Accelerated discovery of multi-elemental reverse water-gas shift catalysts using extrapolative machine learning approach.

Designing novel catalysts is key to solving many energy and environmental challenges. Despite the promise that data science approaches, including machine learning (ML), can accelerate the development of catalysts, truly novel catalysts have rarely been discovered through ML approaches because of one of its most common limitations and criticisms-the assumed inability to extrapolate and identify extraordinary materials. Herein, we demonstrate an extrapolative ML approach to develop new multi-elemental reverse water-gas shift catalysts. Using 45 catalysts as the initial data points and performing 44 cycles of the closed loop discovery system (ML prediction + experiment), we experimentally tested a total of 300 catalysts and identified more than 100 catalysts with superior activity compared to those of the previously reported high-performance catalysts. The composition of the optimal catalyst discovered was Pt(3)/Rb(1)-Ba(1)-Mo(0.6)-Nb(0.2)/TiO2. Notably, niobium (Nb) was not included in the original dataset, and the catalyst composition identified was not predictable even by human experts.

Calcium sparks enhance the tissue fluidity within epithelial layers and promote apical extrusion of transformed cells.

In vertebrates, newly emerging transformed cells are often apically extruded from epithelial layers through cell competition with surrounding normal epithelial cells. However, the underlying molecular mechanism remains elusive. Here, using phospho-SILAC screening, we show that phosphorylation of AHNAK2 is elevated in normal cells neighboring RasV12 cells soon after the induction of RasV12 expression, which is mediated by calcium-dependent protein kinase C. In addition, transient upsurges of intracellular calcium, which we call calcium sparks, frequently occur in normal cells neighboring RasV12 cells, which are mediated by mechanosensitive calcium channel TRPC1 upon membrane stretching. Calcium sparks then enhance cell movements of both normal and RasV12 cells through phosphorylation of AHNAK2 and promote apical extrusion. Moreover, comparable calcium sparks positively regulate apical extrusion of RasV12-transformed cells in zebrafish larvae as well. Hence, calcium sparks play a crucial role in the elimination of transformed cells at the early phase of cell competition.

The role of Mediator and Little Elongation Complex in transcription termination.

Mediator is a coregulatory complex that regulates transcription of Pol II-dependent genes. Previously, we showed that human Mediator subunit MED26 plays a role in the recruitment of Super Elongation Complex (SEC) or Little Elongation Complex (LEC) to regulate the expression of certain genes. MED26 plays a role in recruiting SEC to protein-coding genes including c-myc and LEC to small nuclear RNA (snRNA) genes. However, how MED26 engages SEC or LEC to regulate distinct genes is unclear. Here, we provide evidence that MED26 recruits LEC to modulate transcription termination of non-polyadenylated transcripts including snRNAs and mRNAs encoding replication-dependent histone (RDH) at Cajal bodies. Our findings indicate that LEC recruited by MED26 promotes efficient transcription termination by Pol II through interaction with CBC-ARS2 and NELF/DSIF, and promotes 3' end processing by enhancing recruitment of Integrator or Heat Labile Factor to snRNA or RDH genes, respectively.

Obesity Suppresses Cell-Competition-Mediated Apical Elimination of RasV12-Transformed Cells from Epithelial Tissues.

Recent studies have revealed that newly emerging transformed cells are often eliminated from epithelial tissues via cell competition with the surrounding normal epithelial cells. This cancer preventive phenomenon is termed epithelial defense against cancer (EDAC). However, it remains largely unknown whether and how EDAC is diminished during carcinogenesis. In this study, using a cell competition mouse model, we show that high-fat diet (HFD) feeding substantially attenuates the frequency of apical elimination of RasV12-transformed cells from intestinal and pancreatic epithelia. This process involves both lipid metabolism and chronic inflammation. Furthermore, aspirin treatment significantly facilitates eradication of transformed cells from the epithelial tissues in HFD-fed mice. Thus, our work demonstrates that obesity can profoundly influence competitive interaction between normal and transformed cells, providing insights into cell competition and cancer preventive medicine.

Exploring phenotype patterns of breast cancer within somatic mutations: a modicum in the intrinsic code.

Triple-negative (TN) breast cancer (BC) patients have limited treatment options and poor prognosis even after extant treatments and standard chemotherapeutic regimens. Linking TN patients to clinically known phenotypes with appropriate treatments is vital. Location-specific sequence variants are expected to be useful for this purpose by identifying subgroups within a disease population. Single gene mutational signatures have been widely reported, with related phenotypes in literature. We thoroughly survey currently available mutations (and mutated genes), linked to BC phenotypes, to demonstrate their limited performance as sole predictors/biomarkers to assign phenotypes to patients. We then explore mutational combinations, as a pilot study, using The Cancer Genome Atlas Research Network mutational data of BC and three machine learning methods: association rules (limitless arity multiple procedure), decision tree and hierarchical disjoint clustering. The study results in a patient classification scheme through combinatorial mutations in Phosphatidylinositol-4,5-Bisphosphate 3-Kinase and tumor protein 53, being consistent with all three methods, implying its validity from a diverse viewpoint. However, it would warrant further research to select multi-gene signatures to identify phenotypes specifically and be clinically used routinely.

Predictions of Cleavability of Calpain Proteolysis by Quantitative Structure-Activity Relationship Analysis Using Newly Determined Cleavage Sites and Catalytic Efficiencies of an Oligopeptide Array.

Calpains are intracellular Ca(2+)-regulated cysteine proteases that are essential for various cellular functions. Mammalian conventional calpains (calpain-1 and calpain-2) modulate the structure and function of their substrates by limited proteolysis. Thus, it is critically important to determine the site(s) in proteins at which calpains cleave. However, the calpains' substrate specificity remains unclear, because the amino acid (aa) sequences around their cleavage sites are very diverse. To clarify calpains' substrate specificities, 84 20-mer oligopeptides, corresponding to P10-P10' of reported cleavage site sequences, were proteolyzed by calpains, and the catalytic efficiencies (kcat/Km) were globally determined by LC/MS. This analysis revealed 483 cleavage site sequences, including 360 novel ones. Thekcat/Kms for 119 sites ranged from 12.5-1,710 M(-1)s(-1) Although most sites were cleaved by both calpain-1 and -2 with a similarkcat/Km, sequence comparisons revealed distinct aa preferences at P9-P7/P2/P5'. The aa compositions of the novel sites were not statistically different from those of previously reported sites as a whole, suggesting calpains have a strict implicit rule for sequence specificity, and that the limited proteolysis of intact substrates is because of substrates' higher-order structures. Cleavage position frequencies indicated that longer sequences N-terminal to the cleavage site (P-sites) were preferred for proteolysis over C-terminal (P'-sites). Quantitative structure-activity relationship (QSAR) analyses using partial least-squares regression and >1,300 aa descriptors achievedkcat/Kmprediction withr= 0.834, and binary-QSAR modeling attained an 87.5% positive prediction value for 132 reported calpain cleavage sites independent of our model construction. These results outperformed previous calpain cleavage predictors, and revealed the importance of the P2, P3', and P4' sites, and P1-P2 cooperativity. Furthermore, using our binary-QSAR model, novel cleavage sites in myoglobin were identified, verifying our predictor. This study increases our understanding of calpain substrate specificities, and opens calpains to "next-generation,"i.e.activity-related quantitative and cooperativity-dependent analyses.

The cell competition-based high-throughput screening identifies small compounds that promote the elimination of RasV12-transformed cells from epithelia.

Recent studies have revealed that cell competition can occur between normal and transformed epithelial cells; normal epithelial cells recognize the presence of the neighboring transformed cells and actively eliminate them from epithelial tissues. Here, we have established a brand-new high-throughput screening platform that targets cell competition. By using this platform, we have identified Rebeccamycin as a hit compound that specifically promotes elimination of RasV12-transformed cells from the epithelium, though after longer treatment it shows substantial cytotoxic effect against normal epithelial cells. Among several Rebeccamycin-derivative compounds, we have found that VC1-8 has least cytotoxicity against normal cells but shows the comparable effect on the elimination of transformed cells. This cell competition-promoting activity of VC1-8 is observed both in vitro and ex vivo. These data demonstrate that the cell competition-based screening is a promising tool for the establishment of a novel type of cancer preventive medicine.

Ribosomes in a stacked array: elucidation of the step in translation elongation at which they are stalled during S-adenosyl-L-methionine-induced translation arrest of CGS1 mRNA.

Expression of CGS1, which codes for an enzyme of methionine biosynthesis, is feedback-regulated by mRNA degradation in response to S-adenosyl-L-methionine (AdoMet). In vitro studies revealed that AdoMet induces translation arrest at Ser-94, upon which several ribosomes stack behind the arrested one, and mRNA degradation occurs at multiple sites that presumably correspond to individual ribosomes in a stacked array. Despite the significant contribution of stacked ribosomes to inducing mRNA degradation, little is known about the ribosomes in the stacked array. Here, we assigned the peptidyl-tRNA species of the stacked second and third ribosomes to their respective codons and showed that they are arranged at nine-codon intervals behind the Ser-94 codon, indicating tight stacking. Puromycin reacts with peptidyl-tRNA in the P-site, releasing the nascent peptide as peptidyl-puromycin. This reaction is used to monitor the activity of the peptidyltransferase center (PTC) in arrested ribosomes. Puromycin reaction of peptidyl-tRNA on the AdoMet-arrested ribosome, which is stalled at the pre-translocation step, was slow. This limited reactivity can be attributed to the peptidyl-tRNA occupying the A-site at this step rather than to suppression of PTC activity. In contrast, puromycin reactions of peptidyl-tRNA with the stacked second and third ribosomes were slow but were not as slow as pre-translocation step ribosomes. We propose that the anticodon end of peptidyl-tRNA resides in the A-site of the stacked ribosomes and that the stacked ribosomes are stalled at an early step of translocation, possibly at the P/E hybrid state.

An in silico model for interpreting polypharmacology in drug-target networks.

Recent analysis on polypharmacology leads to the idea that only small fragments of drugs and targets are a key to understanding their interactions forming polypharmacology. This idea motivates us to build an in silico approach of finding significant substructure patterns from drug-target (molecular graph-amino acid sequence) pairs. This article introduces an efficient in silico method for enumerating, from given drug-target pairs, all frequent subgraph-subsequence pairs, which can then be further examined by hypothesis testing for statistical significance. Unique features of the method are its scalability, computational efficiency, and technical soundness in terms of computer science and statistics. The presented method was applied to 11,219 drug-target pairs in DrugBank to obtain significant substructure pairs, which can divide most of the original 11,219 pairs into eight highly exclusive clusters, implying that the obtained substructure pairs are indispensable components for interpreting polypharmacology.

Mining significant substructure pairs for interpreting polypharmacology in drug-target network.

A current key feature in drug-target network is that drugs often bind to multiple targets, known as polypharmacology or drug promiscuity. Recent literature has indicated that relatively small fragments in both drugs and targets are crucial in forming polypharmacology. We hypothesize that principles behind polypharmacology are embedded in paired fragments in molecular graphs and amino acid sequences of drug-target interactions. We developed a fast, scalable algorithm for mining significantly co-occurring subgraph-subsequence pairs from drug-target interactions. A noteworthy feature of our approach is to capture significant paired patterns of subgraph-subsequence, while patterns of either drugs or targets only have been considered in the literature so far. Significant substructure pairs allow the grouping of drug-target interactions into clusters, covering approximately 75% of interactions containing approved drugs. These clusters were highly exclusive to each other, being statistically significant and logically implying that each cluster corresponds to a distinguished type of polypharmacology. These exclusive clusters cannot be easily obtained by using either drug or target information only but are naturally found by highlighting significant substructure pairs in drug-target interactions. These results confirm the effectiveness of our method for interpreting polypharmacology in drug-target network.