A validated hypoxia-inducible factor (HIF) signature across tissue compartments predicts clinical outcome in human lung fibrosis

We previously reported that oxidative stress drives pseudohypoxic hypoxia-inducible factor (HIF) pathway activation to promote pathogenetic collagen structure-function in human lung fibrosis (Brereton et al., 2022). Here, through bioinformatic studies we investigate HIF pathway activation status in patients with idiopathic pulmonary fibrosis (IPF) and whether this has prognostic significance. Applying a well-established HIF gene expression signature, we classified publicly available datasets into HIF score-high and score-low groups across multiple tissue compartments. TheHIF scores in lung tissue, bronchoalveolar lavage (BAL) and peripheral blood mononuclear cells (PBMC) were increased in IPF patients and significantly correlated with an oxidative stress signature consistent with pseudohypoxic HIF pathway activation. A high HIF score in BAL and in PBMC was a strong independent predictor of mortality in multivariate analysis. Thus, a validated HIF gene signature predicts survival across tissue compartments in IPF and merits prospective study as a non-invasive biomarker of lung fibrosis progression.

Cell adhesion and immune response, two main functions altered in the transcriptome of seasonally regressed testes of two mammalian species

In species with seasonal breeding, male specimens undergo substantial testicular regression during the non-breeding period of the year. However, the molecular mechanisms that control this biological process are largely unknown. Here, we report a transcriptomic analysis on the Iberian mole, Talpa occidentalis, in which the desquamation of live, non-apoptotic germ cells is the major cellular event responsible for testis regression. By comparing testes at different reproductive states (active, regressing and inactive), we demonstrate that the molecular pathways controlling the cell adhesion function in the seminiferous epithelium, such as the MAPK, ERK and TGF-beta signalling, are altered during the regression process. In addition, inactive testes display a global upregulation of genes associated with immune response, indicating a selective loss of the immune privilege that normally operates in sexually active testes. Interspecies comparative analyses using analogous data from the Mediterranean pine vole, a rodent species where testis regression is controlled by halting meiosis entry, revealed a common gene expression signature in the regressed testes of these two evolutionary distant species. Our study advances in the knowledge of the molecular mechanisms associated to gonadal seasonal breeding, highlighting the existence of a conserved transcriptional program of testis involution across mammalian clades.

Whole blood transcriptional signatures associated with rapid antidepressant response to ketamine in patients with treatment resistant depression

Ketamine has rapid and sustained antidepressant effects in patients with treatment-resistant depression (TRD). However, the underlying mechanisms of action are not well understood. There is increasing evidence that TRD is associated with a pro-inflammatory state and that ketamine may inhibit inflammatory processes. We thus investigated whole blood transcriptional profiles related to TRD and gene expression changes associated with treatment response to ketamine. Whole blood was collected at baseline (21 healthy controls [HC], 26 patients with TRD) and then again in patients with TRD 24 hours following a single intravenous infusion of ketamine (0.5 mg/kg). We performed RNA-sequencing and analyzed (a) baseline transcriptional profiles between patients with TRD and HC, (b) responders vs. non-responders before ketamine treatment, and (c) gene expression signatures associated with clinical improvement. At baseline, patients with TRD compared to HC showed a gene expression signature indicative of interferon signaling pathway activation. Prior to ketamine administration, the metabotropic glutamate receptor gene GRM2 and the ionotropic glutamate receptor gene GRIN2D were upregulated in responders compared to non-responders. Response to ketamine was associated with a distinct transcriptional signature, however, we did not observe gene expression changes indicative of an anti-inflammatory effect. Future studies are needed to determine the role of the peripheral immune system in the antidepressant effect of ketamine.

A NEUROGENIC GENE EXPRESSION SIGNATURE SUPPORTS HUMAN THERMOGENIC ADIPOSE TISSUE DEVELOPMENT IN VIVO.

Human beige/brite thermogenic adipose tissue exerts beneficial metabolic effects and may be harnessed to improve metabolic health. To uncover mechanisms by which thermogenic adipose tissue is generated and maintained we developed a species-hybrid model in which human mesenchymal progenitor cells are induced in vitro to differentiate into white or thermogenic adipocytes and are then implanted into immuno-compromised mice. Upon implantation, thermogenic adipocytes form a more densely vascularized and innervated adipose tissue compared to non-thermogenic adipocytes. Mouse endothelial and stem/progenitor cells recruited by implanted human thermogenic adipocytes are also qualitatively different, with differentially expressed genes mapping predominantly to circadian rhythm pathways. We trace the formation of this enhanced neurovascular architecture to higher expression of a distinct set of genes directly associated with neurogenesis (THBS4, TNC, NTRK3 and SPARCL1), and to lower expression of genes associated with neurotransmitter degradation (MAOA, ACHE) by adipocytes in the developed tissue. Further analysis reveals that MAOA is abundant in human adipocytes but absent in mouse adipocytes, revealing species-specific mechanisms of neurotransmitter tone regulation. In summary, our work discovers specific neurogenic genes associated with development and maintenance of human thermogenic adipose tissue, reveals species-specific mechanisms of control of neurotransmitter tone, and suggests that targeting adipocyte MAOA may be a strategy for enhancing thermogenic adipose tissue activity in humans.

A 17-Gene Expression Signature for Early Identification of Poor Prognosis in Clear Cell Renal Cell Carcinoma

The Identification of reliable Biomarkers able to predict the outcome after nephrectomy of patients with clear cell renal cell carcinoma (ccRCC) is an unmet need. The gene expression analysis in tumor tissues represents a promising tool for better stratification of ccRCC subtypes and patients’ evaluation. Methods: In our study we retrospectively analyzed using Next-Generation expression analysis (NanoString), the expression of a gene panel in tumor tissue from 46 consecutive patients treated with nephrectomy for non-metastatic ccRCC at two Italian Oncological Centres. Significant differences in expression levels of selected genes was sought. Additionally, we performed a univariate and a multivariate analysis on overall survival according to Cox regression model. Results: A 17-gene expression signature of patients with a recurrence-free survival (RFS) < 1 year (unfavorable genomic signature (UGS)) and of patients with a RFS > 5 years (favorable genomic signature (FGS)) was identified and resulted in being significantly correlated with overall survival of the patients included in this analysis (HR 51.37, p < 0.0001). Conclusions: The identified Genomic Signatures may serve as potential biomarkers for prognosis prediction of non-metastatic RCC and could drive both follow-up and treatment personalization in RCC management.

Identification of a Ten-Gene Signature of DNA Damage Response Pathways with Prognostic Value in Esophageal Squamous Cell Carcinoma

Molecular prognostic signatures are critical for treatment decision-making in esophageal squamous cell cancer (ESCC), but the robustness of these signatures is limited. The aberrant DNA damage response (DDR) pathway may lead to the accumulation of mutations and thus accelerate tumor progression in ESCC. Given this, we applied the LASSO Cox regression to the transcriptomic data of DDR genes, and a prognostic DDR-related gene expression signature (DRGS) consisting of ten genes was constructed, including PARP3, POLB, XRCC5, MLH1, DMC1, GTF2H3, PER1, SMC5, TCEA1, and HERC2. The DRGS was independently associated with overall survival in both training and validation cohorts. The DRGS achieved higher accuracy than six previously reported multigene signatures for the prediction of prognosis in comparable cohorts. Furtherly, a nomogram incorporating DRGS and clinicopathological features showed improved predicting performance. Taken together, the DRGS was identified as a novel, robust, and effective prognostic indicator, which may refine the scheme of risk stratification and management in ESCC patients.

A comparison of host response strategies to distinguish bacterial and viral infection

Objectives Compare three host response strategies to distinguish bacterial and viral etiologies of acute respiratory illness (ARI). Methods In this observational cohort study, procalcitonin, a 3-protein panel (CRP, IP-10, TRAIL), and a host gene expression mRNA panel were measured in 286 subjects with ARI from four emergency departments. Multinomial logistic regression and leave-one-out cross validation were used to evaluate the protein and mRNA tests. Results The mRNA panel performed better than alternative strategies to identify bacterial infection: AUC 0.93 vs. 0.83 for the protein panel and 0.84 for procalcitonin (P<0.02 for each comparison). This corresponded to a sensitivity and specificity of 92% and 83% for the mRNA panel, 81% and 73% for the protein panel, and 68% and 87% for procalcitonin, respectively. A model utilizing all three strategies was the same as mRNA alone. For the diagnosis of viral infection, the AUC was 0.93 for mRNA and 0.84 for the protein panel (p<0.05). This corresponded to a sensitivity and specificity of 89% and 82% for the mRNA panel, and 85% and 62% for the protein panel, respectively. Conclusions A gene expression signature was the most accurate host response strategy for classifying subjects with bacterial, viral, or non-infectious ARI.

Gene expression signature of exercise and change of diet on non-alcoholic fatty liver disease in mice

Non-alcoholic fatty liver diseases (NAFLD) are particularly prevalent in the general Western adult population, with around one third of the population suffering from the disease. Evidence shows that NAFLD is associated with metabolic syndromes such as obesity, insulin resistance, and hypertension. Currently, the sole therapy for NAFLD involves exercise intervention. Studies showed that, with and without weight loss, exercise interventions produced a significant cutback in intrahepatic lipid content in humans, but better controlled studies that can investigate the cellular and molecular mechanisms are still lacking. In the current study we perform RNA sequencing analysis on liver samples from C57BL/6 mice submitted to aerobic exercise and diet interventions that are human-translatable and determine the genetic expression signature of exercise in the NAFLD onset. We show that aerobic exercise affects genes and pathways related to liver metabolism, muscle contraction and relaxation, immune response and inflammation, and development of liver cancer, counteracting non-alcoholic steatohepatitis and hepatocellular carcinoma development. While genes and pathways implicating immune response are activated by aerobic exercise in all interventions, the most effective intervention in terms of improvement of NASH is the combination of aerobic exercise with change of diet.

Predicting Breast Cancer Gene Expression Signature by Applying Deep Convolutional Neural Networks From Unannotated Pathological Images

We proposed a highly versatile two-step transfer learning pipeline for predicting the gene signature defining the intrinsic breast cancer subtypes using unannotated pathological images. Deciphering breast cancer molecular subtypes by deep learning approaches could provide a convenient and efficient method for the diagnosis of breast cancer patients. It could reduce costs associated with transcriptional profiling and subtyping discrepancy between IHC assays and mRNA expression. Four pretrained models such as VGG16, ResNet50, ResNet101, and Xception were trained with our in-house pathological images from breast cancer patient with recurrent status in the first transfer learning step and TCGA-BRCA dataset for the second transfer learning step. Furthermore, we also trained ResNet101 model with weight from ImageNet for comparison to the aforementioned models. The two-step deep learning models showed promising classification results of the four breast cancer intrinsic subtypes with accuracy ranging from 0.68 (ResNet50) to 0.78 (ResNet101) in both validation and testing sets. Additionally, the overall accuracy of slide-wise prediction showed even higher average accuracy of 0.913 with ResNet101 model. The micro- and macro-average area under the curve (AUC) for these models ranged from 0.88 (ResNet50) to 0.94 (ResNet101), whereas ResNet101_imgnet weighted with ImageNet archived an AUC of 0.92. We also show the deep learning model prediction performance is significantly improved relatively to the common Genefu tool for breast cancer classification. Our study demonstrated the capability of deep learning models to classify breast cancer intrinsic subtypes without the region of interest annotation, which will facilitate the clinical applicability of the proposed models.

Identification and Validation of a Novel Six-Gene Expression Signature for Predicting Hepatocellular Carcinoma Prognosis

BackgroundHepatocellular carcinoma (HCC) is a highly lethal disease. Effective prognostic tools to guide clinical decision-making for HCC patients are lacking.ObjectiveWe aimed to establish a robust prognostic model based on differentially expressed genes (DEGs) in HCC.MethodsUsing datasets from The Cancer Genome Atlas (TCGA), the Gene Expression Omnibus (GEO), and the International Genome Consortium (ICGC), DEGs between HCC tissues and adjacent normal tissues were identified. Using TCGA dataset as the training cohort, we applied the least absolute shrinkage and selection operator (LASSO) algorithm and multivariate Cox regression analyses to identify a multi-gene expression signature. Proportional hazard assumptions and multicollinearity among covariates were evaluated while building the model. The ICGC cohort was used for validation. The Pearson test was used to evaluate the correlation between tumor mutational burden and risk score. Through single-sample gene set enrichment analysis, we investigated the role of signature genes in the HCC microenvironment.ResultsA total of 274 DEGs were identified, and a six-DEG prognostic model was developed. Patients were stratified into low- or high-risk groups based on risk scoring by the model. Kaplan–Meier analysis revealed significant differences in overall survival and progression-free interval. Through univariate and multivariate Cox analyses, the model proved to be an independent prognostic factor compared to other clinic-pathological parameters. Time-dependent receiver operating characteristic curve analysis revealed satisfactory prediction of overall survival, but not progression-free interval. Functional enrichment analysis showed that cancer-related pathways were enriched, while immune infiltration analyses differed between the two risk groups. The risk score did not correlate with levels of PD-1, PD-L1, CTLA4, or tumor mutational burden.ConclusionsWe propose a six-gene expression signature that could help to determine HCC patient prognosis. These genes may serve as biomarkers in HCC and support personalized disease management.