Regulatory networks of KRAB zinc finger genes and transposable elements changed during human brain evolution and disease

Transposable elements (TEs) are repetitive DNA sequences capable of migrating and inserting into new locations within the host genome. When transposed, TEs may induce phenotypic changes in the organism (Bourque et al., 2018; Schrader and Schmitz, 2019). One well-known textbook example is the development of an industrial melanization mutant phenotype in the British peppered moth, Biston betularia, which has been linked to increased expression of the cort transcript due to a TE insertion in its first intron (van Hof et al., 2016). Another classic example for primate evolution is the retrovirus insertion in the salivary amylase gene, AMY1C, with this sequence being essential for primates to evolve as one of the few mammals capable of expressing amylase in saliva (Ting et al., 1992). A further example is the evolutionary loss of tails in humans and apes, which is associated with the insertion of an Alu retrotransposon into the intronic region of the TBXT gene. This insertion creates an alternative splice site with an ancestral Alu retrotransposon in the sequence, leading to the excision of the middle exon and the formation of an isoform (Xia et al., 2024).

TEs comprise about half or even more of the mammalian genome (Platt et al., 2018; Qu et al., 2023). Their positions in the genome can be annotated with tools like RepeatMasker (Lawson et al., 2023). TE insertions can be deleterious, such as the insertion of an L1 element initiating colorectal cancer in humans (Scott et al., 2016). Therefore, a variety of host factors are employed to regulate TE expression, such as small RNAs, chromatin and DNA modification pathways, and KRAB zinc finger (KRAB-ZNF) proteins (Colonna Romano and Fanti, 2022). KRAB-ZNF proteins are the largest family of transcription factors in higher vertebrates (Huntley et al., 2006; Ecco et al., 2017), characterized by fast evolution and contributing to gene expression differences between primates (Nowick et al., 2009; Nowick et al., 2013). KRAB-ZNF proteins bind to the interspersed DNA sequences of TEs and repress their expression upon recruiting the cofactor KAP1 (Groner et al., 2010). Fast evolution was also reported for TEs, which led to hypothesizing an evolutionary arms-race model, in which mutated TEs have the chance to escape the repression from KRAB-ZNF proteins until the KRAB-ZNF proteins evolve again with a suitable recognition ability (Jacobs et al., 2014; Imbeault et al., 2017). For instance, in primates, coevolution between a family of TEs, retroelements in endogenous retroviruses (ERVs), and the tandem repeats in KRAB-ZNF genes has been observed (Thomas and Schneider, 2011).

Studying the expression of KRAB-ZNF genes and TEs in the evolution of the human brain has crucial relevance, since some TEs are actively transcribed in the developing human brain (Bodea et al., 2018), including L1 subfamilies, HERV-K subfamilies, and primate-specific Alu subfamilies (Hancks and Kazazian, 2010; Larsen et al., 2018; Dembny et al., 2020). Furthermore, TE-derived promoters play a role in gene transcription within the human brain, which in turn suggests the significance of TEs in gene regulation during neurodevelopment (Playfoot et al., 2021). Interestingly, a substantial number of differentially expressed primate-specific KRAB-ZNF genes were detected in the adult prefrontal cortex, comparing humans to chimpanzees (Nowick et al., 2009), and newly evolved KRAB-ZNF genes were predominantly detected in the developing human brain (Zhang et al., 2011). Therefore, analyzing the interplay between TEs and KRAB-ZNF genes in the context of primate brain evolution could provide valuable insights into the complex mechanisms shaping human brain development and function.

The dysregulation of TE expression has also been linked to neurodegenerative diseases, including Alzheimer’s disease (AD), the primary cause of dementia (Ravel-Godreuil et al., 2021). One of the biomarkers of AD, the Tau protein, appears to induce the expression of at least some TEs (Guo et al., 2018), and the presence of TE products in the cytosol and endosomes induces neuroinflammation in AD patients (Evering et al., 2023). Previous studies have suggested that cognitive skills are related to zinc finger genes and proteins. For instance, patients carrying the schizophrenia-risk allele of ZNF804A showed differences in their reading and spelling performance (Becker et al., 2012). Additionally, the single nucleotide polymorphism of a KRAB-ZNF gene, ZNF224, is associated with AD neuropathology and cognitive functions (Shulman et al., 2010).

Taken together, the study of the regulatory networks involving KRAB-ZNF genes and TEs is expected to provide insights into the evolution of the human brain and the development of neurodegenerative diseases. Here, we present results from two independent RNA-seq datasets: one comparing different brain regions between humans and multiple nonhuman primates (NHPs) (Primate Brain Data) and the other comparing human control samples with AD samples (Mayo Data). These datasets collectively encompass a total of 514 samples from different species, brain regions, and disease states. Since it is very challenging to quantify and normalize expression of TEs for across-species comparison, we developed the TEKRABber R package for the systematic and comparative analysis of TE subfamilies (abbreviated as TEs in the following content). TEKRABber can further be used to explore expression correlations between TEs and any genes in any species of interest. Here, we used it to explore correlations with KRAB-ZNF genes in primates. Our work reveals an intricate network of TEs and KRAB-ZNF genes in the brain that changed during evolution and is modified in AD brains.

In this study, we conducted systematic examinations of TEs and KRAB-ZNFs expression and correlation patterns in the brain and compared them between humans and NHPs, as well as between healthy human samples and AD. We demonstrated high divergence in the expression of TEs and KRAB-ZNF genes between species, suggesting a prominent evolutionary signature in the regulation of TEs and KRAB-ZNF genes in primates (Figure 2A). We also found that evolutionary younger members of both TEs and KRAB-ZNF genes exhibit significantly lower expression levels compared to older members (Figure 2D), proposing that newly emerged genetic elements might be subjected to more stringent regulatory mechanisms, potentially avoiding disruption at the organismal level. To represent the complexity of interactions between TEs and KRAB-ZNFs, we derived bipartite networks. We showed that humans had higher connectivity in these networks compared to NHPs (Figure 4B). This observation is in line with previous findings that humans also have higher connectivity in the transcription factor networks of the brain compared to NHPs (Nowick et al., 2009; Bakken et al., 2016; Berto et al., 2018). A substantial proportion of young TE:KRAB-ZNF seems to be human-specific, pointing to recent additions to the TE:KRAB-ZNF network in the human lineage and presumably an increase in the complexity of gene regulation (Figure 4F). The network of the 21 TE:KRAB-ZNF that were only discovered in the healthy human temporal cortex but not in other NHPs (Figure 6E and F) indicates potential alterations or loss of control of some TE expression in AD brains. These collective findings suggest an important role of the TE:KRAB-ZNF network in shaping the evolution and in maintaining the functionality of the healthy human brain, and particularly highlight the co-expression involving evolutionary young TEs and young KRAB-ZNFs.

Given the generally acknowledged role of KRAB-ZNF proteins as repressors on TE expressions, we expected to observe many negative correlations between KRAB-ZNFs and TEs. Indeed, many evolutionary young and human-specific correlations involving especially TEs of the Alu subfamilies are negative. However, we also observed numerous positive correlations between KRAB-ZNFs and TEs. Keeping in mind that correlations do not indicate direction of the causation and can also be caused by indirect relationships and other factors, several plausible explanations could be offered for these positive interactions. First, some TEs can function as cis-acting regulatory elements capable of influencing the expression of nearby genes, which have been mentioned to be tissue-specifically co-opted TEs (Sentmanat and Elgin, 2012; Wolf et al., 2020; Coronado-Zamora and González, 2023). Second, it is conceivable that the expression levels of KRAB-ZNF genes themselves are influenced by the presence and activity of TEs within the genome (Pontis et al., 2019; Senft and Macfarlan, 2021). Another perspective is that primate-specific KRAB-ZNFs can bind to gene promoters, regulating gene expression in the primate brain without necessarily targeting TEs (Farmiloe et al., 2020); observed correlations could then stem from indirect regulation. We should note, however, that 19.43% (446/2,296) of positively correlated pairs overlap significantly with the ChIP-exo results, i.e., exist in the presence of a KRAB-ZNF protein binding to the TE (Figure 3B). Furthermore, we discovered that all the human-specific correlations are evolutionary young correlations. Notably, different TE subfamilies interact with KRAB-ZNFs differently, e.g., Alu subfamilies are the only TEs that have more negative correlations than positive ones, and LTR subfamilies have only positive correlations (Figure 4F). Therefore, we encourage future research focusing on primate evolution to take these factors into account.

TEs have also been observed to be dysregulated in certain diseases. Here, we observed that the variances in expression levels of TEs and KRAB-ZNFs were more distinct across various brain regions than between control and AD samples (Figure 5A). This finding points out the difficulties in identifying differences between control and AD samples solely through the consideration of DE. This is also illustrated by the detection of only 6 and 10 differentially expressed KRAB-ZNFs and TEs, respectively (Figure 5C). Upon combining expression data with correlation results and an evolutionary perspective, we determined 21 human-specific TE:KRAB-ZNFs that were not detectable in AD brains (Figure 6B, E, and F), suggesting considerable alterations of connections in the context of a human-specific disease. By comparing healthy and disease samples, we discovered evolutionary young Alu subfamilies that are downregulated in AD patients, such as AluYh9 and AluSp subfamilies, which have been found to be negatively associated with Tau pathologic burden in the human dorsolateral prefrontal cortex (Guo et al., 2018). The AluY and AluSx subfamilies derived Alu-mediated in-frame deletions of the exons 9 and 10 in mutated forms of PSNE1, which was associated with early-onset AD (Le Guennec et al., 2017). Alu subfamilies have further been shown to regulate the expression of ACE in neurons from Alu insertions (Wu et al., 2013) and to play a role in A-to-I RNA editing during neurogenesis in the central nervous system and mitochondrial homeostasis (Larsen et al., 2018). Thus, we suggest the dysregulation of modules of Alu subfamilies with their connected KRAB-ZNFs in the regulatory network (Figure 6F) plays a role in AD progression.

With respect to the arms-race hypothesis (Jacobs et al., 2014), we observed a higher number of negative correlations between young TEs and young KRAB-ZNF genes in humans compared to NHPs. This suggests increased repression from young KRAB-ZNFs on young TEs in the human brain (Figure 4B and F). Still, our findings only weakly support the arms-race hypothesis. First, we noted that young TEs and KRAB-ZNFs exhibit lower expression levels than their old counterparts (Figures 2D and 5B). This pattern may not align with the expectation that young TEs have recently escaped repression, which would likely result in higher expression of the young TE or the young KRAB-ZNF to keep the TE in check. However, it remains challenging to discern at which stage of a potential arms race the TE:KRAB-ZNF pair presently is, i.e., whether the TE is on the rise with its expression or already repressed by the KRAB-ZNF. In addition, older TEs might be allowed to be expressed more highly as they are not harmful anymore. Not in line with the arms-race model is further evidence that some young TEs were also negatively correlated with old KRAB-ZNF genes, leading to weak assortativity regarding age inference. We acknowledge, however, that this is not a contradiction, since old KRAB-ZNF genes might be repurposed to repress young TEs, which could be more ‘cost-effective’ than evolving a new KRAB-ZNF gene. Potentially, restricting the analysis to KRAB-ZNFs and TEs younger than 25 mya would be more suitable for investigating the arms-race hypothesis in humans. During this period, we might expect a more direct arms-race scenario, as the evolutionary pressures between rapidly evolving TEs and their regulatory mechanisms should be more pronounced. However, it is important to note that such an analysis would have been limited by the detectable correlations of only 3 out of 9 KRAB-ZNF genes and 9 out of 92 TE subfamilies in our dataset (Supplementary file 1, table S7).

In response to the need for systematically comparing expression profiles of orthologous genes and TEs across diverse species or conditions, we developed TEKRABber, an R Bioconductor package that equips users to effortlessly adapt its comprehensive pipeline for their analyses. TEKRABber efficiently processes mapped raw counts from various TE quantification tools and normalizes transcriptomic data across species by extending the usage of the scaling-based normalization method (Zhou et al., 2019). To optimize computational efficiency and parallel computing during the calculations of pairwise correlations, we harnessed the capabilities of RCpp (Eddelbuettel and François, 2011) and doParallel (Corporation and Weston, 2011). This approach allows for effective scaling, accommodating larger datasets and leveraging additional computational resources as needed. Taking these advantages, users can adapt the TEKRABber’s pipeline to analyze selected orthologous genes and TEs from their expression data within an acceptable time frame. Taking one of the examples in this study, calculating pairwise correlations of 178 orthologs with 836 TEs (neglecting sample number) can be executed in approximately 5 min on standard hardware (Intel Core i7 2.6 GHz, 16 GB memory). Future studies could use TEKRABber, for instance, to investigate correlations between TEs and all genes, to identify candidates of genes whose expression is influenced by certain TEs. Another option would be to explore which factors might repress TE expression in invertebrates, which lack KRAB-ZNF proteins.

Nonetheless, it is crucial to recognize that our present approach is constrained by certain limitations. These constraints primarily stem from variations in the lengths and positions of the same TE across individual genomes of the same species, leaving some room for improving the normalization of TE expression levels. We only used the available reference genomes for our analysis; however, it becomes increasingly clear that substantial individual differences in the presence of TEs exist, which are not covered by a single reference genome of a species. Currently, there are emerging methods designed to detect TE expression, including tools for advancements in sequencing resolution like long-read sequencing (Marx, 2023), de novo TE annotations (Storer et al., 2022; Orozco-Arias et al., 2024), and locus-specific expression detection in single-cell RNA-seq analysis (Rodríguez-Quiroz and Valdebenito-Maturana, 2022). These developments hold promise for achieving a more precise depiction of the variations between samples and the reference, ultimately enhancing our understanding of TE-associated expression patterns. Hence, we acknowledge the significant potential of integrating improved TE orthologous information into our method.

The current manuscript is a computational study, so no data have been generated for this manuscript. Source code for the analysis has been uploaded to GitHub (copy archived at Chen, 2024).

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Author details

1. Yao-Chung Chen

   1. Human Biology and Primate Evolution, Institute of Biology, Freie Universität Berlin, Berlin, Germany
   2. Institute of Bioinformatics, Freie Universität Berlin, Berlin, Germany

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Validation, Visualization, Methodology, Writing – original draft, Writing – review and editing

   For correspondence

   yao-chung.chen@fu-berlin.de

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-9927-9130

2. Arnaud Maupas

   Institut de Biologie de l'École Normale Supérieure, Ecole Normale supérieure de Paris, Paris, France

   Contribution

   Formal analysis, Visualization, Methodology, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0009-0002-6471-884X

3. Katja Nowick

   1. Human Biology and Primate Evolution, Institute of Biology, Freie Universität Berlin, Berlin, Germany
   2. Institute of Bioinformatics, Freie Universität Berlin, Berlin, Germany

   Contribution

   Conceptualization, Supervision, Funding acquisition, Writing – original draft, Project administration, Writing – review and editing

   For correspondence

   katja.nowick@fu-berlin.de

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-3993-4479

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