KRAS Dimerization Impacts MEK Inhibitor Sensitivity and Oncogenic Activity of Mutant KRAS.

The mechanism by which the wild-type KRAS allele imparts a growth inhibitory effect to oncogenic KRAS in various cancers, including lung adenocarcinoma (LUAD), is poorly understood. Here, using a genetically inducible model of KRAS loss of heterozygosity (LOH), we show that KRAS dimerization mediates wild-type KRAS-dependent fitness of human and murine KRAS mutant LUAD tumor cells and underlies resistance to MEK inhibition. These effects are abrogated when wild-type KRAS is replaced by KRASD154Q, a mutant that disrupts dimerization at the α4-α5 KRAS dimer interface without changing other fundamental biochemical properties of KRAS, both in vitro and in vivo. Moreover, dimerization has a critical role in the oncogenic activity of mutant KRAS. Our studies provide mechanistic and biological insights into the role of KRAS dimerization and highlight a role for disruption of dimerization as a therapeutic strategy for KRAS mutant cancers.

THP9 enhances seed protein content and nitrogen-use efficiency in maize.

Teosinte, the wild ancestor of maize (Zea mays subsp. mays), has three times the seed protein content of most modern inbreds and hybrids, but the mechanisms that are responsible for this trait are unknown1,2. Here we use trio binning to create a contiguous haplotype DNA sequence of a teosinte (Zea mays subsp. parviglumis) and, through map-based cloning, identify a major high-protein quantitative trait locus, TEOSINTE HIGH PROTEIN 9 (THP9), on chromosome 9. THP9 encodes an asparagine synthetase 4 enzyme that is highly expressed in teosinte, but not in the B73 inbred, in which a deletion in the tenth intron of THP9-B73 causes incorrect splicing of THP9-B73 transcripts. Transgenic expression of THP9-teosinte in B73 significantly increased the seed protein content. Introgression of THP9-teosinte into modern maize inbreds and hybrids greatly enhanced the accumulation of free amino acids, especially asparagine, throughout the plant, and increased seed protein content without affecting yield. THP9-teosinte seems to increase nitrogen-use efficiency, which is important for promoting a high yield under low-nitrogen conditions.

Fibroblast growth factor 13 is a microtubule-stabilizing protein regulating neuronal polarization and migration.

Secretory fibroblast growth factors (FGFs) and their receptors are known for their regulatory function in the early stages of neural development. FGF13, a nonsecretory protein of the FGF family, is expressed in cerebral cortical neurons during development and is a candidate gene for syndromal and nonspecific forms of X-chromosome-linked mental retardation (XLMR). However, its function during development remains unclear. We show that FGF13 acts intracellularly as a microtubule-stabilizing protein required for axon and leading process development and neuronal migration in the cerebral cortex. FGF13 is enriched in axonal growth cones and interacts directly with microtubules. Furthermore, FGF13 polymerizes tubulins and stabilizes microtubules. The loss of FGF13 impairs neuronal polarization and increases the branching of axons and leading processes. Genetic deletion of FGF13 in mice results in neuronal migration defects in both the neocortex and the hippocampus. FGF13-deficient mice also exhibit weakened learning and memory, which is correlated to XLMR patients' intellectual disability.

HIV-1 p17 matrix protein enhances type I interferon responses through the p17-OLA1-STING axis.

Stimulator of IFN genes (STING; also known as STING1) is an important adaptor protein for detecting cytosolic double-stranded DNA, which can come from HIV infection. Several HIV proteins, such as p6, Vpx and Vif, can influence STING-mediated innate immunity, but the function of p17 is still unknown. In this study, we find that HIV-1 p17, but not HIV-2 p17 or SIV p17, promotes STING signaling induced by cyclic GMP-AMP (cGAMP) treatment. Mechanistically, HIV-1 p17 binds to Obg-like ATPase 1 (OLA1) and inhibits the regulation of STING by OLA1. Here, OLA1 interacts with STING and inhibits the translocation and phosphorylation of STING upon cGAMP stimulation. Furthermore, compared with HIV-2 and SIV, the ATPase and GTPase activities of OLA1 are only promoted by HIV-1 p17. Our study shows that the p17 of HIV-1, but not HIV-2 or SIV, promotes STING-mediated innate immunity by interfering the interaction between OLA1 and STING, thus providing a new clue for specific immune activation of HIV-1.

Elevated incidence of somatic mutations at prevalent genetic sites.

The common loci represent a distinct set of the human genome sites that harbor genetic variants found in at least 1% of the population. Small somatic mutations occur at the common loci and non-common loci, i.e. csmVariants and ncsmVariants, are presumed with similar probabilities. However, our work revealed that within the coding region, common loci constituted only 1.03% of all loci, yet they accounted for 5.14% of TCGA somatic mutations. Furthermore, the small somatic mutation incidence rate at these common loci was 2.7 times that observed in the non-common. Notably, the csmVariants exhibited an impressive recurrent rate of 36.14%, which was 2.59 times of the ncsmVariants. The C-to-T transition at the CpG sites accounted for 32.41% of the csmVariants, which was 2.93 times for the ncsmVariants. Interestingly, the aging-related mutational signature contributed to 13.87% of the csmVariants, 5.5 times that of ncsmVariants. Moreover, 35.93% of the csmVariants contexts exhibited palindromic features, outperforming ncsmVariant contexts by 1.84 times. Notably, cancer patients with higher csmVariants rates had better progression-free survival. Furthermore, cancer patients with high-frequency csmVariants enriched with mismatch repair deficiency were also associated with better progression-free survival. The accumulation of csmVariants during cancerogenesis is a complex process influenced by various factors. These include the presence of a substantial percentage of palindromic sequences at csmVariants sites, the impact of aging and DNA mismatch repair deficiency. Together, these factors contribute to the higher somatic mutation incidence rates of common loci and the overall accumulation of csmVariants in cancer development.

The P-body component DECAPPING5 and the floral repressor SISTER OF FCA regulate FLOWERING LOCUS C transcription in Arabidopsis.

Flowering is the transition from vegetative to reproductive growth and is critical for plant adaptation and reproduction. FLOWERING LOCUS C (FLC) plays a central role in flowering time control, and dissecting its regulation mechanism provides essential information for crop improvement. Here, we report that DECAPPING5 (DCP5), a component of processing bodies (P-bodies), regulates FLC transcription and flowering time in Arabidopsis (Arabidopsis thaliana). DCP5 and its interacting partner SISTER OF FCA (SSF) undergo liquid-liquid phase separation (LLPS) that is mediated by their prion-like domains (PrDs). Enhancing or attenuating the LLPS of both proteins using transgenic methods greatly affects their ability to regulate FLC and flowering time. DCP5 regulates FLC transcription by modulating RNA polymerase II enrichment at the FLC locus. DCP5 requires SSF for FLC regulation, and loss of SSF or its PrD disrupts DCP5 function. Our results reveal that DCP5 interacts with SSF, and the nuclear DCP5-SSF complex regulates FLC expression at the transcriptional level.

Enhancing insights into diseases through horizontal gene transfer event detection from gut microbiome.

Horizontal gene transfer (HGT) phenomena pervade the gut microbiome and significantly impact human health. Yet, no current method can accurately identify complete HGT events, including the transferred sequence and the associated deletion and insertion breakpoints from shotgun metagenomic data. Here, we develop LocalHGT, which facilitates the reliable and swift detection of complete HGT events from shotgun metagenomic data, delivering an accuracy of 99.4%-verified by Nanopore data-across 200 gut microbiome samples, and achieving an average F1 score of 0.99 on 100 simulated data. LocalHGT enables a systematic characterization of HGT events within the human gut microbiome across 2098 samples, revealing that multiple recipient genome sites can become targets of a transferred sequence, microhomology is enriched in HGT breakpoint junctions (P-value = 3.3e-58), and HGTs can function as host-specific fingerprints indicated by the significantly higher HGT similarity of intra-personal temporal samples than inter-personal samples (P-value = 4.3e-303). Crucially, HGTs showed potential contributions to colorectal cancer (CRC) and acute diarrhoea, as evidenced by the enrichment of the butyrate metabolism pathway (P-value = 3.8e-17) and the shigellosis pathway (P-value = 5.9e-13) in the respective associated HGTs. Furthermore, differential HGTs demonstrated promise as biomarkers for predicting various diseases. Integrating HGTs into a CRC prediction model achieved an AUC of 0.87.

PhageScope: a well-annotated bacteriophage database with automatic analyses and visualizations.

Bacteriophages are viruses that infect bacteria or archaea. Understanding the diverse and intricate genomic architectures of phages is essential to study microbial ecosystems and develop phage therapy strategies. However, the existing phage databases are short of meticulous annotations. To this end, we propose PhageScope (https://phagescope.deepomics.org), an online phage database with comprehensive annotations. PhageScope harbors a collection of 873 718 phage sequences from various sources. Applying fifteen state-of-the-art tools to perform systematic annotations and analyses, PhageScope provides annotations on genome completeness, host range, lifestyle information, taxonomy classification, nine types of structural and functional genetic elements, and three types of comparative genomic studies for curated phages. Additionally, PhageScope incorporates automatic analyses and visualizations for curated and customized phages, serving as an efficient platform for phage study.

Similar photosynthetic but different yield responses of C3 and C4 crops to elevated O3.

The deleterious effects of ozone (O3) pollution on crop physiology, yield, and productivity are widely acknowledged. It has also been assumed that C4 crops with a carbon concentrating mechanism and greater water use efficiency are less sensitive to O3 pollution than C3 crops. This assumption has not been widely tested. Therefore, we compiled 46 journal articles and unpublished datasets that reported leaf photosynthetic and biochemical traits, plant biomass, and yield in five C3 crops (chickpea, rice, snap bean, soybean, and wheat) and four C4 crops (sorghum, maize, Miscanthus × giganteus, and switchgrass) grown under ambient and elevated O3 concentration ([O3]) in the field at free-air O3 concentration enrichment (O3-FACE) facilities over the past 20 y. When normalized by O3 exposure, C3 and C4 crops showed a similar response of leaf photosynthesis, but the reduction in chlorophyll content, fluorescence, and yield was greater in C3 crops compared with C4 crops. Additionally, inbred and hybrid lines of rice and maize showed different sensitivities to O3 exposure. This study quantitatively demonstrates that C4 crops respond less to elevated [O3] than C3 crops. This understanding could help maintain cropland productivity in an increasingly polluted atmosphere.

Plant biochemistry influences tropospheric ozone formation, destruction, deposition, and response.

Tropospheric ozone (O3) is among the most damaging air pollutant to plants. Plants alter the atmospheric O3 concentration in two distinct ways: (i) by the emission of volatile organic compounds (VOCs) that are precursors of O3; and (ii) by dry deposition, which includes diffusion of O3 into vegetation through stomata and destruction by nonstomatal pathways. Isoprene, monoterpenes, and higher terpenoids are emitted by plants in quantities that alter tropospheric O3. Deposition of O3 into vegetation is related to stomatal conductance, leaf structural traits, and the detoxification capacity of the apoplast. The biochemical fate of O3 once it enters leaves and reacts with aqueous surfaces is largely unknown, but new techniques for the tracking and identification of initial products have the potential to open the black box.

Causation and familial confounding as explanations for the associations of polygenic risk scores with breast cancer: Evidence from innovative ICE FALCON and ICE CRISTAL analyses.

A polygenic risk score (PRS) combines the associations of multiple genetic variants that could be due to direct causal effects, indirect genetic effects, or other sources of familial confounding. We have developed new approaches to assess evidence for and against causation by using family data for pairs of relatives (Inference about Causation from Examination of FAmiliaL CONfounding [ICE FALCON]) or measures of family history (Inference about Causation from Examining Changes in Regression coefficients and Innovative STatistical AnaLyses [ICE CRISTAL]). Inference is made from the changes in regression coefficients of relatives' PRSs or PRS and family history before and after adjusting for each other. We applied these approaches to two breast cancer PRSs and multiple studies and found that (a) for breast cancer diagnosed at a young age, for example, <50 years, there was no evidence that the PRSs were causal, while (b) for breast cancer diagnosed at later ages, there was consistent evidence for causation explaining increasing amounts of the PRS-disease association. The genetic variants in the PRS might be in linkage disequilibrium with truly causal variants and not causal themselves. These PRSs cause minimal heritability of breast cancer at younger ages. There is also evidence for nongenetic factors shared by first-degree relatives that explain breast cancer familial aggregation. Familial associations are not necessarily due to genes, and genetic associations are not necessarily causal.

Breast and bowel cancers diagnosed in people 'too young to have cancer': A blueprint for research using family and twin studies.

Young breast and bowel cancers (e.g., those diagnosed before age 40 or 50 years) have far greater morbidity and mortality in terms of years of life lost, and are increasing in incidence, but have been less studied. For breast and bowel cancers, the familial relative risks, and therefore the familial variances in age-specific log(incidence), are much greater at younger ages, but little of these familial variances has been explained. Studies of families and twins can address questions not easily answered by studies of unrelated individuals alone. We describe existing and emerging family and twin data that can provide special opportunities for discovery. We present designs and statistical analyses, including novel ideas such as the VALID (Variance in Age-specific Log Incidence Decomposition) model for causes of variation in risk, the DEPTH (DEPendency of association on the number of Top Hits) and other approaches to analyse genome-wide association study data, and the within-pair, ICE FALCON (Inference about Causation from Examining FAmiliaL CONfounding) and ICE CRISTAL (Inference about Causation from Examining Changes in Regression coefficients and Innovative STatistical AnaLysis) approaches to causation and familial confounding. Example applications to breast and colorectal cancer are presented. Motivated by the availability of the resources of the Breast and Colon Cancer Family Registries, we also present some ideas for future studies that could be applied to, and compared with, cancers diagnosed at older ages and address the challenges posed by young breast and bowel cancers.

African swine fever virus infection regulates pyroptosis by cleaving gasdermin A via active caspase-3 and caspase-4.

African swine fever, caused by the African swine fever virus (ASFV), is a viral hemorrhagic disease that affects domestic pigs and wild boars. ASFV infection causes extensive tissue damage, and the associated mechanism is poorly understood. Pyroptosis is characterized by the activation of inflammatory caspases and pore formation in the cellular plasma membrane, resulting in the release of inflammatory cytokines and cell damage. How ASFV infection regulates pyroptosis remains unclear. Here, using siRNA assay and overexpression methods, we report that ASFV infection regulated pyroptosis by cleaving the pyroptosis execution protein gasdermin A (GSDMA). ASFV infection activated caspase-3 and caspase-4, which specifically cleaved GSDMA at D75-P76 and D241-V242 to produce GSDMA into five fragments, including GSDMA-N1-75, GSDMA-N1-241, and GSDMA-N76-241 fragments at the N-terminal end of GSDMA. Only GSDMA-N1-241, which was produced in the late stage of ASFV infection, triggered pyroptosis and inhibited ASFV replication. The fragments, GSDMA-N1-75 and GSDMA-N76-241, lose the ability to induce pyroptosis. Overall ASFV infection differentially regulates pyroptosis by GSDMA in the indicated phase, which may be conducive to its own replication. Our findings reveal a novel molecular mechanism for the regulation of pyroptosis.

Deciphering PDH1's role in mung bean domestication: a genomic perspective on pod dehiscence.

Mung bean (Vigna radiata) stands as a crucial legume crop in Asia, contributing to food security. However, our understanding of the underlying genetic foundation governing domesticated agronomic traits, especially those linked to pod architecture, remains largely unexplored. In this study, we delved into the genomic divergence between wild and domesticated mung bean varieties, leveraging germplasm obtained from diverse sources. Our findings unveiled pronounced variation in promoter regions (35%) between the two mung bean subpopulations, suggesting substantial changes in gene expression patterns during domestication. Leveraging transcriptome analysis using distinct reproductive stage pods and subpopulations, we identified candidate genes responsible for pod and seed architecture development, along with Genome-Wide Association Studies (GWAS) and Quantitative Trait Locus (QTL) analysis. Notably, our research conclusively confirmed PDH1 as a parallel domesticated gene governing pod dehiscence in legumes. This study imparts valuable insights into the genetic underpinnings of domesticated agronomic traits in mung bean, and simultaneously highlighting the parallel domestication of pivotal traits within the realm of legume crops.

Association Analysis Provides Insights into Plant Mitonuclear Interactions.

Cytonuclear interaction refers to the complex and ongoing process of coevolution between nuclear and organelle genomes, which are responsible for cellular respiration, photosynthesis, lipid metabolism, etc. and play a significant role in adaptation and speciation. There have been a large number of studies to detect signatures of cytonuclear interactions. However, identification of the specific nuclear and organelle genetic polymorphisms that are involved in these interactions within a species remains relatively rare. The recent surge in whole genome sequencing has provided us an opportunity to explore cytonuclear interaction from a population perspective. In this study, we analyzed a total of 3,439 genomes from 7 species to identify signals of cytonuclear interactions by association (linkage disequilibrium) analysis of variants in both the mitochondrial and nuclear genomes across flowering plants. We also investigated examples of nuclear loci identified based on these association signals using subcellular localization assays, gene editing, and transcriptome sequencing. Our study provides a novel perspective on the investigation of cytonuclear coevolution, thereby enriching our understanding of plant fitness and offspring sterility.

Segregation analysis of 17,425 population-based breast cancer families: Evidence for genetic susceptibility and risk prediction.

Rare pathogenic variants in known breast cancer-susceptibility genes and known common susceptibility variants do not fully explain the familial aggregation of breast cancer. To investigate plausible genetic models for the residual familial aggregation, we studied 17,425 families ascertained through population-based probands, 86% of whom were screened for pathogenic variants in BRCA1, BRCA2, PALB2, CHEK2, ATM, and TP53 via gene-panel sequencing. We conducted complex segregation analyses and fitted genetic models in which breast cancer incidence depended on the effects of known susceptibility genes and other unidentified major genes and a normally distributed polygenic component. The proportion of familial variance explained by the six genes was 46% at age 20-29 years and decreased steadily with age thereafter. After allowing for these genes, the best fitting model for the residual familial variance included a recessive risk component with a combined genotype frequency of 1.7% (95% CI: 0.3%-5.4%) and a penetrance to age 80 years of 69% (95% CI: 38%-95%) for homozygotes, which may reflect the combined effects of multiple variants acting in a recessive manner, and a polygenic variance of 1.27 (95% CI: 0.94%-1.65), which did not vary with age. The proportion of the residual familial variance explained by the recessive risk component was 40% at age 20-29 years and decreased with age thereafter. The model predicted age-specific familial relative risks consistent with those observed by large epidemiological studies. The findings have implications for strategies to identify new breast cancer-susceptibility genes and improve disease-risk prediction, especially at a young age.

Enhanced antigen-specific CD8 T cells contribute to early protection against FMDV through swine DC vaccination.

Foot-and-mouth disease virus (FMDV) remains a challenge for cloven-hooved animals. The currently licensed FMDV vaccines induce neutralizing antibody (NAb)-mediated protection but show defects in the early protection. Dendritic cell (DC) vaccines have shown great potency in inducing rapid T-cell immunity in humans and mice. Whether DC vaccination could enhance early protection against FMDV has not been elaborately explored in domestic pigs. In this study, we employed DC vaccination as an experimental approach to study the roles of cellular immunity in the early protection against FMDV in pigs. Autologous DCs were differentiated from the periphery blood mononuclear cells of each pig, pulsed with inactivated FMDV (iFMDV-DC) and treated with LPS, and then injected into the original pigs. The cellular immune responses and protective efficacy elicited by the iFMDV-DC were examined by multicolor flow cytometry and tested by FMDV challenge. The results showed that autologous iFMDV-DC immunization induced predominantly FMDV-specific IFN-γ-producing CD4+ T cells and cytotoxic CD8+ T cells (CTLs), high NAb titers, compared to the inactivated FMDV vaccine, and accelerated the development of memory CD4 and CD8 T cells, which was concomitantly associated with early protection against FMDV virulent strain in pigs. Such early protection was associated with the rapid proliferation of secondary T-cell response after challenge and significantly contributed by secondary CD8 effector memory T cells. These results demonstrated that rapid induction of cellular immunity through DC immunization is important for improving early protection against FMDV. Enhancing cytotoxic CD8+ T cells may facilitate the development of more effective FMDV vaccines.IMPORTANCEAlthough the currently licensed FMDV vaccines provide NAb-mediated protection, they have defects in early immune protection, especially in pigs. In this study, we demonstrated that autologous swine DC immunization augmented the cellular immune response and induced an early protective response against FMDV in pigs. This approach induced predominantly FMDV-specific IFN-γ-producing CD4+ T cells and cytotoxic CD8+ T cells, high NAb titers, and rapid development of memory CD4 and CD8 T cells. Importantly, the early protection conferred by this DC immunization is more associated with secondary CD8+ T response rather than NAbs. Our findings highlighted the importance of enhancing cytotoxic CD8+ T cells in early protection to FMDV in addition to Th1 response and identifying a strategy or adjuvant comparable to the DC vaccine might be a future direction for improving the current FMDV vaccines.

Deep autoregressive generative models capture the intrinsics embedded in T-cell receptor repertoires.

T-cell receptors (TCRs) play an essential role in the adaptive immune system. Probabilistic models for TCR repertoires can help decipher the underlying complex sequence patterns and provide novel insights into understanding the adaptive immune system. In this work, we develop TCRpeg, a deep autoregressive generative model to unravel the sequence patterns of TCR repertoires. TCRpeg largely outperforms state-of-the-art methods in estimating the probability distribution of a TCR repertoire, boosting the average accuracy from 0.672 to 0.906 measured by the Pearson correlation coefficient. Furthermore, with promising performance in probability inference, TCRpeg improves on a range of TCR-related tasks: profiling TCR repertoire probabilistically, classifying antigen-specific TCRs, validating previously discovered TCR motifs, generating novel TCRs and augmenting TCR data. Our results and analysis highlight the flexibility and capacity of TCRpeg to extract TCR sequence information, providing a novel approach for deciphering complex immunogenomic repertoires.

TEINet: a deep learning framework for prediction of TCR-epitope binding specificity.

The adaptive immune response to foreign antigens is initiated by T-cell receptor (TCR) recognition on the antigens. Recent experimental advances have enabled the generation of a large amount of TCR data and their cognate antigenic targets, allowing machine learning models to predict the binding specificity of TCRs. In this work, we present TEINet, a deep learning framework that utilizes transfer learning to address this prediction problem. TEINet employs two separately pretrained encoders to transform TCR and epitope sequences into numerical vectors, which are subsequently fed into a fully connected neural network to predict their binding specificities. A major challenge for binding specificity prediction is the lack of a unified approach to sampling negative data. Here, we first assess the current negative sampling approaches comprehensively and suggest that the Unified Epitope is the most suitable one. Subsequently, we compare TEINet with three baseline methods and observe that TEINet achieves an average AUROC of 0.760, which outperforms baseline methods by 6.4-26%. Furthermore, we investigate the impacts of the pretraining step and notice that excessive pretraining may lower its transferability to the final prediction task. Our results and analysis show that TEINet can make an accurate prediction using only the TCR sequence (CDR3$\beta $) and the epitope sequence, providing novel insights to understand the interactions between TCRs and epitopes.

ZetaDesign: an end-to-end deep learning method for protein sequence design and side-chain packing.

Computational protein design has been demonstrated to be the most powerful tool in the last few years among protein designing and repacking tasks. In practice, these two tasks are strongly related but often treated separately. Besides, state-of-the-art deep-learning-based methods cannot provide interpretability from an energy perspective, affecting the accuracy of the design. Here we propose a new systematic approach, including both a posterior probability and a joint probability parts, to solve the two essential questions once for all. This approach takes the physicochemical property of amino acids into consideration and uses the joint probability model to ensure the convergence between structure and amino acid type. Our results demonstrated that this method could generate feasible, high-confidence sequences with low-energy side conformations. The designed sequences can fold into target structures with high confidence and maintain relatively stable biochemical properties. The side chain conformation has a significantly lower energy landscape without delegating to a rotamer library or performing the expensive conformational searches. Overall, we propose an end-to-end method that combines the advantages of both deep learning and energy-based methods. The design results of this model demonstrate high efficiency, and precision, as well as a low energy state and good interpretability.