Unveiling hormone-stimulated gene mechanisms in prostate cancer: A prognostic model, immune infiltration analysis, and drug sensitivity study.

Hormones promote the progression of prostate cancer (PRCA) through the activation of a complex regulatory network. Inhibition of hormones or modulation of specific network nodes alone is insufficient to suppress the entire oncogenic network. Therefore, it is imperative to elucidate the mechanisms underlying the occurrence and development of PRCA in order to identify reliable diagnostic markers and therapeutic targets. To this end, we used publicly available data to analyze the potential mechanisms of hormone-stimulated genes in PRCA, construct a prognostic model, and assess immune infiltration and drug sensitivity. The single-cell RNA-sequencing data of PRCA were subjected to dimensionality reduction clustering and annotation, and the cells were categorized into two groups based on hormone stimulus-related scores. The differentially expressed genes between the two groups were screened and incorporated into the least absolute shrinkage and selection operator machine learning algorithm, and a prognostic model comprising six genes (ZNF862, YIF1A, USP22, TAF7, SRSF3, and SPARC) was constructed. The robustness of the model was validation through multiple methods. Immune infiltration scores in the two risk groups were calculated using three different algorithms. In addition, the relationship between the model genes and immune cell infiltration, and that between risk score and immune cell infiltration were analyzed. Drug sensitivity analysis was performed for the model genes and risk score using public databases to identify potential candidate drugs. Our findings provide novel insights into the mechanisms of hormone-stimulated genes in PRCA progression, prognosis, and drug screening.

Integrated analysis of single-cell and bulk transcriptome identifies a signature based on NK cell marker genes to predict prognosis and therapeutic response in clear cell renal cell carcinoma.

Background: Accumulating evidence has highlighted the effects of natural killer (NK) cells on shaping anti-tumor immunity. This study aimed to construct an NK cell marker gene signature (NKMS) to predict prognosis and therapeutic response of clear cell renal cell carcinoma (ccRCC) patients. Methods: Publicly available single-cell and bulk RNA profiles with matched clinical information of ccRCC patients were collected from Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), ArrayExpress, and International Cancer Genome Consortium (ICGC) databases. A novel NKMS was constructed, and its prognostic value, associated immunogenomic features and predictive capability to immune checkpoint inhibitors (ICIs) and anti-angiogenic therapies were evaluated in ccRCC patients. Results: We identified 52 NK cell marker genes by single-cell RNA-sequencing (scRNA-seq) analysis in GSE152938 and GSE159115. After least absolute shrinkage and selection operator (LASSO) and Cox regression, the most prognostic 7 genes (CLEC2B, PLAC8, CD7, SH3BGRL3, CALM1, KLRF1, and JAK1) composed NKMS using bulk transcriptome from TCGA. Survival and time-dependent receiver operating characteristic (ROC) analysis exhibited exceptional predictive capability of the signature in the training set and two independent validation cohorts (E-MTAB-1980 and RECA-EU cohorts). The seven-gene signature was able to identify patients within high Fuhrman grade (G3-G4) and American Joint Committee on Cancer (AJCC) stage (III-IV). Multivariate analysis confirmed the independent prognostic value of the signature, and a nomogram was built for clinical utility. The high-risk group was characterized by a higher tumor mutation burden (TMB) and greater infiltration of immunocytes, particularly CD8+ T cells, regulatory T (Treg) cells and follicular helper T (Tfh) cells, in parallel with higher expression of genes negatively regulating anti-tumor immunity. Moreover, high-risk tumors exhibited higher richness and diversity of T-cell receptor (TCR) repertoire. In two therapy cohorts of ccRCC patients (PMID32472114 and E-MTAB-3267), we demonstrated that high-risk group showed greater sensitivity to ICIs, whereas the low-risk group was more likely to benefit from anti-angiogenic therapy. Conclusions: We identified a novel signature that can be utilized as an independent predictive biomarker and a tool for selecting the individualized treatment for ccRCC patients.

Identification and expression analysis of chemosensory receptors in the tarsi of fall armyworm, Spodoptera frugiperda (J. E. Smith).

Chemosensation of tarsi provides moths with the ability to detect chemical signals which are important for food recognition. However, molecular mechanisms underlying the chemosensory roles of tarsi are still unknown. The fall armyworm Spodoptera frugiperda is a serious moth pest that can damage many plants worldwide. In the current study, we conducted transcriptome sequencing with total RNA extracted from S. frugiperda tarsi. Through sequence assembly and gene annotation, 23 odorant receptors 10 gustatory receptors and 10 inotropic receptors (IRs) were identified. Further phylogenetic analysis with these genes and homologs from other insect species indicated specific genes, including ORco, carbon dioxide receptors, fructose receptor, IR co-receptors, and sugar receptors were expressed in the tarsi of S. frugiperda. Expression profiling with RT-qPCR in different tissues of adult S. frugiperda showed that most annotated SfruORs and SfruIRs were mainly expressed in the antennae, and most SfruGRs were mainly expressed in the proboscises. However, SfruOR30, SfruGR9, SfruIR60a, SfruIR64a, SfruIR75d, and SfruIR76b were also highly enriched in the tarsi of S. frugiperda. Especially SfruGR9, the putative fructose receptor, was predominantly expressed in the tarsi, and with its levels significantly higher in the female tarsi than in the male ones. Moreover, SfruIR60a was also found to be expressed with higher levels in the tarsi than in other tissues. This study not only improves our insight into the tarsal chemoreception systems of S. frugiperda but also provides useful information for further functional studies of chemosensory receptors in S. frugiperda tarsi.

Heterogeneity of PTEN and PPAR-γ in cancer and their prognostic application to bladder cancer.

The aim of the present study was to determine the prognostic value of peroxisome proliferator-activated receptor-γ (PPAR-γ) and phosphatase and tensin homologue deleted on chromosome ten (PTEN) for bladder cancer. Data were collected from The Cancer Genome Atlas (TCGA), a public database, and were analyzed to assess PTEN and PPAR-γ heterogeneity as well as distinct trends in bladder cancers. Furthermore, PPAR-γ and PTEN expression levels and their association with one another were evaluated. Finally, the prognostic significance of PPAR-γ and PTEN for bladder cancer was validated in vivo using clinical samples. Based on the TCGA database, PTEN levels were significantly increased in bladder cancers (P<0.001); whereas PPAR-γ expression was downregulated in the same samples (P<0.05). Furthermore, linear correlation analysis indicated that in bladder cancers, PPAR-γ and PTEN are inversely correlated (P<0.001). The assessment and analysis of clinical samples revealed that PPAR-γ was significantly elevated in tumor tissues (P<0.001); however, PTEN was downregulated in cancer tissues (P<0.001). Furthermore, PPAR-γ expression determined by immunohistochemistry grey level (P=0.002) was also elevated in high-grade and invasive bladder cancers compared with low-grade and superficial tumors, whereas PTEN levels exhibited the opposite in this analysis (P=0.001). In individuals with lymphoid metastasis, PPAR-γ was significantly increased (P<0.001), and PTEN was significantly decreased (P<0.001). Pearson analysis revealed a significant negative correlation between PPAR-γ and PTEN expression (r=-0.604, P<0.05). In conclusion, tissue heterogeneity was observed with respect to PPAR-γ and PTEN expression in bladder cancer. PTEN and PPAR-γ expression are negatively correlated and may be excellent indicators of bladder cancer tumorigenesis and progression.

Single-shot time-gated fluorescence lifetime imaging using three-frame images.

Qualitative and quantitative measurements of complex flows demand for fast single-shot fluorescence lifetime imaging (FLI) technology with high precision. A method, single-shot time-gated fluorescence lifetime imaging using three-frame images (TFI-TGFLI), is presented. To our knowledge, it is the first work to combine a three-gate rapid lifetime determination (RLD) scheme and a four-channel framing camera to achieve this goal. Different from previously proposed two-gate RLD schemes, TFI-TGFLI can provide a wider lifetime range 0.6 ~ 13ns with reasonable precision. The performances of the proposed approach have been examined by both Monte-Carlo simulations and toluene seeded gas mixing jet diagnosis experiments. The measured average lifetimes of the whole excited areas agree well with the results obtained by the streak camera, and they are 7.6ns (N2 = 7L/min; O2 < 0.1L/min) and 2.6ns (N2 = 19L/min; O2 = 1L/min) with the standard deviations of 1.7ns and 0.8ns among the lifetime image pixels, respectively. The concentration distributions of the quenchers and fluorescent species were further analyzed, and they are consistent with the experimental settings.

Artificial neural network approaches for fluorescence lifetime imaging techniques.

A novel high-speed fluorescence lifetime imaging (FLIM) analysis method based on artificial neural networks (ANN) has been proposed. In terms of image generation, the proposed ANN-FLIM method does not require iterative searching procedures or initial conditions, and it can generate lifetime images at least 180-fold faster than conventional least squares curve-fitting software tools. The advantages of ANN-FLIM were demonstrated on both synthesized and experimental data, showing that it has great potential to fuel current revolutions in rapid FLIM technologies.

Fast bi-exponential fluorescence lifetime imaging analysis methods: publisher's note.

Table 1 of an earlier paper [Opt. Lett.40, 336 (2015)10.1364/OL.40.000336] contained an incorrect mathematical expression. The error is rectified here.

Estimation of Fluorescence Lifetimes Via Rotational Invariance Techniques.

Estimation of signal parameters via rotational invariance techniques is a classical algorithm widely used in array signal processing for direction-of-arrival estimation of emitters. Inspired by this method, a new signal model and new fluorescence lifetime estimation via rotational invariance techniques (FLERIT) were developed for multiexponential fluorescence lifetime imaging (FLIM) experiments. The FLERIT only requires a few time bins of a histogram generated by a time-correlated single-photon counting FLIM system, greatly reducing the data throughput from the imager to the signal processing units. As a noniterative method, the FLERIT does not require initial conditions, prior information nor model selection that are usually required by widely used traditional fitting methods, including nonlinear least square methods or maximum-likelihood methods. Moreover, its simplicity means it is suitable for implementations in embedded systems for real-time applications. FLERIT was tested on synthesized and experimental fluorescent cell data showing the potentials to be widely applied in FLIM data analysis.

Fast bi-exponential fluorescence lifetime imaging analysis methods.

A new hardware-friendly bi-exponential fluorescence lifetime imaging (FLIM) algorithm has been proposed. Compared to conventional FLIM software, the proposed algorithms are noniterative offering direct calculation of lifetimes and therefore suitable for real-time applications. They are applicable to single-channel or 2D multichannel time-correlated single-photon counting (TCSPC) systems. The proposed methods have been tested on both synthesized and realistic FLIM data, and we have compared their performances with other recently proposed nonfitting bi-exponential techniques showing promising applications in future massive solid-state TCSPC imagers.

Expression, purification and crystallization of CTB-MPR, a candidate mucosal vaccine component against HIV-1.

CTB-MPR is a fusion protein between the B subunit of cholera toxin (CTB) and the membrane-proximal region of gp41 (MPR), the transmembrane envelope protein of Human immunodeficiency virus 1 (HIV-1), and has previously been shown to induce the production of anti-HIV-1 antibodies with antiviral functions. To further improve the design of this candidate vaccine, X-ray crystallography experiments were performed to obtain structural information about this fusion protein. Several variants of CTB-MPR were designed, constructed and recombinantly expressed in Escherichia coli. The first variant contained a flexible GPGP linker between CTB and MPR, and yielded crystals that diffracted to a resolution of 2.3 Å, but only the CTB region was detected in the electron-density map. A second variant, in which the CTB was directly attached to MPR, was shown to destabilize pentamer formation. A third construct containing a polyalanine linker between CTB and MPR proved to stabilize the pentameric form of the protein during purification. The purification procedure was shown to produce a homogeneously pure and monodisperse sample for crystallization. Initial crystallization experiments led to pseudo-crystals which were ordered in only two dimensions and were disordered in the third dimension. Nanocrystals obtained using the same precipitant showed promising X-ray diffraction to 5 Šresolution in femtosecond nanocrystallography experiments at the Linac Coherent Light Source at the SLAC National Accelerator Laboratory. The results demonstrate the utility of femtosecond X-ray crystallography to enable structural analysis based on nano/microcrystals of a protein for which no macroscopic crystals ordered in three dimensions have been observed before.

A high-efficiency real-time digital signal averager for time-of-flight mass spectrometry.

RATIONALE: Analog-to-digital converter (ADC)-based acquisition systems are widely applied in time-of-flight mass spectrometers (TOFMS) due to their ability to record the signal intensity of all ions within the same pulse. However, the acquisition system raises the requirement for data throughput, along with increasing the conversion rate and resolution of the ADC. It is therefore of considerable interest to develop a high-performance real-time acquisition system, which can relieve the limitation of data throughput. METHODS: We present in this work a high-efficiency real-time digital signal averager, consisting of a signal conditioner, a data conversion module and a signal processing module. Two optimization strategies are implemented using field programmable gate arrays (FPGAs) to enhance the efficiency of the real-time processing. A pipeline procedure is used to reduce the time consumption of the accumulation strategy. To realize continuous data transfer, a high-efficiency transmission strategy is developed, based on a ping-pong procedure. RESULTS: The digital signal averager features good responsiveness, analog bandwidth and dynamic performance. The optimal effective number of bits reaches 6.7 bits. For a 32 µs record length, the averager can realize 100% efficiency with an extraction frequency below 31.23 kHz by modifying the number of accumulation steps. In unit time, the averager yields superior signal-to-noise ratio (SNR) compared with data accumulation in a computer. CONCLUSIONS: The digital signal averager is combined with a vacuum ultraviolet single-photon ionization time-of-flight mass spectrometer (VUV-SPI-TOFMS). The efficiency of the real-time processing is tested by analyzing the volatile organic compounds (VOCs) from ordinary printed materials. In these experiments, 22 kinds of compounds are detected, and the dynamic range exceeds 3 orders of magnitude.

Systematic characterization of mutations in yeast acetohydroxyacid synthase. Interpretation of herbicide-resistance data.

Acetohydroxyacid synthase (AHAS, EC 4.1.3.18) catalyses the first step in branched-chain amino acid biosynthesis and is the target for sulfonylurea and imidazolinone herbicides, which act as potent and specific inhibitors. Mutants of the enzyme have been identified that are resistant to particular herbicides. However, the selectivity of these mutants towards various sulfonylureas and imidazolinones has not been determined systematically. Now that the structure of the yeast enzyme is known, both in the absence and presence of a bound herbicide, a detailed understanding of the molecular interactions between the enzyme and its inhibitors becomes possible. Here we construct 10 active mutants of yeast AHAS, purify the enzymes and determine their sensitivity to six sulfonylureas and three imidazolinones. An additional three active mutants were constructed with a view to increasing imidazolinone sensitivity. These three variants were purified and tested for their sensitivity to the imidazolinones only. Substantial differences are observed in the sensitivity of the 13 mutants to the various inhibitors and these differences are interpreted in terms of the structure of the herbicide-binding site on the enzyme.