Genetic and QTL Analysis for Hilum-Eye Types in Cowpea (Vigna Unguiculata L. Walp)

Cowpea is an important food legume widely grown in the semi-arid tropics and serves as a main source of dietary protein, minerals, and vitamins. However, varieties differ from region to region based on the consumer’s preference for seed types determined by seed size, seed coat texture, seed color, and hilum-eye types. The genetics of seed size, seed color, and seed coat texture have been well documented, but the hilum-eye types have not been studied well because they represent seven different types with complex interactions. We studied the genetic segregation for hilum-eye types and determined the number of genes involved in a recombinant inbred line (RIL) population derived from a cross between a small eye parent ‘GEC’ and a Watson eye parent ‘IT98K-476-8’. The results demonstrated a three-gene model, W (Watson), S (small), and R (large), for cowpea seed hilum-eye type pattern and the interaction of these three genes, W, S, and R, resulted in five phenotypes, viz. self, Watson, small, large, and ring hilum-eye types. Moreover, we also mapped the RILs for hilum-eye types, identified three quantitative trait loci (QTLs), and aligned to the cowpea reference genome as QTL qHilum7.1, qHilum9.1, and qHilum10.1, corresponding to these three genes, Ring type (R), Watson type (W), and Small type (S) hilum-eye type patterns, respectively. Therefore, there was a complete agreement between the genetic analysis and QTL mapping for the number of genes controlling the hilum types in cowpea.

Improvement of the submergence stress tolerance of local South Sumatran rice through the introgression of the Sub1 gene by using marker-assisted selection

Submergence stress due to unpredictable soil flooding is one of the main constraints encountered in rainfed growing areas, especially in Southern Sumatran riparian swamps. The development of submergence-stress-tolerant cultivars through the introgression of Sub1 via marker-assisted backcrossing (MABC) is an ideal solution. This study was carried out during 2020 at Sriwijaya University, Palembang, Indonesia, with the aim to select Sub1-introgressed lines in BC3F1 generations on the basis of MABC and to screen out the SSR markers that were unlinked to the target gene for application in subsequent background selection studies. Results revealed that almost all the backcrossed progenies segregated from the rice parental cultivars ‘FR13A’ and ‘Pegagan’. The backcrossed lines showed significantly improved submergence stress tolerance and recovery rates compared with their parents. Sub1 introgression into the BC3F1 generation was confirmed by the tightly linked Sub1 marker SUB1C173, and marker RM23915 was used for recombinant selection. These markers followed the expected marker segregation ratio in accordance with the Mendelian single gene model. In the parental polymorphism survey, 84 out of 237 SSR markers that were unlinked to the target loci were found to be available for background study. Twenty-seven backcrossed lines were selected on the basis of foreground selection. Seven plants were selected on the basis of the recombinant marker RM23915. Five backcrossed lines were further selected on the basis of submergence stress tolerance and agronomic performance.

Kinetic characteristics of transcriptional bursting in a complex gene model with cyclic promoter structure

While transcription occurs often in a bursty manner, various possible regulations can lead to complex promoter patterns such as promoter cycles, giving rise to an important issue: How do promoter kinetics shape transcriptional bursting kinetics? Here we introduce and analyze a general model of the promoter cycle consisting of multi-OFF states and multi-ON states, focusing on the effects of multi-ON mechanisms on transcriptional bursting kinetics. The derived analytical results indicate that bust size follows a mixed geometric distribution rather than a single geometric distribution assumed in previous studies, and ON and OFF times obey their own mixed exponential distributions. In addition, we find that the multi-ON mechanism can lead to bimodal burst-size distribution, antagonistic timing of ON and OFF, and diverse burst frequencies, each further contributing to cell-to-cell variability in the mRNA expression level. These results not only reveal essential features of transcriptional bursting kinetics patterns shaped by multi-state mechanisms but also can be used to the inferences of transcriptional bursting kinetics and promoter structure based on experimental data.

The Cymbidium genome reveals the evolution of unique morphological traits

The marvelously diverse Orchidaceae constitutes the largest family of angiosperms. The genus Cymbidium in Orchidaceae is well known for its unique vegetation, floral morphology, and flower scent traits. Here, a chromosome-scale assembly of the genome of Cymbidium ensifolium (Jianlan) is presented. Comparative genomic analysis showed that C. ensifolium has experienced two whole-genome duplication (WGD) events, the most recent of which was shared by all orchids, while the older event was the τ event shared by most monocots. The results of MADS-box genes analysis provided support for establishing a unique gene model of orchid flower development regulation, and flower shape mutations in C. ensifolium were shown to be associated with the abnormal expression of MADS-box genes. The most abundant floral scent components identified included methyl jasmonate, acacia alcohol and linalool, and the genes involved in the floral scent component network of C. ensifolium were determined. Furthermore, the decreased expression of photosynthesis-antennae and photosynthesis metabolic pathway genes in leaves was shown to result in colorful striped leaves, while the increased expression of MADS-box genes in leaves led to perianth-like leaves. Our results provide fundamental insights into orchid evolution and diversification.

Identification of the Nerve-Cancer Cross-Talk-Related Prognostic Gene Model in Head and Neck Squamous Cell Carcinoma

The incidence of head and neck squamous cell carcinoma (HNSC) is increasing year by year. The nerve is an important component of the tumor microenvironment, which has a wide range of cross-talk with tumor cells and immune cells, especially in highly innervated organs, such as head and neck cancer and pancreatic cancer. However, the role of cancer-nerve cross-talk-related genes (NCCGs) in HNSC is unclear. In our study, we constructed a prognostic model based on genes with prognostic value in NCCGs. We used Pearson’s correlation to analyze the relationship between NCCGs and immune infiltration, microsatellite instability, tumor mutation burden, drug sensitivity, and clinical stage. We used single-cell sequencing data to analyze the expression of genes associated with stage in different cells and explored the possible pathways affected by these genes via gene set enrichment analysis. In the TCGA-HNSC cohort, a total of 23 genes were up- or downregulated compared with normal tissues. GO and KEGG pathway analysis suggested that NCCGs are mainly concentrated in membrane potential regulation, chemical synapse, axon formation, and neuroreceptor-ligand interaction. Ten genes were identified as prognosis genes by Kaplan-Meier plotter and used as candidate genes for LASSO regression. We constructed a seven-gene prognostic model (NTRK1, L1CAM, GRIN3A, CHRNA5, CHRNA6, CHRNB4, CHRND). The model could effectively predict the 1-, 3-, and 5-year survival rates in the TCGA-HNSC cohort, and the effectiveness of the model was verified by external test data. The genes included in the model were significantly correlated with immune infiltration, microsatellite instability, tumor mutation burden, drug sensitivity, and clinical stage. Single-cell sequencing data of HNSC showed that CHRNB4 was mainly expressed in tumor cells, and multiple metabolic pathways were enriched in high CHRNB4 expression tumor cells. In summary, we used comprehensive bioinformatics analysis to construct a prognostic gene model and revealed the potential of NCCGs as therapeutic targets and prognostic biomarkers in HNSC.

Identification of Myeloma-Initiating Cells By Single-Cell RNA Sequencing

Background: The outcomes of patients with multiple myeloma (MM) have improved due to treatment advances. However, some patients still experience rapid progression, multiple drug resistance or recurrent relapse. Tumor-initiating cells, also known as cancer stem cells (CSC) in some cancer types, have been speculated to induce recurrence of the disease. The aim of this study is to infer the identity of myeloma-initiating cells (MICs) utilizing single-cell transcriptome analysis and explore the unique biological characteristics relating with high-risk and drug-resistance. Method: We applied single cell RNA sequencing to fresh bone marrow mononuclear cell samples collecting from 7 healthy donors and 12 newly diagnosed MM patients utilizing 10x Chromium platform. Results: Firstly, we segregated the patients by tumor cell infiltration at single cell resolution and found that Myc pathway was significantly enriched in patients with high tumor burden (HTB). Next, we performed clustering analysis to tumor cells and identified 13 tumor subpopulations in total. Surprisingly, the distribution pattern of tumor subpopulations presented similarity among HTB patients, whereas we did not find the uniform subpopulation composition among the patients with low (LTB) or medium tumor burden (MTB). Via the tumor subpopulation analysis, we clarified the divergence in biological characteristics of these 13 malignant subpopulations. We identified plasmablasts as displaying high expression of B-cell gene signatures (CD19, CD27, MS4A1 and CD79B) and relatively low expression of plasma cell gene signatures (SDC1 and BCMA). Additionally, we also noted that they showed high level of CD24, which has been validated to be the marker gene for MICs. We next examined proliferative capability and utilized the 70 high-risk gene model and 56 drug resistance-related gene model to further distinguish subpopulations with the most malignant gene expression features. Notably, we found that plasmablasts possessed characteristics of high proliferation, drug-resistance and high-risk gene profiling, indicating their role as the root of myeloma, namely MIC subpopulation. Gene enrichment analysis also implicated that Wnt pathway, Notch pathway, stem cell differentiation pathway and Hedgehog pathway were enriched in MIC subpopulation which were associated with the proliferation, migration and drug resistance of MM. Differentially expressed gene (DEG) analysis showed that common driver genes in myeloma, such as CCND2, ITGB7 and CD74, were upregulated in MIC subpopulation comparing with other subpopulations. Conclusion: Our work presents an integral profiling for tumor cells in myeloma at single cell resolution. We uncovered divergence in the distribution of malignant subclusters across patients and distinct heterogeneity in gene expression across malignant subclusters as well. Plasmablasts expressing high level of CD24, CD27 and dim CD138 presented as the MICs with characteristics of higher proliferation, drug-resistance and high-risk gene profiling. Disclosures No relevant conflicts of interest to declare.

Multi-Omics Analysis of the Effects of Smoking on Human Tumors

Comprehensive studies on cancer patients with different smoking histories, including non-smokers, former smokers, and current smokers, remain elusive. Therefore, we conducted a multi-omics analysis to explore the effect of smoking history on cancer patients. Patients with smoking history were screened from The Cancer Genome Atlas database, and their multi-omics data and clinical information were downloaded. A total of 2,317 patients were included in this study, whereby current smokers presented the worst prognosis, followed by former smokers, while non-smokers showed the best prognosis. More importantly, smoking history was an independent prognosis factor. Patients with different smoking histories exhibited different immune content, and former smokers had the highest immune cells and tumor immune microenvironment. Smokers are under a higher incidence of genomic instability that can be reversed following smoking cessation in some changes. We also noted that smoking reduced the sensitivity of patients to chemotherapeutic drugs, whereas smoking cessation can reverse the situation. Competing endogenous RNA network revealed that mir-193b-3p, mir-301b, mir-205-5p, mir-132-3p, mir-212-3p, mir-1271-5p, and mir-137 may contribute significantly in tobacco-mediated tumor formation. We identified 11 methylation driver genes (including EIF5A2, GBP6, HGD, HS6ST1, ITGA5, NR2F2, PLS1, PPP1R18, PTHLH, SLC6A15, and YEATS2), and methylation modifications of some of these genes have not been reported to be associated with tumors. We constructed a 46-gene model that predicted overall survival with good predictive power. We next drew nomograms of each cancer type. Interestingly, calibration diagrams and concordance indexes are verified that the nomograms were highly accurate for the prognosis of patients. Meanwhile, we found that the 46-gene model has good applicability to the overall survival as well as to disease-specific survival and progression-free intervals. The results of this research provide new and valuable insights for the diagnosis, treatment, and follow-up of cancer patients with different smoking histories.

A Novel Overall Survival Prediction Signature Based on Cancer Stem Cell-Related Genes in Osteosarcoma

Background: Osteosarcoma is the most general bone malignancy that mostly affects children and adolescents. Numerous stem cell-related genes have been founded in distinct forms of cancer. This study aimed at identifying a stem cell-related gene model for the expected assessment of the prognosis of osteosarcoma patients.Methods: We obtained the genes expression data and relevant clinical materials from Therapeutically Applicable Research to Generate Effective Treatments (TARGET) and Gene Expression Omnibus (GEO) databases. We identified differentially expressed genes (DEGs) from the GEO dataset, whereas prognostic stem cell-related genes were obtained from the TARGET database. Subsequently, univariate, LASSO and multivariate Cox regression analyses were applied to establish the stem cell-related signature. Finally, the prognostic value of the signature was validated in the GEO dataset.Results: Twenty-five genes were prognostic ferroptosis-related DEGs. Consequently, we identified eight stem cell-related genes as a signature of prognosis of osteosarcoma patients. Then, the Kaplan–Meier (K-M) curve, the AUC value of ROC, and Cox regression analysis verified that the eight stem cell-related gene model were a new and substantial prognostic marker independent of other clinical traits. Moreover, the nomogram on the foundation of risk score and other clinical traits was established for predicting the survival rate of osteosarcoma patients. Biological function analyses displayed that tumor related pathways were affluent.Conclusion: The expression level of stem cell-related genes offers novel prognostic markers as well as underlying therapeutic targets for the therapy and prevention of osteosarcoma.

A Novel Immunomodulatory 27-Gene Signature to Predict Response to Neoadjuvant Immunochemotherapy for Primary Triple-Negative Breast Cancer

A precise predictive biomarker for TNBC response to immunochemotherapy is urgently needed. We previously established a 27-gene IO signature for TNBC derived from a previously established 101-gene model for classifying TNBC. Here we report a pilot study to assess the performance of a 27-gene IO signature in predicting the pCR of TNBC to preoperative immunochemotherapy. We obtained RNA sequencing data from the primary tumors of 55 patients with TNBC, who received neoadjuvant immunochemotherapy with the PD-L1 blocker durvalumab. We determined the power and accuracy in predicting pCR for the immunomodulatory (IM) subtype identified by the 101-gene model, the 27-gene IO signature, and PD-L1 expression by immunohistochemistry (IHC). The pCR rate was 45% (25/55). The odds ratios for pCR were as follows: IM subtype by 101-gene model, 3.14 (p = 0.054); 27-gene IO signature, 4.13 (p = 0.012); PD-L1 expression by IHC, 2.63 (p = 0.106); 27-gene IO signature in combination with PD-L1 expression by IHC, 6.53 (p = 0.003). The 27-gene IO signature has the potential to predict the pCR of primary TNBC to neoadjuvant immunochemotherapy. Further analysis in a large cohort is needed.