Transcriptomic divergence of the Rheum palmatum complex derived from top-geoherb and non-geoherb areas provides the insights into geoherbalism properties of rhubarb.

Geoherb usually represents high-quality medicinal herbs with better clinical therapeutic effects, and elucidating the geoherbalism is essential for the quality improvement of traditional Chinese Medicine. However, few researches were conducted to clarify the geoherbalism based on a large scale of transcriptomics. In the present study, we compared the transcriptomes of Rheum palmatum complex derived from top-geoherb and non-geoherb areas to show the geoherbalism properties of rhubarb. A total of 412.32 Gb clean reads were obtained with unigene numbers of 100,615 after assembly. Based on the obtained transcriptome datasets, key enzyme-encoding genes involved in the anthraquinones biosynthesis were also obtained. We also found that 21 anthraquinone-related unigenes were differentially expressed between two different groups, and some of these DEGs were correlated to the content accumulation of five free anthraquinones, indicating that the gene expression profiles may promote the geoherbalism formation of rhubarb. In addition, the selective pressure analyses indicated that most paired orthologous genes between these two groups were subject to negative selection, and only a low proportion of orthologs under positive selection were detected. Functional annotation analyses indicated that these positive-selected genes related to the functions such as gene expression, substance transport, stress response and metabolism, indicating that discrepant environment also enhanced the formation of geoherbalism. Our study not only provided insights for the genetic mechanism of geoherbalism of rhubarb, but also laid more genetic cues for the future rhubarb germplasms improvement and utilization.

Genomic identification and evolutionary analysis of chemosensory receptor gene families in two Phthorimaea pest species: insights into chemical ecology and host adaptation.

BACKGROUND: Insects rely on sophisticated sensitive chemosensory systems to sense their complex chemical environment. This sensory process involves a combination of odorant receptors (ORs), gustatory receptors (GRs) and ionotropic receptors (IRs) in the chemosensory system. This study focused on the identification and characterization of these three types of chemosensory receptor genes in two closely related Phthorimaea pest species, Phthorimaea operculella (potato tuber moth) and Phthorimaea absoluta (tomato leaf miner). RESULTS: Based on manual annotation of the genome, we identified a total of 349 chemoreceptor genes from the genome of P. operculella, including 93 OR, 206 GR and 50 IR genes, while for P. absoluta, we identified 72 OR, 122 GR and 46 IR genes. Through phylogenetic analysis, we observed minimal differences in the number and types of ORs and IRs between the potato tuber moth and tomato leaf miner. In addition, we found that compared with those of tomato leaf miners, the gustatory receptor branch of P. operculella has undergone a large expansion, which may be related to P. absoluta having a narrower host range than P. operculella. Through analysis of differentially expressed genes (DEGs) of male and female antennae, we uncovered 45 DEGs (including 32ORs, 9 GRs, and 4 IRs). CONCLUSIONS: Our research provides a foundation for exploring the chemical ecology of these two pests and offers new insights into the dietary differentiation of lepidopteran insects, while simultaneously providing molecular targets for developing environmentally friendly pest control methods based on insect chemoreception.

Genome-wide identification of GATA transcription factor family and the effect of different light quality on the accumulation of terpenoid indole alkaloids in Uncaria rhynchophylla.

Uncaria rhynchophylla is an evergreen vine plant, belonging to the Rubiaceae family, that is rich in terpenoid indole alkaloids (TIAs) that have therapeutic effects on hypertension and Alzheimer's disease. GATA transcription factors (TF) are a class of transcription regulators that participate in the light response regulation, chlorophyll synthesis, and metabolism, with the capability to bind to GATA cis-acting elements in the promoter region of target genes. Currently the charactertics of GATA TFs in U. rhynchophylla and how different light qualities affect the expression of GATA and key enzyme genes, thereby affecting the changes in U. rhynchophylla alkaloids have not been investigated. In this study, 25 UrGATA genes belonging to four subgroups were identified based on genome-wide analysis. Intraspecific collinearity analysis revealed that only segmental duplications were identified among the UrGATA gene family. Collinearity analysis of GATA genes between U. rhynchophylla and four representative plant species, Arabidopsis thaliana, Oryza sativa, Coffea Canephora, and Catharanthus roseus was also performed. U. rhynchophylla seedlings grown in either red lights or under reduced light intensity had altered TIAs content after 21 days. Gene expression analysis reveal a complex pattern of expression from the 25 UrGATA genes as well as a number of key TIA enzyme genes. UrGATA7 and UrGATA8 were found to have similar expression profiles to key enzyme TIA genes in response to altered light treatments, implying that they may be involved in the regulation TIA content. In this research, we comprehensively analyzed the UrGATA TFs, and offered insight into the involvement of UrGATA TFs from U. rhynchophylla in TIAs biosynthesis.

An enhanced cascade-based deep forest model for drug combination prediction.

Combination therapy has shown an obvious curative effect on complex diseases, whereas the search space of drug combinations is too large to be validated experimentally even with high-throughput screens. With the increase of the number of drugs, artificial intelligence techniques, especially machine learning methods, have become applicable for the discovery of synergistic drug combinations to significantly reduce the experimental workload. In this study, in order to predict novel synergistic drug combinations in various cancer cell lines, the cell line-specific drug-induced gene expression profile (GP) is added as a new feature type to capture the cellular response of drugs and reveal the biological mechanism of synergistic effect. Then, an enhanced cascade-based deep forest regressor (EC-DFR) is innovatively presented to apply the new small-scale drug combination dataset involving chemical, physical and biological (GP) properties of drugs and cells. Verified by the dataset, EC-DFR outperforms two state-of-the-art deep neural network-based methods and several advanced classical machine learning algorithms. Biological experimental validation performed subsequently on a set of previously untested drug combinations further confirms the performance of EC-DFR. What is more prominent is that EC-DFR can distinguish the most important features, making it more interpretable. By evaluating the contribution of each feature type, GP feature contributes 82.40%, showing the cellular responses of drugs may play crucial roles in synergism prediction. The analysis based on the top contributing genes in GP further demonstrates some potential relationships between the transcriptomic levels of key genes under drug regulation and the synergism of drug combinations.

Evidence for within-species transition between drought response strategies in Nicotiana benthamiana.

Nicotiana benthamiana is predominantly distributed in arid habitats across northern Australia. However, none of six geographically isolated accessions shows obvious xerophytic morphological features. To investigate how these tender-looking plants withstand drought, we examined their responses to water deprivation, assessed phenotypic, physiological, and cellular responses, and analysed cuticular wax composition and wax biosynthesis gene expression profiles. Results showed that the Central Australia (CA) accession, globally known as a research tool, has evolved a drought escape strategy with early vigour, short life cycle, and weak, water loss-limiting responses. By contrast, a northern Queensland (NQ) accession responded to drought by slowing growth, inhibiting flowering, increasing leaf cuticle thickness, and altering cuticular wax composition. Under water stress, NQ increased the heat stability and water impermeability of its cuticle by extending the carbon backbone of cuticular long-chain alkanes from c. 25 to 33. This correlated with rapid upregulation of at least five wax biosynthesis genes. In CA, the alkane chain lengths (c. 25) and gene expression profiles remained largely unaltered. This study highlights complex genetic and environmental control over cuticle composition and provides evidence for divergence into at least two fundamentally different drought response strategies within the N. benthamiana species in < 1 million years.

RNA Sequencing Screens the Key Genes and Pathways in a Mouse Model of HFpEF.

INTRODUCTION: Heart failure with preserved ejection fraction (HFpEF) is a common syndrome with high morbidity and mortality but without available evidence-based therapies. It is essential to investigate changes in gene expression profiles in preclinical HFpEF animal models, with the aim of searching for novel therapeutic targets. METHODS: Wild-type male C57BL/6J mice were administrated with a combination of high-fat diet (HFD) and inhibition of constitutive nitric oxide synthase using N-nitro-l-arginine methyl ester (l-NAME) for 5 and 7 weeks. RNA sequencing was conducted to detect gene expression profiles, and bioinformatic analysis was performed to identify the core genes, pathways, and biological processes involved. RESULTS: A total of 1,347 genes were differentially expressed in the heart at week 5 and 7 post-intervention. Gene Ontology enrichment analysis indicated that these greatly changed genes were involved mainly in cell adhesion, neutrophil chemotaxis, cell communication, and other functions. Using hierarchical cluster analysis, these differentially expressed genes were classified into 16 profiles. Of these, three significant profiles were ultimately identified. Gene co-expression network analysis suggested troponin T type 1 (Tnnt1) directly regulated 31 neighboring genes and was considered to be at the core of the associated gene network. CONCLUSION: The combined application of RNA sequencing, hierarchical cluster analysis, and gene network analysis identified Tnnt1 as the most important gene in the development of HFpEF.

Association between miRNAs in serum at 10-14 gestational weeks and spontaneous preterm delivery.

INTRODUCTION: Preterm delivery (PTD) is the leading cause of death in children under 5 years of age. Cervical shortening detected by ultrasound can be used to predict PTD, but prediction is not perfect, and complementary diagnostic markers are needed. Recently, specific plasma microribonucleic acid (miRNAs) detected in early second trimester were shown to be associated with spontaneous PTD in high-risk women with a singleton pregnancy. The aim of this study was to explore to what extent these miRNAs are associated with spontaneous PTD and cervical length in a general population. MATERIAL AND METHODS: This study is a nested case-control study within the CERVIX study. The CERVIX study evaluated the ability of cervical length screening with transvaginal ultrasound to identify women at risk of PTD. In the present study, women who delivered spontaneously <34 weeks (n = 61) were compared with a control group of women who delivered at full term (39 + 0 to 40 + 6 gestational weeks, n = 205). Archived serum samples were analyzed with RT-qPCR for miRNA expression levels of let-7a-5p, miR-150-5p, miR-15b-5p, miR-185-5p, miR-191-5p, miR-19b-3p, miR-23a-3p, miR-374a-5p, and miR-93-5p. The mean relative expression was compared between the groups. Sub-analyses were performed for women delivering <32, <30, and <28 weeks versus the full-term group. RESULTS: The analyzed miRNAs were not significantly differentially expressed in women delivering <34 weeks compared to those delivering at full term. MiR-191-5p and miR-93-5p were significantly overexpressed in women who delivered <32 weeks, and further increase in fold change was observed with decreasing gestational age at delivery. The level of miR-15b-5p was significantly higher in women delivering at <30 weeks compared to those delivering at full term. CONCLUSIONS: Our study shows that overexpression of miR-93-5p, miR-15b-5p, and miR-191-5p in serum at early gestation is associated with spontaneous PTD in a general population. Further research is needed to evaluate the potential of these miRNAs as future biomarkers for spontaneous PTD, as well as their pathophysiological role in spontaneous PTD.

CCLA: an accurate method and web server for cancer cell line authentication using gene expression profiles.

Cancer cell lines (CCLs) as important model systems play critical roles in cancer research. The misidentification and contamination of CCLs are serious problems, leading to unreliable results and waste of resources. Current methods for CCL authentication are mainly based on the CCL-specific genetic polymorphism, whereas no method is available for CCL authentication using gene expression profiles. Here, we developed a novel method and homonymic web server (CCLA, Cancer Cell Line Authentication, http://bioinfo.life.hust.edu.cn/web/CCLA/) to authenticate 1291 human CCLs of 28 tissues using gene expression profiles. CCLA showed an excellent speed advantage and high accuracy for CCL authentication, a top 1 accuracy of 96.58 or 92.15% (top 3 accuracy of 100 or 95.11%) for microarray or RNA-Seq validation data (719 samples, 461 CCLs), respectively. To the best of our knowledge, CCLA is the first approach to authenticate CCLs using gene expression data. Users can freely and conveniently authenticate CCLs using gene expression profiles or NCBI GEO accession on CCLA website.

Deep stratification by transcriptome molecular characters for precision treatment of patients with systemic lupus erythematosus.

OBJECTIVES: To leverage the high clinical heterogeneity of systemic lupus erythematosus (SLE), we developed and validated a new stratification scheme by integrating genome-scale transcriptomic profiles to identify patient subtypes sharing similar transcriptomic markers and drug targets. METHODS: A normalized compendium of transcription profiles was created from peripheral blood mononuclear cells (PBMCs) of 1046 SLE patients and 86 healthy controls (HCs), covering an intersection of 13 689 genes from six microarray datasets. Upregulated differentially expressed genes were subjected to functional and network analysis in which samples were grouped using unsupervised clustering to identify patient subtypes. Then, clustering stability was evaluated by the stratification of six integrated RNA-sequencing datasets using the same method. Finally, the Xgboost classifier was applied to the independent datasets to identify factors associated with treatment outcomes. RESULTS: Based on 278 upregulated DEGs of the transcript profiles, SLE patients were classified into three subtypes (subtype A-C) each with distinct molecular and cellular signatures. Neutrophil activation-related pathways were markedly activated in subtype A (named NE-driving), whereas lymphocyte and IFN-related pathways were more enriched in subtype B (IFN-driving). As the most severe subtype, subtype C [NE-IFN-dual-driving (Dual-driving)] shared functional mechanisms with both NE-driving and IFN-driving, which was closely associated with clinical features and could be used to predict the responses of treatment. CONCLUSION: We developed the largest cohesive SLE transcriptomic compendium for deep stratification using the most comprehensive microarray and RNA sequencing datasets to date. This result could guide future design of molecular diagnosis and the development of stratified therapy for SLE patients.

Genome-wide identification and analysis of the MAPKK gene family in Chinese mitten crab (Eriocheir sinensis) and its response to bacterial challenge.

Protein kinases of the MAPK cascade family (MAPKKK-MAPKK-MAPK) play an important role in the growth and development of organisms and their response to environmental stress. The MAPKK gene families in the Chinese mitten crab Eriocheir sinensis have never been systematically analyzed. We identified four MAPKK family genes, EsMEK, EsMAPKK4, EsMAPKK6, and EsMAPKK7, in E. sinensis and analyzed their molecular features and expression patterns. All four MAPKK genes are composed of multiple exons and introns, all have a conserved domain, and all have 10 conserved motifs (except EsMEK and EsMAPKK7 which are missing motif 10). The four MAPKK genes are on four different chromosomes and have no gene duplications, and the results of phylogenetic tree analysis indicate that the ESMAPKK gene family is highly conserved evolutionarily. The EsMAPKK genes were widely expressed in all the examined tissues with higher expression in hemocytes, hepatopancreas, and gills. Notably, EsMAPKK6 was also highly expressed in the ovary. Vibrio parahaemolyticus infection significantly increased the mRNA levels of the EsMAPKK genes in hemocytes. Further disruption of the EsMAPKK gene family expression affects the expression levels of multiple antimicrobial peptides in hemocytes. Our experimental results provide a starting point for a more in-depth study of the innate immunity functional roles of members of the MAPKK gene families in E. sinensis.

A pilot study on identifying gene signatures as markers for predicting patient response to antiseizure medications.

The majority of the biomarkers were associated with the diagnosis of epilepsy and few of them can be applied to predict the response to antiseizure medications (ASMs). In this study, we identified 26 significantly up-regulated genes and 32 down-regulated genes by comparing the gene expression profiles of patients with epilepsy that responded to valproate with those without applying any ASM. The results of gene set enrichment analysis indicated that the ferroptosis pathway was significantly impacted (p = 0.0087) in patients who responded to valproate. Interestingly, the gene NCOA4 in this pathway exhibited significantly different expression levels between the two groups, indicating that NCOA4 could serve as a potential biomarker to better understand the mechanism of valproate resistance. In addition, six up-regulated genes SF3A2, HMGN2, PABPN1, SSBP3, EFTUD2, and CREB3L2 as well as six down-regulated genes ZFP36L1, ACRC, SUB1, CALM2, TLK1, and STX2 also showed significantly different expression patterns between the two groups. Moreover, based on the gene expression profiles of the patients with the treatment of valproate, carbamazepine, and phenytoin, we proposed a strategy for predicting the response to the ASMs by using the Connectivity Map scoring method. Our findings could be helpful for better understanding the mechanisms of drug resistance of ASMs and improving the clinical treatment of epilepsy.

Characteristic gene prognostic model of type 1 diabetes mellitus via machine learning strategy.

The present study was designed to detect possible biomarkers associated with Type 1 diabetes mellitus (T1DM) incidence in an effort to develop novel treatments for this condition. Three mRNA expression datasets of peripheral blood mononuclear cells (PBMCs) were obtained from the GEO database. Differentially expressed genes (DEGs) between T1DM patients and healthy controls were identified by Limma package in R, and using the DEGs to conduct GO and DO pathway enrichment. The LASSO-SVM were used to screen the hub genes. We performed immune correlation analysis of hub genes and established a T1DM prognosis model. CIBERSORT algorithm was used to identify the different immune cells in distribution between T1DM and normal samples. The correlation of the hub genes and immune cells was analyzed by Spearman. ROC curves were used to assess the diagnostic value of genes in T1DM. A total of 60 immune related DEGs were obtained from the T1DM and normal samples. Then, DEGs were further screened to obtain 3 hub genes, ANP32A-IT1, ESCO2 and NBPF1. CIBERSORT analysis revealed the percentage of immune cells in each sample, indicating that there was significant difference in monocytes, T cells CD8+, gamma delta T cells, naive CD4+ T cells and activated memory CD4+ T cells between T1DM and normal samples. The area under curve (AUC) of ESCO2, ANP32A-IT1 and NBPF1 were all greater than 0.8, indicating that these three genes have high diagnostic value for T1DM. Together, the findings of these bioinformatics analyses thus identified key hub genes associated with T1DM development.

Gene expression and demographic analyses in women with the poor ovarian response: a computational approach.

PURPOSE: Poor response to ovarian stimulation (POR) typically is reflected as decreased follicular response and low estradiol (E2) levels following ovarian stimulation by FSH/HMG. Many genes are involved in oocyte maturation, and demographic features and lifestyle can affect the oocyte maturity and developmental competence. The present study was conducted to investigate the magnitude of gene expression and lifestyle habits in POR women as compared to healthy women, using different statistical and computational methods. METHODS: Fifty women in the two groups were studied. The study groups included POR women (n = 25) with 1-9 released oocytes, and the control group (normal women, n = 25) with 9-15 released oocytes. Quantitative PCR was used to estimate the expression of FIGLA, ZAR1, WNT4, LHX8, APC, H1FOO, MOS, and DMC1 genes in granulosa cells. RESULTS: The results showed no significant difference in the magnitude of the studied genes' expression and linear discriminant analysis did not differentiate the studied groups based on all the genes together. Redundancy analysis (RDA) and latent factor mixed model (LFMM) results produce no significant association between the genes' expression magnitude and the geographical variables of the patients' local habitat. Linear discriminant analysis (LDA) of the demographic features differentiated the two groups of women. CONCLUSION: Our results indicate that demographic features may have an effect on sample gene expression levels.

Effect of Long-Term Low-Dose Arsenic Exposure on DNA Methylation and Gene Expression in Human Liver Cells.

Millions of people around the world are exposed to elevated levels of arsenic through food or drinking water. Epidemiological studies have linked chronic arsenic exposure to an increased risk of several cancers, cardiovascular disease, central nervous system neuropathies, and genotoxic as well as immunotoxic effects. In addition to the induction of oxidative stress and inhibition of DNA repair processes, epigenetic effects, including altered DNA methylation patterns resulting in aberrant gene expression, may contribute to carcinogenicity. However, the underlying mechanisms by which chronic micromolar concentrations of arsenite affect the methylation status of DNA are not fully understood. In this study, human HepG2 hepatocarcinoma cells were treated with 0.5-10 µM sodium arsenite for 24 h, 10, or 20 days. During these periods, the effects on global DNA methylation, cell cycle phase distribution, and gene expression were investigated. While no impact on DNA methylation was seen after short-term exposure, global hypomethylation was observed at both long-term exposure periods, with concomitant induction of the DNA methyltransferase genes DNMT1 and DNMT3B, while DNMT3A was slightly down-regulated. Pronounced time- and concentration-dependent effects were also seen in the case of genes involved in DNA damage response and repair, inflammation, oxidative stress response, and metal homeostasis. These results suggest that chronic low-dose arsenite exposure can lead to global hypomethylation. As an underlying mechanism, the consistent down-regulation of DNA methyltransferase genes could be excluded; alternatively, interactions at the protein level could play an important role.

MammaPrint and BluePrint comprehensively capture the cancer hallmarks in early-stage breast cancer patients.

MammaPrint® (MP) is a 70-gene signature that stratifies early-stage breast cancer patients into low- and high risk of distant relapse. Further stratification of MP risk results identifies four risk subgroups, ultra-low (UL), low, high 1, and high 2, with specific prognostic and predictive outcomes. BluePrint® (BP) is an 80-gene signature that classifies breast tumors as basal, luminal, or HER2 molecular subtype. To gain insight into their biological significance, we annotated the MP 70- and BP 80-genes with respect to the 10 hallmarks of cancer (HoC). Furthermore, we related gene expression profiles of the extreme ends of the MP low- and high-risk patients (here called, ultra-low (UL) and ultra-high (UH) or High2, respectively), to the 10 HoC per BP subtype by differential gene expression and pathway analysis. MP and BP gene functions reflected all 10 HoCs. Most MP and BP genes were associated with sustaining proliferative signaling, followed by genome instability and mutation categories. Based on the gene expression profiles, UL and UH subgroup pathways were down -or upregulated, respectively, reflecting proliferative and metastatic features, such as G2M checkpoint, DNA repair, oxidative phosphorylation, immune invasion, PI3K/AKT/mTOR signaling, and hypoxia pathways. Notably, the UH HER2-type was enriched in several immune signaling pathways, such as IL2/STAT5 signaling and TNFα signaling via NFκB. Our results show that MP and BP gene signatures represent and capture all 10 HoCs and highlight underlying biological processes of MP extreme samples, which might guide treatment decisions as the signature captures the full spectrum of early breast cancers.

Genome-wide identification, molecular evolution and expression analysis of the non-specific lipid transfer protein (nsLTP) family in Setaria italica.

BACKGROUND: Foxtail millet (Setaria italica L.) is a millet species with high tolerance to stressful environments. Plant non-specific lipid transfer proteins (nsLTPs) are a kind of small, basic proteins involved in many biological processes. So far, the genome of S. italica has been fully sequenced, and a comprehensive understanding of the evolution and expression of the nsLTP family is still lacking in foxtail millet. RESULTS: Forty-five nsLTP genes were identified in S. italica and clustered into 5 subfamilies except three single genes (SinsLTP38, SinsLTP7, and SinsLTP44). The proportion of SinsLTPs was different in each subfamily, and members within the same subgroup shared conserved exon-intron structures. Besides, 5 SinsLTP duplication events were investigated. Both tandem and segmental duplication contributed to nsLTP expansion in S. italica, and the duplicated SinsLTPs had mainly undergone purifying selection pressure, which suggested that the function of the duplicated SinsLTPs might not diverge much. Moreover, we identified the nsLTP members in 5 other monocots, and 41, 13, 10, 4, and 1 orthologous gene pairs were identified between S. italica and S. viridis, S. bicolor, Z. mays, O. sativa, and B. distachyon, respectively. The functional divergence within the nsLTP orthologous genes might be limited. In addition, the tissue-specific expression patterns of the SinsLTPs were investigated, and the expression profiles of the SinsLTPs in response to abiotic stress were analyzed, all the 10 selected SinsLTPs were responsive to drought, salt, and cold stress. Among the selected SinsLTPs, 2 paired duplicated genes shared almost equivalent expression profiles, suggesting that these duplicated genes might retain some essential functions during subsequent evolution. CONCLUSIONS: The present study provided the first systematic analysis for the phylogenetic classification, conserved domain and gene structure, expansion pattern, and expression profile of the nsLTP family in S. italica. These findings could pave a way for further comparative genomic and evolution analysis of nsLTP family in foxtail millet and related monocots, and lay the foundation for the functional analysis of the nsLTPs in S. italica.

Integrative analyses of maternal plasma cell-free DNA nucleosome footprint differences reveal chromosomal aneuploidy fetuses gene expression profile.

BACKGROUND: Chromosomal aneuploidy is the most common birth defect. However, the developmental mechanism and gene expression profile of fetuses with chromosomal aneuploidy are relatively unknown, and the maternal immune changes induced by fetal aneuploidy remain unclear. The inability to obtain the placenta multiple times in real-time is a bottleneck in research on aneuploid pregnancies. Plasma cell-free DNA (cfDNA) carries the gene expression profile information of its source cells and may be used to evaluate the development of fetuses with aneuploidy and the immune changes induced in the mother owing to fetal aneuploidy. METHODS: Here, we carried out whole-genome sequencing of the plasma cfDNA of 101 pregnant women carrying a fetus with trisomy (trisomy 21, n = 42; trisomy 18, n = 28; trisomy 13, n = 31) based on non-invasive prenatal testing (NIPT) screening and 140 normal pregnant women to identify differential genes according to the cfDNA nucleosome profile in the region around the transcription start sites (TSSs). RESULTS: The plasma cfDNA promoter profiles were found to differ between aneuploid and euploid pregnancies. A total of 158 genes with significant differences were identified, of which 43 genes were upregulated and 98 genes were downregulated. Functional enrichment and signaling pathway analysis were performed based on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases found that these signal pathways were mainly related to the coordination of developmental signals during embryonic development, the control of cell growth and development, regulation of neuronal survival, and immune regulation, such as the MAPK, Hippo, TGF-ß, and Rap1 signaling pathways, which play important roles in the development of embryonic tissues and organs. Furthermore, based on the results of differential gene analysis, a total of 14 immune-related genes with significant differences from the ImmPort database were collected and analyzed. These significantly different immune genes were mainly associated with the maintenance of embryonic homeostasis and normal development. CONCLUSIONS: These results suggest that the distribution characteristics of cfDNA nucleosomes in maternal plasma can be used to reflect the status of fetal development and changes of the immune responses in trisomic pregnancies. Overall, our findings may provide research ideas for non-invasive detection of the physiological and pathological states of other diseases.

DEPTH2: an mRNA-based algorithm to evaluate intratumor heterogeneity without reference to normal controls.

BACKGROUND: Intratumor heterogeneity (ITH) is associated with tumor progression, unfavorable prognosis, immunosuppression, genomic instability, and therapeutic resistance. Thus, evaluation of ITH levels is valuable in cancer diagnosis and treatment. METHODS: We proposed a new mRNA-based ITH evaluation algorithm (DEPTH2) without reference to normal controls. DEPTH2 evaluates ITH levels based on the standard deviations of absolute z-scored transcriptome levels in tumors, reflecting the asynchronous level of transcriptome alterations relative to the central tendency in a tumor. RESULTS: By analyzing 33 TCGA cancer types, we demonstrated that DEPTH2 ITH was effective in measuring ITH for its significant associations with tumor progression, unfavorable prognosis, genomic instability, reduced antitumor immunity and immunotherapy response, and altered drug response in diverse cancers. Compared to other five ITH evaluation algorithms (MATH, PhyloWGS, ABSOLUTE, DEPTH, and tITH), DEPTH2 ITH showed a stronger association with unfavorable clinical outcomes, and in characterizing other properties of ITH, such as its associations with genomic instability and antitumor immunosuppression, DEPTH2 also displayed competitive performance. CONCLUSIONS: DEPTH2 is expected to have a wider spectrum of applications in evaluating ITH in comparison to other algorithms.

Deep learning applications for the accurate identification of low-transcriptional activity drugs and their mechanism of actions.

Analysis of drug-induced expression profiles facilitated comprehensive understanding of drug properties. However, many compounds exhibit weak transcription responses though they mostly possess definite pharmacological effects. Actually, as a representative example, over 66.4% of 312,438 molecular signatures in the Library of Integrated Cellular Signatures (LINCS) database exhibit low-transcriptional activities (i.e. TAS-low signatures). When computing the association between TAS-low signatures with shared mechanism of actions (MOAs), commonly used algorithms showed inadequate performance with an average area under receiver operating characteristic curve (AUROC) of 0.55, but the computation accuracy of the same task can be improved by our developed tool Genetic profile activity relationship (GPAR) with an average AUROC of 0.68. Up to 36 out of 74 TAS-low MOAs were well trained with AUROC ≥ 0.7 by GPAR, higher than those by other approaches. Further studies showed that GPAR benefited from the size of training samples more significantly than other approaches. Lastly, in biological validation of the MOA prediction for a TAS-low drug Tropisetron, we found an unreported mechanism that Tropisetron can bind to the glucocorticoid receptor. This study indicated that GPAR can serve as an effective approach for the accurate identification of low-transcriptional activity drugs and their MOAs, thus providing a good tool for drug repurposing with both TAS-low and TAS-high signatures.

Adaptive strategies of uropathogenic Escherichia coli CFT073: from growth in lab media to virulence during host cell adhesion.

Urinary tract infections (UTIs) are a major concern in public health. The prevalent uropathogenic bacterium in healthcare settings is Escherichia coli. The increasing rate of antibiotic-resistant strains demands studies to understand E. coli pathogenesis to drive the development of new therapeutic approaches. This study compared the gene expression profile of selected target genes in the prototype uropathogenic E. coli (UPEC) strain CFT073 grown in Luria Bertani (LB), artificial urine (AU), and during adhesion to host bladder cells by semi-quantitative real-time PCR (RT-PCR) assays. AU effectively supported the growth of strain CFT073 as well as other E. coli strains with different lifestyles, thereby confirming the appropriateness of this medium for in vitro models. Unexpectedly, gene expression of strain CFT073 in LB and AU was quite similar; conversely, during the adhesion assay, adhesins and porins were upregulated, while key global regulators were downregulated with respect to lab media. Interestingly, fimH and papGII genes were significantly expressed in all tested conditions. Taken together, these results provide for the first time insights of the metabolic and pathogenic profile of strain CFT073 during the essential phase of host cell adhesion.