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Pancreatic ductal adenocarcinoma (PDAC) is projected to be the second leading cause of cancer mortality in the United States by 2030. Given that resistance to cytotoxic therapy is pervasive, there is a critical need to elucidate salient gene expression programs and spatial relationships among malignant and stromal cells in the tumor microenvironment (TME), particularly in residual disease. We developed and applied a single-nucleus RNA-seq (snRNA-seq) technique to 43 banked frozen primary PDAC specimens that either received neoadjuvant therapy (n=25) or were treatment-naïve (n=18). We discovered expression programs across malignant cell and fibroblast profiles that formed the basis for a refined molecular taxonomy, including a novel neural-like progenitor (NRP) malignant program enriched with neoadjuvant treatment in tumors and organoids, and associated with the worst prognosis in bulk profiles from independent cohorts. To elucidate how neoadjuvant treatment and cancer cell- and fibroblast-intrinsic programs modulate the composition of multicellular neighborhoods, we performed spatial profiling with the GeoMx[1] platform (NanoString) on 21 formalin-fixed paraffin-embedded sections using the human whole transcriptome atlas (WTA). Each tumor showed intra-tumoral heterogeneity in tissue architecture and regions of interest (ROIs) with diverse patterns of neoplastic cells, cancer-associated fibroblasts (CAFs), and immune cells were selected for profiling. We deconvolved the WTA data with our snRNA-seq cell type signatures and mapped expression programs onto the tumor architecture to reveal three distinct multicellular neighborhoods, which we annotated as classical, squamoid-basaloid, and treatment-enriched. The observed enrichment in post-treatment residual disease of multiple spatially-defined receptor-ligand interactions and a neighborhood featuring the NRP program, neurotropic CAF program, and CD8+ T cells may open new therapeutic opportunities. Next, we mapped malignant/CAF programs and immune cell subsets at single-cell spatial resolution by performing spatial molecular imaging (SMI[2]; NanoString CosMx) using a panel of 960 RNA targets on a subset of seven tumors (2 untreated, 5 treated) and captured over 200,000 cells with an average of more than 450 transcripts detected per cell. Correlating ROIs from whole-transcriptome DSP to matched fields of view in kiloplex SMI enabled further dissection of PDAC architecture and treatment-associated remodeling of cell type distributions and receptor-ligand interactions. Ongoing functional studies have begun to elucidate the key regulatory elements underlying the distinct treatment-associated NRP malignant program and its interactions with the TME. Overall, the complementary combination of snRNA-seq, whole-transcriptome DSP, and kiloplex SMI provides a high-resolution molecular framework that can be harnessed to augment precision oncology efforts in pancreatic cancer. [1] GeoMx DSP is for Research Use Only and not for use in diagnostic procedures. [2] CosMx SMI is for Research Use Only and not for use in diagnostic procedures. Citation Format: William L. Hwang, Karthik A. Jagadeesh, Jimmy A. Guo, Hannah I. Hoffman, Carina Shiau, Jennifer Su, Payman Yadollahpour, Jason W. Reeves, Youngmi Kim, Sean Kim, Mark Gregory, Prajan Divakar, Eric Miller, Michael Rhodes, Sarah Warren, Erroll Rueckert, Kit Fuhrman, Daniel R. Zollinger, Robin Fropf, Joseph M. Beechem, Arnav Mehta, Toni Delorey, Cristin McCabe, Jaimie L. Barth, Piotr Zelga, Cristina R. Ferrone, Motaz Qadan, Keith D. Lillemoe, Rakesh K. Jain, Jennifer Y. Wo, Theodore S. Hong, Ramnik Xavier, Orit Rozenblatt-Rosen, Andrew J. Aguirre, Carlos Fernandez-Del Castillo, Andrew S. Liss, Mari Mino-Kenudson, David T. Ting, Tyler Jacks, Aviv Regev. Multicellular spatial community featuring a novel neuronal-like malignant phenotype is enriched in pancreatic cancer after neoadjuvant chemotherapy and radiotherapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr SY12-04.

Background Complex interactions between the gut microbiome and immune cells in infancy are thought to be part of the pathogenesis for the marked rise in pediatric allergic diseases, particularly food allergies. Food protein-induced allergic proctocolitis (FPIAP) is commonly the earliest recognized non-immunoglobulin E (IgE)-mediated food allergy in infancy and is associated with atopic dermatitis and subsequent IgE-mediated food allergy later in childhood. Yet, a large prospective longitudinal study of the microbiome of infants with FPIAP, including samples prior to symptom onset, has not been done. Results Here, we analyzed 954 longitudinal samples from 160 infants in a nested case-control study (81 who developed FPIAP and 79 matched controls) from 1 week to 1 year of age by 16S rRNA ribosomal gene sequencing as part of the Gastrointestinal Microbiome and Allergic Proctocolitis (GMAP) study. We found key differences in the microbiome of infants with FPIAP, most strongly a higher abundance of a genus of Enterobacteriaceae and a lower abundance of a family of Clostridiales during the symptomatic period. We saw some of these significant taxonomic differences even prior to symptom onset. There were no consistent longitudinal differences in richness or stability diversity metrics between infants with FPIAP and healthy controls. Conclusions This study is the first to identify differences in the infant gut microbiome in children who develop FPIAP, some even before they develop symptoms, and provides a foundation for more mechanistic investigation into the pathogenesis of FPIAP and subsequent food allergic diseases in childhood.

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal and treatment-refractory cancer. Molecular stratification in pancreatic cancer remains rudimentary and does not yet inform clinical management or therapeutic development. Here, we construct a high-resolution molecular landscape of the cellular subtypes and spatial communities that compose PDAC using single-nucleus RNA sequencing and whole-transcriptome digital spatial profiling (DSP) of 43 primary PDAC tumor specimens that either received neoadjuvant therapy or were treatment naive. We uncovered recurrent expression programs across malignant cells and fibroblasts, including a newly identified neural-like progenitor malignant cell program that was enriched after chemotherapy and radiotherapy and associated with poor prognosis in independent cohorts. Integrating spatial and cellular profiles revealed three multicellular communities with distinct contributions from malignant, fibroblast and immune subtypes: classical, squamoid-basaloid and treatment enriched. Our refined molecular and cellular taxonomy can provide a framework for stratification in clinical trials and serve as a roadmap for therapeutic targeting of specific cellular phenotypes and multicellular interactions.

Mucosal and barrier tissues such as the gut, lung or skin, are composed of a complex network of cells and microbes forming a tight niche that prevents pathogen colonization and supports host-microbiome symbiosis. Characterizing these networks at high molecular and cellular resolution is crucial for our understanding of homeostasis and disease. Spatial transcriptomics has emerged as a key technology to positionally profile RNAs at high resolution in tissues. Here, we present spatial host-microbiome sequencing, an all-sequencing based approach that captures tissue histology, polyadenylated RNAs and bacterial 16S sequences directly from tissues on spatially barcoded glass surfaces. We apply our approach to the mouse gut as a model system, use a novel deep learning approach for data mapping and detect spatial niches impacted by microbial biogeography. Spatial host-microbiome sequencing should enhance study of native host-microbe interactions in health and disease.

The gastrointestinal tract is constitutively exposed to proteases including trypsin, a serine protease originating from the pancreas1. Elevated trypsin levels in the large intestine have been implicated in pathological conditions including infectious and inflammatory bowel disease2-4. Here we show that trypsin is regulated via degradation by members of the gut microbiota. After passing through the small intestine, trypsin activity is markedly reduced in the caecum of specific pathogen-free (SPF) mice, whereas germ-free (GF) mice have high luminal trypsin levels. We have successfully identified and isolated Paraprevotella strains from the faecal microbiome of healthy human donors as potent trypsin-degrading commensals. Mechanistically, Paraprevotella recruit trypsin to the bacterial surface through type IX secretion system-dependent polysaccharide-anchoring proteins and promote trypsin autolysis. Paraprevotella colonization protects IgA from trypsin degradation and enhances the effectiveness of oral vaccines against Citrobacter rodentium. Moreover, Paraprevotella colonization inhibits lethal infection with murine hepatitis virus, a mouse coronavirus dependent on trypsin and trypsin-like proteases for entry into host cells5,6. Congruently, carriage of putative genes involved in trypsin degradation in the gut microbiome was associated with reduced diarrhoea severity in patients with SARS-CoV2 infection. Therefore, trypsin-degrading commensal colonization may contribute to the maintenance of intestinal homeostasis and protection from pathogen infection.

SARS-CoV-2 infection leads to a broad range of outcomes and immune responses, with the development of neutralizing antibodies generally correlated with protection against reinfection. Here, we have characterized both neutralizing activity and T cell responses in a cluster of subjects with mild disease linked to a single spreading event. Surprisingly, we observed sex-specific associations between spike- and particularly nucleoprotein-specific T cell responses and neutralization, with pro-inflammatory cytokines being linked to higher titers only in males. Using single cell immunoprofiling, which provided matched transcriptome and T-cell receptor (TCR) profiles in restimulated CD4 + and CD8 + cells from these subjects, we identified differences in type I IFN signaling that may underlie this difference in antibody generation. Finally, we also identified several TCRs associated with cytokine producing T cells. Altogether, our work maps the breadth of immunological outcomes of SARS-CoV2 infections and highlight the potential role of sex-specific feedback loops during the generation of neutralizing antibodies.

Microbiome studies of inflammatory bowel diseases (IBD) have achieved a scale for meta-analysis of dysbioses among populations. To enable microbial community meta-analyses generally, we develop MMUPHin for normalization, statistical meta-analysis, and population structure discovery using microbial taxonomic and functional profiles. Applying it to ten IBD cohorts, we identify consistent associations, including novel taxa such as Acinetobacter and Turicibacter, and additional exposure and interaction effects. A single gradient of dysbiosis severity is favored over discrete types to summarize IBD microbiome population structure. These results provide a benchmark for characterization of IBD and a framework for meta-analysis of any microbial communities.

Complex interactions between the gut microbiome and immune cells in infancy are thought to be part of the pathogenesis for the marked rise in pediatric allergic diseases, particularly food allergies. Food protein-induced allergic proctocolitis (FPIAP) is commonly the earliest recognized non-IgE-mediated food allergy in infancy and is associated with atopic dermatitis and subsequent IgE-mediated food allergy later in childhood. Yet, a large prospective longitudinal study of the microbiome of infants with FPIAP (including samples prior to symptom onset) has not been done. Here we analyzed 954 longitudinal samples from 160 infants in a nested case-control study (81 who developed FPIAP, and 79 matched controls) from 1 week to 1 year of age by 16S rRNA ribosomal gene sequencing as part of the Gastrointestinal Microbiome and Allergic Proctocolitis (GMAP) Study. We confirmed that vaginally delivered infants had a greater abundance of Bacteroides, infants who received any breast milk had a greater abundance of Bifidobacterium, and that overall bacterial richness rose over the first year. We found key differences in the microbiome of infants with FPIAP, most strongly a higher abundance of a genus of Enterobacteriaceae and a lower abundance of a family of Clostridiales during the symptomatic period, as well as other key taxonomic differences across symptom states including prior to symptom onset. This study contributes to the larger body of literature examining structural development of the early life gut microbiome and provides a foundation for more mechanistic investigation into the pathogenesis and microbial effects on FPIAP and subsequent food allergic diseases in childhood.

Pangenome analysis on two distinct clades of R. gnavus. Pangenome analysis of R. gnavus using PanPhlAn showing the presence or absence of every gene in the R. gnavus pangenome. The x-axis shows metagenomic samples as well as R. gnavus reference genomes; the y-axis shows gene clusters from the R. gnavus pangenome. Clustering the results reveals two clades of R. gnavus, which we call clade 1 and clade 2. Using metagenomic samples from LSS/Lewis/HMP cohorts, we found that R. gnavus strains from all healthy adult controls and some IBD samples were functionally similar to R. gnavus clade 1. On the other hand, only IBD samples had R. gnavus strains which were functionally similar to R. gnavus clade 2. The red transparent rectangle contains genes enriched in R. gnavus group IBD. The green transparent rectangle contains genes many genes missing from R. gnavus group IBD. (PDF 2396 kb)

Pangenome analysis on two distinct clades of R. gnavus. Pangenome analysis of R. gnavus using PanPhlAn showing the presence or absence of every gene in the R. gnavus pangenome. The x-axis shows metagenomic samples as well as R. gnavus reference genomes; the y-axis shows gene clusters from the R. gnavus pangenome. Clustering the results reveals two clades of R. gnavus, which we call clade 1 and clade 2. Using metagenomic samples from LSS/Lewis/HMP cohorts, we found that R. gnavus strains from all healthy adult controls and some IBD samples were functionally similar to R. gnavus clade 1. On the other hand, only IBD samples had R. gnavus strains which were functionally similar to R. gnavus clade 2. The red transparent rectangle contains genes enriched in R. gnavus group IBD. The green transparent rectangle contains genes many genes missing from R. gnavus group IBD. (PDF 2396 kb)

Distributions of selected clinical variables in our data (colorful) and as collected by the human microbiome project [22] (gray). (PDF 3586 kb)

Hierarchical clustering of the (A) phylogenetic and (B) functional profiles of the samples. Profiles (rows) are colored by the cluster assignment (kâ =â 15), and samples (columns) are colored by the twin variable. The medoid profile per cluster (written in black) was chosen as a representative and shown on Fig. 2. (PDF 3597 kb)

Marker genes abundance profiles for the Human Microbiome Project data [21] of (A) Akkermansia muciniphila and (B) Dialister invisus, formatted as in Fig. 5b and c with differential markers colored in orange. (PDF 3628 kb)

Scatter plots of data collected for the Human Microbiome Project [22] for (A) blood pressure vs. M. smithii abundance and (B) BMI vs. E. sireum. In our data, both these associations have a threshold-like behavior, however this is not observed in the HMP data [21]. (PDF 3520 kb)

Correlation between selected clinical variables in our data. (PDF 3598 kb)

The average microbial prevalence (A) and abundance (B) as compared between the Human Microbiome Project [21] and our data. Prevalence was defined as percent of samples with >0.001 relative abundance for each species, and average abundance was calculated only for samples passing that criterion. (PDF 3577 kb)

Distributions of selected clinical variables in our data (colorful) and as collected by the human microbiome project [22] (gray). (PDF 3586 kb)

The strain similarity of present microbes in our data, estimated by the Bray-Curtis distance between self-samples over time (x-axis) and twin samples (y-axis). Species highlighted in bold are shown in detail on Fig. 5b and c and Additional file 13: Figure S10. (PDF 3544 kb)

Correlation between selected clinical variables in our data. (PDF 3598 kb)

A correlation matrix comparing the microbial and functional profiles, similar to that show in Fig. 4, calculated for the Human Microbiome Project [21] data. (PDF 3729 kb)

The average microbial prevalence (A) and abundance (B) as compared between the Human Microbiome Project [21] and our data. Prevalence was defined as percent of samples with >0.001 relative abundance for each species, and average abundance was calculated only for samples passing that criterion. (PDF 3577 kb)

Hierarchical clustering of the (A) phylogenetic and (B) functional profiles of the samples. Profiles (rows) are colored by the cluster assignment (kâ =â 15), and samples (columns) are colored by the twin variable. The medoid profile per cluster (written in black) was chosen as a representative and shown on Fig. 2. (PDF 3597 kb)

Scatter plots of selected associations of microbial (x-axis) and functional (y-axis) profiles. Each plot represents a single entry in the correlation matrix at Fig. 4, and the R^2 value of this correlation dictates the color of the matrix entry. (PDF 3643 kb)

Emerging evidence suggests that microRNA (miRNA) expression signatures in cancer may have important diagnostic, prognostic, and therapeutic value, but there is no data on miRNA expression in chordoma. The purpose of this study was to identify the role of miRNAs in human chordoma. We analyzed miRNA expression in chordoma-derived cell lines and chordoma tissue by using miRNA microarray technology with unsupervised hierarchical clustering analysis. The relative expression levels of these miRNAs were confirmed by real-time quantitative RT-PCR and Northern blot analysis. To characterize the potential role of miRNA-1, miRNA-1 was stably transfected into a chordoma cell line, UCH1. The expression of miRNA-1 targeted gene Met in chordoma tissues was also studied. We observe that human chordoma tissues and cell lines can be distinguished from normal muscle tissue by comparing miRNA expression profiles. Several miRNAs were differentially expressed in chordoma cell lines compared to controls, and similar expression patterns were found in primary chordoma tissues. Importantly, we were able to show for the first time, to our knowledge, that expression of miRNA-1 and miRNA-206, two miRNAs implicated in a number of other cancer types, were markedly decreased in both chordoma tissues and cell lines. When chordoma cell lines were transfected with miRNA-1, downregulation of known miRNA-1 targets was observed. These targets included Met and HDAC4-two genes that were observed to be overexpressed in chordoma. Our results demonstrate that some miRNAs are differentially expressed in chordoma and, in particular, miRNA-1 may have a functional effect on chordoma tumor pathogenesis.

The cytoplasmic innate immune receptor NOD2 is required for antibacterial immune response. Optimal regulation of NOD2 is essential for controlling bacterial infections and inflammatory disorders. Recently, ubiquitination has emerged as an important posttranslational modification required to control NOD2-signaling. Yet, the specific ubiquitination mechanisms that negatively regulate NOD2-signalling during both acute and chronic (e.g., MDP-tolerance) stimulations are incompletely understood. Through a human genome-wide-RNA-interference screen, we identified an E3-ubiquitin ligase {termed as NOD2 Signaling Regulator (NSR)} as a novel negative-regulator of NOD2-mediated NF-KB, cytokine, and antibacterial responses. Mechanistically, NSR induced K48-linked ubiquitination of RIP2 (NOD2-adaptor), and promoted RIP2 degradation through the endoplasmic-reticulum-associated-degradation (ERAD) pathway. We identified the endoplasmic reticulum as a new regulatory site for NOD2-signaling, where RIP2 was localized and interacted with NSR and ERAD. In vivo NSR knock-down in mice resulted in increased NOD2-mediated NF-KB activation. NSR-mediated RIP2 degradation was critically required for MDP-tolerance induction both in vivo and in vitro. Thus, this study unravels a key molecular mechanism by which NOD2 pathway is attenuated, and provide new directions to understand the role of NOD2 pathway in both infectious and inflammatory diseases.

Lipid rafts are dynamic structures made up of proteins and lipids that float freely within the liquid-disordered bilayer of cellular membranes and have the ability to cluster to form larger, more-ordered platforms. These clustered structures have been identified in all cell types and have been shown to play critical roles in signal transduction, cellular transport, and cell-cell communication. Lipid rafts also have been implicated in facilitating bacterial/viral entry into host cells and in human disease, highlighting the significance of understanding the role lipid rafts play in physiological and pathological signaling outcomes. In this chapter, we provide protocols to isolate lipid rafts from polarized and nonpolarized cells and outline novel technologies to analyze signal transduction cascades in vivo.

The pace of biological investigation has been greatly accelerated by technologies that permit comprehensive inventory of gene expression under diverse conditions. We have been developing and using approaches to understand gene function that are based on functional assessments of gene activity, using automated expression cloning followed by gene knockdown using shRNA or by gene knockout. Some recent developments in expression screens and methods to expedite the formation of targeted mutations in mice are discussed. A resource for large scale quantitative profiling of mRNA abundance is also described.

A classic hallmark in the autophagic process is the reversible conjugation of LC3 to the autophagosomal membrane. Autophagy has been shown to target pathogens that reside in the cytosol or within autophagosomes for lysosomal degradation. Therefore, components of the autophagy pathway can participate in the effective elimination of viruses, bacteria, and parasites, though their role in fungal immunity is poorly understood. We show that activation of Dectin-1 by its ligand β-1,3-glucan leads to recruitment of LC3 to the phagosome and describe multiple intermediate signaling steps. The cytoplasmic tail of Dectin-1 contains an immunoreceptor tyrosine-based activation (ITAM)-like motif which is phosphorylated in response to binding of β-1,3-glucan, leading to relocation from the cell surface to the phagosome. We show that Dectin-1 activation by intact fungal pathogens or by chemically defined ligands leads to LC3 recruitment to the phagosomal membrane. Mutation of the tyrosine phosphorylation site in Dectin-1 abolished LC3 recruitment, suggesting the involvement of spleen tyrosine kinase (Syk) in this subcellular redistribution of LC3. Likewise, impairment of ROS generation by knockout of NADPH oxidase (NCF4) downstream of Syk inhibited LC3 recruitment to the phagosome, whereas knockout of Card9 did not affect LC3 recruitment. These observations suggest a previously unobserved signaling pathway connects Dectin-1 to the autophagic machinery via Syk signaling and ROS generation.

Lymphocyte migration is at the heart of normal and pathological immune responses in the intestinal mucosa. We and others have shown that, in addition to activating lymphocytes, gut-associated dendritic cells (GALT-DC) can metabolize dietary vitamin A into all-trans retinoic acid (RA), which is required for inducing gut-tropic lymphocytes and IgA antibody-secreting cells (IgA-ASC). GALT-DC from mice deficient in the intracellular signaling adaptor MyD88, which lack most Toll-like receptor (TLR) signals, were significantly impaired in their capacity to induce gut-homing T cells and IgA-ASC and expressed low levels of retinal dehydrogenases (RALDH), which are critical enzymes for RA biosynthesis. MyD88-/- mice were also impaired in generating gut-tropic T cells upon immunization and exhibited low numbers of intestinal IgA-ASC. Pre-treatment of murine spleen-DC and human monocyte-derived DC with a TLR1/2 agonist was sufficient to induce RALDH and to confer these extra-intestinal DC with the capacity to induce gut-homing lymphocytes and IgA-ASC via a mechanism dependent on MyD88 and c-JNK/MAPK. Thus, MyD88-dependent TLR signals are both necessary and sufficient to educate DC with gut-specific imprinting properties.

Sequencing and variant call quality metrics. A. Percentage of contamination and chimeric reads. B. Various metrics based on variant calling. Figure S2. Distribution of genic and intergenic variants. Figure S3. Combined PCA between 1000 Genomes and HMP300. Figure S4. Correlation between high-level genetic features and microbial species in non-gut body sites. Figure S5. Correlation between high-level genetic features and microbial metabolic pathways in non-gut body sites. Figure S6. Quantile-quantile plots for association analysis between microbial species and GWAS Catalog SNVs. Figure S7. Quantile-quantile plots for association analysis between microbial metabolic pathways and GWAS Catalog SNVs. Figure S8. Putative SNV-microbial species associations. Figure S9. Putative SNV-microbial metabolic pathway associations. Table S1. Raw statistics for genetic principal component analysis. Table S2. Top SNV and microbiome association results. (PDF 1953 kb)

Supplementary tables and figures. Contains supplementary tables and figures referenced in the paper. (DOCX 838 kb)

Sequencing and variant call quality metrics. A. Percentage of contamination and chimeric reads. B. Various metrics based on variant calling. Figure S2. Distribution of genic and intergenic variants. Figure S3. Combined PCA between 1000 Genomes and HMP300. Figure S4. Correlation between high-level genetic features and microbial species in non-gut body sites. Figure S5. Correlation between high-level genetic features and microbial metabolic pathways in non-gut body sites. Figure S6. Quantile-quantile plots for association analysis between microbial species and GWAS Catalog SNVs. Figure S7. Quantile-quantile plots for association analysis between microbial metabolic pathways and GWAS Catalog SNVs. Figure S8. Putative SNV-microbial species associations. Figure S9. Putative SNV-microbial metabolic pathway associations. Table S1. Raw statistics for genetic principal component analysis. Table S2. Top SNV and microbiome association results. (PDF 1953 kb)

Supplementary tables and figures. Contains supplementary tables and figures referenced in the paper. (DOCX 838 kb)