Changes in the Transcriptome Profile in Young Chickens after Infection with LaSota Newcastle Disease Virus.

Understanding gene expression changes in chicks after vaccination against Newcastle Disease (ND) can reveal vaccine biomarkers. There are limited data on chicks' early immune response after ND vaccination. Two trials focused on this knowledge gap. In experiment one, 42 13-day-old specific-pathogen-free (SPF) chicks were used. Harderian glands (Hgs) and tracheas (Tcs) from five birds per group were sampled at 12, 24, and 48 h post-vaccination (hpv) to evaluate the gene transcription levels by RNA sequencing (RNA-seq) and RT-qPCR. The results of RNA-seq were compared by glmFTest, while results of RT-qPCR were compared by t-test. With RNA-seq, a significant up-regulation of interferon-related genes along with JAK-STAT signaling pathway regulation was observed in the Hgs at 24 hpv. None of the differentially expressed genes (DEGs) identified by RNA-seq were positive for RT-qPCR. Experiment 2 used 112 SPF and commercial chickens that were 1 day old and 14 days old. Only the commercial birds had maternal antibodies for Newcastle Disease virus (NDV). By RNA-seq, 20 core DEGs associated with innate immunity and viral genome replication inhibition were identified. Genes previously unlinked to NDV response, such as USP41, were identified. This research present genes with potential as immunity biomarkers for vaccines, yet further investigation is needed to correlate the core gene expression with viral shedding post-vaccination.

Single cell genomics based insights into the impact of cell-type specific microbial internalization on disease severity.

Host-microbe interactions are complex and ever-changing, especially during infections, which can significantly impact human physiology in both health and disease by influencing metabolic and immune functions. Infections caused by pathogens such as bacteria, viruses, fungi, and parasites are the leading cause of global mortality. Microbes have evolved various immune evasion strategies to survive within their hosts, which presents a multifaceted challenge for detection. Intracellular microbes, in particular, target specific cell types for survival and replication and are influenced by factors such as functional roles, nutrient availability, immune evasion, and replication opportunities. Identifying intracellular microbes can be difficult because of the limitations of traditional culture-based methods. However, advancements in integrated host microbiome single-cell genomics and transcriptomics provide a promising basis for personalized treatment strategies. Understanding host-microbiota interactions at the cellular level may elucidate disease mechanisms and microbial pathogenesis, leading to targeted therapies. This article focuses on how intracellular microbes reside in specific cell types, modulating functions through persistence strategies to evade host immunity and prolong colonization. An improved understanding of the persistent intracellular microbe-induced differential disease outcomes can enhance diagnostics, therapeutics, and preventive measures.

Immune evasion impacts the landscape of driver genes during cancer evolution.

BACKGROUND: Carcinogenesis is driven by interactions between genetic mutations and the local tumor microenvironment. Recent research has identified hundreds of cancer driver genes; however, these studies often include a mixture of different molecular subtypes and ecological niches and ignore the impact of the immune system. RESULTS: In this study, we compare the landscape of driver genes in tumors that escaped the immune system (escape +) versus those that did not (escape -). We analyze 9896 primary tumors from The Cancer Genome Atlas using the ratio of non-synonymous to synonymous mutations (dN/dS) and find 85 driver genes, including 27 and 16 novel genes, in escape - and escape + tumors, respectively. The dN/dS of driver genes in immune escaped tumors is significantly lower and closer to neutrality than in non-escaped tumors, suggesting selection buffering in driver genes fueled by immune escape. Additionally, we find that immune evasion leads to more mutated sites, a diverse array of mutational signatures and is linked to tumor prognosis. CONCLUSIONS: Our findings highlight the need for improved patient stratification to identify new therapeutic targets for cancer treatment.

Single-cell transcriptome landscape of circulating CD4+ T cell populations in autoimmune diseases.

CD4+ T cells are key mediators of various autoimmune diseases; however, their role in disease progression remains unclear due to cellular heterogeneity. Here, we evaluated CD4+ T cell subpopulations using decomposition-based transcriptome characterization and canonical clustering strategies. This approach identified 12 independent gene programs governing whole CD4+ T cell heterogeneity, which can explain the ambiguity of canonical clustering. In addition, we performed a meta-analysis using public single-cell datasets of over 1.8 million peripheral CD4+ T cells from 953 individuals by projecting cells onto the reference and cataloging cell frequency and qualitative alterations of the populations in 20 diseases. The analyses revealed that the 12 transcriptional programs were useful in characterizing each autoimmune disease and predicting its clinical status. Moreover, genetic variants associated with autoimmune diseases showed disease-specific enrichment within the 12 gene programs. The results collectively provide a landscape of single-cell transcriptomes of CD4+ T cell subpopulations involved in autoimmune disease.

Optimal population-specific HLA imputation with dimension reduction.

Human genomics has quickly evolved, powering genome-wide association studies (GWASs). SNP-based GWASs cannot capture the intense polymorphism of HLA genes, highly associated with disease susceptibility. There are methods to statistically impute HLA genotypes from SNP-genotypes data, but lack of diversity in reference panels hinders their performance. We evaluated the accuracy of the 1000 Genomes data as a reference panel for imputing HLA from admixed individuals of African and European ancestries, focusing on (a) the full dataset, (b) 10 replications from 6 populations, and (c) 19 conditions for the custom reference panels. The full dataset outperformed smaller models, with a good F1-score of 0.66 for HLA-B. However, custom models outperformed the multiethnic or population models of similar size (F1-scores up to 0.53, against up to 0.42). We demonstrated the importance of using genetically specific models for imputing populations, which are currently underrepresented in public datasets, opening the door to HLA imputation for every genetic population.

Utilizing immunogenomic approaches to prioritize targetable neoantigens for personalized cancer immunotherapy.

Advancements in sequencing technologies and bioinformatics algorithms have expanded our ability to identify tumor-specific somatic mutation-derived antigens (neoantigens). While recent studies have shown neoantigens to be compelling targets for cancer immunotherapy due to their foreign nature and high immunogenicity, the need for increasingly accurate and cost-effective approaches to rapidly identify neoantigens remains a challenging task, but essential for successful cancer immunotherapy. Currently, gene expression analysis and algorithms for variant calling can be used to generate lists of mutational profiles across patients, but more care is needed to curate these lists and prioritize the candidate neoantigens most capable of inducing an immune response. A growing amount of evidence suggests that only a handful of somatic mutations predicted by mutational profiling approaches act as immunogenic neoantigens. Hence, unbiased screening of all candidate neoantigens predicted by Whole Genome Sequencing/Whole Exome Sequencing may be necessary to more comprehensively access the full spectrum of immunogenic neoepitopes. Once putative cancer neoantigens are identified, one of the largest bottlenecks in translating these neoantigens into actionable targets for cell-based therapies is identifying the cognate T cell receptors (TCRs) capable of recognizing these neoantigens. While many TCR-directed screening and validation assays have utilized bulk samples in the past, there has been a recent surge in the number of single-cell assays that provide a more granular understanding of the factors governing TCR-pMHC interactions. The goal of this review is to provide an overview of existing strategies to identify candidate neoantigens using genomics-based approaches and methods for assessing neoantigen immunogenicity. Additionally, applications, prospects, and limitations of some of the current single-cell technologies will be discussed. Finally, we will briefly summarize some of the recent models that have been used to predict TCR antigen specificity and analyze the TCR receptor repertoire.

A Prediction Model for Deciphering Intratumoral Heterogeneity Derived from the Microglia/Macrophages of Glioma Using Non-Invasive Radiogenomics.

Microglia and macrophages play a major role in glioma immune responses within the glioma microenvironment. We aimed to construct a prognostic prediction model for glioma based on microglia/macrophage-correlated genes. Additionally, we sought to develop a non-invasive radiogenomics approach for risk stratification evaluation. Microglia/macrophage-correlated genes were identified from four single-cell datasets. Hub genes were selected via lasso-Cox regression, and risk scores were calculated. The immunological characteristics of different risk stratifications were assessed, and radiomics models were constructed using corresponding MRI imaging to predict risk stratification. We identified eight hub genes and developed a relevant risk score formula. The risk score emerged as a significant prognostic predictor correlated with immune checkpoints, and a relevant nomogram was drawn. High-risk groups displayed an active microenvironment associated with microglia/macrophages. Furthermore, differences in somatic mutation rates, such as IDH1 missense variant and TP53 missense variant, were observed between high- and low-risk groups. Lastly, a radiogenomics model utilizing five features from magnetic resonance imaging (MRI) T2 fluid-attenuated inversion recovery (Flair) effectively predicted the risk groups under a random forest model. Our findings demonstrate that risk stratification based on microglia/macrophages can effectively predict prognosis and immune functions in glioma. Moreover, we have shown that risk stratification can be non-invasively predicted using an MRI-T2 Flair-based radiogenomics model.

Harnessing tumor immunogenomics: Tumor neoantigens in ovarian cancer and beyond.

Ovarian cancer is a major cause of death among gynecological cancers due to its highly aggressive nature. Immunotherapy has emerged as a promising avenue for ovarian cancer treatment, offering targeted approaches with reduced off-target effects. With the advent of next-generation sequencing, it has become possible to identify genomic alterations that can serve as potential targets for immunotherapy. Furthermore, immunogenomics research has revealed the importance of genetic alterations in shaping the cancer immune responses. However, the heterogeneity of immunogenicity and the low tumor mutation burden pose challenges for neoantigen-based immunotherapies. Further research is needed to identify neoantigen-specific tumor-infiltrating lymphocytes (TIL) and establish guidelines for patient inclusion criteria in TIL-based therapy. The study of neoantigens and their implications in ovarian cancer immunotherapy holds great promise, and efforts focused on personalized treatment strategies, refined neoantigen selection, and optimized therapeutic combinations will contribute to improving patient outcomes in the future.

Genomic and comparative analysis of the T cell receptor gamma locus in two Equus species.

The genus Equus is the only extant genus of the Equidae family, which belongs to Perissodactyla, an order of mammals characterized by an odd number of toes (odd-toes ungulates). Taking advantage of the latest release of the genome assembly, we studied, for the first time in two organisms belonging to the Equus genus, the horse (Equus caballus) and the donkey (Equus asinus), the T cell receptor gamma (TRG) locus encoding the gamma chain of the γδ T cell receptor. Forty-five Variable (TRGV) genes belonging to the seven IMGT-NC validated mammalian TRGV subgroups, 25 Joining (TRGJ) and 17 Constant (TRGC) genes organized in 17 V-J-(J)-C cassettes, in tandem on about 1100 Kb, characterize the horse TRG locus, making the horse TRG locus the one with the greatest extension and with a significantly higher number of genes than the orthologous loci of the other mammalian species. A clonotype analysis of an RNA-seq transcriptomic dataset derived from spleen of an adult healthy horse, using the complete set of the horse TRGJ germline gene sequences as a probe, revealed that, in addition to the most prominent V-J rearrangements within each cassette, there is a relevant proportion of trans-cassette V-J recombination, whereby the same TRGV genes can recombine with different TRGJ genes spliced to the corresponding TRGC genes. This recombinant event strongly contributes to the diversity of the γ chain repertoire. In the donkey TRG locus, 34 TRGV, 21 TRGJ and 14 TRGC genes distributed in 14 V-J-(J)-C cassettes were found in a region of approximately 860 kb. Although the donkey's TRG is smaller than that of the horse, in Equus genus, this is still the second largest locus so far found in any mammalian species. Finally, the comparative analysis highlighted differences in size and gene content between the horse and donkey TRG loci, despite belonging to the same genus, indicating a good level of diversification within Equus. These data is in agreement with the evolutionary idea of the existence of a Equus recent common ancestor in rapid evolution, for which a mutation rate between horses and donkeys is more comparable to that between species belonging to different genera rather than to species of the same genus.

Comment on 'rigorous benchmarking of T cell receptor repertoire profiling methods for cancer RNA sequencing'.

Transcriptome sequencing has become common in cancer research, resulting in the generation of a substantial volume of RNA sequencing (RNA-Seq) data. The ability to extract immune repertoires from these data is crucial for obtaining information on infiltrating T- and B-lymphocyte clones when dedicated amplicon T-cell/B-cell receptors sequencing (TCR-Seq/BCR-Seq) methods are unavailable. In response to this demand, several dedicated computational methods have been developed, including MiXCR, TRUST and ImRep. In the recent publication in Briefings in Bioinformatics, Peng et al. have conducted an intensive, systematic comparison of the three previously mentioned tools. Although their effort is commendable, we do have a few constructive critiques regarding technical elements of their analysis.

Transcriptional immunogenomic analysis reveals distinct immunological clusters in paediatric nervous system tumours.

BACKGROUND: Cancer immunotherapies including immune checkpoint inhibitors and Chimeric Antigen Receptor (CAR) T-cell therapy have shown variable response rates in paediatric patients highlighting the need to establish robust biomarkers for patient selection. While the tumour microenvironment in adults has been widely studied to delineate determinants of immune response, the immune composition of paediatric solid tumours remains relatively uncharacterized calling for investigations to identify potential immune biomarkers. METHODS: To inform immunotherapy approaches in paediatric cancers with embryonal origin, we performed an immunogenomic analysis of RNA-seq data from 925 treatment-naïve paediatric nervous system tumours (pedNST) spanning 12 cancer types from three publicly available data sets. RESULTS: Within pedNST, we uncovered four broad immune clusters: Paediatric Inflamed (10%), Myeloid Predominant (30%), Immune Neutral (43%) and Immune Desert (17%). We validated these clusters using immunohistochemistry, methylation immune inference and segmentation analysis of tissue images. We report shared biology of these immune clusters within and across cancer types, and characterization of specific immune cell frequencies as well as T- and B-cell repertoires. We found no associations between immune infiltration levels and tumour mutational burden, although molecular cancer entities were enriched within specific immune clusters. CONCLUSIONS: Given the heterogeneity of immune infiltration within pedNST, our findings suggest personalized immunogenomic profiling is needed to guide selection of immunotherapeutic strategies.

Multi-omics analysis uncovers clinical, immunological, and pharmacogenomic implications of cuproptosis in clear cell renal cell carcinoma.

OBJECTIVE: The latest research proposed a novel copper-dependent programmed cell death named cuproptosis. We aimed to elucidate the influence of cuproptosis in clear cell renal cell carcinoma (ccRCC) from a multi-omic perspective. METHODS: This study systematically assessed mRNA expression, methylation, and genetic alterations of cuproptosis genes in TCGA ccRCC samples. Through unsupervised clustering analysis, the samples were classified as different cuproptosis subtypes, which were verified through NTP method in the E-MTAB-1980 dataset. Next, the cuproptosis score (Cuscore) was computed based on cuproptosis-related genes via PCA. We also evaluated clinical and immunogenomic features, drug sensitivity, immunotherapeutic response, and post-transcriptional regulation. RESULTS: Cuproptosis genes presented multi-layer alterations in ccRCC, and were linked with patients' survival and immune microenvironment. We defined three cuproptosis subtypes [C1 (moderate cuproptosis), C2 (low cuproptosis), and C3 (high cuproptosis)], and the robustness and reproducibility of this classification was further proven. Overall survival was best in C3, moderate in C1, and worst in C2. C1 had the highest sensitivity to pazopanib, and sorafenib, while C2 was most sensitive to sunitinib. Furthermore, C1 patients benefited more from anti-PD-1 immunotherapy. Patients with high Cuscore presented the notable survival advantage. Cuscore was highly linked with immunogenomic features, and post-transcriptional events that contributed to ccRCC development. Finally, several potential compounds and druggable targets (NMU, RARRES1) were selected for low Cuscore group. CONCLUSION: Overall, our study revealed the non-negligible role of cuproptosis in ccRCC development. Evaluation of the cuproptosis subtypes improves our cognition of immunogenomic features and better guides personalized prognostication and precision therapy.

Identification of cytokine-predominant immunosuppressive class and prognostic risk signatures in glioma.

PURPOSE: The advent of immune checkpoint blockade (ICB) therapies this year has changed the way glioblastoma (GBM) is treated. Meanwhile, some patients with strong PD-L1 expression remain immune checkpoint resistant. To better understand the molecular processes that influence the immune environment, there is an urgent need to characterize the immunosuppressive tumor microenvironment and identify biomarkers to predict patient survival outcomes. PATIENTS AND METHODS: Our study analyzed RNA-sequencing data from 178 GBM samples. Their unique gene expression patterns in the tumor microenvironment were analyzed by an unsupervised clustering algorithm. Through these expression patterns, a panel of T-cell exhaustion signatures, immunosuppressive cells, and clinical features correlates with immunotherapy response. The presence or absence of immune status and prognostic signatures was then validated with the test dataset. RESULTS: 38.2% of GBM patients showed increased expression of anti-inflammatory cytokines, significant enrichment of T cell exhaustion signals, higher proportion of immunosuppressive cells (macrophages and CD4 regulatory T cells) and nine inhibitory checkpoints (CTLA4, PDCD1, LAG3, BTLA, TIGIT, HAVCR2, IDO1, SIGLEC7, and VISTA). The immunodepleted class (IDC) was used to classify these immunocompromised individuals. Despite the high density of tumor-infiltrating lymphocytes shown by IDC, such patients have a poor prognosis. Although PD-L1 was highly expressed in IDC, it suggested that there might be ICB resistance. There are many IDC predictive signatures to discover. CONCLUSION: PD-1 is strongly expressed in a novel immunosuppressive class of GBM, but this cluster may be resistant to ICB therapy. A comprehensive description of this drug-resistant tumor microenvironment could provide new insights into drug resistance mechanisms and improved immunotherapy techniques.

Identification of immunosuppressive signature subtypes and prognostic risk signatures in triple-negative breast cancer.

Purpose: Immune checkpoint blockade (ICB) therapy has transformed the treatment of triple-negative breast cancer (TNBC) in recent years. However, some TNBC patients with high programmed death-ligand 1 (PD-L1) expression levels develop immune checkpoint resistance. Hence, there is an urgent need to characterize the immunosuppressive tumor microenvironment and identify biomarkers to construct prognostic models of patient survival outcomes in order to understand biological mechanisms operating within the tumor microenvironment. Patients and methods: RNA sequence (RNA-seq) data from 303 TNBC samples were analyzed using an unsupervised cluster analysis approach to reveal distinctive cellular gene expression patterns within the TNBC tumor microenvironment (TME). A panel of T cell exhaustion signatures, immunosuppressive cell subtypes and clinical features were correlated with the immunotherapeutic response, as assessed according to gene expression patterns. The test dataset was then used to confirm the occurrence of immune depletion status and prognostic features and to formulate clinical treatment recommendations. Concurrently, a reliable risk prediction model and clinical treatment strategy were proposed based on TME immunosuppressive signature differences between TNBC patients with good versus poor survival status and other clinical prognostic factors. Results: Significantly enriched TNBC microenvironment T cell depletion signatures were detected in the analyzed RNA-seq data. A high proportion of certain immunosuppressive cell subtypes, 9 inhibitory checkpoints and enhanced anti-inflammatory cytokine expression profiles were noted in 21.4% of TNBC patients that led to the designation of this group of immunosuppressed patients as the immune depletion class (IDC). Although IDC group TNBC samples contained tumor-infiltrating lymphocytes present at high densities, IDC patient prognosis was poor. Notably, PD-L1 expression was relatively elevated in IDC patients that indicated their cancers were resistant to ICB treatment. Based on these findings, a set of gene expression signatures predicting IDC group PD-L1 resistance was identified then used to develop risk models for use in predicting clinical therapeutic outcomes. Conclusion: A novel TNBC immunosuppressive tumor microenvironment subtype associated with strong PD-L1 expression and possible resistance to ICB treatment was identified. This comprehensive gene expression pattern may provide fresh insights into drug resistance mechanisms for use in optimizing immunotherapeutic approaches for TNBC patients.

Rigorous benchmarking of T-cell receptor repertoire profiling methods for cancer RNA sequencing.

The ability to identify and track T-cell receptor (TCR) sequences from patient samples is becoming central to the field of cancer research and immunotherapy. Tracking genetically engineered T cells expressing TCRs that target specific tumor antigens is important to determine the persistence of these cells and quantify tumor responses. The available high-throughput method to profile TCR repertoires is generally referred to as TCR sequencing (TCR-Seq). However, the available TCR-Seq data are limited compared with RNA sequencing (RNA-Seq). In this paper, we have benchmarked the ability of RNA-Seq-based methods to profile TCR repertoires by examining 19 bulk RNA-Seq samples across 4 cancer cohorts including both T-cell-rich and T-cell-poor tissue types. We have performed a comprehensive evaluation of the existing RNA-Seq-based repertoire profiling methods using targeted TCR-Seq as the gold standard. We also highlighted scenarios under which the RNA-Seq approach is suitable and can provide comparable accuracy to the TCR-Seq approach. Our results show that RNA-Seq-based methods are able to effectively capture the clonotypes and estimate the diversity of TCR repertoires, as well as provide relative frequencies of clonotypes in T-cell-rich tissues and low-diversity repertoires. However, RNA-Seq-based TCR profiling methods have limited power in T-cell-poor tissues, especially in highly diverse repertoires of T-cell-poor tissues. The results of our benchmarking provide an additional appealing argument to incorporate RNA-Seq into the immune repertoire screening of cancer patients as it offers broader knowledge into the transcriptomic changes that exceed the limited information provided by TCR-Seq.

Peripheral T-cell receptor repertoire dynamics in small cell lung cancer.

Background: Identifying a circulating biomarker predictive of immune checkpoint inhibitor (ICI) benefit in patients with small cell lung cancer (SCLC) remains an unmet need. Characteristics of peripheral and intratumoral T-cell receptor (TCR) repertoires have been shown to predict clinical outcomes in non-small cell lung cancer (NSCLC). Recognizing a knowledge gap, we sought to characterize circulating TCR repertoires and their relationship with clinical outcomes in SCLC. Methods: SCLC patients with limited (n=4) and extensive (n=10) stage disease were prospectively enrolled for blood collection and chart review. Targeted next-generation sequencing of TCR beta and alpha chains of peripheral blood samples was performed. Unique TCR clonotypes were defined by identical CDR3, V gene, and J gene nucleotide sequences of the beta chain and subsequently used to calculate TCR diversity indices. Results: Patients with stable versus progressive and limited versus extensive stage disease did not demonstrate significant differences in V gene usage. Kaplan-Meier curve and log-rank analysis did not identify a statistical difference in progression-free survival (PFS) (P=0.900) or overall survival (OS) (P=0.200) between high and low on-treatment TCR diversity groups, although the high diversity group exhibited a trend toward increased OS. Conclusions: We report the second study investigating peripheral TCR repertoire diversity in SCLC. With a limited sample size, no statistically significant associations between peripheral TCR diversity and clinical outcomes were observed, though further study is warranted.

Transmission of stimulus-induced epigenetic changes through cell division is coupled to continuous transcription factor activity.

Trained immunity, or innate immune memory, has been attributed to the long-term retention of stimulus-induced histone post-translational modifications (PTMs) following clearance of the initial stimulus. Yet, it remains unknown how this epigenetic memory can persist for months in dividing cells given the lack of any known mechanism for stimulus-induced histone PTMs to be directly copied from parent to daughter strand during DNA replication. Here, using time course RNA-seq, ChIP-seq, and infection assays, we find that trained macrophages are transcriptionally, epigenetically, and functionally re-programmed for at least 14 cell divisions after stimulus washout. However, the epigenetic changes observed after multiple rounds of cell division do not result from the self-sustained propagation of stimulus-induced epigenetic changes through cell division. Instead, long-lasting epigenetic differences between trained and non-trained cells are always coupled with changes in transcription factor (TF) activity, emphasizing the central role played by TFs, and gene expression changes more broadly, in driving the transmission of stimulus-induced epigenetic changes across cell divisions.

Immunogenomic analysis of human brain metastases reveals diverse immune landscapes across genetically distinct tumors.

Brain metastases (BrMs) are the most common form of brain tumors in adults and frequently originate from lung and breast primary cancers. BrMs are associated with high mortality, emphasizing the need for more effective therapies. Genetic profiling of primary tumors is increasingly used as part of the effort to guide targeted therapies against BrMs, and immune-based strategies for the treatment of metastatic cancer are gaining momentum. However, the tumor immune microenvironment (TIME) of BrM is extremely heterogeneous, and whether specific genetic profiles are associated with distinct immune states remains unknown. Here, we perform an extensive characterization of the immunogenomic landscape of human BrMs by combining whole-exome/whole-genome sequencing, RNA sequencing of immune cell populations, flow cytometry, immunofluorescence staining, and tissue imaging analyses. This revealed unique TIME phenotypes in genetically distinct lung- and breast-BrMs, thereby enabling the development of personalized immunotherapies tailored by the genetic makeup of the tumors.