Single-cell view into the role of microbiota shaping host immunity in the larynx.

Microbiota play a critical role in the development and training of host innate and adaptive immunity. We present the cellular landscape of the upper airway, specifically the larynx, by establishing a reference single-cell atlas, while dissecting the role of microbiota in cell development and function at single-cell resolution. We highlight the larynx's cellular heterogeneity with the identification of 16 cell types and 34 distinct subclusters. Our data demonstrate that commensal microbiota have extensive impact on the laryngeal immune system by regulating cell differentiation, increasing the expression of genes associated with host defense, and altering gene regulatory networks. We uncover macrophages, innate lymphoid cells, and multiple secretory epithelial cells, whose cell proportions and expressions vary with microbial exposure. These cell types play pivotal roles in maintaining laryngeal and upper airway health and provide specific guidance into understanding the mechanism of immune system regulation by microbiota in laryngeal health and disease.

Genomic analysis of human brain metastases treated with stereotactic radiosurgery reveals unique signature based on treatment failure.

Stereotactic radiosurgery (SRS) has been shown to be efficacious for the treatment of limited brain metastasis (BM); however, the effects of SRS on human brain metastases have yet to be studied. We performed genomic analysis on resected brain metastases from patients whose resected lesion was previously treated with SRS. Our analyses demonstrated for the first time that patients possess a distinct genomic signature based on type of treatment failure including local failure, leptomeningeal spread, and radio-necrosis. Examination of the center and peripheral edge of the tumors treated with SRS indicated differential DNA damage distribution and an enrichment for tumor suppressor mutations and DNA damage repair pathways along the peripheral edge. Furthermore, the two clinical modalities used to deliver SRS, LINAC and GK, demonstrated differential effects on the tumor landscape even between controlled primary sites. Our study provides, in human, biological evidence of differential effects of SRS across BM's.

Intermolecular Protein-Water Coupling Impedes the Coupling Between the Amide A and Amide I Mode in Interfacial Proteins.

The coupling between different vibrational modes in proteins is essential for chemical dynamics and biological functions and is linked to the propagation of conformational changes and pathways of allosteric communication. However, little is known about the influence of intermolecular protein-H2O coupling on the vibrational coupling between amide A (NH) and amide I (C═O) bands. Here, we investigate the NH/CO coupling strength in various peptides with different secondary structures at the lipid cell membrane/H2O interface using femtosecond time-resolved sum frequency generation vibrational spectroscopy (SFG-VS) in which a femtosecond infrared pump is used to excite the amide A band, and SFG-VS is used to probe transient spectral evolution in the amide A and amide I bands. Our results reveal that the NH/CO coupling strength strongly depends on the bandwidth of the amide I mode and the coupling of proteins with water molecules. A large extent of protein-water coupling significantly reduces the delocalization of the amide I mode along the peptide chain and impedes the NH/CO coupling strength. A large NH/CO coupling strength is found to show a strong correlation with the high energy transfer rate found in the light-harvesting proteins of green sulfur bacteria, which may understand the mechanism of energy transfer through a molecular system and assist in controlling vibrational energy transfer by engineering the molecular structures to achieve high energy transfer efficiency.

Probing the Molecular Structure and Dynamics of Membrane-Bound Proteins during Misfolding Processes by Sum-Frequency Generation Vibrational Spectroscopy.

Protein misfolding and amyloid formation are implicated in the protein dysfunction, but the underlying mechanism remains to be clarified due to the lack of effective tools for detecting the transient intermediates. Sum frequency generation vibrational spectroscopy (SFG-VS) has emerged as a powerful tool for identifying the structure and dynamics of proteins at the interfaces. In this review, we summarize recent SFG-VS studies on the structure and dynamics of membrane-bound proteins during misfolding processes. This paper first introduces the methods for determining the secondary structure of interfacial proteins: combining chiral and achiral spectra of amide A and amide I bands and combining amide I, amide II, and amide III spectral features. To demonstrate the ability of SFG-VS in investigating the interfacial protein misfolding and amyloid formation, studies on the interactions between different peptides/proteins (islet amyloid polypeptide, amyloid ß, prion protein, fused in sarcoma protein, hen egg-white lysozyme, fusing fusion peptide, class I hydrophobin SC3 and class II hydrophobin HFBI) and surfaces such as lipid membranes are discussed. These molecular-level studies revealed that SFG-VS can provide a unique understanding of the mechanism of interfacial protein misfolding and amyloid formation in real time, in situ and without any exogenous labeling.

Visualizing Water Monomers and Chiral OH-(H2O) Complexes Infiltrated in a Macroscopic Hydrophobic Teflon Matrix.

Insights into the interaction of fluoroalkyl groups with water are crucial to understanding the polar hydrophobicity of fluorinated compounds, such as Teflon. While an ordered hydrophobic-like 2D water layer has been demonstrated to be present on the surface of macroscopically hydrophobic fluorinated polymers, little is known about how the water infiltrates into the Teflon and what is the molecular structure of the water infiltrated into the Teflon. Using highly sensitive femtosecond sum frequency generation vibrational spectroscopy (SFG-VS), we observe for the first time that monomeric H2O and chiral OH-(H2O) complexes are present in macroscopically hydrophobic Teflon. The species are inhomogeneously distributed inside the Teflon matrix and at the Teflon surface. No water clusters or single-file water "wires" are observed in the matrix. SFG free induction decay (SFG-FID) experiments demonstrate that the OH oscillators of physically absorbed molecular water at the surface dephase on the time scale of <230 fs, whereas the water monomers and hydrated hydroxide ions infiltrated in the Teflon matrix dephase much more slowly (680-830 fs), indicating that the embedded monomeric H2O and OH-(H2O) complexes are decoupled from the outer environment. Our findings can well interpret ultrafast water permeation through fluorous nanochannels and the charging mechanism of Teflon, which may tailor the desired applications of organofluorines.

Serial KinderMiner (SKiM) discovers and annotates biomedical knowledge using co-occurrence and transformer models.

BACKGROUND: The PubMed archive contains more than 34 million articles; consequently, it is becoming increasingly difficult for a biomedical researcher to keep up-to-date with different knowledge domains. Computationally efficient and interpretable tools are needed to help researchers find and understand associations between biomedical concepts. The goal of literature-based discovery (LBD) is to connect concepts in isolated literature domains that would normally go undiscovered. This usually takes the form of an A-B-C relationship, where A and C terms are linked through a B term intermediate. Here we describe Serial KinderMiner (SKiM), an LBD algorithm for finding statistically significant links between an A term and one or more C terms through some B term intermediate(s). The development of SKiM is motivated by the observation that there are only a few LBD tools that provide a functional web interface, and that the available tools are limited in one or more of the following ways: (1) they identify a relationship but not the type of relationship, (2) they do not allow the user to provide their own lists of B or C terms, hindering flexibility, (3) they do not allow for querying thousands of C terms (which is crucial if, for instance, the user wants to query connections between a disease and the thousands of available drugs), or (4) they are specific for a particular biomedical domain (such as cancer). We provide an open-source tool and web interface that improves on all of these issues. RESULTS: We demonstrate SKiM's ability to discover useful A-B-C linkages in three control experiments: classic LBD discoveries, drug repurposing, and finding associations related to cancer. Furthermore, we supplement SKiM with a knowledge graph built with transformer machine-learning models to aid in interpreting the relationships between terms found by SKiM. Finally, we provide a simple and intuitive open-source web interface ( https://skim.morgridge.org ) with comprehensive lists of drugs, diseases, phenotypes, and symptoms so that anyone can easily perform SKiM searches. CONCLUSIONS: SKiM is a simple algorithm that can perform LBD searches to discover relationships between arbitrary user-defined concepts. SKiM is generalized for any domain, can perform searches with many thousands of C term concepts, and moves beyond the simple identification of an existence of a relationship; many relationships are given relationship type labels from our knowledge graph.

Efficient derivation of transgene-free porcine induced pluripotent stem cells enables in vitro modeling of species-specific developmental timing.

Sus scrofa domesticus (pig) has served as a superb large mammalian model for biomedical studies because of its comparable physiology and organ size to humans. The derivation of transgene-free porcine induced pluripotent stem cells (PiPSCs) will, therefore, benefit the development of porcine-specific models for regenerative biology and its medical applications. In the past, this effort has been hampered by a lack of understanding of the signaling milieu that stabilizes the porcine pluripotent state in vitro. Here, we report that transgene-free PiPSCs can be efficiently derived from porcine fibroblasts by episomal vectors along with microRNA-302/367 using optimized protocols tailored for this species. PiPSCs can be differentiated into derivatives representing the primary germ layers in vitro and can form teratomas in immunocompromised mice. Furthermore, the transgene-free PiPSCs preserve intrinsic species-specific developmental timing in culture, known as developmental allochrony. This is demonstrated by establishing a porcine in vitro segmentation clock model that, for the first time, displays a specific periodicity at ∼3.7 h, a timescale recapitulating in vivo porcine somitogenesis. We conclude that the transgene-free PiPSCs can serve as a powerful tool for modeling development and disease and developing transplantation strategies. We also anticipate that they will provide insights into conserved and unique features on the regulations of mammalian pluripotency and developmental timing mechanisms.

Targeted inhibition of BET proteins in HPV-16 associated head and neck squamous cell carcinoma reveals heterogeneous transcription response.

Integrated human papillomavirus (HPV-16) associated head and neck squamous cell carcinoma (HNSCC) tumors have worse survival outcomes compared to episomal HPV-16 HNSCC tumors. Therefore, there is a need to differentiate treatment for HPV-16 integrated HNSCC from other viral forms. We analyzed TCGA data and found that HPV+ HNSCC expressed higher transcript levels of the bromodomain and extra terminal domain (BET) family of transcriptional coregulators. However, the mechanism of BET protein-mediated transcription of viral-cellular genes in the integrated viral-HNSCC genomes needs to be better understood. We show that BET inhibition downregulates E6 significantly independent of the viral transcription factor, E2, and there was overall heterogeneity in the downregulation of viral transcription in response to the effects of BET inhibition across HPV-associated cell lines. Chemical BET inhibition was phenocopied with the knockdown of BRD4 and mirrored downregulation of viral E6 and E7 expression. Strikingly, there was heterogeneity in the reactivation of p53 levels despite E6 downregulation, while E7 downregulation did not alter Rb levels significantly. We identified that BET inhibition directly downregulated c-Myc and E2F expression and induced CDKN1A expression. Overall, our studies show that BET inhibition provokes a G1-cell cycle arrest with apoptotic activity and suggests that BET inhibition regulates both viral and cellular gene expression in HPV-associated HNSCC.

Serial KinderMiner (SKiM) Discovers and Annotates Biomedical Knowledge Using Co-Occurrence and Transformer Models.

Background: The PubMed database contains more than 34 million articles; consequently, it is becoming increasingly difficult for a biomedical researcher to keep up-to-date with different knowledge domains. Computationally efficient and interpretable tools are needed to help researchers find and understand associations between biomedical concepts. The goal of literature-based discovery (LBD) is to connect concepts in isolated literature domains that would normally go undiscovered. This usually takes the form of an A-B-C relationship, where A and C terms are linked through a B term intermediate. Here we describe Serial KinderMiner (SKiM), an LBD algorithm for finding statistically significant links between an A term and one or more C terms through some B term intermediate(s). The development of SKiM is motivated by the the observation that there are only a few LBD tools that provide a functional web interface, and that the available tools are limited in one or more of the following ways: 1) they identify a relationship but not the type of relationship, 2) they do not allow the user to provide their own lists of B or C terms, hindering flexibility, 3) they do not allow for querying thousands of C terms (which is crucial if, for instance, the user wants to query connections between a disease and the thousands of available drugs), or 4) they are specific for a particular biomedical domain (such as cancer). We provide an open-source tool and web interface that improves on all of these issues. Results: We demonstrate SKiM's ability to discover useful A-B-C linkages in three control experiments: classic LBD discoveries, drug repurposing, and finding associations related to cancer. Furthermore, we supplement SKiM with a knowledge graph built with transformer machine-learning models to aid in interpreting the relationships between terms found by SKiM. Finally, we provide a simple and intuitive open-source web interface ( https://skim.morgridge.org ) with comprehensive lists of drugs, diseases, phenotypes, and symptoms so that anyone can easily perform SKiM searches. Conclusions: SKiM is a simple algorithm that can perform LBD searches to discover relationships between arbitrary user-defined concepts. SKiM is generalized for any domain, can perform searches with many thousands of C term concepts, and moves beyond the simple identification of an existence of a relationship; many relationships are given relationship type labels from our knowledge graph.

Rare penetrant mutations confer severe risk of common diseases.

We examined 454,712 exomes for genes associated with a wide spectrum of complex traits and common diseases and observed that rare, penetrant mutations in genes implicated by genome-wide association studies confer ~10-fold larger effects than common variants in the same genes. Consequently, an individual at the phenotypic extreme and at the greatest risk for severe, early-onset disease is better identified by a few rare penetrant variants than by the collective action of many common variants with weak effects. By combining rare variants across phenotype-associated genes into a unified genetic risk model, we demonstrate superior portability across diverse global populations compared with common-variant polygenic risk scores, greatly improving the clinical utility of genetic-based risk prediction.

Rare penetrant mutations confer severe risk of common diseases.

We examined 454,712 exomes for genes associated with a wide spectrum of complex traits and common diseases and observed that rare, penetrant mutations in genes implicated by genome-wide association studies confer ∼10-fold larger effects than common variants in the same genes. Consequently, an individual at the phenotypic extreme and at the greatest risk for severe, early-onset disease is better identified by a few rare penetrant variants than by the collective action of many common variants with weak effects. By combining rare variants across phenotype-associated genes into a unified genetic risk model, we demonstrate superior portability across diverse global populations compared to common variant polygenic risk scores, greatly improving the clinical utility of genetic-based risk prediction. One sentence summary: Rare variant polygenic risk scores identify individuals with outlier phenotypes in common human diseases and complex traits.

Genomic Analysis of Human Brain Metastases Treated with Stereotactic Radiosurgery Under the Phase-II Clinical Trial (NCT03398694) Reveals DNA Damage Repair at the Peripheral Tumor Edge.

Stereotactic Radiosurgery (SRS) is one of the leading treatment modalities for oligo brain metastasis (BM), however no comprehensive genomic data assessing the effect of radiation on BM in humans exist. Leveraging a unique opportunity, as part of the clinical trial (NCT03398694), we collected post-SRS, delivered via Gamma-knife or LINAC, tumor samples from core and peripheral-edges of the resected tumor to characterize the genomic effects of overall SRS as well as the SRS delivery modality. Using these rare patient samples, we show that SRS results in significant genomic changes at DNA and RNA levels throughout the tumor. Mutations and expression profiles of peripheral tumor samples indicated interaction with surrounding brain tissue as well as elevated DNA damage repair. Central samples show GSEA enrichment for cellular apoptosis while peripheral samples carried an increase in tumor suppressor mutations. There are significant differences in the transcriptomic profile at the periphery between Gamma-knife vs LINAC.

SpotClean adjusts for spot swapping in spatial transcriptomics data.

Spatial transcriptomics is a powerful and widely used approach for profiling the gene expression landscape across a tissue with emerging applications in molecular medicine and tumor diagnostics. Recent spatial transcriptomics experiments utilize slides containing thousands of spots with spot-specific barcodes that bind RNA. Ideally, unique molecular identifiers (UMIs) at a spot measure spot-specific expression, but this is often not the case in practice due to bleed from nearby spots, an artifact we refer to as spot swapping. To improve the power and precision of downstream analyses in spatial transcriptomics experiments, we propose SpotClean, a probabilistic model that adjusts for spot swapping to provide more accurate estimates of gene-specific UMI counts. SpotClean provides substantial improvements in marker gene analyses and in clustering, especially when tissue regions are not easily separated. As demonstrated in multiple studies of cancer, SpotClean improves tumor versus normal tissue delineation and improves tumor burden estimation thus increasing the potential for clinical and diagnostic applications of spatial transcriptomics technologies.

Protein-water coupling tunes the anharmonicity of amide I modes in the interfacial membrane-bound proteins.

The diagonal anharmonicity of an amide I mode of protein backbones plays a critical role in a protein's vibrational dynamics and energy transfer. However, this anharmonicity of long-chain peptides and proteins in H2O environment is still lacking. Here, we investigate the anharmonicity of the amide I band of proteins at the lipid membrane/H2O interface using a surface-sensitive pump-probe setup in which a femtosecond infrared pump is followed by a femtosecond broadband sum frequency generation vibrational spectroscopy probe. It is found that the anharmonicity of the amide I mode in ideal α-helical and ß-sheet structures at hydrophobic environments is 3-4 cm-1, indicating that the amide I mode in ideal α-helical and ß-sheet structures is delocalized over eight peptide bonds. The anharmonicity increases as the bandwidth of the amide I mode increases due to the exposure of peptide bonds to H2O. More H2O exposure amounts lead to a larger anharmonicity. The amide I mode of the peptides with large H2O exposure amounts is localized in one to two peptide bonds. Our finding reveals that the coupling between the amide I mode and the H2O bending mode does not facilitate the delocalization of the amide I mode along the peptide chain, highlighting the impact of H2O on energy transfer and structural dynamics of proteins.

SpatialCorr identifies gene sets with spatially varying correlation structure.

Recent advances in spatially resolved transcriptomics technologies enable both the measurement of genome-wide gene expression profiles and their mapping to spatial locations within a tissue. A first step in spatial transcriptomics data analysis is identifying genes with expression that varies spatially, and robust statistical methods exist to address this challenge. While useful, these methods do not detect spatial changes in the coordinated expression within a group of genes. To this end, we present SpatialCorr, a method for identifying sets of genes with spatially varying correlation structure. Given a collection of gene sets pre-defined by a user, SpatialCorr tests for spatially induced differences in the correlation of each gene set within tissue regions, as well as between and among regions. An application to cutaneous squamous cell carcinoma demonstrates the power of the approach for revealing biological insights not identified using existing methods.

The Anna Karenina Model of ß-Cell Maturation in Development and Their Dedifferentiation in Type 1 and Type 2 Diabetes.

Loss of mature ß-cell function and identity, or ß-cell dedifferentiation, is seen in both type 1 and type 2 diabetes. Two competing models explain ß-cell dedifferentiation in diabetes. In the first model, ß-cells dedifferentiate in the reverse order of their developmental ontogeny. This model predicts that dedifferentiated ß-cells resemble ß-cell progenitors. In the second model, ß-cell dedifferentiation depends on the type of diabetogenic stress. This model, which we call the "Anna Karenina" model, predicts that in each type of diabetes, ß-cells dedifferentiate in their own way, depending on how their mature identity is disrupted by any particular diabetogenic stress. We directly tested the two models using a ß-cell-specific lineage-tracing system coupled with RNA sequencing in mice. We constructed a multidimensional map of ß-cell transcriptional trajectories during the normal course of ß-cell postnatal development and during their dedifferentiation in models of both type 1 diabetes (NOD) and type 2 diabetes (BTBR-Lepob/ob ). Using this unbiased approach, we show here that despite some similarities between immature and dedifferentiated ß-cells, ß-cell dedifferentiation in the two mouse models is not a reversal of developmental ontogeny and is different between different types of diabetes.

Normalization by distributional resampling of high throughput single-cell RNA-sequencing data.

MOTIVATION: Normalization to remove technical or experimental artifacts is critical in the analysis of single-cell RNA-sequencing experiments, even those for which unique molecular identifiers are available. The majority of methods for normalizing single-cell RNA-sequencing data adjust average expression for library size (LS), allowing the variance and other properties of the gene-specific expression distribution to be non-constant in LS. This often results in reduced power and increased false discoveries in downstream analyses, a problem which is exacerbated by the high proportion of zeros present in most datasets. RESULTS: To address this, we present Dino, a normalization method based on a flexible negative-binomial mixture model of gene expression. As demonstrated in both simulated and case study datasets, by normalizing the entire gene expression distribution, Dino is robust to shallow sequencing, sample heterogeneity and varying zero proportions, leading to improved performance in downstream analyses in a number of settings. AVAILABILITY AND IMPLEMENTATION: The R package, Dino, is available on GitHub at https://github.com/JBrownBiostat/Dino. The Dino package is further archived and freely available on Zenodo at https://doi.org/10.5281/zenodo.4897558. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

CHARTS: a web application for characterizing and comparing tumor subpopulations in publicly available single-cell RNA-seq data sets.

BACKGROUND: Single-cell RNA-seq (scRNA-seq) enables the profiling of genome-wide gene expression at the single-cell level and in so doing facilitates insight into and information about cellular heterogeneity within a tissue. This is especially important in cancer, where tumor and tumor microenvironment heterogeneity directly impact development, maintenance, and progression of disease. While publicly available scRNA-seq cancer data sets offer unprecedented opportunity to better understand the mechanisms underlying tumor progression, metastasis, drug resistance, and immune evasion, much of the available information has been underutilized, in part, due to the lack of tools available for aggregating and analysing these data. RESULTS: We present CHARacterizing Tumor Subpopulations (CHARTS), a web application for exploring publicly available scRNA-seq cancer data sets in the NCBI's Gene Expression Omnibus. More specifically, CHARTS enables the exploration of individual gene expression, cell type, malignancy-status, differentially expressed genes, and gene set enrichment results in subpopulations of cells across tumors and data sets. Along with the web application, we also make available the backend computational pipeline that was used to produce the analyses that are available for exploration in the web application. CONCLUSION: CHARTS is an easy to use, comprehensive platform for exploring single-cell subpopulations within tumors across the ever-growing collection of public scRNA-seq cancer data sets. CHARTS is freely available at charts.morgridge.org.

CB2 improves power of cell detection in droplet-based single-cell RNA sequencing data.

An important challenge in pre-processing data from droplet-based single-cell RNA sequencing protocols is distinguishing barcodes associated with real cells from those binding background reads. Existing methods test barcodes individually and consequently do not leverage the strong cell-to-cell correlation present in most datasets. To improve cell detection, we introduce CB2, a cluster-based approach for distinguishing real cells from background barcodes. As demonstrated in simulated and case study datasets, CB2 has increased power for identifying real cells which allows for the identification of novel subpopulations and improves the precision of downstream analyses.