Common and ethnic-specific derangements in skeletal muscle transcriptome associated with obesity.

BACKGROUND: Obesity is a common disease with a higher prevalence among African Americans. Obesity alters cellular function in many tissues, including skeletal muscle, and is a risk factor for many life-threatening diseases, including cardiovascular disease and diabetes. The similarities and differences in molecular mechanisms that may explain ethnic disparities in obesity between African and European ancestry individuals have not been studied. METHODS: In this study, data from transcriptome-wide analyses on skeletal muscle tissues from well-powered human cohorts were used to compare genes and biological pathways affected by obesity in European and African ancestry populations. Data on obesity-induced differentially expressed transcripts and GWAS-identified SNPs were integrated to prioritize target genes for obesity-associated genetic variants. RESULTS: Linear regression analysis in the FUSION (European, N = 301) and AAGMEx (African American, N = 256) cohorts identified a total of 2569 body mass index (BMI)-associated transcripts (q < 0.05), of which 970 genes (at p < 0.05) are associated in both cohorts, and the majority showed the same direction of effect on BMI. Biological pathway analyses, including over-representation and gene-set enrichment analyses, identified enrichment of protein synthesis pathways (e.g., ribosomal function) and the ceramide signaling pathway in both cohorts among BMI-associated down- and up-regulated transcripts, respectively. A comparison using the IPA-tool suggested the activation of inflammation pathways only in Europeans with obesity. Interestingly, these analyses suggested repression of the mitochondrial oxidative phosphorylation pathway in Europeans but showed its activation in African Americans. Integration of SNP-to-Gene analyses-predicted target genes for obesity-associated genetic variants (GWAS-identified SNPs) and BMI-associated transcripts suggested that these SNPs might cause obesity by altering the expression of 316 critical target genes (e.g., GRB14) in the muscle. CONCLUSIONS: This study provides a replication of obesity-associated transcripts and biological pathways in skeletal muscle across ethnicities, but also identifies obesity-associated processes unique in either African or European ancestry populations.

Comparative Transcriptomic Assessment of Chemosensory Genes in Adult and Larval Olfactory Organs of Cnaphalocrocis medinalis.

The rice leaf folder, Cnaphalocrocis medinalis (Lepidoptera: Pyralidae), is a notorious pest of rice in Asia. The larvae and adults of C. medinalis utilize specialized chemosensory systems to adapt to different environmental odors and physiological behaviors. However, the differences in chemosensory genes between the olfactory organs of these two different developmental stages remain unclear. Here, we conducted a transcriptome analysis of larvae heads, male antennae, and female antennae in C. medinalis and identified 131 putative chemosensory genes, including 32 OBPs (8 novel OBPs), 23 CSPs (2 novel CSPs), 55 ORs (17 novel ORs), 19 IRs (5 novel IRs) and 2 SNMPs. Comparisons between larvae and adults of C. medinalis by transcriptome and RT-qPCR analysis revealed that the number and expression of chemosensory genes in larval heads were less than that of adult antennae. Only 17 chemosensory genes (7 OBPs and 10 CSPs) were specifically or preferentially expressed in the larval heads, while a total of 101 chemosensory genes (21 OBPs, 9 CSPs, 51 ORs, 18 IRs, and 2 SNMPs) were specifically or preferentially expressed in adult antennae. Our study found differences in chemosensory gene expression between larvae and adults, suggesting their specialized functions at different developmental stages of C. medinalis. These results provide a theoretical basis for screening chemosensory genes as potential molecular targets and developing novel management strategies to control C. medinalis.

[Exploration of transcriptome SSR markers and its application in genetic diversity assessment of Asarum sieboldii].

To explore the genetic diversity of Asarum sieboldii this study developed SSR markers based on transcriptome sequencing results and five populations of A.sieboldii from different regions were used as samples for genetic diversity assessment using software such as GenALEx 6.5, NTSYS 2.1, and Structure 2.3.4. The results showed that 16 SSR markers with high polymorphism and good repeatability were selected from the A.sieboldii transcriptome. Primers designed based on the flanking sequences of these markers successfully amplified 56 polymorphic fragments from 150 individual samples of the five A.sieboldii populations. On average, each primer amplified 3.5 polymorphic fragments, ranging from 2 to 8. The mean values of expected heterozygosity(H_e), Shannon's diversity index(I), Nei's gene diversity index(H), and the polymorphic information content(PIC) were 0.172, 0.281, 0.429, and 0.382, respectively. The mean population differentiation coefficient(F_(ST)) was 0.588, consistent with the analysis of molecular variance(AMOVA) results, which indicated greater genetic variation among A.sieboldii populations(69%) than that within populations(31%). The percentage of polymorphic loci(PPL) ranged from highest to lowest as SNJ>LN>SY>SZ>TB. Principal coordinate analysis(PCoA) and UPGMA clustering analysis further revealed genetic clustering of A.sieboldii individuals based on their geographical distribution, consistent with the results of the structure clustering analysis. In summary, the SSR markers developed from the transcriptome effectively assessed the genetic differentiation and population structure of natural A.sieboldii populations, revealing a relatively low genetic diversity in A.sieboldii, with genetic variation primarily observed at the population level and a correlation between population differentiation and geographic distance.

A comparative full-length transcriptomic resource provides insight into the perennial monocarpic mass flowering.

Perennial monocarpic mass flowering represents as a key developmental innovation in flowering time diversity in several biological and economical essential families, such as the woody bamboos and the shrubby Strobilanthes. However, molecular and genetic mechanisms underlying this important biodiversity remain poorly investigated. Here, we generated a full-length transcriptome resource incorporated into the BlueOmics database (http://blueomics.iflora.cn) for two Strobilanthes species, which feature contrasting flowering time behaviors. Using about 112 and 104 Gb Iso-seq reads together with ~185 and ~75 Gb strand-specific RNA seq data, we annotated 80 971 and 79 985 non-redundant full-length transcripts for the perennial polycarpic Strobilanthes tetrasperma and the perennial monocarpic Strobilanthes biocullata, respectively. In S. tetrasperma, we identified 8794 transcripts showing spatiotemporal expression in nine tissues. In leaves and shoot apical meristems at two developmental stages, 977 and 1121 transcripts were differentially accumulated in S. tetrasperma and S. biocullata, respectively. Interestingly, among the 33 transcription factors showing differential expression in S. tetrasperma but without differential expression in S. biocullata, three were involved potentially in the photoperiod and circadian-clock pathway of flowering time regulation (FAR1 RELATED SEQUENCE 12, FRS12; NUCLEAR FACTOR Y A1, NFYA1; PSEUDO-RESPONSE REGULATOR 5, PRR5), hence provides an important clue in deciphering the flowering diversity mechanisms. Our data serve as a key resource for further dissection of molecular and genetic mechanisms underpinning key biological innovations, here, the perennial monocarpic mass flowering.

Integrating imaging-based classification and transcriptomics for quality assessment of human oocytes according to their reproductive efficiency.

PURPOSE: Utilising non-invasive imaging parameters to assess human oocyte fertilisation, development and implantation; and their influence on transcriptomic profiles. METHODS: A ranking tool was designed using imaging data from 957 metaphase II stage oocytes retrieved from 102 patients undergoing ART. Hoffman modulation contrast microscopy was conducted with an Olympus IX53 microscope. Images were acquired prior to ICSI and processed using ImageJ for optical density and grey-level co-occurrence matrices texture analysis. Single-cell RNA sequencing of twenty-three mature oocytes classified according to their competence was performed. RESULT(S): Overall fertilisation, blastulation and implantation rates were 73.0%, 62.6% and 50.8%, respectively. Three different algorithms were produced using binary logistic regression methods based on "optimal" quartiles, resulting in an accuracy of prediction of 76.6%, 67% and 80.7% for fertilisation, blastulation and implantation. Optical density, gradient, inverse difference moment (homogeneity) and entropy (structural complexity) were the parameters with highest predictive properties. The ranking tool showed high sensitivity (68.9-90.8%) but with limited specificity (26.5-62.5%) for outcome prediction. Furthermore, five differentially expressed genes were identified when comparing "good" versus "poor" competent oocytes. CONCLUSION(S): Imaging properties can be used as a tool to assess differences in the ooplasm and predict laboratory and clinical outcomes. Transcriptomic analysis suggested that oocytes with lower competence may have compromised cell cycle either by non-reparable DNA damage or insufficient ooplasmic maturation. Further development of algorithms based on image parameters is encouraged, with an increased balanced cohort and validated prospectively in multicentric studies.

TEQUILA-seq: a versatile and low-cost method for targeted long-read RNA sequencing.

Long-read RNA sequencing (RNA-seq) is a powerful technology for transcriptome analysis, but the relatively low throughput of current long-read sequencing platforms limits transcript coverage. One strategy for overcoming this bottleneck is targeted long-read RNA-seq for preselected gene panels. We present TEQUILA-seq, a versatile, easy-to-implement, and low-cost method for targeted long-read RNA-seq utilizing isothermally linear-amplified capture probes. When performed on the Oxford nanopore platform with multiple gene panels of varying sizes, TEQUILA-seq consistently and substantially enriches transcript coverage while preserving transcript quantification. We profile full-length transcript isoforms of 468 actionable cancer genes across 40 representative breast cancer cell lines. We identify transcript isoforms enriched in specific subtypes and discover novel transcript isoforms in extensively studied cancer genes such as TP53. Among cancer genes, tumor suppressor genes (TSGs) are significantly enriched for aberrant transcript isoforms targeted for degradation via mRNA nonsense-mediated decay, revealing a common RNA-associated mechanism for TSG inactivation. TEQUILA-seq reduces the per-reaction cost of targeted capture by 2-3 orders of magnitude, as compared to a standard commercial solution. TEQUILA-seq can be broadly used for targeted sequencing of full-length transcripts in diverse biomedical research settings.

Transcriptome diversity assessment of Gossypium arboreum (FDH228) leaves under control, drought and whitefly infestation using PacBio long reads.

Alternative splicing (AS) and alternative polyadenylation (APA) are common mechanisms in eukaryotes to increase the complexity of transcriptomes and subsequently proteomes. Analysis of long reads transcriptomics data can result in the discovery of novel transcripts, splice sites, AS or APA events. Gossypium arboreum is an important cultivated cotton species and a putative contributor of the A sub-genome to the modern tetraploid cotton; and inherently tolerant to several biotic and abiotic stresses. Specifically, its variety 'FDH228' is considered to be an important resistance source. In this study, we sequenced the G. arboreum (var. FDH228) transcriptome using PacBio IsoSeq and illumina short read sequencing under three different conditions i.e. untreated/healthy, treated with biotic stress through whitefly infestation, and treated with abiotic stress via water deprivation, for the discovery and surveying of canonical and non-canonical AS, APA and transcript fusion events. We were able to obtain 15,419 unique transcripts from all samples representing 11,343 genes, out of which 10,832 were annotated and 520 were novel with respect to the published reference genome. These transcripts were grouped into different structural categories including 60 Antisense, 11,959 having a full-splice match, 999 with incomplete-splice match, 30 fusion transcripts, 177 genic, 479 intergenic, 771 novels in the catalog, and 944 Novel but not found in the catalog. Subsequently, randomly selected candidate transcripts were experimentally validated using qRT-PCR. Our comprehensive identification of canonical and non-canonical splicing events, and novel and fusion transcripts aids in the understanding of the resistance mechanisms for this specific germplasm.

Assessment of spatial transcriptomics for oncology discovery.

Tumor heterogeneity is a major challenge for oncology drug discovery and development. Understanding of the spatial tumor landscape is key to identifying new targets and impactful model systems. Here, we test the utility of spatial transcriptomics (ST) for oncology discovery by profiling 40 tissue sections and 80,024 capture spots across a diverse set of tissue types, sample formats, and RNA capture chemistries. We verify the accuracy and fidelity of ST by leveraging matched pathology analysis, which provides a ground truth for tissue section composition. We then use spatial data to demonstrate the capture of key tumor depth features, identifying hypoxia, necrosis, vasculature, and extracellular matrix variation. We also leverage spatial context to identify relative cell-type locations showing the anti-correlation of tumor and immune cells in syngeneic cancer models. Lastly, we demonstrate target identification approaches in clinical pancreatic adenocarcinoma samples, highlighting tumor intrinsic biomarkers and paracrine signaling.

A Bayesian modified Ising model for identifying spatially variable genes from spatial transcriptomics data.

A recent technology breakthrough in spatial molecular profiling (SMP) has enabled the comprehensive molecular characterizations of single cells while preserving spatial information. It provides new opportunities to delineate how cells from different origins form tissues with distinctive structures and functions. One immediate question in SMP data analysis is to identify genes whose expressions exhibit spatially correlated patterns, called spatially variable (SV) genes. Most current methods to identify SV genes are built upon the geostatistical model with Gaussian process to capture the spatial patterns. However, the Gaussian process models rely on ad hoc kernels that could limit the models' ability to identify complex spatial patterns. In order to overcome this challenge and capture more types of spatial patterns, we introduce a Bayesian approach to identify SV genes via a modified Ising model. The key idea is to use the energy interaction parameter of the Ising model to characterize spatial expression patterns. We use auxiliary variable Markov chain Monte Carlo algorithms to sample from the posterior distribution with an intractable normalizing constant in the model. Simulation studies using both simulated and synthetic data showed that the energy-based modeling approach led to higher accuracy in detecting SV genes than those kernel-based methods. When applied to two real spatial transcriptomics (ST) datasets, the proposed method discovered novel spatial patterns that shed light on the biological mechanisms. In summary, the proposed method presents a new perspective for analyzing ST data.

Recent advances of transcriptomics and proteomics in triple-negative breast cancer prognosis assessment.

Triple-negative breast cancer (TNBC), a heterogeneous tumour that lacks the expression of oestrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2), is often characterized by aggressiveness and tends to recur or metastasize. TNBC lacks therapeutic targets compared with other subtypes and is not sensitive to endocrine therapy or targeted therapy except chemotherapy. Therefore, identifying the prognostic characteristics and valid therapeutic targets of TNBC could facilitate early personalized treatment. Due to the rapid development of various technologies, researchers are increasingly focusing on integrating 'big data' and biological systems, which is referred to as 'omics', as a means of resolving it. Transcriptomics and proteomics analyses play an essential role in exploring prospective biomarkers and potential therapeutic targets for triple-negative breast cancers, which provides a powerful engine for TNBC's therapeutic discovery when combined with complementary information. Here, we review the recent progress of TNBC research in transcriptomics and proteomics to identify possible therapeutic goals and improve the survival of patients with triple-negative breast cancer. Also, researchers may benefit from this article to catalyse further analysis and investigation to decipher the global picture of TNBC cancer.

Transcriptome and hyperspectral profiling allows assessment of phosphorus nutrient status in rice under field conditions.

Phosphorus (P) is one of the macronutrients indispensable for crop production, and therefore it is important to understand the potential of plants to adapt to low P conditions. We compared growth and leaf genome-wide transcriptome of four rice cultivars during growth between two fields with different amount of available phosphate and further analysed the acceptable range of P levels for normal growth from the view of both appearance traits and internal P nutrient status, which was measured by profiling the expression of the P indicator gene. This demonstrated that rice plants have a robustness to moderate P-deficient conditions expressing a system for P acquisition and usage without any effects on yield potential and that P indicator gene expression could be a useful index for early diagnosis of P status in plants. To develop a simple method for assessment of P status, we tried to predict the expression level using reflectance spectroscopy and hyperspectral imaging, thereby providing models with good performance. Our findings suggest that rice plants have the potential to adapt to moderate low P conditions in the field and showed that the hyperspectral technique is one of the useful tools for simple measurement of molecular-level dynamics reflecting internal nutrient conditions.

Integrated Transcriptomics Profiling in Chahua and Digao Chickens' Breast for Assessment Molecular Mechanism of Meat Quality Traits.

Meat quality traits are an important economic trait and remain a major argument, from the producer to the consumer. However, there are a few candidate genes and pathways of chicken meat quality traits that were reported for chicken molecular breeding. The purpose of the present study is to identify the candidate genes and pathways associated with meat quality underlying variations in meat quality. Hence, transcriptome profiles of breast tissue in commercial Digao (DG, 5 male) and Chahua (CH, 5 male) native chicken breeds were analyzed at the age of 100 days. The results found 3525 differentially expressed genes (DEGs) in CH compared to DG with adjusted p-values of ≤0.05 and log2FC ≥ 0.1 FDR ≤ 0.05. Functional analysis of GO showed that the DEGs are mainly involved in the two types of processes of meat quality, such as positive regulation of the metabolic process, extracellular structure organization, collagen trimer, cellular amino acid metabolic process, cellular amino acid catabolic process, and heme binding. Functional analysis of KEGG showed that the DEGs are mainly involved in the two types of processes of meat quality, such as oxidative phosphorylation, carbon metabolism, valine, leucine, and isoleucine degradation, and fatty acid degradation. Many of the DEGs are well known to be related to meat quality, such as COL28A1, COL1A2, MB, HBAD, HBA1, ACACA, ACADL, ACSL1, ATP8A1, CAV1, FADS2, FASN, DCN, CHCHD10, AGXT2, ALDH3A2, and MORN4. Therefore, the current study detected multiple pathways and genes that could be involved in the control of the meat quality traits of chickens. These findings should be used as an essential resource to improve the accuracy of selection for meat traits in chickens using marker-assisted selection based on differentially expressed genes.

Model-based assessment of mammalian cell metabolic functionalities using omics data.

Omics experiments are ubiquitous in biological studies, leading to a deluge of data. However, it is still challenging to connect changes in these data to changes in cell functions because of complex interdependencies between genes, proteins, and metabolites. Here, we present a framework allowing researchers to infer how metabolic functions change on the basis of omics data. To enable this, we curated and standardized lists of metabolic tasks that mammalian cells can accomplish. Genome-scale metabolic networks were used to define gene sets associated with each metabolic task. We further developed a framework to overlay omics data on these sets and predict pathway usage for each metabolic task. We demonstrated how this approach can be used to quantify metabolic functions of diverse biological samples from the single cell to whole tissues and organs by using multiple transcriptomic datasets. To facilitate its adoption, we integrated the approach into GenePattern (www.genepattern.org-CellFie).

Biphasic (5-2%) oxygen concentration strategy significantly improves the usable blastocyst and cumulative live birth rates in in vitro fertilization.

Oxygen (O2) concentration is approximately 5% in the fallopian tube and 2% in the uterus in humans. A "back to nature" approach could increase in vitro fertilization (IVF) outcomes. This hypothesis was tested in this monocentric observational retrospective study that included 120 couples who underwent two IVF cycles between 2014 and 2019. Embryos were cultured at 5% from day 0 (D0) to D5/6 (monophasic O2 concentration strategy) in the first IVF cycle, and at 5% O2 from D0 to D3 and 2% O2 from D3 to D5/6 (biphasic O2 concentration strategy) in the second IVF cycle. The total and usable blastocyst rates (44.4% vs. 54.8%, p = 0.049 and 21.8% vs. 32.8%, p = 0.002, respectively) and the cumulative live birth rate (17.9% vs. 44.1%, p = 0.027) were significantly higher with the biphasic (5%-2%) O2 concentration strategy. Whole transcriptome analysis of blastocysts donated for research identified 707 RNAs that were differentially expressed in function of the O2 strategy (fold-change > 2, p value < 0.05). These genes are mainly involved in embryo development, DNA repair, embryonic stem cell pluripotency, and implantation potential. The biphasic (5-2%) O2 concentration strategy for preimplantation embryo culture could increase the "take home baby rate", thus improving IVF cost-effectiveness and infertility management.

Urinary extracellular vesicles: Assessment of pre-analytical variables and development of a quality control with focus on transcriptomic biomarker research.

Urinary extracellular vesicles (uEV) are a topical source of non-invasive biomarkers for health and diseases of the urogenital system. However, several challenges have become evident in the standardization of uEV pipelines from collection of urine to biomarker analysis. Here, we studied the effect of pre-analytical variables and developed means of quality control for uEV isolates to be used in transcriptomic biomarker research. We included urine samples from healthy controls and individuals with type 1 or type 2 diabetes and normo-, micro- or macroalbuminuria and isolated uEV by ultracentrifugation. We studied the effect of storage temperature (-20°C vs. -80°C), time (up to 4 years) and storage format (urine or isolated uEV) on quality of uEV by nanoparticle tracking analysis, electron microscopy, Western blotting and qPCR. Urinary EV RNA was compared in terms of quantity, quality, and by mRNA or miRNA sequencing. To study the stability of miRNA levels in samples isolated by different methods, we created and tested a list of miRNAs commonly enriched in uEV isolates. uEV and their transcriptome were preserved in urine or as isolated uEV even after long-term storage at -80°C. However, storage at -20°C degraded particularly the GC-rich part of the transcriptome and EV protein markers. Transcriptome was preserved in RNA samples extracted with and without DNAse, but read distributions still showed some differences in e.g. intergenic and intronic reads. MiRNAs commonly enriched in uEV isolates were stable and concordant between different EV isolation methods. Analysis of never frozen uEV helped to identify surface characteristics of particles by EM. In addition to uEV, qPCR assays demonstrated that uEV isolates commonly contained polyoma viruses. Based on our results, we present recommendations how to store and handle uEV isolates for transcriptomics studies that may help to expedite standardization of the EV biomarker field.

Assessment of reference genes at six different developmental stages of Schistosoma mansoni for quantitative RT-PCR.

Reverse-transcription quantitative real-time polymerase chain reaction (RT-qPCR) is the most used, fast, and reproducible method to confirm large-scale gene expression data. The use of stable reference genes for the normalization of RT-qPCR assays is recognized worldwide. No systematic study for selecting appropriate reference genes for usage in RT-qPCR experiments comparing gene expression levels at different Schistosoma mansoni life-cycle stages has been performed. Most studies rely on genes commonly used in other organisms, such as actin, tubulin, and GAPDH. Therefore, the present study focused on identifying reference genes suitable for RT-qPCR assays across six S. mansoni developmental stages. The expression levels of 25 novel candidates that we selected based on the analysis of public RNA-Seq datasets, along with eight commonly used reference genes, were systematically tested by RT-qPCR across six developmental stages of S. mansoni (eggs, miracidia, cercariae, schistosomula, adult males and adult females). The stability of genes was evaluated with geNorm, NormFinder and RefFinder algorithms. The least stable candidate reference genes tested were actin, tubulin and GAPDH. The two most stable reference genes suitable for RT-qPCR normalization were Smp_101310 (Histone H4 transcription factor) and Smp_196510 (Ubiquitin recognition factor in ER-associated degradation protein 1). Performance of these two genes as normalizers was successfully evaluated with females maintained unpaired or paired to males in culture for 8 days, or with worm pairs exposed for 16 days to double-stranded RNAs to silence a protein-coding gene. This study provides reliable reference genes for RT-qPCR analysis using samples from six different S. mansoni life-cycle stages.

Cell Heterogeneity Analysis in Single-Cell RNA-seq Data Using Mixture Exponential Graph and Markov Random Field Model.

Advanced single-cell profiling technologies promote exploration of cell heterogeneity, and clustering of single-cell RNA (scRNA-seq) data enables discovery of coexpression genes and network relationships between genes. In particular, single-cell profiling of circulating tumor cells (CTCs) can provide unique insights into tumor heterogeneity (including in triple-negative breast cancer (TNBC)), while scRNA-seq leads to better understanding of subclonal architecture and biological function. Despite numerous reports suggesting a direct correlation between circulating tumor cells (CTCs) and poor clinical outcomes, few studies have provided a thorough heterogeneity characterization of CTCs. In addition, TNBC is a disease with not only intertumor but also intratumor heterogeneity and represents various biological distinct subgroups that may have relationships with immune functions that are not clearly established yet. In this article, we introduce a new scheme for detecting genotypic characterization of single-cell heterogeneities and apply it to CTC and TNBC single-cell RNA-seq data. First, we use an existing mixture exponential family graph model to partition the cell-cell network; then, with the Markov random field model, we obtain more flexible network rewiring. Finally, we find the cell heterogeneity and network relationships according to different high coexpression gene modules in different cell subsets. Our results demonstrate that this scheme provides a reasonable and effective way to model different cell clusters and different biological enrichment gene clusters. Thus, using different internal coexpression genes of different cell clusters, we can infer the differences in tumor composition and diversity.

Construction of Decision Trees Based on Gene Expression Omnibus Data to Classify Bladder Cancer and Its Subtypes.

BACKGROUND Bladder cancer is a malignant tumor of the genitourinary system. Different subtypes of bladder cancer have different treatment methods and prognoses. Therefore, identifying hub genes affecting other genes is of great significance for the treatment of bladder cancer. MATERIAL AND METHODS We obtained expression profiles from the GSE13507 and GSE77952 datasets from the Gene Expression Omnibus database. First, principal component analysis was used to identify the difference in gene expression in different types of tissues. Differential expression analysis was used to find the differentially expressed genes between normal and tumor tissues, and between tumors with and without muscle infiltration. Further, based on differentially expressed genes, we constructed 2 decision trees for differentiating between tumor and normal tissues, and between muscle-infiltrating and non-muscle-infiltrating tumor tissues. A receiver operating characteristic curve was used to evaluate the prediction effect of the decision trees. RESULTS FAM107A and C8orf4 showed significantly lower expression in bladder cancer tissues than in normal tissues. Regarding muscle infiltration, CTHRC1 showed lower expression and HMGCS2 showed higher expression in non-muscle-infiltrating samples than in those with muscle infiltration. We constructed 2 decision trees for differentiating between tumor and normal tissue, and between tissues with and without muscle infiltration. Both decision trees showed good prediction results. CONCLUSIONS These newly discovered hub genes will be helpful in understanding the occurrence and development of different subtypes of bladder cancer, and will provide new therapeutic targets and biomarkers for bladder cancer.

A Gene Rank Based Approach for Single Cell Similarity Assessment and Clustering.

Single-cell RNA sequencing (scRNA-seq) technology provides quantitative gene expression profiles at single-cell resolution. As a result, researchers have established new ways to explore cell population heterogeneity and genetic variability of cells. One of the current research directions for scRNA-seq data is to identify different cell types accurately through unsupervised clustering methods. However, scRNA-seq data analysis is challenging because of their high noise level, high dimensionality and sparsity. Moreover, the impact of multiple latent factors on gene expression heterogeneity and on the ability to accurately identify cell types remains unclear. How to overcome these challenges to reveal the biological difference between cell types has become the key to analyze scRNA-seq data. For these reasons, the unsupervised learning for cell population discovery based on scRNA-seq data analysis has become an important research area. A cell similarity assessment method plays a significant role in cell clustering. Here, we present BioRank, a new cell similarity assessment method based on annotated gene sets and gene ranks. To evaluate the performances, we cluster cells by two classical clustering algorithms based on the similarity between cells obtained by BioRank. In addition, BioRank can be used by any clustering algorithm that requires a similarity matrix. Applying BioRank to 12 public scRNA-seq datasets, we show that it is better than or at least as well as several popular similarity assessment methods for single cell clustering.

Drought responsiveness in black pepper (Piper nigrum L.): Genes associated and development of a web-genomic resource.

Black pepper (Piper nigrum L.; 2n = 52; Piperaceae), the king of spices, is a perennial, trailing woody flowering vine and has global importance with widespread dietary, medicinal, and preservative uses. It is an economically important germplasm cultivated for its fruit and the major cash crop in >30 tropical countries. Crop production is mainly affected by drought stress. The present study deals with the candidate gene identification from drought-affected black pepper leaf transcriptome generated by Illumina Hiseq2000. It also aims to mine putative molecular markers (namely SSRs, SNPs, and InDels) and generate primers for them. The identification of transcription factors and pathways involved in drought tolerance is also reported here. De novo transcriptome assembly was performed with trinity assembler. In total, 4914 differential expressed genes, 2110 transcriptional factors, 786 domains and 1137 families, 20,124 putative SSR markers, and 259,236 variants were identified. At2g30105 (unidentified gene containing leucine-rich repeats and ubiquitin-like domain), serine threonine protein kinase, Mitogen-activated protein kinase, Nucleotide Binding Site-Leucine Rich Repeat, Myeloblastosis-related proteins, basic helix-loop-helix are all found upregulated and are reported to be associated with plant tolerance against drought condition. All these information are catalogued in the Black Pepper Drought Transcriptome Database (BPDRTDb), freely accessible for academic use at http://webtom.cabgrid.res.in/bpdrtdb/. This database is a good foundation for the genetic improvement of pepper plants, breeding programmes, and mapping population of this crop. Putative markers can also be a reliable genomic resource to develop drought-tolerant variety for better black pepper productivity.