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Virus-host cell interactions during hepatitis C virus RNA replication: impact of polyprotein expression on the cellular transcriptome and cell cycle association with viral RNA synthesis.

Considerable controversy surrounds the impact of hepatitis C virus (HCV) protein expression on viability of host cells and regulation of the cell cycle. Both promotion of cellular proliferation and apoptosis have been observed in different experimental systems. To determine whether expression of the entire complement of HCV proteins in the context of ongoing viral RNA replication significantly alters the host cell transcriptome and cell cycle regulatory processes, we carried out high-density oligonucleotide microarray studies and analyzed cell cycle distributions and S-phase entry in Huh7 cell clones harboring selectable, full-length, replicating HCV RNAs that express the entire genotype 1b, HCV-N polyprotein, and clonally related cells in which all viral RNA was eliminated by prior treatment with alpha interferon. Oligonucleotide microarray analyses revealed only subtle, coordinated differences in the mRNA profiles of cells containing replicating viral RNA and their interferon-cured progeny, with variation between different cell clones having a greater influence on the cellular transcriptome than the presence or absence of replicating HCV RNA. Flow cytometric analysis demonstrated no significant differences in cell cycle distribution among populations of asynchronously growing cells of both types. Cell lines containing replicating viral RNA and their interferon-cured progeny were able to reenter the cell cycle similarly after transient G(1) arrest. In contrast, although viral protein expression and genome replication did not alter cell cycle control in these cells, HCV genome replication was highly dependent on cellular proliferation, with viral RNA synthesis strongly decreased in poorly proliferating, confluent, or serum-starved cells and substantially enhanced in the S phase of the cell cycle.

Cell Cycle↗

Single-cell multi-omics dissects transcript isoform and immune repertoire dynamics in human immunosenescence.

Immunosenescence, a major hallmark of systemic aging, refers to the progressive functional decline of the immune system. This decline not only compromises host defense and immunological memory but also fuels chronic inflammation and tissue degeneration (collectively known as inflammaging). While single-cell RNA sequencing (scRNA-seq) has revealed transcriptomic alterations associated with immune aging, analyses restricted to transcript abundance fail to capture deeper regulatory layers, such as transcript isoform diversity and the remodeling of immune receptor repertoires. To address this limitation, we present a human peripheral immune single-cell multi-omics atlas that integrates gene expression, transcript isoform diversity, and immune receptor repertoires. By combining single-cell full-length transcriptome sequencing (scCycloneSEQ), short-read scRNA-seq, and single-cell immune receptor sequencing (scTCR/BCR-seq), we systematically profiled peripheral blood mononuclear cells (PBMCs) from healthy donors aged 30-40 and 60-70 years. Our analyses uncovered extensive age-related remodeling of immune cell composition, functional states, and TCR/BCR diversity. Notably, we found that CD4+ effector memory T cells exhibited widespread differential isoform usage (DIU), 3'UTR length variation, and a marked reshaping of cytotoxic T lymphocyte (CTL) clonotypes-all of which were closely associated with aging-related inflammation and cellular senescence. This multi-omics atlas delineates key molecular features of immunosenescence and provides a high-resolution resource for deciphering the regulatory architecture underlying immune aging.

TCR/BCR↗

Anopheles infection responses; laboratory models versus field malaria transmission systems.

The molecular biology of disease vectors, particularly mosquitoes, has experienced a remarkable progress in the past two decades. This is mainly attributed to methodological advances and the emerging genome sequences of vector species, which have brought experimental biology to an unprecedented level. It is now possible to determine the entire transcriptome of Anopheles gambiae at a variety of conditions, with a low per-gene effort and cost. Proteomic profiles can be generated for as small samples as the hemolymph, and transient reverse genetic and stable germ line based transgenic analyses can be performed to analyze gene function. High throughput screening for receptors and ligands can be used to characterize interactions between vectors and pathogens. At the current breathtaking rates of data production it is essential to question and evaluate the relevance of laboratory infection models to the real disease transmission systems. The majority of scientific discoveries in mosquito molecular biology have been based on highly inbred laboratory strains and rodent malaria parasite infection models, which may differ substantially to their counterparts that transmit human malaria in the field. This review addresses the recent advances in high throughput transcription analyses of Anopheles responses to infection, and discusses considerations for the use of laboratory malaria infection models.

Animals↗

Nitrogen induction of sugar catabolic gene expression in Synechocystis sp. PCC 6803.

Nitrogen starvation requires cells to change their transcriptome in order to cope with this essential nutrient limitation. Here, using microarray analysis, we investigated changes in transcript profiles following nitrogen depletion in the unicellular cyanobacterium Synechocystis sp. PCC 6803. Results revealed that genes for sugar catabolic pathways including glycolysis, oxidative pentose phosphate (OPP) pathway, and glycogen catabolism were induced by nitrogen depletion, and activities of glucose-6-phosphate dehydrogenase (G6PD) and 6-phosphogluconate dehydrogenase (6PGD), two key enzymes of the OPP pathway, were demonstrated to increase under this condition. We recently showed that a group 2 sigma factor SigE, which is under the control of the global nitrogen regulator NtcA, positively regulated these sugar catabolic pathways. However, increases of transcript levels of these sugar catabolic genes under nitrogen starvation were still observed even in a sigE-deficient mutant, indicating the involvement of other regulatory element(s) in addition to SigE. Since these nitrogen activations were abolished in an ntcA mutant, and since these genes were not directly included in the NtcA regulon, we suggested that sugar catabolic genes were induced by nitrogen depletion under complex and redundant regulations including SigE and other unknown factor(s) under the control of NtcA.

Bacterial Proteins↗

Density of points clustering, application to transcriptomic data analysis.

With the increasing amount of data produced by high-throughput technologies in many fields of science, clustering has become an integral step in exploratory data analysis in order to group similar elements into classes. However, many clustering algorithms can only work properly if aided by human expertise. For example, one parameter which is crucial and often manually set is the number of clusters present in the analyzed set. We present a novel stopping rule to find the optimal number of clusters based on the comparison of the density of points inside the clusters and between them. The method is evaluated on synthetic as well as on real transcriptomic data and compared with two current methods. Finally, we illustrate its usefulness in the analysis of the expression profiles of promyelocytic cells before and after treatment with all-trans retinoic acid. Simultaneous clustering for gene regulation and absolute initial expression levels allowed the identification of numerous genes associated with signal transduction revealing the complexity of retinoic acid signaling.

Algorithms↗

Comprehensive in silico functional specification of mouse retina transcripts.

BACKGROUND: The retina is a well-defined portion of the central nervous system (CNS) that has been used as a model for CNS development and function studies. The full specification of transcripts in an individual tissue or cell type, like retina, can greatly aid the understanding of the control of cell differentiation and cell function. In this study, we have integrated computational bioinformatics and microarray experimental approaches to classify the tissue specificity and developmental distribution of mouse retina transcripts. RESULTS: We have classified a set of retina-specific genes using sequence-based screening integrated with computational and retina tissue-specific microarray approaches. 33,737 non-redundant sequences were identified as retina transcript clusters (RTCs) from more than 81,000 mouse retina ESTs. We estimate that about 19,000 to 20,000 genes might express in mouse retina from embryonic to adult stages. 39.1% of the RTCs are not covered by 60,770 RIKEN full-length cDNAs. Through comparison with 2 million mouse ESTs, spectra of neural, retinal, late-generated retinal, and photoreceptor -enriched RTCs have been generated. More than 70% of these RTCs have data from biological experiments confirming their tissue-specific expression pattern. The highest-grade retina-enriched pool covered almost all the known genes encoding proteins involved in photo-transduction. CONCLUSION: This study provides a comprehensive mouse retina transcript profile for further gene discovery in retina and suggests that tissue-specific transcripts contribute substantially to the whole transcriptome.

Animals↗

Scalable single-cell total RNA-seq reveals non-coding programs in immunity, infection, and brain development.

Non-coding RNAs represent a widespread and diverse layer of post-transcriptional regulation across cell types and states, yet much of their diversity remains uncharted at single-cell resolution. This gap stems from the limitations of widely used single-cell RNA-sequencing protocols, which focus on polyadenylated transcripts and miss many short or non-polyadenylated RNAs. Here, we adapted single-cell RNA-sequencing on the 10x Genomics platform to capture a broad complement of coding and non-coding RNAs-including miRNAs, tRNAs, lncRNAs, histone RNAs, and non-adenylated viral transcripts. This approach enabled the discovery of rich, dynamic non-coding RNA programs across immune cells, virally infected hepatocytes, and the developing human brain. In dengue virus-infected hepatocytes, we detect non-adenylated viral transcripts and distinguish active from transcriptionally quiescent infected states, each with distinct host regulatory signatures. In brain tissue, we identify biotype-specific, cell-type-restricted non-coding RNAs, including miRNAs whose expression anticorrelates with predicted targets, consistent with post-transcriptional regulatory relationships. We show that MIR137, one of the strongest GWAS loci associated with schizophrenia and intellectual disability, is expressed specifically in Cajal-Retzius cells, an early-born but transient population that guides subsequent cortical neuron migration. These findings demonstrate the importance of non-coding RNAs in defining cell identity and state, and show how expanded transcriptome coverage can reveal additional layers of gene control-now accessible through practical and scalable single-cell profiling.

Journal Article↗

SHMT2: a Metabolic and Immune Biomarker of Aggressive Lung Adenocarcinoma.

Serine/glycine-one-carbon (SGOC) metabolism is frequently altered in lung adenocarcinoma (LUAD), but its relationship to tumor behavior and predicted immunotherapy responsiveness remains incompletely defined. Metabolomic profiling of 23 paired LUAD and adjacent normal lung tissues was performed using internal extractive electrospray ionization mass spectrometry. Transcriptomic and clinical data from The Cancer Genome Atlas LUAD cohort (TCGA-LUAD) were analyzed to assess SHMT2 expression, prognosis, differentially expressed genes, and immune-related features. Predicted response to immune checkpoint blockade was evaluated using Tumor Immune Dysfunction and Exclusion (TIDE) and The Cancer Immunome Atlas (TCIA), and drug sensitivity was inferred using oncoPredict. Single-cell RNA-seq data were used to examine the cellular distribution of SHMT2. Experimental validation included quantitative reverse-transcription PCR (RT-qPCR), western blotting, Human Protein Atlas (HPA) immunohistochemistry, and short hairpin RNA (shRNA)-mediated SHMT2 knockdown followed by proliferation, wound-healing and colony formation assays. Metabolomic analysis identified glycine, serine, and threonine metabolism as a prominently altered pathway in LUAD. SHMT2 was upregulated in LUAD and associated with worse overall survival and adverse clinicopathological features. SHMT2-high tumors displayed enrichment of cell-cycle and SGOC-related transcriptional programs, lower immune and stromal scores, and reduced predicted responsiveness to immunotherapy. Single-cell analysis showed relative enrichment of SHMT2 expression in B cell populations. In vitro, SHMT2 was overexpressed in LUAD cells, and its knockdown suppressed proliferation, migration, and clonogenic growth. Collectively, SHMT2 is associated with SGOC metabolic reprogramming, aggressive tumor phenotypes, and an immune-disadvantaged state in LUAD, supporting its potential relevance as a biomarker; therapeutic targeting requires additional pharmacologic and in vivo validation.

Humans↗

The human pituitary proteome: the characterization of differentially expressed proteins in an adenoma compared to a control.

In order to clarify the basic molecular mechanisms that participate in the formation of human pituitary macroadenomas, this study, for the first time, describes the comparative proteomics between a pituitary adenoma tissue and a control tissue. A vertical, two-dimensional polyacrylamide gel electrophoresis system and PDQuest image analysis software were used to provide a high level of between-gel reproducibility and electrophoretic separation to accurately locate each differentially expressed protein. Mass spectrometry (MALDI-TOF and LC-ESI-Q-IT) and protein databases were used to characterize each differentially expressed protein. A total of 137 differential gel spots (37 increased spot volumes, 39 decreased, 19 new and 42 lost) were found when we compared an adenoma proteome to a control proteome. Seventy-one spots (20 increased, 27 decreased, 13 new, 11 lost), representing 39 differentially regulated proteins, were identified. Five differentially regulated proteins (prolactin, cellular retinoic acid-binding protein II, G-protein beta subunit 3, secretagogin and calreticulin) were also validated with results from a comparative transcriptomics study of pituitary adenomas and controls. The functional characteristics of these differentially expressed proteins provide a differential proteomic profile between a pituitary adenoma and a control.

Adenoma↗

Verification of biological markers of subacute cutaneous lupus erythematosus via TMT labelling proteomics combined with transcriptome data.

OBJECTIVE: This study aimed to investigate biological markers in subacute cutaneous lupus erythematosus (SCLE). METHODS: The tandem mass tag (TMT)-labelling proteomics method was used to explore differentially expressed proteins between SCLE lesions and normal skin tissues. The differences in transcriptomic data between SCLE tissues and normal skin tissues were analysed from the GEO database (GSE81071, GSE109248 and GSE112943). The differences in transcriptomic data from peripheral blood mononuclear cells (PBMCs) of patients with systemic lupus erythematosus (SLE) and normal controls were analysed (GSE81622 and GSE154851). The 35 healthy controls, 30 SCLE patients, 35 SLE patients and 30 lupus nephritis (LN) patients were diagnosed and enrolled. The serum expression levels of IFI44 and EPSTI1 were detected. Data were presented as the mean&#xa0;&#xb1;&#xa0;standard deviation or frequency and were analysed using Student's t-test, Chi-square test and one-way ANOVA between the groups. Receiver operating characteristic (ROC) curves were used to analyse the clinical efficacy of IFI44 and EPSTI1 in distinguishing SCLE from SLE. RESULTS: In a comparative analysis of SCLE lesions and normal skin tissues, proteomics studies identified 376 proteins that exhibited significant differential expression. In GO and KEGG analyses, the enriched terms mainly included the interferon-gamma-mediated signalling pathway (p&#xa0;<&#xa0;.001), immune receptor activity (p&#xa0;<&#xa0;.001) and cell adhesion molecules (p&#xa0;<&#xa0;.001). The top 10 hub genes were screened in SCLE as follows: CD8A, CXCL10, IFI44, CD7, CCL5, TLR4, EPSTI1, ISG15, KLRD1 and SELL using Cytoscape (3.10.1) software. The 15 common proteins/genes between proteomics and three datasets results were found, including CXCL10, OAS1, DDX60L, CFB, IFI6, HERC6, IFI44L, GBP1, EPSTI1, OAS2, CXCL11, TYMP, IFI44, ISG15 and IFIT3. The 61 differentially expressed genes in GSE81622 and the top 100 differentially expressed genes in GSE154851, alongside the 15 identified genes described above through Venn diagram analysis. Four common genes, IFI44L, IFI44, EPSTI1 and OAS1, were identified. Two common genes, IFI44 and EPSTI1, were found in hub genes from the proteomics results. The serum levels of IFI44 and EPSTI1 in LN were significantly higher than those in SLE patients (p&#xa0;<&#xa0;.05). ROC curve analysis demonstrated that serum levels of IFI44 and EPSTI1 could differentiate SCLE from SLE with an area under the curve (AUC) of 0.898 and 0.847, respectively. CONCLUSIONS: The IFI44 and EPSTI1 proved to be closely involved in the progression from SCLE to SLE, and can represent new candidate diagnostic molecular markers of occurrence and progression of SCLE.

Humans↗

Under the influence of the active deodorant ingredient 4-hydroxy-3-methoxybenzyl alcohol, the skin bacterium Corynebacterium jeikeium moderately responds with differential gene expression.

A 70mer oligonucleotide microarray was constructed to analyze genome-wide expression profiles of Corynebacterium jeikeium, a skin bacterium that is predominantly present in the human axilla and involved in axillary odor formation. Oligonucleotides representing 100% of the predicted coding regions of the C. jeikeium K411 genome were designed and spotted in quadruplicate onto epoxy-coated glass slides. The quality of the printed microarray was demonstrated by co-hybridization with fluorescently labeled cDNA probes obtained from exponentially growing C. jeikeium cultures. Accordingly, genes detected with different intensities resulting in log(2) transformed ratios greater than 0.8 or smaller than -0.8 can be regarded as differentially expressed with a confidence level greater than 99%. In an application example, we measured global changes of gene expression during growth of C. jeikeium in the presence of different concentrations of the deodorant component 4-hydroxy-3-methoxybenzyl alcohol that is active in preventing body odor formation. Global expression profiling revealed that low concentrations of 4-hydroxy-3-methoxybenzyl alcohol (0.5 and 2.5mg/ml) had almost no detectable effect on the transcriptome of C. jeikeium. A slightly higher concentration of 4-hydroxy-3-methoxybenzyl alcohol (5mg/ml) resulted in differential expression of 95 genes, 86 of which showed an enhanced expression when compared to a control culture. Besides many genes encoding proteins that apparently participate in transcription and translation, the drug resistance determinant cmx and the predicted virulence factors sapA and sapD showed significantly enhanced expression levels. Differential expression of relevant genes was validated by real-time reverse transcription PCR assays.

Anti-Infective Agents↗

Region-Resolved Integrative Multi-Omic Characterization Reveals Diverse Tumor and Microenvironment Features of Pituitary Neuroendocrine Tumors.

Pituitary neuroendocrine tumors are frequently invasive, with cavernous sinus invasion leading to poor treatment outcomes and high recurrence. Regional differences within these tumors remain poorly understood, hindering targeted therapy development. Here, we present the first integrative multi-omics analysis combining proteomics, metabolomics and single-cell transcriptomics to characterize tumors from the cavernous sinus and saddle regions. Our results reveal profound regional and cellular heterogeneity: cavernous sinus tumors exhibit significantly enhanced cell proliferation, driven by cancer-associated fibroblasts through the IGF1-IGF1R-MAPK1 axis. Cancer-associated fibroblasts in the cavernous sinus secrete IGF1 under regulation of the transcription factor FOXO1, which binds to receptors on tumor cells to activate proliferation. Metabolomic profiling identifies proline as a key enriched metabolite that stimulates cancer-associated fibroblasts to produce collagen fibers, reinforcing a pro-tumorigenic microenvironment. Single-cell transcriptomics further delineates a distinct subpopulation of receptor-positive malignant cells and a high abundance of cancer-associated fibroblasts in the cavernous sinus. These findings establish core mechanisms underlying the aggressive behavior of cavernous sinus-invading tumors, providing novel actionable targets for precision therapeutic strategies tailored to distinct tumor regions.

Humans↗

Changes in gene expression during male meiosis in Petunia hybrida.

We analyzed changes in gene expression during male meiosis in Petunia by combining the meiotic staging of pollen mother cells from a single anther with cDNA-AFLP transcript profiling of mRNA from the synchronously developing sister anthers. The transcript profiling experiments focused on the identification of genes with a modulated expression profile during meiosis, while premeiotic archesporial cells and postmeiotic microspores served as a reference. About 8000 transcript tags, estimated at 30% of the total transcriptome, were generated, of which around 6% exhibited a modulated gene expression pattern at meiosis. Cluster analysis revealed a transcriptional cascade that coincides with the initiation and progression through all stages of the two meiotic divisions. Fragments that exhibited high expression specifically during meiosis I were characterized further by sequencing; 90 out of the 293 sequenced fragments showed homology with known genes, belonging to a wide range of gene classes, including previously characterized meiotic genes. In-situ hybridization experiments were performed to determine the spatial expression pattern for five selected transcript tags. Its concurrence with cDNA-AFLP transcript profiles indicates that this is an excellent approach to study genes involved in specialized processes such as meiosis. Our data set provides the potential to unravel unique meiotic genes that are as yet elusive to reverse genetics approaches.

DNA, Complementary↗

Metabolomic networks in plants: Transitions from pattern recognition to biological interpretation.

Nowadays techniques for non-targeted metabolite profiling allow for the generation of huge amounts of relevant data essential for the construction of dynamic metabolomic networks. Thus, metabolomics, besides transcriptomics or proteomics, provides a major tool for the characterization of postgenomic processes. In this work, we introduce comparative correlation analysis as a complementary approach to characterize the physiological states of various organs of diverse plant species with focus on specific participation of metabolites in different reaction networks. The correlations observed are induced by diminutive fluctuations in environmental conditions, which propagate through the system and induce specific patterns depending on the genomic background. In order to examine this hypothesis, numeric examples of such fluctuations are computed and compared with experimentally obtained metabolite data.

Algorithms↗

Detecting and profiling tissue-selective genes.

The widespread use of DNA microarray technologies has generated large amounts of data from various tissue and/or cell types. These data set the stage to answer the question of tissue specificity of human transcriptome in a comprehensive manner. Our focus is to uncover the tissue-gene relationship by identifying genes that are preferentially expressed in a small number of tissue types. The tissue selectivity would shed light on the potential physiological functions of these genes and provides an indispensable reference to compare against disease pathophysiology and to identify or validate tissue-specific drug targets. Here we describe a systematic computational and statistical approach to profile gene expression data to identify tissue-selective genes with the use of a more extensive data set and a well-established multiple-comparison procedure with error rate control. Expression data of 35,152 probe sets in 97 normal human tissue types were analyzed, and 3,919 genes were identified to be selective to one or a few tissue types. We presented results of these tissue-selective genes and compared them to those identified by other studies.

Animals↗

Toward the identification of distinct molecular and clinical entities of multiple myeloma using global gene expression profiling.

Multiple myeloma (MM) is a poorly understood and uniformly fatal malignancy of antibody-secreting plasma cells (PC). Although several key molecular events in disease initiation or progression have been confirmed (such as 14q32 translocations) or implicated (chromosome 13 deletion), a unifying mechanism of myelomagenesis has proved elusive. Furthermore, while MM is generally indistinguishable morphologically, the disease exhibits tremendous variability in its clinical course, with some patients surviving only months and others many years, suggesting that MM is composed of distinct clinical entities. As abnormal gene expression is central to most, if not all cancers, high-throughput global gene expression profiling has become a powerful tool to investigate the molecular biology and clinical behavior of malignancy. Here we discuss recent progress made in the development of molecular-based diagnostics and prognostics for MM through the dissection of the transcriptome of PCs from healthy individuals and patients with MM and other PC dyscrasias.

Cell Differentiation↗

Transcriptome of Salmonella enterica serovar Typhi within macrophages revealed through the selective capture of transcribed sequences.

The cDNA obtained by selective capture of transcribed sequences is a complex mixture that can be used in conjunction with microarrays to determine global gene expression by a pathogen during infection. We used this method to study genes expressed by Salmonella enterica serovar Typhi, the etiological agent of typhoid fever, within human macrophages. Global expression profiles of Typhi grown in vitro and within macrophages at different time points were obtained and compared. Known virulence factors, such as the SPI-1- and SPI-2-encoded type III secretion systems, were found to be expressed as predicted during infection by Salmonella, which validated our data. Typhi inside macrophages showed increased expression of genes encoding resistance to antimicrobial peptides, used the glyoxylate bypass for fatty acid utilization, and did not induce the SOS response or the oxidative stress response. Genes coding for the flagellar apparatus, chemotaxis, and iron transport systems were down-regulated in vivo. Many cDNAs corresponding to genes with unknown functions were up-regulated inside human macrophages and will be important to consider for future studies to elucidate the intracellular lifestyle of this human-specific pathogen. Real-time quantitative PCR was consistent with the microarray results. The combined use of selective capture of transcribed sequences and microarrays is an effective way to determine the bacterial transcriptome in vivo and could be used to investigate transcriptional profiles of other bacterial pathogens without the need to recover many nanograms of bacterial mRNA from host and without increasing the multiplicity of infection beyond what is seen in nature.

Cell Line↗

Profiling Caenorhabditis elegans non-coding RNA expression with a combined microarray.

Small non-coding RNAs (ncRNAs) are encoded by genes that function at the RNA level, and several hundred ncRNAs have been identified in various organisms. Here we describe an analysis of the small non-coding transcriptome of Caenorhabditis elegans, microRNAs excepted. As a substantial fraction of the ncRNAs is located in introns of protein-coding genes in C.elegans, we also analysed the relationship between ncRNA and host gene expression. To this end, we designed a combined microarray, which included probes against ncRNA as well as host gene mRNA transcripts. The microarray revealed pronounced differences in expression profiles, even among ncRNAs with housekeeping functions (e.g. snRNAs and snoRNAs), indicating distinct developmental regulation and stage-specific functions of a number of novel transcripts. Analysis of ncRNA-host mRNA relations showed that the expression of intronic ncRNA loci with conserved upstream motifs was not correlated to (and much higher than) expression levels of their host genes. Even promoter-less intronic ncRNA loci, though showing a clear correlation to host gene expression, appeared to have a surprising amount of 'expressional freedom', depending on host gene function. Taken together, our microarray analysis presents a more complete and detailed picture of a non-coding transcriptome than hitherto has been presented for any other multicellular organism.

Animals↗