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.     

Structure-based protein design with deep learning

Since the first revelation of proteins functioning as macromolecular machines through their three dimensional structures, researchers have been intrigued by the marvelous ways the biochemical processes are carried out by proteins. The aspiration to understand protein structures has fueled extensive efforts across different scientific disciplines. In recent years, it has been demonstrated that proteins with new functionality or shapes can be designed via structure-based modeling methods, and the design strategies have combined all available information — but largely piece-by-piece — from sequence derived statistics to the detailed atomic-level modeling of chemical interactions. Despite the significant progress, incorporating data-derived approaches through the use of deep learning methods can be a game changer. In this review, we summarize current progress, compare the arc of developing the deep learning approaches with the conventional methods, and describe the motivation and concepts behind current strategies that may lead to potential future opportunities.     

Phosphoproteome Profiling of the Receptor Tyrosine Kinase MuSK Identifies Tyrosine Phosphorylation of Rab GTPases

Muscle-specific receptor tyrosine kinase (MuSK) agonist antibodies were developed 2 decades ago to explore the benefits of receptor activation at the neuromuscular junction. Unlike agrin, the endogenous agonist of MuSK, agonist antibodies function independently of its coreceptor low-density lipoprotein receptor–related protein 4 to delay the onset of muscle denervation in mouse models of ALS. Here, we performed dose–response and time-course experiments on myotubes to systematically compare site-specific phosphorylation downstream of each agonist. Remarkably, both agonists elicited similar intracellular responses at known and newly identified MuSK signaling components. Among these was inducible tyrosine phosphorylation of multiple Rab GTPases that was blocked by MuSK inhibition. Importantly, mutation of this site in Rab10 disrupts association with its effector proteins, molecule interacting with CasL 1/3. Together, these data provide in-depth characterization of MuSK signaling, describe two novel MuSK inhibitors, and expose phosphorylation of Rab GTPases downstream of receptor tyrosine kinase activation in myotubes.     

DNA Sequences from Formalin-Fixed Nematodes: Integrating Molecular and Morphological Approaches to Taxonomy

To effectively integrate DNA sequence analysis and classical nematode taxonomy, we must be able to obtain DNA sequences from formalin-fixed specimens. Microdissected sections of nematodes were removed from specimens fixed in formalin, using standard protocols and without destroying morphological features. The fixed sections provided sufficient template for multiple polymerase chain reaction-based DNA sequence analyses.     

Trichoderma reesei sequences that bind to the nuclear matrix enhance transformation frequency

Three DNA fragments, trs1, 2 and 3, were isolated from the Trichoderma reesei genome on the basis of their ability to promote autonomous replication of plasmids in Saccharomyces cerevisiae. Each trs element bound specifically to the isolated T. reesei nuclear matrix in vitro, and two of them bound in vivo, indicating that they are matrix attachment regions (MARs). A similar sequence previously isolated from Aspergillus nidulans (ans1) was also shown to bind specifically to the T. reesei nuclear matrix in vitro. The T. reesei MARs are AT-rich sequences containing 70%, 86% and 73% A+T over 2.9, 0.8 and 3.7 kb, respectively for trs1, 2 and 3. They exhibited no significant sequence homology, but were shown to contain a number of sequence motifs that occur frequently in many MARs identified in other eukaryotes. However, these motifs occurred as frequently in the trs elements as in randomly generated sequences with the same A+T content. trs1 and 3 were shown to be present as single copies in the T. reesei genome. The presence of the trs elements in transforming plasmids enhanced the frequency of integrative transformation of T. reesei up to five fold over plasmids without a trs. No evidence was obtained to suggest that the trs elements promoted efficient replication of plasmids in T. reseei. A mechanism for the enhancement of transformation frequency by the trs elements is proposed. Received: 1 March 1997 / Accepted: 13 May 1997     

Expression level of enzymes related to in situ estrogen synthesis and clinicopathological parameters in breast cancer patients

In order to evaluate the importance of estrogen production in tumor and surrounding tissues, we measured mRNA expression levels of 5 enzymes participating to estrogen synthesis in situ and 4 breast cancer-related proteins in 27 pairs of tumor and non-malignant tissues. Steroid sulfatase (STS) mRNA was more frequently detected in tumor tissues rather than in their non-malignant counterparts. Estrogen sulfotransferase (EST) was constantly expressed with high level not only in tumor tissues but also in their surrounding non-malignant counterparts. In contrast, mRNA expression levels of aromatase, and 17β-hydroxysteroid dehydrogenase type I and II were relatively low and detected only in small proportion of the patients. We also measured the mRNA expression levels of the same nine genes in tumor tissues of 197 breast cancer patients, and analyzed relationship between the mRNA expression level and the clinicopathological parameters. The mRNA expression levels of STS, aromatase and erbB2 in tumor tissues increased as breast cancer progressed. The tumoral mRNA expression levels of STS, estrogen receptor β, and erbB2 in patients with recurrence were higher than those in patients without recurrence. Upregulation of STS expression plays an important role in tumor progression of human breast cancer and is considered to be responsible for estrogen production in tumor and surrounding tissues.     

UDP-N-acetylglucosamine 1-carboxyvinyl-transferase from Acinetobacter calcoaceticus

Abstract We have analyzed the sequence downstream of rpoN from Zcinetobacter calcoaceticus and identified an open reading frame encoding a protein with high similarity to UDP- N -acetylgucosamine 1-carboxyvinyl-transferase (MurZ). Multicopy plasmids encoding this enzyme conferred phosphomycin resistance to A. calcoaceticus . The polar effect of a rpoN mutation on the phosphomycin resistance level suggests that murZ is, in part, cotranscribed with rpoN . These observations confirm that A. calcoaceticus represents the first exceptin from a conserved genetic context of rpoN observed in several other Gram-negative bacteria.     

Nucleotide sequence of the ethidium efflux gene from Escherichia coli

The nucleotide sequence of the gene specifying the ethidium efflux system of Escherichia coli has been determined. The translated open reading frame has identified a membrane-bound polypeptide of 110 amino acids (11,960 Da) which shares 42% identity with a staphylococcal protein specifying resistance to ethidium.