Bibliographic record
Abstract
Our genetic code – our genome or DNA – is what defines our biology, how we interact with the environment and which traits give us an advantage in adapting, surviving and reproducing. That was the staple of genetics for years, but it was not the complete story. Later, we found out that it is not only the genome but also something more dynamic that is important, something ‘above’ the genome: the epigenome. Conrad Waddington coined the term epigenetics in the 1940s to refer to the interactions between the environment and the genetic code. It is how our DNA quickly ‘learns’ during development, under changing environmental conditions, without any modifications to the genetic code. DNA methylation, histone modification, alterations to chromatin structure and small non-coding RNAs are some of the ways in which genes can be up-regulated or down-regulated, leading to different phenotypes without any changes to the genetic code. Recent studies have pointed to yet another epigenetic method, which may contribute to the development of resistance to various chemicals in insects: long non-coding RNAs (lncRNAs).Recent advances in deep sequencing techniques have made it possible to identify and classify various types of lncRNAs. Although in the past they had been considered nothing more than transcriptional noise, it is now becoming clear that lncRNAs are involved in a multitude of biological pathways, such as growth and development, cell differentiation and gene expression. Through their study, Kayvan Etebari and colleagues have provided, for the first time, a glimpse of the lncRNA profile of the cabbage moth (Plutella xylostella L.). They also analysed the role that long intergenic non-coding RNAs (lincRNAs), a class of lncRNAs, play in insecticide resistance.The authors found significant correlations between lincRNAs and the number of protein-coding genes in the genome scaffolds that they examined. As lincRNAs are generally expressed with their neighbouring proteins, the authors determined that cabbage moth lincRNAs play a role in protein-binding activities, such as DNA and RNA binding, and transcription regulation. When cabbage moth lincRNA sequences were compared with those of two closely related species, the silk moth (Bombyx mori) and the fall armyworm (Spodoptera frugiperda), only 14 similarities were detected among all three species. This lack of conservation among identified lincRNAs has been reported in vertebrates as well, making it difficult to identify the specific roles they play across various species.When the lincRNA expression profiles of pesticide-resistant and Bacillus thuringiensis (Bt) endotoxin-resistant cabbage moths were compared with those of susceptible individuals, Etebari and colleagues noticed that approximately 70% of the lincRNAs examined were over-expressed in insecticide-resistant populations, and only 50% were up-regulated in Bt-resistant individuals. While the same lincRNAs were commonly altered in the pesticide- and toxin-resistant individuals, the way in which they were modified differed from one population to another, suggesting that their roles in resistance are chemical specific. The authors also found that direct exposure of cabbage moth larvae to insecticides significantly impacted the transcript level of several lincRNAs in insecticide-resistant individuals when compared with non-exposed controls. Whether lincRNAs contribute to resistance development through epigenetic mechanisms, or are a part of the chemical detoxification pathways, remains to be resolved. However, it is now clear that what was previously known as transcriptional noise may indeed play a larger role in gene regulation than was initially thought.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".