Identifying Hipk1 as a target of Mir-22-3p enhancing recombinant protein production from Hek 293 by using microarray and Htp sirna screen

Enhancing protein production in mammalian cells is of interest in the biomedical field for a variety of reasons, including structural studies and antibody production. Using small non-protein coding RNA such as microRNA has recently been a promising method of increasing protein expression. A high throughput human microRNA screen in HEK 293 cells previously identified miRNA 22-3p as a promising candidate for increasing recombinant protein expression. This microRNA enhanced the expression of luciferase, two hard-to-express membrane proteins and a secreted hFc-fusion protein. In order to explore the mechanisms of this increase in protein production and to understand the intracellular events, we conducted a gene expression analysis of cells transfected with a mir-22-3p mimic against a negative control. Following the microarray analysis, several genes that were differentially regulated were identified. These were cross-referenced with predicted mir-22-3p targets along with the results of a high throughput siRNA screen. We will present our selected gene, HIPK1, and its possible involvement in the process of enhanced cells productivity

A synthesis of evidence for policy from behavioural science during COVID-19

Scientific evidence regularly guides policy decisions1, with behavioural science increasingly part of this process2. In April 2020, an influential paper3 proposed 19 policy recommendations (‘claims’) detailing how evidence from behavioural science could contribute to efforts to reduce impacts and end the COVID-19 pandemic. Here we assess 747 pandemic-related research articles that empirically investigated those claims. We report the scale of evidence and whether evidence supports them to indicate applicability for policymaking. Two independent teams, involving 72 reviewers, found evidence for 18 of 19 claims, with both teams finding evidence supporting 16 (89%) of those 18 claims. The strongest evidence supported claims that anticipated culture, polarization and misinformation would be associated with policy effectiveness. Claims suggesting trusted leaders and positive social norms increased adherence to behavioural interventions also had strong empirical support, as did appealing to social consensus or bipartisan agreement. Targeted language in messaging yielded mixed effects and there were no effects for highlighting individual benefits or protecting others. No available evidence existed to assess any distinct differences in effects between using the terms ‘physical distancing’ and ‘social distancing’. Analysis of 463 papers containing data showed generally large samples; 418 involved human participants with a mean of 16,848 (median of 1,699). That statistical power underscored improved suitability of behavioural science research for informing policy decisions. Furthermore, by implementing a standardized approach to evidence selection and synthesis, we amplify broader implications for advancing scientific evidence in policy formulation and prioritization

A synthesis of evidence for policy from behavioural science during COVID-19

Scientific evidence regularly guides policy decisions 1, with behavioural science increasingly part of this process 2. In April 2020, an influential paper 3 proposed 19 policy recommendations (‘claims’) detailing how evidence from behavioural science could contribute to efforts to reduce impacts and end the COVID-19 pandemic. Here we assess 747 pandemic-related research articles that empirically investigated those claims. We report the scale of evidence and whether evidence supports them to indicate applicability for policymaking. Two independent teams, involving 72 reviewers, found evidence for 18 of 19 claims, with both teams finding evidence supporting 16 (89%) of those 18 claims. The strongest evidence supported claims that anticipated culture, polarization and misinformation would be associated with policy effectiveness. Claims suggesting trusted leaders and positive social norms increased adherence to behavioural interventions also had strong empirical support, as did appealing to social consensus or bipartisan agreement. Targeted language in messaging yielded mixed effects and there were no effects for highlighting individual benefits or protecting others. No available evidence existed to assess any distinct differences in effects between using the terms ‘physical distancing’ and ‘social distancing’. Analysis of 463 papers containing data showed generally large samples; 418 involved human participants with a mean of 16,848 (median of 1,699). That statistical power underscored improved suitability of behavioural science research for informing policy decisions. Furthermore, by implementing a standardized approach to evidence selection and synthesis, we amplify broader implications for advancing scientific evidence in policy formulation and prioritization

A synthesis of evidence for policy from behavioural science during COVID-19

DATA AVAILABILITY : All data and study material are provided either in the Supplementary information or through the two online repositories (OSF and Tableau Public, both accessible via https://psyarxiv.com/58udn). No code was used for analyses in this work.Scientific evidence regularly guides policy decisions, with behavioural science increasingly part of this process. In April 2020, an influential paper proposed 19 policy recommendations (‘claims’) detailing how evidence from behavioural science could contribute to efforts to reduce impacts and end the COVID-19 pandemic. Here we assess 747 pandemic-related research articles that empirically investigated those claims. We report the scale of evidence and whether evidence supports them to indicate applicability for policymaking. Two independent teams, involving 72 reviewers, found evidence for 18 of 19 claims, with both teams finding evidence supporting 16 (89%) of those 18 claims. The strongest evidence supported claims that anticipated culture, polarization and misinformation would be associated with policy effectiveness. Claims suggesting trusted leaders and positive social norms increased adherence to behavioural interventions also had strong empirical support, as did appealing to social consensus or bipartisan agreement. Targeted language in messaging yielded mixed effects and there were no effects for highlighting individual benefits or protecting others. No available evidence existed to assess any distinct differences in effects between using the terms ‘physical distancing’ and ‘social distancing’. Analysis of 463 papers containing data showed generally large samples; 418 involved human participants with a mean of 16,848 (median of 1,699). That statistical power underscored improved suitability of behavioural science research for informing policy decisions. Furthermore, by implementing a standardized approach to evidence selection and synthesis, we amplify broader implications for advancing scientific evidence in policy formulation and prioritization.The National Science Foundation; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (Brazilian Federal Agency for the Support and Evaluation of Graduate Education); Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (Brazilian Federal Agency for the Support and Evaluation of Graduate Education); the Ministry of Science, Technology and Innovation | Conselho Nacional de Desenvolvimento Científico e Tecnológico (National Council for Scientific and Technological Development); National Science Foundation grants; the European Research Council; the Canadian Institutes of Health Research.http://www.nature.com/naturehj2024Gordon Institute of Business Science (GIBS)Non

Statistical models for the analysis of heterogeneous biological data sets

The focus of this thesis is on developing methods of integrating heterogeneous biological feature sets into structured statistical models, so as to improve model predictions and further understanding of the complex systems that they emulate. Combining data from different sources is an important task in genomics because of the increasing variety of large-scale data being generated, all of which reflect different components of the same complicated network of biological interactions that make up an organism. We contend that traditional machine learning techniques are too general to accurately model heterogeneous biological data and provide insufficient feedback to researchers concerning the systems being modeled. In contrast, we will show that interpretable statistical models specifically designed for and inspired by the underlying structure of biological problems yield more accurate predictions and provide valuable insight into biological systems. Toward proving this thesis, we introduce maximum entropy biological sequence models. Maximum entropy sequence models have been used previously to integrate arbitrary features in other (non-biological) domains, such as natural language modeling. Here, we apply the same model structure to amino acid and nucleotide sequences. We first propose a broad variety of biologically inspired features, define them mathematically, and test their ability to improve models of amino acid sequences. Of these features, particular attention is paid to long distance features such as triggers, which incorporate information unavailable to more conventional Markovian models and reflect the non-local nature of protein sequence constraints. The ability of these features to improve gene-finding models is demonstrated. We next extend maximum entropy models to nucleotide coding sequences and apply them to the detection of lateral gene transfer. This allows us to evaluate a diverse set of features in a statistically rigorous manner, improving understanding of the problem and eliminating the tendency to inaccurately label short genes. We also develop methods for integrating positional and gene expression data with our maximum entropy sequence model, allowing more accurate predictions of lateral gene transfer and resulting in significant biological insight

Statistical models for the analysis of heterogeneous biological data sets

The focus of this thesis is on developing methods of integrating heterogeneous biological feature sets into structured statistical models, so as to improve model predictions and further understanding of the complex systems that they emulate. Combining data from different sources is an important task in genomics because of the increasing variety of large-scale data being generated, all of which reflect different components of the same complicated network of biological interactions that make up an organism. We contend that traditional machine learning techniques are too general to accurately model heterogeneous biological data and provide insufficient feedback to researchers concerning the systems being modeled. In contrast, we will show that interpretable statistical models specifically designed for and inspired by the underlying structure of biological problems yield more accurate predictions and provide valuable insight into biological systems. Toward proving this thesis, we introduce maximum entropy biological sequence models. Maximum entropy sequence models have been used previously to integrate arbitrary features in other (non-biological) domains, such as natural language modeling. Here, we apply the same model structure to amino acid and nucleotide sequences. We first propose a broad variety of biologically inspired features, define them mathematically, and test their ability to improve models of amino acid sequences. Of these features, particular attention is paid to long distance features such as triggers, which incorporate information unavailable to more conventional Markovian models and reflect the non-local nature of protein sequence constraints. The ability of these features to improve gene-finding models is demonstrated. We next extend maximum entropy models to nucleotide coding sequences and apply them to the detection of lateral gene transfer. This allows us to evaluate a diverse set of features in a statistically rigorous manner, improving understanding of the problem and eliminating the tendency to inaccurately label short genes. We also develop methods for integrating positional and gene expression data with our maximum entropy sequence model, allowing more accurate predictions of lateral gene transfer and resulting in significant biological insight

Maximum Entropy Methods for Biological Sequence Modeling

Many of the same modeling methods used in natural languages, specifically Markov models and HMM's, have also been applied to biological sequence analysis. In recent years, natural language models have been improved upon by using maximum entropy methods which allow information based upon the entire history of a sequence to be considered. This is in contrast to the Markov models, whose predictions generally are based on some fixed number of previous emissions, that have been the standard for most biological sequence models. To test the utility of Maximum Entropy modeling for biological sequence analysis, we used these methods to model amino acid sequences. Our results show that there is significant long-distance information in amino acid sequences and suggests that maximum entropy techniques may be beneficial for a range of biological sequence analysis problems

C911: A Bench-Level Control for Sequence Specific siRNA Off-Target Effects

<div><p>Small interfering RNAs (siRNAs) have become a ubiquitous experimental tool for down-regulating mRNAs. Unfortunately, off-target effects are a significant source of false positives in siRNA experiments and an effective control for them has not previously been identified. We introduce two methods of mismatched siRNA design for negative controls based on changing bases in the middle of the siRNA to their complement bases. To test these controls, a test set of 20 highly active siRNAs (10 true positives and 10 false positives) was identified from a genome-wide screen performed in a cell-line expressing a simple, constitutively expressed luciferase reporter. Three controls were then synthesized for each of these 20 siRNAs, the first two using the proposed mismatch design methods and the third being a simple random permutation of the sequence (scrambled siRNA). When tested in the original assay, the scrambled siRNAs showed significantly reduced activity in comparison to the original siRNAs, regardless of whether they had been identified as true or false positives, indicating that they have little utility as experimental controls. In contrast, one of the proposed mismatch design methods, dubbed C911 because bases 9 through 11 of the siRNA are replaced with their complement, was able to completely distinguish between the two groups. False positives due to off-target effects maintained most of their activity when the C911 mismatch control was tested, whereas true positives whose phenotype was due to on-target effects lost most or all of their activity when the C911 mismatch was tested. The ability of control siRNAs to distinguish between true and false positives, if widely adopted, could reduce erroneous results being reported in the literature and save research dollars spent on expensive follow-up experiments.</p></div