Copyrights in Faculty-Created Works: How Licensing Can Solve the Academic Work-for-Hire Dilemma

Many copyrightable works of university faculty members may be works-for-hire as defined under current U.S. copyright laws. Copyrights in works-for-hire are treated differently than copyrights in other works with respect to ownership, duration, termination rights, and requirements for transfer. Ambiguity over whether a specific faculty-created work is a work-for-hire creates legal uncertainties and potential future litigation about the initial ownership of the copyright, length of the copyright term, and termination rights which could impact all future transfers and licensing. Many universities have attempted to define ownership of faculty-created works through university policies. These policies are ineffective to alter the presumption of university ownership of works-for-hire, as they do not meet the requirements of U.S. copyright laws for a transfer of such ownership. This Comment argues that the best way to resolve these ambiguities is for the university to retain ownership of the copyrights in faculty-created works and provide the faculty creator with a license to the copyrighted work. Although perhaps counterintuitive, this Comment suggests that a licensing approach would actually result in greater certainty and better protection of the interests of both the faculty member and the university

A cis-regulatory logic simulator

<p>Abstract</p> <p>Background</p> <p>A major goal of computational studies of gene regulation is to accurately predict the expression of genes based on the cis-regulatory content of their promoters. The development of computational methods to decode the interactions among cis-regulatory elements has been slow, in part, because it is difficult to know, without extensive experimental validation, whether a particular method identifies the correct cis-regulatory interactions that underlie a given set of expression data. There is an urgent need for test expression data in which the interactions among cis-regulatory sites that produce the data are known. The ability to rapidly generate such data sets would facilitate the development and comparison of computational methods that predict gene expression patterns from promoter sequence.</p> <p>Results</p> <p>We developed a gene expression simulator which generates expression data using user-defined interactions between cis-regulatory sites. The simulator can incorporate additive, cooperative, competitive, and synergistic interactions between regulatory elements. Constraints on the spacing, distance, and orientation of regulatory elements and their interactions may also be defined and Gaussian noise can be added to the expression values. The simulator allows for a data transformation that simulates the sigmoid shape of expression levels from real promoters. We found good agreement between sets of simulated promoters and predicted regulatory modules from real expression data. We present several data sets that may be useful for testing new methodologies for predicting gene expression from promoter sequence.</p> <p>Conclusion</p> <p>We developed a flexible gene expression simulator that rapidly generates large numbers of simulated promoters and their corresponding transcriptional output based on specified interactions between cis-regulatory sites. When appropriate rule sets are used, the data generated by our simulator faithfully reproduces experimentally derived data sets. We anticipate that using simulated gene expression data sets will facilitate the direct comparison of computational strategies to predict gene expression from promoter sequence. The source code is available online and as additional material. The test sets are available as additional material.</p

Phylogeny based discovery of regulatory elements

BACKGROUND: Algorithms that locate evolutionarily conserved sequences have become powerful tools for finding functional DNA elements, including transcription factor binding sites; however, most methods do not take advantage of an explicit model for the constrained evolution of functional DNA sequences. RESULTS: We developed a probabilistic framework that combines an HKY85 model, which assigns probabilities to different base substitutions between species, and weight matrix models of transcription factor binding sites, which describe the probabilities of observing particular nucleotides at specific positions in the binding site. The method incorporates the phylogenies of the species under consideration and takes into account the position specific variation of transcription factor binding sites. Using our framework we assessed the suitability of alignments of genomic sequences from commonly used species as substrates for comparative genomic approaches to regulatory motif finding. We then applied this technique to Saccharomyces cerevisiae and related species by examining all possible six base pair DNA sequences (hexamers) and identifying sequences that are conserved in a significant number of promoters. By combining similar conserved hexamers we reconstructed known cis-regulatory motifs and made predictions of previously unidentified motifs. We tested one prediction experimentally, finding it to be a regulatory element involved in the transcriptional response to glucose. CONCLUSION: The experimental validation of a regulatory element prediction missed by other large-scale motif finding studies demonstrates that our approach is a useful addition to the current suite of tools for finding regulatory motifs

Proteasome Inhibitor-Related Cardiotoxicity: Mechanisms, Diagnosis, and Management.

Multiple myeloma is the second most common hematologic malignancy in the USA, with over 32,000 new cases and nearly 13,000 deaths expected in 2019. The past few decades in myeloma research have yielded significant advances, leading to the expansion of novel anti-myeloma agents. This review describes the incidence and mechanisms of cardiotoxicity for the FDA-approved proteasome inhibitors in myeloma and proposes strategies to assess and manage resultant cardiovascular adverse events.Proteasome inhibition precipitates protein aggregation and alters transcriptional activation of NF-κB targets which contributes to a pro-apoptotic signaling cascade in myeloma cells. Similar effects in cardiomyocytes and vascular smooth muscle endothelium, along with off-target downregulation of autophagy and signaling alterations of nitric oxide homeostasis, may be linked to observed cardiotoxic effects. There is preliminary evidence for cardioprotective potential for rutin, dexrazoxane, and apremilast that could have clinical applicability in the future. Of the proteasome inhibitors used in clinical practice, carfilzomib is the most strongly associated with cardiotoxicity. Patients with anticipated carfilzomib treatment should undergo assessment and optimization of baseline cardiovascular risk, with close monitoring during treatment. Previous clinical trials were not specifically designed to assess proteasome inhibitor-related cardiotoxicity, creating a need for future studies to identify and risk stratify vulnerable individuals and to develop potential cardioprotective strategies in attenuating cardiac injury

Evaluating annotations of an Agilent expression chip suggests that many features cannot be interpreted

<p>Abstract</p> <p>Background</p> <p>While attempting to reanalyze published data from Agilent 4 × 44 human expression chips, we found that some of the 60-mer olignucleotide features could not be interpreted as representing single human genes. For example, some of the oligonucleotides align with the transcripts of more than one gene. We decided to check the annotations for all autosomes and the X chromosome systematically using bioinformatics methods.</p> <p>Results</p> <p>Out of 42683 reporters, we found that 25505 (60%) passed all our tests and are considered "fully valid". 9964 (23%) reporters did not have a meaningful identifier, mapped to the wrong chromosome, or did not pass basic alignment tests preventing us from correlating the expression values of these reporters with a unique annotated human gene. The remaining 7214 (17%) reporters could be associated with either a unique gene or a unique intergenic location, but could not be mapped to a transcript in RefSeq. The 7214 reporters are further partitioned into three different levels of validity.</p> <p>Conclusion</p> <p>Expression array studies should evaluate the annotations of reporters and remove those reporters that have suspect annotations. This evaluation can be done systematically and semi-automatically, but one must recognize that data sources are frequently updated leading to slightly changing validation results over time.</p

Environment-specific combinatorial cis-regulation in synthetic promoters

When a cell's environment changes, a large transcriptional response often takes place. The exquisite sensitivity and specificity of these responses are controlled in large part by the combinations of cis-regulatory elements that reside in gene promoters and adjacent control regions. Here, we present a study aimed at accurately modeling the relationship between combinations of cis-regulatory elements and the expression levels they drive in different environments. We constructed four libraries of synthetic promoters in yeast, consisting of combinations of transcription factor binding sites and assayed their expression in four different environments. Thermodynamic models relating promoter sequences to their corresponding four expression levels explained at least 56% of the variation in expression in each library through the different conditions. Analyses of these models suggested that a large fraction of regulated gene expression is explained by changes in the effective concentration of sequence-specific transcription factors, and we show that in most cases, the corresponding transcription factors are expressed in a pattern that is predicted by the thermodynamic models. Our analysis uncovered two binding sites that switch from activators to repressors in different environmental conditions. In both the cases, the switch was not the result of a single transcription factor changing regulatory modes, but most likely due to competition between multiple factors binding to the same site. Our analysis suggests that this mode of regulation allows for large and steep changes in expression in response to changing transcription factor concentrations. Our results demonstrate that many complex changes in gene expression are accurately explained by simple changes in the effective concentrations of transcription factors

Genome-wide changes in protein translation efficiency are associated with autism

We previously proposed that changes in the efficiency of protein translation are associated with autism spectrum disorders (ASDs). This hypothesis connects environmental factors and genetic factors because each can alter translation efficiency. For genetic factors, we previously tested our hypothesis using a small set of ASD-associated genes, a small set of ASD-associated variants, and a statistic to quantify by how much a single nucleotide variant (SNV) in a protein coding region changes translation speed. In this study, we confirm and extend our hypothesis using a published set of 1,800 autism quartets (parents, one affected child and one unaffected child) and genome-wide variants. Then, we extend the test statistic to combine translation efficiency with other possibly relevant variables: ribosome profiling data, presence/absence of CpG dinucleotides, and phylogenetic conservation. The inclusion of ribosome profiling abundances strengthens our results for male–male sibling pairs. The inclusion of CpG information strengthens our results for female–female pairs, giving an insight into the significant gender differences in autism incidence. By combining the single-variant test statistic for all variants in a gene, we obtain a single gene score to evaluate how well a gene distinguishes between affected and unaffected siblings. Using statistical methods, we compute gene sets that have some power to distinguish between affected and unaffected siblings by translation efficiency of gene variants. Pathway and enrichment analysis of those gene sets suggest the importance of Wnt signaling pathways, some other pathways related to cancer, ATP binding, and ATP-ase pathways in the etiology of ASDs