Biology subjects

Di, C.

Publications and source records attributed to Di, C..

13 recordsLinked to original sources

bioRxiv2026

Pervasive cryptic selection in the human noncoding genome

The prevailing dogma in evolutionary genetics holds that mutations within sequences that are conserved across a phylogeny are deleterious in those species, and mutations outside are neutrally evolving. Indeed, such comparative genomic approaches have estimated that mutations in approximately 5% of the human genome experience negative selection. However, sites that have biological function in certain lineages but not in others, i.e. functional turnover, may violate this assumption since these sites may be invisible to comparative genomic approaches. Thus, the extent of such cryptic, or hidden, negative selection remains elusive. Here, we developed a statistical test to detect cryptic selection in human polymorphism data. Applying our approach to simulated data shows that cryptic selection shapes the site frequency spectrum (SFS) and the statistical detection power depends on the proportion of mutations experiencing cryptic selection, the amount of sequence tested, and the sample size. We applied our method to polymorphism data from the 1000 Genomes Project, comparing variants in putatively functional noncoding regions to those in putatively neutral regions. We detected pervasive signals of cryptic selection in putatively functional regions, even after filtering out the top 70% of conserved sites. Using simulations with varying levels of cryptic selection, we estimated the extent of genome-wide constraint in the human genome. Our approximation suggests that mutations in at least 7% of the human genome are under negative selection, which is greater than the estimates from conservation-based methods, and that many of these mutations have escaped detection by comparative genomic methods. In sum, our results highlight the evolutionary dynamic nature of the noncoding genome and suggest the need to account for functional turnover when identifying putatively neutral variants for evolutionary analyses.

evolutionary biology↗

bioRxiv2026

Causal inference clarifies the roles of background selection and mutation rate variation in shaping human genetic diversity

Decades of theoretical and empirical work have struggled to reconcile competing views on how evolution shapes patterns of genetic variation. We now understand the genome as a mosaic molded by both neutral and selective forces, but quantifying their relative contributions remains an open challenge. A major obstacle has been the tendency to analyze each evolutionary process in isolation. But different processes may leave similar signatures on genetic variation, making it challenging to draw conclusions from correlations alone. To address this gap, we make predictions of the landscape of diversity based on background selection and mutation rate variation. We then develop structural equation models describing how mutation, recombination and selection jointly shape the genomic landscape of diversity. This approach offers a more realistic representation of biological interactions and enables rigorous evaluation of hypothesized causal structures, marking both conceptual and practical improvements over previous studies. Analyses of human data reveal large variation in the explanatory power of candidate models across chromosomes. We find that previous studies likely overestimated the predictive accuracy of background selection; although it emerges as the overall main driver of diversity, in some chromosomes mutation rate variation has a comparable impact. We also show that recombination increases diversity more strongly through its influence on background selection than through its mutagenic effect, resolving a longstanding debate. This work demonstrates that modeling variation inherent in genome biology substantially improves our ability to explain human genetic diversity. At the same time, evidence for unmodeled covariance between mutation rates and density of constrained sites reinvigorates an ongoing discussion about the evolution of the mutation landscape, although additional work is needed to determine its origins.

evolutionary biology↗

bioRxiv2025

R-loops orchestrate the maternal-to-zygotic transition by harnessing RNA polymerase II pause release

R-loops, ubiquitous three-stranded nucleic acid structures abundant in the mammalian genome, play pivotal roles in diverse biological processes1-3. However, the dynamics and functions of R-loops during mammalian preimplantation embryonic development remain poorly understood. Here we optimized an innovative, ultra-sensitive RIAN-seq method capable of mapping genome-wide R-loop landscape using ultra-low cell numbers to investigate R-loop dynamics in mouse gametes and early embryos. Our findings reveal the widespread presence of AT-rich R-loops during early mouse embryonic development and highlight their critical roles in orchestrating transcription machinery during zygotic genome activation (ZGA). We demonstrate that the stability and inheritance of R-loops through developmental stages are governed by their sequence composition, with GC-rich R-loops exhibiting greater stability. Notably, R-loops, particularly AT-rich R-loops, inhibit DDX21 helicase activity on the 7SK/HEXIM1 snRNP complex, thereby reducing CDK9 release. This, in turn, modulates the phosphorylation of Ser2 at C-terminal domain (CTD) of RNA polymerase II (RNAPIIS2p) at the promoters of major ZGA and maternal genes. Furthermore, R-loops promote RNAPII accumulation at major ZGA gene promoters, preventing RNAPII from premature release and ensuring timely activation of major ZGA genes. Conversely, R-loops enhance the retention of stalled RNAPII at maternal gene promoters, effectively repressing their expression and facilitating the maternal-to-zygotic transition. In summary, our study unveils a key role for R-loops in regulating the maternal-to-zygotic transcription transition and early embryonic development in mice.

developmental biology↗

bioRxiv2025

The landscape of fitness effects of putatively functional noncoding mutations in humans

While annotations of noncoding regions in the human genome are increasing, the fitness effects of mutations in these regions remain unclear. Here, we leverage these functional genomic annotations and human polymorphism data to infer the distributions of fitness effects of new noncoding mutations in humans. Our novel approach controls for mutation rate variation and linked selection along the genome. We find distinct patterns of selection in putative enhancers, promoters, and conserved noncoding regions. While mutations in enhancers are often neutral, approximately 30% of mutations in promoters are deleterious. The most conserved noncoding regions, showing reduced divergence across mammals and primates, have the highest proportion of deleterious mutations. Notably, while we infer the most conserved sites across mammals and primates are enriched for deleterious mutations, such conserved sites only account for a minority of the deleterious mutations in noncoding regions. For example, the top 5% of conserved noncoding sites encompass fewer than 20% of deleterious mutations, indicating that functional noncoding regions vary widely in the distribution of their evolutionary constraint. Our findings highlight the dynamic evolution of gene regulation and shifting selection pressures over deep evolutionary timescales. Consistent with this finding, we infer mutations in [~]7-9% of the noncoding genome are deleterious. These insights have broad implications for using comparative genomics to identify non-neutrally evolving sequences in the human genome.

evolutionary biology↗

bioRxiv2025

Capturing R-loops at base-pair resolution unveils clustered R-loops regulate gene expression in a number-dependent manner

R-loops are prevalent triplex nucleic strands found across various organisms, involved in numerous biological processes. However, the physiological and pathological functions of R-loops remain largely unknown due to a lack of effective and high-resolution detection methods. Here, using nuclease P1, T5 exonuclease, and lambda exonuclease mediated digestion of ssRNA, ssDNA, and dsDNA while preserving RNA:DNA hybrid, we report a method named R-loop identification assisted by nuclease and high-throughput sequencing (RIAN-seq) for genome-wide mapping of R-loops at base-pair resolution. RIAN-seq represents ultra-accuracy in position and size of R-loops and identifies an order of magnitude more R-loops than current methods. Notably, we find the majority of R-loops ranging from 60 bp to 130 bp and reveal previously unresolvable patterns of R-loops aggerated in clusters across the genome. Clustered R-loops at gene promoters recruit zinc finger transcription factors (VEZF1 and SP5) to promote transcription. The number of R-loops within a cluster positively correlates with the diversity of bound transcription factors. Furthermore, clustered R-loops are less susceptible to transcription perturbation as the number of R-loops within clusters increases. Overall, we demonstrate the ability to identify R-loops at unprecedented resolution and facilitate investigating the mechanisms of clustered R-loops in gene regulation in diverse biological processes.

molecular biology↗

bioRxiv2024

Evolutionary consequences of domestication on the selective effects of new amino acid changing mutations in canids

The domestication of wild canids led to dogs no longer living in the wild but instead residing alongside humans. Extreme changes in behavior and diet associated with domestication may have led to the relaxation of the selective pressure on traits that may be less important in the domesticated context. Thus, here we hypothesize that strongly deleterious mutations may have become less deleterious in domesticated populations. We test this hypothesis by estimating the distribution of fitness effects (DFE) for new amino acid changing mutations using whole-genome sequence data from 24 gray wolves and 61 breed dogs. We find that the DFE is strikingly similar across canids, with 26-28% of new amino acid changing mutations being neutral/nearly neutral (|s| < 1e-5), and 41-48% under strong purifying selection (|s| > 1e-2). Our results are robust to different model assumptions suggesting that the DFE is stable across short evolutionary timescales, even in the face of putative drastic changes in the selective pressure caused by artificial selection during domestication and breed formation. On par with previous works describing DFE evolution, our data indicate that the DFE of amino acid changing mutations depends more strongly on genome structure and organismal characteristics, and less so on shifting selective pressures or environmental factors. Given the constant DFE and previous data showing that genetic variants that differentiate wolf and dog populations are enriched in regulatory elements, we speculate that domestication may have had a larger impact on regulatory variation than on amino acid changing mutations. Significance StatementDomestication of dogs to live alongside humans resulted in a dramatic shift in the pressures of natural selection. Thus, comparing dogs and wolves offers a unique opportunity to assess how these shifts in selective pressures have impacted the fitness effects of individual mutations. In this project, we use patterns of genetic variation in dogs and wolves to estimate the distribution of fitness effects (DFE), or the proportions of amino acid changing mutations with varying fitness effects throughout the genome. Overall, we find that the DFE for amino acid changing mutations is similar between dogs and wolves. Even genes thought to be most affected by domestication show a similar DFE, suggesting that the DFE has remained stable over evolutionary time.

genetics↗

bioRxiv2024

TOMM40-APOE chimera linking Alzheimer's highest risk genes: a new pathway for mitochondria regulation and APOE4 pathogenesis

The patho-mechanism of apolipoprotein variant, APOE4, the strongest genetic risk for late-onset Alzheimers disease (AD) and longevity, remains unclear. APOEs neighboring gene, TOMM40 (mitochondria protein transport channel), is associated with brain trauma outcome and aging-related cognitive decline, however its role in AD APOE4-independently is controversial. We report that TOMM40 is prone to transcription readthrough into APOE that can generate spliced TOMM40-APOE mRNA chimera (termed T9A2) detected in human neurons and other cells and tissues. T9A2 translation tethers APOE (normal APOE3 or APOE4) to near-full-length TOM40 that is targeted to mitochondria. Importantly, T9A2-APOE3 boosts mitochondrial bioenergetic capacity and decreases oxidative stress significantly more than T9A2-APOE4 and APOE3, and lacking in APOE4. We describe detailed interactomes of these actors that may inform about the activities and roles in pathogenesis. T9A2 uncovers a new candidate pathway for mitochondria regulation and oxidative stress-protection that are impaired in APOE4 genotypes and could initiate neurodegeneration.

cell biology↗

bioRxiv2022

Stability evolution as a major mechanism of human protein adaptation in response to viruses

Pathogens were a major driver of genetic adaptation during human evolution. Viruses in particular were a dominant driver of adaptation in the thousands of proteins that physically interact with viruses (VIPs for Virus-Interacting Proteins). This however poses a conundrum. The best understood cases of virus-driven adaptation in specialized immune antiviral factors or in host viral receptors are numerically vastly insufficient to explain abundant adaptations in VIPs. What adaptive mechanisms can then at least partly close this gap? VIPs tend to be broadly conserved proteins with conserved host native molecular functions. Because many amino acid changes in a protein can alter its stability -the balance between the folded and unfolded forms of a protein- without destroying conserved native activities, here we ask if stability evolution was an important mechanism of virus-driven human protein adaptation. Using predictions of protein stability changes based on Alphafold 2 structures and validated by multiple lines of evidence, we find that amino acid changes that altered stability experienced highly elevated adaptative evolution in VIPs, compared to changes with a weaker impact on stability. We further find that RNA viruses, rather DNA viruses, predominantly drove strong adaptation through stability changes in VIPs. We also observe that stability in immune antiviral VIPs preferentially evolved under directional selection. Conversely, stability in proviral VIPs needed by viruses evolved under compensatory evolution following viral epidemics. Together, these results suggest that stability evolution, and thus functional host protein abundance evolution, was a prominent mechanism of host protein adaptation during viral epidemics.

evolutionary biology↗

bioRxiv2021

Decreased adaptation at human disease genes as a possible consequence of interference between advantageous and deleterious variants

Advances in genome sequencing have dramatically improved our understanding of the genetic basis of human diseases, and thousands of human genes have been associated with different diseases. Despite our expanding knowledge of gene-disease associations, and despite the medical importance of disease genes, their evolution has not been thoroughly studied across diverse human populations. In particular, recent genomic adaptation at disease genes has not been well characterized, even though multiple evolutionary processes are expected to connect disease and adaptation at the gene level. Understanding the relationship between disease and adaptation at the gene level in the human genome is severely hampered by the fact that we dont even know whether disease genes have experienced more, less, or as much adaptation as non-disease genes during recent human evolution. Here, we compare the rate of strong recent adaptation in the form of selective sweeps between disease genes and non-disease genes across 26 distinct human populations from the 1,000 Genomes Project. We find that disease genes have experienced far less selective sweeps compared to non-disease genes during recent human evolution. This sweep deficit at disease genes is particularly visible in Africa, and less visible in East Asia or Europe, likely due to more intense genetic drift in the latter populations creating more spurious selective sweeps signals. Investigating further the possible causes of the sweep deficit at disease genes, we find that this deficit is very strong at disease genes with both low recombination rates and with high numbers of associated disease variants, but is inexistant at disease genes with higher recombination rates or lower numbers of associated disease variants. Because recessive deleterious variants have the ability to interfere with adaptive ones, these observations strongly suggest that adaptation has been slowed down by the presence of interfering recessive deleterious variants at disease genes. These results clarify the evolutionary relationship between disease genes and recent genomic adaptation, and suggest that disease genes suffer not only from a higher load of segregating deleterious mutations, but also an inability to adapt as much, and/or as fast as the rest of the genome.

evolutionary biology↗

bioRxiv2019

Genes derived from ancient polyploidy have higher genetic diversity and are associated with domestication in Brassica rapa

O_LIMany crops are polyploid or have a polyploid ancestry. Recent phylogenetic analyses have found that polyploidy often preceded the domestication of crop plants. One explanation for this observation is that increased genetic diversity following polyploidy may have been important during the strong artificial selection that occurs during domestication. C_LIO_LITo test the connection between domestication and polyploidy, we identified and examined candidate genes associated with the domestication of the diverse crop varieties of Brassica rapa. Like all "diploid" flowering plants, B. rapa has a diploidized paleopolyploid genome and experienced many rounds of whole genome duplication (WGD). We analyzed transcriptome data of more than 100 cultivated B. rapa accessions. C_LIO_LIUsing a combination of approaches, we identified more than 3,000 candidate genes associated with the domestication of four major B. rapa crop varieties. Consistent with our expectation, we found that the candidate genes were significantly enriched with genes derived from the Brassiceae mesohexaploidy. We also observed that paleologs were significantly more diverse than non-paleologs. C_LIO_LIOur analyses find evidence for that genetic diversity derived from ancient polyploidy played a key role in the domestication of B. rapa and provide support for its importance in the success of modern agriculture. C_LI

plant biology↗

bioRxiv2019

U1 snRNP regulates cancer cell migration and invasion

Stimulated cells and cancer cells have widespread shortening of mRNA 3-utranslated regions (3UTRs) and switches to shorter mRNA isoforms due to usage of more proximal polyadenylation signals (PASs) in the last exon and in introns. U1 snRNA (U1), vertebrates most abundant non-coding (spliceosomal) small nuclear RNA, silences proximal PASs and its inhibition with antisense morpholino oligonucleotides (U1 AMO) triggers widespread mRNA shortening. Here we show that U1 AMO also modulates cancer cells phenotype, dose-dependently increasing migration and invasion in vitro by up to 500%, whereas U1 over-expression has the opposite effect. In addition to 3UTR length, numerous transcriptome changes that could contribute to this phenotype are observed, including alternative splicing, and mRNA expression levels of proto-oncogenes and tumor suppressors. These findings reveal an unexpected link between U1 regulation and oncogenic and activated cell states, and suggest U1 as a potential target for their modulation.

molecular biology↗

bioRxiv2018

Identification and quantification of Lyme pathogen strains by deep sequencing of outer surface protein C (ospC) amplicons

Mixed infection of a single tick or host by Lyme disease spirochetes is common and a unique challenge for diagnosis, treatment, and surveillance of Lyme disease. Here we describe a novel protocol for differentiating Lyme strains based on deep sequencing of the hypervariable outer-surface protein C locus (ospC). Improving upon the traditional DNA-DNA hybridization method, the next-generation sequencing-based protocol is high-throughput, quantitative, and able to detect new pathogen strains. We applied the method to over one hundred infected Ixodes scapularis ticks collected from New York State, USA in 2015 and 2016. Analysis of strain distributions within individual ticks suggests an overabundance of multiple infections by five or more strains, inhibitory interactions among co-infecting strains, and presence of a new strain closely related to Borreliella bissettiae. A supporting bioinformatics pipeline has been developed. With the newly designed pair of universal ospC primers targeting intergenic sequences conserved among all known Lyme pathogens, the protocol could be used for culture-free identification and quantification of Lyme pathogens in wildlife and clinical specimens across the globe.

microbiology↗

bioRxiv2018

Phylogeny Recapitulates Learning: Self-Optimization of Genetic Code

Learning algorithms have been proposed as a non-selective mechanism capable of creating complex adaptive systems in life. Evolutionary learning however has not been demonstrated to be a plausible cause for the origin of a specific molecular system. Here we show that genetic codes as optimal as the Standard Genetic Code (SGC) emerge readily by following a molecular analog of the Hebbs rule (\"neurons fire together, wire together\"). Specifically, error-minimizing genetic codes are obtained by maximizing the number of physio-chemically similar amino acids assigned to evolutionarily similar codons. Formulating genetic code as a Traveling Salesman Problem (TSP) with amino acids as \"cities\" and codons as \"tour positions\" and implemented with a Hopfield neural network, the unsupervised learning algorithm efficiently finds an abundance of genetic codes that are more error-minimizing than SGC. Drawing evidence from molecular phylogenies of contemporary tRNAs and aminoacyl-tRNA synthetases, we show that co-diversification between gene sequences and gene functions, which cumulatively captures functional differences with sequence differences and creates a genomic \"memory\" of the living environment, provides the biological basis for the Hebbian learning algorithm. Like the Hebbs rule, the locally acting phylogenetic learning rule, which may simply be stated as increasing phylogenetic divergence for increasing functional difference, could lead to complex and robust life systems. Natural selection, while essential for maintaining gene function, is not necessary to act at system levels. For molecular systems that are self-organizing through phylogenetic learning, the TSP model and its Hopfield network solution offer a promising framework for simulating emerging behavior, forecasting evolutionary trajectories, and designing optimal synthetic systems.

evolutionary biology↗