Molecular and functional imaging insights into the role of hypoxia in cancer aggression.

Hypoxia in cancers has evoked significant interest since 1955 when Thomlinson and Gray postulated the presence of hypoxia in human lung cancers, based on the observation of necrosis occurring at the diffusion limit of oxygen from the nearest blood vessel, and identified the implication of these observations for radiation therapy. Coupled with discoveries in 1953 by Gray and others that anoxic cells were resistant to radiation damage, these observations have led to an entire field of research focused on exploiting oxygenation and hypoxia to improve the outcome of radiation therapy. Almost 65 years later, tumor heterogeneity of nearly every parameter measured including tumor oxygenation, and the dynamic landscape of cancers and their microenvironments are clearly evident, providing a strong rationale for cancer personalized medicine. Since hypoxia is a major cause of extracellular acidosis in tumors, here, we have focused on the applications of imaging to understand the effects of hypoxia in tumors and to target hypoxia in theranostic strategies. Molecular and functional imaging have critically important roles to play in personalized medicine through the detection of hypoxia, both spatially and temporally, and by providing new understanding of the role of hypoxia in cancer aggressiveness. With the discovery of the hypoxia-inducible factor (HIF), the intervening years have also seen significant progress in understanding the transcriptional regulation of hypoxia-induced genes. These advances have provided the ability to silence HIF and understand the associated molecular and functional consequences to expand our understanding of hypoxia and its role in cancer aggressiveness. Most recently, the development of hypoxia-based theranostic strategies that combine detection and therapy are further establishing imaging-based treatment strategies for precision medicine of cancer.

Characterization of lipomatous tumors with high-resolution 1H MRS at 17.6T: Do benign lipomas, atypical lipomatous tumors and liposarcomas have a distinct metabolic signature?

Background: Distinguishing between some benign lipomas (BLs), atypical lipomatous tumors (ALTs), and dedifferentiated liposarcomas (DDLs) can be challenging due to overlapping magnetic resonance imaging characteristics, and poorly understood molecular mechanisms underlying the malignant transformation of liposarcomas. Purpose: To identify metabolic biomarkers of the lipomatous tumor spectrum by examining human tissue specimens using high-resolution 1H magnetic resonance spectroscopy (MRS). Materials and methods: In this prospective study, human tissue specimens were obtained from participants who underwent surgical resection for radiologically-indeterminate lipomatous tumors between November 2016 and May 2019. Tissue specimens were obtained from normal subcutaneous fat (n=9), BLs (n=10), ALTs (n=7) and DDLs (n=8). Extracts from specimens were examined with high-resolution MRS at 17.6T. Computational modeling of pattern recognition-based cluster analysis was utilized to identify significant differences in metabolic signatures between the lipomatous tumor types. Results: Significant differences between BLs and ALTs were observed for multiple metabolites, including leucine, valine, branched chain amino acids, alanine, acetate, glutamine, and formate. DDLs were distinguished from ALTs by increased glucose and lactate, and increased phosphatidylcholine. Multivariate principal component analysis showed clear clustering identifying distinct metabolic signatures of the tissue types. Conclusion: Metabolic signatures identified in 1H MR spectra of lipomatous tumors provide new insights into malignant progression and metabolic targeting. The metabolic patterns identified provide the foundation of developing noninvasive MRS or PET imaging biomarkers to distinguish between BLs, ALTs, and DDLs.

Text-Based Emotion Recognition Using Deep Learning Approach.

Sentiment analysis is a method to identify people's attitudes, sentiments, and emotions towards a given goal, such as people, activities, organizations, services, subjects, and products. Emotion detection is a subset of sentiment analysis as it predicts the unique emotion rather than just stating positive, negative, or neutral. In recent times, many researchers have already worked on speech and facial expressions for emotion recognition. However, emotion detection in text is a tedious task as cues are missing, unlike in speech, such as tonal stress, facial expression, pitch, etc. To identify emotions from text, several methods have been proposed in the past using natural language processing (NLP) techniques: the keyword approach, the lexicon-based approach, and the machine learning approach. However, there were some limitations with keyword- and lexicon-based approaches as they focus on semantic relations. In this article, we have proposed a hybrid (machine learning + deep learning) model to identify emotions in text. Convolutional neural network (CNN) and Bi-GRU were exploited as deep learning techniques. Support vector machine is used as a machine learning approach. The performance of the proposed approach is evaluated using a combination of three different types of datasets, namely, sentences, tweets, and dialogs, and it attains an accuracy of 80.11%.

Hypoxia theranostics of a human prostate cancer xenograft and the resulting effects on the tumor microenvironment.

Hypoxia is frequently observed in human prostate cancer, and is associated with chemoresistance, radioresistance, metastasis, and castrate-resistance. Our purpose in these studies was to perform hypoxia theranostics by combining in vivo hypoxia imaging and hypoxic cancer cell targeting in a human prostate cancer xenograft. This was achieved by engineering PC3 human prostate cancer cells to express luciferase as well as a prodrug enzyme, yeast cytosine deaminase, under control of hypoxic response elements (HREs). Cancer cells display an adaptive response to hypoxia through the activation of several genes mediated by the binding of hypoxia inducible factors (HIFs) to HRE in the promoter region of target gene that results in their increased transcription. HIFs promote key steps in tumorigenesis, including angiogenesis, metabolism, proliferation, metastasis, and differentiation. HRE-driven luciferase expression allowed us to detect hypoxia in vivo to time the administration of the nontoxic prodrug 5-fluorocytosine that was converted by yeast cytosine deaminase, expressed under HRE regulation, to the chemotherapy agent 5-fluorouracil to target hypoxic cells. Conversion of 5-fluorocytosine to 5-fluorouracil was detected in vivo by 19F magnetic resonance spectroscopy. Morphological and immunohistochemical staining and molecular analyses were performed to characterize tumor microenvironment changes in cancer-associated fibroblasts, cell viability, collagen 1 fiber patterns, and HIF-1α. These studies expand our understanding of the effects of eliminating hypoxic cancer cells on the tumor microenvironment and in reducing stromal cell populations such as cancer-associated fibroblasts.

Brain metabolites in cholinergic and glutamatergic pathways are altered by pancreatic cancer cachexia.

BACKGROUND: Cachexia is a major cause of morbidity in pancreatic ductal adenocarcinoma (PDAC) patients. Our purpose was to understand the impact of PDAC-induced cachexia on brain metabolism in PDAC xenograft studies, to gain new insights into the causes of cachexia-induced morbidity. Changes in mouse and human plasma metabolites were characterized to identify underlying causes of brain metabolic changes. METHODS: We quantified metabolites, detected with high-resolution 1 H magnetic resonance spectroscopy, in the brain and plasma of normal mice (n = 10) and mice bearing cachexia (n = 10) or non-cachexia (n = 9) inducing PDAC xenografts as well as in human plasma obtained from normal individuals (n = 24) and from individuals with benign pancreatic disease (n = 20) and PDAC (n = 20). Statistical significance was defined as a P value ≤0.05. RESULTS: The brain metabolic signature of cachexia-inducing PDAC was characterized by a significant depletion of choline of -27% and -21% as well as increases of glutamine of 13% and 9% and formate of 21% and 14%, relative to normal controls and non-cachectic tumour-bearing mice, respectively. Good to moderate correlations with percent weight change were found for choline (r = 0.70), glutamine (r = -0.58), and formate (r = -0.43). Significant choline depletion of -38% and -30%, relative to normal controls and non-cachectic tumour-bearing mice, respectively, detected in the plasma of cachectic mice likely contributed to decreased brain choline in cachectic mice. Similarly, relative to normal controls and patients with benign disease, choline levels in human plasma samples of PDAC patients were significantly lower by -12% and -20% respectively. A comparison of plasma metabolites from PDAC patients with and without weight loss identified significant changes in glutamine metabolism. CONCLUSIONS: Disturbances in metabolites of the choline/cholinergic and glutamine/glutamate/glutamatergic neurotransmitter pathways may contribute to morbidity. Metabolic normalization may provide strategies to reduce morbidity. The human plasma metabolite changes observed may lead to the development of companion diagnostic markers to detect PDAC and PDAC-induced cachexia.

Hypoxia Patterns in Primary and Metastatic Prostate Cancer Environments.

Metastatic dissemination continues to be a major cause of prostate cancer (PCa) mortality, creating a compelling need to understand factors that play a role in the metastatic cascade. Since hypoxia plays an important role in PCa aggressiveness, we characterized patterns of hypoxia in the primary tumor and metastatic environments of a human PCa xenograft. We previously developed and characterized an imaging strategy based on the hypoxia response element (HRE)-driven expression of long-lived enhanced green fluorescent protein (EGFP) and short-lived luciferase (luc) fused to the oxygen-dependent degradation domain in human PCa PC-3 cells. Both reporter proteins were placed under the transcriptional control of a five-tandem repeat HRE sequence. PC-3 cells also constitutively expressed the tdTomato red fluorescent protein, allowing cancer cell detection in vivo. This "timer" strategy can provide information on the temporal evolution of HIF activity and hypoxia in tumors. Here, for the first time, we performed in vivo and ex vivo imaging of this dual HIF reporter system in PC-3 metastatic tumors implanted orthotopically in the prostate and PC-3 nonmetastatic tumors implanted subcutaneously. We observed distinct patterns of EGFP and luc expression in subcutaneous and orthotopic tumors, and in metastatic nodules, that provide new insights into the presence of hypoxia at primary and metastatic tumor sites, and of the role of hypoxia in metastasis.

Metabolite profiling reveals abiotic stress tolerance in Tn5 mutant of Pseudomonas putida.

Pseudomonas is an efficient plant growth-promoting rhizobacteria (PGPR); however, intolerance to drought and high temperature limit its application in agriculture as a bioinoculant. Transposon 5 (Tn5) mutagenesis was used to generate a stress tolerant mutant from a PGPR Pseudomonas putida NBRI1108 isolated from chickpea rhizosphere. A mutant NBRI1108T, selected after screening of nearly 10,000 transconjugants, exhibited significant tolerance towards high temperature and drought. Southern hybridization analysis of EcoRI and XhoI restricted genomic DNA of NBRI1108T confirmed that it had a single Tn5 insertion. The metabolic changes in the polar and non-polar extracts of NBRI1108 and NBRI1108T were examined using 1H, 31P nuclear magnetic resonance (NMR) spectroscopy and gas chromatography-mass spectrometry (GC-MS). Thirty six chemically diverse metabolites consisting of amino acids, fatty acids and phospholipids were identified and quantified. Insertion of Tn5 influenced amino acid and phospholipid metabolism and resulted in significantly higher concentration of aspartic acid, glutamic acid, glycinebetaine, glycerophosphatidylcholine (GPC) and putrescine in NBRI1108T as compared to that in NBRI1108. The concentration of glutamic acid, glycinebetaine and GPC increased by 34%, 95% and 100%, respectively in the NBRI1108T as compared to that in NBRI1108. High concentration of glycerophosphatidylethanolamine (GPE) and undetected GPC in NBRI1108 indicates that biosynthesis of GPE may have taken place via the methylation pathway of phospholipid biosynthesis. However, high GPC and low GPE concentration in NBRI1108T suggest that methylation pathway and phosphatidylcholine synthase (PCS) pathway of phospholipid biosynthesis are being followed in the NBRI1108T. Application of multivariate principal component analysis (PCA) on the quantified metabolites revealed clear variations in NBRI1108 and NBRI1108T in polar and non-polar metabolites. Identification of abiotic stress tolerant metabolites from the NBRI1108T suggest that Tn5 mutagenesis enhanced tolerance towards high temperature and drought. Tolerance to drought was further confirmed in greenhouse experiments with maize as host plant, where NBRI1108T showed relatively high biomass under drought conditions.

Study of interaction of human serum albumin with curcumin by NMR and docking.

Curcumin has been reported to be therapeutically active but has poor bioavailability, half life, and high rate of metabolic detoxifcation. Most of the hydrophobic and acidic drugs get transported through human serum albumin (HSA). Binding of drugs to serum protein increases their half-life. The present study is focused to analyze interaction of curcumin with HSA by NMR and docking studies. In order to investigate the binding affinity of curcumin with HSA, NMR based diffusion techniques and docking study have been carried out. We report that curcumin has shown comparable binding affinity value vis-a-vis standard, the accessible surface area (ASA) of human serum albumin (uncomplexed) and its docked complex with curcumin at both binding sites was calculated and found to be close to that of warfarin and diazepam respectively. Conclusion drawn from our study demonstrates that curcumin interacts with HSA strongly thereby its poor half life is due to high rate of its metabolic detoxification as reported in literature.

Polycyclic aromatic hydrocarbons and their quinones modulate the metabolic profile and induce DNA damage in human alveolar and bronchiolar cells.

The release of particulate pollutants into the air through burning of coal, crude oil, diesel, coal tar, etc. raises concerns of potential health hazards to the exposed human population. Polycyclic aromatic hydrocarbons (PAHs) are major toxic constituents of particulate matter (PM), which upon ingestion get metabolized to even more toxic metabolites such as quinones. The PAHs levels were assessed in both respirable particulate matter (RSPM, <10µM size) and suspended particulate matter (SPM, >10µM size) of urban ambient air (UAA) and that of major contributors viz. diesel exhaust particles (DEPs) and coal tar combustions emissions (CTCE). Seven US Environmental Protection Agency (USEPA) prioritized PAHs in RSPM and 10 in SPM were detected in UAA. Ten and 15 prioritized PAHs, respectively, were also detected in diesel exhaust particles (DEP) and coal tar combustion emission (CTCE) evidencing their release in the air. These PM associated PAHs for UAA, DEP and CTCE showed significant increase (p<0.05) in mutagenicity and mammalian genotoxicity in the order CTCE>DEP>UAA. Human lung alveolar (A549) and bronchiolar (BEAS-2B) cells when treated with PAH-metabolites viz. 1,4-benzoquinone (1,4-BQ), hydroquinone (HQ), 1,2-naphthoquinone (1,2-NQ), 1,4-naphthoquinone (1,4-NQ) and 9,10-phenanthroquinone (9,10-PQ) showed metabolic modulation in these cell lines with significant depletion of principal cellular metabolites viz. NADP, uracil, asparagines, glutamine, and histidine and accumulation of di-methyl amine and beta-hydroxybutyrate, identified using (1)H NMR spectroscopy. These results suggest that PAH-quinones induce genotoxic effects by modulating the metabolic machinery inside the cells by a combined effect of oxidative stress and energy depletion. Our data for metabolic profiling of human lung cells could also help in understanding the mechanism of toxicity of other xenobiotics.