Measurement and Modeling of Signaling at the Single-Cell Level

It has long been recognized that a deeper understanding of cell function, with respect to execution of phenotypic behaviors and their regulation by the extracellular environment, is likely to be achieved by analyzing the underlying molecular processes for individual cells selected from across a population, rather than averages of many cells comprising that population. In recent years, experimental and computational methods for undertaking these analyses have advanced rapidly. In this review, we provide a perspective on both measurement and modeling facets of biochemistry at a single-cell level. Our central focus is on receptor-mediated signaling networks that regulate cell phenotypic functions.David H. Koch Institute for Integrative Cancer Research at MIT (Ludwig Fellowship)National Institutes of Health (U.S.) (grant R01-EB010246)National Institutes of Health (U.S.) (grant P50-GM68762)United States. Army Research Office (Institute for Collaborative Biotechnologies, Grant W911NF-09-0001

Eukaryotic initiator tRNA: Finely tuned and ready for action

AbstractThe initiator tRNA must serve functions distinct from those of other tRNAs, evading binding to elongation factors and instead binding directly to the ribosomal P site with the aid of initiation factors. It plays a key role in decoding the start codon, setting the frame for translation of the mRNA. Sequence elements and modifications of the initiator tRNA distinguish it from the elongator methionyl tRNA and help it to perform its varied tasks. These identity elements appear to finely tune the structure of the initiator tRNA, and growing evidence suggests that the body of the tRNA is involved in transmitting the signal that the start codon has been found to the rest of the pre-initiation complex

Leveraging existing data sets to generate new insights into Alzheimer’s disease biology in specific patient subsets

To generate new insights into the biology of Alzheimer’s Disease (AD), we developed methods to combine and reuse a wide variety of existing data sets in new ways. We first identified genes consistently associated with AD in each of four separate expression studies, and confirmed this result using a fifth study. We next developed algorithms to search hundreds of thousands of Gene Expression Omnibus (GEO) data sets, identifying a link between an AD-associated gene (NEUROD6) and gender. We therefore stratified patients by gender along with APOE4 status, and analyzed multiple SNP data sets to identify variants associated with AD. SNPs in either the region of NEUROD6 or SNAP25 were significantly associated with AD, in APOE4+ females and APOE4+ males, respectively. We developed algorithms to search Connectivity Map (CMAP) data for medicines that modulate AD-associated genes, identifying hypotheses that warrant further investigation for treating specific AD patient subsets. In contrast to other methods, this approach focused on integrating multiple gene expression datasets across platforms in order to achieve a robust intersection of disease-affected genes, and then leveraging these results in combination with genetic studies in order to prioritize potential genes for targeted therapy

Microfluidic probe for single-cell analysis in adherent tissue culture

Single-cell analysis provides information critical to understanding key disease processes that are characterized by significant cellular heterogeneity. Few current methods allow single-cell measurement without removing cells from the context of interest, which not only destroys contextual information but also may perturb the process under study. Here we present a microfluidic probe that lyses single adherent cells from standard tissue culture and captures the contents to perform single-cell biochemical assays. We use this probe to measure kinase and housekeeping protein activities, separately or simultaneously, from single human hepatocellular carcinoma cells in adherent culture. This tool has the valuable ability to perform measurements that clarify connections between extracellular context, signals and responses, especially in cases where only a few cells exhibit a characteristic of interest.National Institutes of Health (U.S.) (Grant P50-GM068762)United States. Army Research Office (Institute for Collaborative Biotechnologies Grant W911NF-09-0001

Yeast initiator tRNA identity elements cooperate to influence multiple steps of translation initiation

All three kingdoms of life employ two methionine tRNAs, one for translation initiation and the other for insertion of methionines at internal positions within growing polypeptide chains. We have used a reconstituted yeast translation initiation system to explore the interactions of the initiator tRNA with the translation initiation machinery. Our data indicate that in addition to its previously characterized role in binding of the initiator tRNA to eukaryotic initiation factor 2 (eIF2), the initiator-specific A1:U72 base pair at the top of the acceptor stem is important for the binding of the eIF2•GTP•Met-tRNA(i) ternary complex to the 40S ribosomal subunit. We have also shown that the initiator-specific G:C base pairs in the anticodon stem of the initiator tRNA are required for the strong thermodynamic coupling between binding of the ternary complex and mRNA to the ribosome. This coupling reflects interactions that occur within the complex upon recognition of the start codon, suggesting that these initiator-specific G:C pairs influence this step. The effect of these anticodon stem identity elements is influenced by bases in the T loop of the tRNA, suggesting that conformational coupling between the D-loop–T-loop substructure and the anticodon stem of the initiator tRNA may occur during AUG codon selection in the ribosomal P-site, similar to the conformational coupling that occurs in A-site tRNAs engaged in mRNA decoding during the elongation phase of protein synthesis

Detecting Kinase Activities from Single Cell Lysate Using Concentration-Enhanced Mobility Shift Assay

Electrokinetic preconcentration coupled with mobility shift assays can give rise to very high detection sensitivities. We describe a microfluidic device that utilizes this principle to detect cellular kinase activities by simultaneously concentrating and separating substrate peptides with different phosphorylation states. This platform is capable of reliably measuring kinase activities of single adherent cells cultured in nanoliter volume microwells. We also describe a novel method utilizing spacer peptides that significantly increase separation resolution while maintaining high concentration factors in this device. Thus, multiplexed kinase measurements can be implemented with single cell sensitivity. Multiple kinase activity profiling from single cell lysate could potentially allow us to study heterogeneous activation of signaling pathways that can lead to multiple cell fates.National Institutes of Health (U.S.) (Grant P50-GM068762)Singapore. Agency for Science, Technology and Research (National Science Scholarship

Advanced bioinformatics rapidly identifies existing therapeutics for patients with coronavirus disease-2019 (COVID-19)

BACKGROUND: The recent global pandemic has placed a high priority on identifying drugs to prevent or lessen clinical infection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), caused by Coronavirus disease-2019 (COVID-19). METHODS: We applied two computational approaches to identify potential therapeutics. First, we sought to identify existing FDA approved drugs that could block coronaviruses from entering cells by binding to ACE2 or TMPRSS2 using a high-throughput AI-based binding affinity prediction platform. Second, we sought to identify FDA approved drugs that could attenuate the gene expression patterns induced by coronaviruses, using our Disease Cancelling Technology (DCT) platform. RESULTS: Top results for ACE2 binding iincluded several ACE inhibitors, a beta-lactam antibiotic, two antiviral agents (Fosamprenavir and Emricasan) and glutathione. The platform also assessed specificity for ACE2 over ACE1, important for avoiding counterregulatory effects. Further studies are needed to weigh the benefit of blocking virus entry against potential counterregulatory effects and possible protective effects of ACE2. However, the data herein suggest readily available drugs that warrant experimental evaluation to assess potential benefit. DCT was run on an animal model of SARS-CoV, and ranked compounds by their ability to induce gene expression signals that counteract disease-associated signals. Top hits included Vitamin E, ruxolitinib, and glutamine. Glutathione and its precursor glutamine were highly ranked by two independent methods, suggesting both warrant further investigation for potential benefit against SARS-CoV-2. CONCLUSIONS: While these findings are not yet ready for clinical translation, this report highlights the potential use of two bioinformatics technologies to rapidly discover existing therapeutic agents that warrant further investigation for established and emerging disease processes