Author Response:
Public Reviews:
Reviewer #1 (Public Review):
Summary:
In this study, Bonnifet et al. profile the presence of L1 ORF1p in the mouse and human brain. They claim that ORF1p is expressed in the human and mouse brain at a steady state and that there is an age-dependent increase in expression. This is a timely report as two recent papers have extensively documented the presence of full-length L1 transcripts in the mouse and human brain (PMID: 38773348 & PMID: 37910626). Thus, the finding that L1 ORF1p is consistently expressed in the brain is not surprising, but important to document.
Thank you for recognizing the importance of this study. The two cited papers have indeed reported the presence of full-length transcripts in the mouse and human brain. However, the first (PMID: 38773348) report has shown evidence of flL1 RNA and ORF1 protein expression in the mouse hippocampus (but not elsewhere) and the second (PMID: 37910626) shows full-length LINE-1 RNA expression and H3K4me3-ChIP data in the frontal and temporal lobe of the human brain, but not protein expression.
Strengths:
Several parts of this manuscript appear to be well done and include the necessary controls. In particular, the evidence for steady-state expression of ORF1p in the mouse brain appears robust.
Weaknesses:
Several parts of the manuscript appear to be more preliminary and need further experiments to validate their claims. In particular, the data suggesting expression of L1 ORF1p in the human brain and the data suggesting increased expression in the aged brain need further validation. Detailed comments:
(1) The expression of ORF1p in the human brain shown in Figure 1j is not convincing. Why are there two strong bands in the WB? How can the authors be sure that this signal represents ORF1p expression and not nonspecific labelling? Additional validations and controls are needed to verify the specificity of this signal.
We have validated the antibody (Abcam 245249 - https://www.abcam.com/en-us/products/primary-antibodies/line-1-orf1p-antibody-epr22227-6-ab245249), which we use for Western blotting experiments like in Fig1j), by several means. We have done immunoprecipitations (IPs) and co-immunoprecipitations (co-IPs) followed by quantitative mass spectrometry (LC-MS/MS). We efficiently detect ORF1p in IPs (Western blot) and by quantitative mass spectrometry (5 independent samples per IP-ORF1p and IP-IgG: ORF1p/IgG ratio: 40.86; adj p-value 8.7e-07; human neurons in culture). We also did co-IPs followed by Western blot using two different antibodies, the Millipore or the Abcam antibody to immunoprecipitate and the Abcam antibody for Western blotting (the Millipore AB does not work well on WB in our hands) which consistently showed a double band indicating that both bands are ORF1p-derived. We can provide this data to the revised manuscript, although some of it (the MS data) is subject of another study in preparation. Abcam also reports a double band, and they suspect that the lower band is a truncated form (see the link to their website above). ORF1p Western blots done by other labs with different antibodies have detected a second band in human samples
(1) Sato, S. et al. LINE-1 ORF1p as a candidate biomarker in high grade serous ovarian carcinoma. Sci Rep 13, 1537 (2023) in Figure 1D
(2) McKerrow, W. et al. LINE-1 expression in cancer correlates with p53 mutation, copy number alteration, and S phase checkpoint. Proc. Natl. Acad. Sci. U.S.A. 119, e2115999119 (2022)) showing a Western blot of an inducible LINE-1 (ORFeus) detected by the MABC1152 ORF1p antibody from Millipore Sigma in Figure 7 3) in a publication in eLife (Walter et al. eLife 2016;5:e11418. DOI: 10.7554/eLife.11418) in mouse ES cells with an antibody made in-house from another lab (gift) – Figure 2B
The lower band might thus be a truncated form of ORF1p or a degradation product which appears to be shared by mouse and human ORF1p. We will mention this in the revised version of the paper. In addition, we have used the very well characterized antibody from Millipore (https://www.merckmillipore.com/CH/en/product/Anti-LINE-1-ORF1p-Antibody-clone-4H1,MM_NF-MABC1152?ReferrerURL=https%3A%2F%2Fwww.google.com%2F) for immunostainings and detect ORF1p staining in human neurons in the very same brain regions (Fig 2H) including the cerebellum (selectively in Purkinje cells as in mice in Fig1B panel 10: human images not shown).
Altogether, based on our experimental validations and evidence from the literature, we are very confident that it is ORF1p that we detect on the blots.
(2) The data shown in Figure 2g are not convincing. How can the authors be sure that this signal represents ORF1p expression and not non-specific labelling? Extensive additional validations and controls are needed to verify the specificity of this signal.
Figure 2g shows a Western blot using an extensively used and well characterized ORF1p antibody from abcam (mouse ORF1p - (https://www.abcam.com/en-us/products/primary-antibodies/line-1-orf1p-antibody-epr21844108-ab216324; cited in at least 11 publications) after FACS-sorting of neurons (NeuN+) of the mouse brain. We have validated this ORF1p antibody ourselves in IPs (see Fig 6A) and co-IP followed by mass spectrometry (LC/MS-MS; see Fig 6, where we detect ORF1p exclusively in the 5 independent ORF1p-IP samples and not at all in 5 independent IgG-IP control samples, see Suppl Table 2). This together makes us very confident that we are looking at a specific ORF1p signal. Please note that in the IP of ORF1p shown in Fig6A, there is a double band as well, strongly suggesting that the lower band might be a truncated or processed form of ORF1p. As stated above, this double band has been detected in other studies (Walter et al. eLife 2016;5:e11418. DOI: 10.7554/eLife.11418) in mouse ES cells using an in-house generated antibody against mouse ORF1p. Thus, with either commercial or in-house generated antibodies in some mouse and human samples, there is a double band corresponding to full-length ORF1p and a truncated or processed version of it.
We noticed that we have not added the references of the primary antibodies used in Western blot experiments in the manuscript, which will be corrected in the revised version.
(3) The data showing a reduction in ORF1p expression in the aged mouse brain is confusing and maybe even misleading. Although there is an increase in the intensity of the ORF1p signal in ORF1p+ cells, the data clearly shows that fewer cells express ORF1p in the aged brain. If these changes indicate an overall loss or gain of ORF1p, expression in the aged brain is not resolved. Thus, conclusions should be more carefully phrased in this section. It is important to show the quantification of NeuN+ and NeuN- cells in young vs aged (not only the proportions as shown in Figure 3b) to determine if the difference in the number of ORF1p+ cells is due to loss of neurons or perhaps a sampling issue. More so, it would be essential to perform WB and/or proteomics experiments to complement the IHC data for the aged mouse samples.
The data presented in Fig3 C-I show a modest but widespread and reproducible increase in expression of ORF1p per cell. What decreases is the proportion of ORF1p+/NeuN+ cells (Fig3A, B), indicating that fewer cells might express ORF1p in the brain. However, the proportion or number/mm2 of ORF1p+ cells overall does not decrease significantly, neither does the proportion or number/mm2 of NeuN+ cells (data will be added to the revision). We show data of the % of NeuN+ and NeuN- cells in the ventral midbrain (Suppl Fig3C, quantified on confocal images)) which indeed indicates that in this region, there are less neurons in the aged mouse brain compared to the young. There might thus be a very regional decrease in neurons with age in the midbrain motor region. We will, however, as suggested, plot the number of NeuN+ and NeuN- cells per mm2 for the whole brain as well as the different regions in young vs aged to compare actual cell numbers per volume. While it is true that we cannot say that there is an overall loss or gain of ORF1p expression in the aged mouse brain, we believe that this is not of the highest importance as what most likely matters biologically in the context of aging is the quantity of ORF1p per cell (and possibly full-length LINE-1 RNA and ORF2p) and not “per brain”.
We also plan on doing Western blots on mouse brain tissues from young and aged individuals, however, we might run into limits regarding tissue availability of aged mice.
(4) The transcriptomic data presented in Figure 4 and Figure 5 are not convincing. Quantification of transposon expression on short read sequencing has important limitations. Longer reads and complementary approaches are needed to study the expression of evolutionarily young L1s (see PMID: 38773348 & PMID: 37910626 for examples of the current state of the art). Given the read length and the unstranded sequencing approach, I would at least ask the authors to add genome browser tracks of the upregulated loci so that we can properly assess the clarity of the results. I would also suggest adding the mappability profile of the elements in question. In addition, since this manuscript focuses on ORF1p, it would be essential to document changes in protein levels (and not just transcripts) in the ageing human brain.
We agree that there are limitations to the analysis of TEs with short read sequencing and we will add more text on this aspect in a revised version. The approaches shown in PMID: 38773348 & PMID: 37910626 or even a combination of them, would be ideal of course. However, here we reanalyzed a unique existing dataset (Dong et al, Nature Neuroscience, 2018; http://dx.doi.org/10.1038/s41593-018-0223-0), which contains RNA-seq data of human post-mortem dopaminergic neurons in a relatively high number of brain-healthy individuals of a wide age range including some “young” individuals which is rare in post-mortem studies. Such data is unfortunately not available with long read sequencing or any other more appropriate approach yet. Limitations are evident, but all limitations will apply equally to both groups of individuals that we compare. We will add genome browser tracks of the differentially expressed elements. The general mappability profile of the full-length LINE-1 “UIDs” is shown in Suppl Fig 6A. We will color-highlight the specific elements in this graph and will add genome browser data for these elements in a revised version.
We will not be able to document changes in protein levels in aged human dopaminergic neurons as we do not have access to this material. We have tried to obtain human substantia nigra tissues but were not able to get sufficient amounts to do laser-capture microdissection or FACS analyses, especially of young individuals. There are still important limitations to tissue availability, especially of regions of interest like the substantia nigra pars compacta affected in Parkinson’s disease.
(5) More information is needed on RNAseq of microdissections of dopaminergic neurons from 'healthy' postmortem samples of different ages. No further information on these samples is provided. I would suggest adding a table with the clinical information of these samples (especially age, sex, and cause of death). The authors should also discuss whether this experiment has sufficient power. The human ageing cohort seems very small to me.
This is a re-analysis of a published dataset (Dong et al, Nat Neurosci, 2018; doi:10.1038/s41593-018-0223-0), available through dbgap (phs001556.v1.p1). In this original article, the criteria for inclusion as a brain-healthy control were as follows:
“…Subjects… were without clinicopathological diagnosis of a neurodegenerative disease meeting the following stringent inclusion and exclusion criteria. Inclusion criteria: (i) absence of clinical or neuropathological diagnosis of a neurodegenerative disease, for example, PD according to the UKPDBB criteria47, Alzheimer’s disease according to NIA-Reagan criteria48, or dementia with Lewy bodies by revised consensus criteria49; for the purpose of this analysis incidental Lewy body cases (not meeting clinicopathological diagnostic criteria for PD or other neurodegenerative disease) were accepted for inclusion; (ii) PMI ≤ 48 h; (iii) RIN50 ≥ 6.0 by Agilent Bioanalyzer (good RNA integrity); and (iv) visible ribosomal peaks on the electropherogram. Exclusion criteria were: (i) a primary intracerebral event as the cause of death; (2) brain tumor (except incidental meningiomas); (3) systemic disorders likely to cause chronic brain damage.”
We do not have access to the cause of death, but we will add available metadata to the manuscript.
We will perform a post-hoc power analysis and add the result to the revision.
(6) The findings in this manuscript apply to both human and mouse brains. However, the landscape of the evolutionarily young L1 subfamilies between these two species is very different and should be part of the discussion. For example, the regulatory sequences that drive L1 expression are quite different in human and mouse L1s. This should be discussed.
Indeed, they are very different. We will add this to the discussion.
(7) On page 3 the authors write: "generally accepted that TE activation can be both, a cause and consequence of aging". This statement does not reflect the current state of the field. On the contrary, this is still an area of extensive investigation and many of the findings supporting this hypothesis need to be confirmed in independent studies. This statement should be revised to reflect this reality.
We agree, this is overstated, we will change this sentence accordingly.
Reviewer #2 (Public Review):
Summary:
Bonnifet et al. sought to characterize the expression pattern of L1 ORF1p expression across the entire mouse brain, in young and aged animals, and to corroborate their characterization with Western blotting for L1 ORF1p and L1 RNA expression data from human samples. They also queried L1 ORF1p interacting partners in the mouse brain by IP-MS.
Strengths:
A major strength of the study is the use of two approaches: a deep-learning detection method to distinguish neuronal vs. non-neuronal cells and ORF1p+ cells vs. ORF1p- cells across large-scale images encompassing multiple brain regions mapped by comparison to the Allen Brain Atlas, and confocal imaging to give higher resolution on specific brain regions. These results are also corroborated by Western blotting on six mouse brain regions. Extension of their analysis to post-mortem human samples, to the extent possible, is another strength of the paper. The identification of novel ORF1p interactors in the brain is also a strength in that it provides a novel dataset for future studies.
Thank you for highlighting the strength of our study.
Weaknesses:
The main weakness of the study is that cell type specificity of ORF1p expression was not examined beyond neuron (NeuN+) vs non-neuron (NeuN-). Indeed, a recent study (Bodea et al. 2024, Nature Neuroscience) found that ORF1p expression is characteristic of parvalbumin-positive interneurons, and it would be very interesting to query whether other neuronal subtypes in different brain regions are distinguished by ORF1p expression.
We agree that this point is important to address. We do provide indications for this in the manuscript. For instance, we detect staining in mouse and human Purkinje cells of the cerebellum in accordance with data from Takahashi et al, Neuron, 2022; DOI: 10.1016/j.neuron.2022.08.011. We also know from previous work, that in the mouse ventral midbrain, dopaminergic neurons (TH+/NeuN+) express ORF1p and that these neurons express higher levels of ORF1p than adjacent non-dopaminergic neurons (TH-/NeuN+; Blaudin de Thé et al, EMBO J, 2018). Others have shown evidence of full-length L1 RNA expression in both excitatory and inhibitory neurons but much less expression in non-neuronal cells (Garza et al, SciAdv, 2023). In sum, although this has not been investigated systematically brain-wide, it does not seem as if ORF1p expression is restricted to PV cells overall. We will deepen the discussion of this aspect in the revised manuscript. To address this question experimentally, we will try to perform ORF1p stainings on different brain regions together with PV stainings and add this data to a revised version, if possible.
The data suggesting that ORF1p expression is increased in aged mouse brains is intriguing, although it seems to be based upon modestly (up to 27%, dependent on brain region) higher intensity of ORF1p staining rather than a higher proportion of ORF1+ neurons. Indeed, the proportion of NeuN+/Orf1p+ cells actually decreased in aged animals. It is difficult to interpret the significance and validity of the increase in intensity, as Hoechst staining of DNA, rather than immunostaining for a protein known to be stably expressed in young and aged neurons, was used as a control for staining intensity.
It would have been indeed interesting to have another marker than DNA as a control. However, this requires a protein that is indeed stably expressed throughout the brain and throughout age. We are not aware of a protein for which this has been established. DNA staining with Hoechst does control for technical artefacts. We have whole-brain imaging data for the protein Rbfox3 (NeuN) which we used as a marker for cell identity. If this protein turns out to be stable, we could add this data to a revised version.
The main weakness of the IP-MS portion of the study is that none of the interactors were individually validated or subjected to follow-up analyses. The list of interactors was compared to previously published datasets, but not to ORF1p interactors in any other mouse tissue.
As stated in the manuscript, the list of previously published datasets does include a mouse dataset with ORF1p interacting proteins in mouse spermatocytes (please see line 434-435: “ORF1p interactors found in mouse spermatocytes were also present in our analysis including CNOT10, CNOT11, PRKRA and FXR2 among others (Suppl_Table4).”) -> De Luca, C., Gupta, A. & Bortvin, A. Retrotransposon LINE-1 bodies in the cytoplasm of piRNA-deficient mouse spermatocytes: Ribonucleoproteins overcoming the integrated stress response. PLoS Genet 19, e1010797 (2023)). We indeed did not validate any interactors for several reasons (economic reasons and time constraints (post-doc leaving)). However, we feel that the significant overlap with previously published interactors highlights the validity of our data and we anticipate that this list of ORF1p protein interactors in the mouse brain will be of further use for the community.
The authors achieved the goals of broadly characterizing ORF1p expression across different regions of the mouse brain, and identifying putative ORF1p interactors in the mouse brain. However, findings from both parts of the study are somewhat superficial in depth.
This provides a useful dataset to the field, which likely will be used to justify and support numerous future studies into L1 activity in the aging mammalian brain and in neurodegenerative disease. Similarly, the list of ORF1p interacting proteins in the brain will likely be taken up and studied in greater depth.
Reviewer #3 (Public Review):
The question about whether L1 exhibits normal/homeostatic expression in the brain (and in general) is interesting and important. L1 is thought to be repressed in most somatic cells (with the exception of some stem/progenitor compartments). However, to our knowledge, this has not been authoritatively / systematically examined and the literature is still developing with respect to this topic. The full gamut of biological and pathobiological roles of L1 remains to be shown and elucidated and this area has garnered rapidly increasing interest, year-by-year. With respect to the brain, L1 (and repeat sequences in general) have been linked with neurodegeneration, and this is thought to be an aging-related consequence or contributor (or both) of inflammation. This study provides an impressive and apparently comprehensive imaging analysis of differential L1 ORF1p expression in mouse brain (with some supporting analysis of the human brain), compatible with a narrative of non-pathological expression of retrotransposition-competent L1 sequences. We believe this will encourage and support further research into the functional roles of L1 in normal brain function and how this may give way to pathological consequences in concert with aging. However, we have concerns with conclusions drawn, in some cases regardless of the lack of statistical support from the data. We note a lack of clarity about how the 3rd party pre-trained machine learning models perform on the authors' imaging data (validation/monitoring tests are not reported), as well as issues (among others) with the particular implementation of co-immunoprecipitation (ORF1p is not among the highly enriched proteins and apparently does not reach statistical significance for the comparison) - neither of which may be sufficiently rigorous.
Thank you for your comments on our manuscript.
In a revised version and a more in-depth response, we will address the concerns about the machine learning paradigm. Concerning the co-IP-MS, we can confirm that ORF1p is among the highly enriched proteins as it was not found in the IgG control (in 5 independent samples), only in the ORF1p-IP (in 5 out of 5 independent samples). This is what the infinite sign in Suppl Table 2 indicates and this is why there is no p-value assigned as infinite/0 doesn’t allow to calculate a p-value. We will make this clearer in a revised version of the manuscript.
