Identification of transcriptional signature change and critical transcription factors involved during the differentiation of mouse trophoblast stem cell into maternal blood vessel associated trophoblast giant cell.

The placenta is essential organ for oxygen and nutrient exchange between the mother and the developing fetus. Trophoblast lineage differentiation is closely related to the normal function of the placenta. Trophoblast stem cells (TSCs) can differentiate into all placental trophoblast subtypes and are widely used as in vitro stem cell models to study placental development and trophoblast lineage differentiation. Although extensive research has been conducted on the differentiation of TSCs, the possible parallels between trophoblast giant cells (TGCs) that are differentiated from TSCs in vitro and the various subtypes of TGC lineages in vivo are still poorly understood. In this study, mouse TSCs (mTSCs) were induced to differentiate into TGCs, and our mRNA sequencing (RNA-seq) data revealed that mTSCs and TGCs have distinct transcriptional signatures. We conducted a comparison of mTSCs and TGCs transcriptomes with the published transcriptomes of TGC lineages in murine placenta detected by single-cell RNA-seq and found that mTSCs tend to differentiate into maternal blood vessel-associated TGCs in vitro. Moreover, we identified the transcription factor (TF) ZMAT1, which may be responsible for the differentiation of mTSCs into sinusoid TGCs, and the TFs EGR1 and MITF, which are likely involved in the differentiation of mTSCs into spiral artery-associated TGCs. Thus, our findings provide a valuable resource for the mechanisms of trophoblast lineage differentiation and placental deficiency-associated diseases development.

Definition of a Multi-Omics Signature for Esophageal Adenocarcinoma Prognosis Prediction.

Esophageal cancer is a highly lethal malignancy, representing 5% of all cancer-related deaths. The two main subtypes are esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC). While most research has focused on ESCC, few studies have analyzed EAC for transcriptional signatures linked to diagnosis or prognosis. In this study, we utilized single-cell RNA sequencing and bulk RNA sequencing to identify specific immune cell types that contribute to anti-tumor responses, as well as differentially expressed genes (DEGs). We have characterized transcriptional signatures, validated against a wide cohort of TCGA patients, that are capable of predicting clinical outcomes and the prognosis of EAC post-surgery with efficacy comparable to the currently accepted prognostic factors. In conclusion, our findings provide insights into the immune landscape and therapeutic targets of EAC, proposing novel immunological biomarkers for predicting prognosis, aiding in patient stratification for post-surgical outcomes, follow-up, and personalized adjuvant therapy decisions.

Single-cell RNA-sequencing of human eosinophils in allergic inflammation in the esophagus.

BACKGROUND: Eosinophils are elusive cells involved in allergic inflammation. Single-cell RNA-sequencing (scRNA-seq) is an emerging approach to deeply characterize cellular properties, heterogeneity, and functionality. OBJECTIVES: We sought to comprehensively characterize the transcriptome and biological functions of human eosinophils at a site of severe allergic inflammation in the esophagus (ie, eosinophilic esophagitis [EoE]). METHODS: We employed a gravity-based scRNA-seq methodology to sequence blood eosinophils from patients with EoE and control individuals compared to a reanalyzed public scRNA-seq dataset of human esophageal eosinophils of EoE patients. We used flow cytometry, immunostaining, and a stimulation assay to verify mRNA findings. RESULTS: In total, scRNA-seq was obtained from 586 eosinophils (188 from blood [n = 6 individuals] and 398 from esophagus [n = 6 individuals]). The esophageal eosinophils were composed of a population of activated eosinophils (enriched in 659 genes compared with peripheral blood-associated eosinophils) and a small population of eosinophils resembling peripheral blood eosinophils (enriched in 62 genes compared with esophageal eosinophils). Esophageal eosinophils expressed genes involved in sensing and responding to diverse stimuli, most notably IFN-γ, IL-10, histamine and leukotrienes, and succinate. Esophageal eosinophils were most distinguished from other esophageal populations by gene expression of the receptors CCR3, HRH4, SUCNR1, and VSTM1; transcription factors CEBPE, OLIG1, and OLIG2; protease PRSS33; and the hallmark eosinophil gene CLC. A web of bidirectional eosinophil interactions with other esophageal populations was derived. Comparing esophageal eosinophils and mast cells revealed that esophageal eosinophils expressed genes involved in DNAX-activation protein-12 (also known as TYROBP) interactions, IgG receptor-triggered events, immunoregulation, and IL-10 signaling. CONCLUSIONS: In EoE, esophageal eosinophils exist as 2 populations, a minority population resembling blood eosinophils and the other population characterized by high de novo transcription of diverse sensing receptors and inflammatory mediators readying them to potentially intersect with diverse cell types.

Machine Learning Gene Signature to Metastatic ccRCC Based on ceRNA Network.

Clear-cell renal-cell carcinoma (ccRCC) is a silent-development pathology with a high rate of metastasis in patients. The activity of coding genes in metastatic progression is well known. New studies evaluate the association with non-coding genes, such as competitive endogenous RNA (ceRNA). This study aims to build a ceRNA network and a gene signature for ccRCC associated with metastatic development and analyze their biological functions. Using data from The Cancer Genome Atlas (TCGA), we constructed the ceRNA network with differentially expressed genes, assembled nine preliminary gene signatures from eight feature selection techniques, and evaluated the classification metrics to choose a final signature. After that, we performed a genomic analysis, a risk analysis, and a functional annotation analysis. We present an 11-gene signature: SNHG15, AF117829.1, hsa-miR-130a-3p, hsa-mir-381-3p, BTBD11, INSR, HECW2, RFLNB, PTTG1, HMMR, and RASD1. It was possible to assess the generalization of the signature using an external dataset from the International Cancer Genome Consortium (ICGC-RECA), which showed an Area Under the Curve of 81.5%. The genomic analysis identified the signature participants on chromosomes with highly mutated regions. The hsa-miR-130a-3p, AF117829.1, hsa-miR-381-3p, and PTTG1 were significantly related to the patient's survival and metastatic development. Additionally, functional annotation resulted in relevant pathways for tumor development and cell cycle control, such as RNA polymerase II transcription regulation and cell control. The gene signature analysis within the ceRNA network, with literature evidence, suggests that the lncRNAs act as "sponges" upon the microRNAs (miRNAs). Therefore, this gene signature presents coding and non-coding genes and could act as potential biomarkers for a better understanding of ccRCC.

Single-cell transcriptional signature-based drug repurposing and in vitro evaluation in colorectal cancer.

BACKGROUND: The need for intelligent and effective treatment of diseases and the increase in drug design costs have raised drug repurposing as one of the effective strategies in biomedicine. There are various computational methods for drug repurposing, one of which is using transcription signatures, especially single-cell RNA sequencing (scRNA-seq) data, which show us a clear and comprehensive view of the inside of the cell to compare the state of disease and health. METHODS: In this study, we used 91,103 scRNA-seq samples from 29 patients with colorectal cancer (GSE144735 and GSE132465). First, differential gene expression (DGE) analysis was done using the ASAP website. Then we reached a list of drugs that can reverse the gene signature pattern from cancer to normal using the iLINCS website. Further, by searching various databases and articles, we found 12 drugs that have FDA approval, and so far, no one has reported them as a drug in the treatment of any cancer. Then, to evaluate the cytotoxicity and performance of these drugs, the MTT assay and real-time PCR were performed on two colorectal cancer cell lines (HT29 and HCT116). RESULTS: According to our approach, 12 drugs were suggested for the treatment of colorectal cancer. Four drugs were selected for biological evaluation. The results of the cytotoxicity analysis of these drugs are as follows: tezacaftor (IC10 = 19 µM for HCT-116 and IC10 = 2 µM for HT-29), fenticonazole (IC10 = 17 µM for HCT-116 and IC10 = 7 µM for HT-29), bempedoic acid (IC10 = 78 µM for HCT-116 and IC10 = 65 µM for HT-29), and famciclovir (IC10 = 422 µM for HCT-116 and IC10 = 959 µM for HT-29). CONCLUSIONS: Cost, time, and effectiveness are the main challenges in finding new drugs for diseases. Computational approaches such as transcriptional signature-based drug repurposing methods open new horizons to solve these challenges. In this study, tezacaftor, fenticonazole, and bempedoic acid can be introduced as promising drug candidates for the treatment of colorectal cancer. These drugs were evaluated in silico and in vitro, but it is necessary to evaluate them in vivo.

Immunological signatures unveiled by integrative systems vaccinology characterization of dengue vaccination trials and natural infection.

Introduction: Dengue virus infection is a global health problem lacking specific therapy, requiring an improved understanding of DENV immunity and vaccine responses. Considering the recent emerging of new dengue vaccines, here we performed an integrative systems vaccinology characterization of molecular signatures triggered by the natural DENV infection (NDI) and attenuated dengue virus infection models (DVTs). Methods and results: We analyzed 955 samples of transcriptomic datasets of patients with NDI and attenuated dengue virus infection trials (DVT1, DVT2, and DVT3) using a systems vaccinology approach. Differential expression analysis identified 237 common differentially expressed genes (DEGs) between DVTs and NDI. Among them, 28 and 60 DEGs were up or downregulated by dengue vaccination during DVT2 and DVT3, respectively, with 20 DEGs intersecting across all three DVTs. Enriched biological processes of these genes included type I/II interferon signaling, cytokine regulation, apoptosis, and T-cell differentiation. Principal component analysis based on 20 common DEGs (overlapping between DVTs and our NDI validation dataset) distinguished dengue patients by disease severity, particularly in the late acute phase. Machine learning analysis ranked the ten most critical predictors of disease severity in NDI, crucial for the anti-viral immune response. Conclusion: This work provides insights into the NDI and vaccine-induced overlapping immune response and suggests molecular markers (e.g., IFIT5, ISG15, and HERC5) for anti-dengue-specific therapies and effective vaccination development.

MOASL: Predicting drug mechanism of actions through similarity learning with transcriptomic signature.

Understanding the mechanisms of actions (MOAs) of compounds is crucial in drug discovery. A common step in drug MOAs annotation is to query the dysregulated gene signatures induced by drugs in a reference library of pre-defined signatures. However, traditional similarity-based computational strategies face challenges when dealing with high-dimensional and noisy transcriptional signature data. To address this issue, we introduce MOASL (MOAs prediction via Similarity Learning), a novel approach that contrastive to learn similarity embeddings among signatures with shared MOAs automatically. We evaluated the accuracy of signature matching on various transcriptional activity score (TAS) datasets and individual cell lines by using MOASL. The results show MOASL achieved higher performance over several statistical and machine learning methods. Furthermore, we provided the rationale of our model by visualizing the signature annotation procedure. Using MOASL, the MOAs label of query signature could be conveniently defined by calculating the similarity between the query embedding and the reference embeddings. Finally, we applied MOASL to repurpose thousands of compounds as glucocorticoid receptor (GR) agonists, accurately identifying 8 out of the top 10 compounds. MOASL is conveniently accessible on GitHub at https://github.com/jianglikun/MOASL, empowering researchers and practitioners in the field of drug discovery to predict the MOAs of drug.

Inflammatory Bone Marrow Mesenchymal Stem Cells in Multiple Myeloma: Transcriptional Signature and In Vitro Modeling.

Bone marrow mesenchymal stem cells (BM MSCs) play a tumor-supportive role in promoting drug resistance and disease relapse in multiple myeloma (MM). Recent studies have discovered a sub-population of MSCs, known as inflammatory MSCs (iMSCs), exclusive to the MM BM microenvironment and implicated in drug resistance. Through a sophisticated analysis of public expression data from unexpanded BM MSCs, we uncovered a positive association between iMSC signature expression and minimal residual disease. While in vitro expansion generally results in the loss of the iMSC signature, our meta-analysis of additional public expression data demonstrated that cytokine stimulation, including IL1-ß and TNF-α, as well as immune cells such as neutrophils, macrophages, and MM cells, can reactivate the signature expression of iMSCs to varying extents. These findings underscore the importance and potential utility of cytokine stimulation in mimicking the gene expression signature of early passage of iMSCs for functional characterizations of their tumor-supportive roles in MM.

Comprehensive identification of immuno-related transcriptional signature for active pulmonary tuberculosis by integrated analysis of array and single cell RNA-seq.

BACKGROUND: Tuberculosis (TB) continues to be a major cause of morbidity and mortality worldwide. However, the molecular mechanism underlying immune response to human infection with Mycobacterium tuberculosis (Mtb) remains unclear. Assessing changes in transcript abundance in blood between health and disease on a genome-wide scale affords a comprehensive view of the impact of Mtb infection on the host defense and a reliable way to identify novel TB biomarkers. METHODS: We combined expression profiling by array and single cell RNA-sequencing (scRNA-seq) via 10X Genomics platform to better illustrate the immuno-related transcriptional signature of TB and explore potential diagnostic markers for differentiating TB from latent tuberculosis infection (LTBI) and healthy control (HC). FINDINGS: Pathway analysis based on differential expressed genes (DEGs) revealed that immune transcriptional profiling could effectively differ TB with LTBI and HC. Following WGCNA and PPI network analysis based on DEGs, we screened out three key immuno-related hub genes (ADM, IFIT3 and SERPING1) highly associated with TB. Further validation found only ADM expression significantly increased in TB patients in both adult and children's datasets. By comparing the scRNA-seq datasets from TB, LTBI and HC, we observed a remarkable elevated expression level and proportion of ADM in TB Myeloid cells, further supporting that ADM expression changes could distinguish patients with TB from LTBI and HC. Besides, the hsa-miR-24-3p-NEAT1-ADM-CEBPB regulation pathway might be one of the critical networks regulating the pathogenesis of TB. Although further investigation in a larger cohort is warranted, we provide useful and novel insight to explore the potential candidate genes for TB diagnosis and intervention. INTERPRETATION: We propose that the expression of ADM in peripheral blood could be used as a novel biomarker for differentiating TB with LTBI and HC.

Distinct Transcriptional Profile of PDZ Genes after Activation of Human Macrophages and Dendritic Cells.

The PDZ (PSD95, Dlg and ZO-1) genes encode proteins that primarily function as scaffolds of diverse signaling pathways. To date, 153 PDZ genes have been identified in the human genome, most of which have multiple protein isoforms widely studied in epithelial and neural cells. However, their expression and function in immune cells have been poorly studied. Herein, we aimed to assess the transcriptional profiles of 83 PDZ genes in human macrophages (Mɸ) and dendritic cells (DCs) and changes in their relative expression during cell PRR stimulation. Significantly distinct PDZ gene transcriptional profiles were identified under different stimulation conditions. Furthermore, a distinct PDZ gene transcriptional signature was found in Mɸ and DCs under the same phagocytic stimuli. Notably, more than 40 PDZ genes had significant changes in expression, with potentially relevant functions in antigen-presenting cells (APCs). Given that several PDZ proteins are targeted by viral products, our results support that many of these proteins might be viral targets in APCs as part of evasion mechanisms. Our results suggest a distinct requirement for PDZ scaffolds in Mɸ and DCs signaling pathways activation. More assessments on the functions of PDZ proteins in APCs and their role in immune evasion mechanisms are needed.

Using bioinformatics and systems biology to discover common pathogenetic processes between sarcoidosis and COVID-19.

The coronavirus disease (COVID-19) pandemic caused by SARS-CoV-2 is ongoing. Individuals with sarcoidosis tend to develop severe COVID-19; however, the underlying pathological mechanisms remain elusive. To determine common transcriptional signatures and pathways between sarcoidosis and COVID-19, we investigated the whole-genome transcriptome of peripheral blood mononuclear cells (PBMCs) from patients with COVID-19 and sarcoidosis and conducted bioinformatic analysis, including gene ontology and pathway enrichment, protein-protein interaction (PPI) network, and gene regulatory network (GRN) construction. We identified 33 abnormally expressed genes that were common between COVID-19 and sarcoidosis. Functional enrichment analysis showed that these differentially expressed genes were associated with cytokine production involved in the immune response and T cell cytokine production. We identified several hub genes from the PPI network encoded by the common genes. These hub genes have high diagnostic potential for COVID-19 and sarcoidosis and can be potential biomarkers. Moreover, GRN analysis identified important microRNAs and transcription factors that regulate the common genes. This study provides a novel characterization of the transcriptional signatures and biological processes commonly dysregulated in sarcoidosis and COVID-19 and identified several critical regulators and biomarkers. This study highlights a potential pathological association between COVID-19 and sarcoidosis, establishing a theoretical basis for future clinical trials.

A transcriptional signature detects homologous recombination deficiency in pancreatic cancer at the individual level.

Pancreatic cancer (PC) with homologous recombination deficiency (HRD) has been reported to benefit from poly ADP-ribose polymerase (PARP) inhibitors. However, accurate identification of HRD status for PC patients from the transcriptional level is still a great challenge. Here, based on a relative expression ordering (REO)-based algorithm, we developed an HRD signature including 24 gene pairs (24-GPS) using PC transcriptional profiles from The Cancer Genome Atlas (TCGA). HRD samples classified by 24-GPS showed worse overall survival (p = 4.4E-3 for TCGA; p = 1.2E-3 for International Cancer Genome Consortium-Australia cohort; p = 6.4E-2 for GSE17891; p = 7.5E-2 for GSE57495) and higher HRD scores than non-HRD samples (p = 1.4E-4). HRD samples showed highly unstable genomic characteristics and also displayed HRD-related alterations at the epigenomic and proteomic levels. Moreover, HRD cell lines identified by 24-GPS tended to be sensitive to PARP inhibitors (p = 6.6E-2 for olaparib; p = 2.6E-3 for niraparib). Compared with the non-HRD group, the HRD group presented lower immune scores and CD4/CD8 T cell infiltration proportion. Interestingly, PC tumor cells with co-inhibition of PARP-related genes and ATR showed reduced survival ability. In conclusion, 24-GPS can robustly identify PC patients with HRD status at the individualized level.

A novel blood-based assay for treatment monitoring of tuberculosis.

OBJECTIVES: A novel 3-gene host transcriptional signature (GBP5, DUSP3 and KLF2) has been validated for tuberculosis (TB) treatment monitoring using laboratory-based RNA sequencing platforms. The signature was recently translated by Cepheid into a prototype cartridge-based test that can be run on the GeneXpert instrument. In this study, we prospectively evaluated the change in the expression of the cartridge-based 3-gene signature following treatment initiation among pulmonary TB patients who were microbiologically cured at the end of treatment. RESULTS: The 3-gene signature expression level (TB score) changed significantly over time with respect to baseline among 31 pulmonary TB patients. The greatest increase in TB score occurred within the first month of treatment (median fold-increase in TB score: 1.08 [IQR 0.54-1.52]) and plateaued after 4 months of treatment (median TB score: 1.97 [IQR: 1.03-2.33]). The rapid and substantial increase of the TB score in the first month of treatment holds promise for the early identification of patients that respond to TB treatment. The plateau in TB score at 4 months may indicate early clearance of disease and could direct treatment to be shortened. These hypotheses need to be further explored with larger prospective treatment monitoring studies.

Network-based transcriptomic analysis identifies the genetic effect of COVID-19 to chronic kidney disease patients: A bioinformatics approach.

COVID-19 has emerged as global health threats. Chronic kidney disease (CKD) patients are immune-compromised and may have a high risk of infection by the SARS-CoV-2. We aimed to detect common transcriptomic signatures and pathways between COVID-19 and CKD by systems biology analysis. We analyzed transcriptomic data obtained from peripheral blood mononuclear cells (PBMC) infected with SARS-CoV-2 and PBMC of CKD patients. We identified 49 differentially expressed genes (DEGs) which were common between COVID-19 and CKD. The gene ontology and pathways analysis showed the DEGs were associated with "platelet degranulation", "regulation of wound healing", "platelet activation", "focal adhesion", "regulation of actin cytoskeleton" and "PI3K-Akt signalling pathway". The protein-protein interaction (PPI) network encoded by the common DEGs showed ten hub proteins (EPHB2, PRKAR2B, CAV1, ARHGEF12, HSP90B1, ITGA2B, BCL2L1, E2F1, TUBB1, and C3). Besides, we identified significant transcription factors and microRNAs that may regulate the common DEGs. We investigated protein-drug interaction analysis and identified potential drugs namely, aspirin, estradiol, rapamycin, and nebivolol. The identified common gene signature and pathways between COVID-19 and CKD may be therapeutic targets in COVID-19 patients with CKD comorbidity.

Two novel qualitative transcriptional signatures robustly applicable to non-research-oriented colorectal cancer samples with low-quality RNA.

Currently, due to the low quality of RNA caused by degradation or low abundance, the accuracy of gene expression measurements by transcriptome sequencing (RNA-seq) is very challenging for non-research-oriented clinical samples, majority of which are preserved in hospitals or tissue banks worldwide with complete pathological information and follow-up data. Molecular signatures consisting of several genes are rarely applied to such samples. To utilize these resources effectively, 45 stage II non-research-oriented samples which were formalin-fixed paraffin-embedded (FFPE) colorectal carcinoma samples (CRC) using RNA-seq have been analysed. Our results showed that although gene expression measurements were significantly affected, most cancer features, based on the relative expression orderings (REOs) of gene pairs, were well preserved. We then developed two REO-based signatures, which consisted of 136 gene pairs for early diagnosis of CRC, and 4500 gene pairs for predicting post-surgery relapse risk of stage II and III CRC. The performance of our signatures, which included hundreds or thousands of gene pairs, was more robust for non-research-oriented clinical samples, compared to that of two published concise REO-based signatures. In conclusion, REO-based signatures with relatively more gene pairs could be robustly applied to non-research-oriented CRC samples.

A qualitative transcriptional signature of recurrence risk for stages II-III gastric cancer patients after surgical resection.

BACKGROUND AND AIM: Metastasis is the leading cause of recurrence in gastric cancer. However, the imaging techniques and pathological examinations for tumor metastasis have a high false-positive rate or a high false-negative rate, and many proposed that metastasis-related molecular biomarkers can hardly be validated in independent datasets. METHODS: We propose to use significantly stable gene pairs with reversal relative expression orderings (REOs) between non-metastasis and metastasis gastric cancer samples as the metastasis-related gene pairs. Based on the REOs of these gene pairs, we developed a qualitative transcriptional signature for predicting the recurrence risk of stages II-III gastric cancer patients after surgical resection. RESULTS: A REOs-based signature, consisting of 19 gene pairs (19-GPS), was selected from 77 stages II-III gastric cancer patients and validated in two independent datasets. Samples in the high-risk group had shorter disease-free survival time and overall survival time than those in the low-risk group. Differentially expressed genes (DEGs) between the high- and low-risk groups classified by 19-GPS were highly reproducible comparing with those between lymph node metastasis and lymph node non-metastasis groups. Functional enrichment analysis showed that these DEGs were significantly enriched in metastasis-related pathways, such as PI3K-Akt and Rap1 signaling pathways. The multi-omics analyses suggested that the epigenetic and genomic features might cause transcriptional differences between two subgroups, which help to characterize the mechanism of gastric cancer metastasis. CONCLUSIONS: The signature could robustly identify patients at high recurrence risk after resection surgery, and the multi-omics analyses might aid in revealing the metastasis-related characteristics of gastric cancer.

Ultra-low Dose Aerosol Infection of Mice with Mycobacterium tuberculosis More Closely Models Human Tuberculosis.

Tuberculosis (TB) is a heterogeneous disease manifesting in a subset of individuals infected with aerosolized Mycobacterium tuberculosis (Mtb). Unlike human TB, murine infection results in uniformly high lung bacterial burdens and poorly organized granulomas. To develop a TB model that more closely resembles human disease, we infected mice with an ultra-low dose (ULD) of between 1-3 founding bacteria, reflecting a physiologic inoculum. ULD-infected mice exhibited highly heterogeneous bacterial burdens, well-circumscribed granulomas that shared features with human granulomas, and prolonged Mtb containment with unilateral pulmonary infection in some mice. We identified blood RNA signatures in mice infected with an ULD or a conventional Mtb dose (50-100 CFU) that correlated with lung bacterial burdens and predicted Mtb infection outcomes across species, including risk of progression to active TB in humans. Overall, these findings highlight the potential of the murine TB model and show that ULD infection recapitulates key features of human TB.

A Qualitative Transcriptional Signature for Predicting CpG Island Methylator Phenotype Status of the Right-Sided Colon Cancer.

A part of colorectal cancer which is characterized by simultaneous numerous hypermethylation CpG islands sites is defined as CpG island methylator phenotype (CIMP) status. Stage II and III CIMP-positive (CIMP+) right-sided colon cancer (RCC) patients have a better prognosis than CIMP-negative (CIMP-) RCC treated with surgery alone. However, there is no gold standard available in defining CIMP status. In this work, we selected the gene pairs whose relative expression orderings (REOs) were associated with the CIMP status, to develop a qualitative transcriptional signature to individually predict CIMP status for stage II and III RCC. Based on the REOs of gene pairs, a signature composed of 19 gene pairs was developed to predict the CIMP status of RCC through a feature selection process. A sample is predicted as CIMP+ when the gene expression orderings of at least 12 gene pairs vote for CIMP+; otherwise the CIMP-. The difference of prognosis between the predicted CIMP+ and CIMP- groups was more significantly different than the original CIMP status groups. There were more differential methylation and expression characteristics between the two predicted groups. The hierarchical clustering analysis showed that the signature could perform better for predicting CIMP status of RCC than current methods. In conclusion, the qualitative transcriptional signature for classifying CIMP status at the individualized level can predict outcome and guide therapy for RCC patients.

Differential Markers of Bacterial and Viral Infections in Children for Point-of-Care Testing.

Children suffering from infectious diseases, both bacterial and viral, are often treated with empirical antibiotics. Keeping in mind both the menace of microorganisms and antibiotic toxicity, it is imperative to develop point-of-care testing (POCT) to discriminate bacterial from viral infections, and to define indications for antibiotic treatment. This article reviews potential protein biomarkers and host-derived gene expression signatures for differentiating between bacterial and viral infections in children, and focuses on emerging multiplex POCT devices for the simultaneous detection of sets of protein biomarkers or streamlined gene expression signatures that may provide rapid and cost-effective pathogen-discriminating tools.

Transcriptional signature of resting-memory CD4 T cells differentiates spontaneous from treatment-induced HIV control.

The HIV reservoir is the main barrier to eradicating HIV infection, and resting memory CD4 T (Trm) cells are one of the most relevant cellular component harboring latent proviruses. This is the first study analyzing the transcriptional profile of Trm cells, in two well-characterized groups of HIV patients with distinct mechanisms of viral replication control (spontaneous versus treatment-induced). We use a systems biology approach to unravel subtle but important differences in the molecular mechanisms operating at the cellular level that could be associated with the host's ability to control virus replication and persistence. Despite the absence of significant differences in the transcriptome of Trm cells between Elite Controllers (ECs) and cART-treated (TX) patients at the single gene level, we found 353 gene ontology (GO) categories upregulated in EC compared with TX. Our results suggest the existence of mechanisms at two different levels: first boosting both adaptive and innate immune responses, and second promoting active viral replication and halting HIV latency in the Trm cell compartment of ECs as compared with TX patients. These differences in the transcriptional profile of Trm cells could be involved in the lower HIV reservoir observed in ECs compared with TX individuals, although mechanistic studies are needed to confirm this hypothesis. Combining transcriptome analysis and systems biology methods is likely to provide important findings to help us in the design of therapeutic strategies aimed at purging the HIV reservoir. KEY MESSAGES: HIV-elite controllers have the lowest HIV-DNA content in resting memory CD4 T cells. HIV-ECs show a particular transcriptional profile in resting memory CD4 T cells. Molecular mechanisms of enhanced adaptative and innate immune response in HIV-ECs. High viral replication and low viral latency establishment associate to the EC status.