Rapid <i>KRAS</i> Mutation Detection via Hybridization-Induced Aggregation of Microbeads

Using hybridization-induced aggregation (HIA), a unique bead-based DNA detection technology scalable for a microchip platform, we describe a simplistic, low-cost method for rapid mutation testing. HIA utilizes a pair of sequence-specific oligonucleotide probes bound to magnetic microbeads. Hybridization to a target DNA strand tethers the beads together, inducing bead aggregation. By simply using the extent of bead aggregation as a measure of the hybridization efficiency, we avoid the need for additional labels and sophisticated analytical equipment. Through strategic manipulation of the assay design and experimental parameters, we use HIA to facilitate, for the first time, the detection of single base mutations in a gene segment and, specifically, the detection of activating <i>KRAS</i> mutations. Following the development and optimization of the assay, we apply it for <i>KRAS</i> mutation analysis of four human cancer cell lines. Ultimately, we present a proof-of-principle method for detecting any of the common <i>KRAS</i> mutations in a single-step, 2 min assay, using only one set of oligonucleotide probes, for a total analysis time of less than 10 min post-PCR. The assay is performed at room temperature and uses simple, inexpensive instrumentation that permits multiplexed analysis

Normalization strategy and sorting of PHASTpep.

<p>(A) For each screen, the frequencies were divided by the total number of reads of the screen, followed by the frequency of that sequence in the reference library. (B) In order to demonstrate the sorting process, small libraries were created that represented a reference library, two positive screens, and 2 negative screens. For each sequence, a qualitative ranking was determined (predicted ranking) based on the level of frequency assigned in each library. For example, GVTHKLQ was absent in the reference library, high in both positive screens, and absent in both negative screens. Therefore, it was predicted to be ranked very high. Conversely, TPSIYFL was only high in the negative screens and absent elsewhere. Thus it was predicted to rank very low. For each test case (sequence), the predicted ranking was compared to the actual ranking after running the test libraries through our sorting software. R, reference; PS, positive screen; NS, negative screen; A, absent; L, low; H, high.</p

Summary of Streptavidin screens.

<p>Positive and negative screens were carried out for a protein target, Streptavidin, and processed with PHASTpep.</p

Validation of PHASTpep software translation and frequency calculations.

<p>(A) The raw data pulled from the fastq file of the Illumina sequencer showing the unique flanking regions (red) surrounding the portion of the DNA sequence corresponding to the displayed peptides (blue). (B) The unique flanking regions were used to isolate the peptide sequences, which were then translated into amino acids. For each sequence, a frequency was calculated corresponding to the number of times it appeared in the run. (C) GUI of the PHASTpep software presented in this paper to automate the data processing and analysis.</p

Comparison matrices.

<p>A heat map matrix visualization was generated using conditional formatting in Excel for the top 40 sequences from streptavidin (A) and CAF (B) sets of screens. The first two streptavidin screens used glycine to elute; whereas, the third and forth streptavidin screens were eluted with biotin. Scatter plots compare the frequencies and average frequencies of peptide sequences across independent screen replicates for streptavidin (C) and CAF (D) screens.</p

Summary of CAF screens.

<p>Positive and negative screens were carried out for a cell target, CAFs, and processed with PHASTpep.</p

Peptide signatures of various cells and tissues.

<p>Peptides identified from screens performed on cell lines, ex vivo tissue specimens and in vivo screens were processed and analyzed using PHASTpep. They are presented as a heat map generated via conditional formatting in Excel. PDEC, pancreatic ductal epithelial cell; gl, glucose; B, b cells; TIL, tumor infiltrating lymphocyte; Eff, effector; Omm, ommental; SVF, stromal vascular fraction; Ob, obese; CHO, chinese hamster ovary.</p

<i>In vitro</i> and <i>in vivo</i> peptide sequence validation.

<p>(A) An ELISA compares the binding of phage displaying the peptides to CAFs versus normal fibroblasts (MRC5). The first three sequences were selected using our selectivity analysis; whereas, the next six sequences were found using a traditional phage display approach. The dashed line indicates a fold change of 1.2. (B) Flow cytometry was performed by binding fluorescently-labeled phage to cells with a live-dead violet stain. Data was gated on cell population, live cells, and phage positive cells. (C) An ELISA compares binding of phage to HPSC and MRC5. Statistical significance was measured with a student t-test between HPSC and MRC5 where ^#p<0.01 and *p<0.02. (D) An ELISA compares binding of phage to HPSC and BXPC3. Statistical significance was measured with a student t-test between HPSC and BXPC3 where *p<0.02 and ^Φp<0.06. (E) Fluorescently-labeled phage were injected into mice bearing subcutaneous admix CAF/BXPC3 tumors or BXPC3-only tumors (n = 6 tumors per group) and tumor accumulation was measured on an FMT using a region-of-interest around the tumor area. Statistical significance was determined using student’s t-test of each type of displayed peptide versus KE with ^#p<0.01 and *p<0.02. (F) FMT images of mice with admix CAF/BXPC3 tumors scanned 4 h post-injection. Tumor regions have been circled with dashed lines. (G) Tumor sections of admix tumors were fixed, sectioned, and stained with anti-αSMA (green), then mounted with prolong gold anti-fade with DAPI (blue). The fluorescent labeling of the phage is colored red. Mander’s correlation coefficients (M) are indicated at the bottom of each image. For each phage type, images are representative of two tumors, three tumor sections each. Scale bars, 10 um.</p

Approach to finding candidate peptide sequences.

<p>(A) The Illumina sequencer outputs fastq files that are separated by barcodes. For each of these files, the portion of DNA corresponding to the displayed peptides was isolated and translated. The number of times each sequence was read in a run was summed to obtain the frequency associated with that sequence, which was subsequently divided by the total number of reads from the run and then by the frequency of that sequence in the reference library. This processing resulted in a normalized frequency for each sequence of a run. (B) Sequences present in one screen but absent in another were set to the non-zero mode of the absent screen rather than zero to prevent later division by zero. The normalized frequencies across all positive screens were averaged as well as across all negative screens. The average positive normalized frequency was divided by the average negative normalized frequency and this ratio was used to sort the sequences so that sequences high across positive screens and low across negative screens distilled to the top fraction. Sequences ordered by ratio created the rows of the comparison matrix showing all of the normalized frequencies for each sequence across all screens, facilitating identification of the most selective sequences. * PhD libraries from NEB are generated with constrained codons. When using this library, sequences containing codons not represented in the library are removed.</p

Different neurotoxic insults damage neurons in different brain areas, leading to astrocyte and microglia activation.

<p>Activation of STAT3 (pTYR 705) represents a signaling event common to neurotoxic insults and neuroinflammation. Neurotoxicity-related damage results in astrogliosis dependent on activation of STAT3 but does not require upstream signaling from proinflammatory cytokines and chemokines. Suppression of this neuroinflammatory response to neurotoxicity does not affect expression of GFAP or pSTAT3. LPS causes brain-wide neuroinflammation (represented by flames) characterized by activation of microglia and elaboration of proinflammatory cytokines/chemokines but not neurodegeneration. Neuroinflammation-related activation of microglia due to LPS does not lead to astrogliosis but also is associated with activation of STAT3 suppressible by glucocorticoids. Neuroinflammation likely activates a separate STAT3 pathway, perhaps in microglia. Identification of upstream effectors in these STAT3 pathways will aid in defining and manipulating signal transduction events that likely play roles in repair and neuroimmune responses.</p