Advances in expression of heterologous protein products by Streptococcus gordonii

Streptococcus gordonii is a bacterial species that naturally colonizes the oral cavity of most healthy humans. It resides in the mouth as an adherent to dental surfaces and, with few exceptions, does not cause disease in individuals it inhabits. It possesses qualities that encourage its use as a vector to deliver human vaccines against pathogens that enter at mucosal surfaces such as the mouth, throat, lungs, gastrointestinal or urogenital tracts. S. gordonii is easily engineered in the laboratory to produce foreign, or heterologous, proteins derived from pathogenic organisms. Thus, S. gordonii engineered to produce particular viral may stimulate protective immunity against smallpox, a potential biowarfare agent, without causing the harmful side effects associated with the current vaccine. The research presented in this dissertation desribes new ways that S. gordonii can be genetically engineered for greater production of heterologous products derived from pathogens. S. gordonii naturally secretes two proteins, glucosyltransferase and amylase-binding protein during growth, and the genetics S. gordonii utilizes to produce these products were reproduced and situated on a plasmid to facilitate production of a heterologous product. Although some plasmids constructs could not be introduced into E. coli, it was found that the addition of a synthetic peptide to growth medium enhanced the efficiency with which S. gordonii assimilated genetic material, thereby allowing the plasmid to be directly introduced into S. gordonii. The expression plasmid is well retained by S. gordonii that colonized the oral cavities of mice. The mechanisms that S. gordonii uses to export products from the cell are capable of accommodating high-level expression of at least two heterologous products, smallpox vaccine candidates A27L and B5R. Further, particular viral products that had previously proven recalcitrant to expression were produced by S. gordonii by engineering the fusion of viral sequences with those derived from a bacterial protein

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Same data, different conclusions : radical dispersion in empirical results when independent analysts operationalize and test the same hypothesis

In this crowdsourced initiative, independent analysts used the same dataset to test two hypotheses regarding the effects of scientists’ gender and professional status on verbosity during group meetings. Not only the analytic approach but also the operationalizations of key variables were left unconstrained and up to individual analysts. For instance, analysts could choose to operationalize status as job title, institutional ranking, citation counts, or some combination. To maximize transparency regarding the process by which analytic choices are made, the analysts used a platform we developed called DataExplained to justify both preferred and rejected analytic paths in real time. Analyses lacking sufficient detail, reproducible code, or with statistical errors were excluded, resulting in 29 analyses in the final sample. Researchers reported radically different analyses and dispersed empirical outcomes, in a number of cases obtaining significant effects in opposite directions for the same research question. A Boba multiverse analysis demonstrates that decisions about how to operationalize variables explain variability in outcomes above and beyond statistical choices (e.g., covariates). Subjective researcher decisions play a critical role in driving the reported empirical results, underscoring the need for open data, systematic robustness checks, and transparency regarding both analytic paths taken and not taken. Implications for organizations and leaders, whose decision making relies in part on scientific findings, consulting reports, and internal analyses by data scientists, are discussed

Same data, different conclusions: Radical dispersion in empirical results when independent analysts operationalize and test the same hypothesis

In this crowdsourced initiative, independent analysts used the same dataset to test two hypotheses regarding the effects of scientists’ gender and professional status on verbosity during group meetings. Not only the analytic approach but also the operationalizations of key variables were left unconstrained and up to individual analysts. For instance, analysts could choose to operationalize status as job title, institutional ranking, citation counts, or some combination. To maximize transparency regarding the process by which analytic choices are made, the analysts used a platform we developed called DataExplained to justify both preferred and rejected analytic paths in real time. Analyses lacking sufficient detail, reproducible code, or with statistical errors were excluded, resulting in 29 analyses in the final sample. Researchers reported radically different analyses and dispersed empirical outcomes, in a number of cases obtaining significant effects in opposite directions for the same research question. A Boba multiverse analysis demonstrates that decisions about how to operationalize variables explain variability in outcomes above and beyond statistical choices (e.g., covariates). Subjective researcher decisions play a critical role in driving the reported empirical results, underscoring the need for open data, systematic robustness checks, and transparency regarding both analytic paths taken and not taken. Implications for organizations and leaders, whose decision making relies in part on scientific findings, consulting reports, and internal analyses by data scientists, are discussed