A human-based multi-gene signature enables quantitative drug repurposing for metabolic disease

Insulin resistance (IR) contributes to the pathophysiology of diabetes, dementia, viral infection, and cardiovascular disease. Drug repurposing (DR) may identify treatments for IR; however, barriers include uncertainty whether in vitro transcriptomic assays yield quantitative pharmacological data, or how to optimise assay design to best reflect in vivo human disease. We developed a clinical-based human tissue IR signature by combining lifestyle-mediated treatment responses (>500 human adipose and muscle biopsies) with biomarkers of disease status (fasting IR from >1200 biopsies). The assay identified a chemically diverse set of >130 positively acting compounds, highly enriched in true positives, that targeted 73 proteins regulating IR pathways. Our multi-gene RNA assay score reflected the quantitative pharmacological properties of a set of epidermal growth factor receptor-related tyrosine kinase inhibitors, providing insight into drug target specificity; an observation supported by deep learning-based genome-wide predicted pharmacology. Several drugs identified are suitable for evaluation in patients, particularly those with either acute or severe chronic IR.

ATP6V1B2 and IFI27 and their intrinsic functional genomic characteristics associated with SARS-CoV-2

Genes functionally associated with SARS-CoV-2 and genes functionally related to COVID-19 disease can be different, whose distinction will become the first essential step for successfully fighting against the COVID-19 pandemic. Unfortunately, this first step has not been completed in all biological and medical research. Using a newly developed max-competing logistic classifier, two genes, ATP6V1B2 and IFI27, stand out to be critical in transcriptional response to SARS-CoV-2 with differential expressions derived from NP/OP swab PCR. This finding is evidenced by combining these two genes with one another gene in predicting disease status to achieve better-indicating power than existing classifiers with the same number of genes. In addition, combining these two genes with three other genes to form a five-gene classifier outperforms existing classifiers with ten or more genes. With their exceptional predicting power, these two genes can be critical in fighting against the COVID-19 pandemic as a new focus and direction. Comparing the functional effects of these genes with a five-gene classifier with 100% accuracy identified and tested from blood samples in the literature, genes and their transcriptional response and functional effects to SARS-CoV-2 and genes and their functional signature patterns to COVID-19 antibody are significantly different, which can be interpreted as the former is the point of a phenomenon, and the latter is the essence of the disease. Such significant findings can help explore the causal and pathological clue between SARS-CoV-2 and COVID-19 disease and fight against the disease with more targeted vaccines, antiviral drugs, and therapies.

Is Circulating DNA and Tumor Cells in Myeloma the Way Forward?

Multiple myeloma (MM) is the second deadliest hematological cancer. Despite the enormous innovation on MM treatment in the last decades, still 48% of patients die within 5 years after diagnosis. MM diagnosis and therapeutic strategy mainly rely on direct bone marrow (BM) assessment. Given the MM heterogeneity, BM biopsies do not accurately reflect the whole disease status, hampering accurate disease prognosis. Moreover, biopsies are painful and invasive procedures, highlighting the need for non-invasive and more accurate source of biomarkers. Liquid biopsies are promising sources of biomarkers that may overcome these limitations. Peripheral blood carries circulating myeloma components that are being extensively explored since the last few years as an alternative to BM aspirates. These include circulating tumor cells (CTC), cell-free DNA (cfDNA), and extracellular vesicles containing miRNA and proteins. The current review summarizes scientific evidence establishing BM as a gold standard for the diagnosis, prognosis, and evaluation of minimal residual disease. We discuss the last advances regarding cfDNA and CTC biomarkers from peripheral blood in patients with MM as well as the statistical validations. This paper addresses the technological hurdles associated with liquid biopsies and examines the missing steps for their inclusion into the clinical practice.

Synergetic Effect of Physical Activity and Fruit-vegetable Intake on the Decreasing of Cognitive Decline in Older Taiwanese

Factors which are associated with cognitive decline among elders include physical activity and the intake of fruit and vegetable, however, long-term effects and the concurrence of physical activity and fruit-vegetable intake are unknown. The present study explores this potential synergy for the mitigation of cognitive decline among a cohort of older Taiwanese in a 16-year longitudinal study. Five population-based surveys from the Taiwan Longitudinal Survey on Aging (1995–2015) involving 4,440 respondents over 53 years old in 1999 were studied. Trends in decrease of cognitive decline were observed for 16 years. Cognitive function was assessed using the Short Portable Mental Status Questionnaire (SPMSQ). Adjustments made on regression analysis included demographic, socioeconomic, health behavioral, and disease status covariates. The risk of cognitive decline decreased 63% when high physical activity group and high intake of fruit-vegetable group were combined (OR :0.37, 95% CI: 0.23–0.59). More physical activity was combined with greater fruit-vegetable intake, cognitive decline among older Taiwanese was mitigated. This indicates that there may be a synergistic effect of physical activity and fruit-vegetable intake on mitigating cognitive decline. Synergistic personal behavior is a safe, effective, and economical approach to health in later life.

Machine learning for exploring microbial inter-kingdom associations in Cystic Fibrosis and Bronchiectasis

Background: Cystic fibrosis (CF) and non-CF bronchiectasis (BX) are lung diseases characterised by severe chronic infections. Fungal and bacterial components of infection are both recognized. Recent molecular investigation of sputum from patients with CF and BX has revealed a complex mycobiome. However, little is known about how fungal and bacterial organisms interact or whether the interactions impact on disease outcomes. Methods: Quantitative PCR and next generation sequencing of ITS2 and 16S rRNA gene was carried out on 107 patients with CF and BX and defined clinical fungal infection status. Fungal and bacterial communities were explored using supervised and unsupervised machine learning to understand associations between fungal and bacterial communities and their relationship to disease. Results: Fungal and bacterial communities both had significantly higher biomass and lower diversity in CF compared to BX patients. Random forest modelling demonstrated that the fungal and bacterial communities were distinct between CF and BX patients. Within the CF group, bacterial communities contained no predictive signal for fungal disease status. Neither bacterial nor fungal community composition were predictive of the presence of CF pulmonary exacerbation (CFPE). Intra-kingdom correlations were far stronger than those between the two kingdoms. Dirichlet mixture components analysis identified two distinct clusters of bacteria related to the relative abundance of Pseudomonas. Fungal community composition contained no predictive signal for bacterial clusters. Conclusions: Clear changes in diversity were observed between patients with different clinical disease status. Although our results demonstrate that bacterial community composition differs in the presence of fungal disease, no direct relationship between bacterial and fungal OTUs was found.

Preliminary Clinical Validation of a Filtration-Based CTC Assay for Tumor Burden and HER2 Status Monitoring in Metastatic Breast Cancer

<b><i>Background:</i></b> Bearing multidimensional tumor-relevant information ranging from genomic alterations to proteomic makeup, circulating tumor cells (CTCs) constitute a promising material for liquid biopsy. The clinical validity of CTCs has been most extensively studied in metastatic breast cancer (MBC). The Cellsearch assay is currently the most widely used, while alternative strategies are pursued. A filtration-based microfluidic device has been described for CTC enrichment, but its clinical relevance remains unknown. <b><i>Methods:</i></b> In this preliminary study, we prospectively enrolled 47 MBC patients and evaluated the performance of the abovementioned CTC assay for tumor burden monitoring and human epidermal growth factor receptor 2 (HER2) status determination. <b><i>Results:</i></b> At baseline, 51.1% patients (24/47) were CTC positive. CTC count and positivity were also significantly higher in samples that accompanied poorer radiographic response evaluations. Serial blood draws suggested that CTC count enabled more accurate monitoring of tumor burden than serum markers carcinoembryonic antigen and cancer antigen 15-3. Also, in contrast to previous reports, CTC-HER2 status was moderately consistent with tumor-HER2 status. CTC-HER2 status assessment was further supported by <i>HER2</i> copy number measurements in select samples. <b><i>Conclusion:</i></b> The preliminary results from this study suggest promise for the interrogated CDC assay in several aspects, including sensitive CTC detection, accurate disease status reflection, and HER2 status determination. More studies are warranted to validate these findings and further characterize the value of CTC assay.

Gut Microbiota Metabolites in Major Depressive Disorder. Deep Insights into Their Pathophysiological Role and Potential Translational Applications

The gut microbiota is a complex and dynamic ecosystem essential for the proper functioning of the organism, affecting the health and disease status of the individuals. There is continuous and bidirectional communication between gut microbiota and the host, conforming to a unique entity known as “holobiont”. Among these crosstalk mechanisms, the gut microbiota synthesizes a broad spectrum of bioactive compounds or metabolites which exert pleiotropic effects on the human organism. Many of these microbial metabolites can cross the blood–brain barrier (BBB) or have significant effects on the brain, playing a key role in the so-called microbiota-gut-brain axis. An altered Microbiota-Gut-Brain (MGB) axis is a major characteristic of many neuropsychiatric disorders, including major depressive disorder (MDD). Significative differences between gut eubiosis and dysbiosis in mental disorders like MDD with their different metabolite composition and concentrations are being discussed. In the present review, the main microbial metabolites (short-chain fatty acids -SCFAs-, bile acids, amino acids, tryptophan -trp- derivatives, and more), their signaling pathways and functions will be summarized to explain part of MDD pathophysiology. Conclusions from promising translational approaches related to microbial metabolome will be addressed in more depth to discuss their possible clinical value in the management of MDD patients.

Quantile-specific heritability of plasma fibrinogen concentrations

Background Fibrinogen is a moderately heritable blood protein showing different genetic effects by sex, race, smoking status, pollution exposure, and disease status. These interactions may be explained in part by “quantile-dependent expressivity”, where the effect size of a genetic variant depends upon whether the phenotype (e.g. plasma fibrinogen concentration) is high or low relative to its distribution. Purpose Determine whether fibrinogen heritability (h2) is quantile-specific, and whether quantile-specific h2 could account for fibrinogen gene-environment interactions. Methods Plasma fibrinogen concentrations from 5689 offspring-parent pairs and 1932 sibships from the Framingham Heart Study were analyzed. Quantile-specific heritability from offspring-parent (βOP, h2 = 2βOP/(1+rspouse)) and full-sib regression slopes (βFS, h2 = {(1+8rspouseβFS)0.05–1}/(2rspouse)) were robustly estimated by quantile regression with nonparametric significance assigned from 1000 bootstrap samples. Results Quantile-specific h2 (±SE) increased with increasing percentiles of the offspring’s age- and sex-adjusted fibrinogen distribution when estimated from βOP (Ptrend = 5.5x10-6): 0.30±0.05 at the 10th, 0.37±0.04 at the 25th, 0.48±0.05 at the 50th, 0.61±0.06 at the 75th, and 0.65±0.08 at the 90th percentile, and when estimated from βFS (Ptrend = 0.008): 0.28±0.04 at the 10th, 0.31±0.04 at the 25th, 0.36±0.03 at the 50th, 0.41±0.05 at the 75th, and 0.50±0.06 at the 90th percentile. The larger genetic effect at higher average fibrinogen concentrations may contribute to fibrinogen’s greater heritability in women than men and in Blacks than Whites, and greater increase from smoking and air pollution for the FGB -455G>A A-allele. It may also explain greater fibrinogen differences between: 1) FGB -455G>A genotypes during acute phase reactions than usual conditions, 2) GTSM1 and IL-6 -572C>G genotypes in smokers than nonsmokers, 3) FGB -148C>T genotypes in untreated than treated diabetics, and LPL PvuII genotypes in macroalbuminuric than normoalbuminuric patients. Conclusion Fibrinogen heritability is quantile specific, which may explain or contribute to its gene-environment interactions. The analyses do not disprove the traditional gene-environment interpretations of these examples, rather quantile-dependent expressivity provides an alternative explanation that warrants consideration.