Differential co-expression network analysis elucidated genes associated with sensitivity to farnesyltransferase inhibitor and prognosis of acute myeloid leukemia.

Acute myeloid leukemia (AML) is a heterogeneous disease and the most common form of acute leukemia with a poor prognosis. Due to its complexity, the disease requires the identification of biomarkers for reliable prognosis. To identify potential disease genes that regulate patient prognosis, we used differential co-expression network analysis and transcriptomics data from relapsed, refractory, and previously untreated AML patients based on their response to treatment in the present study. In addition, we combined functional genomics and transcriptomics data to identify novel and therapeutically potential systems biomarkers for patients who do or do not respond to treatment. As a result, we constructed co-expression networks for response and non-response cases and identified a highly interconnected group of genes consisting of SECISBP2L, MAN1A2, PRPF31, VASP, and SNAPC1 in the response network and a group consisting of PHTF2, SLC11A2, PDLIM5, OTUB1, and KLRD1 in the non-response network, both of which showed high prognostic performance with hazard ratios of 4.12 and 3.66, respectively. Remarkably, ETS1, GATA2, AR, YBX1, and FOXP3 were found to be important transcription factors in both networks. The prognostic indicators reported here could be considered as a resource for identifying tumorigenesis and chemoresistance to farnesyltransferase inhibitor. They could help identify important research directions for the development of new prognostic and therapeutic techniques for AML.

Acute Myeloid Leukemia: New Multiomics Molecular Signatures and Implications for Systems Medicine Diagnostics and Therapeutics Innovation.

Acute myeloid leukemia (AML) is a common, complex, and multifactorial malignancy of the hematopoietic system. AML diagnosis and treatment outcomes display marked heterogeneity and patient-to-patient variations. To date, AML-related biomarker discovery research has employed single omics inquiries. Multiomics analyses that reconcile and integrate the data streams from multiple levels of the cellular hierarchy, from genes to proteins to metabolites, offer much promise for innovation in AML diagnostics and therapeutics. We report, in this study, a systems medicine and multiomics approach to integrate the AML transcriptome data and reporter biomolecules at the RNA, protein, and metabolite levels using genome-scale biological networks. We utilized two independent transcriptome datasets (GSE5122, GSE8970) in the Gene Expression Omnibus database. We identified new multiomics molecular signatures of relevance to AML: miRNAs (e.g., mir-484 and miR-519d-3p), receptors (ACVR1 and PTPRG), transcription factors (PRDM14 and GATA3), and metabolites (in particular, amino acid derivatives). The differential expression profiles of all reporter biomolecules were crossvalidated in independent RNA-Seq and miRNA-Seq datasets. Notably, we found that PTPRG holds important prognostication potential as evaluated by Kaplan-Meier survival analyses. The multiomics relationships unraveled in this analysis point toward the genomic pathogenesis of AML. These multiomics molecular leads warrant further research and development as potential diagnostic and therapeutic targets.

Evaluating the effect of microalga Haematococcus pluvialis bioaugmentation on aerobic membrane bioreactor in terms of performance, membrane fouling and microbial community structure.

In this study, considering the enhancement potential of microalgae and MBRs for wastewater treatment, the microalgae Haematococcus pluvialis, which is a freshwater species of Chlorophyta with a high capacity to synthesize astaxanthin, was bioaugmented into an aerobic MBR to investigate its potential on treatment of antibiotics in wastewater, reducing membrane biofouling, and impact on the microbial community structure. For this purpose, two control MBRs, with and without antibiotics, alongside an MBR bioaugmented with H. pluvialis, were set under mesophilic conditions, using inoculum from a local wastewater treatment facility and synthetic wastewater. The common antibiotics sulfamethoxazole (SMX), tetracycline (TET) and erythromycin (ERY) were selected to investigate removal efficiencies by Haematococcus pluvialis in an MBR for this study. In the bioaugmented reactor, membrane biofouling was delayed by 33% and chemical oxygen demand removal increased by 6%. The highest removal of antibiotics was observed for TET with a 20% enhancement from 69.75% (C2) to 89.73% (HP). The results also suggested that H. pluvialis reconstructed indigenous and biofilm microbial communities in MBR. The biodegradation network was modified and the relative abundance of Proteobacteria increased, while Firmicutes and Bacteroidetes were significantly reduced.

The asymmetric effect eco-innovation and tourism towards carbon neutrality target in Turkey.

After the Paris Climate Conference (Conference of the Paris COP21), emerging countries also start progressing as the world has shown serious concern towards carbon neutrality targets. In doing this, the tourism and hospitality businesses have also emerged as an industry that has shown tremendous economic positive outputs and support to an economy with the lease inputs. However, tourism has also been reported to lead to increased environmental degradation as foreigners came to the host country, whereby ecological innovation emerged as a potential solution for eliminating the level of carbon emissions. In contrast, researchers are in agreement in terms of its beneficial relationship. Therefore, based on Turkey as the contextual gap and the application of quantile autoregressive distributed lag (QARDL) as the methodological contribution, the present study explores the relationship of tourism and ecological innovation within the framework of Environment Kuznets Curve (EKC) on the environment using two different proxies (carbon emissions and ecological footprint) spanning from 1995 to 2018. The results confirm the EKC curve whereby the parameter estimating tourism and ecological innovation on carbon emissions and ecological footprint, both of the integrations are found negative and statistically significant across most quantiles. Based on the findings, the study is accordingly concluded, followed by the managerial implications.