PARP1 UFMylation ensures the stability of stalled replication forks.

The S-phase checkpoint involving CHK1 is essential for fork stability in response to fork stalling. PARP1 acts as a sensor of replication stress and is required for CHK1 activation. However, it is unclear how the activity of PARP1 is regulated. Here, we found that UFMylation is required for the efficient activation of CHK1 by UFMylating PARP1 at K548 during replication stress. Inactivation of UFL1, the E3 enzyme essential for UFMylation, delayed CHK1 activation and inhibits nascent DNA degradation during replication blockage as seen in PARP1-deficient cells. An in vitro study indicated that PARP1 is UFMylated at K548, which enhances its catalytic activity. Correspondingly, a PARP1 UFMylation-deficient mutant (K548R) and pathogenic mutant (F553L) compromised CHK1 activation, the restart of stalled replication forks following replication blockage, and chromosome stability. Defective PARP1 UFMylation also resulted in excessive nascent DNA degradation at stalled replication forks. Finally, we observed that PARP1 UFMylation-deficient knock-in mice exhibited increased sensitivity to replication stress caused by anticancer treatments. Thus, we demonstrate that PARP1 UFMylation promotes CHK1 activation and replication fork stability during replication stress, thus safeguarding genome integrity.

High time-resolved studies of stick-slip show similar dilatancy to fast and slow earthquakes.

Fast and slow earthquakes are two modes of energy release by the slip in tectonic fault rupture. Although fast and slow slips were observed in the laboratory stick-slip experiments, due to the sampling rate limitation, the details of the fault thickness variation were poorly understood. Especially, why a single fault would show different modes of slip remains elusive. Herein, we report on ring shear experiments with an ultrahigh sampling rate (10 MHz) that illuminate the different physical processes between fast and slow slip events. We show that the duration of slips ranged from dozens to hundreds of milliseconds. Fast slip events are characterized by continuous large-amplitude AE (acoustic emission) and somewhat intricate variation of the sample thickness: A short compaction pulse during the rapid release of stress is followed by dilation and vibrations of the sample thickness. As the slip ends, the thickness of the sample first recovers by slow compaction and then dilates again before nucleation of the following slip event. In contrast, during slow slip events, the shear stress reduction is accompanied by intermittent bursts of low-amplitude AE and sample dilation. We observed the detailed thickness variation during slips and found that dilation occurs during both fast and slow slips, which is consistent with natural observations of coseismic dilatation. This study may be used to reveal the mechanism of fault slips during fast and slow earthquakes, which explain the potential effect of fast and slow slips on stress redistribution and structural rearrangement in faults.

Transcriptional regulation of FACT involves Coordination of chromatin accessibility and CTCF binding.

Histone chaperone FACT (facilitates chromatin transcription) is well known to promote chromatin recovery during transcription. However, the mechanism how FACT regulates genome-wide chromatin accessibility and transcription factor binding has not been fully elucidated. Through loss-of-function studies, we show here that FACT component Ssrp1 is required for DNA replication and DNA damage repair and is also essential for progression of cell phase transition and cell proliferation in mouse embryonic fibroblast cells. On the molecular level, absence of the Ssrp1 leads to increased chromatin accessibility, enhanced CTCF binding, and a remarkable change in dynamic range of gene expression. Our study thus unequivocally uncovers a unique mechanism by which FACT complex regulates transcription by coordinating genome-wide chromatin accessibility and CTCF binding.

Predicting metabolite-disease associations based on auto-encoder and non-negative matrix factorization.

Metabolism refers to a series of orderly chemical reactions used to maintain life activities in organisms. In healthy individuals, metabolism remains within a normal range. However, specific diseases can lead to abnormalities in the levels of certain metabolites, causing them to either increase or decrease. Detecting these deviations in metabolite levels can aid in diagnosing a disease. Traditional biological experiments often rely on a lot of manpower to do repeated experiments, which is time consuming and labor intensive. To address this issue, we develop a deep learning model based on the auto-encoder and non-negative matrix factorization named as MDA-AENMF to predict the potential associations between metabolites and diseases. We integrate a variety of similarity networks and then acquire the characteristics of both metabolites and diseases through three specific modules. First, we get the disease characteristics from the five-layer auto-encoder module. Later, in the non-negative matrix factorization module, we extract both the metabolite and disease characteristics. Furthermore, the graph attention auto-encoder module helps us obtain metabolite characteristics. After obtaining the features from three modules, these characteristics are merged into a single, comprehensive feature vector for each metabolite-disease pair. Finally, we send the corresponding feature vector and label to the multi-layer perceptron for training. The experiment demonstrates our area under the receiver operating characteristic curve of 0.975 and area under the precision-recall curve of 0.973 in 5-fold cross-validation, which are superior to those of existing state-of-the-art predictive methods. Through case studies, most of the new associations obtained by MDA-AENMF have been verified, further highlighting the reliability of MDA-AENMF in predicting the potential relationships between metabolites and diseases.

Predicting potential interactions between lncRNAs and proteins via combined graph auto-encoder methods.

Long noncoding RNA (lncRNA) is a kind of noncoding RNA with a length of more than 200 nucleotide units. Numerous research studies have proven that although lncRNAs cannot be directly translated into proteins, lncRNAs still play an important role in human growth processes by interacting with proteins. Since traditional biological experiments often require a lot of time and material costs to explore potential lncRNA-protein interactions (LPI), several computational models have been proposed for this task. In this study, we introduce a novel deep learning method known as combined graph auto-encoders (LPICGAE) to predict potential human LPIs. First, we apply a variational graph auto-encoder to learn the low dimensional representations from the high-dimensional features of lncRNAs and proteins. Then the graph auto-encoder is used to reconstruct the adjacency matrix for inferring potential interactions between lncRNAs and proteins. Finally, we minimize the loss of the two processes alternately to gain the final predicted interaction matrix. The result in 5-fold cross-validation experiments illustrates that our method achieves an average area under receiver operating characteristic curve of 0.974 and an average accuracy of 0.985, which is better than those of existing six state-of-the-art computational methods. We believe that LPICGAE can help researchers to gain more potential relationships between lncRNAs and proteins effectively.

Single-cell causal network inferred by cross-mapping entropy.

Gene regulatory networks (GRNs) reveal the complex molecular interactions that govern cell state. However, it is challenging for identifying causal relations among genes due to noisy data and molecular nonlinearity. Here, we propose a novel causal criterion, neighbor cross-mapping entropy (NME), for inferring GRNs from both steady data and time-series data. NME is designed to quantify 'continuous causality' or functional dependency from one variable to another based on their function continuity with varying neighbor sizes. NME shows superior performance on benchmark datasets, comparing with existing methods. By applying to scRNA-seq datasets, NME not only reliably inferred GRNs for cell types but also identified cell states. Based on the inferred GRNs and further their activity matrices, NME showed better performance in single-cell clustering and downstream analyses. In summary, based on continuous causality, NME provides a powerful tool in inferring causal regulations of GRNs between genes from scRNA-seq data, which is further exploited to identify novel cell types/states and predict cell type-specific network modules.

Modeling and analyzing single-cell multimodal data with deep parametric inference.

The proliferation of single-cell multimodal sequencing technologies has enabled us to understand cellular heterogeneity with multiple views, providing novel and actionable biological insights into the disease-driving mechanisms. Here, we propose a comprehensive end-to-end single-cell multimodal analysis framework named Deep Parametric Inference (DPI). DPI transforms single-cell multimodal data into a multimodal parameter space by inferring individual modal parameters. Analysis of cord blood mononuclear cells (CBMC) reveals that the multimodal parameter space can characterize the heterogeneity of cells more comprehensively than individual modalities. Furthermore, comparisons with the state-of-the-art methods on multiple datasets show that DPI has superior performance. Additionally, DPI can reference and query cell types without batch effects. As a result, DPI can successfully analyze the progression of COVID-19 disease in peripheral blood mononuclear cells (PBMC). Notably, we further propose a cell state vector field and analyze the transformation pattern of bone marrow cells (BMC) states. In conclusion, DPI is a powerful single-cell multimodal analysis framework that can provide new biological insights into biomedical researchers. The python packages, datasets and user-friendly manuals of DPI are freely available at https://github.com/studentiz/dpi.

dSCOPE: a software to detect sequences critical for liquid-liquid phase separation.

Membrane-based cells are the fundamental structural and functional units of organisms, while evidences demonstrate that liquid-liquid phase separation (LLPS) is associated with the formation of membraneless organelles, such as P-bodies, nucleoli and stress granules. Many studies have been undertaken to explore the functions of protein phase separation (PS), but these studies lacked an effective tool to identify the sequence segments that critical for LLPS. In this study, we presented a novel software called dSCOPE (http://dscope.omicsbio.info) to predict the PS-driving regions. To develop the predictor, we curated experimentally identified sequence segments that can drive LLPS from published literature. Then sliding sequence window based physiological, biochemical, structural and coding features were integrated by random forest algorithm to perform prediction. Through rigorous evaluation, dSCOPE was demonstrated to achieve satisfactory performance. Furthermore, large-scale analysis of human proteome based on dSCOPE showed that the predicted PS-driving regions enriched various protein post-translational modifications and cancer mutations, and the proteins which contain predicted PS-driving regions enriched critical cellular signaling pathways. Taken together, dSCOPE precisely predicted the protein sequence segments critical for LLPS, with various helpful information visualized in the webserver to facilitate LLPS-related research.

Structural and functional basis of low-affinity SAM/SAH-binding in the conserved MTase of the multi-segmented Alongshan virus distantly related to canonical unsegmented flaviviruses.

Alongshan virus (ALSV), a newly discovered member of unclassified Flaviviridae family, is able to infect humans. ALSV has a multi-segmented genome organization and is evolutionarily distant from canonical mono-segmented flaviviruses. The virus-encoded methyltransferase (MTase) plays an important role in viral replication. Here we show that ALSV MTase readily binds S-adenosyl-L-methionine (SAM) and S-adenosyl-L-homocysteine (SAH) but exhibits significantly lower affinities than canonical flaviviral MTases. Structures of ALSV MTase in the free and SAM/SAH-bound forms reveal that the viral enzyme possesses a unique loop-element lining side-wall of the SAM/SAH-binding pocket. While the equivalent loop in flaviviral MTases half-covers SAM/SAH, contributing multiple hydrogen-bond interactions; the pocket-lining loop of ALSV MTase is of short-length and high-flexibility, devoid of any physical contacts with SAM/SAH. Subsequent mutagenesis data further corroborate such structural difference affecting SAM/SAH-binding. Finally, we also report the structure of ALSV MTase bound with sinefungin, an SAM-analogue MTase inhibitor. These data have delineated the basis for the low-affinity interaction between ALSV MTase and SAM/SAH and should inform on antiviral drug design.

Development of a bispecific nanobody conjugate broadly neutralizes diverse SARS-CoV-2 variants and structural basis for its broad neutralization.

The continuous emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants with increased transmissibility and profound immune-escape capacity makes it an urgent need to develop broad-spectrum therapeutics. Nanobodies have recently attracted extensive attentions due to their excellent biochemical and binding properties. Here, we report two high-affinity nanobodies (Nb-015 and Nb-021) that target non-overlapping epitopes in SARS-CoV-2 S-RBD. Both nanobodies could efficiently neutralize diverse viruses of SARS-CoV-2. The neutralizing mechanisms for the two nanobodies are further delineated by high-resolution nanobody/S-RBD complex structures. In addition, an Fc-based tetravalent nanobody format is constructed by combining Nb-015 and Nb-021. The resultant nanobody conjugate, designated as Nb-X2-Fc, exhibits significantly enhanced breadth and potency against all-tested SARS-CoV-2 variants, including Omicron sub-lineages. These data demonstrate that Nb-X2-Fc could serve as an effective drug candidate for the treatment of SARS-CoV-2 infection, deserving further in-vivo evaluations in the future.

Immunophilin FKB20-2 participates in oligomerization of Photosystem I in Chlamydomonas.

PSI is a sophisticated photosynthesis protein complex that fuels the light reaction of photosynthesis in algae and vascular plants. While the structure and function of PSI have been studied extensively, the dynamic regulation on PSI oligomerization and high light response is less understood. In this work, we characterized a high light-responsive immunophilin gene FKB20-2 (FK506-binding protein 20-2) required for PSI oligomerization and high light tolerance in Chlamydomonas (Chlamydomonas reinhardtii). Biochemical assays and 77-K fluorescence measurement showed that loss of FKB20-2 led to the reduced accumulation of PSI core subunits and abnormal oligomerization of PSI complexes and, particularly, reduced PSI intermediate complexes in fkb20-2. It is noteworthy that the abnormal PSI oligomerization was observed in fkb20-2 even under dark and dim light growth conditions. Coimmunoprecipitation, MS, and yeast 2-hybrid assay revealed that FKB20-2 directly interacted with the low molecular weight PSI subunit PsaG, which might be involved in the dynamic regulation of PSI-light-harvesting complex I supercomplexes. Moreover, abnormal PSI oligomerization caused accelerated photodamage to PSII in fkb20-2 under high light stress. Together, we demonstrated that immunophilin FKB20-2 affects PSI oligomerization probably by interacting with PsaG and plays pivotal roles during Chlamydomonas tolerance to high light.

Placental transcriptomic signatures of prenatal and preconceptional maternal stress.

Prenatal exposure to maternal psychological stress is associated with increased risk for adverse birth and child health outcomes. Accumulating evidence suggests that preconceptional maternal stress may also be transmitted intergenerationally to negatively impact offspring. However, understanding of mechanisms linking these exposures to offspring outcomes, particularly those related to placenta, is limited. Using RNA sequencing, we identified placental transcriptomic signatures associated with maternal prenatal stressful life events (SLEs) and childhood traumatic events (CTEs) in 1 029 mother-child pairs in two birth cohorts from Washington state and Memphis, Tennessee. We evaluated individual gene-SLE/CTE associations and performed an ensemble of gene set enrichment analyses combing across 11 popular enrichment methods. Higher number of prenatal SLEs was significantly (FDR < 0.05) associated with increased expression of ADGRG6, a placental tissue-specific gene critical in placental remodeling, and decreased expression of RAB11FIP3, an endocytosis and endocytic recycling gene, and SMYD5, a histone methyltransferase. Prenatal SLEs and maternal CTEs were associated with gene sets related to several biological pathways, including upregulation of protein processing in the endoplasmic reticulum, protein secretion, and ubiquitin mediated proteolysis, and down regulation of ribosome, epithelial mesenchymal transition, DNA repair, MYC targets, and amino acid-related pathways. The directional associations in these pathways corroborate prior non-transcriptomic mechanistic studies of psychological stress and mental health disorders, and have previously been implicated in pregnancy complications and adverse birth outcomes. Accordingly, our findings suggest that maternal exposure to psychosocial stressors during pregnancy as well as the mother's childhood may disrupt placental function, which may ultimately contribute to adverse pregnancy, birth, and child health outcomes.

Multiomics sequencing and immune microenvironment characteristics define three subtypes of small cell neuroendocrine carcinoma of the cervix.

Small cell cervical carcinoma (SCCC) is the most common neuroendocrine tumor in the female genital tract, with an unfavorable prognosis and lacking an evidence-based therapeutic approach. Until now, the distinct subtypes and immune characteristics of SCCC combined with genome and transcriptome have not been described. We performed genomic (n = 18), HPV integration (n = 18), and transcriptomic sequencing (n = 19) of SCCC samples. We assessed differences in immune characteristics between SCCC and conventional cervical cancer, and other small cell neuroendocrine carcinomas, through bioinformatics analysis and immunohistochemical assays. We stratified SCCC patients through non-negative matrix factorization and described the characteristics of these distinct types. We further validated it using multiplex immunofluorescence (n = 77) and investigated its clinical prognostic effect. We confirmed a high frequency of PIK3CA and TP53 alterations and HPV18 integrations in SCCC. SCCC and other small cell carcinoma had similar expression signatures and immune cell infiltration patterns. Comparing patients with SCCC to those with conventional cervical cancer, the former presented immune excluded or 'desert' infiltration. The number of CD8+ cells in the invasion margin of SCCC patients predicted favorable clinical outcomes. We identified three transcriptome subtypes: an inflamed phenotype with high-level expression of genes related to the MHC-II complex (CD74) and IFN-α/ß (SCCC-I), and two neuroendocrine subtypes with high-level expression of ASCL1 or NEUROD1, respectively. Combined with multiple technologies, we found that the neuroendocrine groups had more TP53 mutations and SCCC-I had more PIK3CA mutations. Multiplex immunofluorescence validated these subtypes and SCCC-I was an independent prognostic factor of overall survival. These results provide insights into SCCC tumor heterogeneity and potential therapies. © 2024 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Predicting drug-induced liver injury using graph attention mechanism and molecular fingerprints.

Drug-induced liver injury (DILI) is a significant issue in drug development and clinical treatment due to its potential to cause liver dysfunction or damage, which, in severe cases, can lead to liver failure or even fatality. DILI has numerous pathogenic factors, many of which remain incompletely understood. Consequently, it is imperative to devise methodologies and tools for anticipatory assessment of DILI risk in the initial phases of drug development. In this study, we present DMFPGA, a novel deep learning predictive model designed to predict DILI. To provide a comprehensive description of molecular properties, we employ a multi-head graph attention mechanism to extract features from the molecular graphs, representing characteristics at the level of compound nodes. Additionally, we combine multiple fingerprints of molecules to capture features at the molecular level of compounds. The fusion of molecular fingerprints and graph features can more fully express the properties of compounds. Subsequently, we employ a fully connected neural network to classify compounds as either DILI-positive or DILI-negative. To rigorously evaluate DMFPGA's performance, we conduct a 5-fold cross-validation experiment. The obtained results demonstrate the superiority of our method over four existing state-of-the-art computational approaches, exhibiting an average AUC of 0.935 and an average ACC of 0.934. We believe that DMFPGA is helpful for early-stage DILI prediction and assessment in drug development.

fMRI evidence of movement familiarization effects on recognition memory in professional dancers.

Dual-process theories propose that recognition memory involves recollection and familiarity; however, the impact of motor expertise on memory recognition, especially the interplay between familiarity and recollection, is relatively unexplored. This functional magnetic resonance imaging study used videos of a dancer performing International Latin Dance Styles as stimuli to investigate memory recognition in professional dancers and matched controls. Participants observed and then reported whether they recognized dance actions, recording the level of confidence in their recollections, whereas blood-oxygen-level-dependent signals measured encoding and recognition processes. Professional dancers showed higher accuracy and hit rates for high-confidence judgments, whereas matched controls exhibited the opposite trend for low-confidence judgments. The right putamen and precentral gyrus showed group-based moderation effects, especially for high-confidence (vs. low-confidence) action recognition in professional dancers. During action recognition, the right superior temporal gyrus and insula showed increased activation for accurate recognition and high-confidence retrieval, particularly in matched controls. These findings highlighting enhanced action memory of professional dancers-evident in their heightened recognition confidence-not only supports the dual-processing model but also underscores the crucial role of expertise-driven familiarity in bolstering successful recollection. Additionally, they emphasize the involvement of the action observation network and frontal brain regions in facilitating detailed encoding linked to intention processing.

Activation of nicotinic acetylcholine receptor α7 subunit limits Zika viral infection via promoting autophagy and ferroptosis.

Vagus nerve regulates viral infection and inflammation via the alpha 7 nicotinic acetylcholine receptor (α7 nAChR); however, the role of α7 nAChR in ZIKA virus (ZIKV) infection, which can cause severe neurological diseases such as microcephaly and Guillain-Barré syndrome, remains unknown. Here, we first examined the role of α7 nAChR in ZIKV infection in vitro. A broad effect of α7 nAChR activation was identified in limiting ZIKV infection in multiple cell lines. Combined with transcriptomics analysis, we further demonstrated that α7 nAChR activation promoted autophagy and ferroptosis pathways to limit cellular ZIKV viral loads. Additionally, activation of α7 nAChR prevented ZIKV-induced p62 nucleus accumulation, which mediated an enhanced autophagy pathway. By regulating proteasome complex and an E3 ligase NEDD4, activation of α7 nAChR resulted in increased amount of cellular p62, which further enhanced the ferroptosis pathway to reduce ZIKV infection. Moreover, utilizing in vivo neonatal mouse models, we showed that α7 nAChR is essential in controlling the disease severity of ZIKV infection. Taken together, our findings identify an α7 nAChR-mediated effect that critically contributes to limiting ZIKV infection, and α7 nAChR activation offers a novel strategy for combating ZIKV infection and its complications.

qPTM: an updated database for PTM dynamics in human, mouse, rat and yeast.

Post-translational modifications (PTMs) are critical molecular mechanisms that regulate protein functions temporally and spatially in various organisms. Since most PTMs are dynamically regulated, quantifying PTM events under different states is crucial for understanding biological processes and diseases. With the rapid development of high-throughput proteomics technologies, massive quantitative PTM proteome datasets have been generated. Thus, a comprehensive one-stop data resource for surfing big data will benefit the community. Here, we updated our previous phosphorylation dynamics database qPhos to the qPTM (http://qptm.omicsbio.info). In qPTM, 11 482 553 quantification events among six types of PTMs, including phosphorylation, acetylation, glycosylation, methylation, SUMOylation and ubiquitylation in four different organisms were collected and integrated, and the matched proteome datasets were included if available. The raw mass spectrometry based false discovery rate control and the recurrences of identifications among datasets were integrated into a scoring system to assess the reliability of the PTM sites. Browse and search functions were improved to facilitate users in swiftly and accurately acquiring specific information. The results page was revised with more abundant annotations, and time-course dynamics data were visualized in trend lines. We expected the qPTM database to be a much more powerful and comprehensive data repository for the PTM research community.

BRD9-mediated control of the TGF-ß/Activin/Nodal pathway regulates self-renewal and differentiation of human embryonic stem cells and progression of cancer cells.

Bromodomain-containing protein 9 (BRD9) is a specific subunit of the non-canonical SWI/SNF (ncBAF) chromatin-remodeling complex, whose function in human embryonic stem cells (hESCs) remains unclear. Here, we demonstrate that impaired BRD9 function reduces the self-renewal capacity of hESCs and alters their differentiation potential. Specifically, BRD9 depletion inhibits meso-endoderm differentiation while promoting neural ectoderm differentiation. Notably, supplementation of NODAL, TGF-ß, Activin A or WNT3A rescues the differentiation defects caused by BRD9 loss. Mechanistically, BRD9 forms a complex with BRD4, SMAD2/3, ß-CATENIN and P300, which regulates the expression of pluripotency genes and the activity of TGF-ß/Nodal/Activin and Wnt signaling pathways. This is achieved by regulating the deposition of H3K27ac on associated genes, thus maintaining and directing hESC differentiation. BRD9-mediated regulation of the TGF-ß/Activin/Nodal pathway is also demonstrated in the development of pancreatic and breast cancer cells. In summary, our study highlights the crucial role of BRD9 in the regulation of hESC self-renewal and differentiation, as well as its participation in the progression of pancreatic and breast cancers.

ASPM promotes ATR-CHK1 activation and stabilizes stalled replication forks in response to replication stress.

ASPM is a protein encoded by primary microcephaly 5 (MCPH5) and is responsible for ensuring spindle position during mitosis and the symmetrical division of neural stem cells. We recently reported that ASPM promotes homologous recombination (HR) repair of DNA double strand breaks. However, its potential role in DNA replication and replication stress response remains elusive. Interestingly, we found that ASPM is dispensable for DNA replication under unperturbed conditions. However, ASPM is enriched at stalled replication forks in a RAD17-dependent manner in response to replication stress and promotes RAD9 and TopBP1 loading onto chromatin, facilitating ATR-CHK1 activation. ASPM depletion results in failed fork restart and nuclease MRE11-mediated nascent DNA degradation at the stalled replication fork. The overall consequence is chromosome instability and the sensitization of cancer cells to replication stressors. These data support a role for ASPM in loading RAD17-RAD9/TopBP1 onto chromatin to activate the ATR-CHK1 checkpoint and ultimately ensure genome stability.

Genetic adaptation of skin pigmentation in highland Tibetans.

Strong ultraviolet (UV) radiation at high altitude imposes a serious selective pressure, which may induce skin pigmentation adaptation of indigenous populations. We conducted skin pigmentation phenotyping and genome-wide analysis of Tibetans in order to understand the underlying mechanism of adaptation to UV radiation. We observe that Tibetans have darker baseline skin color compared with lowland Han Chinese, as well as an improved tanning ability, suggesting a two-level adaptation to boost their melanin production. A genome-wide search for the responsible genes identifies GNPAT showing strong signals of positive selection in Tibetans. An enhancer mutation (rs75356281) located in GNPAT intron 2 is enriched in Tibetans (58%) but rare in other world populations (0 to 18%). The adaptive allele of rs75356281 is associated with darker skin in Tibetans and, under UVB treatment, it displays higher enhancer activities compared with the wild-type allele in in vitro luciferase assays. Transcriptome analyses of gene-edited cells clearly show that with UVB treatment, the adaptive variant of GNPAT promotes melanin synthesis, likely through the interactions of CAT and ACAA1 in peroxisomes with other pigmentation genes, and they act synergistically, leading to an improved tanning ability in Tibetans for UV protection.