Microglial Transcriptional Signatures in the Central Nervous System: Toward A Future of Unraveling Their Function in Health and Disease.

Microglia, the resident immune cells of the central nervous system (CNS), are primarily derived from the embryonic yolk sac and make their way to the CNS during early development. They play key physiological and immunological roles across the life span, throughout health, injury, and disease. Recent transcriptomic studies have identified gene transcript signatures expressed by microglia that may provide the foundation for unprecedented insights into their functions. Microglial gene expression signatures can help distinguish them from macrophage cell types to a reasonable degree of certainty, depending on the context. Microglial expression patterns further suggest a heterogeneous population comprised of many states that vary according to the spatiotemporal context. Microglial diversity is most pronounced during development, when extensive CNS remodeling takes place, and following disease or injury. A next step of importance for the field will be to identify the functional roles performed by these various microglial states, with the perspective of targeting them therapeutically.

Development and validation of AI-assisted transcriptomic signatures to personalize adjuvant chemotherapy in patients with pancreatic ductal adenocarcinoma.

BACKGROUND: After surgical resection of pancreatic ductal adenocarcinoma (PDAC), patients are predominantly treated with adjuvant chemotherapy, commonly consisting of gemcitabine (GEM)-based regimens or the modified FOLFIRINOX (mFFX) regimen. While mFFX regimen has been shown to be more effective than GEM-based regimens, it is also associated with higher toxicity. Current treatment decisions are based on patient performance status rather than on the molecular characteristics of the tumor. To address this gap, the goal of this study was to develop drug-specific transcriptomic signatures for personalized chemotherapy treatment. PATIENTS AND METHODS: We used PDAC datasets from preclinical models, encompassing chemotherapy response profiles for the mFFX regimen components. From them we identified specific gene transcripts associated with chemotherapy response. Three transcriptomic artificial intelligence signatures were obtained by combining independent component analysis and the least absolute shrinkage and selection operator-random forest approach. We integrated a previously developed GEM signature with three newly developed ones. The machine learning strategy employed to enhance these signatures incorporates transcriptomic features from the tumor microenvironment, leading to the development of the 'Pancreas-View' tool ultimately clinically validated in a cohort of 343 patients from the PRODIGE-24/CCTG PA6 trial. RESULTS: Patients who were predicted to be sensitive to the administered drugs (n = 164; 47.8%) had longer disease-free survival (DFS) than the other patients. The median DFS in the mFFX-sensitive group treated with mFFX was 50.0 months [stratified hazard ratio (HR) 0.31, 95% confidence interval (CI) 0.21-0.44, P < 0.001] and 33.7 months (stratified HR 0.40, 95% CI 0.17-0.59, P < 0.001) in the GEM-sensitive group when treated with GEM. Comparatively patients with signature predictions unmatched with the treatments (n = 86; 25.1%) or those resistant to all drugs (n = 93; 27.1%) had shorter DFS (10.6 and 10.8 months, respectively). CONCLUSIONS: This study presents a transcriptome-based tool that was developed using preclinical models and machine learning to accurately predict sensitivity to mFFX and GEM.

Sncg, Mybpc1, and Parm1 Classify subpopulations of VIP-expressing interneurons in layers 2/3 of the somatosensory cortex.

Neocortical vasoactive intestinal polypeptide-expressing (VIP+) interneurons display highly diverse morpho-electrophysiological and molecular properties. To begin to understand the function of VIP+ interneurons in cortical circuits, they must be clearly and comprehensively classified into distinct subpopulations based on specific molecular markers. Here, we utilized patch-clamp RT-PCR (Patch-PCR) to simultaneously obtain the morpho-electric properties and mRNA profiles of 155 VIP+ interneurons in layers 2 and 3 (L2/3) of the mouse somatosensory cortex. Using an unsupervised clustering method, we identified 3 electrophysiological types (E-types) and 2 morphological types (M-types) of VIP+ interneurons. Joint clustering based on the combined electrophysiological and morphological features resulted in 3 morpho-electric types (ME-types). More importantly, we found these 3 ME-types expressed distinct marker genes: ~94% of Sncg+ cells were ME-type 1, 100% of Mybpc1+ cells were ME-type 2, and ~78% of Parm1+ were ME-type 3. By clarifying the properties of subpopulations of cortical L2/3 VIP+ interneurons, this study establishes a basis for future investigations aiming to elucidate their physiological roles.

Identifying a distinct fibrosis subset of NAFLD via molecular profiling and the involvement of profibrotic macrophages.

BACKGROUND: There are emerging studies suggesting that non-alcoholic fatty liver disease (NAFLD) is a heterogeneous disease with multiple etiologies and molecular phenotypes. Fibrosis is the key process in NAFLD progression. In this study, we aimed to explore molecular phenotypes of NAFLD with a particular focus on the fibrosis phenotype and also aimed to explore the changes of macrophage subsets in the fibrosis subset of NAFLD. METHODS: To assess the transcriptomic alterations of key factors in NAFLD and fibrosis progression, we included 14 different transcriptomic datasets of liver tissues. In addition, two single-cell RNA sequencing (scRNA-seq) datasets were included to construct transcriptomic signatures that could represent specific cells. To explore the molecular subsets of fibrosis in NAFLD based on the transcriptomic features, we used a high-quality RNA-sequencing (RNA-seq) dataset of liver tissues from patients with NAFLD. Non-negative matrix factorization (NMF) was used to analyze the molecular subsets of NAFLD based on the gene set variation analysis (GSVA) enrichment scores of key molecule features in liver tissues. RESULTS: The key transcriptomic signatures on NAFLD including non-alcoholic steatohepatitis (NASH) signature, fibrosis signature, non-alcoholic fatty liver (NAFL) signature, liver aging signature and TGF-ß signature were constructed by liver transcriptome datasets. We analyzed two liver scRNA-seq datasets and constructed cell type-specific transcriptomic signatures based on the genes that were highly expressed in each cell subset. We analyzed the molecular subsets of NAFLD by NMF and categorized four main subsets of NAFLD. Cluster 4 subset is mainly characterized by liver fibrosis. Patients with Cluster 4 subset have more advanced liver fibrosis than patients with other subsets, or may have a high risk of liver fibrosis progression. Furthermore, we identified two key monocyte-macrophage subsets which were both significantly correlated with the progression of liver fibrosis in NAFLD patients. CONCLUSION: Our study revealed the molecular subtypes of NAFLD by integrating key information from transcriptomic expression profiling and liver microenvironment, and identified a novel and distinct fibrosis subset of NAFLD. The fibrosis subset is significantly correlated with the profibrotic macrophages and M2 macrophage subset. These two liver macrophage subsets may be important players in the progression of liver fibrosis of NAFLD patients.

Profiling transcriptional heterogeneity of epithelium, fibroblasts, and immune cells in esophageal squamous cell carcinoma by single-cell RNA sequencing.

Esophageal squamous cell carcinoma (ESCC) is one of the most aggressive malignancies with complex tumor microenvironment (TME) which has been proven to be associated with therapeutic failure or resistance. A deeper understanding of the complex TME and cellular heterogeneity is urgently needed in ESCC. Here, we generated single-cell RNA sequencing (scRNA-seq) of 25 796 immune and 8197 non-immune cells from three primary tumor and paired normal samples in ESCC patients. The results revealed intratumoral and intertumoral epithelium heterogeneity and tremendously differences in tumor and normal epithelium. The infiltration of myofibroblasts, one subtype of fibroblasts, might play important roles in the progression of ESCC. We also found that some differentially expressed genes and markers in epithelium and fibroblast subtypes showed prognostic values for ESCC. Diverse cell subtypes of T cells and myeloid cells were identified, including tumor-enriched HAVCR2+ CD4+ T cells with significantly exhausted signature. The epithelium and myeloid cells had more frequent cell-cell communication compared with epithelium and T cells. Taken together, this study provided in-depth insights into the cellular heterogeneity of TME in ESCC and highlighted potential therapeutic targets including for immunotherapy.

Inhibitor of DNA Binding Protein 3 (ID3) and Nuclear Respiratory Factor 1 (NRF1) Mediated Transcriptional Gene Signatures are Associated with the Severity of Cerebral Amyloid Angiopathy.

Cerebral amyloid angiopathy (CAA) is a degenerative vasculopathy. We have previously shown that transcription regulating proteins- inhibitor of DNA binding protein 3 (ID3) and the nuclear respiratory factor 1 (NRF1) contribute to vascular dysregulation. In this study, we have identified sex specific ID3 and NRF1-mediated gene networks in CAA patients diagnosed with Alzheimer's Disease (AD). High expression of ID3 mRNA coupled with low NRF1 mRNA levels was observed in the temporal cortex of men and women CAA patients. Low NRF1 mRNA expression in the temporal cortex was found in men with severe CAA. High ID3 expression was found in women with the genetic risk factor APOE4. Low NRF1 expression was also associated with APOE4 in women with CAA. Genome wide transcriptional activity of both ID3 and NRF1 paralleled their mRNA expression levels. Sex specific differences in transcriptional gene signatures of both ID3 and NRF1 were observed. These findings were further corroborated by Bayesian machine learning and the GeNIe simulation models. Dynamic machine learning using a Monte Carlo Markov Chain (MCMC) gene ordering approach revealed that ID3 was associated with disease severity in women. NRF1 was associated with CAA and severity of this disease in men. These findings suggest that aberrant ID3 and NRF1 activity presumably plays a major role in the pathogenesis and severity of CAA. Further analyses of ID3- and NRF1-regulated molecular drivers of CAA may provide new targets for personalized medicine and/or prevention strategies against CAA.

Aberrant adaptive immune response underlies genetic susceptibility to tuberculosis.

Mycobacterium tuberculosis (Mtb) remains a major threat worldwide, although only a fraction of infected individuals develops tuberculosis (TB). TB susceptibility is shaped by multiple genetic factors, and we performed comparative immunological analysis of two mouse strains to uncover relevant mechanisms underlying susceptibility and resistance. C57BL/6 mice are relatively TB-resistant, whereas I/St mice are prone to develop severe TB, partly due to the MHC-II allelic variant that shapes suboptimal CD4+ T cell receptor repertoire. We investigated the repertoires of lung-infiltrating helper T cells and B cells at the progressed stage in both strains. We found that lung CD4+ T cell repertoires of infected C57BL/6 but not I/St mice contained convergent TCR clusters with functionally confirmed Mtb specificity. Transcriptomic analysis revealed a more prominent Th1 signature in C57BL/6, and expression of pro-inflammatory IL-16 in I/St lung-infiltrating helper T cells. The two strains also showed distinct Th2 signatures. Furthermore, the humoral response of I/St mice was delayed, less focused, and dominated by IgG/IgM isotypes, whereas C57BL/6 mice generated more Mtb antigen-focused IgA response. We conclude that the inability of I/St mice to produce a timely and efficient anti-Mtb adaptive immune responses arises from a suboptimal helper T cell landscape that also impacts the humoral response, leading to diffuse inflammation and severe disease.

Sequential Infection with Influenza A Virus Followed by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Leads to More Severe Disease and Encephalitis in a Mouse Model of COVID-19.

COVID-19 is a spectrum of clinical symptoms in humans caused by infection with SARS-CoV-2. The coalescence of SARS-CoV-2 with seasonal respiratory viruses, particularly influenza viruses, is a global health concern. To understand this, transgenic mice expressing the human ACE2 receptor (K18-hACE2) were infected with influenza A virus (IAV) followed by SARS-CoV-2 and the host response and effect on virus biology was compared to K18-hACE2 mice infected with IAV or SARS-CoV-2 alone. The sequentially infected mice showed reduced SARS-CoV-2 RNA synthesis, yet exhibited more rapid weight loss, more severe lung damage and a prolongation of the innate response compared to the singly infected or control mice. Sequential infection also exacerbated the extrapulmonary encephalitic manifestations associated with SARS-CoV-2 infection. Conversely, prior infection with a commercially available, multivalent live-attenuated influenza vaccine (Fluenz Tetra) elicited the same reduction in SARS-CoV-2 RNA synthesis, albeit without the associated increase in disease severity. This suggests that the innate immune response stimulated by IAV inhibits SARS-CoV-2. Interestingly, infection with an attenuated, apathogenic influenza vaccine does not result in an aberrant immune response and enhanced disease severity. Taken together, the data suggest coinfection ('twinfection') is deleterious and mitigation steps should be instituted as part of the comprehensive public health and management strategy of COVID-19.

Mesenchymal stem cells derived from adipose tissue and umbilical cord reveal comparable efficacy upon radiation-induced colorectal fibrosis in rats.

Chemoradiotherapy (CRT) and radiotherapy (RT) have served as anticancer treatments and neoadjuvant therapies for conquering multimodal rectal cancers including colorectal carcinoma (CRC), yet the concomitant radiation-induced colorectal fibrosis (RICF) has caused chronic toxicity and stenosis in the colorectal mucosa of patients. Mesenchymal stem/stromal cells (MSCs) with unique bidirectional immunoregulation and anti-fibrotic effect have been recognized as splendid sources for regenerative purposes including intestinal diseases. Herein, we are aiming to verify the feasibility and variations of MSC-based cytotherapy for the remission of RICF from the pathological features and the potential impact upon the transcriptomic signatures of RICF rats. For the purpose, we utilized our well-established RICF Sprague-Dawley (SD) rats by radiation for five weeks, and conducted consecutive intraperitoneal injection of two distinct MSCs for treatment, including MSCs derived from adult adipose tissue (AD-MSCs) and perinatal umbilical cord (UC-MSCs). On the one hand, the efficacy of AD-MSCs and UC-MSCs was assessed by diverse indicators, including weight change, pathological detections (e.g., H&E staining, Masson staining, EVG staining, IF staining, and IHC staining), and proinflammatory and fibrotic factor expression. On the other hand, we turned to RNA-sequencing (RNA-SEQ) and multifaceted bioinformatics analyses (e.g., GOBP, Venn Map, KEGG, and GSEA) to compare the impact of AD-MSC and UC-MSC treatment upon the gene expression profiling and genetic variations. RICF rats after consecutive AD-MSC and UC-MSC administration revealed comparable remission in histopathogenic features and significant suppression of diverse proinflammatory and fibrotic factors expression. Meanwhile, RICF rats after both MSC treatment revealed decrease and variations in the alterations in diverse gene expression and somatic mutations compared to RICF rats. Collectively, our data indicated the comparable therapeutic effect of AD-MSCs and UC-MSCs upon RICF in SD rats, together with the conservations in gene expression profiling and the diverse variations in genetic mutations. Our findings indicated the multifaceted impact of MSC infusion for the supervision of RICF both at the therapeutic and transcriptomic levels, which would provide novel references for the further evaluation and development of MSC-based regimens in future.

A computational framework to improve cross-platform implementation of transcriptomics signatures.

The emergence of next-generation sequencing technologies and computational advances have expanded our understanding of gene expression regulation (i.e., the transcriptome). This has also led to an increased interest in using transcriptomic biomarkers to improve disease diagnosis and stratification, to assess prognosis and predict the response to treatment. Significant progress in identifying transcriptomic signatures for various clinical needs has been made, with large discovery studies accounting for challenges such as patient variability, unwanted batch effects, and data complexities; however, obstacles related to the technical aspects of cross-platform implementation still hinder the successful integration of transcriptomic technologies into standard diagnostic workflows. In this article, we discuss the challenges associated with integrating transcriptomic signatures derived using high-throughput technologies (such as RNA-sequencing) into clinical diagnostic tools using nucleic acid amplification (NAA) techniques. The novelty of the proposed approach lies in our aim to embed constraints related to cross-platform implementation in the process of signature discovery. These constraints could include technical limitations of amplification platform and chemistry, the maximal number of targets imposed by the chosen multiplexing strategy, and the genomic context of identified RNA biomarkers. Finally, we propose to build a computational framework that would integrate these constraints in combination with existing statistical and machine learning models used for signature identification. We envision that this could accelerate the integration of RNA signatures discovered by high-throughput technologies into NAA-based approaches suitable for clinical applications.

Long Intergenic Non-Coding RNAs of Human Chromosome 18: Focus on Cancers.

Malignant neoplasms are characterized by high molecular heterogeneity due to multilevel deregulation of gene expression and cellular functions. It is known that non-coding RNAs, including long intergenic non-coding RNAs (lincRNAs), can play significant roles in cancer biology. The current review focuses on a systematical analysis of genomic, transcriptomic, epigenomic, interactomic, and literature data on 65 lincRNAs of human chromosome 18 in the context of pan-cancer studies. The entire group of lincRNAs can be conditionally divided into 4 subgroups depending on experimental evidence on direct or indirect involvement in cancers and the biological associations with cancers, which we found during the data-mining process: the most studied (5 lincRNAs), moderately or poorly studied (11 lincRNAs), and understudied (31 lincRNAs). For the remaining 18 lincRNAs, data for analysis were fragmentary or missing. Among the key findings were the following: Of the lincRNAs of human chromosome 18, 40% have tissue-specific expression patterns, 22% of lincRNAs are known to have gene fusions, 40% of lincRNAs are prone to gene amplifications and/or deletions in cancers at a frequency greater than 3%, and 23% of lincRNAs are differentially expressed across cancer types, whereas 7% have subtype-specific expression patterns. LincRNAs' interactomes consist of 'master' microRNAs and 47 proteins (including cancer-associated proteins and microRNAs) that can interact with 3 or more lincRNAs. Functional enrichment analysis of a set of highly co-expressed genes retrieved for 17 lincRNAs in different cancer types indicated the potential associations of these lincRNAs with cellular signaling pathways. Six lincRNAs encoded small open-reading frame (smORF) proteins with emerging roles in cancers, and microRNAs as well as proteins with known functions in molecular carcinogenesis can bind to coding regions of smORFs. We identified seven transcriptomic signatures with potential prognostic value, consisting of two to seven different lincRNAs only. Taken together, the literature, biomedical, and molecular biology data analyzed indicated that only five of all lincRNAs of human chromosome 18 are cancer-associated, while eleven other lincRNAs have the tendency to be associated with cancers.

Utility of a three-gene transcriptomic signature in the diagnosis of tuberculosis in a low-endemic hospital setting.

BACKGROUND: Host transcriptomic blood signatures have demonstrated diagnostic potential for tuberculosis (TB), requiring further validation across different geographical settings. Discriminating TB from other diseases with similar clinical manifestations is crucial for the development of an accurate immunodiagnostic tool. In this exploratory cohort study, we evaluated the performance of potential blood-based transcriptomic signatures in distinguishing TB disease from non-TB lower respiratory tract infections in hospitalised patients in a TB low-endemic country. METHOD: Quantitative real-time polymerase chain reaction qPCR) was used to evaluate 26 previously published genes in blood from 31 patients (14 TB and 17 lower respiratory tract infection cases) admitted to Oslo University Hospital in Norway. The diagnostic accuracies of differentially expressed genes were determined by receiver operating characteristic curves. RESULTS: A significant difference (p < .01) in the age distribution was observed between patients with TB (mean age, 40 ± 15 years) and lower respiratory tract infection (mean age 59 ± 12 years). Following adjustment for age, ETV7, GBP1, GBP5, P2RY14 and BLK were significantly differentially expressed between patients with TB and those with LRI. A general discriminant analysis generated a three-gene signature (BAFT2, ETV7 and CD1C), which diagnosed TB with an area under the receiver operating characteristic curve (AUC) of 0.86 (95% CI, 0.69 - 1.00), sensitivity of 69.23% (95% CI, 38.57%-90.91%) and specificity of 94.12% (95% CI, 71.31%-99.85%). CONCLUSION: The three-genes signature may have potential to improve diagnosis of TB in a hospitalised low-burden setting. However, the influence of confounding variables or covariates such as age requires further evaluation in larger studies.

Integrating host transcriptomic signatures for distinguishing autoimmune encephalitis in cerebrospinal fluid by metagenomic sequencing.

BACKGROUND: The early accurate diagnoses for autoimmune encephalitis (AE) and infectious encephalitis (IE) are essential since the treatments for them are different. This study aims to discover some specific and sensitive biomarkers to distinguish AE from IE at early stage to give specific treatments for good outcomes. RESULTS: We compared the host gene expression profiles and microbial diversities of cerebrospinal fluid (CSF) from 41 patients with IE and 18 patients with AE through meta-transcriptomic sequencing. Significant differences were found in host gene expression profiles and microbial diversities in CSF between patients with AE and patients with IE. The most significantly upregulated genes in patients with IE were enriched in pathways related with immune response such as neutrophil degranulation, antigen processing and presentation and adaptive immune system. In contrast, those upregulated genes in patients with AE were mainly involved in sensory organ development such as olfactory transduction, as well as synaptic transmission and signaling. Based on the differentially expressed genes, a classifier consisting of 5 host genes showed outstanding performance with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.95. CONCLUSIONS: This study provides a promising classifier and is the first to investigate transcriptomic signatures for differentiating AE from IE by using meta-transcriptomic next-generation sequencing technology.

Host-pathogen relationship in retreated tuberculosis with major rifampicin resistance-conferring mutations.

Introduction: It is assumed that host defense systems eliminating the pathogen and regulating tissue damage make a strong impact on the outcome of tuberculosis (TB) disease and that these processes are affected by rifampicin (RIF) resistance-conferring mutations of Mycobacterium tuberculosis (Mtb). However, the host responses to the pathogen harboring different mutations have not been studied comprehensively in clinical settings. We analyzed clinico-epidemiological factors and blood transcriptomic signatures associated with major rpoB mutations conferring RIF resistance in a cohort study. Methods: Demographic data were collected from 295 active pulmonary TB patients with treatment history in Hanoi, Vietnam. When recruited, drug resistance-conferring mutations and lineage-specific variations were identified using whole-genome sequencing of clinical Mtb isolates. Before starting retreatment, total RNA was extracted from the whole blood of HIV-negative patients infected with Mtb that carried either the rpoB H445Y or rpoB S450L mutation, and the total RNA was subjected to RNA sequencing after age-gender matching. The individual RNA expression levels in the blood sample set were also measured using real-time RT-PCR. Logistic and linear regression models were used to assess possible associations. Results: In our cohort, rpoB S450L and rpoB H445Y were major RIF resistance-conferring mutations [32/87 (36.8%) and 15/87 (17.2%), respectively]. H445Y was enriched in the ancient Beijing genotype and was associated with nonsynonymous mutations of Rv1830 that has been reported to regulate antibiotic resilience. H445Y was also more frequently observed in genetically clustered strains and in samples from patients who had received more than one TB treatment episode. According to the RNA sequencing, gene sets involved in the interferon-γ and-α pathways were downregulated in H445Y compared with S450L. The qRT-PCR analysis also confirmed the low expression levels of interferon-inducible genes, including BATF2 and SERPING1, in the H445Y group, particularly in patients with extensive lesions on chest X-ray. Discussion: Our study results showed that rpoB mutations as well as Mtb sublineage with additional genetic variants may have significant effects on host response. These findings strengthen the rationale for investigation of host-pathogen interactions to develop countermeasures against epidemics of drug-resistant TB.

Encorafenib plus cetuximab for the treatment of BRAF-V600E-mutated metastatic colorectal cancer.

B-type RAF (BRAF)-V600E mutations in metastatic colorectal cancer (mCRC) have been described in up to 12% of the patients. This mutation confers a bad prognostic and poor response with standard chemotherapy. Unlike the scenario for BRAF mutant melanoma, successful BRAF blockade in mCRC has emerged as a complex path, primarily due to the complex underlying biology of mCRC. The BEACON trial has reshaped the therapeutic landscape of BRAF mCRC demonstrating the benefit of the BRAF inhibitor encorafenib in combination with the anti-epidermal growth factor receptor cetuximab. This paper aims to review the main features of BRAF mCRC as well as to review the development of targeted therapy and biomarkers in this specific population. Finally, a deep insight into the underlying biology and molecular classification of BRAF-V600E mCRC has also been performed. The words 'BRAF-V600E mutation', 'colorectal cancer', 'BRAF inhibitors', 'consensus molecular subtypes', 'encorafenib', and 'cetuximab' were used to identify the clinical trials from phase I to phase III related to the development of BRAF inhibitors in this population. A deep search among international meetings (American Society of Clinical Oncology and European Society of Medical Oncology) has been performed to incorporate the last trials presented. BRAF-V600E mCRC is a challenging disease, mostly because of its molecular biology. The BEACON trial has been the most important therapeutic change in the last decade. Nevertheless, new information regarding biomarkers or novel combinations including BRAF inhibitors plus immune checkpoint inhibitors are also promising.

Decoding the multidimensional signatures of resident and expanded natural killer cells generated from perinatal blood.

Natural killer (NK) cells are lymphocytes and play a pivotal role in innate and adaptive immune responses against infections and malignancies. Longitudinal studies have indicated the feasibility of perinatal blood for large-scale NK cell generation, yet the systematic and detailed comparations of the signatures of resident and expanded NK cells (rNKs, eNKs) are largely obscure. Herein, we harvested rNKs from umbilical cord blood (rUC-NKs) and placental blood (rP-NKs) as well as the corresponding eNKs (eUC-NKs, eP-NKs). Furthermore, the biological properties and transcriptomic signatures including cellular subpopulations, cytotoxicity, gene expression profiling, genetic characteristics, signaling pathways and gene set-related biological process were investigated. The enriched rNKs and eNKs exhibited diversity in biomarker expression pattern, and eNKs with higher percentages of NKG2D+, NKG2A+, NKp44+ and NKp46+ subsets. rNKs or eNKs with different origins showed more similarities in transcriptomic signatures than those with the same origin. Our data revealed multifaceted similarities and differences of the indicated rNKs and pNKs both at the cellular and molecular levels. Our findings provide new references for further dissecting the efficacy and molecular mechanisms of rNKs and eNKs, which will collectively benefit the fundamental and translational studies of NK cell-based immunotherapy.

Identification of new drug treatments to combat COVID19: A signature-based approach using iLINCS.

The COVID-19 pandemic caused by the novel SARS-CoV-2 is more contagious than other coronaviruses and has higher rates of mortality than influenza. As no vaccine or drugs are currently approved to specifically treat COVID-19, identification of effective therapeutics is crucial to treat the afflicted and limit disease spread. We deployed a bioinformatics workflow to identify candidate drugs for the treatment of COVID-19. Using an "omics" repository, the Library of Integrated Network-Based Cellular Signatures (LINCS), we simultaneously probed transcriptomic signatures of putative COVID-19 drugs and signatures of coronavirus-infected cell lines to identify therapeutics with concordant signatures and discordant signatures, respectively. Our findings include three FDA approved drugs that have established antiviral activity, including protein kinase inhibitors, providing a promising new category of candidates for COVID-19 interventions.

Human Peripheral Blood-Derived Endothelial Colony-Forming Cells Are Highly Similar to Mature Vascular Endothelial Cells yet Demonstrate a Transitional Transcriptomic Signature.

Endothelial colony-forming cells (ECFC) are currently considered as a promising cell population for the pre-endothelialization or pre-vascularization of tissue-engineered constructs, including small-diameter biodegradable vascular grafts. However, the extent of heterogeneity between ECFC and mature vascular endothelial cells (EC) is unclear. Here, we performed a transcriptome-wide study to compare gene expression profiles of ECFC, human coronary artery endothelial cells (HCAEC), and human umbilical vein endothelial cells (HUVEC). Characterization of the abovementioned cell populations was carried out by immunophenotyping, tube formation assay, and evaluation of proliferation capability while global gene expression profiling was conducted by means of RNA-seq. ECFC were similar to HUVEC in terms of immunophenotype (CD31+vWF+KDR+CD146+CD34-CD133-CD45-CD90-) and tube formation activity yet had expectedly higher proliferative potential. HCAEC and HUVEC were generally similar to ECFC with regards to their global gene expression profile; nevertheless, ECFC overexpressed specific markers of all endothelial lineages (NRP2, NOTCH4, LYVE1), in particular lymphatic EC (LYVE1), and had upregulated extracellular matrix and basement membrane genes (COL1A1, COL1A2, COL4A1, COL4A2). Proteomic profiling for endothelial lineage markers and angiogenic molecules generally confirmed RNA-seq results, indicating ECFC as an intermediate population between HCAEC and HUVEC. Therefore, gene expression profile and behavior of ECFC suggest their potential to be applied for a pre-endothelialization of bioartificial vascular grafts, whereas in terms of endothelial hierarchy they differ from HCAEC and HUVEC, having a transitional phenotype.

Red-legged partridge (Alectoris rufa) de-novo transcriptome assembly and identification of gene-related markers.

The red-legged partridge (Alectoris rufa) has a great socio-economic importance as a game species and is reared by millions in farms in several European countries. The ability to respond to a wide spectrum of pathogens and environmental changes is key for farm-reared animals that, as such, face even higher pathogen exposure and specifically for those submitted to restocking programs. In this study, RNA-sequencing and de-novo assembly of genes expressed in different immune tissues were performed. The raw FASTQ files were submitted to the NCBI SRA database with accession number PRJNA289204. A total of 94.2 million reads were obtained and assembled into 51,403 contigs using OASES software. The final annotated partridge immune transcriptome comprises almost 7000 unigenes, available as FASTA in the supplementary material. A total of 12,828 microsatellites and 33,857 Single Nucleotide Polymorphisms (SNPs) were identified. The candidate gene sequences and the large number of potential genetic markers from the red-legged partridge transcriptome reliably identified through the use for the first time of a high coverage 100-bp paired-end RNA-seq protocol, provide new tools for future studies in this and related species, thus contributing to the ongoing development of genomic resources in avian species. Further investigation into candidate genes and gene-associated markers will help to uncover individual variability in the resistance to infections and other external aggressions in partridges.

Characterization of Dendritic Cell Subsets Through Gene Expression Analysis.

Dendritic cells (DCs) are immune sentinels of the body and play a key role in the orchestration of the communication between the innate and the adaptive immune systems. DCs can polarize innate and adaptive immunity toward a variety of functions, sometimes with opposite roles in the overall control of immune responses (e.g., tolerance or immunosuppression versus immunity) or in the balance between various defense mechanisms promoting the control of different types of pathogens (e.g., antiviral versus antibacterial versus anti-worm immunity). These multiple DC functions result both from the plasticity of individual DC to exert different activities and from the existence of various DC subsets specialized in distinct functions. Functional genomics represents a powerful, unbiased, approach to better characterize these two levels of DC plasticity and to decipher its molecular regulation. Indeed, more and more experimental immunologists are generating high-throughput data in order to better characterize different states of DC based, for example, on their belonging to a specific subpopulation and/or on their exposure to specific stimuli and/or on their ability to exert a specific function. However, the interpretation of this wealth of data is severely hampered by the bottleneck of their bioinformatics analysis. Indeed, most experimental immunologists lack advanced computational or bioinformatics expertise and do not know how to translate raw gene expression data into potential biological meaning. Moreover, subcontracting such analyses is generally disappointing or financially not sustainable, since companies generally propose canonical analysis pipelines that are often unadapted for the structure of the data to analyze or for the precise type of questions asked. Hence, there is an important need of democratization of the bioinformatics analyses of gene expression profiling studies, in order to accelerate interpretation of the results by the researchers at the origin of the research project, of the data and who know best the underlying biology. This chapter will focus on the analysis of DC subset transcriptomes as measured by microarrays. We will show that simple bioinformatics procedures, applied one after the other in the framework of a pipeline, can lead to the characterization of DC subsets. We will develop two tutorials based on the reanalysis of public gene expression data. The first tutorial aims at illustrating a strategy for establishing the identity of DC subsets studied in a novel context, here their in vitro generation in cultures of human CD34(+) hematopoietic progenitors. The second tutorial aims at illustrating how to perform a posteriori bioinformatics analyses in order to evaluate the risk of contamination or of improper identification of DC subsets during preparation of biological samples, such that this information is taken into account in the final interpretation of the data and can eventually help to redesign the sampling strategy.