instruction:
In this task, you're given a paragraph from the research paper and your task is to generate a suitable title for the research paper based on the given paper. Under 100 words is a good title length.
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Background: Nonadditivity in fitness effects from two or more mutations, termed epistasis, can result in compensation of deleterious mutations or negation of beneficial mutations. Recent evidence shows the importance of epistasis in individual evolutionary pathways. However, an unresolved question in molecular evolution is how often and how significantly fitness effects change in alternative genetic backgrounds. Results: To answer this question, we quantified the effects of all single mutations and double mutations between all positions in the IgG-binding domain of protein G (GB1). By observing the first two steps of all possible evolutionary pathways using this fitness profile, we were able to characterize the extent and magnitude of pairwise epistasis throughout an entire protein molecule. Furthermore, we developed a novel approach to quantitatively determine the effects of single mutations on structural stability (DDG U ). This enabled determination of the importance of stability effects in functional epistasis. Conclusions: Our results illustrate common biophysical mechanisms for occurrences of positive and negative epistasis. Our results show pervasive positive epistasis within a conformationally dynamic network of residues. The stability analysis shows that significant negative epistasis, which is more common than positive epistasis, mostly occurs between combinations of destabilizing mutations. Furthermore, we show that although significant positive epistasis is rare, many deleterious mutations are beneficial in at least one alternative mutational background. The distribution of conditionally beneficial mutations throughout the domain demonstrates that the functional portion of sequence space can be significantly expanded by epistasis.
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Article A Comprehensive Biophysical Description of Pairwise Epistasis throughout an Entire Protein Domain

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The emerging novel human betacoronavirus 2c EMC/2012 (HCoV-EMC) was recently isolated from patients with severe pneumonia and renal failure and was associated with an unexplained high crude fatality rate of 56%. We performed a cell line susceptibility study with 28 cell lines. HCoV-EMC was found to infect the human respiratory tract ( polarized airway epithelium cell line Calu-3, embryonic fibroblast cell line HFL, and lung adenocarcinoma cell line A549), kidney (embryonic kidney cell line HEK), intestinal tract (colorectal adenocarcinoma cell line Caco-2), liver cells (hepatocellular carcinoma cell line Huh-7), and histiocytes (malignant histiocytoma cell line His-1), as evident by detection of high or increasing viral load in culture supernatants, detection of viral nucleoprotein expression by immunostaining, and/or detection of cytopathic effects. Although an infected human neuronal cell line (NT2) and infected monocyte and T lymphocyte cell lines (THP-1, U937, and H9) had increased viral loads, their relatively lower viral production corroborated with absent nucleoprotein expression and cytopathic effects. This range of human tissue tropism is broader than that for all other HCoVs, including SARS coronavirus, HCoV-OC43, HCoV-HKU1, HCoV-229E, and HCoV-NL63, which may explain the high mortality associated with this disease. A recent cell line susceptibility study showed that HCoV-EMC can infect primate, porcine, and bat cells and therefore may jump interspecies barriers. We found that HCoV-EMC can also infect civet lung fibroblast and rabbit kidney cell lines. These findings have important implications for the diagnosis, pathogenesis, and transmission of HCoV-EMC.
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Differential Cell Line Susceptibility to the Emerging Novel Human Betacoronavirus 2c EMC/2012: Implications for Disease Pathogenesis and Clinical Manifestation

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Classifying the mechanisms of antibiotic failure has led to the development of new treatment strategies for killing bacteria. Among the currently described mechanismswhich include resistance, persistence and tolerancewe propose defiance as a subclass of antibiotic failure specific to prodrugs. Using locked antimicrobial peptides (AMP) that are activated by bacterial 5 proteases as a prototypic prodrug, we observe that although treatment eliminates bacteria across the vast majority of environmental conditions (e.g., temperature, concentration of growth nutrients), bacteria spontaneously switch from susceptibility to defiance under conditions that alter the competing rates between bacterial proliferation and prodrug activation. To identify the determinants of this switch-like behavior, we model bacteria-prodrug dynamics as a multi-rate 10 feedback system and identify a dimensionless quantity we call the Bacterial Advantage Heuristic (BAH) that perfectly classifies bacteria as either defiant or susceptible across a broad range of treatment conditions. By recognizing that the bacterial switch from susceptibility to defiance behaves analogously to electronic transistors, we construct prodrug logic gates (e.g., AND, OR, NOT, etc.) to allow assembly of an integrated 3-bit multi-prodrug circuit that kills defiant bacteria 15 under all possible combinations of BAH values (i.e., 000, 001, , 111) that represent a broad range of possible treatment conditions. Our study identifies a form of bacterial resistance specific to prodrugs that is described by a predictive dimensionless constant to reveal logic-based treatment strategies using multi-prodrug biological circuits.
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Prodrug defiance reveals logic-based strategies for treating bacterial resistance