The pan-cancer landscape of abnormal DNA methylation and intratumor microorganisms.

Microorganisms play very important roles in carcinogenesis, tumor progression, and resistance upon treatment. Due to the challenge of accurately acquiring samples and quantifying low-biomass tissue microorganisms, most studies have focused on the effect of gut microorganisms on cancer treatments, especially the efficacy of immunotherapy. Although recent publications reveal the potential interactions between intratumor microorganisms and the immune microenvironment, whether and to what extent the intratumor microorganism could affect progression and treatment outcome remain controversial. This study is aiming to evaluate the associations among intratumor microorganisms, DNA methylation cancer driver genes, immune response, and clinical outcomes from a pan-cancer perspective, using 6,876 TCGA samples across 21 cancer types. We revealed that tumor microorganism dysbiosis is closely associated with the abnormal tumor methylome and/or tumor microenvironment, which might serve to enhance the proliferation ability and fitness for the therapy of tumors. These findings shed the light on a better understanding of the interactions between tumor cells and carcinogens during and after tumor formation, as well as microorganism-associated methylation alterations that could further serve as biomarkers for clinical outcome assessment.

Diet and human reproductive system: Insight of omics approaches.

Nutrition and lifestyle have a great impact on reproduction and infertility in humans, as they are essential for certain processes such as implantation, placental growth, angiogenesis, and the transfer of nutrients from the mother to the fetus. The aim of this review is to provide the interconnection between nutrition and reproductive health through the insight of omics approaches (including metabolomics and nutrigenomics). The effect of various macronutrients, micronutrients, and some food-associated components on male and female reproduction was discussed. Recent research work was collected through database search from 2010 to 2020 to identify eligible studies. Alterations of metabolic pathways in pregnant women were deliberated with an emphasis on different strategies of lifestyle and dietary interventions. Several nutritional methods, which are important for embryonic and child neurological development, nutritional supplements to lactation, and improved gestational length along with birth weight have been emphasized. Considerable advances in omics strategies show potential technological development for improving human reproductive health.

Mechanisms and Advances in Anti-Ovarian Cancer with Natural Plants Component.

Ovarian cancer ranks seventh in the most common malignant tumors among female disease, which seriously threatens female reproductive health. It is characterized by hidden pathogenesis, missed diagnosis, high reoccurrence rate, and poor prognosis. In clinic, the first-line treatment prioritized debulking surgery with paclitaxel-based chemotherapy. The harsh truth is that female patients are prone to relapse due to the dissemination of tumor cells and drug resistance. In these circumstances, the development of new therapy strategies combined with traditional approaches is conductive to improving the quality of treatment. Among numerous drug resources, botanical compounds have unique advantages due to their potentials in multitarget functions, long application history, and wide availability. Previous studies have revealed the therapeutic effects of bioactive plant components in ovarian cancer. These natural ingredients act as part of the initial treatment or an auxiliary option for maintenance therapy, further reducing the tumor and metastatic burden. In this review, we summarized the functions and mechanisms of natural botanical components applied in human ovarian cancer. We focused on the molecular mechanisms of cell apoptosis, autophagy, RNA and DNA lesion, ROS damage, and the multiple-drug resistance. We aim to provide a theoretical reference for in-depth drug research so as to manage ovarian cancer better in clinic.

Met is required for oligodendrocyte progenitor cell migration in Danio rerio.

During vertebrate central nervous system development, most oligodendrocyte progenitor cells (OPCs) are specified in the ventral spinal cord and must migrate throughout the neural tube until they become evenly distributed, occupying non-overlapping domains. While this process of developmental OPC migration is well characterized, the nature of the molecular mediators that govern it remain largely unknown. Here, using zebrafish as a model, we demonstrate that Met signaling is required for initial developmental migration of OPCs, and, using cell-specific knock-down of Met signaling, show that Met acts cell-autonomously in OPCs. Taken together, these findings demonstrate in vivo, the role of Met signaling in OPC migration and provide new insight into how OPC migration is regulated during development.

Dulaglutide, a long-acting GLP-1 receptor agonist, can improve hyperandrogenemia and ovarian function in DHEA-induced PCOS rats.

OBJECTIVE: The purpose of this study was to explore the effect of dulaglutide on DHEA induced PCOS rats and its mechanism, to provide new drugs and research directions for clinical treatment of PCOS. METHODS: In this study, the PCOS model was established by giving female SD rats subcutaneous injection of DHEA for 21 consecutive days. After modeling, the treatment group was injected subcutaneously with three doses of dulaglutide for 3 weeks. The model group was injected with sterile ultrapure water, and the normal group did not get any intervention. The body weight changes of rats in each group were recorded from the first day when rats received the administration of dulaglutide. Three weeks later, the rats were fasted the night after the last treatment, determined fasting insulin and fasting glucose the next day. After the rats were anesthetized by chloral hydrate, more blood was collected from the heart of the rat. The serum insulin, testosterone and sex hormone binding globulin (SHBG) levels were detected by the enzyme-linked immunoassay method. After removing the adipose tissue, the obtained rat ovary tissue was used for subsequent experimental detection, using HE staining for morphology and follicular development analysis; qRT-PCR for the detection of 3ßHSD, CYP17α1, CYP19α1, and StAR gene expression in ovarian tissue; and western blotting analysis of CYP17α1, CYP19α1, StAR protein expression and insulin level to verify whether dulaglutide has a therapeutic effect on PCOS in rats. RESULTS: After treated with different concentrations of dulaglutide, we found that the body weight of rats in the treatment groups were reduced. Compared with the rats in PCOS group, the serum androgen level of rats in the treatment groups was significantly decreased, and the serum sex hormone binding protein content was significantly increased, and there was statistically significant difference between these groups and PCOS group. In terms of protein expression and gene regulation, the expression of 3ßHSD, CYP19α1 and StAR in the ovarian tissue of rats in treatment groups were decreased significantly after received the treatment of dulaglutide, and there was statistically significant difference between these groups and PCOS group. In addition, dulaglutide reduced the insulin content in the ovarian tissue of PCOS rats. CONCLUSION: Dulaglutide may reduce the hyperandrogenemia of PCOS rats by regulating the content of serum SHBG and the expression of 3ßHSD, CYP19α1, and StAR related genes and proteins, thereby inhibiting the excessive development of small follicles and the formation of cystic follicles in the ovaries of PCOS rats, thereby improving polycystic ovary in PCOS rats. In addition, dulaglutide may reduce the weight of PCOS rats, further reducing the level of high androgen in PCOS rats, and improving the morphology of their polycystic ovaries.

Multimodal Neuroimaging Predictors of Learning Performance of Sensorimotor Rhythm Up-Regulation Neurofeedback.

Electroencephalographic (EEG) neurofeedback (NFB) is a popular neuromodulation method to help one selectively enhance or inhibit his/her brain activities by means of real-time visual or auditory feedback of EEG signals. Sensory motor rhythm (SMR) NFB protocol has been applied to improve cognitive performance, but a large proportion of participants failed to self-regulate their brain activities and could not benefit from NFB training. Therefore, it is important to identify the neural predictors of SMR up-regulation NFB training performance for a better understanding the mechanisms of individual difference in SMR NFB. Twenty-seven healthy participants (12 males, age: 23.1 ± 2.36) were enrolled to complete three sessions of SMR up-regulation NFB training and collection of multimodal neuroimaging data [resting-state EEG, structural magnetic resonance imaging (MRI), and resting-state functional MRI (fMRI)]. Correlation analyses were performed between within-session NFB learning index and anatomical and functional brain features extracted from multimodal neuroimaging data, in order to identify the neuroanatomical and neurophysiological predictors for NFB learning performance. Lastly, machine learning models were trained to predict NFB learning performance using features from each modality as well as multimodal features. According to our results, most participants were able to successfully increase the SMR power and the NFB learning performance was significantly correlated with a set of neuroimaging features, including resting-state EEG powers, gray/white matter volumes from MRI, regional and functional connectivity (FC) of resting-state fMRI. Importantly, results of prediction analysis indicate that NFB learning index can be better predicted using multimodal features compared with features of single modality. In conclusion, this study highlights the importance of multimodal neuroimaging technique as a tool to explain the individual difference in within-session NFB learning performance, and could provide a theoretical framework for early identification of individuals who cannot benefit from NFB training.

The glucagon-like peptide-1 (GLP-1) analog exenatide ameliorates intrauterine adhesions in mice.

OBJECTIVE: The purpose of the experiments in this study was to explore the effect of exenatide on intrauterine adhesions (IUAs) and to elucidate its mechanism to provide new ideas for the clinical treatment of IUAs. METHODS: In this study, an animal model of IUAs was established by double stimulation using mechanical curettage and inflammation. After modeling, the treatment group was injected subcutaneously with three doses of exenatide for two weeks. The model group was injected with sterile ultrapure water, and the sham operation group was treated the same as the normal group, except for the observation of abdominal wound changes. Two weeks later, all mice were sacrificed by cervical dysfunction. The obtained mouse uterine tissue was used for subsequent experimental detection, using HE and Masson staining for histomorphological and pathological analysis; qRT-PCR for the detection of TGF-ß1, α-SMA, and MMP-9 gene expression in uterine tissue; and western blotting analysis of TGF-ß1, α-SMA, and collagen 1 protein expression to verify whether exenatide has a therapeutic effect on IUAs in mice. RESULTS: In the high-dose exenatide treatment group, the endometrial glands significantly increased in size, and the deposition area of collagen fibers in the endometrial tissue was significantly reduced. We observed that the mRNA expression of TGF-ß1 and α-SMA in the endometrial tissue of IUAs mice in this group was significantly reduced, while the expression of MMP-9 was significantly increased. In addition, we found that the protein expression of TGF-ß1, α-SMA, and collagen 1 remarkably decreased after treatment with exenatide. CONCLUSION: Exenatide may reduce the deposition of collagen fibers in the uterus of IUAs mice and promote the proliferation of endometrial glands in mice.

The Emerging Roles of Extracellular Vesicles in Ovarian Cancer.

Ovarian cancer (OC) is one of the deadliest gynecological malignancy. Epithelial ovarian cancer (EOC) is its most common form. OC has both, a poor prognosis and a high mortality rate due to the difficulties of early diagnosis, limitation of current treatment and resistance to chemotherapy. Extracellular vesicles (EVs) is a heterogeneous group of cell-derived submicron vesicles, which can be detected in body fluids, and it can be classified into three main types including exosomes, micro-vesicles, and apoptotic bodies. Cancer cells can produce more EVs than healthy cells. Moreover, the contents of these EVs have been found distinctive from each other. It has been considered that EVs shedding from tumor cells may be implicated in clinical applications, such as a tool for tumor diagnosis, prognosis and potential treatment of certain cancers. In this review, we provide a brief description of EVs. in diagnosis, prognosis, treatment, and drug-resistantance of OC. Cancer-related EVs show powerful influences on tumors by various biological mechanisms. However, the contents mentioned above remain in the laboratory stage and there is a lack of large-scale clinical trials, and the maturity of the purification and detection methods is a constraint. In addition, amplification of oncogenes on ecDNA is remarkably prevalent in cancer. It may be possible that ecDNA can be encapsulated in EVs and thus detected by us. In summary, much more research on EVs needs to be performed to reveal breakthroughs in OC and to accelerate the process of its application in clinic.

Targeted realignment of LC-MS profiles by neighbor-wise compound-specific graphical time warping with misalignment detection.

MOTIVATION: Liquid chromatography-mass spectrometry (LC-MS) is a standard method for proteomics and metabolomics analysis of biological samples. Unfortunately, it suffers from various changes in the retention times (RT) of the same compound in different samples, and these must be subsequently corrected (aligned) during data processing. Classic alignment methods such as in the popular XCMS package often assume a single time-warping function for each sample. Thus, the potentially varying RT drift for compounds with different masses in a sample is neglected in these methods. Moreover, the systematic change in RT drift across run order is often not considered by alignment algorithms. Therefore, these methods cannot effectively correct all misalignments. For a large-scale experiment involving many samples, the existence of misalignment becomes inevitable and concerning. RESULTS: Here, we describe an integrated reference-free profile alignment method, neighbor-wise compound-specific Graphical Time Warping (ncGTW), that can detect misaligned features and align profiles by leveraging expected RT drift structures and compound-specific warping functions. Specifically, ncGTW uses individualized warping functions for different compounds and assigns constraint edges on warping functions of neighboring samples. Validated with both realistic synthetic data and internal quality control samples, ncGTW applied to two large-scale metabolomics LC-MS datasets identifies many misaligned features and successfully realigns them. These features would otherwise be discarded or uncorrected using existing methods. The ncGTW software tool is developed currently as a plug-in to detect and realign misaligned features present in standard XCMS output. AVAILABILITY AND IMPLEMENTATION: An R package of ncGTW is freely available at Bioconductor and https://github.com/ChiungTingWu/ncGTW. A detailed user's manual and a vignette are provided within the package. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SynQuant: an automatic tool to quantify synapses from microscopy images.

MOTIVATION: Synapses are essential to neural signal transmission. Therefore, quantification of synapses and related neurites from images is vital to gain insights into the underlying pathways of brain functionality and diseases. Despite the wide availability of synaptic punctum imaging data, several issues are impeding satisfactory quantification of these structures by current tools. First, the antibodies used for labeling synapses are not perfectly specific to synapses. These antibodies may exist in neurites or other cell compartments. Second, the brightness of different neurites and synaptic puncta is heterogeneous due to the variation of antibody concentration and synapse-intrinsic differences. Third, images often have low signal to noise ratio due to constraints of experiment facilities and availability of sensitive antibodies. These issues make the detection of synapses challenging and necessitates developing a new tool to easily and accurately quantify synapses. RESULTS: We present an automatic probability-principled synapse detection algorithm and integrate it into our synapse quantification tool SynQuant. Derived from the theory of order statistics, our method controls the false discovery rate and improves the power of detecting synapses. SynQuant is unsupervised, works for both 2D and 3D data, and can handle multiple staining channels. Through extensive experiments on one synthetic and three real datasets with ground truth annotation or manually labeling, SynQuant was demonstrated to outperform peer specialized unsupervised synapse detection tools as well as generic spot detection methods. AVAILABILITY AND IMPLEMENTATION: Java source code, Fiji plug-in, and test data are available at https://github.com/yu-lab-vt/SynQuant. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Aberrant Calcium Signaling in Astrocytes Inhibits Neuronal Excitability in a Human Down Syndrome Stem Cell Model.

Down syndrome (DS) is a genetic disorder that causes cognitive impairment. The staggering effects associated with an extra copy of human chromosome 21 (HSA21) complicates mechanistic understanding of DS pathophysiology. We examined the neuron-astrocyte interplay in a fully recapitulated HSA21 trisomy cellular model differentiated from DS-patient-derived induced pluripotent stem cells (iPSCs). By combining calcium imaging with genetic approaches, we discovered the functional defects of DS astroglia and their effects on neuronal excitability. Compared with control isogenic astroglia, DS astroglia exhibited more-frequent spontaneous calcium fluctuations, which reduced the excitability of co-cultured neurons. Furthermore, suppressed neuronal activity could be rescued by abolishing astrocytic spontaneous calcium activity either chemically by blocking adenosine-mediated signaling or genetically by knockdown of inositol triphosphate (IP3) receptors or S100B, a calcium binding protein coded on HSA21. Our results suggest a mechanism by which DS alters the function of astrocytes, which subsequently disturbs neuronal excitability.

DETECTION AND TRACKING OF MIGRATING OLIGODENDROCYTE PROGENITOR CELLS FROM IN VIVO FLUORESCENCE TIME-LAPSE IMAGING DATA.

In this work, we develop a fully automatic algorithm named "MCDT" (Migrating Cell Detector and Tracker) for the integrated task of migrating cell detection, segmentation and tracking from in vivo fluorescence time-lapse microscopy imaging data. The interest of detecting and tracking migrating cells arouses from the scientific question in understanding the impact of oligodendrocyte progenitor cells (OPCs) migration in vivo, using advanced microscopy imaging techniques. Current practice of OPC mobility analysis relies on manual labeling, suffering from massive human labor, subjective biases, and weak reproducibility. Existing cell tracking methods have difficulties in analyzing such challenging data due to the extra complexity of in vivo data. Designed for in vivo data, MCDT circumvents the common strong assumption of separable feature distributions between foreground and background. Besides, by focusing on migrating cells (OPCs) only, MCDT relieves the burden of tracking all irrelevant cells correctly, not only accelerating the analysis but also achieving better accuracy in OPCs. Seed based segmentation and tracking by topology-preserved motion estimation endows MCDT with robustness to complex surroundings of the cell under tracking and to occasional inaccurate segmentation in some frames. We tested MCDT on imaging data of transgenic zebrafish larval spinal cord and MCDT showed very promising performance.

LncRNA HOTTIP modulates cancer stem cell properties in human pancreatic cancer by regulating HOXA9.

Our previous study demonstrated that long non-coding RNA (lncRNA) HOTTIP was maximally expressed in PDAC, and promoted cancer cell progression and epithelial to mesenchymal transition (EMT). Numerous studies indicated that lncRNAs or EMT supported cancer stem cells. However, the role of HOTTIP in pancreatic cancer stem cells (PCSCs) remains unclear. Here, we evaluated the role and mechanism of HOTTIP in PCSCs. First, we analyzed the relationship between HOTTIP expression and overall or disease-free survival in 90 patients with PDAC after radical resection. Patients with higher HOTTIP expression had shorter disease-free survival and overall survival than those with lower expression. Expression of HOTTIP and other lncRNAs was detected in PCSCs and non-PCSCs by laser capture microdissection (LCM). HOTTIP was highly expressed in PCSCs. In addition, in vitro assays showed that HOTTIP alterations affected stemness, including sphericity, tumorigenesis, and stem factors (LIN28, NANOG, OCT4, and SOX2) and markers (ALDH1, CD44, and CD133). Mechanistically, HOTTIP mediated HOXA9 to enhance the Wnt/ß-catenin pathway by binding to WDR5 in PCSCs. In vivo results showed that HOTTIP or HOXA9 alterations influenced stemness. Our results indicate that the HOTTIP/WDR5/HOXA9/Wnt axis contributes to PCSC stemness and is a potential therapeutic target for PDAC.

Automated Functional Analysis of Astrocytes from Chronic Time-Lapse Calcium Imaging Data.

Recent discoveries that astrocytes exert proactive regulatory effects on neural information processing and that they are deeply involved in normal brain development and disease pathology have stimulated broad interest in understanding astrocyte functional roles in brain circuit. Measuring astrocyte functional status is now technically feasible, due to recent advances in modern microscopy and ultrasensitive cell-type specific genetically encoded Ca2+ indicators for chronic imaging. However, there is a big gap between the capability of generating large dataset via calcium imaging and the availability of sophisticated analytical tools for decoding the astrocyte function. Current practice is essentially manual, which not only limits analysis throughput but also risks introducing bias and missing important information latent in complex, dynamic big data. Here, we report a suite of computational tools, called Functional AStrocyte Phenotyping (FASP), for automatically quantifying the functional status of astrocytes. Considering the complex nature of Ca2+ signaling in astrocytes and low signal to noise ratio, FASP is designed with data-driven and probabilistic principles, to flexibly account for various patterns and to perform robustly with noisy data. In particular, FASP explicitly models signal propagation, which rules out the applicability of tools designed for other types of data. We demonstrate the effectiveness of FASP using extensive synthetic and real data sets. The findings by FASP were verified by manual inspection. FASP also detected signals that were missed by purely manual analysis but could be confirmed by more careful manual examination under the guidance of automatic analysis. All algorithms and the analysis pipeline are packaged into a plugin for Fiji (ImageJ), with the source code freely available online at https://github.com/VTcbil/FASP.

The long non-coding RNA HOTTIP promotes progression and gemcitabine resistance by regulating HOXA13 in pancreatic cancer.

BACKGROUND: The human genome encodes many long non-coding RNAs (lncRNAs). However, their biological functions, molecular mechanisms, and the prognostic value associated with pancreatic ductal adenocarcinoma (PDAC) remain to be elucidated. Here, we identify a fundamental role for the lncRNA HOXA transcript at the distal tip (HOTTIP) in the progression and chemoresistance of PDAC. METHODS: High-throughput microarrays were performed to detect the expression profiles of lncRNAs and messenger RNAs in eight human PDAC tissues and four pancreatic tissues. Quantitative real-time PCR was used to determine the levels of HOTTIP and HOXA13 transcripts in PDAC cell lines and 90 PDAC samples from patients. HPDE6 cells (immortalized human pancreatic ductal epithelial cells) and corresponding adjacent non-neoplastic tissues were used as controls, respectively. The functions of HOTTIP and HOXA13 in cell proliferation, invasion, and epithelial-mesenchymal transition were evaluated by targeted knockdown in vitro. CCK-8 assays, colony formation assays, and xenografts in nude mice were used to investigate whether targeted silencing of HOTTIP could sensitize pancreatic cancer cells to gemcitabine. Immunohistochemistry was performed to investigate the relationship between HOXA13 expression and patient outcome. RESULTS: Microarray analyses revealed that HOTTIP was one of the most significantly upregulated lncRNAs in PDAC tissues compared with pancreatic tissues. Quantitative PCR further verified that HOTTIP levels were increased in PDAC cell lines and patient samples compared with controls. Functionally, HOTTIP silencing resulted in proliferation arrest by altering cell-cycle progression, and impaired cell invasion by inhibiting epithelial-mesenchymal transition in pancreatic cancer. Additionally, inhibition of HOTTIP potentiated the antitumor effects of gemcitabine in vitro and in vivo. Furthermore, knockdown of HOXA13 by RNA interference (siHOXA13) revealed that HOTTIP promoted PDAC cell proliferation, invasion, and chemoresistance, at least partly through regulating HOXA13. Immunohistochemistry results revealed that higher HOXA13 expression was correlated with lymph node metastasis, poor histological differentiation, and decreased overall survival in PDAC patients. CONCLUSIONS: As a crucial tumor promoter, HOTTIP promotes cell proliferation, invasion, and chemoresistance by modulating HOXA13. Therefore, the HOTTIP/HOXA13 axis is a potential therapeutic target and molecular biomarker for PDAC.