From genetic to genomic regulation: iterativity in microRNA research

Question-driven stepwise experimental discoveries in biochemistry: two case studies

Philosophers of science diverge on the question what drives the growth of scientific knowledge. Most of the twentieth century was dominated by the notion that theories propel that growth whereas experiments play secondary roles of operating within the theoretical framework or testing theoretical predictions. New experimentalism, a school of thought pioneered by Ian Hacking in the early 1980s, challenged this view by arguing that theory-free exploratory experimentation may in many cases effectively probe nature and potentially spawn higher evidence-based theories. Because theories are often powerless to envisage workings of complex biological systems, theory-independent experimentation is common in the life sciences. Some such experiments are triggered by compelling observation, others are prompted by innovative techniques or instruments, whereas different investigations query big data to identify regularities and underlying organizing principles. A distinct fourth type of experiments is motivated by a major question. Here I describe two question-guided experimental discoveries in biochemistry: the cyclic adenosine monophosphate mediator of hormone action and the ubiquitin-mediated system of protein degradation. Lacking underlying theories, antecedent data bases, or new techniques, the sole guides of the two discoveries were respective substantial questions. Both research projects were similarly instigated by theory-free exploratory experimentation and continued in alternating phases of results-based interim working hypotheses, their examination by experiment, provisional hypotheses again, and so on. These two cases designate theory-free, question-guided, stepwise biochemical investigations as a distinct subtype of the new experimentalism mode of scientific enquiry.

When is it Safe to Edit the Human Germline?

In the fall of 2018 Jiankui He shocked the international community with the following announcement: two female babies, "Lulu" and "Nana," whose germlines had been modified by the cutting edge, yet profoundly unsafe CRISPR-Cas9 technology had been born. This event galvanized policy makers and scientists to advocate for more explicit and firm regulation of human germline gene editing (GGE). Recent policy proposals attempt to integrate safety considerations and public input to identify specific types of diseases that may be safe targets for human GGE (Sarkar forthcoming; Guttinger. 2019. "Editing the Reactive Genome: Towards Postgenomic Ethics of Germline Editing." Journal of Applied Philosophy. "Editing the Reactive Genome: Towards Postgenomic Ethics of Germline Editing." Journal of Applied Philosophy. "Editing the Reactive Genome: Towards Postgenomic Ethics of Germline Editing." Journal of Applied Philosophy; Lander et al., 2019). This paper argues these policy proposals are inadequate in different ways. While Sarkar (forthcoming) intends to incorporate input from the disability community for the purpose of deciding the value of human GGE, I argue that his strategy for doing so is inadequate. I'll argue that an iterative, deliberative process is a more appropriate framework for allowing the disability community to inform policy on human GGE. Further policy proposals have been framed in terms of monogenetic or single-gene diseases (Guttinger. 2019. "Editing the Reactive Genome: Towards Postgenomic Ethics of Germline Editing." Journal of Applied Philosophy. "Editing the Reactive Genome: Towards Postgenomic Ethics of Germline Editing." Journal of Applied Philosophy. "Editing the Reactive Genome: Towards Postgenomic Ethics of Germline Editing." Journal of Applied Philosophy; Lander et al., 2019). I argue that this way of conceptualizing disease is not what matters for deciding which disorders are viable candidates for human GGE. Instead, what matters is that (1) the disease in question must have (among its set of causes) genes that have a high degree of causal control with respect to the disease and (2) alternative nucleic acid sequences variants that are likely to produce traits deemed desirable must be identified. Previous policy proposals leave (2) unspecified. What conditions must be met for satisfying condition (2) should not be left to individual scientists to decide for themselves. The present proposal offers some guidance on this issue.

Small RNA research and the scientific repertoire: a tale about biochemistry and genetics, crops and worms, development and disease

The discovery of RNA interference in 1998 has made a lasting impact on biological research. Identifying the regulatory role of small RNAs changed the modes of molecular biological inquiry as well as biologists' understanding of genetic regulation. This article examines the early years of small RNA biology's success story. I query which factors had to come together so that small RNA research came into life in the blink of an eye. I primarily look at scientific repertoires as facilitators of rapid scientific change. I show that for a short period of time, between the years 1998 and 2002, different model organism communities, investigative strategies, technological innovations, and research interests could be successfully aligned to take small RNA research off the ground. I discuss how the keystone discoveries were situated in specific experimental traditions and what strategies were employed to establish these discoveries as more general phenomena. Providing thus a practice-based approach of rapid scientific change, I ask how to relate the change in propositional bits of scientific knowledge with changes in scientific practice.

Data-Intensive Science and Research Integrity

In this commentary, we consider questions related to research integrity in data-intensive science and argue that there is no need to create a distinct category of misconduct that applies to deception related to processing, analyzing, or interpreting data. The best way to promote integrity in data-intensive science is to maintain a firm commitment to epistemological and ethical values, such as honesty, openness, transparency, and objectivity, which apply to all types of research, and to promote education, policy development, and scholarly debate concerning appropriate uses of statistics.

Conceptions of Good Science in Our Data-Rich World

Scientists have been debating for centuries the nature of proper scientific methods. Currently, criticisms being thrown at data-intensive science are reinvigorating these debates. However, many of these criticisms represent long-standing conflicts over the role of hypothesis testing in science and not just a dispute about the amount of data used. Here, we show that an iterative account of scientific methods developed by historians and philosophers of science can help make sense of data-intensive scientific practices and suggest more effective ways to evaluate this research. We use case studies of Darwin's research on evolution by natural selection and modern-day research on macrosystems ecology to illustrate this account of scientific methods and the innovative approaches to scientific evaluation that it encourages. We point out recent changes in the spheres of science funding, publishing, and education that reflect this richer account of scientific practice, and we propose additional reforms.

MicroRNA-181d is a tumor suppressor in human esophageal squamous cell carcinoma inversely regulating Derlin-1

Esophageal squamous cell carcinoma (ESCC) is the predominant subtype of human esophageal cancer in East Asia. In the present study, we explored the tumor suppressive function of microRNA-181d (miR-181d) in ESCC. Quantitative RT-PCR was used to assess gene levels of miR‑181d in both ESCC cell lines and clinical samples. Lentiviral transduction was used to overexpress miR-181d in ECA109 and Kyse30 cells. The possible tumor suppressive effects of miR-181d overexpression on ESCC proliferation, migration and cell cycle transition in vitro, and tumorigenicity in vivo were examined. Downstream target gene of miR‑181d in ESCC, Derlin-1 (DERL1) was assessed by luciferase assay and qRT-PCR. DERL1 was also force expressed in miR‑181d-overexperssed ECA109 and Kyse30 cells to evaluate its reverse effect on miR-181d mediated tumor suppression in ESCC. miR-181d was aberrantly downregulated in both ESCC cell lines and human tumors. Lentivirus-mediated miR-181d overexpression had significant tumor suppressing functions by inhibiting cancer proliferation, migration and arresting cell cycle transition in vitro, as well as inhibiting tumorigenicity in vivo. DERL1 was identified as downstream target gene of miR-181d in ESCC cells. Forced overexpression of DERL1 in ESCC cells was shown to greatly reverse the tumor suppressive effects of miR-181d upregulation on ESCC in vitro proliferation, migration and cell cycle arrest. Out data suggest that miR-181d is a tumor suppressor in ESCC inversely regulating its downstream target gene of DERL1.

Variations in Scientific Data Production: What Can We Learn from #Overlyhonestmethods?

In recent months months the hashtag #overlyhonestmethods has steadily been gaining popularity. Posts under this hashtag--presumably by scientists--detail aspects of daily scientific research that differ considerably from the idealized interpretation of scientific experimentation as standardized, objective and reproducible. Over and above its entertainment value, the popularity of this hashtag raises two important points for those who study both science and scientists. Firstly, the posts highlight that the generation of data through experimentation is often far less standardized than is commonly assumed. Secondly, the popularity of the hashtag together with its relatively blasé reception by the scientific community reveal that the actions reported in the tweets are far from shocking and indeed may be considered just "part of scientific research". Such observations give considerable pause for thought, and suggest that current conceptions of data might be limited by failing to recognize this "inherent variability" within the actions of generation--and thus within data themselves. Is it possible, we must ask, that epistemic virtues such as standardization, consistency, reportability and reproducibility need to be reevaluated? Such considerations are, of course, of particular importance to data sharing discussions and the Open Data movement. This paper suggests that the notion of a "moral professionalism" for data generation and sharing needs to be considered in more detail if the inherent variability of data are to be addressed in any meaningful manner.

Can patents prohibit research? On the social epistemology of patenting and licensing in science

A topic of growing importance within philosophy of science is the epistemic implications of the organization of research. This paper identifies a promising approach to social epistemology--nonideal systems design--and uses it to examine one important aspect of the organization of research, namely the system of patenting and licensing and its role in structuring the production and dissemination of knowledge. The primary justification of patenting in science and technology is consequentialist in nature. Patenting should incentivize research and thereby promote the development of knowledge, which in turn facilitates social progress. Some have disputed this argument, maintaining that patenting actually inhibits knowledge production. In this paper, I make a stronger argument; in some areas of research in the US--in particular, research on GM seeds--patents and patent licenses can be, and are in fact being, used to prohibit some research. I discuss three potential solutions to this problem: voluntary agreements, eliminating patents, and a research exemption. I argue against eliminating patents, and I show that while voluntary agreements and a research exemption could be helpful, they do not sufficiently address the problems of access that are discussed here. More extensive changes in the organization of research are necessary.

Scientific perspectivism: A philosopher of science's response to the challenge of big data biology

Big data biology-bioinformatics, computational biology, systems biology (including 'omics'), and synthetic biology-raises a number of issues for the philosophy of science. This article deals with several such: Is data-intensive biology a new kind of science, presumably post-reductionistic? To what extent is big data biology data-driven? Can data 'speak for themselves?' I discuss these issues by way of a reflection on Carl Woese's worry that "a society that permits biology to become an engineering discipline, that allows that science to slip into the role of changing the living world without trying to understand it, is a danger to itself." And I argue that scientific perspectivism, a philosophical stance represented prominently by Giere, Van Fraassen, and Wimsatt, according to which science cannot as a matter of principle transcend our human perspective, provides the best resources currently at our disposal to tackle many of the philosophical issues implied in the modeling of complex, multilevel/multiscale phenomena.