Lactate Is a Natural Suppressor of RLR Signaling by Targeting MAVS.

RLR-mediated type I IFN production plays a pivotal role in elevating host immunity for viral clearance and cancer immune surveillance. Here, we report that glycolysis, which is inactivated during RLR activation, serves as a barrier to impede type I IFN production upon RLR activation. RLR-triggered MAVS-RIG-I recognition hijacks hexokinase binding to MAVS, leading to the impairment of hexokinase mitochondria localization and activation. Lactate serves as a key metabolite responsible for glycolysis-mediated RLR signaling inhibition by directly binding to MAVS transmembrane (TM) domain and preventing MAVS aggregation. Notably, lactate restoration reverses increased IFN production caused by lactate deficiency. Using pharmacological and genetic approaches, we show that lactate reduction by lactate dehydrogenase A (LDHA) inactivation heightens type I IFN production to protect mice from viral infection. Our study establishes a critical role of glycolysis-derived lactate in limiting RLR signaling and identifies MAVS as a direct sensor of lactate, which functions to connect energy metabolism and innate immunity.

Inositol serves as a natural inhibitor of mitochondrial fission by directly targeting AMPK.

Mitochondrial dynamics regulated by mitochondrial fusion and fission maintain mitochondrial functions, whose alterations underline various human diseases. Here, we show that inositol is a critical metabolite directly restricting AMPK-dependent mitochondrial fission independently of its classical mode as a precursor for phosphoinositide generation. Inositol decline by IMPA1/2 deficiency elicits AMPK activation and mitochondrial fission without affecting ATP level, whereas inositol accumulation prevents AMPK-dependent mitochondrial fission. Metabolic stress or mitochondrial damage causes inositol decline in cells and mice to elicit AMPK-dependent mitochondrial fission. Inositol directly binds to AMPKγ and competes with AMP for AMPKγ binding, leading to restriction of AMPK activation and mitochondrial fission. Our study suggests that the AMP/inositol ratio is a critical determinant for AMPK activation and establishes a model in which AMPK activation requires inositol decline to release AMPKγ for AMP binding. Hence, AMPK is an inositol sensor, whose inactivation by inositol serves as a mechanism to restrict mitochondrial fission.

LDER-GE estimates phenotypic variance component of gene-environment interactions in human complex traits accurately with GE interaction summary statistics and full LD information.

Gene-environment (GE) interactions are essential in understanding human complex traits. Identifying these interactions is necessary for deciphering the biological basis of such traits. In this study, we review state-of-art methods for estimating the proportion of phenotypic variance explained by genome-wide GE interactions and introduce a novel statistical method Linkage-Disequilibrium Eigenvalue Regression for Gene-Environment interactions (LDER-GE). LDER-GE improves the accuracy of estimating the phenotypic variance component explained by genome-wide GE interactions using large-scale biobank association summary statistics. LDER-GE leverages the complete Linkage Disequilibrium (LD) matrix, as opposed to only the diagonal squared LD matrix utilized by LDSC (Linkage Disequilibrium Score)-based methods. Our extensive simulation studies demonstrate that LDER-GE performs better than LDSC-based approaches by enhancing statistical efficiency by ~23%. This improvement is equivalent to a sample size increase of around 51%. Additionally, LDER-GE effectively controls type-I error rate and produces unbiased results. We conducted an analysis using UK Biobank data, comprising 307 259 unrelated European-Ancestry subjects and 966 766 variants, across 217 environmental covariate-phenotype (E-Y) pairs. LDER-GE identified 34 significant E-Y pairs while LDSC-based method only identified 23 significant E-Y pairs with 22 overlapped with LDER-GE. Furthermore, we employed LDER-GE to estimate the aggregated variance component attributed to multiple GE interactions, leading to an increase in the explained phenotypic variance with GE interactions compared to considering main genetic effects only. Our results suggest the importance of impacts of GE interactions on human complex traits.

CRISPR screening uncovers nucleolar RPL22 as a heterochromatin destabilizer and senescence driver.

Dysfunction of the ribosome manifests during cellular senescence and contributes to tissue aging, functional decline, and development of aging-related disorders in ways that have remained enigmatic. Here, we conducted a comprehensive CRISPR-based loss-of-function (LOF) screen of ribosome-associated genes (RAGs) in human mesenchymal progenitor cells (hMPCs). Through this approach, we identified ribosomal protein L22 (RPL22) as the foremost RAG whose deficiency mitigates the effects of cellular senescence. Consequently, absence of RPL22 delays hMPCs from becoming senescent, while an excess of RPL22 accelerates the senescence process. Mechanistically, we found in senescent hMPCs, RPL22 accumulates within the nucleolus. This accumulation triggers a cascade of events, including heterochromatin decompaction with concomitant degradation of key heterochromatin proteins, specifically heterochromatin protein 1γ (HP1γ) and heterochromatin protein KRAB-associated protein 1 (KAP1). Subsequently, RPL22-dependent breakdown of heterochromatin stimulates the transcription of ribosomal RNAs (rRNAs), triggering cellular senescence. In summary, our findings unveil a novel role for nucleolar RPL22 as a destabilizer of heterochromatin and a driver of cellular senescence, shedding new light on the intricate mechanisms underlying the aging process.

GmEID1 modulates light signaling through the Evening Complex to control flowering time and yield in soybean.

Soybean (Glycine max) morphogenesis and flowering time are accurately regulated by photoperiod, which determine the yield potential and limit soybean cultivars to a narrow latitudinal range. The E3 and E4 genes, which encode phytochrome A photoreceptors in soybean, promote the expression of the legume-specific flowering repressor E1 to delay floral transition under long-day (LD) conditions. However, the underlying molecular mechanism remains unclear. Here, we show that the diurnal expression pattern of GmEID1 is opposite to that of E1 and targeted mutations in the GmEID1 gene delay soybean flowering regardless of daylength. GmEID1 interacts with J, a key component of circadian Evening Complex (EC), to inhibit E1 transcription. Photoactivated E3/E4 interacts with GmEID1 to inhibit GmEID1-J interaction, promoting J degradation resulting in a negative correlation between daylength and the level of J protein. Notably, targeted mutations in GmEID1 improved soybean adaptability by enhancing yield per plant up to 55.3% compared to WT in field trials performed in a broad latitudinal span of more than 24°. Together, this study reveals a unique mechanism in which E3/E4-GmEID1-EC module controls flowering time and provides an effective strategy to improve soybean adaptability and production for molecular breeding.

Oncolytic strategy using new bifunctional HDACs/BRD4 inhibitors against virus-associated lymphomas.

Primary effusion lymphoma (PEL) caused by Kaposi sarcoma-associated herpesvirus (KSHV) is an aggressive malignancy with poor prognosis even under chemotherapy. Currently, there is no specific treatment for PEL therefore requiring new therapies. Both histone deacetylases (HDACs) and bromodomain-containing protein 4 (BRD4) have been found as therapeutic targets for PEL through inducing viral lytic reactivation. However, the strategy of dual targeting with one agent and potential synergistic effects have never been explored. In the current study, we first demonstrated the synergistic effect of concurrently targeting HDACs and BRD4 on KSHV reactivation by using SAHA or entinostat (HDACs inhibitors) and (+)-JQ1 (BRD4 inhibitor), which indicated dual blockage of HDACs/BRD4 is a viable therapeutic approach. We were then able to rationally design and synthesize a series of new small-molecule inhibitors targeting HDACs and BRD4 with a balanced activity profile by generating a hybrid of the key binding motifs between (+)-JQ1 and entinostat or SAHA. Upon two iterative screenings of optimized compounds, a pair of epimers, 009P1 and 009P2, were identified to better inhibit the growth of KSHV positive lymphomas compared to (+)-JQ1 or SAHA alone at low nanomolar concentrations, but not KSHV negative control cells or normal cells. Mechanistic studies of 009P1 and 009P2 demonstrated significantly enhanced viral reactivation, cell cycle arrest and apoptosis in KSHV+ lymphomas through dually targeting HDACs and BRD4 signaling activities. Importantly, in vivo preclinical studies showed that 009P1 and 009P2 dramatically suppressed KSHV+ lymphoma progression with oral bioavailability and minimal visible toxicity. These data together provide a novel strategy for the development of agents for inducing lytic activation-based therapies against these viruses-associated malignancies.

Statistical methods for assessing the effects of de novo variants on birth defects.

With the development of next-generation sequencing technology, de novo variants (DNVs) with deleterious effects can be identified and investigated for their effects on birth defects such as congenital heart disease (CHD). However, statistical power is still limited for such studies because of the small sample size due to the high cost of recruiting and sequencing samples and the low occurrence of DNVs. DNV analysis is further complicated by genetic heterogeneity across diseased individuals. Therefore, it is critical to jointly analyze DNVs with other types of genomic/biological information to improve statistical power to identify genes associated with birth defects. In this review, we discuss the general workflow, recent developments in statistical methods, and future directions for DNV analysis.

Impaired macroautophagy confers substantial risk for intellectual disability in children with autism spectrum disorders.

Autism spectrum disorder (ASD) represents a complex of neurological and developmental disabilities characterized by clinical and genetic heterogeneity. While the causes of ASD are still unknown, many ASD risk factors are found to converge on intracellular quality control mechanisms that are essential for cellular homeostasis, including the autophagy-lysosomal degradation pathway. Studies have reported impaired autophagy in ASD human brain and ASD-like synapse pathology and behaviors in mouse models of brain autophagy deficiency, highlighting an essential role for defective autophagy in ASD pathogenesis. To determine whether altered autophagy in the brain may also occur in peripheral cells that might provide useful biomarkers, we assessed activities of autophagy in lympoblasts from ASD and control subjects. We find that lymphoblast autophagy is compromised in a subset of ASD participants due to impaired autophagy induction. Similar changes in autophagy are detected in postmortem human brains from ASD individuals and in brain and peripheral blood mononuclear cells from syndromic ASD mouse models. Remarkably, we find a strong correlation between impaired autophagy and intellectual disability in ASD participants. By depleting the key autophagy gene Atg7 from different brain cells, we provide further evidence that autophagy deficiency causes cognitive impairment in mice. Together, our findings suggest autophagy dysfunction as a convergent mechanism that can be detected in peripheral blood cells from a subset of autistic individuals, and that lymphoblast autophagy may serve as a biomarker to stratify ASD patients for the development of targeted interventions.

EFHD1 promotes osteosarcoma proliferation and drug resistance by inhibiting the opening of the mitochondrial membrane permeability transition pore (mPTP) by binding to ANT3.

Chemoresistance is the main obstacle in the clinical treatment of osteosarcoma (OS). In this study, we investigated the role of EF-hand domain-containing protein 1 (EFHD1) in OS chemotherapy resistance. We found that the expression of EFHD1 was highly correlated with the clinical outcome after chemotherapy. We overexpressed EFHD1 in 143B cells and found that it increased their resistance to cell death after drug treatment. Conversely, knockdown of EFHD1 in 143BR cells (a cisplatin-less-sensitive OS cell line derived from 143B cells) increased their sensitivity to treatment. Mechanistically, EFHD1 bound to adenine nucleotide translocase-3 (ANT3) and inhibited its conformational change, thereby inhibiting the opening of the mitochondrial membrane permeability transition pore (mPTP). This effect could maintain mitochondrial function, thereby favoring OS cell survival. The ANT3 conformational inhibitor carboxyatractyloside (CATR), which can promote mPTP opening, enhanced the chemosensitivity of EFHD1-overexpressing cells when combined with cisplatin. The ANT3 conformational inhibitor bongkrekic acid (BKA), which can inhibit mPTP opening, restored the resistance of EFHD1 knockdown cells. In conclusion, our results suggest that EFHD1-ANT3-mPTP might be a promising target for OS therapy in the future.

Network assisted analysis of de novo variants using protein-protein interaction information identified 46 candidate genes for congenital heart disease.

De novo variants (DNVs) with deleterious effects have proved informative in identifying risk genes for early-onset diseases such as congenital heart disease (CHD). A number of statistical methods have been proposed for family-based studies or case/control studies to identify risk genes by screening genes with more DNVs than expected by chance in Whole Exome Sequencing (WES) studies. However, the statistical power is still limited for cohorts with thousands of subjects. Under the hypothesis that connected genes in protein-protein interaction (PPI) networks are more likely to share similar disease association status, we developed a Markov Random Field model that can leverage information from publicly available PPI databases to increase power in identifying risk genes. We identified 46 candidate genes with at least 1 DNV in the CHD study cohort, including 18 known human CHD genes and 35 highly expressed genes in mouse developing heart. Our results may shed new insight on the shared protein functionality among risk genes for CHD.