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Abstract Background To examine the expression characteristics of single nucleotide polymorphisms (SNPs) in the SRD5A2 gene and investigate their potential association with differences in the clinical characteristics between sexes in patients with chronic hepatitis B virus (HBV) infection. Methods A total of 30 loci in six genes primarily involved in the metabolism and signaling of sex hormones/sex hormone receptors, namely AKR1C2, AKR1C3, HSD17B6, SRD5A1, SRD5A2, and ESR1, were genotyped in 1007 patients from eight counties (cities) in Northeastern China with chronic HBV infection and 1040 healthy controls, and their association with viral replication characteristics and the differences in disease severity between sexes was assessed. Western blotting was conducted to determine the hepatic SRD5A2 protein level and its relationship with the inflammatory activity and fibrosis degree in male and female patients. Results Two SNP loci in the SRD5A2 gene (rs12470143 and rs7594951) exhibited significant differences in genotype and allele frequencies between sexes, with the proportion of T alleles significantly higher in males than in females. It was found that the incidence and severity of HBV-related liver fibrosis were significantly higher in patients with the T/T genotype in SRD5A2 rs12470143 and rs7594951 than those with the non-T/T genotype. Additionally, serum HBV DNA levels were significantly elevated in T/T patients compared to non-T/T patients. Female patients exhibited significantly lower serum DNA levels compared to male patients. Western blot analysis indicated that greater hepatic SRD5A2 protein levels were associated with higher METAVIR inflammation and fibrosis scores. Furthermore, multivariate analysis showed that the two genetic variants in the SRD5A2 gene (rs12470143 C > T, r7594951 C > T), together with the male sex, age > 50 years old, HBeAg positive status, elevated serum HBsAg load, high serum HBV DNA load, and HBV genotype C, were independent risk factors for HBV-related liver fibrosis. Conclusions This study demonstrated that two genetic variants in the SRD5A2 gene (rs12470143 C > T, r7594951 C > T) are associated with sex differences in the clinical characteristics of patients with chronic HBV infection.

Abstract Background Single-cell RNA sequencing (scRNA-seq) has revolutionized the transcriptomics field by advancing analyses from tissue-level to cell-level resolution. Despite the great advances in the development of computational methods for various steps of scRNA-seq analyses, one major bottleneck of the existing technologies remains in identifying the molecular relationship between disease phenotype and cell subpopulations, where “disease phenotype” refers to the clinical characteristics of each patient sample, and subpopulation refer to groups of single cells, which often do not correspond to clusters identified by standard single-cell clustering analysis. Here, we present PACSI, a method aimed at distinguishing cell subpopulations associated with disease phenotypes at the single-cell level. Results PACSI takes advantage of the topological properties of biological networks to introduce a proximity-based measure that quantifies the correlation between each cell and the disease phenotype of interest. Applied to simulated data and four case studies, PACSI accurately identified cells associated with disease phenotypes such as diagnosis, prognosis, and response to immunotherapy. In addition, we demonstrated that PACSI can also be applied to spatial transcriptomics data and successfully label spots that are associated with poor survival of breast carcinoma. Conclusions PACSI is an efficient method to identify cell subpopulations associated with disease phenotypes. Our research shows that it has a broad range of applications in revealing mechanistic and clinical insights of diseases.

Abstract Background Neuroinflammation in the rostral ventrolateral medulla (RVLM) has been associated with the pathogenesis of stress-induced hypertension (SIH). Neuronal mitochondrial dysfunction is involved in many pathological and physiological processes. However, the impact of neuroinflammation on neuronal mitochondrial homeostasis and the involved signaling pathway in the RVLM during SIH are largely unknown. Methods The morphology and phenotype of microglia and the neuronal mitochondrial injury in vivo were analyzed by immunofluorescence, Western blot, RT-qPCR, transmission electron microscopy, and kit detection. The underlying mechanisms of microglia-derived tumor necrosis factor‐α (TNF-α) on neuronal mitochondrial function were investigated through in vitro and in vivo experiments such as immunofluorescence and Western blot. The effect of TNF-α on blood pressure (BP) regulation was determined in vivo via intra-RVLM microinjection of TNF-α receptor antagonist R7050. Results The results demonstrated that BP, heart rate (HR), renal sympathetic nerve activity (RSNA), plasma norepinephrine (NE), and electroencephalogram (EEG) power increased in SIH rats. Furthermore, the branching complexity of microglia in the RVLM of SIH rats decreased and polarized into M1 phenotype, accompanied by upregulation of TNF‐α. Increased neuronal mitochondria injury was observed in the RVLM of SIH rats. Mechanistically, Sirtuin 3 (Sirt3) and p-AMPK expression were markedly downregulated in both SIH rats and TNF-α–treated N2a cells. AMPK activator A769662 upregulated AMPK–Sirt3 signaling pathway and consequently reversed TNF-α–induced mitochondrial dysfunction. Microinjection of TNF-α receptor antagonist R7050 into the RVLM of SIH rats significantly inhibited the biological activities of TNF-α, increased p‐AMPK and Sirt3 levels, and alleviated neuronal mitochondrial injury, thereby reducing c-FOS expression, RSNA, plasma NE, and BP. Conclusions This study revealed that microglia-derived TNF-α in the RVLM impairs neuronal mitochondrial function in SIH possibly through inhibiting the AMPK–Sirt3 pathway. Therefore, microglia-derived TNF-α in the RVLM may be a possible therapeutic target for the intervention of SIH.

Abstract Background CircRNAs are essential for the regulation of post-transcriptional gene expression, including as miRNA sponges, and play an important role in disease development. Some computational tools have been proposed recently to predict circRNA, since only one classifier is used, there is still much that can be done to improve the performance. Results StackCirRNAPred was proposed, the computational classification of long circRNA from other lncRNA based on stacking strategy. In order to cope with the potential problem that a single feature might not be able to distinguish circRNA well from other lncRNA, we first extracted features from different sources, including nucleic acid composition, sequence spatial features and physicochemical properties, Alu and tandem repeats. We innovatively apply the stacking strategy to integrate the more advantageous classifiers of RF, LightGBM, XGBoost. This allows the model to incorporate these features more flexibly. StackCirRNAPred was found to be significantly better than other tools, with precision, accuracy, F1, recall and MCC of 0.843, 0.833, 0.831, 0.819 and 0.666 respectively. We tested it directly on the mouse dataset. StackCirRNAPred was still significantly better than other methods, with precision, accuracy, F1, recall and MCC of 0.837, 0.839, 0.839, 0.841, 0.677. Conclusions We proposed StackCirRNAPred based on stacking strategy to distinguish long circRNAs from other lncRNAs. With the test results demonstrating the validity and robustness of StackCirRNAPred, we hope StackCirRNAPred will complement existing circRNA prediction methods and is helpful in down-stream research.

Abstract Background Anovulation is one of the main causes of female infertility. This study will evaluate the effectiveness and safety of Bushen Culuan Decoction for anovulatory infertility caused by six diseases, including anovulatory abnormal uterine bleeding, polycystic ovarian syndrome, hyperprolactinemia, luteinized unruptured follicle syndrome, corpus luteum insufficiency, and premature ovarian insufficiency. Methods This is a randomized, double-blinded, double-dummy, parallel, positively controlled, adaptive, multicenter clinical trial. All participants will be randomly allocated by a central randomization system to the treatment group or the control group in a 1:1 ratio. The treatment group will undergo a 14-day treatment with Bushen Culuan Decoction 13 g three times a day and a 5-day treatment with clomiphene citrate placebo tablets 50 mg once a day starting on day 5 of every menstrual period. The control group will undergo a 14-day treatment with Bushen Culuan Decoction placebo 13 g three times a day and a 5-day treatment with clomiphene citrate tablets 50 mg once a day from day 5 in every menstrual period. The whole treatment will last through 3 menstrual periods or 6 menstrual periods, depending on whether ovulation is regained in the first 3 menstrual periods. All statistical analyses will be performed in SPSS 21.0 (SPSS, Chicago, Illinois, USA), and a p value

Abstract Background As a metastasis-related protein, NEDD9 has been reported in breast cancer (BC) metastasis research. However, there are few studies on the upstream regulators of NEDD9, especially involving the potential role of miRNAs. The purpose of this study was to explain whether miR-107 potentially regulates NEDD9, which may lead to invasion and metastasis of BC. Methods MCF-7 and MDA-MB-231 cells were transduced with lentiviruses to construct stably transduced cells with miR-107 overexpression, miR-107 silencing or empty vectors. A luciferase reporter assay was performed to verify the binding of miR-107 and NEDD9. The scratch test and Transwell assay were used to measure cell migration and invasion ability, respectively. For the study of metastasis in vivo, we injected MDA-MB-231 cells into the fat pad of nude mice to develop an orthotopic breast cancer model. Results We found that NEDD9 expression correlates with the prognosis of BC patients. In BC cell lines, NEDD9 was positively correlated with cell migration ability. Further research revealed that miR-107 inhibited NEDD9 expression by targeting the 3′-untranslated region of NEDD9. Overexpression of miR-107 suppressed the expression of NEDD9, thereby inhibiting the invasion, migration and proliferation of BC cells, but interference with miR-107 promoted the expression of NEDD9 as well as invasion, migration and proliferation. In an in vivo model, overexpression of miR-107 decreased the expression of NEDD9 and inhibited tumour growth, invasion and metastasis; however, these effects were reversed by inhibiting miR-107. Conclusions These findings indicated the potential role of miR-107 in regulating NEDD9 in the invasion, migration and proliferation of BC.

Abstract Background Amyloids are insoluble fibrillar aggregates that are highly associated with complex human diseases, such as Alzheimer’s disease, Parkinson’s disease, and type II diabetes. Recently, many studies reported that some specific regions of amino acid sequences may be responsible for the amyloidosis of proteins. It has become very important for elucidating the mechanism of amyloids that identifying the amyloidogenic regions. Accordingly, several computational methods have been put forward to discover amyloidogenic regions. The majority of these methods predicted amyloidogenic regions based on the physicochemical properties of amino acids. In fact, position, order, and correlation of amino acids may also influence the amyloidosis of proteins, which should be also considered in detecting amyloidogenic regions. Results To address this problem, we proposed a novel machine-learning approach for predicting amyloidogenic regions, called ReRF-Pred. Firstly, the pseudo amino acid composition (PseAAC) was exploited to characterize physicochemical properties and correlation of amino acids. Secondly, tripeptides composition (TPC) was employed to represent the order and position of amino acids. To improve the distinguishability of TPC, all possible tripeptides were analyzed by the binomial distribution method, and only those which have significantly different distribution between positive and negative samples remained. Finally, all samples were characterized by PseAAC and TPC of their amino acid sequence, and a random forest-based amyloidogenic regions predictor was trained on these samples. It was proved by validation experiments that the feature set consisted of PseAAC and TPC is the most distinguishable one for detecting amyloidosis. Meanwhile, random forest is superior to other concerned classifiers on almost all metrics. To validate the effectiveness of our model, ReRF-Pred is compared with a series of gold-standard methods on two datasets: Pep-251 and Reg33. The results suggested our method has the best overall performance and makes significant improvements in discovering amyloidogenic regions. Conclusions The advantages of our method are mainly attributed to that PseAAC and TPC can describe the differences between amyloids and other proteins successfully. The ReRF-Pred server can be accessed at http://106.12.83.135:8080/ReRF-Pred/.

Abstract Background Efficient and effective genome scaffolding tools are still in high demand for generating reference-quality assemblies. While long read data itself is unlikely to create a chromosome-scale assembly for most eukaryotic species, the inexpensive Hi-C sequencing technology, capable of capturing the chromosomal profile of a genome, is now widely used to complete the task. However, the existing Hi-C based scaffolding tools either require a priori chromosome number as input, or lack the ability to build highly continuous scaffolds. Results We design and develop a novel Hi-C based scaffolding tool, pin_hic, which takes advantage of contact information from Hi-C reads to construct a scaffolding graph iteratively based on N-best neighbors of contigs. Subsequent to scaffolding, it identifies potential misjoins and breaks them to keep the scaffolding accuracy. Through our tests on three long read based de novo assemblies from three different species, we demonstrate that pin_hic is more efficient than current standard state-of-art tools, and it can generate much more continuous scaffolds, while achieving a higher or comparable accuracy. Conclusions Pin_hic is an efficient Hi-C based scaffolding tool, which can be useful for building chromosome-scale assemblies. As many sequencing projects have been launched in the recent years, we believe pin_hic has potential to be applied in these projects and makes a meaningful contribution.

Abstract Background Methylation of cytosine bases in DNA is a critical epigenetic mark in many eukaryotes and has also been implicated in the development and progression of normal and diseased cells. Therefore, profiling DNA methylation across the genome is vital to understanding the effects of epigenetic. In recent years the Illumina HumanMethylation450 (HM450K) and MethylationEPIC (EPIC) BeadChip have been widely used to profile DNA methylation in human samples. The methods to predict the methylation states of DNA regions based on microarray methylation datasets are critical to enable genome-wide analyses. Result We report a computational approach based on the two layers two-state hidden Markov model (HMM) to identify methylation states of single CpG site and DNA regions in HM450K and EPIC BeadChip. Using this mothed, all CpGs detected by HM450K and EPIC in H1-hESC and GM12878 cell lines are identified as un-methylated, middle-methylated and full-methylated states. A large number of DNA regions are segmented into three methylation states as well. Comparing the identified regions with the result from the whole genome bisulfite sequencing (WGBS) datasets segmented by MethySeekR, our method is verified. Genome-wide maps of chromatin states show that methylation state is inversely correlated with active histone marks. Genes regulated by un-methylated regions are expressed and regulated by full-methylated regions are repressed. Our method is illustrated to be useful and robust. Conclusion Our method is valuable for DNA methylation genome-wide analyses. It is focusing on identification of DNA methylation states on microarray methylation datasets. For the features of array datasets, using two layers two-state HMM to identify to methylation states on CpG sites and regions creatively, our method which takes into account the distribution of genome-wide methylation levels is more reasonable than segmentation with a fixed threshold.

Abstract Background Various methods for differential expression analysis have been widely used to identify features which best distinguish between different categories of samples. Multiple hypothesis testing may leave out explanatory features, each of which may be composed of individually insignificant variables. Multivariate hypothesis testing holds a non-mainstream position, considering the large computation overhead of large-scale matrix operation. Random forest provides a classification strategy for calculation of variable importance. However, it may be unsuitable for different distributions of samples. Results Based on the thought of using an ensemble classifier, we develop a feature selection tool for differential expression analysis on expression profiles (i.e., ECFS-DEA for short). Considering the differences in sample distribution, a graphical user interface is designed to allow the selection of different base classifiers. Inspired by random forest, a common measure which is applicable to any base classifier is proposed for calculation of variable importance. After an interactive selection of a feature on sorted individual variables, a projection heatmap is presented using k-means clustering. ROC curve is also provided, both of which can intuitively demonstrate the effectiveness of the selected feature. Conclusions Feature selection through ensemble classifiers helps to select important variables and thus is applicable for different sample distributions. Experiments on simulation and realistic data demonstrate the effectiveness of ECFS-DEA for differential expression analysis on expression profiles. The software is available at http://bio-nefu.com/resource/ecfs-dea.

Abstract Background Computational prediction of transcription factor (TF) binding sites in different cell types is challenging. Recent technology development allows us to determine the genome-wide chromatin accessibility in various cellular and developmental contexts. The chromatin accessibility profiles provide useful information in prediction of TF binding events in various physiological conditions. Furthermore, ChIP-Seq analysis was used to determine genome-wide binding sites for a range of different TFs in multiple cell types. Integration of these two types of genomic information can improve the prediction of TF binding events. Results We assessed to what extent a model built upon on other TFs and/or other cell types could be used to predict the binding sites of TFs of interest. A random forest model was built using a set of cell type-independent features such as specific sequences recognized by the TFs and evolutionary conservation, as well as cell type-specific features derived from chromatin accessibility data. Our analysis suggested that the models learned from other TFs and/or cell lines performed almost as well as the model learned from the target TF in the cell type of interest. Interestingly, models based on multiple TFs performed better than single-TF models. Finally, we proposed a universal model, BPAC, which was generated using ChIP-Seq data from multiple TFs in various cell types. Conclusion Integrating chromatin accessibility information with sequence information improves prediction of TF binding.The prediction of TF binding is transferable across TFs and/or cell lines suggesting there are a set of universal “rules”. A computational tool was developed to predict TF binding sites based on the universal “rules”.