TEnGExA: an R package based tool for tissue enrichment and gene expression analysis.

RNA-seq data analysis with rapidly advancing high-throughput sequencing technology, nowadays provides large number of transcripts or genes to perform downstream analysis including functional annotation and pathway analysis. However for the data from multiple tissues, downstream analysis with tissue-specific or tissue-enriched transcripts is highly preferable. However, there is still a need of tool for quickly performing tissue-enrichment and gene expression analysis irrespective of number of input genes or tissues at various fragments per kilobase of transcript per million fragments mapped (FPKM) thresholds. To fulfill this need, we presented a freely available R package and web-interface tool, TEnGExA, which allows tissue-enrichment analysis (TEA) for any number of genes or transcripts for any species provided only a read-count or FPKM-value matrix as input. Based on the different FPKM value and fold thresholds, TEnGExA classifies the user provided gene lists into tissue-enriched or tissue-specific transcripts along with other standard classes. By analyzing the published sample data from human, plant and microorganism, we signifies that TEnGExA can easily handle complex or large data from any species to provided tissue-enriched gene list for downstream analysis in quick time. In summary, TEnGExA is quick, easy to use and an efficient tool for TEA. The R package is freely available at https://github.com/ubagithub/TEnGExA/ and the GUI web interface is accessible at http://webtom.cabgrid.res.in/tissue_enrich/.

Misuse of RPKM or TPM normalization when comparing across samples and sequencing protocols.

In recent years, RNA-sequencing (RNA-seq) has emerged as a powerful technology for transcriptome profiling. For a given gene, the number of mapped reads is not only dependent on its expression level and gene length, but also the sequencing depth. To normalize these dependencies, RPKM (reads per kilobase of transcript per million reads mapped) and TPM (transcripts per million) are used to measure gene or transcript expression levels. A common misconception is that RPKM and TPM values are already normalized, and thus should be comparable across samples or RNA-seq projects. However, RPKM and TPM represent the relative abundance of a transcript among a population of sequenced transcripts, and therefore depend on the composition of the RNA population in a sample. Quite often, it is reasonable to assume that total RNA concentration and distributions are very close across compared samples. Nevertheless, the sequenced RNA repertoires may differ significantly under different experimental conditions and/or across sequencing protocols; thus, the proportion of gene expression is not directly comparable in such cases. In this review, we illustrate typical scenarios in which RPKM and TPM are misused, unintentionally, and hope to raise scientists' awareness of this issue when comparing them across samples or different sequencing protocols.

Comprehensive Analysis of Transcript and Protein Relative Abundance During Blood Stages of Plasmodium falciparum Infection.

Plasmodium falciparum is the main causative agent of human malaria. During the intraerythrocytic development cycle, the P. falciparum morphology changes dramatically from circulating young rings to sequestered mature trophozoites and schizonts. Sequestered forms contribute to the pathophysiology of severe malaria as the infected erythrocytes obstruct the microvascular flow in deep organs and induce local inflammation. However, the sequestration mechanism limits the access to the corresponding parasitic form in the clinical samples from patients infected with P. falciparum. To complement this deficiency, we aimed to evaluate the relevance of mRNA study as a proxy of protein expression in sequestered parasites. To do so, we conducted a proteotranscriptomic analysis using five independent P. falciparum laboratory strain samples. RNA sequencing was performed, and the mRNA expression level was assessed on circulating ring-stage parasites. The level of protein expression were measured by LC-MS/MS on the corresponding sequestered mature forms after 18-24 h of maturation. Overall, our results showed a strong transcriptome/transcriptome and a very strong proteome/proteome correlation between samples. Moreover, positive correlations of mRNA and protein expression levels were found between ring-stage transcriptomes and mature form proteomes. However, twice more transcripts were identified at the ring stage than proteins at the mature trophozoite stage. A high level of transcript expression did not guarantee the detection of the corresponding protein. Finally, we pointed out discrepancies at the individual gene level. Taken together, our results show that transcript and protein expressions are overall correlated. However, mRNA abundance is not a perfect proxy of protein expression at the individual level. Importantly, our study shows limitations of the "blind" use of RNA-seq and the importance of multiomics approaches for P. falciparum blood stage study in clinical samples.

Effect of normalization methods on the performance of supervised learning algorithms applied to HTSeq-FPKM-UQ data sets: 7SK RNA expression as a predictor of survival in patients with colon adenocarcinoma.

MOTIVATION: One of the main challenges in machine learning (ML) is choosing an appropriate normalization method. Here, we examine the effect of various normalization methods on analyzing FPKM upper quartile (FPKM-UQ) RNA sequencing data sets. We collect the HTSeq-FPKM-UQ files of patients with colon adenocarcinoma from TCGA-COAD project. We compare three most common normalization methods: scaling, standardizing using z-score and vector normalization by visualizing the normalized data set and evaluating the performance of 12 supervised learning algorithms on the normalized data set. Additionally, for each of these normalization methods, we use two different normalization strategies: normalizing samples (files) or normalizing features (genes). RESULTS: Regardless of normalization methods, a support vector machine (SVM) model with the radial basis function kernel had the maximum accuracy (78%) in predicting the vital status of the patients. However, the fitting time of SVM depended on the normalization methods, and it reached its minimum fitting time when files were normalized to the unit length. Furthermore, among all 12 learning algorithms and 6 different normalization techniques, the Bernoulli naive Bayes model after standardizing files had the best performance in terms of maximizing the accuracy as well as minimizing the fitting time. We also investigated the effect of dimensionality reduction methods on the performance of the supervised ML algorithms. Reducing the dimension of the data set did not increase the maximum accuracy of 78%. However, it leaded to discovery of the 7SK RNA gene expression as a predictor of survival in patients with colon adenocarcinoma with accuracy of 78%.

TPM, FPKM, or Normalized Counts? A Comparative Study of Quantification Measures for the Analysis of RNA-seq Data from the NCI Patient-Derived Models Repository.

BACKGROUND: In order to correctly decode phenotypic information from RNA-sequencing (RNA-seq) data, careful selection of the RNA-seq quantification measure is critical for inter-sample comparisons and for downstream analyses, such as differential gene expression between two or more conditions. Several methods have been proposed and continue to be used. However, a consensus has not been reached regarding the best gene expression quantification method for RNA-seq data analysis. METHODS: In the present study, we used replicate samples from each of 20 patient-derived xenograft (PDX) models spanning 15 tumor types, for a total of 61 human tumor xenograft samples available through the NCI patient-derived model repository (PDMR). We compared the reproducibility across replicate samples based on TPM (transcripts per million), FPKM (fragments per kilobase of transcript per million fragments mapped), and normalized counts using coefficient of variation, intraclass correlation coefficient, and cluster analysis. RESULTS: Our results revealed that hierarchical clustering on normalized count data tended to group replicate samples from the same PDX model together more accurately than TPM and FPKM data. Furthermore, normalized count data were observed to have the lowest median coefficient of variation (CV), and highest intraclass correlation (ICC) values across all replicate samples from the same model and for the same gene across all PDX models compared to TPM and FPKM data. CONCLUSION: We provided compelling evidence for a preferred quantification measure to conduct downstream analyses of PDX RNA-seq data. To our knowledge, this is the first comparative study of RNA-seq data quantification measures conducted on PDX models, which are known to be inherently more variable than cell line models. Our findings are consistent with what others have shown for human tumors and cell lines and add further support to the thesis that normalized counts are the best choice for the analysis of RNA-seq data across samples.

Identification of Prostaglandin Pathway in Dinoflagellates by Transcriptome Data Mining.

Dinoflagellates, a major class of marine eukaryote microalgae composing the phytoplankton, are widely recognised as producers of a large variety of toxic molecules, particularly neurotoxins, which can also act as potent bioactive pharmacological mediators. In addition, similarly to other microalgae, they are also good producers of polyunsaturated fatty acids (PUFAs), important precursors of key molecules involved in cell physiology. Among PUFA derivatives are the prostaglandins (Pgs), important physiological mediators in several physiological and pathological processes in humans, also used as "biological" drugs. Their synthesis is very expensive because of the elevated number of reaction steps required, thus the search for new Pgs production methods is of great relevance. One possibility is their extraction from microorganisms (e.g., diatoms), which have been proved to produce the same Pgs as humans. In the present study, we took advantage of the available transcriptomes for dinoflagellates in the iMicrobe database to search for the Pgs biosynthetic pathway using a bioinformatic approach. Here we show that dinoflagellates express nine Pg-metabolism related enzymes involved in both Pgs synthesis and reduction. Not all of the enzymes were expressed simultaneously in all the species analysed and their expression was influenced by culturing conditions, especially salinity of the growth medium. These results confirm the existence of a biosynthetic pathway for these important molecules in unicellular microalgae other than diatoms, suggesting a broad diffusion and conservation of the Pgs pathway, which further strengthen their importance in living organisms.

Landscape of the intratumroal microenvironment in bladder cancer: Implications for prognosis and immunotherapy.

Introduction: This study aims to present the landscape of the intratumoral microenvironment and by which establish a classification system that can be used to predict the prognosis of bladder cancer patients and their response to anti-PD-L1 immunotherapy. Methods: The expression profiles of 1554 bladder cancer cases were downloaded from seven public datasets. Single-sample gene set enrichment analysis (ssGSEA), univariate Cox regression analysis, and meta-analysis were employed to establish the bladder cancer immune prognostic index (BCIPI). Extensive analyses were executed to investigate the association between BCIPI and overall survival, tumor-infiltrated immunocytes, immunotherapeutic response, mutation load, etc. Results: The results obtained from seven independent cohorts and meta-analyses suggested that the BCIPI is an effective classification system for estimating bladder cancer patients' overall survival. Patients in the BCIPI-High subgroup revealed different immunophenotypic outcomes from those in the BCIPI-Low subgroup regarding tumor-infiltrated immunocytes and mutated genes. Subsequent analysis suggested that patients in the BCIPI-High subgroup were more sensitive to anti-PD-L1 immunotherapy than those in the BCIPI-Low subgroup. Conclusions: The newly established BCIPI is a valuable tool for predicting overall survival outcomes and immunotherapeutic responses in patients with bladder cancer.

Integrative network analysis reveals subtype-specific long non-coding RNA regulatory mechanisms in head and neck squamous cell carcinoma.

Head and neck squamous cell carcinoma (HNSC) is one of most common malignancies with high mortality worldwide. Importantly, the molecular heterogeneity of HNSC complicates the clinical diagnosis and treatment, leading to poor overall survival outcomes. To dissect the complex heterogeneity, recent studies have reported multiple molecular subtyping systems. For instance, HNSC can be subdivided to four distinct molecular subtypes: atypical, basal, classical, and mesenchymal, of which the mesenchymal subtype is characterized by upregulated epithelial-mesenchymal transition (EMT) and associated with poorer survival outcomes. Despite a wealth of studies into the complex molecular heterogeneity, the regulatory mechanism specific to this aggressive subtype remain largely unclear. Herein, we developed a network-based bioinformatics framework that integrates lncRNA and mRNA expression profiles to elucidate the subtype-specific regulatory mechanisms. Applying the framework to HNSC, we identified a clinically relevant lncRNA LNCOG as a key master regulator mediating EMT underlying the mesenchymal subtype. Five genes with strong prognostic values, namely ANXA5, ITGA5, CCBE1, P4HA2, and EPHX3, were predicted to be the putative targets of LNCOG and subsequently validated in other independent datasets. By integrative analysis of the miRNA expression profiles, we found that LNCOG may act as a ceRNA to sponge miR-148a-3p thereby upregulating ITGA5 to promote HNSC progression. Furthermore, our drug sensitivity analysis demonstrated that the five putative targets of LNCOG were also predictive of the sensitivities of multiple FDA-approved drugs. In summary, our bioinformatics framework facilitates the dissection of cancer subtype-specific lncRNA regulatory mechanisms, providing potential novel biomarkers for more optimized treatment of HNSC.

Transcriptomic landscapes of effective and failed liver regeneration in humans.

Background & Aims: Although extensive experimental evidence on the process of liver regeneration exists, in humans, validation is largely missing. However, liver regeneration is critically affected by underlying liver disease. Within this project, we aimed to systematically assess early transcriptional changes during liver regeneration in humans and further assess how these processes differ in people with dysfunctional liver regeneration. Methods: Blood samples of 154 patients and intraoperative tissue samples of 46 patients undergoing liver resection were collected and classified with regard to dysfunctional postoperative liver regeneration. Of those, a matched cohort of 21 patients were used for RNA sequencing. Samples were assessed for circulating cytokines, gene expression dynamics, intrahepatic neutrophil accumulation, and spatial transcriptomics. Results: Individuals with dysfunctional liver regeneration demonstrated an aggravated transcriptional inflammatory response with higher intracellular adhesion molecule-1 induction. Increased induction of this critical leukocyte adhesion molecule was associated with increased intrahepatic neutrophil accumulation and activation upon induction of liver regeneration in individuals with dysfunctional liver regeneration. Comparing baseline gene expression profiles in individuals with and without dysfunctional liver regeneration, we found that dual-specificity phosphatase 4 (DUSP4) expression, a known critical regulator of intracellular adhesion molecule-1 expression in endothelial cells, was markedly reduced in patients with dysfunctional liver regeneration. Mimicking clinical risk factors for dysfunctional liver regeneration, we found liver sinusoidal endothelial cells of two liver disease models to have significantly reduced baseline levels of DUSP4. Conclusions: Exploring the landscape of early transcriptional changes of human liver regeneration, we observed that people with dysfunctional regeneration experience overwhelming intrahepatic inflammation. Subclinical liver disease might account for DUSP4 reduction in liver sinusoidal endothelial cells, which ultimately primes the liver for an aggravated inflammatory response. Impact and implications: Using a unique human biorepository, focused on liver regeneration (LR), we explored the landscape of circulating and tissue-level alterations associated with both functional and dysfunctional LR. In contrast to experimental animal models, people with dysfunctional LR demonstrated an aggravated transcriptional inflammatory response, higher intracellular adhesion molecule-1 (ICAM-1) induction, intrahepatic neutrophil accumulation and activation upon induction of LR. Although inflammatory responses appear rapidly after liver resection, people with dysfunctional LR have exaggerated inflammatory responses that appear to be related to decreased levels of LSEC DUSP4, challenging existing concepts of post-resectional LR.

MAPKAPK2-centric transcriptome profiling reveals its major role in governing molecular crosstalk of IGFBP2, MUC4, and PRKAR2B during HNSCC pathogenesis.

Transcriptome analysis of head and neck squamous cell carcinoma (HNSCC) has been pivotal to comprehending the convoluted biology of HNSCC tumors. MAPKAPK2 or MK2 is a critical modulator of the mRNA turnover of crucial genes involved in HNSCC progression. However, MK2-centric transcriptome profiles of tumors are not well known. This study delves into HNSCC progression with MK2 at the nexus to delineate the biological relevance and intricate crosstalk of MK2 in the tumor milieu. We performed next-generation sequencing-based transcriptome profiling of HNSCC cells and xenograft tumors to ascertain mRNA expression profiles in MK2-wild type and MK2-knockdown conditions. The findings were validated using gene expression assays, immunohistochemistry, and transcript turnover studies. Here, we identified a pool of crucial MK2-regulated candidate genes by annotation and differential gene expression analyses. Regulatory network and pathway enrichment revealed their significance and involvement in the HNSCC pathogenesis. Additionally, 3'-UTR-based filtering recognized important MK2-regulated downstream target genes and validated them by nCounter gene expression assays. Finally, immunohistochemistry and transcript stability studies revealed the putative role of MK2 in regulating the transcript turnover of IGFBP2, MUC4, and PRKAR2B in HNSCC. Conclusively, MK2-regulated candidate genes were identified in this study, and their plausible involvement in HNSCC pathogenesis was elucidated. These genes possess investigative values as targets for diagnosis and therapeutic interventions for HNSCC.

Anopheles gambiae strain (Ag55) cultured cells originated from Anopheles coluzzii and are phagocytic with hemocyte-like gene expression.

Anopheles gambiae and Anopheles coluzzii are closely related species that are predominant vectors of malaria in Africa. Recently, A. gambiae form M was renamed A. coluzzii and we now conclude on the basis of a diagnostic PCR-restriction fragment length polymorphism assay that Ag55 cells were derived from A. coluzzii. We established an Ag55 cell transcriptome, and KEGG pathway analysis showed that Ag55 cells are enriched in phagosome pathway transcripts. The Ag55 transcriptome has an abundance of specific transcripts characteristic of mosquito hemocytes. Functional E. coli bioparticle uptake experiments visualized by fluorescence microscopy and confocal microscopy and quantified by flow cytometry establish the phagocytic competence of Ag55 cells. Results from this investigation of Ag55 cell properties will guide researchers in the use and engineering of the Ag55 cell line to better enable investigations of Plasmodium, other microbes, and insecticidal toxins.

Ketogenic diet containing medium-chain triglyceride ameliorates transcriptome disruption in skeletal muscles of rat models of duchenne muscular dystrophy.

Duchenne muscular dystrophy (DMD) is a myopathy characterized by progressive muscle weakness caused by a mutation in the dystrophin gene on the X chromosome. We recently showed that a medium-chain triglyceride-containing ketogenic diet (MCTKD) improves skeletal muscle myopathy in a CRISPR/Cas9 gene-edited rat model of DMD. We examined the effects of the MCTKD on transcription profiles in skeletal muscles of the model rats to assess the underlying mechanism of the MCTKD-induced improvement in DMD. DMD rats were fed MCTKD or normal diet (ND) from weaning to 9 months, and wild-type rats were fed with the ND, then tibialis anterior muscles were sampled for mRNA-seq analysis. Pearson correlation heatmaps revealed a one-node transition in the expression profile between DMD and wild-type rats. A total of 10,440, 11,555 and 11,348 genes were expressed in the skeletal muscles of wild-type and ND-fed DMD rats the MCTKD-fed DMD rats, respectively. The MCTKD reduced the number of DMD-specific mRNAs from 1624 to 1350 and increased the number of mRNAs in common with wild-type rats from 9931 to 9998. Among 2660 genes were differentially expressed in response to MCTKD intake, the mRNA expression of 1411 and 1249 of them was respectively increased and decreased. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses suggested that the MCTKD significantly suppressed the mRNA expression of genes associated with extracellular matrix organization and inflammation. This suggestion was consistent with our previous findings that the MCTKD significantly suppressed fibrosis and inflammation in DMD rats. In contrast, the MCTKD significantly increased the mRNA expression of genes associated with oxidative phosphorylation and ATP production pathways, suggesting altered energy metabolism. The decreased and increased mRNA expression of Sln and Atp2a1 respectively suggested that Sarco/endoplasmic reticulum Ca2+-ATPase activation is involved in the MCTKD-induced improvement of skeletal muscle myopathy in DMD rats. This is the first report to examine transcription profiles in the skeletal muscle of CRISPR/Cas9 gene-edited DMD model rats and the effect of MCTKD feeding on it.

Differentiating between liver diseases by applying multiclass machine learning approaches to transcriptomics of liver tissue or blood-based samples.

Background & Aims: Liver disease carries significant healthcare burden and frequently requires a combination of blood tests, imaging, and invasive liver biopsy to diagnose. Distinguishing between inflammatory liver diseases, which may have similar clinical presentations, is particularly challenging. In this study, we implemented a machine learning pipeline for the identification of diagnostic gene expression biomarkers across several alcohol-associated and non-alcohol-associated liver diseases, using either liver tissue or blood-based samples. Methods: We collected peripheral blood mononuclear cells (PBMCs) and liver tissue samples from participants with alcohol-associated hepatitis (AH), alcohol-associated cirrhosis (AC), non-alcohol-associated fatty liver disease, chronic HCV infection, and healthy controls. We performed RNA sequencing (RNA-seq) on 137 PBMC samples and 67 liver tissue samples. Using gene expression data, we implemented a machine learning feature selection and classification pipeline to identify diagnostic biomarkers which distinguish between the liver disease groups. The liver tissue results were validated using a public independent RNA-seq dataset. The biomarkers were computationally validated for biological relevance using pathway analysis tools. Results: Utilizing liver tissue RNA-seq data, we distinguished between AH, AC, and healthy conditions with overall accuracies of 90% in our dataset, and 82% in the independent dataset, with 33 genes. Distinguishing 4 liver conditions and healthy controls yielded 91% overall accuracy in our liver tissue dataset with 39 genes, and 75% overall accuracy in our PBMC dataset with 75 genes. Conclusions: Our machine learning pipeline was effective at identifying a small set of diagnostic gene biomarkers and classifying several liver diseases using RNA-seq data from liver tissue and PBMCs. The methodologies implemented and genes identified in this study may facilitate future efforts toward a liquid biopsy diagnostic for liver diseases. Lay summary: Distinguishing between inflammatory liver diseases without multiple tests can be challenging due to their clinically similar characteristics. To lay the groundwork for the development of a non-invasive blood-based diagnostic across a range of liver diseases, we compared samples from participants with alcohol-associated hepatitis, alcohol-associated cirrhosis, chronic hepatitis C infection, and non-alcohol-associated fatty liver disease. We used a machine learning computational approach to demonstrate that gene expression data generated from either liver tissue or blood samples can be used to discover a small set of gene biomarkers for effective diagnosis of these liver diseases.

Integrative RNA profiling of TBEV-infected neurons and astrocytes reveals potential pathogenic effectors.

Tick-borne encephalitis virus (TBEV), the most medically relevant tick-transmitted flavivirus in Eurasia, targets the host central nervous system and frequently causes severe encephalitis. The severity of TBEV-induced neuropathogenesis is highly cell-type specific and the exact mechanism responsible for such differences has not been fully described yet. Thus, we performed a comprehensive analysis of alterations in host poly-(A)/miRNA/lncRNA expression upon TBEV infection in vitro in human primary neurons (high cytopathic effect) and astrocytes (low cytopathic effect). Infection with severe but not mild TBEV strain resulted in a high neuronal death rate. In comparison, infection with either of TBEV strains in human astrocytes did not. Differential expression and splicing analyses with an in silico prediction of miRNA/mRNA/lncRNA/vd-sRNA networks found significant changes in inflammatory and immune response pathways, nervous system development and regulation of mitosis in TBEV Hypr-infected neurons. Candidate mechanisms responsible for the aforementioned phenomena include specific regulation of host mRNA levels via differentially expressed miRNAs/lncRNAs or vd-sRNAs mimicking endogenous miRNAs and virus-driven modulation of host pre-mRNA splicing. We suggest that these factors are responsible for the observed differences in the virulence manifestation of both TBEV strains in different cell lines. This work brings the first complex overview of alterations in the transcriptome of human astrocytes and neurons during the infection by two TBEV strains of different virulence. The resulting data could serve as a starting point for further studies dealing with the mechanism of TBEV-host interactions and the related processes of TBEV pathogenesis.

A Transcriptomic Pipeline Adapted for Genomic Sequence Discovery of Germline-Restricted Sequence in Zebra Finch, Taeniopygia guttata.

Songbirds have an unusual genomic element which is only found in their germline cells, known as the germline-restricted chromosome (GRC). Because germ cells contain both GRC and non-GRC (or A-chromosome) sequences, confidently identifying the GRC-derived elements from genome assemblies has proven difficult. Here, we introduce a new application of a transcriptomic method for GRC sequence identification. By adapting the Stringtie/Ballgown pipeline to use somatic and germline DNA reads, we find that the ratio of fragments per kilobase per million mapped reads can be used to confidently assign contigs to the GRC. Using this comparative coverage analysis, we successfully identify 733 contigs as high confidence GRC sequences (720 newly identified in this study) and 51 contigs which were validated using quantitative polymerase chain reaction. We also identified two new GRC genes, one hypothetical protein and one gene encoding an RNase H-like domain, and placed 16 previously identified but unplaced genes onto their host contigs. With the current focus on sequencing GRCs from different songbirds, our work adds to the genomic toolkit to identify GRC elements, and we provide a detailed protocol and GitHub repository at https://github.com/brachtlab/Comparative_Coverage_Analysis (last accessed May 12, 2021).

Postnatal maturation of calcium signaling in islets of Langerhans from neonatal mice.

Pancreatic islet cells develop mature physiological responses to glucose and other fuels postnatally. In this study, we used fluorescence imaging techniques to measure changes in intracellular calcium ([Ca2+]i) to compare islets isolated from mice on postnatal days 0, 4, and 12 with islets from adult CD-1 mice. In addition, we used publicly available RNA-sequencing data to compare expression levels of key genes in ß-cell physiology with [Ca2+]i data across these ages. We show that islets isolated from mice on postnatal day 0 displayed elevated [Ca2+]i in basal glucose (≤4 mM) but lower [Ca2+]i responses to stimulation by 12-20 mM glucose compared to adult. Neonatal islets displayed more adult-like [Ca2+]i in basal glucose by day 4 but continued to show lower [Ca2+]i responses to 16 and 20 mM glucose stimulation up to at least day 12. A right shift in glucose sensing (EC50) correlated with lower fragment-per-kilobase-of-transcript-per-million-reads-mapped (FPKM) of Slc2a2 (glut2) and Actn3 and increased FPKM for Galk1 and Nupr1. Differences in [Ca2+]i responses to additional stimuli were also observed. Calcium levels in the endoplasmic reticulum were elevated on day 0 but became adult-like by day 4, which corresponded with reduced expression in Atp2a2 (SERCA2) and novel K+-channel Ktd17, increased expression of Pml, Wfs1, Thada, and Herpud1, and basal [Ca2+]i maturing to adult levels. Ion-channel activity also matured rapidly, but RNA sequencing data mining did not yield strong leads. In conclusion, the maturation of islet [Ca2+]i signaling is complex and multifaceted; several possible gene targets were identified that may participate in this process.

Interpopulation differences of retroduplication variations (RDVs) in rice retrogenes and their phenotypic correlations.

Retroduplication variation (RDV), a type of retrocopy polymorphism, is considered to have essential biological significance, but its effect on gene function and species phenotype is still poorly understood. To this end, we analyzed the retrocopies and RDVs in 3,010 rice genomes. We calculated the RDV frequencies in the genome of each rice population; detected the mutated, ancestral and expressed retrogenes in rice genomes; and analyzed their RDV influence on rice phenotypic traits. Collectively, 73 RDVs were identified, and 14 RDVs in ancestral retrogenes can significantly affect rice phenotypes. Our research reveals that RDV plays an important role in rice migration, domestication and evolution. We think that RDV is a good molecular breeding marker candidate. To our knowledge, this is the first study on the relationship between retrogene function, expression, RDV and species phenotype.

Identification of the susceptibility genes for COVID-19 in lung adenocarcinoma with global data and biological computation methods.

INTRODUCTION: The risk of infection with COVID-19 is high in lung adenocarcinoma (LUAD) patients, and there is a dearth of studies on the molecular mechanism underlying the high susceptibility of LUAD patients to COVID-19 from the perspective of the global differential expression landscape. OBJECTIVES: To fill the research void on the molecular mechanism underlying the high susceptibility of LUAD patients to COVID-19 from the perspective of the global differential expression landscape. METHODS: Herein, we identified genes, specifically the differentially expressed genes (DEGs), correlated with the susceptibility of LUAD patients to COVID-19. These were obtained by calculating standard mean deviation (SMD) values for 49 SARS-CoV-2-infected LUAD samples and 24 non-affected LUAD samples, as well as 3931 LUAD samples and 3027 non-cancer lung samples from 40 pooled RNA-seq and microarray datasets. Hub susceptibility genes significantly related to COVID-19 were further selected by weighted gene co-expression network analysis. Then, the hub genes were further analyzed via an examination of their clinical significance in multiple datasets, a correlation analysis of the immune cell infiltration level, and their interactions with the interactome sets of the A549 cell line. RESULTS: A total of 257 susceptibility genes were identified, and these genes were associated with RNA splicing, mitochondrial functions, and proteasomes. Ten genes, MEA1, MRPL24, PPIH, EBNA1BP2, MRTO4, RABEPK, TRMT112, PFDN2, PFDN6, and NDUFS3, were confirmed to be the hub susceptibility genes for COVID-19 in LUAD patients, and the hub susceptibility genes were significantly correlated with the infiltration of multiple immune cells. CONCLUSION: In conclusion, the susceptibility genes for COVID-19 in LUAD patients discovered in this study may increase our understanding of the high risk of COVID-19 in LUAD patients.

Ablation of liver Fxr results in an increased colonic mucus barrier in mice.

BACKGROUND & AIMS: The interorgan crosstalk between the liver and the intestine has been the focus of intense research. Key in this crosstalk are bile acids, which are secreted from the liver into the intestine, interact with the microbiome, and upon absorption reach back to the liver. The bile acid-activated farnesoid X receptor (Fxr) is involved in the gut-to-liver axis. However, liver-to-gut communication and the roles of bile acids and Fxr remain elusive. Herein, we aim to get a better understanding of Fxr-mediated liver-to-gut communication, particularly in colon functioning. METHODS: Fxr floxed/floxed mice were crossed with cre-expressing mice to yield Fxr ablation in the intestine (Fxr-intKO), liver (Fxr-livKO), or total body (Fxr-totKO). The effects on colonic gene expression (RNA sequencing), the microbiome (16S sequencing), and mucus barrier function by ex vivo imaging were analysed. RESULTS: Despite relatively small changes in biliary bile acid concentration and composition, more genes were differentially expressed in the colons of Fxr-livKO mice than in those of Fxr-intKO and Fxr-totKO mice (3272, 731, and 1824, respectively). The colons of Fxr-livKO showed increased expression of antimicrobial genes, Toll-like receptors, inflammasome-related genes and genes belonging to the 'Mucin-type O-glycan biosynthesis' pathway. Fxr-livKO mice have a microbiome profile favourable for the protective capacity of the mucus barrier. The thickness of the inner sterile mucus layer was increased and colitis symptoms reduced in Fxr-livKO mice. CONCLUSIONS: Targeting of FXR is at the forefront in the battle against metabolic diseases. We show that ablation of Fxr in the liver greatly impacts colonic gene expression and increased the colonic mucus barrier. Increasing the mucus barrier is of utmost importance to battle intestinal diseases such as inflammatory bowel disease, and we show that this might be done by antagonising FXR in the liver. LAY SUMMARY: This study shows that the communication of the liver to the intestine is crucial for intestinal health. Bile acids are key players in this liver-to-gut communication, and when Fxr, the master regulator of bile acid homoeostasis, is ablated in the liver, colonic gene expression is largely affected, and the protective capacity of the mucus barrier is increased.

Discovery of tumor immune infiltration-related snoRNAs for predicting tumor immune microenvironment status and prognosis in lung adenocarcinoma.

Lung adenocarcinoma (LUAD) has a high mortality rate and is difficult to diagnose and treat in its early stage. Previous studies have demonstrated that small nucleolar RNAs (snoRNAs) play a critical role in tumor immune infiltration and the development of a variety of solid tumors. However, there have been no studies on the correlation between tumor-infiltrating immune-related snoRNAs (TIISRs) and LUAD. In this study, we filtered six immune-related snoRNAs based on the tissue specificity index (TSI) and expression profile of all snoRNAs between all LUAD cell lines from the Cancer Cell Line Encyclopedia and 21 types of immune cells from the Gene Expression Omnibus database. Further, we performed real-time quantitative polymerase chain reaction (RT-qPCR) to validate the expression status of these snoRNAs on peripheral blood mononuclear cells (PBMCs) and lung cancer cell lines. Next, we developed a TIISR signature based on the expression profiles of snoRNAs from 479 LUAD patients filtered by the random survival forest algorithm. We then analyzed the value of this TIISR signature (TIISR risk score) for assessing tumor immune infiltration, immune checkpoint inhibitor (ICI) treatment response, and the prognosis of LUAD between groups with high and low TIISR risk score. Further, we found that the TIISR risk score groups showed significant differences in biological characteristics and that the risk score could be used to assess the level of tumor immune cell infiltration, thereby predicting prognosis and responsiveness to immunotherapy in LUAD patients.