TCF-1 upregulation identifies early innate lymphoid progenitors in the bone marrow.

The cellular and molecular events that drive the early development of innate lymphoid cells (ILCs) remain poorly understood. We show that the transcription factor TCF-1 is required for the efficient generation of all known adult ILC subsets and their precursors. Using novel reporter mice, we identified a new subset of early ILC progenitors (EILPs) expressing high amounts of TCF-1. EILPs lacked efficient T and B lymphocyte potential but efficiently gave rise to NK cells and all known adult helper ILC lineages, indicating that they are the earliest ILC-committed progenitors identified so far. Our results suggest that upregulation of TCF-1 expression denotes the earliest stage of ILC fate specification. The discovery of EILPs provides a basis for deciphering additional signals that specify ILC fate.

CDKN1A regulates Langerhans cell survival and promotes Treg cell generation upon exposure to ionizing irradiation.

Treatment with ionizing radiation (IR) can lead to the accumulation of tumor-infiltrating regulatory T cells (Treg cells) and subsequent resistance of tumors to radiotherapy. Here we focused on the contribution of the epidermal mononuclear phagocytes Langerhans cells (LCs) to this phenomenon because of their ability to resist depletion by high-dose IR. We found that LCs resisted apoptosis and rapidly repaired DNA damage after exposure to IR. In particular, we found that the cyclin-dependent kinase inhibitor CDKN1A (p21) was overexpressed in LCs and that Cdkn1a(-/-) LCs underwent apoptosis and accumulated DNA damage following IR treatment. Wild-type LCs upregulated major histocompatibility complex class II molecules, migrated to the draining lymph nodes and induced an increase in Treg cell numbers upon exposure to IR, but Cdkn1a(-/-) LCs did not. Our findings suggest a means for manipulating the resistance of LCs to IR to enhance the response of cutaneous tumors to radiotherapy.

Senataxin suppresses the antiviral transcriptional response and controls viral biogenesis.

The human helicase senataxin (SETX) has been linked to the neurodegenerative diseases amyotrophic lateral sclerosis (ALS4) and ataxia with oculomotor apraxia (AOA2). Here we identified a role for SETX in controlling the antiviral response. Cells that had undergone depletion of SETX and SETX-deficient cells derived from patients with AOA2 had higher expression of antiviral mediators in response to infection than did wild-type cells. Mechanistically, we propose a model whereby SETX attenuates the activity of RNA polymerase II (RNAPII) at genes stimulated after a virus is sensed and thus controls the magnitude of the host response to pathogens and the biogenesis of various RNA viruses (e.g., influenza A virus and West Nile virus). Our data indicate a potentially causal link among inborn errors in SETX, susceptibility to infection and the development of neurologic disorders.

The systematic analysis of protein-lipid interactions comes of age.

Lipids tailor membrane identities and function as molecular hubs in all cellular processes. However, the ways in which lipids modulate protein function and structure are poorly understood and still require systematic investigation. In this Innovation article, we summarize pioneering technologies, including lipid-overlay assays, lipid pull-down assays, affinity-purification lipidomics and the liposome microarray-based assay (LiMA), that will enable protein-lipid interactions to be deciphered on a systems level. We discuss how these technologies can be applied to the charting of system-wide networks and to the development of new pharmaceutical strategies.

Glycan microarrays for decoding the glycome.

In the last decade, glycan microarrays have revolutionized the analysis of the specificity of glycan-binding proteins (GBPs), providing information that simultaneously illuminates the biology mediated by them and decodes the informational content of the glycome. Numerous methods have emerged for arraying glycans in a "chip" format, and glycan libraries have been assembled that address the diversity of the human glycome. Such arrays have been successfully used for analysis of GBPs, which mediate mammalian biology, host-pathogen interactions, and immune recognition of glycans relevant to vaccine production and cancer antigens. This review covers the development of glycan microarrays and applications that have provided insights into the roles of mammalian and microbial GBPs.

Shared and distinct transcriptional programs underlie the hybrid nature of iNKT cells.

Invariant natural killer T cells (iNKT cells) are innate-like T lymphocytes that act as critical regulators of the immune response. To better characterize this population, we profiled gene expression in iNKT cells during ontogeny and in peripheral subsets as part of the Immunological Genome Project. High-resolution comparative transcriptional analyses defined developmental and subset-specific programs of gene expression by iNKT cells. In addition, we found that iNKT cells shared an extensive transcriptional program with NK cells, similar in magnitude to that shared with major histocompatibility complex (MHC)-restricted T cells. Notably, the program shared by NK cells and iNKT cells also operated constitutively in γδ T cells and in adaptive T cells after activation. Together our findings highlight a core effector program regulated distinctly in innate and adaptive lymphocytes.

Serum antibody screening using glycan arrays.

Humans and other animals produce a diverse collection of antibodies, many of which bind to carbohydrate chains, referred to as glycans. These anti-glycan antibodies are a critical part of our immune systems' defenses. Whether induced by vaccination or natural exposure to a pathogen, anti-glycan antibodies can provide protection against infections and cancers. Alternatively, when an immune response goes awry, antibodies that recognize self-glycans can mediate autoimmune diseases. In any case, serum anti-glycan antibodies provide a rich source of information about a patient's overall health, vaccination history, and disease status. Glycan microarrays provide a high-throughput platform to rapidly interrogate serum anti-glycan antibodies and identify new biomarkers for a variety of conditions. In addition, glycan microarrays enable detailed analysis of the immune system's response to vaccines and other treatments. Herein we review applications of glycan microarray technology for serum anti-glycan antibody profiling.

A Genome-Wide Association Study of Respiratory Syncytial Virus Infection Severity in Infants.

BACKGROUND: Respiratory syncytial virus (RSV) is a significant cause of infant morbidity and mortality worldwide. Most children experience at least one 1 RSV infection by the age of two 2 years, but not all develop severe disease. However, the understanding of genetic risk factors for severe RSV is incomplete. Consequently, we conducted a genome-wide association study of RSV severity. METHODS: Disease severity was assessed by the ReSVinet scale, in a cohort of 251 infants aged 1 week to 1 year. Genotyping data were collected from multiple European study sites as part of the RESCEU Consortium. Linear regression models were used to assess the impact of genotype on RSV severity and gene expression as measured by microarray. RESULTS: While no SNPs reached the genome-wide statistical significance threshold (P < 5 × 10-8), we identified 816 candidate SNPs with a P-value of <1 × 10-4. Functional annotation of candidate SNPs highlighted genes relevant to neutrophil trafficking and cytoskeletal functions, including LSP1 and RAB27A. Moreover, SNPs within the RAB27A locus significantly altered gene expression (false discovery rate, FDR P < .05). CONCLUSIONS: These findings may provide insights into genetic mechanisms driving severe RSV infection, offering biologically relevant information for future investigations.

Feature-specific quantile normalization and feature-specific mean-variance normalization deliver robust bi-directional classification and feature selection performance between microarray and RNAseq data.

BACKGROUND: Cross-platform normalization seeks to minimize technological bias between microarray and RNAseq whole-transcriptome data. Incorporating multiple gene expression platforms permits external validation of experimental findings, and augments training sets for machine learning models. Here, we compare the performance of Feature Specific Quantile Normalization (FSQN) to a previously used but unvalidated and uncharacterized method we label as Feature Specific Mean Variance Normalization (FSMVN). We evaluate the performance of these methods for bidirectional normalization in the context of nested feature selection. RESULTS: FSQN and FSMVN provided clinically equivalent bidirectional model performance with and without feature selection for colon CMS and breast PAM50 classification. Using principal component analysis, we determine that these methods eliminate batch effects related to technological platforms. Without feature selection, no statistical difference was identified between the performance of FSQN and FSMVN of cross-platform data compared to within-platform distributions. Under optimal feature selection conditions, balanced accuracy was FSQN and FSMVN were statistically equivalent to the within-platform distribution performance in multivariable linear regression analysis. FSQN and FSMVN also provided similar performance to within-platform distributions as the number of selected genes used to create models decreases. CONCLUSIONS: In the context of generating supervised machine learning classifiers for molecular subtypes, FSQN and FSMVN are equally effective. Under optimal modeling conditions, FSQN and FSMVN provide equivalent model accuracy performance on cross-platform normalization data compared to within-platform data. Using cross-platform data should still be approached with caution as subtle performance differences may exist depending on the classification problem, training, and testing distributions.

Identification of biomarkers in multiple myeloma: A comprehensive study combining microarray analysis and Mendelian randomization.

Despite remarkable advancements in the treatment of multiple myeloma (MM), relapse remains a challenge. However, the mechanisms underlying this disease remain unclear. This study aimed to identify potential biomarkers that could open new avenues for MM treatment. Microarray data and clinical characteristics of patients with MM were obtained from the Gene Expression Omnibus database. Differential expression analysis and protein-protein interaction (PPI) network construction were used to identify hub genes associated with MM. Predictive performance was further assessed using receiver operating characteristic curves and nomogram construction. Functional enrichment analysis was conducted to investigate possible mechanisms. Mendelian randomization (MR) was used to evaluate the causal relationship between the crucial gene and MM risk. Topological analysis of the PPI network revealed five hub genes associated with MM, with myeloperoxidase (MPO) being the key gene owing to its highest degree and area under the curve values. MPO showed significant differences between patients with MM and controls across all datasets. Functional enrichment analysis revealed a strong association between MPO and immune-related pathways in MM. MR analysis confirmed a causal relationship between MPO and the risk of MM. By integrating microarray analysis and MR, we successfully identified and validated MPO as a promising biomarker for MM that is potentially implicated in MM pathogenesis and progression through immune-related pathways.

Reusable glycan microarrays using a microwave assisted wet-erase (MAWE) process.

Modern studies on binding of proteins to glycans commonly involve the use of synthetic glycans and their derivatives in which a small amount of the material is covalently printed onto a functionalized slide in a glycan microarray format. While incredibly useful to explore binding interactions with many types of samples, the common techniques involve drying the slides, which leads to irreversible association of the protein to the spots on slides to which they bound, thus limiting a microarray to a single use. We have developed a new technique which we term Microwave Assisted Wet-Erase (MAWE) glycan microarrays. In this approach we image the slides under wet conditions to acquire the data, after which the slides are cleaned of binding proteins by treatment with a denaturing SDS solution along with microwave treatment. Slides cleaned in this way can be reused multiple times, and an example here shows the reuse of a single array 15 times. We also demonstrate that this method can be used for a single-array per slide or multi-array per slide platforms. Importantly, the results obtained using this technique for a variety of lectins sequentially applied to a single array, are concordant to those obtained via the classical dry approaches on multiple slides. We also demonstrate that MAWE can be used for different types of samples, such as serum for antibody binding, and whole cells, such as yeast. This technique will greatly conserve precious glycans and prolong the use of existing and new glycan microarrays.

Modeling interactions between Heparan sulfate and proteins based on the Heparan sulfate microarray analysis.

Heparan sulfate (HS), a sulfated polysaccharide abundant in the extracellular matrix, plays pivotal roles in various physiological and pathological processes by interacting with proteins. Investigating the binding selectivity of HS oligosaccharides to target proteins is essential, but the exhaustive inclusion of all possible oligosaccharides in microarray experiments is impractical. To address this challenge, we present a hybrid pipeline that integrates microarray and in silico techniques to design oligosaccharides with desired protein affinity. Using fibroblast growth factor 2 (FGF2) as a model protein, we assembled an in-house dataset of HS oligosaccharides on microarrays and developed two structural representations: a standard representation with all atoms explicit and a simplified representation with disaccharide units as "quasi-atoms." Predictive Quantitative Structure-Activity Relationship (QSAR) models for FGF2 affinity were developed using the Random Forest (RF) algorithm. The resulting models, considering the applicability domain, demonstrated high predictivity, with a correct classification rate of 0.81-0.80 and improved positive predictive values (PPV) up to 0.95. Virtual screening of 40 new oligosaccharides using the simplified model identified 15 computational hits, 11 of which were experimentally validated for high FGF2 affinity. This hybrid approach marks a significant step toward the targeted design of oligosaccharides with desired protein interactions, providing a foundation for broader applications in glycobiology.

Whole exome sequencing and polygenic assessment of a Swedish cohort with severe developmental language disorder.

Developmental language disorder (DLD) overlaps clinically, genetically, and pathologically with other neurodevelopmental disorders (NDD), corroborating the concept of the NDD continuum. There is a lack of studies to understand the whole genetic spectrum in individuals with DLD. Previously, we recruited 61 probands with severe DLD from 59 families and examined 59 of them and their families using microarray genotyping with a 6.8% diagnostic yield. Herein, we investigated 53 of those probands using whole exome sequencing (WES). Additionally, we used polygenic risk scores (PRS) to understand the within family enrichment of neurodevelopmental difficulties and examine the associations between the results of language-related tests in the probands and language-related PRS. We identified clinically significant variants in four probands, resulting in a 7.5% (4/53) molecular diagnostic yield. Those variants were in PAK2, MED13, PLCB4, and TNRC6B. We also prioritized additional variants for future studies for their role in DLD, including high-impact variants in PARD3 and DIP2C. PRS did not explain the aggregation of neurodevelopmental difficulties in these families. We did not detect significant associations between the language-related tests and language-related PRS. Our results support using WES as the first-tier genetic test for DLD as it can identify monogenic DLD forms. Large-scale sequencing studies for DLD are needed to identify new genes and investigate the polygenic contribution to the condition.

Optical genome mapping enables constitutional chromosomal aberration detection.

Chromosomal aberrations including structural variations (SVs) are a major cause of human genetic diseases. Their detection in clinical routine still relies on standard cytogenetics. Drawbacks of these tests are a very low resolution (karyotyping) and the inability to detect balanced SVs or indicate the genomic localization and orientation of duplicated segments or insertions (copy number variant [CNV] microarrays). Here, we investigated the ability of optical genome mapping (OGM) to detect known constitutional chromosomal aberrations. Ultra-high-molecular-weight DNA was isolated from 85 blood or cultured cells and processed via OGM. A de novo genome assembly was performed followed by structural variant and CNV calling and annotation, and results were compared to known aberrations from standard-of-care tests (karyotype, FISH, and/or CNV microarray). In total, we analyzed 99 chromosomal aberrations, including seven aneuploidies, 19 deletions, 20 duplications, 34 translocations, six inversions, two insertions, six isochromosomes, one ring chromosome, and four complex rearrangements. Several of these variants encompass complex regions of the human genome involved in repeat-mediated microdeletion/microduplication syndromes. High-resolution OGM reached 100% concordance compared to standard assays for all aberrations with non-centromeric breakpoints. This proof-of-principle study demonstrates the ability of OGM to detect nearly all types of chromosomal aberrations. We also suggest suited filtering strategies to prioritize clinically relevant aberrations and discuss future improvements. These results highlight the potential for OGM to provide a cost-effective and easy-to-use alternative that would allow comprehensive detection of chromosomal aberrations and structural variants, which could give rise to an era of "next-generation cytogenetics."

Next-generation cytogenetics: Comprehensive assessment of 52 hematological malignancy genomes by optical genome mapping.

Somatic structural variants (SVs) are important drivers of cancer development and progression. In a diagnostic set-up, especially for hematological malignancies, the comprehensive analysis of all SVs in a given sample still requires a combination of cytogenetic techniques, including karyotyping, FISH, and CNV microarrays. We hypothesize that the combination of these classical approaches could be replaced by optical genome mapping (OGM). Samples from 52 individuals with a clinical diagnosis of a hematological malignancy, divided into simple (<5 aberrations, n = 36) and complex (≥5 aberrations, n = 16) cases, were processed for OGM, reaching on average: 283-fold genome coverage. OGM called a total of 918 high-confidence SVs per sample, of which, on average, 13 were rare and >100 kb. In addition, on average, 73 CNVs were called per sample, of which six were >5 Mb. For the 36 simple cases, all clinically reported aberrations were detected, including deletions, insertions, inversions, aneuploidies, and translocations. For the 16 complex cases, results were largely concordant between standard-of-care and OGM, but OGM often revealed higher complexity than previously recognized. Detailed technical comparison with standard-of-care tests showed high analytical validity of OGM, resulting in a sensitivity of 100% and a positive predictive value of >80%. Importantly, OGM resulted in a more complete assessment than any previous single test and most likely reported the most accurate underlying genomic architecture (e.g., for complex translocations, chromoanagenesis, and marker chromosomes). In conclusion, the excellent concordance of OGM with diagnostic standard assays demonstrates its potential to replace classical cytogenetic tests as well as to rapidly map novel leukemia drivers.

High-Throughput Quantitative Analysis of Amino Acids in Freeze-Dried Drops Using Time-of-Flight Secondary Ion Mass Spectrometry.

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) has become a promising analytical tool for molecular profiling in biological applications. However, its ultrahigh vacuum environment and matrix effects hamper the absolute quantitation of solution samples. Herein, we present a rapid high-throughput platform for quantitative ToF-SIMS analysis of amino acids in matrix deposits formed from freeze-dried solution drops through ice sublimation on a parylene film microarray substrate. Droplets of the amino acid solutions, which were mixed with stable isotope-labeled phenylalanine (F*) of high concentration (10 mM), were loaded on wells of the microarray, then frozen and evaporated slowly below the freezing point, forming continuous solid-phase F* matrix deposits. The amino acids (≤500 µM), adequately well dispersed throughout the F* matrix deposits on each well, were quantitatively analyzed by ToF-SIMS in a rapid and high-throughput fashion. The lower limit of quantitation reached below 10 µM.

Reaping the benefits of liquid handlers for high-throughput gene expression profiling in a marine model invertebrate.

BACKGROUND: Modern high-throughput technologies enable the processing of a large number of samples simultaneously, while also providing rapid and accurate procedures. In recent years, automated liquid handling workstations have emerged as an established technology for reproducible sample preparation. They offer flexibility, making them suitable for an expanding range of applications. Commonly, such approaches are well-developed for experimental procedures primarily designed for cell-line processing and xenobiotics testing. Conversely, little attention is focused on the application of automated liquid handlers in the analysis of whole organisms, which often involves time-consuming laboratory procedures. RESULTS: Here, we present a fully automated workflow for all steps, from RNA extraction to real-time PCR processing, for gene expression quantification in the ascidian marine model Ciona robusta. For procedure validation, we compared the results obtained with the liquid handler with those of the classical manual procedure. The outcome revealed comparable results, demonstrating a remarkable time saving particularly in the initial steps of sample processing. CONCLUSIONS: This work expands the possible application fields of this technology to whole-body organisms, mitigating issues that can arise from manual procedures. By minimizing errors, avoiding cross-contamination, decreasing hands-on time and streamlining the procedure, it could be employed for large-scale screening investigations.

Protective mechanism of TCF7L1 against retinal photoreceptor cell injury following retinitis pigmentosa based on the GEO database.

Recent studies have reported the promising value of differential gene expression analysis and weighted gene coexpression network analysis (WGCNA) for identifying disease biomarkers. Based on this method, this study intends to characterize the hub genes and pathways related to retinal photoreceptor cell (PRC) injury in the context of retinitis pigmentosa (RP). A total of 53 coexpression modules were identified by WGCNA, among which lightpink4, darkolivegreen, tan4, blue2, skyblue2, and navajowhite2 ranked at the top. By analyzing the RP microarrays retrieved from the GEO database, 338 differentially expressed genes (DEGs) were identified in the RP samples. Forty-five candidate genes were selected from these DEGs by intersection with the genes in the coexpression modules. These intersection genes were subjected to GO and KEGG analyses. Furthermore, the genes and pathways involved in PRC damage were identified based on analyses utilizing GeneCards and STRING tools. Transcription factor 7-like 1 (TCF7L1, also called TCF3) was suggested to participate in the RP-associated PRC damage through the Wnt signaling pathway. It was validated in a blue light-irradiated cell model that TCF7L1 overexpression boosted PRC viability and repressed apoptosis. Inhibition of the Wnt signaling pathway also contributed to protective effects. Together, the data mentioned above supported the conclusion that either elevation of TCF7L1 or blockade of the Wnt signaling pathway could prevent RP progression by protecting PRCs from damage.

Diagnostic yield of chromosomal microarray in the largest Latino clinical cohort.

Copy number variants (CNVs) remain a major etiological cause of neurodevelopmental delay and congenital malformations. Chromosomal microarray analysis (CMA) represents the gold standard for CNVs molecular characterization. We applied CMA throughout the patient's clinical diagnostic workup, as the patient's medical provider requested. We collected CMA results of 3380 patients enrolled for 5 years (2016-2021). We found 830 CNVs in 719 patients with potential clinical significance, that is, (i) pathogenic, (ii) likely pathogenic, and (iii) variants of uncertain significance (VUS), from which 10.6% (predominantly involving chromosomes 15 and 22) were most likely the final cause underpinning the patients' clinical phenotype. For those associated with neurodevelopmental phenotypes, the rate of pathogenic or likely pathogenic findings among the patients with CNVs was 60.75%. When considering epileptic phenotypes, it was 59%. Interestingly, our protocol identified two gains harbored in 17q21.31 and 9q34.3, internationally classified initially as VUS. However, because of their high frequency, we propose that these two VUS be reclassified as likely benign in this widely heterogeneous phenotypic population. These results support the diagnostic yield efficiency of CMA in characterizing CNVs to define the final molecular cause of genetic diseases in this cohort of Colombian patients, the most significant sample of patients from a Latino population, and define new benign polymorphic CNVs.

Out-of-Sequence Signal 3 Paralyzes Primary CD4(+) T-Cell-Dependent Immunity.

Primary T cell activation involves the integration of three distinct signals delivered in sequence: (1) antigen recognition, (2) costimulation, and (3) cytokine-mediated differentiation and expansion. Strong immunostimulatory events such as immunotherapy or infection induce profound cytokine release causing "bystander" T cell activation, thereby increasing the potential for autoreactivity and need for control. We show that during strong stimulation, a profound suppression of primary CD4(+) T-cell-mediated immune responses ensued and was observed across preclinical models and patients undergoing high-dose interleukin-2 (IL-2) therapy. This suppression targeted naive CD4(+) but not CD8(+) T cells and was mediated through transient suppressor of cytokine signaling-3 (SOCS3) inhibition of the STAT5b transcription factor signaling pathway. These events resulted in complete paralysis of primary CD4(+) T cell activation, affecting memory generation and induction of autoimmunity as well as impaired viral clearance. These data highlight the critical regulation of naive CD4(+) T cells during inflammatory conditions.