Multiomic Metabolic Enrichment Network Analysis Reveals Metabolite–Protein Physical Interaction Subnetworks Altered in Cancer

Metabolism is recognized as an important driver of cancer progression and other complex diseases, but global metabolite profiling remains a challenge. Protein expression profiling is often a poor proxy since existing pathway enrichment models provide an incomplete mapping between the proteome and metabolism. To overcome these gaps, we introduce multiomic metabolic enrichment network analysis (MOMENTA), an integrative multiomic data analysis framework for more accurately deducing metabolic pathway changes from proteomics data alone in a gene set analysis context by leveraging protein interaction networks to extend annotated metabolic models. We apply MOMENTA to proteomic data from diverse cancer cell lines and human tumors to demonstrate its utility at revealing variation in metabolic pathway activity across cancer types, which we verify using independent metabolomics measurements. The novel metabolic networks we uncover in breast cancer and other tumors are linked to clinical outcomes, underscoring the pathophysiological relevance of the findings.     

The SKS of the KMSKS signature of class I aminoacyl-tRNA synthetases corresponds to the GKT/S sequence characteristic of the ATP-binding site of many proteins

On the basis of protein modification studies and primary structure comparison, we propose that the SKS sequence within the KMSKS signature of the class 1 aminoacyl-tRNA synthetases corresponds to the GKT(or S) sequence considered as a signature of the nucleotide triphosphate-binding site of many proteins.     

Proteomic fingerprinting enables quantitative biodiversity assessments of species and ontogenetic stages in Calanus congeners (Copepoda,Crustacea) from the Arctic Ocean

Species identification is pivotal in biodiversity assessments and proteomic fingerprinting by MALDI-TOF mass spectrometry has already been shown to reliably identify calanoid copepods to species level. However, MALDI-TOF data may contain more information beyond mere species identification. In this study, we investigated different ontogenetic stages (copepodids C1–C6 females) of three co-occurring Calanus species from the Arctic Fram Strait, which cannot be identified to species level based on morphological characters alone. Differentiation of the three species based on mass spectrometry data was without any error. In addition, a clear stage-specific signal was detected in all species, supported by clustering approaches as well as machine learning using Random Forest. More complex mass spectra in later ontogenetic stages as well as relative intensities of certain mass peaks were found as the main drivers of stage distinction in these species. Through a dilution series, we were able to show that this did not result from the higher amount of biomass that was used in tissue processing of the larger stages. Finally, the data were tested in a simulation for application in a real biodiversity assessment by using Random Forest for stage classification of specimens absent from the training data. This resulted in a successful stage-identification rate of almost 90%, making proteomic fingerprinting a promising tool to investigate polewards shifts of Atlantic Calanus species and, in general, to assess stage compositions in biodiversity assessments of Calanoida, which can be notoriously difficult using conventional identification methods.     

A genome-wide perspective on the diversity and selection signatures in indigenous goats using 53 K single nucleotide polymorphism array

Tibetan goats, Taihang goats, Jining grey goats, and Meigu goats are the representative indigenous goats in China, found in Qinghai-Tibet Plateau, Western pastoral area, Northern and Southern agricultural regions. Very few studies have conducted a comprehensive analysis of the genomic diversity and selection of these breeds. We genotyped 96 unrelated individuals, using goat 53 K Illumina BeadChip array, of the following goat breeds: Tibetan (TG), Taihang (THG), Jining grey (JGG), and Meigu (MGG). A total of 45 951 single nucleotide polymorphisms were filtered to estimate the genetic diversity and selection signatures. All breeds had a high proportion (over 95%) of polymorphic loci. The observed and excepted heterozygosity ranged from 0.338 (MGG) to 0.402 (JGG) and 0.339 (MGG) to 0.395 (JGG), respectively. Clustering analysis displayed a genetically distinct lineage for each breed, and their Fst were greater than 0.25, indicating that they had a higher genetic differentiation between groups. Furthermore, effective population size reduced in all four populations, indicating a loss of genetic diversity. In addition, runs of homozygosity were mainly distributed in 5–10 Mb. Lastly, we identified signature genes, which were closely related to high-altitude adaptation (ADIRF) and prolificity (CNTROB, SMC3, and PTEN). This study provides a valuable resource for future studies on genome-wide perspectives on the diversity and selection signatures of Chinese indigenous goats.     

弯曲菌染色体DNA限制性内切酶带型研究

从鸡、羊、鹅、鸭粪便中分离出的空肠弯曲菌,提取染色体DNA做酶切分析,得到几乎相同的带型;与从比利时引进的结肠弯曲菌及海鸥弯曲菌比较,带型完全不同;同时,从人胃粘膜中分离出的幽门螺旋菌,做染色体DNA酶切分析,得到与前者完全不同的带型。     

Characterization of WiDr: A human colon carcinoma cell line

Summary We describe the establishment and characterization of WiDr, a cell line derived from a human colon carcinoma. It produces carcinoembryonic antigen in culture, and has a doubling time of 15 hr with plating efficiency of 51%. The HLA antigenic profile and the allozyme genetic signature (composed of eight gene-enzyme systems) of WiDr cells are different from those of HeLa cells. Furthermore, WiDr cells possess three marker chromosomes, again distinct from the HeLa marker chromosomes. Finally, it is highly tumorigenic in four different xenogeneic animal models. Based on these studies, WiDr represents a useful model cell line for tumor cell biology investigations.     

Cancer: Towards a general theory of the target

General theories (GT) are reductionist explications of apparently independent facts. Here, in reviewing the literature, I develop a GT to simplify the cluttered landscape of cancer therapy targets by revealing they cluster parsimoniously according to only a few underlying principles. The first principle is that targets can be only exploited by either or both of two fundamentally different approaches: causality‐inhibition, and ‘acausal’ recognition of some marker or signature. Nonetheless, each approach must achieve both of two separate goals, efficacy (reduction in cancer burden) and selectivity (sparing of normal cells); if the mechanisms are known, this provides a definition of rational treatment. The second principle is target fragmentation, whereby the target may perform up to three categoric functions (cytoreduction, modulation, cytoprotection), potentially mediated by physically different target molecules, even on different cell types, or circulating freely. This GT remains incomplete until the minimal requirements for cure, or alternatively, proof that cure is impossible, become predictable.     

A novel prognostic signature of immune‐related genes for patients with colorectal cancer

Colorectal cancer (CRC) is one of the most commonly diagnosed cancers with an estimated 1.8 million new cases worldwide and associated with high mortality rates of 881 000 CRC‐related deaths in 2018. Screening programs and new therapies have only marginally improved the survival of CRC patients. Immune‐related genes (IRGs) have attracted attention in recent years as therapeutic targets. The aim of this study was to identify an immune‐related prognostic signature for CRC. To this end, we combined gene expression and clinical data from the CRC data sets of The Cancer Genome Atlas (TCGA) into an integrated immune landscape profile. We identified a total of 476 IRGs that were differentially expressed in CRC vs normal tissues, of which 18 were survival related according to univariate Cox analysis. Stepwise multivariate Cox proportional hazards analysis established an immune‐related prognostic signature consisting of SLC10A2, FGF2, CCL28, NDRG1, ESM1, UCN, UTS2 and TRDC. The predictive ability of this signature for 3‐ and 5‐year overall survival was determined using receiver operating characteristics (ROC), and the respective areas under the curve (AUC) were 79.2% and 76.6%. The signature showed moderate predictive accuracy in the validation and GSE38832 data sets as well. Furthermore, the 8‐IRG signature correlated significantly with tumour stage, invasion, lymph node metastasis and distant metastasis by univariate Cox analysis, and was established an independent prognostic factor by multivariate Cox regression analysis for CRC. Gene set enrichment analysis (GSEA) revealed a relationship between the IRG prognostic signature and various biological pathways. Focal adhesions and ECM‐receptor interactions were positively correlated with the risk scores, while cytosolic DNA sensing and metabolism‐related pathways were negatively correlated. Finally, the bioinformatics results were validated by real‐time RT?qPCR. In conclusion, we identified and validated a novel, immune‐related prognostic signature for patients with CRC, and this signature reflects the dysregulated tumour immune microenvironment and has a potential for better CRC patient management.     

Comprehensive analysis of mRNA‐level and miRNA‐level subpathway activities for identifying robust ovarian cancer prognostic signatures

Ovarian cancer (OvCa) causes the highest mortality among all gynaecologic cancers. A large number of mRNA‐ or miRNA‐based signatures were identified for OvCa patient prognosis. However, the comprehensive analysis of function‐level prognostic signatures is currently not considered in OvCa. In the present study, we respectively inferred subpathway activities from mRNA and miRNA levels based on high‐throughput expression profiles and reconstructed subpathways. Firstly, the activities of two tumour pathways were calculated and the difference between normal and tumour samples were analysed using multiple tumour types. Then, we calculated subpathway activities for OvCa based on the expression profiles from both mRNA and miRNA levels. Furthermore, based on these subpathway activity matrices, we performed bootstrap analysis to obtain sub‐training sets and utilized univariate method to identify robust OvCa prognostic subpathways. A comprehensive comparison of subpathway results between these two levels was performed. As a result, we observed subpathway mutual exclusion trend between the levels of mRNA and miRNA, which indicated the necessary of combining mRNA‐miRNA levels. Finally, by using ICGC data as testing sets, we utilized two strategies to verify survival predictive power of the mRNA‐miRNA combined subpathway signatures and performed comparisons with results from individual levels. It was confirmed that our framework displayed application to identify robust and efficient prognostic signatures for OvCa, and the combined signatures indeed exhibited advantages over individual ones. In the study, we took a step forward in relevant novel integrated functional signatures for OvCa prognosis.     

Intracoronary application of nicorandil regulates the inflammatory response induced by percutaneous coronary intervention

Intracoronary application of nicorandil can effectively reduce the myocardial no‐reflow (MNR) after percutaneous coronary intervention (PCI). We sought to investigate the mechanisms of nicorandil in preventing MNR, besides that of dilating the coronary microvasculature. A total of 60 patients undergoing PCI were enrolled and randomly divided into a nicorandil group and a control group. Before PCI, 2 mg of nicorandil or an equal volume of normal saline was injected into the coronary artery. Blood samples were collected before, 24 hours and 1 week after PCI and inflammatory cytokines were tested. In the control group, the expression of pro‐inflammatory cytokines was significantly increased, while the anti‐inflammatory cytokines were decreased 24 hours after PCI. In contrast, these changes were reversed in the nicorandil group, indicating that nicorandil regulated the inflammatory response induced by PCI. Then, proteomic analysis was performed to further elucidate the potential mechanisms. A total of 53 differentially expressed proteins (DEPs) were found 24 hours after PCI in the control group, and the changes of these relevant genes were reversed in the nicorandil group. These DEPs were significantly enriched in the inflammatory pathways. In conclusion, the intracoronary application of nicorandil before PCI can regulate the inflammatory responses induced by PCI, which might be an important mechanism of nicorandil in preventing MNR.