Contribution of mobile elements to the uniqueness of human genome with more than 15,000 human-specific insertions
Bibliographic record
Abstract
Abstract Mobile elements (MEs) collectively constituted to at least 51% of the human genome. Due to their past incremental accumulation and ongoing DNA transposition of members from certain subfamilies, MEs serve as a significant source for both inter- and intra-species genetic diversity during primate and human evolution. Since MEs can exert direct impact on gene function via a plethora of mechanism, it is believed that the ME-derived genetic diversity has contributed to the phenotypic differences between human and non-human primates, as well as among human populations and individuals. To define the specific contribution of MEs in making Human sapiens as a biologically unique species, we aim to compile a complete list of MEs that are only uniquely present in the human genome, i.e., human-specific MEs (HS-MEs). By making use of the most recent reference genome sequences for human and many other primates and a unbiased more robust and integrative multi-way comparative genomic approach, we identified a total of 15,463 HS-MEs. This list of HS-MEs represents a 120% increase from prior studies with over 8,000 being newly identified as HS-MEs. Collectively, these ~15,000 HS-MEs have contributed to a total of 15 million base pair (Mbp) sequence increase through insertion, generation of target site duplications, and transductions, as well as a 0.5 Mbp sequence loss via insertion- mediated deletions, leading to a net total of 14.5 Mbp genome size increase. Other new observations made with these HS-MEs include: 1) identification of several additional ME subfamilies with significant transposition activities not visible with prior smaller datasets (e.g. L1HS, L1PA2, and HERV-K); 2) A clear similarity of the retrotransposition mechanism among L1, Alu s, and SVAs that is distinct from HERVs based on the pre- integration site sequence motifs; 3) Y-chromosome as a strikingly hot target for HS-MEs, particularly for LTRs, which showed an insertion rate 15 times higher than the genome average; 4) among the ME types, SVAs seem to show a very strong bias in inserting into existing SVAs. Among the HS-MEs, more than 8,000 elements were integrated into the vicinity of ~4900 unique genes, in regions including CDS, untranslated exon regions, promoters, and introns of protein coding genes, as well as promoters and exons of non- coding RNAs. In seven cases, MEs participate in protein coding. Furthermore, 1,213 HS-MEs contributed to a total of 3,124 experimentally identified binding sites for 146 of the 161 transcriptional factors in association with 622 genes. All these data suggest that these HS-MEs, despite being very young, already showed sufficient sign for their participation in gene function via regulation of transcription, splicing, and protein coding, with more potential for future participation. In conclusion, our results demonstrate that the amount of MEs uniquely occurred in the human genome is much higher than previously known, and we predict that the same is true regarding their impact on human genome evolution and function. The comprehensive list of HS-MEs provides an important reference resource for studying the impact of DNA transposition in human genome evolution and gene function.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".