Untargeted and stable isotope-assisted metabolomic analysis of MDA-MB-231 cells under hypoxia

Introduction

Hypoxia commonly occurs in cancers and is highly related with the occurrence, development and metastasis of cancer. Treatment of triple negative breast cancer remains challenge. Knowledge about the metabolic status of triple negative breast cancer cell lines in hypoxia is valuable for the understanding of molecular mechanisms of this tumor subtype to develop effective therapeutics.

Objectives

Comprehensively characterize the metabolic profiles of triple negative breast cancer cell line MDA-MB-231 in normoxia and hypoxia and the pathways involved in metabolic changes in hypoxia.

Methods

Differences in metabolic profiles affected pathways of MDA-MB-231 cells in normoxia and hypoxia were characterized using GC–MS based untargeted and stable isotope assisted metabolomic techniques.

Results

Thirty-three metabolites were significantly changed in hypoxia and nine pathways were involved. Hypoxia increased glycolysis, inhibited TCA cycle, pentose phosphate pathway and pyruvate carboxylation, while increased glutaminolysis in MDA-MB-231 cells.

Conclusion

The current results provide metabolic differences of MDA-MB-231 cells in normoxia and hypoxia conditions as well as the involved metabolic pathways, demonstrating the power of combined use of untargeted and stable isotope-assisted metabolomic methods in comprehensive metabolomic analysis.

     

Integrative pathway-based survival prediction utilizing the interaction between gene expression and DNA methylation in breast cancer

Background

Integrative analysis on multi-omics data has gained much attention recently. To investigate the interactive effect of gene expression and DNA methylation on cancer, we propose a directed random walk-based approach on an integrated gene-gene graph that is guided by pathway information.

Methods

Our approach first extracts a single pathway profile matrix out of the gene expression and DNA methylation data by performing the random walk over the integrated graph. We then apply a denoising autoencoder to the pathway profile to further identify important pathway features and genes. The extracted features are validated in the survival prediction task for breast cancer patients.

Results

The results show that the proposed method substantially improves the survival prediction performance compared to that of other pathway-based prediction methods, revealing that the combined effect of gene expression and methylation data is well reflected in the integrated gene-gene graph combined with pathway information. Furthermore, we show that our joint analysis on the methylation features and gene expression profile identifies cancer-specific pathways with genes related to breast cancer.

Conclusions

In this study, we proposed a DRW-based method on an integrated gene-gene graph with expression and methylation profiles in order to utilize the interactions between them. The results showed that the constructed integrated gene-gene graph can successfully reflect the combined effect of methylation features on gene expression profiles. We also found that the selected features by DA can effectively extract topologically important pathways and genes specifically related to breast cancer.

     

Influence of media selection on NMR based metabolic profiling of human cell lines

Introduction

Comparative metabolic profiling of different human cancer cell lines can reveal metabolic pathways up-regulated or down-regulated in each cell line, potentially providing insight into distinct metabolism taking place in different types of cancer cells. It is noteworthy, however, that human cell lines available from public repositories are deposited with recommended media for optimal growth, and if cell lines to be compared are cultured on different growth media, this introduces a potentially serious confounding variable in metabolic profiling studies designed to identify intrinsic metabolic pathways active in each cell line.

Objectives

The goal of this study was to determine if the culture media used to grow human cell lines had a significant impact on the measured metabolic profiles.

Methods

NMR-based metabolic profiles of hydrophilic extracts of three human pancreatic cancer cell lines, AsPC-1, MiaPaCa-2 and Panc-1, were compared after culture on Dulbecco’s Modified Eagle Medium (DMEM) or Roswell Park Memorial Institute (RPMI-1640) medium.

Results

Comparisons of the same cell lines cultured on different media revealed that the concentrations of many metabolites depended strongly on the choice of culture media. Analyses of different cell lines grown on the same media revealed insight into their metabolic differences.

Conclusion

The choice of culture media can significantly impact metabolic profiles of human cell lines and should be considered an important variable when designing metabolic profiling studies. Also, the metabolic differences of cells cultured on media recommended for optimal growth in comparison to a second growth medium can reveal critical insight into metabolic pathways active in each cell line.

     

Characterizing biomarkers in osteosarcoma metastasis based on an ego-network

Objectives

To characterize biomarkers that underlie osteosarcoma (OS) metastasis based on an ego-network.

Results

From the microarray data, we obtained 13,326 genes. By combining PPI data and microarray data, 10,520 shared genes were found and constructed into ego-networks. 17 significant ego-networks were identified with p < 0.05. In the pathway enrichment analysis, seven ego-networks were identified with the most significant pathway.

Conclusions

These significant ego-modules were potential biomarkers that reveal the potential mechanisms in OS metastasis, which may contribute to understanding cancer prognoses and providing new perspectives in the treatment of cancer.

     

Selective measurement of α smooth muscle actin: why β-actin can not be used as a housekeeping gene when tissue fibrosis occurs

Background

Prevalence of fibroproliferative diseases, including chronic kidney disease is rapidly increasing and has become a major public health problem worldwide. Fibroproliferative diseases are characterized by increased expression of α smooth muscle actin (α-SMA) that belongs to the family of the six conserved actin isoforms showing high degree homology. The aim of the present study was to develop real-time PCRs that clearly discriminate α-SMA and ß-actin from other actin isoforms.

Results

Real-time PCRs using self-designed mouse, human and rat specific α-SMA or ß-actin primer pairs resulted in the specific amplification of the artificial DNA templates corresponding to mouse, human or rat α-SMA or ß-actin, however ß-actin showed cross-reaction with the housekeeping γ-cyto-actin. We have shown that the use of improperly designed literary primer pairs significantly affects the results of PCRs measuring mRNA expression of α-SMA or ß-actin in the kidney of mice underwent UUO.

Conclusion

We developed a set of carefully designed primer pairs and PCR conditions to selectively determine the expression of mouse, human or rat α-SMA and ß-actin isoforms. We demonstrated the importance of primer specificity in experiments where the results are normalized to the expression of ß-actin especially when fibrosis and thus increased expression of α-SMA is occur.

     

Comparisons of gene coexpression network modules in breast cancer and ovarian cancer

Background

Breast cancer and ovarian cancer are hormone driven and are known to have some predisposition genes in common such as the two well known cancer genes BRCA1 and BRCA2. The objective of this study is to compare the coexpression network modules of both cancers, so as to infer the potential cancer-related modules.

Methods

We applied the eigen-decomposition to the matrix that integrates the gene coexpression networks of both breast cancer and ovarian cancer. With hierarchical clustering of the related eigenvectors, we obtained the network modules of both cancers simultaneously. Enrichment analysis on Gene Ontology (GO), KEGG pathway, Disease Ontology (DO), and Gene Set Enrichment Analysis (GSEA) in the identified modules was performed.

Results

We identified 43 modules that are enriched by at least one of the four types of enrichments. 31, 25, and 18 modules are enriched by GO terms, KEGG pathways, and DO terms, respectively. The structure of 29 modules in both cancers is significantly different with p-values less than 0.05, of which 25 modules have larger densities in ovarian cancer. One module was found to be significantly enriched by the terms related to breast cancer from GO, KEGG and DO enrichment. One module was found to be significantly enriched by ovarian cancer related terms.

Conclusion

Breast cancer and ovarian cancer share some common properties on the module level. Integration of both cancers helps identifying the potential cancer associated modules.

     

Human amylin proteotoxicity impairs protein biosynthesis,and alters major cellular signaling pathways in the heart,brain and liver of humanized diabetic rat model in vivo

Introduction

Chronic hypersecretion of the 37 amino acid amylin is common in type 2 diabetics (T2D). Recent studies implicate human amylin aggregates cause proteotoxicity (cell death induced by misfolded proteins) in both the brain and the heart.

Objectives

Identify systemic mechanisms/markers by which human amylin associated with cardiac and brain defects might be identified.

Methods

We investigated the metabolic consequences of amyloidogenic and cytotoxic amylin oligomers in heart, brain, liver, and plasma using non-targeted metabolomics analysis in a rat model expressing pancreatic human amylin (HIP model).

Results

Four metabolites were significantly different in three or more of the four compartments (heart, brain, liver, and plasma) in HIP rats. When compared to a T2D rat model, HIP hearts uniquely had significant DECREASES in five amino acids (lysine, alanine, tyrosine, phenylalanine, serine), with phenylalanine decreased across all four tissues investigated, including plasma. In contrast, significantly INCREASED circulating phenylalanine is reported in diabetics in multiple recent studies.

Conclusion

DECREASED phenylalanine may serve as a unique marker of cardiac and brain dysfunction due to hyperamylinemia that can be differentiated from alterations in T2D in the plasma. While the deficiency in phenylalanine was seen across tissues including plasma and could be monitored, reduced tyrosine was seen only in the brain. The 50 % reduction in phenylalanine and tyrosine in HIP brains is significant given their role in supporting brain chemistry as a precursor for catecholamines (dopamine, norepinephrine, epinephrine), which may contribute to the increased morbidity and mortality in diabetics at a multi-system level beyond the effects on glucose metabolism.

     

Tissue-specific spatial organization of genomes

Background

Genomes are organized in vivo in the form of chromosomes. Each chromosome occupies a distinct nuclear subvolume in the form of a chromosome territory. The spatial positioning of chromosomes within the interphase nucleus is often nonrandom. It is unclear whether the nonrandom spatial arrangement of chromosomes is conserved among tissues or whether spatial genome organization is tissue-specific.

Results

Using two-dimensional and three-dimensional fluorescence in situ hybridization we have carried out a systematic analysis of the spatial positioning of a subset of mouse chromosomes in several tissues. We show that chromosomes exhibit tissue-specific organization. Chromosomes are distributed tissue-specifically with respect to their position relative to the center of the nucleus and also relative to each other. Subsets of chromosomes form distinct types of spatial clusters in different tissues and the relative distance between chromosome pairs varies among tissues. Consistent with the notion that nonrandom spatial proximity is functionally relevant in determining the outcome of chromosome translocation events, we find a correlation between tissue-specific spatial proximity and tissue-specific translocation prevalence.

Conclusions

Our results demonstrate that the spatial organization of genomes is tissue-specific and point to a role for tissue-specific spatial genome organization in the formation of recurrent chromosome arrangements among tissues.

     

Methylation mediated Gadd45β enhanced the chemosensitivity of hepatocellular carcinoma by inhibiting the stemness of liver cancer cells

Background

Defects of the growth arrest DNA damage-inducible gene 45β (Gadd45β) play an important role in the progression of tumor and confer resistance to chemotherapy. However, the role of Gadd45β in the apoptosis of hepatocellular carcinoma is still not clear. Purpose of this study was to explore the effect of Gadd45β on the apoptosis of liver cancer cells, and the possible mechanism was examined.

Result

In this study, we first confirmed the decreased expression of Gadd45β in human liver cancer tissues and human liver cancer cell lines, when compared to the peri-tumor liver tissue and normal liver cells. And, it was found that Gadd45β could inhibit the stemness of liver cancer cells, enhancing the apoptosis of cancer cells induced by chemotherapy. Furthermore, the results showed that HCC tissues and cell lines showed a higher methylation status in Gadd45β promoter than that in peri-tumor tissues and normal liver cells. Methylation was then reversed by pretreatment of SMMC-7721 and Hep-3B with 5-azacytidine which is the DNA methyltransferase inhibitor. And the 5-azacytidine decreased the stemness of SMMC-7721 and Hep-3B, enhanced the sensitivity of SMMC-7721 and Hep-3B to cisplatin.

Conclusions

Methylation mediated Gadd45β expression inhibited the stemness of liver cancer cells, promoting the chemotherapy-induced apoptosis. Thus Gadd45β may be the potential target for enhancing the chemosensitivity of human hepatocellular carcinoma.

     

Intratumoral anti-HuD immunotoxin therapy for small cell lung cancer and neuroblastoma

Background

Most patients with small cell lung cancer (SCLC) or neuroblastoma (NB) already show clinically detectable metastases at diagnosis and have an extremely poor prognosis even when treated with combined modalities. The HuD-antigen is a neuronal RNA-binding protein that is expressed in 100% of SCLC tumor cells and over 50% of neuroblastoma cells. The correlation between high titers of circulating anti-HuD antibodies in patients and spontaneous tumor remission suggests that the HuD-antigen might be a potential molecular target for immunotherapy.

Methods

We have constructed a new antibody-toxin compound (called BW-2) by assembling a mouse anti-human-HuD monoclonal antibody onto streptavidin/saporin complexes.

Results

We found that the immunotoxin BW-2 specifically killed HuD-positive human SCLC and NB cancer cells at very low concentrations in vitro. Moreover, intratumoral immunotoxin therapy in a nude mouse model of human SCLC (n?=?6) significantly reduced local tumor progression without causing toxicity. When the same intratumoral immunotoxin protocol was applied to an immunocompetent A/J mouse model of NB, significant inhibition of local tumor growth was also observed. In neuroblastoma allografted A/J mice (n?=?5) treated twice with intratumoral immunotoxin, significant tumor regression occurred in over 80% of the animals and their duration of tumor response was significantly prolonged.

Conclusions

Our study suggests that anti-HuD based immunotoxin therapy may prove to be an effective alternative treatment for patients with SCLC and NB.

     

Comparison of the canine and human olfactory receptor gene repertoires

Background

Olfactory receptors (ORs), the first dedicated molecules with which odorants physically interact to arouse an olfactory sensation, constitute the largest gene family in vertebrates, including around 900 genes in human and 1,500 in the mouse. Whereas dogs, like many other mammals, have a much keener olfactory potential than humans, only 21 canine OR genes have been described to date.

Results

In this study, 817 novel canine OR sequences were identified, and 640 have been characterized. Of the 661 characterized OR sequences, representing half of the canine repertoire, 18% are predicted to be pseudogenes, compared with 63% in human and 20% in mouse. Phylogenetic analysis of 403 canine OR sequences identified 51 families, and radiation-hybrid mapping of 562 showed that they are distributed on 24 dog chromosomes, in 37 distinct regions. Most of these regions constitute clusters of 2 to 124 closely linked genes. The two largest clusters (124 and 109 OR genes) are located on canine chromosomes 18 and 21. They are orthologous to human clusters located on human chromosomes 11q11-q13 and HSA11p15, containing 174 and 115 ORs respectively.

Conclusions

This study shows a strongly conserved genomic distribution of OR genes between dog and human, suggesting that OR genes evolved from a common mammalian ancestral repertoire by successive duplications. In addition, the dog repertoire appears to have expanded relative to that of humans, leading to the emergence of specific canine OR genes.

     

Identification of cancer subtypes from single-cell RNA-seq data using a consensus clustering method

Background

Human cancers are complex ecosystems composed of cells with distinct molecular signatures. Such intratumoral heterogeneity poses a major challenge to cancer diagnosis and treatment. Recent advancements of single-cell techniques such as scRNA-seq have brought unprecedented insights into cellular heterogeneity. Subsequently, a challenging computational problem is to cluster high dimensional noisy datasets with substantially fewer cells than the number of genes.

Methods

In this paper, we introduced a consensus clustering framework conCluster, for cancer subtype identification from single-cell RNA-seq data. Using an ensemble strategy, conCluster fuses multiple basic partitions to consensus clusters.

Results

Applied to real cancer scRNA-seq datasets, conCluster can more accurately detect cancer subtypes than the widely used scRNA-seq clustering methods. Further, we conducted co-expression network analysis for the identified melanoma subtypes.

Conclusions

Our analysis demonstrates that these subtypes exhibit distinct gene co-expression networks and significant gene sets with different functional enrichment.

     

Probability-based collaborative filtering model for predicting gene–disease associations

Background

Accurately predicting pathogenic human genes has been challenging in recent research. Considering extensive gene–disease data verified by biological experiments, we can apply computational methods to perform accurate predictions with reduced time and expenses.

Methods

We propose a probability-based collaborative filtering model (PCFM) to predict pathogenic human genes. Several kinds of data sets, containing data of humans and data of other nonhuman species, are integrated in our model. Firstly, on the basis of a typical latent factorization model, we propose model I with an average heterogeneous regularization. Secondly, we develop modified model II with personal heterogeneous regularization to enhance the accuracy of aforementioned models. In this model, vector space similarity or Pearson correlation coefficient metrics and data on related species are also used.

Results

We compared the results of PCFM with the results of four state-of-arts approaches. The results show that PCFM performs better than other advanced approaches.

Conclusions

PCFM model can be leveraged for predictions of disease genes, especially for new human genes or diseases with no known relationships.