Epithelial-Mesenchymal Transition and Metabolic Reprogramming in Human Breast Cancer Cells

Metastasis is the leading cause of cancer related deaths and yet there are no targeted therapies for metastatic cancers. Epithelial-mesenchymal transition (EMT) promotes metastasis by inducing invasive properties in epithelial tumors. Although EMT-mediated cellular and molecular changes are well understood, very little is known about EMT-induced metabolic changes. To determine whether EMT induces metabolic alterations, HER2-positive BT-474 breast cancer cells were induced to undergo a stable EMT using mammosphere culture, as previously described by us for the ERα-positive MCF-7 breast cancer cells. Two epithelial breast cancer cell lines (BT-474 and MCF-7) were compared to their respective EMT-derived mesenchymal progeny (BT-474EMT and MCF-7EMT) for changes in metabolic pathways including glycolysis, glycogen metabolism, pentosephosphate pathway, hexosamine biosynthetic pathway, serine biosynthetic pathway, de novolipogenesis pathway and gluconeogenesis. Both EMT-derived breast cancer cells displayed enhanced aerobic glycolysis along with overexpression of specific glucose transporters (GLUT3, GLUT12), lactate dehydrogenase isoforms (LDHA, LDHB), monocarboxylate transporters (MCT2, MCT4) and the glycogen phosphorylase isoform, PYGL. In contrast, both EMT-derived breast cancer cells suppressed the expression of crucial enzymes in oxidative pentosephosphate pathway, serine biosynthetic pathway, de novo lipogenesis and gluconeogenesis. STAT3, a transcription factor involved in tumor initiation and progression, plays a role in the EMT-related changes in the expression of several specific enzymes and transporters. Both EMT-derived breast cancer cells show loss of the expression of miR-200 family, a group of microRNAs that inhibits EMT and promotes epithelial differentiation. miR-200c, a member of miR-200 family, suppresses the expression of PSAT1, a serine biosynthetic pathway enzyme, and ACC1, a de novo lipogenesis enzyme, in parental epithelial breast cancer cell lines. Both EMT-derived breast cancer cells express PSAT1 and ACC1 due to the loss of miR-200c. These miR-200c-induced changes may alter the acetylation status of specific nuclear and non-nuclear proteins. This study provides a broad overview of similar metabolic changes induced by EMT in two independent and substantially different epithelial breast cancer cell lines. These metabolic changes may be exploited to develop novel drugs to specifically target metastatic breast cancer cells

Global Analysis of Mouse Polyomavirus Infection Reveals Dynamic Regulation of Viral and Host Gene Expression and Promiscuous Viral RNA Editing

<div><p>Mouse polyomavirus (MPyV) lytically infects mouse cells, transforms rat cells in culture, and is highly oncogenic in rodents. We have used deep sequencing to follow MPyV infection of mouse NIH3T6 cells at various times after infection and analyzed both the viral and cellular transcriptomes. Alignment of sequencing reads to the viral genome illustrated the transcriptional profile of the early-to-late switch with both early-strand and late-strand RNAs being transcribed at all time points. A number of novel insights into viral gene expression emerged from these studies, including the demonstration of widespread RNA editing of viral transcripts at late times in infection. By late times in infection, 359 host genes were seen to be significantly upregulated and 857 were downregulated. Gene ontology analysis indicated transcripts involved in translation, metabolism, RNA processing, DNA methylation, and protein turnover were upregulated while transcripts involved in extracellular adhesion, cytoskeleton, zinc finger binding, SH3 domain, and GTPase activation were downregulated. The levels of a number of long noncoding RNAs were also altered. The long noncoding RNA MALAT1, which is involved in splicing speckles and used as a marker in many late-stage cancers, was noticeably downregulated, while several other abundant noncoding RNAs were strongly upregulated. We discuss these results in light of what is currently known about the MPyV life cycle and its effects on host cell growth and metabolism.</p></div

Viral RNA splicing events during infection.

<p>Average and standard deviation of aligned reads spanning viral splice junctions during the time course across three biological replicates. Percentages shown are with respect to total early or total late splice junction alignments.</p><p>Viral RNA splicing events during infection.</p

Accumulation of hyper-editing clusters during the time course.

<p>Reads from the time course were aligned with the host mouse genome and discarded. Reads were then aligned to the Py59RA reference genome and discarded. As in reads that did not align to the Py59RA genome were changed to Gs and realigned to a Py59RA reference genome that itself had all As changed to Gs. This allowed for reads that did not originally align to the unaltered Py59RA genome due to hyperediting to be captured. The process was repeated for all combinations of single base mismatch. Clusters of A-G mismatches from the time course consistent with hyperediting by ADAR were mapped to the Py59RA genome.</p

Example of host protein coding transcripts significantly upregulated or downregulated in infected samples compared to mock infection.

<p>Protein coding reads from the time course were aligned to the host genome. <b>(A)</b> Expression of Hist1ha increases compared to mock infection. <b>(B)</b> Col1a2 decreases compared to mock infection.</p

Polyomavirus genome and work flow.

<p><b>(A)</b> Polyomavirus genome including origin and polyadenylation sites. Early gene splicing shown in blue. Late gene splicing shown in red. <b>(B)</b> Schematic of read-through of the polyadenylation site during the late phase of infection. Late transcripts must read through the entire viral genome at least once to allow for the late leader exon (L) to splice properly. This results in spliced late mRNAs with at least two tandem repeats of the late leader exon. <b>(C)</b> Work flow of experiments. NIH 3T6 cells were infected with the Py59RA strain of polyomavirus and either harvested at different time points or treated with aphidicolin to block DNA replication and keep the infection in the early phase for 48 hours. Total RNA was collected and used to synthesize stranded cDNA libraries using the Illumina TruSeq Stranded Total RNA Preparation kit. Samples were run on the Hiseq 2000 sequencer and aligned to both the Py59RA and mouse host genomes.</p

Alignment of 48 hour aphidicolin + and - reads to Py59RA genome.

<p>NIH 3T6 cells were infected with Py59RA at an MOI of 50 pfu/cell or with a mock infection and treated with or without the DNA replication inhibitor aphidicolin at a concentration of 2μg/ml (5.9μM). Total RNA was harvested at 48 hours and stranded cDNA libraries were prepared for sequencing on the HiSeq 2000 sequencer. Reads were aligned to the Py59RA genome and visualized on the UCSC genome browser. <b>(A)</b> Samples of 48 hr infections with and without the DNA replication inhibitor aphidicolin to block the initiation of late phase aligned to the Py59RA genome unscaled to show differential expression between early and late strands. <b>(B)</b> Scaled to show changes in early strand alignment.</p

Transfind transcription factor prediction.

<p>The web based application Transfind was used to predict common transcription factor binding sites among the lists of upregulated and downregulated host genes. Predictions of mouse transcription factor binding sites were made with promoter sets of 1000nt from 800nt upstream to 200nt downstream and compared with 1000 genes with the highest predicted factor affinities among 33,837 total mouse genes. Possible factors were represented by the TRANSFAC matrix.</p><p>Transfind transcription factor prediction.</p

Example of host noncoding transcripts significantly upregulated or downregulated in infected samples compared to mock infection.

<p>Noncoding RNA reads from the time course were aligned to the host genome. <b>(A)</b> Expression of Snhg2 increases compared to mock infection. <b>(B)</b> Malat1 decreases compared to mock infection.</p

Gene ontology analysis.

<p>The web based application Database for Annotation, Visualization and Integrated Discovery (DAVID) was used to sort lists of upregulated or downregulated genes by common function and enrichment using the <i>Mus musculus</i> background. A false discovery threshold of 1E-03 was used.</p><p>Gene ontology analysis.</p