The Small Molecule Antagonist KCI807 Disrupts Association of the Amino-Terminal Domain of the Androgen Receptor with ELK1 by Modulating the Adjacent DNA Binding Domain.

The androgen receptor (AR) is a crucial coactivator of ELK1 for prostate cancer (PCa) growth, associating with ELK1 through two peptide segments (358-457 and 514-557) within the amino-terminal domain (NTD) of AR. The small-molecule antagonist 5-hydroxy-2-(3-hydroxyphenyl)chromen-4-one (KCI807) binds to AR, blocking ELK1 binding and inhibiting PCa growth. We investigated the mode of interaction of KCI807 with AR using systematic mutagenesis coupled with ELK1 coactivation assays, testing polypeptide binding and Raman spectroscopy. In full-length AR, deletion of neither ELK1 binding segment affected sensitivity of residual ELK1 coactivation to KCI807. Although the NTD is sufficient for association of AR with ELK1, interaction of the isolated NTD with ELK1 was insensitive to KCI807. In contrast, coactivation of ELK1 by the AR-V7 splice variant, comprising the NTD and the DNA binding domain (DBD), was sensitive to KCI807. Deletions and point mutations within DBD segment 558-595, adjacent to the NTD, interfered with coactivation of ELK1, and residual ELK1 coactivation by the mutants was insensitive to KCI807. In a glutathione S-transferase pull-down assay, KCI807 inhibited ELK1 binding to an AR polypeptide that included the two ELK1 binding segments and the DBD but did not affect ELK1 binding to a similar AR segment that lacked the sequence downstream of residue 566. Raman spectroscopy detected KCI807-induced conformational change in the DBD. The data point to a putative KCI807 binding pocket within the crystal structure of the DBD and indicate that either mutations or binding of KCI807 at this site will induce conformational changes that disrupt ELK1 binding to the NTD. SIGNIFICANCE STATEMENT: The small-molecule antagonist KCI807 disrupts association of the androgen receptor (AR) with ELK1, serving as a prototype for the development of small molecules for a novel type of therapeutic intervention in drug-resistant prostate cancer. This study provides basic information needed for rational KCI807-based drug design by identifying a putative binding pocket in the DNA binding domain of AR through which KCI807 modulates the amino-terminal domain to inhibit ELK1 binding.

Accurate identification of breast cancer margins in microenvironments of ex-vivo basal and luminal breast cancer tissues using Raman spectroscopy.

Better knowledge of the breast tumor microenvironment is required for surgical resection and understanding the processes of tumor development. Raman spectroscopy is a promising tool that can assist in uncovering the molecular basis of disease and provide quantifiable molecular information for diagnosis and treatment evaluation. In this work, eighty-eight frozen breast tissue sections, including forty-four normal and forty-four tumor sections, were mapped in their entirety using a 250-µm-square measurement grid. Two or more smaller regions of interest within each tissue were additionally mapped using a 25 µm-square step size. A deep learning algorithm, convolutional neural network (CNN), was developed to distinguish histopathologic features with-in individual and across multiple tissue sections. Cancerous breast tissue were discriminated from normal breast tissue with 90 % accuracy, 88.8 % sensitivity and 90.8 % specificity with an excellent Area Under the Receiver Operator Curve (AUROC) of 0.96. Features that contributed significantly to the model were identified and used to generate RGB images of the tissue sections. For each grid point (pixel) on a Raman map, color was assigned to intensities at frequencies of 1002 cm-1 (Phenylalanine), 869 cm-1 (Proline, CC stretching of hydroxyproline-collagen assignment, single bond stretching vibrations for the amino acids proline, valine and polysaccharides) and 1309 cm-1 (CH3/CH2 twisting or bending mode of lipids). The Raman images clearly associate with hematoxylin and eosin stained tissue sections and allow clear visualization of boundaries between normal adipose, connective tissue and tumor. We demonstrated that this simple imaging technique allows high-resolution, straightforward molecular interpretation of Raman images. Raman spectroscopy provides rapid, label-free imaging of microscopic features with high accuracy. This method has application as laboratory tool and can assist with intraoperative tissue assessment during Breast Conserving surgery.

Rapid Detection of Clostridium difficile Toxins in Stool by Raman Spectroscopy.

BACKGROUND: Clinical practice guidelines define Clostridium difficile infections (CDI) as diarrhea (≥3 unformed stools in 24 h) with either a positive C difficile stool test or detection of pseudomembranous colitis. Diagnostic modalities such as toxigenic culture and nucleic acid amplification testing can identify the presence of toxigenic C difficile in stools. But these tests are confounded by the presence of asymptomatic colonization of toxigenic C difficile and lead to overdiagnosis of CDI. The presence of two large toxins, toxin A and B (TcdA and TcdB) is necessary for pathogenicity. Detection of toxins using toxin enzyme immunoassay is difficult as it has low sensitivity and moderate specificity. Raman spectroscopy (RS) is a novel technology that is used to detect bacteria and their toxins. RS does not require any reagents for detection such as antibodies, enzymes, primers, or stains. We hypothesize that RS is a sensitive method to detect C difficile toxins in stool and will solve the problem of overdiagnosis of CDI. MATERIALS AND METHODS: CDI negative stool samples were spiked with concentrations (1 ng/mL, 100 pg/mL, 1 pg/mL, and 0.1 pg/mL) of TcdA and TcdB. RS was performed on air-dried smeared samples of stool supernatant on a mirror-polished stainless-steel slide. As RS of feces is difficult because of confounding background material and autofluorescence, samples were photo-bleached before spectral acquisition to reduce autofluorescence. Raman spectra were obtained, background corrected, and vector normalized. The data were split into training (70%) and test (30%) datasets. The machine learning methods used on the training data set were Support Vector Machine with Linear and Radial Kernels, Random Forest, Stochastic Gradient Boosting Machine, and Principle Component Analysis-Linear Discriminant Analysis. Results were validated using a test data set. The best model was chosen, and its accuracy, sensitivity, and specificity were determined. RESULTS: In our preliminary results, at all concentrations (1 ng/mL, 100 pg/mL, 1 pg/mL, and 0.1 pg/mL), TcdA or TcdB spiked stool was distinguished from unspiked stool by all models with accuracies ranging from 64% to 77%. Gradient Boosting Machine, Principle Component Analysis-Linear Discriminant Analysis, and Support Vector Machine Linear Kernel performed best with sensitivities ranging from 69% to 90% and specificities ranging from 43% to 78%. CONCLUSIONS: Using RS, we successfully detected TcdA and TcdB in stool samples albeit with moderate-to-high sensitivity and low-to-moderate specificity. Sensitivity and specificity could be further increased with the implementation of deep learning methods, which require large sample sizes. In terms of sensitivity, RS performs better than toxin enzyme immunoassay and has the potential to rapidly detect C difficile toxins in stool at clinically relevant concentrations and thereby help mitigate overdiagnosis of CDI.

Rapid Detection of Clostridium difficile Toxins in Serum by Raman Spectroscopy.

BACKGROUND: Clostridium difficile infection (CDI) is due to the effects of toxins, toxin A and toxin B on the host. Severe CDI is associated with systemic signs of infection. Animal models of CDI demonstrate a strong correlation between systemic toxemia and the occurrence of severe disease. However, current technologies have low sensitivity to detect C difficile toxemia in human subjects. Raman spectroscopy (RS) is an upcoming technology that is used to detect bacteria and their toxins. We speculate that RS may be a sensitive method to detect clinically relevant concentrations of C difficile toxins in serum. MATERIALS AND METHODS: Serum samples were spiked with varying concentrations of toxin A, toxin B, and both. RS was performed on an air-dried serum drop that was placed on a mirror-polished stainless steel slide. Raman spectra were obtained, background corrected, vector normalized, and analyzed by Partial Least Square Linear Discriminant Analysis and Support Vector Machine for Classification. Model accuracy was measured by cross-validation and bootstrap methods. RESULTS: Toxin-spiked sera of various concentrations (1 ng/mL, 1 pg/mL, and 0.1 pg/mL) were distinguished from control serum 100% with cross-validation error rate ranging from 0% to 18% and bootstrap error rate ranging from 0% to 12% for various concentrations. The sensitivity ranged from 87% to 100% and specificity ranged from 77% to 100% for various concentrations of toxin-spiked serum. CONCLUSIONS: We conclude that RS may be a sensitive method to detect clinically relevant concentrations of C difficile toxins in serum and thus to help diagnose severe CDI in patients in real-time at the point of care.

Identification of regions of normal grey matter and white matter from pathologic glioblastoma and necrosis in frozen sections using Raman imaging.

In neurosurgical applications, a tool capable of distinguishing grey matter, white matter, and areas of tumor and/or necrosis in near-real time could greatly aid in tumor resection decision making. Raman spectroscopy is a non-destructive spectroscopic technique which provides molecular information about the tissue under examination based on the vibrational properties of the constituent molecules. With careful measurement and data processing, a spatial step and repeat acquisition of Raman spectra can be used to create Raman images. Forty frozen brain tissue sections were imaged in their entirety using a 300-µm-square measurement grid, and two or more regions of interest within each tissue were also imaged using a 25 µm-square step size. Molecular correlates for histologic features of interest were identified within the Raman spectra, and novel imaging algorithms were developed to compare molecular features across multiple tissues. In previous work, the relative concentration of individual biomolecules was imaged. Here, the relative concentrations of 1004, 1300:1344, and 1660 cm(-1), which correspond primarily to protein and lipid content, were simultaneously imaged across all tissues. This provided simple interpretation of boundaries between grey matter, white matter, and diseased tissue, and corresponded with findings from adjacent hematoxylin and eosin-stained sections. This novel, yet simple, multi-channel imaging technique allows clinically-relevant resolution with straightforward molecular interpretation of Raman images not possible by imaging any single peak. This method can be applied to either surgical or laboratory tools for rapid, non-destructive imaging of grey and white matter.

Raman molecular imaging of brain frozen tissue sections.

Raman spectroscopy provides a molecular signature of the region being studied. It is ideal for neurosurgical applications because it is non-destructive, label-free, not impacted by water concentration, and can map an entire region of tissue. The objective of this paper is to demonstrate the meaningful spatial molecular information provided by Raman spectroscopy for identification of regions of normal brain, necrosis, diffusely infiltrating glioma and solid glioblastoma (GBM). Five frozen section tissues (1 normal, 1 necrotic, 1 GBM, and 2 infiltrating glioma) were mapped in their entirety using a 300-µm-square step size. Smaller regions of interest were also mapped using a 25-µm step size. The relative concentrations of relevant biomolecules were mapped across all tissues and compared with adjacent hematoxylin and eosin-stained sections, allowing identification of normal, GBM, and necrotic regions. Raman peaks and peak ratios mapped included 1003, 1313, 1431, 1585, and 1659 cm(-1). Tissue maps identified boundaries of grey and white matter, necrosis, GBM, and infiltrating tumor. Complementary information, including relative concentration of lipids, protein, nucleic acid, and hemoglobin, was presented in a manner which can be easily adapted for in vivo tissue mapping. Raman spectroscopy can successfully provide label-free imaging of tissue characteristics with high accuracy. It can be translated to a surgical or laboratory tool for rapid, non-destructive imaging of tumor margins.

Raman spectroscopy to distinguish grey matter, necrosis, and glioblastoma multiforme in frozen tissue sections.

The need exists for a highly accurate, efficient and inexpensive tool to distinguish normal brain tissue from glioblastoma multiforme (GBM) and necrosis boundaries rapidly, in real-time, in the operating room. Raman spectroscopy provides a unique biochemical signature of a tissue type, with the potential to provide intraoperative identification of tumor and necrosis boundaries. We aimed to develop a database of Raman spectra from normal brain, GBM, and necrosis, and a methodology for distinguishing these pathologies. Raman spectroscopy was used to measure 95 regions from 40 frozen tissue sections using 785 nm excitation wavelength. Review of adjacent hematoxylin and eosin sections confirmed histology of each region. Three regions each of normal grey matter, necrosis, and GBM were selected as a training set. Ten regions were selected as a validation set, with a secondary validation set of tissue regions containing freeze artifact. Grey matter contained higher lipid (1061, 1081 cm(-1)) content, whereas necrosis revealed increased protein and nucleic acid content (1003, 1206, 1239, 1255-1266, 1552 cm(-1)). GBM fell between these two extremes. Discriminant function analysis showed 99.6, 97.8, and 77.5% accuracy in distinguishing tissue types in the training, validation, and validation with freeze artifact datasets, respectively. Decreased classification in the freeze artifact group was due to tissue preparation damage. This study shows the potential of Raman spectroscopy to accurately identify normal brain, necrosis, and GBM as a tool to augment pathologic diagnosis. Future work will develop mapped images of diffuse glioma and neoplastic margins toward development of an intraoperative surgical tool.

Reliable and highly sensitive biosensor from suspended MoS2 atomic layer on nano-gap electrodes.

The uneven morphology and the trapped charges at the surface of the traditionally used supporting substrate-based 2D biosensors produces a scattering effect, which leads to a irregular signals from individually fabricated devices. Though suspended 2D channel material has the potential to overcome scattering effects from the substrates but achieving reliability and selectivity, have been limiting the using of this biosensor technology. Here, we have demonstrated nanogap electrodes fabrication by using the self-assembly technique, which provides suspension to the 2D-MoS2. These nano-spacing electrodes not only give suspension but also provide robustness strength to the atomic layer, which remains freestanding after coating of the Hafnium oxide (HfO2) as well as linkers and antibodies. For evaluating the electrical characteristics of suspended MoS2 FET, gating potential was applied through an electrolyte on the suspended MoS2 transistor. This helped in achieved a lower subthreshold swing 70 mV/dec and ON/OFF ratio 107. Later, pH detection was conducted at room temperature, which showed an impressive sensitivity of ~880 by changing 1 unit of pH. We have also successfully shown Escherichia coli (E. coli) bacteria sensing from the suspended MoS2 transistor by functionalizing dielectric layer with E. coli antibodies. The reported biosensor has shown the ~9% of conductance changes with a lower concentration of E. coli (10 CFU/mL; colony-forming unit per mL) as well as maintain the constant sensitivity in three fabricated devices. The obtained enhancement in the sensitivity of devices and its effect on biomolecules detection can be extened to other biomolecules and this type of architecture has the potential to detect COVID-19 viruses based biomolecules.

Comparing the release of erythromycin and vancomycin from calcium polyphosphate hydrogel using different drug loading methods.

Calcium polyphosphate (CPP) hydrogel is used to load erythromycin (EM) and vancomycin (VCM) by means of two loading methods: they are either added directly to the formed CPP hydrogel (Gel Mixture method) or mixed with CPP powders, followed by the formation of CPP-antibiotic hydrogel (Powder Mixture method). The release of loaded antibiotics from CPP hydrogel is measured up to 48 hr. Compared to Powder Mixture method, Gel Mixture method significantly reduced the burst release of embedded antibiotics. A significant change in CPP hydrogel Raman characteristic peaks is observed only in Gel Mixture method, indicating a close interaction between embedded antibiotics with CPP hydrogel matrix. In contrast, a similarity between characteristic peaks of CPP hydrogel and Powder Mixture method shows that antibiotic incorporation does not interfere with CPP gel formation, resulting in no ionic interaction between antibiotic and polyphosphate chains. Rheometer analysis further confirms that the hydrophobic nature of EM impacts the viscoelastic properties of CPP hydrogel, whereas the hydrophilic VCM exhibits a higher loading efficiency. The potential application of CPP hydrogel as a ceramic matrix for sustained drug release warrants further investigation.

Setting mechanism of a new injectable Dicalcium Phosphate Dihydrate (DCPD) forming cement.

We previously described the gelation mechanism of calcium polyphosphate (CPP) in the presence of water. In this study, we developed novel and injectable poly-dicalcium phosphate dihydrate (P-DCPD) forming cement by the reaction of acidic CPP gel with alkali tetracalcium phosphate (TTCP). The setting reaction mechanism of P-DCPD is due to the intermolecular interaction between CPP gel and TTCP that was supported by XRD, AFM, Raman spectra analysis and SEM. The setting mechanism of P-DCPD is completely different from the classical calcium phosphate cement (CPC) that achieves crystallization by monophosphates reaction. P-DCPD represents a new type of poly-CPCs with significant advantages, including strong mechanical strength, excellent cohesion and easy of handling. More extensive experiments are currently underway to further evaluate the performance of P-DCPD cements, including biocompatibility, degradation behavior and bone defect hearing efficacy, among others.

Release of vancomycin and tobramycin from polymethylmethacrylate cements impregnated with calcium polyphosphate hydrogel.

The influence of calcium polyphosphate (CPP) gel incorporation on the release of vancomycin and tobramycin from polymethyl methacrylate (PMMA) cement (Simplex P, SP) has been studied. Adding 10% CPP gel to SP led to a much lower burst release of vancomycin and considerably extended release of both vancomycin and tobramycin up to 24 weeks. Antibiotics released from this new material retain their bactericidal activity for up to 15 weeks. The improvement in the antibiotic release is mainly due to the molecular interactions of antibiotics with embedded CPP polyphosphate chains as confirmed by Raman spectroscopy analysis. The inclusion of CPP hydrogel also increased the SP surface roughness and pore sizes, leading to a higher release rate of antibiotics. The new material is biocompatible and has similar handling properties and mechanical strength as compared to SP cements. We believe that incorporating CPP gel provides a better and usable drug carrier for PMMA cement. © 2017 The Authors Journal of Biomedical Materials Research Part B: Applied Biomaterials Published by Wiley Periodicals, Inc. J Biomed Mater Res Part B: Appl Biomater, 106B: 2827-2840, 2018.