Deconvolution Strategy Research Articles

A practical scheme for enhancing consistency and independence in wireless key generation for Wi-Fi networks

Wi-Fi networks benefit from physical layer wireless key generation, a theoretically secure method for improving information transmission security. This paper introduces the Consistent Independent Key Generation (CIKG) scheme based on the Wi-Fi signal transmission model. It addresses the dual challenges of channel noise and virtual carrier low-pass effects. The scheme begins with extracting the channel frequency response (CFR) from the entire signal preamble, including both long and short symbols, followed by applying cubic spline interpolation to the short symbol-derived CFR to achieve fine-grained frequency fading corresponding to the long symbols. This refined CFR is averaged to bolster noise resistance, improving key consistency. Subsequently, the inverse Fourier transform is used to obtain the channel impulse response (CIR), and a deconvolution strategy reduces the impact of virtual carrier low-pass filtering on the multipath information of CIR, thus improving key independence. Implemented within Wi-Fi networks, the effectiveness of the CIKG scheme is rigorously tested across diverse scenarios. Quantitative evaluations indicate that the scheme substantially improves key consistency, achieving a 5 dB enhancement in signal-to-noise ratio over traditional CFR-based schemes and elevates information entropy by 20%, significantly boosting key independence. These advances affirm the potential of the CIKG scheme as a formidable solution for developing robust and secure wireless communication networks.

Hadge: a comprehensive pipeline for donor deconvolution in single-cell studies

Single-cell multiplexing techniques (cell hashing and genetic multiplexing) combine multiple samples, optimizing sample processing and reducing costs. Cell hashing conjugates antibody-tags or chemical-oligonucleotides to cell membranes, while genetic multiplexing allows to mix genetically diverse samples and relies on aggregation of RNA reads at known genomic coordinates. We develop hadge (hashing deconvolution combined with genotype information), a Nextflow pipeline that combines 12 methods to perform both hashing- and genotype-based deconvolution. We propose a joint deconvolution strategy combining best-performing methods and demonstrate how this approach leads to the recovery of previously discarded cells in a nuclei hashing of fresh-frozen brain tissue.

Abstract 5003: Efficacy of the STC-1010 a new allogenic cancer vaccine in different colorectal cancer models

Abstract Colorectal cancer (CRC) is the 2nd cause of cancer-related death worldwide. Recent tremendous progress has installed immunotherapy as a treatment option and opened multiple ways to address therapeutic challenges for CRC patients. Accordingly, Brenus-Pharma developed a therapeutic cancer vaccine, based on stimulating tumor cells (STC) technology with physical (irradiation and heat shock) or chemical (chemotherapy) stress, coupled to haptenization making vaccine more immunogenic. We first validated STC technology using a “one cell line” mouse surrogate vaccine in CT26 mouse model showed an efficacy of the combination of stress plus haptenization. We next prepared a mouse surrogate vaccine using three murine tumoral cell lines the mSTC-1010 (CT26, CMT93 and LTPA), and confirmed the value of using 3 cell lines to increase the antigenicity. In addition, mSTC-1010 associated with immunostimulant (cyclophosphamide and mGM-CSF), combined or not with chemotherapy (FOLFOX or FOLFIRI) led to a significant decrease of tumor volume, a M1-oriented macrophage response (immunohistochemical, iNOS/CD163 >1), and an increase of T lymphocyte infiltration. Following, we confirmed these proofs-of-concept with the human vaccine, STC-1010, composed of 6 drug substances (DS) derived from 3 CRC human cell lines (HCT116, HT29 and Lovo), stressed physically or chemically and then haptenized. Transcriptomic analysis showed the induction of stress-related pathways with a certain specificity for each DS, allowing their quantification in the STC-1010 by deconvolution strategy. Moreover, proteomic analysis showed specific protein expression modification of each DS and confirmed the rationale of using 3 cell lines with 2 stresses to cover the CRC heterogeneity. More than 200 cancer-related proteins from the Atlas proteins’ database were identified in theSTC-1010 including CRC-specific proteins, tumor plasticity and tumor-associated antigens. In ex vivo model, the efficacy of the vaccine was shown, through that CD8+ T cells primed by the STC-1010 treated DCs (versus control), induced massive apoptosis of cancer cells (p<0,001). In chicken embryo Chorio-Allantoid Membrane (CAM) model, STC-1010 significantly increased IL-12, IL-2 and IFN-gamma expression (versus control p<0.02). Results from this immune reactive model showed a significant increase of tumor necrosis (p=0,0267), metastasis regression (-49%), and increased infiltration of CD4+, CD8+ (versus control). The good tolerability and reproducible efficiency of the STC-1010 vaccine in these different clinical models allow to plan a first-in-human phase I/II clinical trial for metastatic CRC(mCRC) patients. A dose-escalation and cohort extension phase I followed by a phase IIa with STC-1010 plus immunostimulant, associated to mFOLFOX6 w/o bevacizumab, will be performed for MSS mCRC patients to evaluate the safety and preliminary effectiveness in human. Citation Format: George Alzeeb, Céline Gongora, Corentin Richard, Romain Boidot, Tanguy Fortin, Yan Wang, Alban Bessede, Benoit Pinteur, Lionel Chalus, Corinne Tortorelli, Paul Bravetti, François Ghiringhelli. Efficacy of the STC-1010 a new allogenic cancer vaccine in different colorectal cancer models [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5003.

DEPArray™ single-cell technology: A validation study for forensic applications

In forensics investigations, it is common to encounter biological mixtures consisting of homogeneous or heterogeneous components from multiple individuals and with different genetic contributions. One promising mixture deconvolution strategy is the DEPArray™ technology, which enables the separation of cell populations before genetic analysis. While technological advances are fundamental, their reliable validation is crucial for successful implementation and use for casework. Thus, this study aimed to 1) systematically validate the DEPArray™ system concerning specificity, sensitivity, repeatability, and contamination occurrences for blood, epithelial, and sperm cells, and 2) evaluate its potential for single-cell analysis in the field of forensic science. Our findings confirmed the effective identification of different cell types and the correct assignment of successfully genotyped single cells to their respective donor(s). Using the NGM Detect™ Amplification Kit, the average profile completeness for diploid cells was approximately 80%, with ∼ 290 RFUs. In contrast, haploid sperm analysis yielded an average completeness of 51% referring to the haploid reference profile, accompanied by mean peak heights of ∼ 176 RFUs. Although certain alleles of heterozygous loci in diploid cells showed strong imbalances, the overall peak balances yielded acceptable values above ≥ 60% with a mean value of 72% ± 0.21, a median of 77%, but with a maximum imbalance of 9% between heterozygous peaks. Locus dropouts were considered stochastic events, exhibiting variations among donors and cell types, with a notable failure incidence observed for TH01. Within the wet-lab experimentation with >500 single cells for the validation, profiling was performed using the consensus approach, where profiles were selected randomly from all data to better mirror real casework results. Nevertheless, complete profiles could be achieved with as few as three diploid cells, while the average success rate increased to 100% when using profiles of 6–10 cells. For sperms, however, a consensus profile with completeness >90% of the autosomal diploid genotype could be attained using ≥15 cells. In addition, the robustness of the consensus approach was evaluated in the absence of the respective reference profile without severe deterioration. Here, increased stutter peaks (≥ 15%) were found as the main artifact in single-cell profiles, while contamination and drop-ins were ascertained as rare events. Lastly, the technique’s potential and limitations are discussed, and practical guidance is provided, particularly valuable for cold cases, multiple perpetrator rapes, and analyses of homogeneous mixed evidence.

Dissection of Targeting Molecular Mechanisms of Celastrol-induced Nephrotoxicity via A Combined Deconvolution Strategy of Chemoproteomics and Metabolomics.

Celastrol (Cel), derived from the traditional herb Tripterygium wilfordii Hook. f., has anti-inflammatory, anti-tumor, and immunoregulatory activities. Renal dysfunction, including acute renal failure, has been reported in patients following the administration of Cel-relative medications. However, the functional mechanism of nephrotoxicity caused by Cel is unknown. This study featured combined use of activity-based protein profiling and metabolomics analysis to distinguish the targets of the nephrotoxic effects of Cel. Results suggest that Cel may bind directly to several critical enzymes participating in metabolism and mitochondrial functions. These enzymes include voltage-dependent anion-selective channel protein 1 (essential for maintaining mitochondrial configurational and functional stability), pyruvate carboxylase (involved in sugar isomerization and the tricarboxylic acid cycle), fatty acid synthase (related to β-oxidation of fatty acids), and pyruvate kinase M2 (associated with aerobic respiration). Proteomics and metabolomics analysis confirmed that Cel-targeted proteins disrupt some metabolic biosynthetic processes and promote mitochondrial dysfunction. Ultimately, Cel aggravated kidney cell apoptosis. These cumulative results deliver an insight into the potential mechanisms of Cel-caused nephrotoxicity. They may also facilitate development of antagonistic drugs to mitigate the harmful effects of Cel on the kidneys and improve its clinical applications.

Deconvolution based on sparsity and continuity improves the quality of ultrasound image

The application of ultrasound (US) image has been limited by its limited resolution, inherent speckle noise, and the impact of clutter and artifacts, especially in the miniaturized devices with restricted hardware conditions. In order to solve these problems, many researchers have explored a number of hardware modifications as well as algorithmic improvements, but further improvements in resolution, signal-to-noise ratio (SNR) and contrast are still needed. In this paper, a deconvolution algorithm based on sparsity and continuity (DBSC) is proposed to obtain the higher resolution, SNR, and, contrast. The algorithm begins with a relatively bold Wiener filtering for initial enhancement of image resolution in preprocessing, but it also introduces ringing noise and compromises the SNR. In further processing, the noise is suppressed based on the characteristic that the adjacent pixels of the US image are continuous as long as Nyquist sampling criterion is met, and the extraction of high-frequency information is balanced by using relatively sparse. Subsequently, the theory and experiments demonstrate that relative sparsity and continuity are general properties of US images. DBSC is compared with other deconvolution strategies through simulations and experiments, and US imaging under different transmission channels is also investigated. The final results show that the proposed method can greatly improve the resolution, as well as provide significant advantages in terms of contrast and SNR, and is also feasible in applications to devices with limited hardware.

Chemoproteomics Reveals Glaucocalyxin A Induces Mitochondria-Dependent Apoptosis of Leukemia Cells via Covalently Binding to VDAC1.

Chronic myelogenous leukemia (CML) that is resistant to tyrosine kinase inhibitors is one of the deadliest hematologic malignancies, and the T315I mutation in the breakpoint cluster region-Abelson (BCR-ABL) kinase domain is the most prominent point mutation responsible for imatinib resistance in CML. Glaucocalyxin A (GLA), a natural bioactive product derived from the Rabdosia rubescens plant, has strong anticancer activity. In this study, the effect and molecular mechanism of GLA on imatinib-sensitive and imatinib-resistant CML cells harboring T315I mutation via a combined deconvolution strategy of chemoproteomics and label-free proteomics is investigated. The data demonstrated that GLA restrains proliferation and induces mitochondria-dependent apoptosis in both imatinib-sensitive and resistant CML cells. GLA covalently binds to the cysteine residues of mitochondrial voltage-dependent anion channels (VDACs), resulting in mitochondrial damage and overflow of intracellular apoptotic factors, eventually leading to apoptosis. In addition, the combination of GLA with elastin, a mitochondrial channel VDAC2/3 inhibitor, enhances mitochondria-dependent apoptosis in imatinib-sensitive and -resistant CML cells, representing a promising therapeutic approach for leukemia treatment. Taken together, the results show that GLA induces mitochondria-dependent apoptosis via covalently targeting VDACs in CML cells. GLA may thus be a candidate compound for the treatment of leukemia.

Hierarchical deconvolution dehazing method based on transmission map segmentation.

Images captured in fog are often affected by scattering. Due to the absorption and scattering of light by aerosols and water droplets, the image quality will be seriously degraded. The specific manifests are brightness decrease, contrast decrease, image blur, and noise increase. In the single-image dehazing method, the image degradation model is essential. In this paper, an effective image degradation model is proposed, in which the hierarchical deconvolution strategy based on transmission map segmentation can effectively improve the accuracy of image restoration. Specifically, the transmission map is obtained by using the dark channel prior (DCP) method, then the transmission histogram is fitted. The next step is to divide the image region according to the fitting results. Furthermore, to more accurately recover images of complex objects with a large depth of field, different levels of inverse convolution are adopted for different regions. Finally, the sub-images of different regions are fused to get the dehazing image. We tested the proposed method using synthetic fog images and natural fog images respectively. The proposed method is compared with eight advanced image dehazing methods on quantitative rating indexes such as peak signal-to-noise ratio (PSNR), structural similarity (SSIM), image entropy, natural image quality evaluator (NIQE), and blind/referenceless image spatial quality evaluator (BRISQUE). Both subjective and objective evaluations show that the proposed method achieves competitive results.

Bisymmetric pupil modification deconvolution strategy for differential optical transfer function (dOTF) wavefront sensing

Context. The differential optical transfer function (dOTF) is a model-independent image-based wavefront sensor for measuring the complex pupil field (phase and amplitude). This method is particularly suitable for compensating non-common path aberrations or for the phasing of segmented telescopes that often prevent the so-called diffraction-limit resolution from being achieved with real-world instruments. Aims. The main problem inherent to the dOTF approach is to address the effect of the convolution. The resolution of the recovered complex pupil field is impacted by the size of the pupil modification. The complex pupil field estimated by the dOTF is blurred by convolution with the complex conjugate of the pupil modification. If the pupil modification involves a non-negligible region of the pupil (actuator or segment poke), it causes significant blurring and resolution loss. Methods. We propose a bisymmetric pupil modification deconvolution strategy to solve this problem. We use two different dOTFs with the opposite-sign pupil modification to identify the pupil modification location and four dOTFs with a symmetric pupil modification to complete the knowledge of their impact on the complex pupil field prior to the deconvolution process in the Fourier domain. The proposed strategy solves the intrinsic limitation of a former deconvolution algorithm, namely the cross-iteration deconvolution algorithm, which is restricted to amplitude pupil modification and precludes its applicability to phase pupil modification. Results. The bissymetric pupil modification deconvolution strategy is a novel probing pattern that permits the extension of iterative cross-deconvolution to phase-only probes. The effectiveness of the proposed approach has been validated analytically and with numerical simulations. Conclusions. The bisymmetric pupil modification deconvolution strategy can improve the resolution and accuracy of dOTF wavefront sensing and contributes to efficient and precise image-based wavefront sensing techniques.

Impact of Deconvolution Strategies in Entrepreneurship Education in Universities on Students' Innovative Behavior and Entrepreneurial Success

This research investigates the impact of entrepreneurship education in Chinese colleges on students' innovative behavior and business success. The major objective of the study is to explore the role of entrepreneurship education in nurturing students' innovative behavior and its contribution to entrepreneurial success. Fourteen semi-structured interviews were conducted with top academicians possessing industry and academic expertise. The data obtained from these interviews underwent a rigorous three-step evaluation process, including open coding, axial coding, and selective coding. Respondents were selected using the intentional convenience sampling approach. The findings emphasize the importance of entrepreneurship education in improving students' innovative behavior by cultivating creativity, problem-solving skills, and an entrepreneurial attitude. Furthermore, the research shows that entrepreneurship education has an important role in boosting entrepreneurial success by providing students with critical knowledge, skills, and support systems. Furthermore, the study identifies important characteristics and methodology of effective entrepreneurship education programs, providing practical insights for program design and execution. The study suggests that entrepreneurship education has a beneficial impact on students' inventive behavior and greatly adds to their success as entrepreneurs. It emphasizes the value of such education in developing creativity, problem-solving skills, and an entrepreneurial perspective. In addition, the study gives useful guidance for developing and implementing effective entrepreneurship education programs. The paper recommends performing quantitative studies to establish causal linkages, researching the processes by which entrepreneurship education affects innovative behavior, and investigating the transferability of effective programs across varied settings for future research.

Stochastic deconvolution of wall statistics in Reynolds‐averaged Navier–Stokes simulations based on one‐dimensional turbulence

AbstractReynolds‐averaged Navier–Stokes simulation (RaNS) is state‐of‐the‐art for numerical analysis of complex flows at high Reynolds number. Standalone RaNS may yield a reasonable estimate of the wall‐shear stress and turbulent drag if a proper wall‐function is prescribed, but detailed turbulence statistics cannot be obtained, especially at the wall. This lack in modeling is addressed here by a stochastic deconvolution strategy based on a stochastic one‐dimensional turbulence (ODT) model. Here, a one‐way coupling strategy is proposed in which a forcing term is computed from the balanced RaNS solution that is in turn utilized in the ODT model. The temporally developing ODT solution exhibits turbulent perturbations but relaxes toward the local RaNS solution due to resolved molecular‐diffusive processes. It is demonstrated that the approach is able to recover the distribution of positive wall‐shear stress fluctuations in turbulent channel flow. When formulated as post‐processing tool, it is suggested that RaNS can be enhanced by ODT providing economical means for local high‐fidelity numerical modeling based on a low‐fidelity flow solution.

CLK2 and CLK4 are regulators of DNA damage-induced NF-κB targeted by novel small molecule inhibitors

Transcription factor NF-κB potently activates anti-apoptotic genes, and its inactivation significantly reduces tumor cell survival following genotoxic stresses. We identified two structurally distinct lead compounds that selectively inhibit NF-κB activation by DNA double-strand breaks, but not by other stimuli, such as TNFα. Our compounds do not directly inhibit previously identified regulators of this pathway, most critically including IκB kinase (IKK), but inhibit signal transmission in-between ATM, PARP1, and IKKγ. Deconvolution strategies, including derivatization and invitro testing in multi-kinase panels, yielded shared targets, cdc-like kinase (CLK) 2 and 4, as essential regulators of DNA damage-induced IKK and NF-κB activity. Both leads sensitize to DNA damaging agents by increasing p53-induced apoptosis, thereby reducing cancer cell viability. We propose that our lead compounds and derivatives can be used in context of genotoxic therapy-induced or ongoing DNA damage to increase tumor cell apoptosis, which may be beneficial in cancer treatment.

A spectral kurtosis based blind deconvolution approach for spur gear fault diagnosis

Unanticipated background noises often convolute fault information in the gearboxes’ vibration response. The Blind Deconvolution strategy has been extensively applied for fault-impulse enhancement to aid gear fault detection. The existing deconvolution strategies involve designing an optimum filter applied in the time domain. Gear tooth wear leads to the excitation of Gear Mesh Frequency harmonics. Hence, spectral analysis is typically used for gearbox fault detection. As such, feature enhancement in the order domain is more practical than existing blind deconvolution approaches. This study proposes a Spectral Kurtosis-based blind deconvolution strategy with filtering done in the order domain, to aid gear fault detection. Experimental analyses show 109.76% and 64.48% better performance for constant and real-world speed operation, respectively, for the proposed method to aid spectral analysis-based fault detection.

ScAnno: a deconvolution strategy-based automatic cell type annotation tool for single-cell RNA-sequencing data sets.

Undoubtedly, single-cell RNA sequencing (scRNA-seq) has changed the research landscape by providing insights into heterogeneous, complex and rare cell populations. Given that more such data sets will become available in the near future, their accurate assessment with compatible and robust models for cell type annotation is a prerequisite. Considering this, herein, we developed scAnno (scRNA-seq data annotation), an automated annotation tool for scRNA-seq data sets primarily based on the single-cell cluster levels, using a joint deconvolution strategy and logistic regression. We explicitly constructed a reference profile for human (30 cell types and 50 human tissues) and a reference profile for mouse (26 cell types and 50 mouse tissues) to support this novel methodology (scAnno). scAnno offers a possibility to obtain genes with high expression and specificity in a given cell type as cell type-specific genes (marker genes) by combining co-expression genes with seed genes as a core. Of importance, scAnno can accurately identify cell type-specific genes based on cell type reference expression profiles without any prior information. Particularly, in the peripheral blood mononuclear cell data set, the marker genes identified by scAnno showed cell type-specific expression, and the majority of marker genes matched exactly with those included in the CellMarker database. Besides validating the flexibility and interpretability of scAnno in identifying marker genes, we also proved its superiority in cell type annotation over other cell type annotation tools (SingleR, scPred, CHETAH and scmap-cluster) through internal validation of data sets (average annotation accuracy: 99.05%) and cross-platform data sets (average annotation accuracy: 95.56%). Taken together, we established the first novel methodology that utilizes a deconvolution strategy for automated cell typing and is capable of being a significant application in broader scRNA-seq analysis. scAnno is available at https://github.com/liuhong-jia/scAnno.

Abstract 4291: Deconvolution of cell type composition in FFPE tissue: Application to benign breast disease

Abstract Purpose: Benign breast disease (BBD) is viewed as a non-obligate precursor stage in the development of breast cancer (BC). Transcriptome profiling using RNA sequencing (RNA-seq) of bulk formalin-fixed paraffin-embedded (FFPE) tissue blocks has been routinely used in BC research. However, RNA-seq of heterogeneous bulk samples represents an averaged expression profile across all cell mixtures. Downstream analyses, such as biomarker discovery, are likely confounded by differences in cell proportions. There is a lack of research in optimizing strategies for cell type deconvolution especially for FFPE samples. We evaluated and optimized strategies for cell type deconvolution using existing single cell RNA-seq (scRNA-seq) and single nucleus RNA-seq (snRNA-seq) breast tissue references, and applied these to a cohort of BBD FFPE samples to assess risk of subsequent BC. Methods: 7 scRNA-seq and 3 snRNA-seq public samples from women’s breast tissue were normalized using SCTransform, then integrated using Harmony to form a complete reference set. Bioinformatics evaluations examined the performance of existing statistical and deep learning-based deconvolution methods. Computational simulations were conducted to evaluate those methods on three scenarios: 1) baseline; 2) incomplete reference set; 3) FFPE artifacts. FFPE artifacts were simulated based on a generalized additive model using 7 in-house paired FFPE-fresh frozen samples. Finally, we applied the best performing method to our BBD cohort of 62 samples. Differential expression analysis adjusting for cell proportions was performed to identify transcriptomic aberrations in BBD cases. Results: snRNA-seq was more sensitive in capturing adipocytes, while scRNA-seq was more sensitive in detecting lymphocytes in breast tissue. The median number of detectable genes per cell was higher for scRNA-seq than snRNA-seq before integration (1264 vs 558). Marker gene-based regression methods (e.g. CIBERSORT) displayed higher variance and lower bias compared to methods directly using scRNA-seq data (e.g. SCDC). Incomplete reference data led to increased bias and little effect on variance. FFPE artifacts had a higher impact on performance compared to incomplete reference data. Overall, Scaden, a deep learning-based method, was the top performing method across all simulation scenarios and was especially robust to FFPE artifacts. 59 differential expressed genes (DEGs) were identified with fold change > 1.5 and p value < 0.01. DEGs involved in BC, e.g. S100A8, WNT4 and HIST1H3H, would be left out if cell proportions were not properly adjusted. Conclusions: We constructed the first complete single cell reference data for women’s breast tissue and benchmarked existing cell type deconvolution methods. Compared to analyses not adjusting for cell proportions, we observed substantial differences in perturbed genes and pathways associated with BC risk for BBD patients. Citation Format: Yuanhang Liu, Chen Wang, Stacey J. Winham, Aditya Bhagwate, Robert A. Vierkant, Julie M. Cunningham, Bryan M. McCauley, William A. Jons, Derek C. Radisky, Amy C. Degnim. Deconvolution of cell type composition in FFPE tissue: Application to benign breast disease. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4291.

Transcriptional profiling of the developing rat ovary following intrauterine exposure to the endocrine disruptors diethylstilbestrol and ketoconazole

Exposure to endocrine-disrupting chemicals (EDCs) during development may cause reproductive disorders in women. Although female reproductive endpoints are assessed in rodent toxicity studies, a concern is that typical endpoints are not sensitive enough to detect chemicals of concern to human health. If so, measured endpoints must be improved or new biomarkers of effects included. Herein, we have characterized the dynamic transcriptional landscape of developing rat ovaries exposed to two well-known EDCs, diethylstilbestrol (DES) and ketoconazole (KTZ), by 3’ RNA sequencing. Rats were orally exposed from day 7 of gestation until birth, and from postnatal day 1 until days 6, 14 or 22. Three exposure doses for each chemical were used: 3, 6 and 12 µg/kg bw/day of DES; 3, 6, 12 mg/kg bw/day of KTZ. The transcriptome changed dynamically during perinatal development in control ovaries, with 1137 differentially expressed genes (DEGs) partitioned into 3 broad expression patterns. A cross-species deconvolution strategy based on a mouse ovary developmental cell atlas was used to map any changes to ovarian cellularity across the perinatal period to allow for characterization of actual changes to gene transcript levels. A total of 184 DEGs were observed across dose groups and developmental stages in DES-exposed ovaries, and 111 DEGs in KTZ-exposed ovaries across dose groups and developmental stages. Based on our analyses, we have identified new candidate biomarkers for female reproductive toxicity induced by EDC, including Kcne2, Calb2 and Insl3.

Discovery of AHCY as an Off-Target of Doxorubicin by Integrative Analysis of Photoaffinity Labeling Chemoproteomics and Untargeted Metabolomics.

Target identification is critically important for understanding the mechanism of action of drugs. Here, we reported a new strategy for deconvolution of drug targets (or off-targets) with photoaffinity labeling chemoproteomics in combination with untargeted metabolomics by using doxorubicin (DOX) as a model. The DOX-derived photoaffinity probes were prepared and applied to capture DOX-interacting proteins in living cells. The captured DOX-interacting proteins were then identified by label-free quantitative proteomics. Totally, 151 significant proteins were identified with high confidence (fold change >4, p-value < 0.005). The gene ontology enrichment analysis suggested that the proteins were mainly involved in carbon metabolism, citrate cycle, fatty acid metabolism, and metabolic pathways. Therefore, untargeted metabolomics was applied to quantify the significantly altered metabolites in cells upon drug treatment. The pathway enrichment analysis suggested that DOX mainly interrupted with the processes of pyrimidine and purine metabolism, carbon metabolism, methionine metabolism, and phosphatidylcholine biosynthesis. Integrative analysis of chemoproteomics and metabolomics indicated that adenosylhomocysteinase (AHCY) is a new target (off-target) of DOX leading to the accumulation of S-adenosyl homocysteine. This deduced DOX target was confirmed by the cellular thermal shift assay, affinity competitive pull-down assay, biochemical assay, and siRNA knock down experiments. Our result suggested that AHCY is the uncovered off-target of DOX.

Deconvolution of main hydration kinetic peaks in properly sulfated Portland cements with boundary nucleation and growth models and relation to early-age concrete strength development

When the hydration kinetics of properly sulfated Portland cements is monitored by heat flow calorimetry, the initial dissolution peak and the dormant period is followed by the main hydration peak – formation of calcium-silicate-hydrates and portlandite, the so-called silicate peak. This main peak is superimposed by a peak associated to further tricalcium aluminate dissolution and a surge in precipitation of sulfoaluminates (the so-called sulfate depletion peak) right after sulfate added for setting control is depleted in the material system. In this paper strategies for the deconvolution of silicate peak and sulfate depletion peak are presented. Hereby, the boundary nucleation and growth model and the classical bulk nucleation and growth model serve as tools for deconvolution of calorimetric data. We discuss obtained kinetic parameters and their temperature dependency. The boundary nucleation and growth model for constant nucleation rate emerged as the most suitable model for the silicate reaction, the bulk nucleation and growth model with an order of three, indicating site saturation, as adequate for the sulfate depletion peak. The reaction enthalpy for tricalcium silicate hydration was obtained as 503 J/g, remarkably close to literature values. Calorimetric data related to the sulfate depletion peak is corroborated by X-ray diffraction analysis of formation history of crystalline hydration products. Furthermore, the history of the (silicate) degree of reaction as predicted by the boundary nucleation and growth model for different temperature histories is related to experimentally obtained early-age strength histories. • Investigation of hydration kinetics of commercial, well-sulfated Portland cement. • Main peak (C-S-H and portlandite formation) superimposed by sulfate depletion peak. • Strategies for deconvolution of peaks with Avrami–Cahn models as tools for analysis. • Degree of silicate reaction history is linked to early-age strength gain.

Systematic evaluation of cell-type deconvolution pipelines for sequencing-based bulk DNA methylomes.

DNA methylation analysis by sequencing is becoming increasingly popular, yielding methylomes at single-base pair and single-molecule resolution. It has tremendous potential for cell-type heterogeneity analysis using intrinsic read-level information. Although diverse deconvolution methods were developed to infer cell-type composition based on bulk sequencing-based methylomes, systematic evaluation has not been performed yet. Here, we thoroughly benchmark six previously published methods: Bayesian epiallele detection, DXM, PRISM, csmFinder+coMethy, ClubCpG and MethylPurify, together with two array-based methods, MeDeCom and Houseman, as a comparison group. Sequencing-based deconvolution methods consist of two main steps, informative region selection and cell-type composition estimation, thus each was individually assessed. With this elaborate evaluation, we aimed to establish which method achieves the highest performance in different scenarios of synthetic bulk samples. We found that cell-type deconvolution performance is influenced by different factors depending on the number of cell types within the mixture. Finally, we propose a best-practice deconvolution strategy for sequencing data and point out limitations that need to be handled. Array-based methods—both reference-based and reference-free—generally outperformed sequencing-based methods, despite the absence of read-level information. This implies that the current sequencing-based methods still struggle with correctly identifying cell-type-specific signals and eliminating confounding methylation patterns, which needs to be handled in future studies.

Identification of antimalarial targets of chloroquine by a combined deconvolution strategy of ABPP and MS-CETSA

BackgroundMalaria is a devastating infectious disease that disproportionally threatens hundreds of millions of people in developing countries. In the history of anti-malaria campaign, chloroquine (CQ) has played an indispensable role, however, its mechanism of action (MoA) is not fully understood.MethodsWe used the principle of photo-affinity labeling and click chemistry-based functionalization in the design of a CQ probe and developed a combined deconvolution strategy of activity-based protein profiling (ABPP) and mass spectrometry-coupled cellular thermal shift assay (MS-CETSA) that identified the protein targets of CQ in an unbiased manner in this study. The interactions between CQ and these identified potential protein hits were confirmed by biophysical and enzymatic assays.ResultsWe developed a novel clickable, photo-affinity chloroquine analog probe (CQP) which retains the antimalarial activity in the nanomole range, and identified a total of 40 proteins that specifically interacted and photo-crosslinked with CQP which was inhibited in the presence of excess CQ. Using MS-CETSA, we identified 83 candidate interacting proteins out of a total of 3375 measured parasite proteins. At the same time, we identified 8 proteins as the most potential hits which were commonly identified by both methods.ConclusionsWe found that CQ could disrupt glycolysis and energy metabolism of malarial parasites through direct binding with some of the key enzymes, a new mechanism that is different from its well-known inhibitory effect of hemozoin formation. This is the first report of identifying CQ antimalarial targets by a parallel usage of labeled (ABPP) and label-free (MS-CETSA) methods.