To realize the full potential of immunotherapy, it is critical to understand the drivers of tumor infiltration by immune cells. Previous studies have linked immune infiltration with tumor neoantigen levels, but the broad applicability of this concept remains unknown. Here, we find that while this observation is true across cancers characterized by recurrent mutations, it does not hold for cancers driven by recurrent copy number alterations, such as breast and pancreatic tumors. To understand immune invasion in these cancers, we developed an integrative multi-omics framework, identifying the DNA damage response protein ATM as a driver of cytokine production leading to increased immune infiltration. This prediction was validated in numerous orthogonal datasets, as well as experimentally in vitro and in vivo by cytokine release and immune cell migration. These findings demonstrate diverse drivers of immune cell infiltration across cancer lineages and may facilitate the clinical adaption of immunotherapies across diverse malignancies.
ABSTRACT For Candida albicans , evidence has suggested that the mating pheromones activate not only the mating response in mating-competent opaque cells but also a unique response in mating-incompetent white cells that includes increased cohesion and adhesion, enhanced biofilm formation, and expression of select mating-related and white cell-specific genes. On the basis of a recent microarray analysis comparing changes in the global expression patterns of white cells in two strains in response to α-pheromone, however, skepticism concerning the validity and generality of the white cell response has been voiced. Here, we present evidence that the response occurs in all tested media (Lee's, RPMI, SpiderM, yeast extract-peptone-dextrose, and a synthetic medium) and in all of the 27 tested strains, including a / a and α/α strains, derivatives of the common laboratory strain SC5314, and representatives from all of the five major clades. The white cell response to pheromone is therefore a general characteristic of MTL -homozygous strains of C. albicans .
Abstract High‐throughput binary protein interaction mapping is continuing to extend our understanding of cellular function and disease mechanisms. However, we remain one or two orders of magnitude away from a complete interaction map for humans and other major model organisms. Completion will require screening at substantially larger scales with many complementary assays, requiring further efficiency gains in proteome‐scale interaction mapping. Here, we report Barcode Fusion Genetics‐Yeast Two‐Hybrid ( BFG ‐Y2H), by which a full matrix of protein pairs can be screened in a single multiplexed strain pool. BFG ‐Y2H uses Cre recombination to fuse DNA barcodes from distinct plasmids, generating chimeric protein‐pair barcodes that can be quantified via next‐generation sequencing. We applied BFG ‐Y2H to four different matrices ranging in scale from ~25 K to 2.5 M protein pairs. The results show that BFG ‐Y2H increases the efficiency of protein matrix screening, with quality that is on par with state‐of‐the‐art Y2H methods.
The ligno-cellulosic biomass is the most abundant and also the most renewable biomaterial on earth. The development of alternative energy technology such as bioconversion of biomass is critically important because of the rising prices of crude oil, security issues regarding the oil supply and air pollution. Many microorganisms in nature are able to attack and degrade lignin, thus making access to cellulose easy. Such organisms are abundantly found in forest leaf litter/composts and especially include the wood rotting fungi, actinomycetes and bacteria. These microorganisms possess enzyme systems to attack, depolymerize and degrade the polymers in lignocellulosic substrates. Fungi such as Trichoderma reesei and Trichoderma harzianumproduce large amounts of extracellular cellulolytic enzymes, whereas higher fungi such as basidiomycetes (e.g. Phanerochaete chrysosporium, Coriolus versicolor, Pleurotus ostreatus, Fusarium sp.) have unique oxidative systems which together with ligninolytic enzymes are responsible for lignocellulose degradation. These lignocellulolytic fungi can prove extremely useful in delignification of lignocellulosic biomass. The present work reports the comparison in chemical compostion of paddy straw pretreated with Coriolus versicolor MTCC 138 (standard) andFusarium sp. (isolated from compost/digested slurry/ plant debris). Lignin loss observed was 27.1 and 17.5% on 20th day in paddy straw pretreated with C. versicolor MTCC 138 and Fusarium sp., respectively. Thus, fungal pretreatment for lignocellulosic substrate can be developed to facilitate efficient degradation of lignocellulosic biomass.
Key words: Delignification, fungal pretreatment, lignocellulosic biomass.
<p>Supplementary Figures 1-10. Figure-S1. Immune score prediction of response to PD-1 immune checkpoint blockade; Figure-S2. Immune mutation burden in cancer; Figure-S3. Transcriptome alteration of immunome in cancer; Figure-S4. Enrichment of differentially expressed genes (DEGs) in immunological gene signatures; Figure-S5. Immune infiltration score in cancer; Figure-S6. Candidate immune responder genes in cancer; Figure-S7. The ROC curves for different signatures in two datasets; Figure-S8. Barplots showing the AUCs of different signatures across 17 datasets; Figure-S9. CNV alteration and QTL of immune-related genes; Figure-S10. QTLs of immune-related genes across cancer types.</p>
// Yongsheng Li 1, * , Nidhi Sahni 1, 2 , Song Yi 1, * 1 Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA 2 Graduate Program in Structural and Computational Biology and Molecular Biophysics, Baylor College of Medicine, Houston, TX 77030, USA * These authors contributed equally to this work Correspondence to: Nidhi Sahni, email: Nsahni@mdanderson.org Song Yi, email: Syi2@mdanderson.org Keywords: comparative network analysis, protein interaction networks, prioritization of cancer genes, network centrality, systems biology Received: August 04, 2016 Accepted: October 14, 2016 Published: October 25, 2016 ABSTRACT Comprehensive understanding of human cancer mechanisms requires the identification of a thorough list of cancer-associated genes, which could serve as biomarkers for diagnoses and therapies in various types of cancer. Although substantial progress has been made in functional studies to uncover genes involved in cancer, these efforts are often time-consuming and costly. Therefore, it remains challenging to comprehensively identify cancer candidate genes. Network-based methods have accelerated this process through the analysis of complex molecular interactions in the cell. However, the extent to which various interactome networks can contribute to prediction of candidate genes responsible for cancer is still enigmatic. In this study, we evaluated different human protein-protein interactome networks and compared their application to cancer gene prioritization. Our results indicate that network analyses can increase the power to identify novel cancer genes. In particular, such predictive power can be enhanced with the use of unbiased systematic protein interaction maps for cancer gene prioritization. Functional analysis reveals that the top ranked genes from network predictions co-occur often with cancer-related terms in literature, and further, these candidate genes are indeed frequently mutated across cancers. Finally, our study suggests that integrating interactome networks with other omics datasets could provide novel insights into cancer-associated genes and underlying molecular mechanisms.
Chopped and moist paddy straw was pretreated with Fusarium sp. to enhance its digestibility and biogas production. The potential of microbial pretreatment of paddy straw was investigated at regular interval of 5, 10, 15 and 20 days by determining the change in Chemical composition of paddy straw like cellulose, hemicellulose, lignin and silica contents. The pretreated straw was used for biogas production in 2l capacity biogas digesters. Results indicate that the cellulose, hemicellulose, lignin and silica contents decreased by 17.2%, 3.4%, 27.1% & 16.5% respectively. Biogas production also increased by 53.8% in 10 days pretreated samples. The significantly higher reduction of silica along with lignin content in the pretreated straw indicates that removal of silica by Fusarium sp. might be more responsible for increasing paddy straw digestibility and biogas production.
Abstract Understanding the determinants of human gene essentiality is critical for developing anticancer therapeutics. Here, we systematically analyzed the human essentialome from a network perspective. We found that essential genes are mostly context-specific and predicted synthetic lethal interactions based on analysis of context-specific essentiality in distinct genetic backgrounds. The predicted gene pairs are significantly overlapped with known synthetic lethality and further validated across more than 300 cancer cell lines. Moreover, analysis of synthetic lethal interactions among actionable genes helps prioritizing rational drug combinations. Surprisingly, genes in human essentialome (especially spliceosome, RNA transport and cell cycle genes) are enriched for cancer mutations with high functional impact and disease risks. Our network analyses suggest that protein interactome topology and neighborhood community integrated with gene expression, are highly predictive of gene essentiality. Thus, we devised network-based method (SYE-NET) to systematically prioritize the essentialome, which is further validated by functional screens in independent cell lines. Taken together, our study provides a holistic insight into molecular determinants for cell fate and implicates potential biomarkers for synthetic lethality and drug combinations in cancer, a critical step towards precision medicine. Citation Format: Yongsheng Li, Nidhi Sahni, S. Stephen Yi. Network biology approach reveals multiple non-essential helper genes to target cancer cell lethality [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr LB183.
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