When less may be more: calorie restriction and response to cancer therapy

Abstract Calorie restriction (CR) extends lifespan and has been shown to reduce age-related diseases including cancer, diabetes, and cardiovascular and neurodegenerative diseases in experimental models. Recent translational studies have tested the potential of CR or CR mimetics as adjuvant therapies to enhance the efficacy of chemotherapy, radiation therapy, and novel immunotherapies. Chronic CR is challenging to employ in cancer patients, and therefore intermittent fasting, CR mimetic drugs, or alternative diets (such as a ketogenic diet), may be more suitable. Intermittent fasting has been shown to enhance treatment with both chemotherapy and radiation therapy. CR and fasting elicit different responses in normal and cancer cells, and reduce certain side effects of cytotoxic therapy. Findings from preclinical studies of CR mimetic drugs and other dietary interventions, such as the ketogenic diet, are promising for improving the efficacy of anticancer therapies and reducing the side effects of cytotoxic treatments. Current and future clinical studies will inform on which cancers, and at which stage of the cancer process, CR, fasting, or CR mimetic regimens will prove most effective

Obesity and Cancer Metabolism: A Perspective on Interacting Tumor–Intrinsic and Extrinsic Factors

Obesity is associated with increased risk and poor prognosis of many types of cancers. Several obesity-related host factors involved in systemic metabolism can influence tumor initiation, progression, and/or response to therapy, and these have been implicated as key contributors to the complex effects of obesity on cancer incidence and outcomes. Such host factors include systemic metabolic regulators including insulin, insulin-like growth factor 1, adipokines, inflammation-related molecules, and steroid hormones, as well as the cellular and structural components of the tumor microenvironment, particularly adipose tissue. These secreted and structural host factors are extrinsic to, and interact with, the intrinsic metabolic characteristics of cancer cells to influence their growth and spread. This review will focus on the interplay of these tumor cell–intrinsic and extrinsic factors in the context of energy balance, with the objective of identifying new intervention targets for preventing obesity-associated cancer

Starving cancer from the outside and inside: separate and combined effects of calorie restriction and autophagy inhibition on Ras-driven tumors

Abstract Background Calorie restriction (CR) prevents obesity and exerts anticancer effects in many preclinical models. CR is also increasingly being used in cancer patients as a sensitizing strategy prior to chemotherapy regimens. While the beneficial effects of CR are widely accepted, the mechanisms through which CR affects tumor growth are incompletely understood. In many cell types, CR and other nutrient stressors can induce autophagy, which provides energy and metabolic substrates critical for cancer cell survival. We hypothesized that limiting extracellular and intracellular substrate availability by combining CR with autophagy inhibition would reduce tumor growth more effectively than either treatment alone. Results A 30 % CR diet, relative to control diet, in nude mice resulted in significant decreases in body fat, blood glucose, and serum insulin, insulin-like growth factor-1, and leptin levels concurrent with increased adiponectin levels. In a xenograft model in nude mice involving H-RasG12V-transformed immortal baby mouse kidney epithelial cells with (Atg5 +/+ ) and without (Atg5 −/−) autophagic capacity, the CR diet (relative to control diet) genetically induced autophagy inhibition and their combination, each reduced tumor development and growth. Final tumor volume was greatest for Atg5 +/+ tumors in control-fed mice, intermediate for Atg5 +/+ tumors in CR-fed mice and Atg5 −/− tumors in control-fed mice, and lowest for Atg5 −/− tumors in CR mice. In Atg5 +/+ tumors, autophagic flux was increased in CR-fed relative to control-fed mice, suggesting that the prosurvival effects of autophagy induction may mitigate the tumor suppressive effects of CR. Metabolomic analyses of CR-fed, relative to control-fed, nude mice showed significant decreases in circulating glucose and amino acids and significant increases in ketones, indicating CR induced negative energy balance. Combining glucose deprivation with autophagy deficiency in Atg5 −/− cells resulted in significantly reduced in vitro colony formation relative to glucose deprivation or autophagy deficiency alone. Conclusions Combined restriction of extracellular (via CR in vivo or glucose deprivation in vitro) and intracellular (via autophagy inhibition) sources of energy and nutrients suppresses Ras-driven tumor growth more effectively than either CR or autophagy deficiency alone. Interventions targeting both systemic energy balance and tumor-cell intrinsic autophagy may represent a novel and effective anticancer strategy

When less may be more: calorie restriction and response to cancer therapy

Abstract Calorie restriction (CR) extends lifespan and has been shown to reduce age-related diseases including cancer, diabetes, and cardiovascular and neurodegenerative diseases in experimental models. Recent translational studies have tested the potential of CR or CR mimetics as adjuvant therapies to enhance the efficacy of chemotherapy, radiation therapy, and novel immunotherapies. Chronic CR is challenging to employ in cancer patients, and therefore intermittent fasting, CR mimetic drugs, or alternative diets (such as a ketogenic diet), may be more suitable. Intermittent fasting has been shown to enhance treatment with both chemotherapy and radiation therapy. CR and fasting elicit different responses in normal and cancer cells, and reduce certain side effects of cytotoxic therapy. Findings from preclinical studies of CR mimetic drugs and other dietary interventions, such as the ketogenic diet, are promising for improving the efficacy of anticancer therapies and reducing the side effects of cytotoxic treatments. Current and future clinical studies will inform on which cancers, and at which stage of the cancer process, CR, fasting, or CR mimetic regimens will prove most effective

An epoxide anchor enables expansion of proteins and RNAs away from each other.

(A) In a standard ExM experiment, target biomolecules (e.g., proteins and nucleic acids, or labels attached to them) in a biological specimen are covalently bound to anchoring molecules bearing vinyl groups (here, an epoxide such as glycidyl methacrylate (GMA), turning biomolecules to methacrylate (MA) forms, e.g., MA-RNA and MA-protein) that can be crosslinked to a swellable polyacrylate hydrogel synthesized throughout the specimen (“Gelation”). After tissue softening with denaturation and/or proteolysis, the sample can be isotropically expanded upon dialysis with low osmolarity solutions (e.g., distilled water), during which the anchored biomolecules are pulled apart (“Expansion”). Target-specific detection can be performed (e.g., antibody staining for proteins and oligonucleotide probe hybridization for RNAs) to realize nanoimaging on conventional microscopes. (B) Using the epoxide anchor GMA, simultaneous detection of nucleic acids and proteins was demonstrated across different sample types. In panel (i), a 50 μm thick coronal section of mouse brain tissue expressing Thy1-YFP was cut in half, one part anchored with 0.01% (w/v) LabelX plus 0.005% (w/v) AcX and the other with 0.1% (w/v) GMA. The samples were then subjected to gelation and proK based digestion. HCR-FISH targeting ACTB mRNAs was performed post-expansion and the FISH signals were quantified together with the retained YFP signals. Scale bars (in pre-expansion units): 1000 μm (whole brain); 250 μm (zoomed-in hippocampal view). Linear expansion factor: 4.1. In panel (ii), mean intensities for YFP within cells and HCR-FISH spots in each image were quantified and compared between the LabelX/AcX and GMA processed tissues (data distribution shown in violin plots, with raw data points presented, and mean values highlighted with solid lines; n = 50 images from 3 different slices, 2 mouse brains; two-sample t-test was performed with p −20 for both YFP and HCR-FISH signals). In panel (iii), multimodal detection of proteins and RNAs with uniExM was demonstrated. Antibody staining for proteins was performed pre-expansion and the samples were anchored with 0.04% (w/v) GMA (for HeLa cells and cultured neurons) or 0.1% (w/v) GMA (for mouse brain slices). After gelation and expansion, HCR-FISH targeting specific RNA targets was performed post-expansion. In the figure captions, capitalized italic fonts represent the RNA target names (ACTB, EEF1A1, GAPDH). The colors in each image correspond to the following fluorescent dyes: blue—DAPI; green—Alexa488; yellow—Alexa546; red—Alexa647. Scale bars (in pre-expansion units): 20 μm. Linear expansion factors: 4.4 for HeLa cells and cultured neurons, 4.1 for mouse brain tissues (prior to re-embedding). All images are shown as maximum z-projection of image stacks (10–15 μm z-depth for cultured HeLa cells and neurons; 50 μm for mouse brain tissues).</p

uniExM-supported <i>in situ</i> RNA sequencing (ExSeq).

(A) Schematic of the workflow for targeted ExSeq (tExSeq): target RNA molecules are reacted with GMA to acquire methacrylate groups (termed MA-RNA) and anchored to the expandable hydrogel. Padlock probes are then introduced to hybridize with the target RNAs in the expanded biological sample. Upon successful hybridization, the sequence of a target mRNA serves as a “splint” for PBCV-1 enzyme-mediated ligation of the bound padlock, as shown in the zoomed-in panel (i). Afterwards, rolling circle amplification (RCA) is applied to amplify the ligated probes that harbor barcodes specific to targets. Finally, the barcodes are read out by in situ sequencing chemistry, as shown in the zoomed-in panel (ii). The full readout of a specific barcode can then be used to reveal the gene identity (e.g., from barcode_a to gene_a) together with its location information. In such way, multiple gene targets can be decoded (represented as differentially colored amplicons). (B) Validation of ExSeq enzymatics and sequencing-by-synthesis (SBS) chemistry in samples processed with the uniExM procedure. (i) GAPDH in HeLa cells was chosen to undergo HCR-FISH or tExSeq. The numbers of detected signal spots per cell were quantitatively compared. No statistically significant difference was observed between the two methods. (Data shown as violin plots, with raw data points presented, and mean values highlighted with solid lines; n = 70 cells from 4 samples, 2 culture batches; two-sample t-test was performed with p > 0.1) (ii) A fluorescence image showing raw ExSeq signals from all four base channels in HeLa cells undergoing targeted ExSeq. Scale bars (in pre-expansion units): 20 μm. (C) Demonstration of ExSeq applying an 87-gene panel in GMA-anchored SA501 PDX breast cancer tissue. (i) Overview of the raw ExSeq reads (gray spots) in the tissue. DAPI staining for nuclei was used for cell segmentation and reads assignment (shown in the zoomed-in images). (ii) The raw ExSeq reads were decoded and colored based on 8 distinct gene function groups (full list in S5 Table in S1 File). Scale bars (in pre-expansion units): 100 μm (for stitched overview images), 10 μm (for zoomed-in images). (iii) Gene maps of 3 selected function groups—DNA repair, proliferation and epithelial-mesenchymal transition (EMT), were used to help visualize heterogeneity of cell status in the whole tissue. The decoded transcripts of genes belonging to each functional group were summed and their ratio to total transcript counts were assigned to the “R”, “G”, “B” color channels of the image, respectively. During the color assigning process, a scaling factor of 3.33 (for DNA repair and proliferation) or 2.5 (for EMT) was introduced; that is, if the EMT group of genes was 40% of the total transcripts in one cell, its assigned “B” channel was given the maximum color intensity (40% X 2.5 = 100%). Then, the three individual channels were combined to make a composite image (right). Scale bars (in pre-expansion units): 100 μm. Linear expansion factor: 3.2 (the expanded gel was re-embedded before sequencing). (D) Unsupervised principal component analysis (PCA) identified two primary gene groups for cell classification. (i) Using these two PCA gene groups, a distribution of different cancer cells was revealed. In this presented image, the summed transcripts of each PCA group normalized to the total transcript count for a given cell were assigned to the “R” channel and “G” channel, respectively (scaling factor: 3.33). Then the two images were overlaid to make a composite. Scale bar (in pre-expansion units): 100 μm. (ii) In the zoomed-in region where differentially colored cells co-exist, 6 marker genes are plotted; their distribution varies across cells in the subregion. Scale bar (in pre-expansion units): 10 μm. (E) Uniform manifold approximation and projection (UMAP) representation of the cell typing results using bulk RNA-seq identified marker genes in the SA501 cancer model. According to the panel design, the 87 gene list could differentiate two primary cancer cell clones that are successfully annotated on UMAP—“Tumor_XIST” and “Tumor_ZNF24”, named after their feature genes. A small group of cells are marked as “Unclassified”, likely attributed to non-cancer interstitial or other cells.</p

Document containing S1–S13 Figs and S1–S5 Tables.

Expansion microscopy (ExM), by physically enlarging specimens in an isotropic fashion, enables nanoimaging on standard light microscopes. Key to existing ExM protocols is the equipping of different kinds of molecules, with different kinds of anchoring moieties, so they can all be pulled apart from each other by polymer swelling. Here we present a multifunctional anchor, an acrylate epoxide, that enables proteins and RNAs to be equipped with anchors in a single experimental step. This reagent simplifies ExM protocols and reduces cost (by 2-10-fold for a typical multiplexed ExM experiment) compared to previous strategies for equipping RNAs with anchors. We show that this united ExM (uniExM) protocol can be used to preserve and visualize RNA transcripts, proteins in biologically relevant ultrastructures, and sets of RNA transcripts in patient-derived xenograft (PDX) cancer tissues and may support the visualization of other kinds of biomolecular species as well. uniExM may find many uses in the simple, multimodal nanoscale analysis of cells and tissues.</div

Characterization of GMA-based uniExM for protein and RNA retention.

(A) uniExM improves imaging resolution and achieves homogenous expansion. (i) Representative images of HeLa cells stained with β-tubulin antibody (pre-expansion staining) are shown. Upon expansion, resolution improvement, expansion factor and distortion were evaluated. Left image: pre-expansion (lower left half) and post-expansion (upper right half) fields of view of the same specimen, where the white diagonal dashed line delineates the boundary between the two images. Middle and right images: zoomed-in view of the region highlighted by the yellow square in the left image. Scale bars (in pre-expansion units): 20 μm (left image), 2 μm (middle and right images). Panels (ii) and (iii) plot the cross-section intensity profiles along the yellow and blue lines, respectively, in the zoomed-in images of panel (i). The raw intensity values (shapes) were fitted with multi-peak Gaussian functions (solid lines). The values presented in panel (iii) are FWHM (full width at half maximum) of the fitted Gaussian functions. In panel (iv), long axes of the same cell were measured before and after expansion to calculate the expansion factor. The linear expansion factor was determined to be 4.2 in this demonstration (n = 30 cells from 2 different batches of culture; mean + standard deviation was presented in bar chart with raw measurements shown as individual points). In panel (v), RMS length measurement error was quantified by benchmarking post-expansion confocal images against pre-expansion super-resolution SoRa images of microtubule staining in HeLa cells (red line, mean value; shaded area, standard deviation; n = 5 samples). (B) uniExM for RNA detection and quantification. (i) GMA-based expansion helps de-crowd densely packed mRNAs and better resolve single transcripts of the highly expressed GAPDH gene in HeLa cells. Left image: pre-expansion (lower left half) and post-expansion (upper right half) images of the same specimen, where the white diagonal dashed line delineates the boundary between these two images. Middle and right images: zoomed-in view of the region highlighted by the yellow square in the left image. Scale bars (in pre-expansion units): 20 μm (left image), 2 μm (middle and right images). (ii) GMA-based uniExM effectively preserves RNA information during the expansion process. HCR-FISH targeting specific genes was performed before and after expansion. Left image: a representative image of HCR-FISH for the USF2 gene in HeLa cells. Number of transcripts per cell was counted, and then FISH probes were stripped off with concentrated formamide and heating. Right image: The same sample was subjected to uniExM, after which HCR-FISH targeting the same gene was performed and quantified. Scale bars (in pre-expansion units): 5 μm. (iii) Three genes—TOP2A, TFRC and USF2 (with the expression level ranging from ~50 to ~500 transcripts per cell)—were chosen to evaluate the RNA anchoring efficiency by GMA in uniExM. Spots/transcripts per cell counted before and after GMA anchoring were fit by linear regression, with an R-squared value of 0.9736, indicating nearly 100% RNA retention (each point in the scatter plot represents one measurement from a single cell; n = 60 cells collected from 3 culture batches).</p

Preservation of protein ultrastructural organization by GMA in uniExM.

(A) Antibody staining against βII-spectrin in mouse hippocampal neurons was performed with 4× expansion (linear expansion factor ~4.2). One segment of axon showing periodic, punctate signals is presented. In the zoomed-in inset, the axial view of βII-spectrin is shown, displaying a ring structure. Scale bar (in pre-expansion units): 1 μm. (B) The intensity profile of βII-spectrin clusters along an axon segment (within the red rectangle of the inset image) was plotted and fitted with multi-peak Gaussian functions (black squares: raw data points; red line: fitting function). A consistent distance around 190 nm between two adjacent peaks was noted. (C) Antibody staining against βII-spectrin in mouse hippocampal neurons was performed with a 7× expansion protocol modified from the TREx protocol (linear expansion factor ~7.3). (i) The intensity profile of over 10 βII-spectrin clusters along an axon segment (within the red rectangle of the inset image) was plotted. (ii) Autocorrelation analysis was performed to calculate the periodicity of βII-spectrin clusters across space (i.e., the similarity between signals as a function of the spatial position lag between them). Based on each fitted autocorrelation function, the first three inter-peak distance values (denoted as P1, P2 and P3) were extracted to calculate the mean periodicity P̅. (D) Mean autocorrelation function of periodicity analysis using 4× expansion and pre-expansion antibody staining. (Solid lines, mean; shaded areas, standard error of mean. n = 40 measurements from 2 culture batches). (E) Post-expansion antibody staining revealed the same periodic distribution pattern of βII-spectrin under 4× expansion. Scale bar (in pre-expansion units): 5 μm. The color code of the image represents z-axis information.</p