Accurate sequencing of DNA motifs able to form alternative (non-B) structures.

Approximately 13% of the human genome at certain motifs have the potential to form noncanonical (non-B) DNA structures (e.g., G-quadruplexes, cruciforms, and Z-DNA), which regulate many cellular processes but also affect the activity of polymerases and helicases. Because sequencing technologies use these enzymes, they might possess increased errors at non-B structures. To evaluate this, we analyzed error rates, read depth, and base quality of Illumina, Pacific Biosciences (PacBio) HiFi, and Oxford Nanopore Technologies (ONT) sequencing at non-B motifs. All technologies showed altered sequencing success for most non-B motif types, although this could be owing to several factors, including structure formation, biased GC content, and the presence of homopolymers. Single-nucleotide mismatch errors had low biases in HiFi and ONT for all non-B motif types but were increased for G-quadruplexes and Z-DNA in all three technologies. Deletion errors were increased for all non-B types but Z-DNA in Illumina and HiFi, as well as only for G-quadruplexes in ONT. Insertion errors for non-B motifs were highly, moderately, and slightly elevated in Illumina, HiFi, and ONT, respectively. Additionally, we developed a probabilistic approach to determine the number of false positives at non-B motifs depending on sample size and variant frequency, and applied it to publicly available data sets (1000 Genomes, Simons Genome Diversity Project, and gnomAD). We conclude that elevated sequencing errors at non-B DNA motifs should be considered in low-read-depth studies (single-cell, ancient DNA, and pooled-sample population sequencing) and in scoring rare variants. Combining technologies should maximize sequencing accuracy in future studies of non-B DNA.

Superconductivity in a Layered Cobalt Oxychalcogenide Na2CoSe2O with a Triangular Lattice.

Unconventional superconductivity in bulk materials under ambient pressure is extremely rare among the 3d transition metal compounds outside the layered cuprates and iron-based family. It is predominantly linked to highly anisotropic electronic properties and quasi-two-dimensional (2D) Fermi surfaces. To date, the only known example of a Co-based exotic superconductor is the hydrated layered cobaltate, NaxCoO2·yH2O, and its superconductivity is realized in the vicinity of a spin-1/2 Mott state. However, the nature of the superconductivity in these materials is still a subject of intense debate, and therefore, finding a new class of superconductors will help unravel the mysteries of their unconventional superconductivity. Here, we report the discovery of superconductivity at ∼6.3 K in our newly synthesized layered compound Na2CoSe2O, in which the edge-shared CoSe6 octahedra form [CoSe2] layers with a perfect triangular lattice of Co ions. It is the first 3d transition metal oxychalcogenide superconductor with distinct structural and chemical characteristics. Despite its relatively low TC, this material exhibits very high superconducting upper critical fields, µ0HC2(0), which far exceeds the Pauli paramagnetic limit by a factor of 3-4. First-principles calculations show that Na2CoSe2O is a rare example of a negative charge transfer superconductor. This cobalt oxychalcogenide with a geometrical frustration among Co spins shows great potential as a highly appealing candidate for the realization of unconventional and/or high-TC superconductivity beyond the well-established Cu- and Fe-based superconductor families and opens a new field in the physics and chemistry of low-dimensional superconductors.

Selection and thermostability suggest G-quadruplexes are novel functional elements of the human genome.

Approximately 1% of the human genome has the ability to fold into G-quadruplexes (G4s)-noncanonical strand-specific DNA structures forming at G-rich motifs. G4s regulate several key cellular processes (e.g., transcription) and have been hypothesized to participate in others (e.g., firing of replication origins). Moreover, G4s differ in their thermostability, and this may affect their function. Yet, G4s may also hinder replication, transcription, and translation and may increase genome instability and mutation rates. Therefore, depending on their genomic location, thermostability, and functionality, G4 loci might evolve under different selective pressures, which has never been investigated. Here we conducted the first genome-wide analysis of G4 distribution, thermostability, and selection. We found an overrepresentation, high thermostability, and purifying selection for G4s within genic components in which they are expected to be functional-promoters, CpG islands, and 5' and 3' UTRs. A similar pattern was observed for G4s within replication origins, enhancers, eQTLs, and TAD boundary regions, strongly suggesting their functionality. In contrast, G4s on the nontranscribed strand of exons were underrepresented, were unstable, and evolved neutrally. In general, G4s on the nontranscribed strand of genic components had lower density and were less stable than those on the transcribed strand, suggesting that the former are avoided at the RNA level. Across the genome, purifying selection was stronger at stable G4s. Our results suggest that purifying selection preserves the sequences of functional G4s, whereas nonfunctional G4s are too costly to be tolerated in the genome. Thus, G4s are emerging as fundamental, functional genomic elements.

Cellular Localization of FOXO3 Determines Its Role in Cataractogenesis.

The transcription factor forkhead box protein (FOX)-O3 is a core regulator of cellular homeostasis, stress response, and longevity. The cellular localization of FOXO3 is closely related to its function. Herein, the role of FOXO3 in cataract formation was explored. FOXO3 showed nuclear translocation in lens epithelial cells (LECs) arranged in a single layer on lens capsule tissues from both human cataract and N-methyl-N-nitrosourea (MNU)-induced rat cataract, also in MNU-injured human (H)-LEC lines. FOXO3 knockdown inhibited the MNU-induced increase in expression of genes related to cell cycle arrest (GADD45A and CCNG2) and apoptosis (BAK and TP53). H2 is highly effective in reducing oxidative impairments in nuclear DNA and mitochondria. When H2 was applied to MNU-injured HLECs, FOXO3 underwent cleavage by MAPK1 and translocated into mitochondria, thereby increasing the transcription of oxidative phosphorylation-related genes (MTCO1, MTCO2, MTND1, and MTND6) in HLECs. Furthermore, H2 mediated the translocation of FOXO3 from the nucleus to the mitochondria within the LECs of cataract capsule tissues of rats exposed to MNU. This intervention ameliorated MNU-induced cataracts in the rat model. In conclusion, there was a correlation between the localization of FOXO3 and its function in cataract formation. It was also determined that H2 protects HLECs from injury by leading FOXO3 mitochondrial translocation via MAPK1 activation. Mitochondrial FOXO3 can increase mtDNA transcription and stabilize mitochondrial function in HLECs.

Lysosome-Targeted and pH-Activatable Phototheranostics for NIR-II Fluorescence Imaging-Guided Nasopharyngeal Carcinoma Phototherapy.

Currently, clinical therapeutic strategies for nasopharyngeal carcinoma (NPC) confront insurmountable dilemmas in which surgical resection is incomplete and chemotherapy/radiotherapy has significant side effects. Phototherapy offers a maneuverable, effective, and noninvasive pattern for NPC therapy. Herein, we developed a lysosome-targeted and pH-responsive nanophototheranostic for near-infrared II (NIR-II) fluorescence imaging-guided photodynamic therapy (PDT) and photothermal therapy (PTT) of NPC. A lysosome-targeted S-D-A-D-S-type NIR-II phototheranostic molecule (IRFEM) is encapsulated within the acid-sensitive amphiphilic DSPE-Hyd-PEG2k to form IRFEM@DHP nanoparticles (NPs). The prepared IRFEM@DHP exhibits a good accumulation in the acidic lysosomes for facilitating the release of IRFEM, which could disrupt lysosomal function by generating an amount of heat and ROS under laser irradiation. Moreover, the guidelines of NIR-II fluorescence enhance the accuracy of PTT/PDT for NPC and avoid damage to normal tissues. Remarkably, IRFEM@DHP enable efficient antitumor effects both in vitro and in vivo, opening up a new avenue for precise NPC theranostics.

Subconjunctival Administration of Mesenchymal Stem Cells Alleviates Ocular Inflammation in a Murine Model of Corneal Alkali Burn.

Corneal alkali burns cause extensive damage not only to the cornea but also to the intraocular tissues. As an anti-inflammatory therapy, subconjunctival administration of mesenchymal stem cells (MSCs) for corneal protection after corneal alkali burn has been explored. Little evidence demonstrates the potential of subconjunctival MSCs delivery in protecting the post-burn intraocular tissues. This study aimed to evaluate the therapeutic efficacy of subconjunctival injection of human placental (hP)-MSCs in protecting against ocular destruction after the burn. hP-MSCs were subconjunctivally administered to C57/BL mice after corneal alkali burn. Western blot of iNOS and CD206 was performed to determine the M1 and M2 macrophage infiltration in the cornea. Infiltration of inflammatory cells in the anterior uvea and retina was analyzed by flow cytometry. The TUNEL assay or Western blot of Bax and Bcl2 was used to evaluate the anti-apoptotic effects of MSCs. MSCs could effectively facilitate cornea repair by suppressing inflammatory cytokines IL-1ß, MCP-1, and MMP9, and polarizing CD206 positive M2 macrophages. Anterior uveal and retinal inflammatory cytokines expression and inflammatory cell infiltration were inhibited in the MSC-treated group. Reduced TUNEL positive staining and Bax/Bcl2 ratio indicated the anti-apoptosis of MSCs. MSC-conditioned medium promoted human corneal epithelial cell proliferation and regulated LPS-stimulated inflammation in RAW 264.7 macrophages, confirming the trophic and immunoregulatory effects of MSCs. Our findings demonstrate that subconjunctival administration of MSCs exerted anti-inflammatory and anti-apoptotic effects in the cornea, anterior uvea, and retina after corneal alkali burn. This strategy may provide a new direction for preventing post-event complications after corneal alkali burn.

Adaptor protein 14-3-3zeta promotes corneal wound healing via regulating cell homeostasis, a potential novel therapy for corneal injury.

Severe corneal injury can lead to blindness even after prompt treatment. 14-3-3zeta, a member of an adaptor protein family, contributes to tissue repair by enhancing cellular viability and inhibiting fibrosis and inflammation in renal disease or arthritis. However, its role in corneal regeneration is less studied. In this study, filter disc of 2-mm diameter soaked in sodium hydroxide with a concentration of 0.5 N was placed at the center of the cornea for 30 s to establish a mouse model of corneal alkali injury. We found that 14-3-3zeta, which is mainly expressed in the epithelial layer, was upregulated following injury. Overexpression of 14-3-3zeta in ocular tissues via adeno-associated virus-mediated subconjunctival delivery promoted corneal wound healing, showing improved corneal structure and transparency. In vitro studies on human corneal epithelial cells showed that 14-3-3zeta was critical for cell proliferation and migration. mRNA-sequencing in conjunction with KEGG analysis and validation experiments revealed that 14-3-3zeta regulated the mRNA levels of ITGB1, PIK3R1, FGF5, PRKAA1 and the phosphorylation level of Akt, suggesting the involvement of the PI3K-Akt pathway in 14-3-3zeta-mediated tissue repair. 14-3-3zeta is a potential novel therapeutic candidate for treating severe corneal injury.

An unsupervised deep learning framework for predicting human essential genes from population and functional genomic data.

BACKGROUND: The ability to accurately predict essential genes intolerant to loss-of-function (LOF) mutations can dramatically improve the identification of disease-associated genes. Recently, there have been numerous computational methods developed to predict human essential genes from population genomic data. While the existing methods are highly predictive of essential genes of long length, they have limited power in pinpointing short essential genes due to the sparsity of polymorphisms in the human genome. RESULTS: Motivated by the premise that population and functional genomic data may provide complementary evidence for gene essentiality, here we present an evolution-based deep learning model, DeepLOF, to predict essential genes in an unsupervised manner. Unlike previous population genetic methods, DeepLOF utilizes a novel deep learning framework to integrate both population and functional genomic data, allowing us to pinpoint short essential genes that can hardly be predicted from population genomic data alone. Compared with previous methods, DeepLOF shows unmatched performance in predicting ClinGen haploinsufficient genes, mouse essential genes, and essential genes in human cell lines. Notably, at a false positive rate of 5%, DeepLOF detects 50% more ClinGen haploinsufficient genes than previous methods. Furthermore, DeepLOF discovers 109 novel essential genes that are too short to be identified by previous methods. CONCLUSION: The predictive power of DeepLOF shows that it is a compelling computational method to aid in the discovery of essential genes.

Direct Incorporation of Dinitrogen into an Aliphatic C-H Bond.

The activation of dinitrogen (N2) and direct incorporation of its N atom into C-H bonds to create aliphatic C-N compounds remains unresolved. Incompatible conditions between dinitrogen reduction and C-H functionalization make this process extremely challenging. Herein, we report the first example of dinitrogen insertion into an aliphatic Csp3-H bond on the ligand scaffold of a 1,3-propane-bridged [N2N]2--type dititanium complex. Mechanistic investigations on the behaviors of dinuclear and mononuclear Ti complexes indicated the intramolecular synergistic effect of two Ti centers at a C-N bond-forming step. Computational studies revealed the critical isomerization between the inactive side-on N2 complex and the active nitridyl complex, which is responsible for the Csp3-H amination. This strategy maps an efficient route toward the future synthesis of aliphatic amines directly from N2.

Dissecting Genomic Determinants of Positive Selection with an Evolution-Guided Regression Model.

In evolutionary genomics, it is fundamentally important to understand how characteristics of genomic sequences, such as gene expression level, determine the rate of adaptive evolution. While numerous statistical methods, such as the McDonald-Kreitman (MK) test, are available to examine the association between genomic features and the rate of adaptation, we currently lack a statistical approach to disentangle the independent effect of a genomic feature from the effects of other correlated genomic features. To address this problem, I present a novel statistical model, the MK regression, which augments the MK test with a generalized linear model. Analogous to the classical multiple regression model, the MK regression can analyze multiple genomic features simultaneously to infer the independent effect of a genomic feature, holding constant all other genomic features. Using the MK regression, I identify numerous genomic features driving positive selection in chimpanzees. These features include well-known ones, such as local mutation rate, residue exposure level, tissue specificity, and immune genes, as well as new features not previously reported, such as gene expression level and metabolic genes. In particular, I show that highly expressed genes may have a higher adaptation rate than their weakly expressed counterparts, even though a higher expression level may impose stronger negative selection. Also, I show that metabolic genes may have a higher adaptation rate than their nonmetabolic counterparts, possibly due to recent changes in diet in primate evolution. Overall, the MK regression is a powerful approach to elucidate the genomic basis of adaptation.

Diagnosis of Clinically Significant Portal Hypertension Using CT- and MRI-based Vascular Model.

Background Currently, the hepatic venous pressure gradient (HVPG) remains the reference standard for diagnosis of clinically significant portal hypertension (CSPH) but is limited by its invasiveness and availability. Purpose To investigate a vascular geometric model for noninvasive diagnosis of CSPH (HVPG ≥10 mm Hg) in patients with liver cirrhosis for both contrast-enhanced CT and MRI. Materials and Methods In this retrospective study, consecutive patients with liver cirrhosis who underwent HVPG measurement from August 2016 to April 2019 were included. Patients without hepatic diseases were included and marked as non-CSPH to balance the ratio of CSPH 1:1. A variety of vascular parameters were extracted from the portal vein, hepatic vein, aorta, and inferior vena cava and then entered into a vascular geometric model for identification of CSPH. Diagnostic performance was assessed with the area under the receiver operating characteristic curve (AUC). Results The model was developed and tested with retrospective data from 250 patients with liver cirrhosis and 273 patients without clinical evidence of hepatic disease at contrast-enhanced CT examination, including 213 patients with CSPH (mean age, 49 years ± 12 [SD]; 138 women) and 310 patients without CSPH (mean age, 50 years ± 9; 177 women). For external validation, an MRI data set with 224 patients with cirrhosis (mean age, 49 years ± 10; 158 women) and a CT data set with 106 patients with cirrhosis (mean age, 53 years ± 12; 71 women) were analyzed. Significant reductions in mean whole-vessel volumes were observed in the portal vein (ranging from 36.9 cm3 ± 16.0 to 29.6 cm3 ± 11.1; P < .05) and hepatic vein (ranging from 35.3 cm3 ± 21.5 to 22.4 cm3 ± 15.7; P < .05) when CSPH occurred. Similarly, the mean whole-vessel lengths were shorter in patients with CSPH (portal vein: 1.7 m ± 1.2 vs 3.0 m ± 2.4, P < .05; hepatic vein: 0.9 m ± 1.5 vs 1.8 m ± 1.5, P < .05) than in those without CSPH. The proposed vascular model performed well in the internal test set (mean AUC, 0.90 ± 0.02) and external test sets (mean AUCs, 0.84 ± 0.12 and 0.87 ± 0.11). Conclusion A contrast-enhanced CT- and MRI-based vascular model was proposed with good diagnostic consistency for hepatic venous pressure gradient measurement. ClinicalTrials.gov registration nos. NCT03138915 and NCT03766880 © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Roldán-Alzate and Reeder in this issue.

Integrated analysis of murine cornea identifies JAK/STAT signaling pathway upregulated specifically in female Vitamin A Deficient mice.

The Keratoconus (KC) is a corneal ectatic disease with unclear etiology. There are increasing studies that reported its association with a variety of inflammatory mechanisms. Vitamin A(VA) is an important nutrient related to inflammation regulation, and its deficiency may cause abnormalities of the ocular surface. However, the proportion of Vitamin A deficiency(VAD) was found surprisingly high among KC patients in our clinic practice. The aim of this study is to explore the effects of VAD on the transcriptome of corneas with the help of the VAD murine model and transcriptomics techniques. Blood samples of KC patients and non-KC controls (NC) were collected and the serum VA concentrations were measured and analyzed. A total of 52 NC and 39 KC were enrolled and the comparison of serum VA showed that the proportion of VAD in KC patients was 48.7% versus 1.9% in NC group. The further analysis of gender differences showed the proportion of VAD in female KC was 88.9% versus 36.7% in KC male patients. To explore the influence of VAD on cornea, the VAD mice fed with VAD diets were used. The RNA sequencing was employed to compare the corneal transcriptomic characteristics between the VAD female mice, NC female mice, VAD male mice and NC male mice. The transcriptome analysis revealed that the upregulated differential genes were mainly enriched in the immune response related pathways in VAD female mice versus NC female mice, especially the genes of JAK-STAT signaling pathway. The downstream molecules of JAK-STAT pathway were also significant after corneal mechanical scratching in female VAD mice. While, the differential genes between VAD male mice and NC male mice were estrogen signaling pathway instead of JAK-STAT pathway. This study indicates that VAD affects the transcriptomics of murine cornea with gender differences, which specifically affects the inflammatory status of the female murine cornea.

Development and validation of a machine learning-based model for varices screening in compensated cirrhosis (CHESS2001): an international multicenter study.

BACKGROUND AND AIMS: The prevalence of high-risk varices (HRV) is low among compensated cirrhotic patients undergoing EGD. Our study aimed to identify a novel machine learning (ML)-based model, named ML EGD, for ruling out HRV and avoiding unnecessary EGDs in patients with compensated cirrhosis. METHODS: An international cohort from 17 institutions from China, Singapore, and India were enrolled (CHESS2001). The variables with the top 3 importance scores (liver stiffness, platelet count, and total bilirubin) were selected by the Shapley additive explanation and input into a light gradient-boosting machine algorithm to develop ML EGD for identification of HRV. Furthermore, we built a web-based calculator for ML EGD, which is free with open access (http://www.pan-chess.cn/calculator/MLEGD_score). Unnecessary EGDs that were not performed and the rates of missed HRV were used to assess the efficacy and safety for varices screening. RESULTS: Of 2794 enrolled patients, 1283 patients formed a real-world cohort from 1 university hospital in China used to develop and internally validate the performance of ML EGD for varices screening. They were randomly assigned into the training (n = 1154) and validation (n = 129) cohorts with a ratio of 9:1. In the training cohort, ML EGD spared 607 (52.6%) unnecessary EGDs with a missed HRV rate of 3.6%. In the validation cohort, ML EGD spared 75 (58.1%) EGDs with a missed HRV rate of 1.4%. To externally test the performance of ML EGD, 966 patients from 14 university hospitals in China (test cohort 1) and 545 from 2 hospitals in Singapore and India (test cohort 2) comprised the 2 test cohorts. In test cohort 1, ML EGD spared 506 (52.4%) EGDs with a missed HRV rate of 2.8%. In test cohort 2, ML EGD spared 224 (41.1%) EGDs with a missed HRV rate of 3.1%. When compared with the Baveno VI criteria, ML EGD spared more screening EGDs in all cohorts (training cohort, 52.6% vs 29.4%; validation cohort, 58.1% vs 44.2%; test cohort 1, 52.4% vs 26.5%; test cohort 2, 41.1% vs 21.1%, respectively; P < .001). CONCLUSIONS: We identified a novel model based on liver stiffness, platelet count, and total bilirubin, named ML EGD, as a free web-based calculator. ML EGD could efficiently help rule out HRV and avoid unnecessary EGDs in patients with compensated cirrhosis. (Clinical trial registration number: NCT04307264.).

Highly Regio- and Diastereoselective Phosphine-Catalyzed [2 + 4] Annulation of Benzofuran-Derived Azadienes with Allyl Carbonates: Access to Spiro[benzofuran-cyclohexanes].

A novel highly regio- and diastereoselective phosphine-catalyzed [2 + 4] annulation of benzofuran-derived azadienes (BDAs) with acidic hydrogen-tethered allyl carbonates has been developed ingeniously. A range of functionalized spiro[benzofuran-cyclohexane] derivatives with two consecutive stereocenters were smoothly obtained in moderate to excellent yields under mild reaction conditions from readily available materials. Moreover, this method is a practical and scalable strategy that creates the core structural motif of the fungistatic drug, griseofulvin.

Strengthening the Stability of the Reconstructed NiOOH Phase for 5-Hydroxymethylfurfural Oxidation.

Electrochemical oxidation of biomass-derived 5-hydroxymethylfurfural (HMF) is a promising approach to produce high-value chemicals such as 2,5-furandicarboxylic acid (FDCA). However, the undesirable stability of catalysts commonly limits its potential application value. In this work, NiOOH derived from Ni(OH)2 was determined as the main catalytic site for HMF oxidation, but the collapse of Ni(OH)2 caused severe instability during the electrocatalytic process because of the crystal structure mismatch between NiOOH and Ni(OH)2. The implantation of Ce in Ni(OH)2 (Ce-Ni(OH)2) was successfully realized to address the stability issue of bare Ni(OH)2, since the larger ion radius of Ce could increase the Ni-O bond length and d-spacing. As a result, the activity of 14%Ce-Ni(OH)2 has not obviously decayed after the 50 cyclic voltammetry (CV)-cycle test. HMF conversion is close to 100%, and the Faraday efficiency (FE) reaches 86.6% at the potential of 0.45 V vs Ag/AgCl. This study provides a new strategy to design stable catalysts for the conversion of biomass derivatives.

Predicting central lymph node metastasis in patients with papillary thyroid carcinoma based on ultrasound radiomic and morphological features analysis.

OBJECTIVES: To build a combined model based on the ultrasound radiomic and morphological features, and evaluate its diagnostic performance for preoperative prediction of central lymph node metastasis (CLNM) in patients with papillary thyroid carcinoma (PTC). METHOD: A total of 295 eligible patients, who underwent preoperative ultrasound scan and were pathologically diagnosed with unifocal PTC were included at our hospital from October 2019 to July 2022. According to ultrasound scanners, patients were divided into the training set (115 with CLNM; 97 without CLNM) and validation set (45 with CLNM; 38 without CLNM). Ultrasound radiomic, morphological, and combined models were constructed using multivariate logistic regression. The diagnostic performance was assessed by the area under the curve (AUC) of the receiver operating characteristic curve, accuracy, sensitivity, and specificity. RESULTS: A combined model was built based on the morphology, boundary, length diameter, and radiomic score. The AUC was 0.960 (95% CI, 0.924-0.982) and 0.966 (95% CI, 0.901-0.993) in the training and validation set, respectively. Calibration curves showed good consistency between prediction and observation, and DCA demonstrated the clinical benefit of the combined model. CONCLUSION: Based on ultrasound radiomic and morphological features, the combined model showed a good performance in predicting CLNM of patients with PTC preoperatively.

Non-B DNA: a major contributor to small- and large-scale variation in nucleotide substitution frequencies across the genome.

Approximately 13% of the human genome can fold into non-canonical (non-B) DNA structures (e.g. G-quadruplexes, Z-DNA, etc.), which have been implicated in vital cellular processes. Non-B DNA also hinders replication, increasing errors and facilitating mutagenesis, yet its contribution to genome-wide variation in mutation rates remains unexplored. Here, we conducted a comprehensive analysis of nucleotide substitution frequencies at non-B DNA loci within noncoding, non-repetitive genome regions, their ±2 kb flanking regions, and 1-Megabase windows, using human-orangutan divergence and human single-nucleotide polymorphisms. Functional data analysis at single-base resolution demonstrated that substitution frequencies are usually elevated at non-B DNA, with patterns specific to each non-B DNA type. Mirror, direct and inverted repeats have higher substitution frequencies in spacers than in repeat arms, whereas G-quadruplexes, particularly stable ones, have higher substitution frequencies in loops than in stems. Several non-B DNA types also affect substitution frequencies in their flanking regions. Finally, non-B DNA explains more variation than any other predictor in multiple regression models for diversity or divergence at 1-Megabase scale. Thus, non-B DNA substantially contributes to variation in substitution frequencies at small and large scales. Our results highlight the role of non-B DNA in germline mutagenesis with implications to evolution and genetic diseases.

Is Surgical Repair With Nerve Allograft More Cost-Effective Than Non-Surgical Management for Persistent Trigeminal Neuropathy? Initial Assessment With Markov Model.

PURPOSE: Persistent trigeminal neuropathy (PTN) is associated with high rates of depression, loss of work, and decreased quality of life (QoL). Nerve allograft repair can achieve functional sensory recovery in a predictable manner; however, it bears significant upfront costs. In patients suffering from PTN, is surgical repair with allogeneic nerve graft, when compared to non-surgical therapy, a more cost-effective treatment option? MATERIALS AND METHODS: A Markov model was constructed with TreeAge Pro Healthcare 2022 (TreeAge Software, Massachusetts) to estimate the direct and indirect costs for PTN. The model ran for 40 years with 1-year-cycles on a 40-year-old model patient with persistent inferior alveolar or lingual nerve injury (S0 to S2+) at 3 months without signs of improvement, and without dysesthesia or neuropathic pain (NPP). The 2 treatment arms were surgery with nerve allograft versus non-surgical management. There were 3 disease states, functional sensory recovery (S3 to S4), hypoesthesia/anesthesia (S0 to S2+), and NPP. Direct surgical costs were calculated using the 2022 Medicare Physician Fee Schedule and verified with standard institutional billing practices. Non-surgical treatment direct costs (follow-up, specialist referral, medications, imaging) and indirect costs (QoL, loss of employment) were determined from historical data and the literature. Direct surgical costs for allograft repair were $13,291. State-specific direct costs for hypoesthesia/anesthesia were $2,127.84 per year, and $3,168.24 for NPP per year. State-specific indirect costs included decreased labor force participation, absenteeism, and decreased QoL. RESULTS: Surgical treatment with nerve allograft was more effective and had a lower long-term cost. The incremental cost-effectiveness ratio was -10,751.94, indicating surgical treatment should be utilized based on efficiency and cost. With a willingness-to-pay threshold of $50,000, the net monetary benefits of surgical treatment are $1,158,339 compared to $830,654 for non-surgical treatment. With a standard threshold incremental cost-effectiveness ratio of 50,000, the sensitivity analysis shows that surgical treatment would remain the preferred choice based on efficiency even if surgical costs were doubled. CONCLUSION: Despite high initial costs of surgical treatment with nerve allograft for PTN, surgical intervention with nerve allograft is a more cost-effective treatment option when compared to non-surgical therapy.

Unified inference of missense variant effects and gene constraints in the human genome.

A challenge in medical genomics is to identify variants and genes associated with severe genetic disorders. Based on the premise that severe, early-onset disorders often result in a reduction of evolutionary fitness, several statistical methods have been developed to predict pathogenic variants or constrained genes based on the signatures of negative selection in human populations. However, we currently lack a statistical framework to jointly predict deleterious variants and constrained genes from both variant-level features and gene-level selective constraints. Here we present such a unified approach, UNEECON, based on deep learning and population genetics. UNEECON treats the contributions of variant-level features and gene-level constraints as a variant-level fixed effect and a gene-level random effect, respectively. The sum of the fixed and random effects is then combined with an evolutionary model to infer the strength of negative selection at both variant and gene levels. Compared with previously published methods, UNEECON shows improved performance in predicting missense variants and protein-coding genes associated with autosomal dominant disorders, and feature importance analysis suggests that both gene-level selective constraints and variant-level predictors are important for accurate variant prioritization. Furthermore, based on UNEECON, we observe a low correlation between gene-level intolerance to missense mutations and that to loss-of-function mutations, which can be partially explained by the prevalence of disordered protein regions that are highly tolerant to missense mutations. Finally, we show that genes intolerant to both missense and loss-of-function mutations play key roles in the central nervous system and the autism spectrum disorders. Overall, UNEECON is a promising framework for both variant and gene prioritization.

Estimation of allele-specific fitness effects across human protein-coding sequences and implications for disease.

A central challenge in human genomics is to understand the cellular, evolutionary, and clinical significance of genetic variants. Here, we introduce a unified population-genetic and machine-learning model, called Linear Allele-Specific Selection InferencE (LASSIE), for estimating the fitness effects of all observed and potential single-nucleotide variants, based on polymorphism data and predictive genomic features. We applied LASSIE to 51 high-coverage genome sequences annotated with 33 genomic features and constructed a map of allele-specific selection coefficients across all protein-coding sequences in the human genome. This map is generally consistent with previous inferences of the bulk distribution of fitness effects but reveals pervasive weak negative selection against synonymous mutations. In addition, the estimated selection coefficients are highly predictive of inherited pathogenic variants and cancer driver mutations, outperforming state-of-the-art variant prioritization methods. By contrasting our estimated model with ultrahigh coverage ExAC exome-sequencing data, we identified 1118 genes under unusually strong negative selection, which tend to be exclusively expressed in the central nervous system or associated with autism spectrum disorder, as well as 773 genes under unusually weak selection, which tend to be associated with metabolism. This combination of classical population genetic theory with modern machine-learning and large-scale genomic data is a powerful paradigm for the study of both human evolution and disease.