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.     

Serendipity and Vision: Two Methods for Discovery Comments on Nickles

Thomas Nickles challenges my thesis that innovative discoveries can be based on deliberately chosen problems and research strategies. He suggests that all significant innovation can be seen as such only in retrospect and that its generation must be serendipitous. Here I argue in response that significant innovations can and do often arise from self conscious critical appraisal of orthodox practice combined with regulated though speculative abductive argumentation to alternative explanatory schemata. Orthodox practice is not based upon monolithic systems of belief about the subject of inquiry. Rather major domains of scientific practice often are approached from different conceptualizations that provide grounds for critical dialectic and consilient empirical information. Abductive arguments that meet appropriate conditions of likelihood, probability and analogy are an important means of justifying allocating resources to innovative practices that have yet to produce products that can rival those of established orthodoxy.     

DIA-Based Proteomics Identifies IDH2 as a Targetable Regulator of Acquired Drug Resistance in Chronic Myeloid Leukemia

Drug resistance is a critical obstacle to effective treatment in patients with chronic myeloid leukemia. To understand the underlying resistance mechanisms in response to imatinib mesylate (IMA) and adriamycin (ADR), the parental K562 cells were treated with low doses of IMA or ADR for 2 months to generate derivative cells with mild, intermediate, and severe resistance to the drugs as defined by their increasing resistance index. PulseDIA-based (DIA [data-independent acquisition]) quantitative proteomics was then employed to reveal the proteome changes in these resistant cells. In total, 7082 proteins from 98,232 peptides were identified and quantified from the dataset using four DIA software tools including OpenSWATH, Spectronaut, DIA-NN, and EncyclopeDIA. Sirtuin signaling pathway was found to be significantly enriched in both ADR-resistant and IMA-resistant K562 cells. In particular, isocitrate dehydrogenase (NADP(+)) 2 was identified as a potential drug target correlated with the drug resistance phenotype, and its inhibition by the antagonist AGI-6780 reversed the acquired resistance in K562 cells to either ADR or IMA. Together, our study has implicated isocitrate dehydrogenase (NADP(+)) 2 as a potential target that can be therapeutically leveraged to alleviate the drug resistance in K562 cells when treated with IMA and ADR.     

Lysine Succinylation of VBS Contributes to Sclerotia Development and Aflatoxin Biosynthesis in Aspergillus flavus

Aspergillus flavus is a common saprophytic and pathogenic fungus, and its secondary metabolic pathways are one of the most highly characterized owing to its aflatoxin (AF) metabolite affecting global economic crops and human health. Different natural environments can cause significant variations in AF synthesis. Succinylation was recently identified as one of the most critical regulatory post-translational modifications affecting metabolic pathways. It is primarily reported in human cells and bacteria with few studies on fungi. Proteomic quantification of lysine succinylation (Ksuc) exploring its potential involvement in secondary metabolism regulation (including AF production) has not been performed under natural conditions in A. flavus. In this study, a quantification method was performed based on tandem mass tag labeling and antibody-based affinity enrichment of succinylated peptides via high accuracy nano-liquid chromatography with tandem mass spectrometry to explore the succinylation mechanism affecting the pathogenicity of naturally isolated A. flavus strains with varying toxin production. Altogether, 1240 Ksuc sites in 768 proteins were identified with 1103 sites in 685 proteins quantified. Comparing succinylated protein levels between high and low AF-producing A. flavus strains, bioinformatics analysis indicated that most succinylated proteins located in the AF biosynthetic pathway were downregulated, which directly affected AF synthesis. Versicolorin B synthase is a key catalytic enzyme for heterochrome B synthesis during AF synthesis. Site-directed mutagenesis and biochemical studies revealed that versicolorin B synthase succinylation is an important regulatory mechanism affecting sclerotia development and AF biosynthesis in A. flavus. In summary, our quantitative study of the lysine succinylome in high/low AF-producing strains revealed the role of Ksuc in regulating AF biosynthesis. We revealed novel insights into the metabolism of AF biosynthesis using naturally isolated A. flavus strains and identified a rich source of metabolism-related enzymes regulated by succinylation.     

东方法老蛛雌蛛的描述（蜘蛛目：跳蛛科）

报道采自甘肃文县的东方法老蛛雌蛛,为该种雌蛛的首次记述。     

Methods to identify protein targets of metal-based drugs

Metal-based anticancer agents occupy a distinct chemical space due to their particular coordination geometry and reactivity. Despite the initial DNA-targeting paradigm for this class of compounds, it is now clear that they can also be tuned to target proteins in cells, depending on the metal and ligand scaffold. Since metallodrug discovery is dominated by phenotypic screenings, tailored proteomics strategies were crucial to identify and validate protein targets of several investigative and clinically advanced metal-based drugs. Here, such experimental approaches are discussed, which showed that metallodrugs based on ruthenium, gold, rhenium and even platinum, can selectively and specifically target proteins with clear-cut down-stream effects. Target identification strategies are expected to support significantly the mechanism-driven clinical translation of metal-based drugs.