the GntI genes gntKU and gntT, whereas GntH was previously suggested to be an activator for the GntII genes gntV and idnDO-gntWH. The helix-turn-helix residues of the two regulators GntR and GntH exhibit extensive homologies. The similarity between the two regulators prompted analysis of the cross-regulation of the GntI genes by GntH. Repression of gntKU and gntT by GntH, as well as GntR, was indeed observed using transcriptional fusions and RNA analysis. High GntH expression, from cloned gntH or induced through 5-ketogluconate, was required to observe repression of GntI genes. Two GntR-binding elements were identified in the promoter-operator region of gntKU and were also shown to be the target sites of GntH by mutational analysis. However, the GntI genes were not induced by gluconate in the presence of enhanced amounts of GntH, whereas repression by GntR was relieved by gluconate. The repression of GntI genes by GntH is thus unusual in that it is not relieved by the availability of substrate. These results led us to propose that GntH activates GntII and represses the GntI genes in the presence of metabolites derived from gluconate, allowing the organism to switch from the GntI to the GntII system. This cross-regulation may explain the progressive changes in gnt gene expression along with phases of cell growth in the presence of gluconate

Hedgehog Signals Mediate Anti-Cancer Drug Resistance in Three-Dimensional Primary Colorectal Cancer Organoid Culture

Colorectal cancer is one of the most common causes of cancer death worldwide. In patients with metastatic colorectal cancer, combination treatment with several anti-cancer drugs is employed and improves overall survival in some patients. Nevertheless, most patients with metastatic disease are not cured owing to the drug resistance. Cancer stem cells are known to regulate resistance to chemotherapy. In the previous study, we established a novel three-dimensional organoid culture model from tumor colorectal tissues of human patients using an air–liquid interface (ALI) method, which contained numerous cancer stem cells and showed resistance to 5-fluorouracil (5-FU) and Irinotecan. Here, we investigate which inhibitor for stem cell-related signal improves the sensitivity for anti-cancer drug treatment in tumor ALI organoids. Treatment with Hedgehog signal inhibitors (AY9944, GANT61) decreases the cell viability of organoids compared with Notch (YO-01027, DAPT) and Wnt (WAV939, Wnt-C59) signal inhibitors. Combination treatment of AY9944 or GANT61 with 5-FU, Irinotecan or Oxaliplatin decreases the cell viability of tumor organoids compared with each anti-cancer drug alone treatment. Treatment with AY9944 or GANT61 inhibits expression of stem cell markers c-Myc, CD44 and Nanog, likely through the decrease of their transcription factor, GLI-1 expression. Combination treatment of AY9944 or GANT61 with 5-FU or Irinotecan also prevents colony formation of colorectal cancer cell lines HCT116 and SW480. These findings suggest that Hedgehog signals mediate anti-cancer drug resistance in colorectal tumor patient-derived ALI organoids and that the inhibitors are useful as a combinational therapeutic strategy against colorectal cancer