Forchlorfenuron-Induced Mitochondrial Respiration Inhibition and Metabolic Shifts in Endometrial Cancer.

Forchlorfenuron (FCF) is a widely used plant cytokinin that enhances fruit quality and size in agriculture. It also serves as a crucial pharmacological tool for the inhibition of septins. However, the precise target of FCF has not yet been fully determined. This study reveals a novel target of FCF and elucidates its downstream signaling events. FCF significantly impairs mitochondrial respiration and mediates metabolic shift toward glycolysis, thus making cells more vulnerable to glycolysis inhibition. Interestingly, FCF's impact on mitochondrial function persists, even in cells lacking septins. Furthermore, the impaired mitochondrial function leads to the degradation of HIF-1α, facilitated by increased cellular oxygen. FCF also induces AMPK activation, suppresses Erk1/2 phosphorylation, and reduces the expression of HER2, ß-catenin, and PD-L1. Endometrial cancer is characterized by metabolic disorders such as diabetes and aberrant HER2/Ras-Erk1/2/ß-catenin signaling. Thus, FCF may hold promise as a potential therapeutic in endometrial cancer.

Analytical performance of the FDA-cleared Parsortix® PC1 system.

Introduction: The Parsortix® PC1 system, Food and Drug Administration (FDA) cleared for use in metastatic breast cancer (MBC) patients, is an epitope-independent microfluidic device for the capture and harvest of circulating tumor cells from whole blood based on cell size and deformability. This report details the analytical characterization of linearity, detection limit, precision, and reproducibility for this device. Methods: System performance was determined using K2-EDTA blood samples collected from self-declared healthy female volunteers (HVs) and MBC patients spiked with prelabeled cultured breast cancer cell lines (SKBR3, MCF7, or Hs578T). Samples were processed on Parsortix® PC1 systems and captured cells were harvested and enumerated. Results: The system captured and harvested live SKBR3, MCF7, and Hs578T cells and fixed SKBR3 cells linearly between 2 and ~100 cells, with average harvest rates of 69%, 73%, 79%, and 90%, respectively. To harvest ≥1 cell ≥95% of the time, the system required 3, 5 or 4 live SKBR3, MCF7 or Hs578T cells, respectively. Average harvest rates from precision studies using 5, 10, and ~50 live cells spiked into blood for each cell line ranged from 63.5% to 76.2%, with repeatability and reproducibility percent coefficient of variation (%CV) estimates ranging from 12.3% to 32.4% and 13.3% to 34.1%, respectively. Average harvest rates using ~20 fixed SKBR3 cells spiked into HV and MBC patient blood samples were 75.0% ± 16.1% (%CV = 22.3%) and 68.4% ± 14.3% (%CV = 21.1%), respectively. Conclusions: These evaluations demonstrate the Parsortix® PC1 system linearly and reproducibly harvests tumor cells from blood over a range of 1 to ~100 cells.

Spatial and clonality-resolved 3D cancer genome alterations reveal enhancer-hijacking as a potential prognostic marker for colorectal cancer.

The regulatory effect of non-coding large-scale structural variations (SVs) on proto-oncogene activation remains unclear. This study investigated SV-mediated gene dysregulation by profiling 3D cancer genome maps from 40 patients with colorectal cancer (CRC). We developed a machine learning-based method for spatial characterization of the altered 3D cancer genome. This revealed a frequent establishment of "de novo chromatin contacts" that can span multiple topologically associating domains (TADs) in addition to the canonical TAD fusion/shuffle model. Using this information, we precisely identified super-enhancer (SE)-hijacking and its clonal characteristics. Clonal SE-hijacking genes, such as TOP2B, are recurrently associated with cell-cycle/DNA-processing functions, which can potentially be used as CRC prognostic markers. Oncogene activation and increased drug resistance due to SE-hijacking were validated by reconstructing the patient's SV using CRISPR-Cas9. Collectively, the spatial and clonality-resolved analysis of the 3D cancer genome reveals regulatory principles of large-scale SVs in oncogene activation and their clinical implications.

Probabilistic establishment of speckle-associated inter-chromosomal interactions.

Inter-chromosomal interactions play a crucial role in genome organization, yet the organizational principles remain elusive. Here, we introduce a novel computational method to systematically characterize inter-chromosomal interactions using in situ Hi-C results from various cell types. Our method successfully identifies two apparently hub-like inter-chromosomal contacts associated with nuclear speckles and nucleoli, respectively. Interestingly, we discover that nuclear speckle-associated inter-chromosomal interactions are highly cell-type invariant with a marked enrichment of cell-type common super-enhancers (CSEs). Validation using DNA Oligopaint fluorescence in situ hybridization (FISH) shows a strong but probabilistic interaction behavior between nuclear speckles and CSE-harboring genomic regions. Strikingly, we find that the likelihood of speckle-CSE associations can accurately predict two experimentally measured inter-chromosomal contacts from Hi-C and Oligopaint DNA FISH. Our probabilistic establishment model well describes the hub-like structure observed at the population level as a cumulative effect of summing individual stochastic chromatin-speckle interactions. Lastly, we observe that CSEs are highly co-occupied by MAZ binding and MAZ depletion leads to significant disorganization of speckle-associated inter-chromosomal contacts. Taken together, our results propose a simple organizational principle of inter-chromosomal interactions mediated by MAZ-occupied CSEs.

scAVENGERS: a genotype-based deconvolution of individuals in multiplexed single-cell ATAC-seq data without reference genotypes.

Genetic differences inferred from sequencing reads can be used for demultiplexing of pooled single-cell RNA-seq (scRNA-seq) data across multiple donors without WGS-based reference genotypes. However, such methods could not be directly applied to single-cell ATAC-seq (scATAC-seq) data owing to the lower read coverage for each variant compared to scRNA-seq. We propose a new software, scATAC-seq Variant-based EstimatioN for GEnotype ReSolving (scAVENGERS), which resolves this issue by calling more individual-specific germline variants and using an optimized mixture model for the scATAC-seq. The benchmark conducted with three synthetic multiplexed scATAC-seq datasets of peripheral blood mononuclear cells and prefrontal cortex tissues showed outstanding performance compared to existing methods in terms of accuracy, doublet detection, and a portion of donor-assigned cells. Furthermore, analyzing the effect of the improved sections provided insight into handling pooled single-cell data in the future. Our source code of the devised software is available at GitHub: https://github.com/kaistcbfg/scAVENGERS.

Hi-C as a molecular rangefinder to examine genomic rearrangements.

The mammalian genome is highly packed into the nucleus. Over the past decade, the development of Hi-C has contributed significantly to our understanding of the three-dimensional (3D) chromatin structure, uncovering the principles and functions of higher-order chromatin organizations. Recent studies have repositioned its property in spatial proximity measurement to address challenging problems in genome analyses including genome assembly, haplotype phasing, and the detection of genomic rearrangements. In particular, the power of Hi-C in detecting large-scale structural variations (SVs) in the cancer genome has been demonstrated, which is challenging to be addressed solely with short-read-based whole-genome sequencing analyses. In this review, we first provide a comprehensive view of Hi-C as an intuitive and effective SV detection tool. Then, we introduce recently developed bioinformatics tools utilizing Hi-C to investigate genomic rearrangements. Finally, we discuss the potential application of single-cell Hi-C to address the heterogeneity of genomic rearrangements and sub-population identification in the cancer genome.

covNorm: An R package for coverage based normalization of Hi-C and capture Hi-C data.

Hi-C and capture Hi-C have greatly advanced our understanding of the principles of higher-order chromatin structure. In line with the evolution of the Hi-C protocols, there is a demand for an advanced computational method that can be applied to the various forms of Hi-C protocols and effectively remove innate biases. To resolve this issue, we developed an implicit normalization method named "covNorm" and implemented it as an R package. The proposed method can perform a complete procedure of data processing for Hi-C and its variants. Starting from the negative binomial model-based normalization for DNA fragment coverages, removal of genomic distance-dependent background and calling of the significant interactions can be applied sequentially. The performance evaluation of covNorm showed enhanced or similar reproducibility in terms of HiC-spector score, correlation of compartment A/B profiles, and detection of reproducible significant long-range chromatin contacts compared to baseline methods in the benchmark datasets. The developed method is powerful in terms of effective normalization of Hi-C and capture Hi-C data, detection of long-range chromatin contacts, and readily extendibility to the other derivative Hi-C protocols. The covNorm R package is freely available at GitHub: https://github.com/kaistcbfg/covNormRpkg.

3DIV update for 2021: a comprehensive resource of 3D genome and 3D cancer genome.

Three-dimensional (3D) genome organization is tightly coupled with gene regulation in various biological processes and diseases. In cancer, various types of large-scale genomic rearrangements can disrupt the 3D genome, leading to oncogenic gene expression. However, unraveling the pathogenicity of the 3D cancer genome remains a challenge since closer examinations have been greatly limited due to the lack of appropriate tools specialized for disorganized higher-order chromatin structure. Here, we updated a 3D-genome Interaction Viewer and database named 3DIV by uniformly processing ∼230 billion raw Hi-C reads to expand our contents to the 3D cancer genome. The updates of 3DIV are listed as follows: (i) the collection of 401 samples including 220 cancer cell line/tumor Hi-C data, 153 normal cell line/tissue Hi-C data, and 28 promoter capture Hi-C data, (ii) the live interactive manipulation of the 3D cancer genome to simulate the impact of structural variations and (iii) the reconstruction of Hi-C contact maps by user-defined chromosome order to investigate the 3D genome of the complex genomic rearrangement. In summary, the updated 3DIV will be the most comprehensive resource to explore the gene regulatory effects of both the normal and cancer 3D genome. '3DIV' is freely available at http://3div.kr.

Characterization of Structural Variations in the Context of 3D Chromatin Structure.

Chromosomes located in the nucleus form discrete units of genetic material composed of DNA and protein complexes. The genetic information is encoded in linear DNA sequences, but its interpretation requires an understanding of threedimensional (3D) structure of the chromosome, in which distant DNA sequences can be juxtaposed by highly condensed chromatin packing in the space of nucleus to precisely control gene expression. Recent technological innovations in exploring higher-order chromatin structure have uncovered organizational principles of the 3D genome and its various biological implications. Very recently, it has been reported that large-scale genomic variations may disrupt higher-order chromatin organization and as a consequence, greatly contribute to disease-specific gene regulation for a range of human diseases. Here, we review recent developments in studying the effect of structural variation in gene regulation, and the detection and the interpretation of structural variations in the context of 3D chromatin structure.

A Deep Learning-Based Automatic Mosquito Sensing and Control System for Urban Mosquito Habitats.

Mosquito control is important as mosquitoes are extremely harmful pests that spread various infectious diseases. In this research, we present the preliminary results of an automated system that detects the presence of mosquitoes via image processing using multiple deep learning networks. The Fully Convolutional Network (FCN) and neural network-based regression demonstrated an accuracy of 84%. Meanwhile, the single image classifier demonstrated an accuracy of only 52%. The overall processing time also decreased from 4.64 to 2.47 s compared to the conventional classifying network. After detection, a larvicide made from toxic protein crystals of the Bacillus thuringiensis serotype israelensis bacteria was injected into static water to stop the proliferation of mosquitoes. This system demonstrates a higher efficiency than hunting adult mosquitos while avoiding damage to other insects.

Septin-2 is overexpressed in epithelial ovarian cancer and mediates proliferation via regulation of cellular metabolic proteins.

Epithelial Ovarian Cancer (EOC) is associated with dismal survival rates due to the fact that patients are frequently diagnosed at an advanced stage and eventually become resistant to traditional chemotherapeutics. Hence, there is a crucial need for new and innovative therapies. Septin-2, a member of the septin family of GTP binding proteins, has been characterized in EOC for the first time and represents a potential future target. Septin-2 was found to be overexpressed in serous and clear cell human patient tissue compared to benign disease. Stable septin-2 knockdown clones developed in an ovarian cancer cell line exhibited a significant decrease in proliferation rates. Comparative label-free proteomic analysis of septin-2 knockdown cells revealed differential protein expression of pathways associated with the TCA cycle, acetyl CoA, proteasome and spliceosome. Further validation of target proteins indicated that septin-2 plays a predominant role in post-transcriptional and translational modifications as well as cellular metabolism, and suggested the potential novel role of septin-2 in promoting EOC tumorigenesis through these mechanisms.

Human Epididymis Secretory Protein 4 (HE4) Compromises Cytotoxic Mononuclear Cells via Inducing Dual Specificity Phosphatase 6.

While selective overexpression of serum clinical biomarker Human epididymis secretory protein 4 (HE4) is indicative of ovarian cancer tumorigenesis, much is still known about the mechanistic role of the HE4 gene or gene product. Here, we examine the role of the secretory glycoprotein HE4 in ovarian cancer immune evasion. Through modified subtractive hybridization analyses of human peripheral blood mononuclear cells (PBMCs), we have characterized gene targets of HE4 and established a preliminary mechanism of HE4-mediated immune failure in ovarian tumors. Dual specificity phosphatase 6 (DUSP6) emerged as the most upregulated gene in PBMCs upon in vitro exposure to HE4. DUSP6 was found to be upregulated in CD8+ cells and CD56+ cells. HE4 exposure reduced Erk1/2 phosphorylation specifically in these cell populations and the effect was erased by co-incubation with a DUSP6 inhibitor, (E)-2-benzylidene-3-(cyclohexylamino)-2,3-dihydro-1H-inden-1-one (BCI). In co-culture with PBMCs, HE4-silenced SKOV3 human ovarian cancer cells exhibited enhanced proliferation upon exposure to external HE4, while this effect was partially attenuated by adding BCI to the culture. Additionally, the reversal effects of BCI were erased in the co-culture with CD8+ / CD56+ cell deprived PBMCs. Taken together, these findings show that HE4 enhances tumorigenesis of ovarian cancer by compromising cytotoxic CD8+ and CD56+ cells through upregulation of self-produced DUSP6.

MineLoC: A Rapid Production of Lab-on-a-Chip Biosensors Using 3D Printer and the Sandbox Game, Minecraft.

Here, MineLoC is described as a pipeline developed to generate 3D printable models of master templates for Lab-on-a-Chip (LoC) by using a popular multi-player sandbox game “Minecraft”. The user can draw a simple diagram describing the channels and chambers of the Lab-on-a-Chip devices with pre-registered color codes which indicate the height of the generated structure. MineLoC converts the diagram into large chunks of blocks (equal sized cube units composing every object in the game) in the game world. The user and co-workers can simultaneously access the game and edit, modify, or review, which is a feature not generally supported by conventional design software. Once the review is complete, the resultant structure can be exported into a stereolithography (STL) file which can be used in additive manufacturing. Then, the Lab-on-a-Chip device can be fabricated by the standard protocol to produce a Lab-on-a-Chip. The simple polydimethylsiloxane (PDMS) device for the bacterial growth measurement used in the previous research was copied by the proposed method. The error calculation by a 3D model comparison showed an accuracy of 86%. It is anticipated that this work will facilitate more use of 3D printer-based Lab-on-a-Chip fabrication, which greatly lowers the entry barrier in the field of Lab-on-a-Chip research.

Visual Estimation of Bacterial Growth Level in Microfluidic Culture Systems.

Microfluidic devices are an emerging platform for a variety of experiments involving bacterial cell culture, and has advantages including cost and convenience. One inevitable step during bacterial cell culture is the measurement of cell concentration in the channel. The optical density measurement technique is generally used for bacterial growth estimation, but it is not applicable to microfluidic devices due to the small sample volumes in microfluidics. Alternately, cell counting or colony-forming unit methods may be applied, but these do not work in situ; nor do these methods show measurement results immediately. To this end, we present a new vision-based method to estimate the growth level of the bacteria in microfluidic channels. We use Fast Fourier transform (FFT) to detect the frequency level change of the microscopic image, focusing on the fact that the microscopic image becomes rough as the number of cells in the field of view increases, adding high frequencies to the spectrum of the image. Two types of microfluidic devices are used to culture bacteria in liquid and agar gel medium, and time-lapsed images are captured. The images obtained are analyzed using FFT, resulting in an increase in high-frequency noise proportional to the time passed. Furthermore, we apply the developed method in the microfluidic antibiotics susceptibility test by recognizing the regional concentration change of the bacteria that are cultured in the antibiotics gradient. Finally, a deep learning-based data regression is performed on the data obtained by the proposed vision-based method for robust reporting of data.

Light Emitting Marker for Robust Vision-Based On-The-Spot Bacterial Growth Detection.

Simple methods using the striped pattern paper marker and FFT (fast Fourier transformation) have been proposed as alternatives to measuring the optical density for determining the level of bacterial growth. The marker-based method can be easily automated, but due to image-processing-base of the method, the presence of light or the color of the culture broth can disturb the detection process. This paper proposes a modified version of marker-FFT-based growth detection that uses a light emitting diode (LED) array as a marker. Since the marker itself can emit the light, the measurements can be performed even when there is no light source or the bacteria are cultured in a large volume of darkly colored broth. In addition, an LED marker can function as a region of interest (ROI) indicator in the image. We expect that the proposed LED-based marker system will allow more robust growth detection compared to conventional methods.

Vision Marker-Based In Situ Examination of Bacterial Growth in Liquid Culture Media.

The detection of bacterial growth in liquid media is an essential process in determining antibiotic susceptibility or the level of bacterial presence for clinical or research purposes. We have developed a system, which enables simplified and automated detection using a camera and a striped pattern marker. The quantification of bacterial growth is possible as the bacterial growth in the culturing vessel blurs the marker image, which is placed on the back of the vessel, and the blurring results in a decrease in the high-frequency spectrum region of the marker image. The experiment results show that the FFT (fast Fourier transform)-based growth detection method is robust to the variations in the type of bacterial carrier and vessels ranging from the culture tubes to the microfluidic devices. Moreover, the automated incubator and image acquisition system are developed to be used as a comprehensive in situ detection system. We expect that this result can be applied in the automation of biological experiments, such as the Antibiotics Susceptibility Test or toxicity measurement. Furthermore, the simple framework of the proposed growth measurement method may be further utilized as an effective and convenient method for building point-of-care devices for developing countries.

A Low Cost/Low Power Open Source Sensor System for Automated Tuberculosis Drug Susceptibility Testing.

In this research an open source, low power sensor node was developed to check the growth of mycobacteria in a culture bottle with a nitrate reductase assay method for a drug susceptibility test. The sensor system reports the temperature and color sensor output frequency change of the culture bottle when the device is triggered. After the culture process is finished, a nitrite ion detecting solution based on a commercial nitrite ion detection kit is injected into the culture bottle by a syringe pump to check bacterial growth by the formation of a pigment by the reaction between the solution and the color sensor. Sensor status and NRA results are broadcasted via a Bluetooth low energy beacon. An Android application was developed to collect the broadcasted data, classify the status of cultured samples from multiple devices, and visualize the data for the end users, circumventing the need to examine each culture bottle manually during a long culture period. The authors expect that usage of the developed sensor will decrease the cost and required labor for handling large amounts of patient samples in local health centers in developing countries. All 3D-printerable hardware parts, a circuit diagram, and software are available online.

Sensor node for remote monitoring of waterborne disease-causing bacteria.

A sensor node for sampling water and checking for the presence of harmful bacteria such as E. coli in water sources was developed in this research. A chromogenic enzyme substrate assay method was used to easily detect coliform bacteria by monitoring the color change of the sampled water mixed with a reagent. Live webcam image streaming to the web browser of the end user with a Wi-Fi connected sensor node shows the water color changes in real time. The liquid can be manipulated on the web-based user interface, and also can be observed by webcam feeds. Image streaming and web console servers run on an embedded processor with an expansion board. The UART channel of the expansion board is connected to an external Arduino board and a motor driver to control self-priming water pumps to sample the water, mix the reagent, and remove the water sample after the test is completed. The sensor node can repeat water testing until the test reagent is depleted. The authors anticipate that the use of the sensor node developed in this research can decrease the cost and required labor for testing samples in a factory environment and checking the water quality of local water sources in developing countries.

HE4 expression is associated with hormonal elements and mediated by importin-dependent nuclear translocation.

Antiestrogens including tamoxifen and fulvestrant have been evaluated as chemotherapeutics for ovarian cancer, particularly in cases of platinum resistant disease. Human epididymis protein 4 (HE4) is highly overexpressed in women with ovarian cancer and overexpression of HE4 has been found to correlate with platinum resistance. However, the role of HE4 in modulating responses to hormones and hormonal therapy has not been characterized in ovarian cancer. Here we demonstrate that 17ß-estradiol, tamoxifen, and fulvestrant induce nuclear and nucleolar translocation of HE4 and that HE4 overexpression induces resistance to antiestrogens. HE4 was found to interact with estrogen receptor-α (ER-α), and HE4 overexpression resulted in ER-α downregulation in vitro and in human ovarian cancers. We identified a novel role for importin-4 in governing the nuclear transport of HE4. Treatment with ivermectin, an importin inhibitor, blocked HE4/importin-4 nuclear accumulation and sensitized HE4-overexpressing ovarian cancer cells to fulvestrant and tamoxifen.