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Agrawal, N.

Publications and source records attributed to Agrawal, N..

2 recordsLinked to original sources

Functional interactions between post-translationally modified amino acids of methyl-coenzyme M reductase in Methanosarcina acetivorans

Methyl-coenzyme M reductase (MCR) plays an important role in mediating global levels of methane by catalyzing a reversible reaction that leads to the production or consumption of this potent greenhouse gas in methanogenic and methanotrophic archaea. In methanogenic archaea, the alpha subunit of MCR (McrA) typically contains four to six post-translationally modified amino acids near the active site. Recent studies have identified genes that install two of these modifications (thioglycine and 5-(S)-methylarginine), yet little is known about the installation and function of the remaining post-translationally modified residues. Here, we provide in vivo evidence that a dedicated SAM-dependent methyltransferase encoded by a gene we designated mcmA is responsible for formation of S-methylcysteine in Methanosarcina acetivorans McrA. Phenotypic analysis of mutants incapable of cysteine methylation suggests that the S-methylcysteine residue plays an important role in adaptation to a mesophilic lifestyle. To examine the interactions between the S-methylcysteine residue and the previously characterized thioglycine, 5-(S)-methylarginine modifications, we generated M. acetivorans mutants lacking the three known modification genes in all possible combinations. Phenotypic analyses revealed complex, physiologically relevant interactions between the modified residues, which alter the thermal stability of MCR in a combinatorial fashion that is not readily predictable from the phenotypes of single mutants. Surprisingly, high-resolution crystal structures of the various unmodified MCRs were indistinguishable from the fully modified enzyme, suggesting that interactions between the post-translationally modified residues do not exert a major influence on the physical structure of the enzyme, but rather serve to fine-tune the activity and efficiency of MCR.

microbiology

Deep learning detects virus presence in cancer histology

Oncogenic viruses like human papilloma virus (HPV) or Epstein Barr virus (EBV) are a major cause of human cancer. Viral oncogenesis has a direct impact on treatment decisions because virus-associated tumors can demand a lower intensity of chemotherapy and radiation or can be more susceptible to immune check-point inhibition. However, molecular tests for HPV and EBV are not ubiquitously available.\n\nWe hypothesized that the histopathological features of virus-driven and non-virus driven cancers are sufficiently different to be detectable by artificial intelligence (AI) through deep learning-based analysis of images from routine hematoxylin and eosin (HE) stained slides. We show that deep transfer learning can predict presence of HPV in head and neck cancer with a patient-level 3-fold cross validated area-under-the-curve (AUC) of 0.89 [0.82; 0.94]. The same workflow was used for Epstein-Barr virus (EBV) driven gastric cancer achieving a cross-validated AUC of 0.80 [0.70; 0.92] and a similar performance in external validation sets. Reverse-engineering our deep neural networks, we show that the key morphological features can be made understandable to humans.\n\nThis workflow could enable a fast and low-cost method to identify virus-induced cancer in clinical trials or clinical routine. At the same time, our approach for feature visualization allows pathologists to look into the black box of deep learning, enabling them to check the plausibility of computer-based image classification.

cancer biology