IGSF8 is an innate immune checkpoint and cancer immunotherapy target.

Antigen presentation defects in tumors are prevalent mechanisms of adaptive immune evasion and resistance to cancer immunotherapy, whereas how tumors evade innate immunity is less clear. Using CRISPR screens, we discovered that IGSF8 expressed on tumors suppresses NK cell function by interacting with human KIR3DL2 and mouse Klra9 receptors on NK cells. IGSF8 is normally expressed in neuronal tissues and is not required for cell survival in vitro or in vivo. It is overexpressed and associated with low antigen presentation, low immune infiltration, and worse clinical outcomes in many tumors. An antibody that blocks IGSF8-NK receptor interaction enhances NK cell killing of malignant cells in vitro and upregulates antigen presentation, NK cell-mediated cytotoxicity, and T cell signaling in vivo. In syngeneic tumor models, anti-IGSF8 alone, or in combination with anti-PD1, inhibits tumor growth. Our results indicate that IGSF8 is an innate immune checkpoint that could be exploited as a therapeutic target.

A high-resolution view of intra-tumoral B cell immunity.

In this issue of Immunity, Meylan et al. (2022) uses spatial transcriptomics to examine B cell immunity within intratumoral tertiary lymphoid structures (TLSs). They find that B cells expand and mature into plasma cells (PCs) within the TLS, migrate along fibroblastic tracks to tumor beds, and produce IgG antibodies that target cancer cells.

TRUST4: immune repertoire reconstruction from bulk and single-cell RNA-seq data.

We introduce the TRUST4 open-source algorithm for reconstruction of immune receptor repertoires in αß/γδ T cells and B cells from RNA-sequencing (RNA-seq) data. Compared with competing methods, TRUST4 supports both FASTQ and BAM format and is faster and more sensitive in assembling longer-even full-length-receptor repertoires. TRUST4 can also call repertoire sequences from single-cell RNA-seq (scRNA-seq) data without V(D)J enrichment, and is compatible with both SMART-seq and 5' 10x Genomics platforms.

Mass cytometry of Hodgkin lymphoma reveals a CD4+ regulatory T-cell-rich and exhausted T-effector microenvironment.

In classical Hodgkin lymphoma (cHL), the host antitumor immune response is ineffective. Hodgkin Reed-Sternberg (HRS) cells have multifaceted mechanisms to evade the immune system, including 9p24.1/CD274(PD-L1)/PDCD1LG2(PD-L2) genetic alterations, overexpression of PD-1 ligands, and associated T-cell exhaustion and additional structural bases of aberrant antigen presentation. The clinical success of PD-1 blockade in cHL suggests that the tumor microenvironment (TME) contains reversibly exhausted T effector cells (Teffs). However, durable responses are observed in patients with ß2-microglobulin/major histocompatibility complex (MHC) class I loss on HRS cells, raising the possibility of non-CD8+ T cell-mediated mechanisms of efficacy of PD-1 blockade. These observations highlight the need for a detailed analysis of the cHL TME. Using a customized time-of-flight mass cytometry panel, we simultaneously assessed cell suspensions from diagnostic cHL biopsies and control reactive lymph node/tonsil (RLNT) samples. Precise phenotyping of immune cell subsets revealed salient differences between cHLs and RLNTs. The TME in cHL is CD4+ T-cell rich, with frequent loss of MHC class I expression on HRS cells. In cHLs, we found concomitant expansion of T helper 1 (Th1)-polarized Teffs and regulatory T cells (Tregs). The cHL Th1 Tregs expressed little or no PD-1, whereas the Th1 Teffs were PD-1+ The differential PD-1 expression and likely functional Th1-polarized CD4+ Tregs and exhausted Teffs may represent complementary mechanisms of immunosuppression in cHL.

RNA-binding protein DDX1 is responsible for fatty acid-mediated repression of insulin translation.

The molecular mechanism in pancreatic ß cells underlying hyperlipidemia and insulin insufficiency remains unclear. Here, we find that the fatty acid-induced decrease in insulin levels occurs due to a decrease in insulin translation. Since regulation at the translational level is generally mediated through RNA-binding proteins, using RNA antisense purification coupled with mass spectrometry, we identify a novel insulin mRNA-binding protein, namely, DDX1, that is sensitive to palmitate treatment. Notably, the knockdown or overexpression of DDX1 affects insulin translation, and the knockdown of DDX1 eliminates the palmitate-induced repression of insulin translation. Molecular mechanism studies show that palmitate treatment causes DDX1 phosphorylation at S295 and dissociates DDX1 from insulin mRNA, thereby leading to the suppression of insulin translation. In addition, DDX1 may interact with the translation initiation factors eIF3A and eIF4B to regulate translation. In high-fat diet mice, the inhibition of insulin translation happens at an early prediabetic stage before the elevation of glucose levels. We speculate that the DDX1-mediated repression of insulin translation worsens the situation of insulin resistance and contributes to the elevation of blood glucose levels in obese animals.

Topological analysis reveals a PD-L1-associated microenvironmental niche for Reed-Sternberg cells in Hodgkin lymphoma.

Signaling between programmed cell death protein 1 (PD-1) and the PD-1 ligands (PD-L1, PD-L2) is essential for malignant Hodgkin Reed-Sternberg (HRS) cells to evade antitumor immunity in classical Hodgkin lymphoma (cHL). Copy number alterations of 9p24.1/CD274(PD-L1)/PDCD1LG2(PD-L2) contribute to robust PD-L1 and PD-L2 expression by HRS cells. PD-L1 is also expressed by nonmalignant tumor-associated macrophages (TAMs), but the relationships among PD-L1+ HRS cells, PD-L1+ TAMs, and PD-1+ T cells remain undefined. We used multiplex immunofluorescence and digital image analysis to examine the topography of PD-L1+ and PD-1+ cells in the tumor microenvironment (TME) of cHL. We find that the majority of PD-L1 in the TME is expressed by the abundant PD-L1+ TAMs, which physically colocalize with PD-L1+ HRS cells in a microenvironmental niche. PD-L1+ TAMs are enriched for contacts with T cells, and PD-L1+ HRS cells are enriched for contacts with CD4+ T cells, a subset of which are PD-1+ Our data define a unique topology of cHL in which PD-L1+ TAMs surround HRS cells and implicate CD4+ T cells as a target of PD-1 blockade.

Analysis of sequencing data for probing RNA secondary structures and protein-RNA binding in studying posttranscriptional regulations.

High-throughput sequencing has been used to study posttranscriptional regulations, where the identification of protein-RNA binding is a major and fast-developing sub-area, which is in turn benefited by the sequencing methods for whole-transcriptome probing of RNA secondary structures. In the study of RNA secondary structures using high-throughput sequencing, bases are modified or cleaved according to their structural features, which alter the resulting composition of sequencing reads. In the study of protein-RNA binding, methods have been proposed to immuno-precipitate (IP) protein-bound RNA transcripts in vitro or in vivo By sequencing these transcripts, the protein-RNA interactions and the binding locations can be identified. For both types of data, read counts are affected by a combination of confounding factors, including expression levels of transcripts, sequence biases, mapping errors and the probing or IP efficiency of the experimental protocols. Careful processing of the sequencing data and proper extraction of important features are fundamentally important to a successful analysis. Here we review and compare different experimental methods for probing RNA secondary structures and binding sites of RNA-binding proteins (RBPs), and the computational methods proposed for analyzing the corresponding sequencing data. We suggest how these two types of data should be integrated to study the structural properties of RBP binding sites as a systematic way to better understand posttranscriptional regulations.

A novel method for discovering local spatial clusters of genomic regions with functional relationships from DNA contact maps.

MOTIVATION: The three-dimensional structure of genomes makes it possible for genomic regions not adjacent in the primary sequence to be spatially proximal. These DNA contacts have been found to be related to various molecular activities. Previous methods for analyzing DNA contact maps obtained from Hi-C experiments have largely focused on studying individual interactions, forming spatial clusters composed of contiguous blocks of genomic locations, or classifying these clusters into general categories based on some global properties of the contact maps. RESULTS: Here, we describe a novel computational method that can flexibly identify small clusters of spatially proximal genomic regions based on their local contact patterns. Using simulated data that highly resemble Hi-C data obtained from real genome structures, we demonstrate that our method identifies spatial clusters that are more compact than methods previously used for clustering genomic regions based on DNA contact maps. The clusters identified by our method enable us to confirm functionally related genomic regions previously reported to be spatially proximal in different species. We further show that each genomic region can be assigned a numeric affinity value that indicates its degree of participation in each local cluster, and these affinity values correlate quantitatively with DNase I hypersensitivity, gene expression, super enhancer activities and replication timing in a cell type specific manner. We also show that these cluster affinity values can precisely define boundaries of reported topologically associating domains, and further define local sub-domains within each domain. AVAILABILITY AND IMPLEMENTATION: The source code of BNMF and tutorials on how to use the software to extract local clusters from contact maps are available at http://yiplab.cse.cuhk.edu.hk/bnmf/ CONTACT: kevinyip@cse.cuhk.edu.hk SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A common set of distinct features that characterize noncoding RNAs across multiple species.

To find signature features shared by various ncRNA sub-types and characterize novel ncRNAs, we have developed a method, RNAfeature, to investigate >600 sets of genomic and epigenomic data with various evolutionary and biophysical scores. RNAfeature utilizes a fine-tuned intra-species wrapper algorithm that is followed by a novel feature selection strategy across species. It considers long distance effect of certain features (e.g. histone modification at the promoter region). We finally narrow down on 10 informative features (including sequences, structures, expression profiles and epigenetic signals). These features are complementary to each other and as a whole can accurately distinguish canonical ncRNAs from CDSs and UTRs (accuracies: >92% in human, mouse, worm and fly). Moreover, the feature pattern is conserved across multiple species. For instance, the supervised 10-feature model derived from animal species can predict ncRNAs in Arabidopsis (accuracy: 82%). Subsequently, we integrate the 10 features to define a set of noncoding potential scores, which can identify, evaluate and characterize novel noncoding RNAs. The score covers all transcribed regions (including unconserved ncRNAs), without requiring assembly of the full-length transcripts. Importantly, the noncoding potential allows us to identify and characterize potential functional domains with feature patterns similar to canonical ncRNAs (e.g. tRNA, snRNA, miRNA, etc) on ∼70% of human long ncRNAs (lncRNAs).

Improved prediction of RNA secondary structure by integrating the free energy model with restraints derived from experimental probing data.

Recently, several experimental techniques have emerged for probing RNA structures based on high-throughput sequencing. However, most secondary structure prediction tools that incorporate probing data are designed and optimized for particular types of experiments. For example, RNAstructure-Fold is optimized for SHAPE data, while SeqFold is optimized for PARS data. Here, we report a new RNA secondary structure prediction method, restrained MaxExpect (RME), which can incorporate multiple types of experimental probing data and is based on a free energy model and an MEA (maximizing expected accuracy) algorithm. We first demonstrated that RME substantially improved secondary structure prediction with perfect restraints (base pair information of known structures). Next, we collected structure-probing data from diverse experiments (e.g. SHAPE, PARS and DMS-seq) and transformed them into a unified set of pairing probabilities with a posterior probabilistic model. By using the probability scores as restraints in RME, we compared its secondary structure prediction performance with two other well-known tools, RNAstructure-Fold (based on a free energy minimization algorithm) and SeqFold (based on a sampling algorithm). For SHAPE data, RME and RNAstructure-Fold performed better than SeqFold, because they markedly altered the energy model with the experimental restraints. For high-throughput data (e.g. PARS and DMS-seq) with lower probing efficiency, the secondary structure prediction performances of the tested tools were comparable, with performance improvements for only a portion of the tested RNAs. However, when the effects of tertiary structure and protein interactions were removed, RME showed the highest prediction accuracy in the DMS-accessible regions by incorporating in vivo DMS-seq data.

Computational identification of protein binding sites on RNAs using high-throughput RNA structure-probing data.

MOTIVATION: High-throughput sequencing has been used to probe RNA structures, by treating RNAs with reagents that preferentially cleave or mark certain nucleotides according to their local structures, followed by sequencing of the resulting fragments. The data produced contain valuable information for studying various RNA properties. RESULTS: We developed methods for statistically modeling these structure-probing data and extracting structural features from them. We show that the extracted features can be used to predict RNA 'zipcodes' in yeast, regions bound by the She complex in asymmetric localization. The prediction accuracy was better than using raw RNA probing data or sequence features. We further demonstrate the use of the extracted features in identifying binding sites of RNA binding proteins from whole-transcriptome global photoactivatable-ribonucleoside-enhanced cross-linking and immunopurification (gPAR-CLIP) data. AVAILABILITY: The source code of our implemented methods is available at http://yiplab.cse.cuhk.edu.hk/probrna/ CONTACT: kevinyip@cse.cuhk.edu.hk Supplementary information: Supplementary data are available at Bioinformatics online.

Protocol for fast clonal family inference and analysis from large-scale B cell receptor repertoire sequencing data.

The expeditious identification and comprehensive analysis of clonal families from extensive B cell receptor (BCR) repertoire sequencing data are imperative for elucidating the intricacies of B cell immune responses. Here, we introduce a computational pipeline designed to swiftly deduce clonal families from bulk BCR heavy-chain sequencing data, accompanied by a suite of functional modules tailored to streamline post-clustering analysis. The outlined methodology encompasses guidelines for software installation, meticulous data preparation, and the systematic inference and analysis of clonal families. For complete details on the use and execution of this protocol, please refer to Wang et al.1.

A glucomannan produced by Bacillus velezensis HY23 and its growth promoting effect on soybeans under salt stress.

The Bacillus genus is widely distributed in nature, has bacteriostatic and growth-promoting activities, and has broad application potential in agriculture. An exopolysaccharide (EPS) was extracted and purified from Bacillus velezensis HY23. Structural characterisation of the EPS was performed by chemical and spectroscopic analyses. Methylation analysis showed that the EPS of HY23 was composed of mannose and glucose at a ratio of 82:18 and was identified as glucomannan. Combined with the nuclear magnetic resonance (NMR) analysis, EPS from HY23 had a backbone of →2)-α-D-Manp-(1 â†’ and →2,6)-α-D-Manp-(1 â†’ branched at C-6 with terminal α-(3-O-Me)-D-Manp-(1 â†’ and →6)-α-D-Manp-(1 â†’ residues as the side chain. A certain amount of ß-D-Glcp residues were also present in backbone. Moreover, EPS significantly improved the nitrogen-fixing activity and salt resistance of soybean seedlings by regulating the antioxidant pool and expression of ion transporters. These findings indicate that EPS from B. velezensis HY23 is a potential biostimulant for enhancing plant resistance to salt stress.

Fast clonal family inference from large-scale B cell repertoire sequencing data.

Advances in high-throughput sequencing technologies have facilitated the large-scale characterization of B cell receptor (BCR) repertoires. However, the vast amount and high diversity of the BCR sequences pose challenges for efficient and biologically meaningful analysis. Here, we introduce fastBCR, an efficient computational approach for inferring B cell clonal families from massive BCR heavy chain sequences. We demonstrate that fastBCR substantially reduces the running time while ensuring high accuracy on simulated datasets with diverse numbers of B cell lineages and varying mutation rates. We apply fastBCR to real BCR sequencing data from peripheral blood samples of COVID-19 patients, showing that the inferred clonal families display disease-associated features, as well as corresponding antigen-binding specificity and affinity. Overall, our results demonstrate the advantages of fastBCR for analyzing BCR repertoire data, which will facilitate the identification of disease-associated antibodies and improve our understanding of the B cell immune response.

Fluorescent detection of brown spot of tobacco caused by Alternaria alternata based on lambda exonuclease-induced DNAzyme amplification.

A rapid, simple, and sensitive fluorescent detection method for brown spot of tobacco is established by lambda exonuclease-induced Mg2+-dependent DNAzyme amplification. It contains hybridization of the Alternaria alternata genome and HP1, digestion of the 5'-phosphorylated strand of the hybrid dsDNA by lambda exonuclease, acquisition of complete Mg2+-dependent DNAzyme, cleavage of the substrate modified with FAM and BHQ-1, and fluorescent detection. The proposed assay exhibits good sensitivity (10 pg L-1), selectivity and reproducibility. The method does not require pure DNA and expensive instruments, and can be performed within 2.5 hours. To the best of our knowledge, this is the first report of fluorescent detection of Alternaria alternata and its tobacco field samples. This method can be applied to the rapid and sensitive detection of Alternaria alternata in tobacco and its seedlings, and is particularly important for the green prevention and control of tobacco brown spot disease.

MetaMHC: a meta approach to predict peptides binding to MHC molecules.

As antigenic peptides binding to major histocompatibility complex (MHC) molecules is the prerequisite of cellular immune responses, an accurate computational predictor will be of great benefit to biologists and immunologists for understanding the underlying mechanism of immune recognition as well as facilitating the process of epitope mapping and vaccine design. Although various computational approaches have been developed, recent experimental results on benchmark data sets show that the development of improved predictors is needed, especially for MHC Class II peptide binding. To make the most of current methods and achieve a higher predictive performance, we developed a new web server, MetaMHC, to integrate the outputs of leading predictors by several popular ensemble strategies. MetaMHC consists of two components: MetaMHCI and MetaMHCII for MHC Class I peptide and MHC Class II peptide binding predictions, respectively. Experimental results by both cross-validation and using an independent data set show that the ensemble approaches outperform individual predictors, being statistically significant. MetaMHC is freely available at http://www.biokdd.fudan.edu.cn/Service/MetaMHC.html.

Identification of specificity determining residues in peptide recognition domains using an information theoretic approach applied to large-scale binding maps.

BACKGROUND: Peptide Recognition Domains (PRDs) are commonly found in signaling proteins. They mediate protein-protein interactions by recognizing and binding short motifs in their ligands. Although a great deal is known about PRDs and their interactions, prediction of PRD specificities remains largely an unsolved problem. RESULTS: We present a novel approach to identifying these Specificity Determining Residues (SDRs). Our algorithm generalizes earlier information theoretic approaches to coevolution analysis, to become applicable to this problem. It leverages the growing wealth of binding data between PRDs and large numbers of random peptides, and searches for PRD residues that exhibit strong evolutionary covariation with some positions of the statistical profiles of bound peptides. The calculations involve only information from sequences, and thus can be applied to PRDs without crystal structures. We applied the approach to PDZ, SH3 and kinase domains, and evaluated the results using both residue proximity in co-crystal structures and verified binding specificity maps from mutagenesis studies. DISCUSSION: Our predictions were found to be strongly correlated with the physical proximity of residues, demonstrating the ability of our approach to detect physical interactions of the binding partners. Some high-scoring pairs were further confirmed to affect binding specificity using previous experimental results. Combining the covariation results also allowed us to predict binding profiles with higher reliability than two other methods that do not explicitly take residue covariation into account. CONCLUSIONS: The general applicability of our approach to the three different domain families demonstrated in this paper suggests its potential in predicting binding targets and assisting the exploration of binding mechanisms.

Subperiosteal orbital hematoma concomitant with abscess in a patient with sinusitis: A case report.

BACKGROUND: Sinusitis-derived subperiosteal orbital collection is uncommon and is usually diagnosed as subperiosteal orbital abscess or, rarely, as hematoma. We report a unique and even rarer case of subperiosteal orbital collection, which is actually a complication of hematoma and abscess. CASE SUMMARY: A 26-year-old female presented with left eyeball pain and ipsilateral chemosis. She had no history of head trauma or upper respiratory infection. Her blood cell count showed an increase in leukocytes. Fiberoptic rhinolaryngoscopy revealed only mucosal edema of the left olfactory crest without apparent discharge. The computed tomography scan results showed an opaque left posterior ethmoid cell with a thickened bony shell and fusiform changes in the periosteal elevation of the medial wall of the left orbit. Emergent surgery revealed an ethmoid mucocele complicated with subperiosteal orbital hematoma and abscess. The pathology of the lamina papyracea between the mucocele and subperiosteal collection was necrotic, and the overlying mucosa was de-epithelialized. CONCLUSION: Subperiosteal orbital hematoma with abscess in a patient with sinusitis adds to the current knowledge of orbital complications of sinusitis.

Comprehensive Characterizations of Immune Receptor Repertoire in Tumors and Cancer Immunotherapy Studies.

We applied our computational algorithm TRUST4 to assemble immune receptor (T-cell receptor/B-cell receptor) repertoires from approximately 12,000 RNA sequencing samples from The Cancer Genome Atlas and seven immunotherapy studies. From over 35 million assembled complete complementary-determining region 3 sequences, we observed that the expression of CCL5 and MZB1 is the most positively correlated genes with T-cell clonal expansion and B-cell clonal expansion, respectively. We analyzed amino acid evolution during B-cell receptor somatic hypermutation and identified tyrosine as the preferred residue. We found that IgG1+IgG3 antibodies together with FcRn were associated with complement-dependent cytotoxicity and antibody-dependent cellular cytotoxicity or phagocytosis. In addition to B-cell infiltration, we discovered that B-cell clonal expansion and IgG1+IgG3 antibodies are also correlated with better patient outcomes. Finally, we created a website, VisualizIRR, for users to interactively explore and visualize the immune repertoires in this study. See related Spotlight by Liu and Han, p. 786.

Combining phage display with SMRTbell next-generation sequencing for the rapid discovery of functional scFv fragments.

Phage display technology in combination with next-generation sequencing (NGS) currently is a state-of-the-art method for the enrichment and isolation of monoclonal antibodies from diverse libraries. However, the current NGS methods employed for sequencing phage display libraries are limited by the short contiguous read lengths associated with second-generation sequencing platforms. Consequently, the identification of antibody sequences has conventionally been restricted to individual antibody domains or to the analysis of single domain binding moieties such as camelid VHH or cartilaginous fish IgNAR antibodies. In this study, we report the application of third-generation sequencing to address this limitation. We used single molecule real time (SMRT) sequencing coupled with hairpin adaptor loop ligation to facilitate the accurate interrogation of full-length single-chain Fv (scFv) libraries. Our method facilitated the rapid isolation and testing of scFv antibodies enriched from phage display libraries within days following panning. Two libraries against CD160 and CD123 were panned and monitored by NGS. Analysis of NGS antibody data sets led to the isolation of several functional scFv antibodies that were not identified by conventional panning and screening strategies. Our approach, which combines phage display selection of immune libraries with the full-length interrogation of scFv fragments, is an easy method to discover functional antibodies, with a range of affinities and biophysical characteristics.