RCPA: An Open-Source R Package for Data Processing, Differential Analysis, Consensus Pathway Analysis, and Visualization.

Identifying impacted pathways is important because it provides insights into the biology underlying conditions beyond the detection of differentially expressed genes. Because of the importance of such analysis, more than 100 pathway analysis methods have been developed thus far. Despite the availability of many methods, it is challenging for biomedical researchers to learn and properly perform pathway analysis. First, the sheer number of methods makes it challenging to learn and choose the correct method for a given experiment. Second, computational methods require users to be savvy with coding syntax, and comfortable with command-line environments, areas that are unfamiliar to most life scientists. Third, as learning tools and computational methods are typically implemented only for a few species (i.e., human and some model organisms), it is difficult to perform pathway analysis on other species that are not included in many of the current pathway analysis tools. Finally, existing pathway tools do not allow researchers to combine, compare, and contrast the results of different methods and experiments for both hypothesis testing and analysis purposes. To address these challenges, we developed an open-source R package for Consensus Pathway Analysis (RCPA) that allows researchers to conveniently: (1) download and process data from NCBI GEO; (2) perform differential analysis using established techniques developed for both microarray and sequencing data; (3) perform both gene set enrichment, as well as topology-based pathway analysis using different methods that seek to answer different research hypotheses; (4) combine methods and datasets to find consensus results; and (5) visualize analysis results and explore significantly impacted pathways across multiple analyses. This protocol provides many example code snippets with detailed explanations and supports the analysis of more than 1000 species, two pathway databases, three differential analysis techniques, eight pathway analysis tools, six meta-analysis methods, and two consensus analysis techniques. The package is freely available on the CRAN repository. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Processing Affymetrix microarrays Basic Protocol 2: Processing Agilent microarrays Support Protocol: Processing RNA sequencing (RNA-Seq) data Basic Protocol 3: Differential analysis of microarray data (Affymetrix and Agilent) Basic Protocol 4: Differential analysis of RNA-Seq data Basic Protocol 5: Gene set enrichment analysis Basic Protocol 6: Topology-based (TB) pathway analysis Basic Protocol 7: Data integration and visualization.

First transcriptomic insight into the working muscles of racing pigeons during a competition flight.

BACKGROUND: The currently known homing pigeon is a result of a sharp one-sided selection for flight characteristics focused on speed, endurance, and spatial orientation. This has led to extremely well-adapted athletic phenotypes in racing birds. METHODS: Here, we identify genes and pathways contributing to exercise adaptation in sport pigeons by applying next-generation transcriptome sequencing of m.pectoralis muscle samples, collected before and after a 300 km competition flight. RESULTS: The analysis of differentially expressed genes pictured the central role of pathways involved in fuel selection and muscle maintenance during flight, with a set of genes, in which variations may therefore be exploited for genetic improvement of the racing pigeon population towards specific categories of competition flights. CONCLUSIONS: The presented results are a background to understanding the genetic processes in the muscles of birds during flight and also are the starting point of further selection of genetic markers associated with racing performance in carrier pigeons.

Enhancing multiple myeloma staging: a novel cell death risk model approach.

The prognostication of survival trajectories in multiple myeloma (MM) patients presents a substantial clinical challenge. Leveraging transcriptomic and clinical profiles from an expansive cohort of 2,088 MM patients, sourced from the Gene Expression Omnibus and The Cancer Genome Atlas repositories, we applied a sophisticated nested lasso regression technique to construct a prognostic model predicated on 28 gene pairings intrinsic to cell death pathways, thereby deriving a quantifiable risk stratification metric. Employing a threshold of 0.15, we dichotomized the MM samples into discrete high-risk and low-risk categories. Notably, the delineated high-risk cohort exhibited a statistically significant diminution in survival duration, a finding which consistently replicated across both training and external validation datasets. The prognostic acumen of our cell death signature was further corroborated by TIME ROC analyses, with the model demonstrating robust performance, evidenced by AUC metrics consistently surpassing the 0.6 benchmark across the evaluated arrays. Further analytical rigor was applied through multivariate COX regression analyses, which ratified the cell death risk model as an independent prognostic determinant. In an innovative stratagem, we amalgamated this risk stratification with the established International Staging System (ISS), culminating in the genesis of a novel, refined ISS categorization. This tripartite classification system was subjected to comparative analysis against extant prognostic models, whereupon it manifested superior predictive precision, as reflected by an elevated C-index. In summation, our endeavors have yielded a clinically viable gene pairing model predicated on cellular mortality, which, when synthesized with the ISS, engenders an augmented prognostic tool that exhibits pronounced predictive prowess in the context of multiple myeloma.

Comparative analysis of infected cassava root transcriptomics reveals candidate genes for root rot disease resistance.

Cassava root-rot incited by soil-borne pathogens is one of the major diseases that reduces root yield. Although the use of resistant cultivars is the most effective method of management, the genetic basis for root-rot resistance remains poorly understood. Therefore, our work analyzed the transcriptome of two contrasting genotypes (BRS Kiriris/resistant and BGM-1345/susceptible) using RNA-Seq to understand the molecular response and identify candidate genes for resistance. Cassava seedlings (resistant and susceptible to root-rot) were both planted in infested and sterilized soil and samples from Initial-time and Final-time periods, pooled. Two controls were used: (i) seedlings collected before planting in infested soil (absolute control) and, (ii) plants grown in sterilized soil (mock treatments). For the differentially expressed genes (DEGs) analysis 23.912 were expressed in the resistant genotype, where 10.307 were differentially expressed in the control treatment, 15 DEGs in the Initial Time-period and 366 DEGs in the Final Time-period. Eighteen candidate genes from the resistant genotype were related to plant defense, such as the MLP-like protein 31 and the peroxidase A2-like gene. This is the first model of resistance at the transcriptional level proposed for the cassava × root-rot pathosystem. Gene validation will contribute to screening for resistance of germplasm, segregating populations and/or use in gene editing in the pursuit to develop most promising cassava clones with resistance to root-rot.

Repression of developmental transcription factor networks triggers aging-associated gene expression in human glial progenitor cells.

Human glial progenitor cells (hGPCs) exhibit diminished expansion competence with age, as well as after recurrent demyelination. Using RNA-sequencing to compare the gene expression of fetal and adult hGPCs, we identify age-related changes in transcription consistent with the repression of genes enabling mitotic expansion, concurrent with the onset of aging-associated transcriptional programs. Adult hGPCs develop a repressive transcription factor network centered on MYC, and regulated by ZNF274, MAX, IKZF3, and E2F6. Individual over-expression of these factors in iPSC-derived hGPCs lead to a loss of proliferative gene expression and an induction of mitotic senescence, replicating the transcriptional changes incurred during glial aging. miRNA profiling identifies the appearance of an adult-selective miRNA signature, imposing further constraints on the expansion competence of aged GPCs. hGPC aging is thus associated with acquisition of a MYC-repressive environment, suggesting that suppression of these repressors of glial expansion may permit the rejuvenation of aged hGPCs.

Periodic heat waves-induced neuronal etiology in the elderly is mediated by gut-liver-brain axis: a transcriptome profiling approach.

Heat stress exposure in intermittent heat waves and subsequent exposure during war theaters pose a clinical challenge that can lead to multi-organ dysfunction and long-term complications in the elderly. Using an aged mouse model and high-throughput sequencing, this study investigated the molecular dynamics of the liver-brain connection during heat stress exposure. Distinctive gene expression patterns induced by periodic heat stress emerged in both brain and liver tissues. An altered transcriptome profile showed heat stress-induced altered acute phase response pathways, causing neural, hepatic, and systemic inflammation and impaired synaptic plasticity. Results also demonstrated that proinflammatory molecules such as S100B, IL-17, IL-33, and neurological disease signaling pathways were upregulated, while protective pathways like aryl hydrocarbon receptor signaling were downregulated. In parallel, Rantes, IRF7, NOD1/2, TREM1, and hepatic injury signaling pathways were upregulated. Furthermore, current research identified Orosomucoid 2 (ORM2) in the liver as one of the mediators of the liver-brain axis due to heat exposure. In conclusion, the transcriptome profiling in elderly heat-stressed mice revealed a coordinated network of liver-brain axis pathways with increased hepatic ORM2 secretion, possibly due to gut inflammation and dysbiosis. The above secretion of ORM2 may impact the brain through a leaky blood-brain barrier, thus emphasizing intricate multi-organ crosstalk.

Construction of ceRNA regulatory networks for active pulmonary tuberculosis.

Delayed diagnosis in patients with pulmonary tuberculosis (PTB) often leads to serious public health problems. High throughput sequencing was used to determine the expression levels of lncRNAs, mRNAs, and miRNAs in the lesions and adjacent health lung tissues of patients with PTB. Their differential expression profiles between the two groups were compared, and 146 DElncRs, 447 DEmRs, and 29 DEmiRs were obtained between lesions and adjacent health tissues in patients with PTB. Enrichment analysis for mRNAs showed that they were mainly involved in Th1, Th2, and Th17 cell differentiation. The lncRNAs, mRNAs with target relationship with miRNAs were predicted respectively, and correlation analysis was performed. The ceRNA regulatory network was obtained by comparing with the differentially expressed transcripts (DElncRs, DEmRs, DEmiRs), then 2 lncRNAs mediated ceRNA networks were established. The expression of genes within the network was verified by quantitative real-time PCR (qRT-PCR). Flow cytometric analysis revealed that the proportion of Th1 cells and Th17 cells was lower in PTB than in controls, while the proportion of Th2 cells increased. Our results provide rich transcriptome data for a deeper investigation of PTB. The ceRNA regulatory network we obtained may be instructive for the diagnosis and treatment of PTB.

Insights into phage-bacteria interaction in cold seep Gigantidas platifrons through metagenomics and transcriptome analyses.

Viruses are crucial for regulating deep-sea microbial communities and biogeochemical cycles. However, their roles are still less characterized in deep-sea holobionts. Bathymodioline mussels are endemic species inhabiting cold seeps and harboring endosymbionts in gill epithelial cells for nutrition. This study unveiled a diverse array of viruses in the gill tissues of Gigantidas platifrons mussels and analyzed the viral metagenome and transcriptome from the gill tissues of Gigantidas platifrons mussels collected from a cold seep in the South Sea. The mussel gills contained various viruses including Baculoviridae, Rountreeviridae, Myoviridae and Siphovirdae, but the active viromes were Myoviridae, Siphoviridae, and Podoviridae belonging to the order Caudovirales. The overall viral community structure showed significant variation among environments with different methane concentrations. Transcriptome analysis indicated high expression of viral structural genes, integrase, and restriction endonuclease genes in a high methane concentration environment, suggesting frequent virus infection and replication. Furthermore, two viruses (GP-phage-contig14 and GP-phage-contig72) interacted with Gigantidas platifrons methanotrophic gill symbionts (bathymodiolin mussels host intracellular methanotrophic Gammaproteobacteria in their gills), showing high expression levels, and have huge different expression in different methane concentrations. Additionally, single-stranded DNA viruses may play a potential auxiliary role in the virus-host interaction using indirect bioinformatics methods. Moreover, the Cro and DNA methylase genes had phylogenetic similarity between the virus and Gigantidas platifrons methanotrophic gill symbionts. This study also explored a variety of viruses in the gill tissues of Gigantidas platifrons and revealed that bacteria interacted with the viruses during the symbiosis with Gigantidas platifrons. This study provides fundamental insights into the interplay of microorganisms within Gigantidas platifrons mussels in deep sea.

Differential expression of angiogenesis-related genes 'VEGF' and 'angiopoietin-1' in metastatic and EMAST-positive colorectal cancer patients.

Abnormal angiogenesis leads to tumor progression and metastasis in colorectal cancer (CRC). This study aimed to elucidate the association between angiogenesis-related genes, including VEGF-A, ANGPT-1, and ANGPT-2 with both metastatic and microsatellite alterations at selected tetranucleotide repeats (EMAST) subtypes of CRC. We conducted a thorough assessment of the ANGPT-1, ANGPT-2, and VEGF-A gene expression utilizing publicly available RNA sequencing and microarray datasets. Then, the experimental validation was performed in 122 CRC patients, considering their disease metastasis and EMAST+/- profile by using reverse transcription polymerase chain reaction (RT-PCR). Subsequently, a competing endogenous RNA (ceRNA) network associated with these angiogenesis-related genes was constructed and analyzed. The expression level of VEGF-A and ANGPT-2 genes were significantly higher in tumor tissues as compared with normal adjacent tissues (P-value < 0.001). Nevertheless, ANGPT-1 had a significantly lower expression in tumor samples than in normal colon tissue (P-value < 0.01). We identified a significantly increased VEGF-A (P-value = 0.002) and decreased ANGPT-1 (P-value = 0.04) expression in EMAST+ colorectal tumors. Regarding metastasis, a significantly increased VEGF-A and ANGPT-2 expression (P-value = 0.001) and decreased ANGPT-1 expression (P-value < 0.05) were established in metastatic CRC patients. Remarkably, co-expression analysis also showed a strong correlation between ANGPT-2 and VEGF-A gene expressions. The ceRNA network was constructed by ANGPT-1, ANGPT-2, VEGF-A, and experimentally validated miRNAs (hsa-miR-190a-3p, hsa-miR-374c-5p, hsa-miR-452-5p, and hsa-miR-889-3p), lncRNAs (AFAP1-AS1, KCNQ1OT1 and MALAT1), and TFs (Sp1, E2F1, and STAT3). Network analysis revealed that colorectal cancer is amongst the 82 significant pathways. We demonstrated a significant differential expression of VEGF-A and ANGPT-1 in colorectal cancer patients exhibiting the EMAST+ phenotype. This finding provides novel insights into the molecular pathogenesis of colorectal cancer, specifically in EMAST subtypes. Yet, the generalization of in silico findings to EMAST+ colorectal cancer warrants future experimental investigations. In the end, this study proposes that the EMAST biomarker could serve as an additional perspective on CMS4 biology which is well-defined by activated angiogenesis and worse overall survival.

Genome-wide identification, molecular evolution and gene expression of P450 gene family in Cyrtotrachelus buqueti.

BACKGROUND: Insect Cytochrome P450 monooxygenase (CYPs or P450s) plays an important role in detoxifying insecticides, causing insect populations to develop resistance. However, the molecular functions of P450 gene family in Cyrtotrachelus buqueti genome are still lacking. RESULTS: In this study, 71 CbuP450 genes have been identified. The amino acids length of CbuP450 proteins was between 183 aa ~ 1041 aa. They are proteins with transmembrane domains. The main component of their secondary structure is α-helix and random coils. Phylogenetic analysis showed that C. buqueti and Rhynchophorus ferrugineus were the most closely related. This gene family has 29 high-frequency codons, which tend to use A/T bases and A/T ending codons. Gene expression analysis showed that CbuP450_23 in the female adult may play an important role on high temperature resistance, and CbuP450_17 in the larval may play an important role on low temperature tolerance. CbuP450_10, CbuP450_17, CbuP450_23, CbuP450_10, CbuP450_16, CbuP450_20, CbuP450_23 and CbuP450_ 29 may be related to the regulation of bamboo fiber degradation genes in C. buqueti. Protein interaction analysis indicates that most CbuP450 proteins are mainly divided into three aspects: encoding the biosynthesis of ecdysteroids, participating in the decomposition of synthetic insecticides, metabolizing insect hormones, and participating in the detoxification of compounds. CONCLUSIONS: We systematically analyzed the gene and protein characteristics, gene expression, and protein interactions of CbuP450 gene family, revealing the key genes involved in the stress response of CbuP450 gene family in the resistance of C. buqueti to high or low temperature stress, and identified the key CbuP450 proteins involved in important life activity metabolism. These results provided a reference for further research on the function of P450 gene family in C. buqueti.

Elucidating the role of exogenous melatonin in mitigating alkaline stress in soybeans across different growth stages: a transcriptomic and metabolomic approach.

BACKGROUND: Soybean (Glycine max), a vital grain and oilseed crop, serves as a primary source of plant protein and oil. Soil salinization poses a significant threat to soybean planting, highlighting the urgency to improve soybean resilience and adaptability to saline stress. Melatonin, recently identified as a key plant growth regulator, plays crucial roles in plant growth, development, and responses to environmental stress. However, the potential of melatonin to mitigate alkali stress in soybeans and the underlying mechanisms remain unclear. RESULTS: This study investigated the effects of exogenous melatonin on the soybean cultivar Zhonghuang 13 under alkaline stress. We employed physiological, biochemical, transcriptomic, and metabolomic analyses throughout both vegetative and pod-filling growth stages. Our findings demonstrate that melatonin significantly counteracts the detrimental effects of alkaline stress on soybean plants, promoting plant growth, photosynthesis, and antioxidant capacity. Transcriptomic analysis during both growth stages under alkaline stress, with and without melatonin treatment, identified 2,834 and 549 differentially expressed genes, respectively. These genes may play a vital role in regulating plant adaptation to abiotic stress. Notably, analysis of phytohormone biosynthesis pathways revealed altered expression of key genes, particularly in the ARF (auxin response factor), AUX/IAA (auxin/indole-3-acetic acid), and GH3 (Gretchen Hagen 3) families, during the early stress response. Metabolomic analysis during the pod-filling stage identified highly expressed metabolites responding to melatonin application, such as uteolin-7-O-(2''-O-rhamnosyl)rutinoside and Hederagenin-3-O-glucuronide-28-O-glucosyl(1,2)glucoside, which helped alleviate the damage caused by alkali stress. Furthermore, we identified 183 differentially expressed transcription factors, potentially playing a critical role in regulating plant adaptation to abiotic stress. Among these, the gene SoyZH13_04G073701 is particularly noteworthy as it regulates the key differentially expressed metabolite, the terpene metabolite Hederagenin-3-O-glucuronide-28-O-glucosyl(1,2)glucoside. WGCNA analysis identified this gene (SoyZH13_04G073701) as a hub gene, positively regulating the crucial differentially expressed metabolite of terpenoids, Hederagenin-3-O-glucuronide-28-O-glucosyl(1,2)glucoside. Our findings provide novel insights into how exogenous melatonin alleviates alkali stress in soybeans at different reproductive stages. CONCLUSIONS: Integrating transcriptomic and metabolomic approaches, our study elucidates the mechanisms by which exogenous melatonin ameliorates the inhibitory effects of alkaline stress on soybean growth and development. This occurs through modulation of biosynthesis pathways for key compounds, including terpenes, flavonoids, and phenolics. Our findings provide initial mechanistic insights into how melatonin mitigates alkaline stress in soybeans, offering a foundation for molecular breeding strategies to enhance salt-alkali tolerance in this crop.

Disregarding multimappers leads to biases in the functional assessment of NGS data.

BACKGROUND: Standard ChIP-seq and RNA-seq processing pipelines typically disregard sequencing reads whose origin is ambiguous ("multimappers"). This usual practice has potentially important consequences for the functional interpretation of the data: genomic elements belonging to clusters composed of highly similar members are left unexplored. RESULTS: In particular, disregarding multimappers leads to the underrepresentation in epigenetic studies of recently active transposable elements, such as AluYa5, L1HS and SVAs. Furthermore, this common strategy also has implications for transcriptomic analysis: members of repetitive gene families, such the ones including major histocompatibility complex (MHC) class I and II genes, are under-quantified. CONCLUSION: Revealing inherent biases that permeate routine tasks such as functional enrichment analysis, our results underscore the urgency of broadly adopting multimapper-aware bioinformatic pipelines -currently restricted to specific contexts or communities- to ensure the reliability of genomic and transcriptomic studies.

A comparison of RNA-Seq data preprocessing pipelines for transcriptomic predictions across independent studies.

BACKGROUND: RNA sequencing combined with machine learning techniques has provided a modern approach to the molecular classification of cancer. Class predictors, reflecting the disease class, can be constructed for known tissue types using the gene expression measurements extracted from cancer patients. One challenge of current cancer predictors is that they often have suboptimal performance estimates when integrating molecular datasets generated from different labs. Often, the quality of the data is variable, procured differently, and contains unwanted noise hampering the ability of a predictive model to extract useful information. Data preprocessing methods can be applied in attempts to reduce these systematic variations and harmonize the datasets before they are used to build a machine learning model for resolving tissue of origins. RESULTS: We aimed to investigate the impact of data preprocessing steps-focusing on normalization, batch effect correction, and data scaling-through trial and comparison. Our goal was to improve the cross-study predictions of tissue of origin for common cancers on large-scale RNA-Seq datasets derived from thousands of patients and over a dozen tumor types. The results showed that the choice of data preprocessing operations affected the performance of the associated classifier models constructed for tissue of origin predictions in cancer. CONCLUSION: By using TCGA as a training set and applying data preprocessing methods, we demonstrated that batch effect correction improved performance measured by weighted F1-score in resolving tissue of origin against an independent GTEx test dataset. On the other hand, the use of data preprocessing operations worsened classification performance when the independent test dataset was aggregated from separate studies in ICGC and GEO. Therefore, based on our findings with these publicly available large-scale RNA-Seq datasets, the application of data preprocessing techniques to a machine learning pipeline is not always appropriate.

Effects of hibernation on two important contractile tissues in tibetan frogs, Nanorana parkeri: a perspective from transcriptomics and metabolomics approaches.

BACKGROUND: In response to seasonal cold and food shortage, the Xizang plateau frogs, Nanorana parkeri (Anura: Dicroglossidae), enter a reversible hypometabolic state where heart rate and oxygen consumption in skeletal muscle are strongly suppressed. However, the effect of winter hibernation on gene expression and metabolic profiling in these two tissues remains unknown. In the present study, we conducted transcriptomic and metabolomic analyses of heart and skeletal muscle from summer- and winter-collected N. parkeri to explore mechanisms involved in seasonal hibernation. RESULTS: We identified 2407 differentially expressed genes (DEGs) in heart and 2938 DEGs in skeletal muscle. Enrichment analysis showed that shared DEGs in both tissues were enriched mainly in translation and metabolic processes. Of these, the expression of genes functionally categorized as "response to stress", "defense mechanisms", or "muscle contraction" were particularly associated with hibernation. Metabolomic analysis identified 24 and 22 differentially expressed metabolites (DEMs) in myocardium and skeletal muscle, respectively. In particular, pathway analysis showed that DEMs in myocardium were involved in the pentose phosphate pathway, glycerolipid metabolism, pyruvate metabolism, citrate cycle (TCA cycle), and glycolysis/gluconeogenesis. By contrast, DEMs in skeletal muscle were mainly involved in amino acid metabolism. CONCLUSIONS: In summary, natural adaptations of myocardium and skeletal muscle in hibernating N. parkeri involved transcriptional alterations in translation, stress response, protective mechanisms, and muscle contraction processes as well as metabolic remodeling. This study provides new insights into the transcriptional and metabolic adjustments that aid winter survival of high-altitude frogs N. parkeri.

Glycometabolism and lipid metabolism related genes predict the prognosis of endometrial carcinoma and their effects on tumor cells.

BACKGROUND: Glycometabolism and lipid metabolism are critical in cancer metabolic reprogramming. The primary aim of this study was to develop a prognostic model incorporating glycometabolism and lipid metabolism-related genes (GLRGs) for accurate prognosis assessment in patients with endometrial carcinoma (EC). METHODS: Data on gene expression and clinical details were obtained from publicly accessible databases. GLRGs were obtained from the Genecards database. Through nonnegative matrix factorization (NMF) clustering, molecular groupings with various GLRG expression patterns were identified. LASSO Cox regression analysis was employed to create a prognostic model. Use rich algorithms such as GSEA, GSVA, xCELL ssGSEA, EPIC,CIBERSORT, MCPcounter, ESTIMATE, TIMER, TIDE, and Oncoppredict to analyze functional pathway characteristics of the forecast signal, immune status, anti-tumor therapy, etc. The expression was assessed using Western blot and quantitative real-time PCR techniques. A total of 113 algorithm combinations were combined to screen out the most significant GLRGs in the signature for in vitro experimental verification, such as colony formation, EdU cell proliferation, wound healing, apoptosis, and Transwell assays. RESULTS: A total of 714 GLRGs were found, and 227 of them were identified as prognostic-related genes. And ten GLRGs (AUP1, ESR1, ERLIN2, ASS1, OGDH, BCKDHB, SLC16A1, HK2, LPCAT1 and PGR-AS1) were identified to construct the prognostic model of patients with EC. Based on GLRGs, the risk model's prognosis and independent prognostic value were established. The signature of GLRGs exhibited a robust correlation with the infiltration of immune cells and the sensitivity to drugs. In cytological experiments, we selected HK2 as candidate gene to verify its value in the occurrence and development of EC. Western blot and qRT-PCR revealed that HK2 was substantially expressed in EC cells. According to in vitro experiments, HK2 knockdown can increase EC cell apoptosis while suppressing EC cell migration, invasion, and proliferation. CONCLUSION: The GLRGs signature constructed in this study demonstrated significant prognostic value for patients with endometrial carcinoma, thereby providing valuable guidance for treatment decisions.

Transcriptome-wide association mapping provides insights into the genetic basis and candidate genes governing flowering, maturity and seed weight in rice bean (Vigna umbellata).

BACKGROUND: Rice bean (Vigna umbellata), an underrated legume, adapts to diverse climatic conditions with the potential to support food and nutritional security worldwide. It is used as a vegetable, minor food crop and a fodder crop, being a rich source of proteins, minerals, and essential fatty acids. However, little effort has been made to decipher the genetic and molecular basis of various useful traits in this crop. Therefore, we considered three economically important traits i.e., flowering, maturity and seed weight of rice bean and identified the associated candidate genes employing an associative transcriptomics approach on 100 diverse genotypes out of 1800 evaluated rice bean accessions from the Indian National Genebank. RESULTS: The transcriptomics-based genotyping of one-hundred diverse rice bean cultivars followed by pre-processing of genotypic data resulted in 49,271 filtered markers. The STRUCTURE, PCA and Neighbor-Joining clustering of 100 genotypes revealed three putative sub-populations. The marker-trait association analysis involving various genome-wide association study (GWAS) models revealed significant association of 82 markers on 48 transcripts for flowering, 26 markers on 22 transcripts for maturity and 22 markers on 21 transcripts for seed weight. The transcript annotation provided information on the putative candidate genes for the considered traits. The candidate genes identified for flowering include HSC80, P-II PsbX, phospholipid-transporting-ATPase-9, pectin-acetylesterase-8 and E3-ubiquitin-protein-ligase-RHG1A. Further, the WRKY1 and DEAD-box-RH27 were found to be associated with seed weight. Furthermore, the associations of PIF3 and pentatricopeptide-repeat-containing-gene with maturity and seed weight, and aldo-keto-reductase with flowering and maturity were revealed. CONCLUSION: This study offers insights into the genetic basis of key agronomic traits in rice bean, including flowering, maturity, and seed weight. The identified markers and associated candidate genes provide valuable resources for future exploration and targeted breeding, aiming to enhance the agronomic performance of rice bean cultivars. Notably, this research represents the first transcriptome-wide association study in pulse crop, uncovering the candidate genes for agronomically useful traits.

Diagnostic leukapheresis reveals distinct phenotypes of NSCLC circulating tumor cells.

BACKGROUND: Circulating tumor cells (CTCs) hold immense promise for unraveling tumor heterogeneity and understanding treatment resistance. However, conventional methods, especially in cancers like non-small cell lung cancer (NSCLC), often yield low CTC numbers, hindering comprehensive analyses. This study addresses this limitation by employing diagnostic leukapheresis (DLA) to cancer patients, enabling the screening of larger blood volumes. To leverage DLA's full potential, this study introduces a novel approach for CTC enrichment from DLAs. METHODS: DLA was applied to six advanced stage NSCLC patients. For an unbiased CTC enrichment, a two-step approach based on negative depletion of hematopoietic cells was used. Single-cell (sc) whole-transcriptome sequencing was performed, and CTCs were identified based on gene signatures and inferred copy number variations. RESULTS: Remarkably, this innovative approach led to the identification of unprecedented 3,363 CTC transcriptomes. The extensive heterogeneity among CTCs was unveiled, highlighting distinct phenotypes related to the epithelial-mesenchymal transition (EMT) axis, stemness, immune responsiveness, and metabolism. Comparison with sc transcriptomes from primary NSCLC cells revealed that CTCs encapsulate the heterogeneity of their primary counterparts while maintaining unique CTC-specific phenotypes. CONCLUSIONS: In conclusion, this study pioneers a transformative method for enriching CTCs from DLA, resulting in a substantial increase in CTC numbers. This allowed the creation of the first-ever single-cell whole transcriptome in-depth characterization of the heterogeneity of over 3,300 NSCLC-CTCs. The findings not only confirm the diagnostic value of CTCs in monitoring tumor heterogeneity but also propose a CTC-specific signature that can be exploited for targeted CTC-directed therapies in the future. This comprehensive approach signifies a major leap forward, positioning CTCs as a key player in advancing our understanding of cancer dynamics and paving the way for tailored therapeutic interventions.

Pigmentation level of human iPSC-derived RPE does not indicate a specific gene expression profile.

Retinal pigment epithelium (RPE) cells show heterogeneous levels of pigmentation when cultured in vitro. To know whether their color in appearance is correlated with the function of the RPE, we analyzed the color intensities of human-induced pluripotent stem cell-derived RPE cells (iPSC-RPE) together with the gene expression profile at the single-cell level. For this purpose, we utilized our recent invention, Automated Live imaging and cell Picking System (ALPS), which enabled photographing each cell before RNA-sequencing analysis to profile the gene expression of each cell. While our iPSC-RPE were categorized into four clusters by gene expression, the color intensity of iPSC-RPE did not project any specific gene expression profiles. We reasoned this by less correlation between the actual color and the gene expressions that directly define the level of pigmentation, from which we hypothesized the color of RPE cells may be a temporal condition not strongly indicating the functional characteristics of the RPE.

The backs of our eyes are lined with retinal pigment epithelial cells (or RPE cells for short). These cells provide nutrition to surrounding cells and contain a pigment called melanin that absorbs excess light that might interfere with vision. By doing so, they support the cells that receive light to enable vision. However, with age, RPE cells can become damaged and less able to support other cells. This can lead to a disease called age-related macular degeneration, which can cause blindness. One potential way to treat this disease is to transplant healthy RPE cells into eyes that have lost them. These healthy cells can be grown in the laboratory from human pluripotent stem cells, which have the capacity to turn into various specialist cells. Stem cell-derived RPE cells growing in a dish contain varying amounts of melanin, resulting in some being darker than others. This raised the question of whether pigment levels affect the function of RPE cells. However, it was difficult to compare single cells containing various amounts of pigment as most previous studies only analyzed large numbers of RPE cells mixed together. Nakai-Futatsugi et al. overcame this hurdle using a technique called Automated Live imaging and cell Picking System (also known as ALPS). More than 2300 stem cell-derived RPE cells were photographed individually and the color of each cell was recorded. The gene expression of each cell was then measured to investigate whether certain genes being switched on or off affects pigment levels and cell function. Analysis did not find a consistent pattern of gene expression underlying the pigmentation of RPE cells. Even gene expression related to the production of melanin was only slightly linked to the color of the cells. These findings suggests that the RPE cell color fluctuates and is not primarily determined by which genes are switched on or off. Future experiments are required to determine whether the findings are the same for RPE cells grown naturally in the eyes and whether different pigment levels affect their capacity to protect the rest of the eye.

Identification of molecular subtypes based on chromatin regulator-related genes and experimental verification of the role of ASCL1 in conferring chemotherapy resistance to breast cancer.

Objective: The aim of this study was to identify the molecular subtypes of breast cancer based on chromatin regulator-related genes. Methods: The RNA sequencing data of The Cancer Genome Atlas-Breast Cancer cohort were obtained from the official website, while the single-cell data were downloaded from the Gene Expression Omnibus database (GSE176078). Validation was performed using the Molecular Taxonomy of Breast Cancer International Consortium dataset. Furthermore, the immune characteristics, tumor stemness, heterogeneity, and clinical characteristics of these molecular subtypes were analyzed. The correlation between chromatin regulators and chemotherapy resistance was examined in vitro using the quantitative real-time polymerase chain reaction (qRT-PCR) and Cell Counting Kit-8 (CCK8) assays. Results: This study identified three stable molecular subtypes with different prognostic and pathological features. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and protein-protein interaction analyses revealed that the differentially expressed genes were associated with disease processes, such as mitotic nuclear division, chromosome segregation, condensed chromosome, and specific chromosome region. The T stage and subtypes were correlated with the clinical features. Tumor heterogeneity (mutant-allele tumor heterogeneity, tumor mutational burden, purity, and homologous recombination deficiency) and tumor stemness (RNA expression-based stemness score, epigenetically regulated RNA expression-based stemness score, DNA methylation-based stemness score, and epigenetically regulated DNA methylation-based stemness score) significantly varied between the three subtypes. Furthermore, Western blotting, qRT-PCR, and CCK8 assays demonstrated that the expression of ASCL1 was positively correlated with chemotherapy resistance in breast cancer. Conclusion: This study identified the subtypes of breast cancer based on chromatin regulators and analyzed their clinical features, gene mutation status, immunophenotype, and drug sensitivity. The results of this study provide effective strategies for assessing clinical prognosis and developing personalized treatment strategies.

Unraveling the immunogenetic landscape of autism spectrum disorder: a comprehensive bioinformatics approach.

Background: Autism spectrum disorder (ASD) is a disease characterized by social disorder. Recently, the population affected by ASD has gradually increased around the world. There are great difficulties in diagnosis and treatment at present. Methods: The ASD datasets were obtained from the Gene Expression Omnibus database and the immune-relevant genes were downloaded from a previously published compilation. Subsequently, we used WGCNA to screen the modules related to the ASD and immune. We also choose the best combination and screen out the core genes from Consensus Machine Learning Driven Signatures (CMLS). Subsequently, we evaluated the genetic correlation between immune cells and ASD used GNOVA. And pleiotropic regions identified by PLACO and CPASSOC between ASD and immune cells. FUMA was used to identify pleiotropic regions, and expression trait loci (EQTL) analysis was used to determine their expression in different tissues and cells. Finally, we use qPCR to detect the gene expression level of the core gene. Results: We found a close relationship between neutrophils and ASD, and subsequently, CMLS identified a total of 47 potential candidate genes. Secondly, GNOVA showed a significant genetic correlation between neutrophils and ASD, and PLACO and CPASSOC identified a total of 14 pleiotropic regions. We annotated the 14 regions mentioned above and identified a total of 6 potential candidate genes. Through EQTL, we found that the CFLAR gene has a specific expression pattern in neutrophils, suggesting that it may serve as a potential biomarker for ASD and is closely related to its pathogenesis. Conclusions: In conclusion, our study yields unprecedented insights into the molecular and genetic heterogeneity of ASD through a comprehensive bioinformatics analysis. These valuable findings hold significant implications for tailoring personalized ASD therapies.