National Science Review

Since Omicron variant of SARS-CoV-2 was first detected in South Africa (SA), it has now dominated in United Kingdom (UK) of Europe and United State (USA) of North America. A prominent feature of this variant is the gathering of spike protein mutations, in particularly at the receptor binding domain (RBD). These RBD mutations essentially contribute to antibody resistance of current immune approaches. During global spillover, combinations of RBD mutations may exist and synergistically contribute to antibody resistance in fact. Using three geographic-stratified genome wide association studies (GWAS), we observed that RBD combinations exhibited a geographic pattern and genetical associated, such as five common mutations in both UK and USA Omicron, six or two specific mutations in UK or USA Omicron. Although the UK specific RBD mutations can be further classified into two separated sub-groups of combination based on linkage disequilibrium analysis. Functional analysis indicated that the common RBD combinations (fold change, -11.59) alongside UK or USA specific mutations significantly reduced neutralization (fold change, -38.72, -18.11). As RBD overlaps with angiotensin converting enzyme 2(ACE2) binding motif, protein-protein contact analysis indicated that the common RBD mutations enhanced ACE2 binding accessibility and were further strengthened by UK or USA-specific RBD mutations. Spatiotemporal evolution analysis indicated that UK-specific RBD mutations largely contribute to global spillover. Collectively, we have provided genetic evidence of RBD combinations and estimated their effects on antibody evasion and ACE2 binding accessibility.

Whether human lineage mutations (HLM) have contributed to human evolution via post-transcriptional modification remained unknown. We applied deep learning models Seqweaver to predict how HLM impacts RNA-binding protein affinity (RBP). At the threshold of the top 1% of human common variation, only 0.27% of HLM had large impacts on RBP. These HLMs enriched in a set of conserved genes that are highly expressed in adult excitatory neurons and prenatal Purkinje neurons, and involved in synapse organization and the GTPase pathway. These genes also carried excess damaging coding mutations that caused neurodevelopmental disorders, ataxia, and Schizophrenia. Among these genes, NTRK2 and ITPR1 had the most aggregated evidence of functional importance, pointing to an essential role in cognition and bipedalism. We concluded that a very small number of human-specific mutations have contributed to human speciation via impacts on post-transcriptional modification of critical brain-related genes. Author summaryPost-transcriptional modification has important functions in biological systems, but its role in evolution has long been a mystery due to the difficulty in predicting how a mutation could impact this process. Here we applied the newly developed deep learning model Seqweaver on human lineage mutations (HLM) to predict their effect on RNA-protein binding affinity profile (RBP). We found that only a very small number HLM are influential to RBP. Influential HLMs enriched in a set of conserved genes that are enriched in cells and functions critical for cognition and bipedal walking, and severe mutations on these genes cause the disruption of cognition (neurodevelopmental disorders) and bipedalism (ataxia). NTRK2 and ITPR1 had the most aggregated pieces of evidence of functional importance, pointing to an important role in cognition and bipedal walking. Taken together, our result demonstrated that there is a small number of human lineage mutations that modified the post-transcriptional modifications of conserved neuronal genes, which contributed to human speciation.

The evolution of SARS-CoV-2 is characterized by the emergence of new variants with a sheer number of mutations compared to their predecessors, which conferred resistance to pre-existing antibodies and/or increased transmissibility. The recently emerged Omicron subvariants also exhibit a strong tendency for immune evasion, suggesting adaptive evolution. However, previous studies have been limited to specific lineages or subsets of mutations, the overall evolutionary trajectory of SARS-CoV-2 and the underlying driving forces are still not fully understood. In this study, we analyzed the mutations present in all open-access SARS-CoV-2 genomes (until November 2022) and correlated the mutations incidence and fitness change with its impact on immune evasion and ACE2 binding affinity. Our results showed that the Omicron lineage had an accelerated mutation rate in the RBD region, while the mutation incidence in other genomic regions did not change dramatically over time. Moreover, mutations in the RBD region (but not in any other genomic regions) exhibited a lineage-specific pattern and tended to become more aggregated over time, and the mutation incidence was positively correlated with the strength of antibody pressure on the specific position. Additionally, the incidence of mutation was also positively correlated with changes in ACE2 binding affinity, but with a lower correlation coefficient than with immune evasion. In contrast, the mutations effect on fitness was more closely correlated with changes in ACE2 binding affinity than immune evasion. In conclusion, our results suggest that immune evasion and ACE2 binding affinity play significant and diverse roles in the evolution of SARS-CoV-2.

A novel coronavirus, SARS-CoV-2, has caused a pandemic of COVID-19. The evolutionary trend of the virus genome may have implications for infection control policy but remains obscure. We introduce an estimation of fold change of translational efficiency based on synonymous variant sites to characterize the adaptation of the virus to hosts. The increased translational efficiency of the M and N genes suggests that the population of SARS-CoV-2 benefits from mutations toward favored codons, while the ORF1ab gene has slightly decreased the translational efficiency. In the coding region of the ORF1ab gene upstream of the -1 frameshift site, the decreasing of the translational efficiency has been weakening parallel to the growth of the epidemic, indicating inhibition of synthesis of RNA-dependent RNA polymerase and promotion of replication of the genome. Such an evolutionary trend suggests that multiple infections increased virulence in the absence of social distancing.

After centuries of genetic studies, one of the most fundamental questions, i.e. in what cell types do DNA mutations regulate a phenotype, remains unanswered for most complex phenotypes. The current availability of hundreds of genome-wide association studies (GWASs) and single-cell RNA sequencing (scRNA-seq) of millions of cells provides a unique opportunity to address the question. In the present study, we firstly constructed an association landscape between over 20,000 single cell clusters and 997 complex phenotypes by a cross annotation framework with scRNA-seq expression profiles and GWAS summary statistics. We then performed an extensive overview of cell-type specificity and pleiotropy in human phenotypes and found most phenotypes (>90%) were moderately selectively associated with a limited number of cell types while a small fraction cell types (<10%) had strong pleiotropy in multiple phenotypes (~100). Moreover, we identified three cell type-phenotype mutual pleiotropy blocks in the landscape. The application of the single cell type-phenotype cross annotation framework (named SPA) also explained the T cell biased lymphopenia and suggested important supporting genes in severe COVID-19 from human genetics angle. All the cell type-phenotype association results can be queried and visualized at http://pmglab.top/spa.

Immune evasion is a pivotal force shaping the evolution of viruses. Nonetheless, the extent to which virus evolution varies among populations with diverse immune backgrounds remains an unsolved mystery. Prior to the widespread SARS-CoV-2 infections in December 2022 and January 2023, the Chinese population possessed a markedly distinct (less potent) immune background due to its low infection rate, compared to countries experiencing multiple infection waves, presenting an unprecedented opportunity to investigate how the virus has evolved under different immune contexts. We compared the mutation spectrum and functional potential of BA.5.2.48, BF.7.14, and BA.5.2.49--variants prevalent in China--with their counterparts in other countries. We found that mutations in the RBD region in these lineages were more widely dispersed and evenly distributed across different epitopes. These mutations led to a higher ACE2 binding affinity and reduced potential for immune evasion compared to their counterparts in other countries. These findings suggest a milder immune pressure and less evident immune imprinting within the Chinese population. Despite the emergence of numerous immune-evading variants in China, none of them exhibited a transmission advantage. Instead, they were replaced by the imported XBB variant with stronger immune evasion since April 2023. Our findings demonstrated that the continuously changing immune background led to varying evolutionary pressures on SARS-CoV-2. Thus, in addition to the viral genome surveillance, immune background surveillance is also imperative for predicting forthcoming mutations and understanding how these variants spread in the population.

Although the "RNA world" hypothesis suggests that RNA played a crucial role in the origin of life [7], the functional framework of RNA in prebiotic protein synthesis and the mechanisms of genetic code formation during the prebiotic period remain poorly understood. Here, using the prebiotic "primordial soup" as a model, we reconstructed the detailed steps that would yield a protein with a stable ordered amino-acid sequence in the "primordial soup" at the prebiotic period. In the "primordial soup", a large number of medium- to large-sized biomolecule-like substances--such as RNA-like and protein-like molecules of various sizes and shapes, as well as related polymers like amino-acid-RNA-like etc.--did generate and accumulate. Moreover, protein-like and RNA-like molecules formed even more intricate complexes. These complexes bound free mRNA-like molecules through complementary base pairing. Subsequently, with an extremely low probability, two adjacent amino-acid-RNA-like molecules became bound to this free mRNA-like molecule, and their amino acids underwent a condensation reaction by the complexes, producing peptides and eventually proteins or polypeptides. This free mRNA-like molecule exhibits a certain flexible structure, whereas the super-large complexes formed by protein-like and RNA-like molecules (which possess certain activities) and the amino-acid-RNA molecules exhibit relatively rigid structures. Long-term evolution and mutual selection led to the emergence of proteins with stable amino acid sequences and moderate catalytic activity. In this way, the nucleotide information embedded in such mRNA-like molecules indirectly express through protein synthesis--a process we term the "A Co-Adaptation Flexible-Rigid Docking Model", where flexible mRNA-like molecules dock onto rigid complexes to enable ordered peptide formation. Finally, we show how trinucleotide codons emerge naturally from the flexible-rigid docking constraints.

The SARS-COV-2 virus, which causes the COVID-19, is rapidly accumulating mutations to adapt to the hosts. We collected SARS-COV-2 sequence data from the end of 2019 to April 2022 to analyze for their evolutionary features during the pandemic. We found that most of the SARS-COV-2 genes are undergoing negative purifying selection, while the spike protein gene (S-gene) is undergoing rapid positive selection. From the original strain to the alpha, delta and omicron variant types, the Ka/Ks of the S-gene increases, while the Ka/Ks within one variant type decreases over time. During the evolution, the codon usage did not evolve towards optimal translation and protein expression. In contrast, only S-gene mutations showed a remarkable trend on accumulating more positive charges. This facilitates the infection via binding human ACE2 for cell entry and binding furin for cleavage. Such a functional evolution emphasizes the survival strategy of SARS-COV-2, and indicated new druggable target to contain the viral infection. The nearly fully positively-charged interaction surfaces indicated that the infectivity of SARS-COV-2 virus may approach a limit.

Modern Japanese have two major ancestral populations: the indigenous Jomon hunter gatherers and continental East Asian farmers. To figure out the formation process of current Japanese population, we developed a reference-free detection method of variants derived from ancestral populations using a summary statistic, the ancestry-marker index (AMI). We confirmed by computer simulations that AMI can detect ancestry-derived variants even in an admixed population of recently diverged source populations with high accuracy, which cannot be achieved by the most widely used statistics, S*, for identifying archaic ancestry. We applied the AMI to modern Japanese samples and identified 208,648 single nucleotide polymorphisms (SNPs) that were likely derived from the Jomon people (Jomon-derived variants). The analysis of Jomon-derived variants in 10,842 modern Japanese individuals recruited from all over Japan revealed that the admixture proportions of the Jomon people varied between prefectures, probably due to the differences of population sizes of immigrants in the final Jomon to the Yayoi period. The estimated allele frequencies of genome-wide SNPs in the ancestral populations of modern Japanese suggested their phenotypic characteristics possibly for adaptation to their respective livelihoods; higher triglycerides and blood sugar for the Jomon ancestry and higher C-reactive protein and eosinophil counts for continental ancestry. According to our findings, we propose a formation model of modern Japanese population; regional variations in admixture proportions of the Jomon people and continental East Asians formed genotypic and phenotypic gradations of current Japanese archipelago populations.

By sensing local conformational strains in DNA, TaqMutS utilizes a push-pull mechanism via the synergy of residues S57 and D472 to modulate {pi}-stacking interactions between the key recognition residue F39 and DNA, thereby achieving specific mismatch-site recognition. Meanwhile, residues K61 and R473 fine-tune non-specific binding of TaqMutS through electrostatic synergy.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cause the most serious pandemics of Coronavirus Disease 2019 (COVID-19), which threatens human health and public safety. SARS-CoV-2 spike (S) protein uses angiotensin-converting enzyme 2 (ACE2) as recognized receptor for its entry into host cell that contributes to the infection of SARS-CoV-2 to hosts. Using computational modeling approach, this study resolved the evolutionary pattern of bonding affinity of ACE2 in 247 jawed vertebrates to the spike (S) protein of SARS-CoV-2. First, high-or-low binding affinity phenotype divergence of ACE2 to the S protein of SARS-CoV-2 has appeared in two ancient species of jawed vertebrates, Scyliorhinus torazame (low-affinity, Chondrichthyes) and Latimeria chalumnae (high-affinity, Coelacanthimorpha). Second, multiple independent affinity divergence events recur in fishes, amphibians-reptiles, birds, and mammals. Third, high affinity phenotypes go up in mammals, possibly implying the rapid expansion of mammals might accelerate the evolution of coronaviruses. Fourth, we found natural mutations at eight amino acid sites of ACE2 can determine most of phenotype divergences of bonding affinity in 247 vertebrates and resolved their related structural basis. Moreover, we also identified high-affinity or low-affinity-associated concomitant mutation group.The group linked to extremely high affinity may provide novel potentials for the development of human recombinant soluble ACE2 (hrsACE2) in treating patients with COVID-19 or for constructing genetically modified SARS-CoV-2 infection models promoting vaccines studies. These findings would offer potential benefits for the treatment and prevention of SARS-CoV-2.

Because of lacking of the systematic investigation of correlations between the physical examination indicators (PEIs), currently most of them are independently used for disease warning. This results in very limited diagnostic values of general physical examination. Here, we first systematically analyzed the correlations between 221 PEIs in healthy and in 34 unhealthy states in 803,614 peoples in China. We revealed rich relevant between PEIs in healthy physical status (7,662 significant correlations, 31.5% of all). However, in disease conditions, the PEI correlations changed. We further focused on the difference of these PEIs between healthy and 35 unhealthy physical status, 1,239 significant PEI difference were discovered suggesting as candidate disease markers. Finally, we established machine learning algorithms to predict the health status by using 15%-16% PEIs by feature extraction, which reached 66%-99% precision predictions depending on the physical state. This new encyclopedia of PEI correlation provides rich information to chronic disease diagnosis. Our developed machine learning algorithms will have fundamental impact in practice of general physical examination.

Revealing the molecular mechanisms underlying neuronal specification and acquisition of specific functions is key to understanding how the nervous system is constructed. In the Drosophila brain, Kenyon cells (KCs) are sequentially generated to assemble the backbone of the mushroom body (MB). Broad-complex, tramtrack and bric-{square}-brac zinc finger transcription factors (BTBzf TFs) specify early-born KCs, whereas the essential TFs for specifying late-born KCs remain unidentified. Here, we report that Pipsqueak domain-containing TF Eip93F promotes the identity of late-born KCs by reciprocally regulating gene expression in main KC types. Moreover, Eip93F not only regulates the expression of calcium channel Ca-1T in late-born KCs to functionally control animal behavior, but it also forms a genetic network with BTBzf TFs to specify the identities of main KC types. Our study provides crucial information linking KC-type diversification to unique function acquisition in the adult MB.

Yeast, a single eukaryotic cell model organism, demonstrates a progressive aging process. In the era of synthetic biology, study of the impact of synthetic chromosomes and aging is urgent and intriguing. Herein, we successfully constructed the 884 Kb synXIII of Saccharomyces cerevisiae and conducted replicative aging studies using the synthetic strains. We verified that the rRNA-related transcriptional factor RRN9 is a major positive controller of replicative lifespan. Using SCRaMbLE and an HSP104 reporter as a biomarker for mutant discovery, we screened 135 SCRaMbLEd synXIII strains with extended lifespan and identified 10 genes on synXIII that potentially serve as aging regulators. In addition, the genome-scale regression analysis of long-replicative lifespan SCRaMbLEd strains revealed distinct dysregulation of nucleus, ribosome, and mitochondrion function networks. Our findings suggest that Sc2.0 yeast has potential for unveiling new aging-related genes and gene-gene interactions underlying replicative lifespan.

Pathogen-host adaptative interaction and complex population demographical processes, including admixture, drift and Darwen selection, have considerably shaped the Neolithic-to-Modern Western Eurasian population structure and genetic susceptibility to modern human diseases. However, the genetic footprints of evolutionary events in East Asia keep unknown as the underrepresentation of genomic diversity and the design of large-scale population studies. We reported one aggregated database of genome-wide-SNP variations from 796 Tai-Kadai (TK) genomes, including Bouyei first reported here, to explore the genetic history, population structure and biological adaptative features of TK-speaking people from Southern China and Southeast Asia. We found geography-related population substructure among TK-speaking people using the state-of-the-art population genetic structure reconstruction techniques based on the allele frequency spectrum and haplotype-resolved phased fragments. We found that the Northern TK-speaking people from Guizhou harboured one TK-dominant ancestry maximised in Bouyei people, and the Southern one from Thailand obtained more influences from Southeast Asians and indigenous people. We reconstructed the fitted admixture models and demographic graphs, which showed that TK-speaking people received gene flow from ancient rice farmer-related lineages related to the Hmong-Mien and Austroasiatic people and Northern millet farmers associated with the Sino-Tibetan people. Biological adaptation focused on our identified unique TK lineages related to Bouyei showed many adaptive signatures conferring Malaria resistance and low-rate lipid metabolism. Further gene enrichment, the allele frequency distribution of derived alleles, and their correlation with the incidence of Malaria further confirmed that CR1 played an essential role in the resistance of Malaria in the ancient "Baiyue" tribes.

Despite decades of antibody research, it remains challenging to predict the specificity of an antibody solely based on its sequence. Two major obstacles are the lack of appropriate models and inaccessibility of datasets for model training. In this study, we curated a dataset of >5,000 influenza hemagglutinin (HA) antibodies by mining research publications and patents, which revealed many distinct sequence features between antibodies to HA head and stem domains. We then leveraged this dataset to develop a lightweight memory B cell language model (mBLM) for sequence-based antibody specificity prediction. Model explainability analysis showed that mBLM captured key sequence motifs of HA stem antibodies. Additionally, by applying mBLM to HA antibodies with unknown epitopes, we discovered and experimentally validated many HA stem antibodies. Overall, this study not only advances our molecular understanding of antibody response to influenza virus, but also provides an invaluable resource for applying deep learning to antibody research.

How animal neural system addresses the object identity-preserving recognition problem is largely unknown. Artificial neural network such as convolution network (CNN) has reached human level performance in recognition tasks, however, animal neural system does not support such kernel scanning operation across retinal neurons, and thus the neuronal responses do not match that of CNN units. Here, we used an alternative recognition-reconstruction network (RRN) architecture as an analogy to animal-like system, and the resulting neural characteristics agreed fairly well with electrophysiological measurements in monkey studies. First, in network development study, the RRN also experienced critical developmental stages characterized by specificities in neuronal types, connectivity strength and firing pattern, from early stage of coarse salience map recognition to mature stage of fine structure recognition. In digit recognition study, we witnessed that the RRN could maintain object invariance representation under various viewing conditions by coordinated adjustment of responses of population neurons. And such concerted population responses contained untangled object identity and properties information that could be accurately extracted via a simple weighted summation decoder. In the learning and forgetting study, novel structure recognition was implemented by adjusting entire synapses in low magnitude while pattern specificities of original synaptic connectivity were preserved, which guaranteed a learning process without disrupting the existing functionalities. This work benefits the understanding of human neural mechanism and the development of humane-like intelligence.

A novel coronavirus disease (COVID-19) caused by SARS-CoV-2 has been pandemic worldwide. The genetic dynamics of quasispecies afford RNA viruses a great fitness on cell tropism and host range. However, no quasispecies data of SARS-CoV-2 have been reported yet. To explore quasispecies haplotypes and its transmission characteristics, we carried out single-molecule real-time (SMRT) sequencing of the full-length of SARS-CoV-2 spike gene within 14 RNA samples from 2 infection clusters, covering first-to third-generation infected-patients. We observed a special quasispecies structure of SARS-CoV-2 (modeled as One-King): one dominant haplotype (mean abundance ~70.15%) followed by numerous minor haplotypes (mean abundance < 0.10%). We not only discovered a novel dominant haplotype of F1040 but also realized that minor quasispecies were also worthy of attention. Notably, some minor haplotypes (like F1040 and currently pandemic one G614) could potentially reveal adaptive and converse into the dominant one. However, minor haplotypes exhibited a high transmission bottleneck (~6% could be stably transmitted), and the new adaptive/dominant haplotypes were likely originated from genetic variations within a host rather than transmission. The evolutionary rate was estimated as 2.68-3.86 x 10-3 per site per year, which was larger than the estimation at consensus genome level. The One-King model and conversion event expanded our understanding of the genetic dynamics of SARS-CoV-2, and explained the incomprehensible phenomenon at the consensus genome level, such as limited cumulative mutations and low evolutionary rate. Moreover, our findings suggested the epidemic strains may be multi-host origin and future traceability would face huge difficulties.

BackgroundMetabolic remodelling and memory in cardiomyocytes is a pivotal mechanism underlying cardiomyopathy pathogenesis. Clinical observations demonstrate persistent progression of hyperglycaemia-induced multiorgan damage following blood glucose stabilization, which is predominantly mediated through epigenetic regulation. While prior studies have identified epigenetic contributions to hyperglycaemic myocardial injury, the involvement of RNA methylation-particularly N7-methylguanosine (m7G) modification-in this regulatory network remains undefined. MethodsClinical specimens were collected from diabetic patients, and cardiomyocyte-specific METTL1 and ob/ob knockout murine models were established in parallel. Multiomic profiling (proteomics, glycoproteomics, ubiquitinomics, m7G-MeRIP sequencing, and metabolomics) was systematically conducted. The molecular mechanisms governing METTL1 regulation via O-GlcNAcylation and ubiquitination were elucidated through integrated in vitro and in vivo assays. A DUB siRNA library and computational strategies combining molecular docking with molecular dynamics simulations were employed for screening drugs targeting METTL1 O-GlcNAcylation, followed by in vivo therapeutic validation. ResultsComparative analysis of diabetic murine and human samples revealed strong METTL1 downregulation in cardiomyopathy contexts. Tamoxifen-inducible METTL1 knockout mice presented exacerbated diabetic cardiomyopathy phenotypes, confirming its cardioprotective function. Multiomic integration demonstrated that METTL1-mediated m7G modification critically regulates cardiomyocyte fatty acid metabolism. Mechanistically, hyperglycaemia was found to induce O-GlcNAcylation at the METTL1-T268 residue, suppressing m7G methyltransferase activity by 38% (p < 0.01). Subsequent investigations revealed that USP5 deubiquitinase activity is impaired under hyperglycaemic conditions, leading to accelerated METTL1 degradation. Notably, administration of the first-in-class small drug HIT106265621 significantly attenuated cardiomyopathy-associated pathological alterations in ob/ob mice in vivo. ConclusionHyperglycaemia promotes METTL1 O-GlcNAcylation, which impedes USP5-mediated deubiquitination, consequently reducing cardiomyocyte METTL1 protein levels and m7G modification. METTL1 deficiency drives diabetic cardiomyopathy progression through fatty acid metabolic dysregulation, inflammatory activation, and myocardial hypertrophy. Pharmacological inhibition of the OGT-METTL1 interaction using HIT106265621 has therapeutic potential for metabolic cardiomyopathy intervention.

Cognitive reasoning, also known as mental programming, is fundamental to our intelligence. A central function in mental programming is working memory (WM), involving both temporarily maintaining the information and manipulating it based on rules. While the neural mechanisms of WM maintenance have been extensively studied, those governing WM manipulation remain largely unknown. To bridge this gap, the present study focuses on an elementary operation in WM manipulation: re-ordering items in WM sequences. We propose a functional, biologically plausible neural circuit model that consists of two interconnected modules: a memory module composed of continuous attractor-based memory slots that store item features, and a control module sending gain-modulating commands to orchestrate specific operations in the memory module. The model successfully implements two-item swapping in a WM sequence, generating neuronal responses similar to recent primate experiments of WM sequence manipulation. By incorporating principles from the algebraic permutation group, we generalize the circuit model to accommodate more complex sequence manipulations. This math foundation reveals how arbitrary permutations can be decomposed into sequences of elementary swapping operations, which can be generated by a hierarchical tree-structured control circuit module. And the mutual inhibition within the control tree ensures that only one program is being executed at the same time. Our study establishes overarching connections among mental programming neural circuit models, neuro-science experiments, and abstract algebraic structure. These insights enhance our understanding of the neural underpinnings of cognitive reasoning and inspire the design of artificial cognitive systems.