Tn-Seq Analysis Identifies Genes Important for Yersinia pestis Adherence during Primary Pneumonic Plague

Following inhalation, Yersinia pestis rapidly colonizes the lung to establish infection during primary pneumonic plague. Although several adhesins have been identified in Yersinia spp., the factors mediating early Y. pestis adherence in the lung remain unknown. To identify genes important for Y. pestis adherence during primary pneumonic plague, we used transposon insertion sequencing (Tn-seq). Wild-type and capsule mutant (Δcaf1) Y. pestis transposon mutant libraries were serially passaged in vivo to enrich for nonadherent mutants in the lung using a mouse model of primary pneumonic plague. Sequencing of the passaged libraries revealed six mutants that were significantly enriched in both the wild-type and Δcaf1 Y. pestis backgrounds. The enriched mutants had insertions in genes that encode transcriptional regulators, chaperones, an endoribonuclease, and YPO3903, a hypothetical protein. Using single-strain infections and a transcriptional analysis, we identified a significant role for YPO3903 in Y. pestis adherence in the lung and showed that YPO3903 regulated transcript levels of psaA, which encodes a fimbria previously implicated in Y. pestis adherence in vitro. Deletion of psaA had a minor effect on Y. pes-tis adherence in the lung, suggesting that YPO3903 regulates other adhesins in addition to psaA. By enriching for mutations in genes that regulate the expression or assembly of multiple genes or proteins, we obtained screen results indicating that there may be not just one dominant adhesin but rather several factors that contribute to early Y. pestis adherence during primary pneumonic plague

Saccharomyces Cerevisiae Transcriptional Reprograming Due To Bacterial Contamination During Industrial Scale Bioethanol Production

Background: The bioethanol production system used in Brazil is based on the fermentation of sucrose from sugarcane feedstock by highly adapted strains of the yeast Saccharomyces cerevisiae. Bacterial contaminants present in the distillery environment often produce yeast-bacteria cellular co-aggregation particles that resemble yeast-yeast cell adhesion (flocculation). The formation of such particles is undesirable because it slows the fermentation kinetics and reduces the overall bioethanol yield. Results: In this study, we investigated the molecular physiology of one of the main S. cerevisiae strains used in Brazilian bioethanol production, PE-2, under two contrasting conditions: typical fermentation, when most yeast cells are in suspension, and co-aggregated fermentation. The transcriptional profile of PE-2 was assessed by RNA-seq during industrial scale fed-batch fermentation. Comparative analysis between the two conditions revealed transcriptional profiles that were differentiated primarily by a deep gene repression in the co-aggregated samples. The data also indicated that Lactobacillus fermentum was likely the main bacterial species responsible for cellular co-aggregation and for the high levels of organic acids detected in the samples. Conclusions: Here, we report the high-resolution gene expression profiling of strain PE-2 during industrial-scale fermentations and the transcriptional reprograming observed under co-aggregation conditions. 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Intercellular communication between airway epithelial cells is mediated by exosome-like vesicles

Airway epithelium structure/function can be altered by local inflammatory/immune signals, and this process is called epithelial remodeling. The mechanism by which this innate response is regulated, which causes mucin/mucus overproduction, is largely unknown. Exosomes are nanovesicles that can be secreted and internalized by cells to transport cellular cargo, such as proteins, lipids, and miRNA. The objective of this study was to understand the role exosomes play in airway remodeling through cell–cell communication. We used two different human airway cell cultures: primary human tracheobronchial (HTBE) cells, and a cultured airway epithelial cell line (Calu-3). After intercellular exosomal transfer, comprehensive proteomic and genomic characterization of cell secretions and exosomes was performed. Quantitative proteomics and exosomal miRNA analysis profiles indicated that the two cell types are fundamentally distinct. HTBE cell secretions were typically dominated by fundamental innate/protective proteins, including mucin MUC5B, and Calu-3 cell secretions were dominated by pathology-associated proteins, including mucin MUC5AC. After exosomal transfer/intake, approximately 20% of proteins, including MUC5AC and MUC5B, were significantly altered in HTBE secretions. After exosome transfer, approximately 90 miRNAs (z4%) were upregulated in HTBE exosomes, whereas Calu-3 exosomes exhibited a preserved miRNA profile. Together, our data suggest that the transfer of exosomal cargo between airway epithelial cells significantly alters the qualitative and quantitative profiles of airway secretions, including mucin hypersecretion, and the miRNA cargo of exosomes in target cells. This finding indicates that cellular information can be carried between airway epithelial cells via exosomes, which may play an important role in airway biology and epithelial remodeling

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types

Driver Fusions and Their Implications in the Development and Treatment of Human Cancers.

Gene fusions represent an important class of somatic alterations in cancer. We systematically investigated fusions in 9,624 tumors across 33 cancer types using multiple fusion calling tools. We identified a total of 25,664 fusions, with a 63% validation rate. Integration of gene expression, copy number, and fusion annotation data revealed that fusions involving oncogenes tend to exhibit increased expression, whereas fusions involving tumor suppressors have the opposite effect. For fusions involving kinases, we found 1,275 with an intact kinase domain, the proportion of which varied significantly across cancer types. Our study suggests that fusions drive the development of 16.5% of cancer cases and function as the sole driver in more than 1% of them. Finally, we identified druggable fusions involving genes such as TMPRSS2, RET, FGFR3, ALK, and ESR1 in 6.0% of cases, and we predicted immunogenic peptides, suggesting that fusions may provide leads for targeted drug and immune therapy

Machine Learning Detects Pan-cancer Ras Pathway Activation in The Cancer Genome Atlas

Precision oncology uses genomic evidence to match patients with treatment but often fails to identify all patients who may respond. The transcriptome of these \u201chidden responders\u201d may reveal responsive molecular states. We describe and evaluate a machine-learning approach to classify aberrant pathway activity in tumors, which may aid in hidden responder identification. The algorithm integrates RNA-seq, copy number, and mutations from 33 different cancer types across The Cancer Genome Atlas (TCGA) PanCanAtlas project to predict aberrant molecular states in tumors. Applied to the Ras pathway, the method detects Ras activation across cancer types and identifies phenocopying variants. The model, trained on human tumors, can predict response to MEK inhibitors in wild-type Ras cell lines. We also present data that suggest that multiple hits in the Ras pathway confer increased Ras activity. The transcriptome is underused in precision oncology and, combined with machine learning, can aid in the identification of hidden responders. Way et al. develop a machine-learning approach using PanCanAtlas data to detect Ras activation in cancer. Integrating mutation, copy number, and expression data, the authors show that their method detects Ras-activating variants in tumors and sensitivity to MEK inhibitors in cell lines

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types

Perspective on Oncogenic Processes at the End of the Beginning of Cancer Genomics

The Cancer Genome Atlas (TCGA) has catalyzed systematic characterization of diverse genomic alterations underlying human cancers. At this historic junction marking the completion of genomic characterization of over 11,000 tumors from 33 cancer types, we present our current understanding of the molecular processes governing oncogenesis. We illustrate our insights into cancer through synthesis of the findings of the TCGA PanCancer Atlas project on three facets of oncogenesis: (1) somatic driver mutations, germline pathogenic variants, and their interactions in the tumor; (2) the influence of the tumor genome and epigenome on transcriptome and proteome; and (3) the relationship between tumor and the microenvironment, including implications for drugs targeting driver events and immunotherapies. These results will anchor future characterization of rare and common tumor types, primary and relapsed tumors, and cancers across ancestry groups and will guide the deployment of clinical genomic sequencing

Somatic Mutational Landscape of Splicing Factor Genes and Their Functional Consequences across 33 Cancer Types

Hotspot mutations in splicing factor genes have been recently reported at high frequency in hematological malignancies, suggesting the importance of RNA splicing in cancer. We analyzed whole-exome sequencing data across 33 tumor types in The Cancer Genome Atlas (TCGA), and we identified 119 splicing factor genes with significant non-silent mutation patterns, including mutation over-representation, recurrent loss of function (tumor suppressor-like), or hotspot mutation profile (oncogene-like). Furthermore, RNA sequencing analysis revealed altered splicing events associated with selected splicing factor mutations. In addition, we were able to identify common gene pathway profiles associated with the presence of these mutations. Our analysis suggests that somatic alteration of genes involved in the RNA-splicing process is common in cancer and may represent an underappreciated hallmark of tumorigenesis

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although the MYC oncogene has been implicated in cancer, a systematic assessment of alterations of MYC, related transcription factors, and co-regulatory proteins, forming the proximal MYC network (PMN), across human cancers is lacking. Using computational approaches, we define genomic and proteomic features associated with MYC and the PMN across the 33 cancers of The Cancer Genome Atlas. Pan-cancer, 28% of all samples had at least one of the MYC paralogs amplified. In contrast, the MYC antagonists MGA and MNT were the most frequently mutated or deleted members, proposing a role as tumor suppressors. MYC alterations were mutually exclusive with PIK3CA, PTEN, APC, or BRAF alterations, suggesting that MYC is a distinct oncogenic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such as immune response and growth factor signaling; chromatin, translation, and DNA replication/repair were conserved pan-cancer. This analysis reveals insights into MYC biology and is a reference for biomarkers and therapeutics for cancers with alterations of MYC or the PMN. We present a computational study determining the frequency and extent of alterations of the MYC network across the 33 human cancers of TCGA. These data, together with MYC, positively correlated pathways as well as mutually exclusive cancer genes, will be a resource for understanding MYC-driven cancers and designing of therapeutics