Sperm, but not oocyte, DNA methylome is inherited by zebrafish early embryos.

5-methylcytosine is a major epigenetic modification that is sometimes called "the fifth nucleotide." However, our knowledge of how offspring inherit the DNA methylome from parents is limited. We generated nine single-base resolution DNA methylomes, including zebrafish gametes and early embryos. The oocyte methylome is significantly hypomethylated compared to sperm. Strikingly, the paternal DNA methylation pattern is maintained throughout early embryogenesis. The maternal DNA methylation pattern is maintained until the 16-cell stage. Then, the oocyte methylome is gradually discarded through cell division and is progressively reprogrammed to a pattern similar to that of the sperm methylome. The passive demethylation rate and the de novo methylation rate are similar in the maternal DNA. By the midblastula stage, the embryo's methylome is virtually identical to the sperm methylome. Moreover, inheritance of the sperm methylome facilitates the epigenetic regulation of embryogenesis. Therefore, besides DNA sequences, sperm DNA methylome is also inherited in zebrafish early embryos.

Adaptation in Unstable Environments and Global Gene Losses: Small but Stable Gene Networks by the May-Wigner Theory.

Although gene loss is common in evolution, it remains unclear whether it is an adaptive process. In a survey of seven major mangrove clades that are woody plants in the intertidal zones of daily environmental perturbations, we noticed that they generally evolved reduced gene numbers. We then focused on the largest clade of Rhizophoreae and observed the continual gene set reduction in each of the eight species. A great majority of gene losses are concentrated on environmental interaction processes, presumably to cope with the constant fluctuations in the tidal environments. Genes of the general processes for woody plants are largely retained. In particular, fewer gene losses are found in physiological traits such as viviparous seeds, high salinity, and high tannin content. Given the broad and continual genome reductions, we propose the May-Wigner theory (MWT) of system stability as a possible mechanism. In MWT, the most effective solution for buffering continual perturbations is to reduce the size of the system (or to weaken the total genic interactions). Mangroves are unique as immovable inhabitants of the compound environments in the land-sea interface, where environmental gradients (such as salinity) fluctuate constantly, often drastically. Extending MWT to gene regulatory network (GRN), computer simulations and transcriptome analyses support the stabilizing effects of smaller gene sets in mangroves vis-à-vis inland plants. In summary, we show the adaptive significance of gene losses in mangrove plants, including the specific role of promoting phenotype innovation and a general role in stabilizing GRN in unstable environments as predicted by MWT.

Surveying mutation density patterns around specific genomic features.

Mutation density patterns reveal unique biological properties of specific genomic regions and shed light on the mechanisms of carcinogenesis. Although previous studies reported insightful mutation density patterns associated with certain genomic regions such as transcription start sites and DNA replication origins, a tool that can systematically investigate mutational spatial patterns is still lacking. Thus, we developed MutDens, a bioinformatic tool for comprehensive analysis of mutation density patterns around genomic features, namely, genomic positions, in humans and model species. By scanning the bidirectional vicinity regions of given positions, MutDens systematically characterizes the mutation density for single-base substitution mutational classes after adjusting for total mutation burden and local nucleotide proportion. Analysis results using MutDens not only verified the previously reported transcriptional strand bias around transcription start sites and replicative strand bias around DNA replication origins, but also identified novel mutation density patterns around other genomics features, such as enhancers and retrotransposon insertion polymorphism sites. To our knowledge, MutDens is the first tool that systematically calculates, examines, and compares mutation density patterns, thus providing a valuable avenue for investigating the mutational landscapes associated with important genomic features.

Canalization of Phenotypes-When the Transcriptome is Constantly but Weakly Perturbed.

Recent studies have increasingly pointed to microRNAs (miRNAs) as the agent of gene regulatory network (GRN) stabilization as well as developmental canalization against constant but small environmental perturbations. To analyze mild perturbations, we construct a Dicer-1 knockdown line (dcr-1 KD) in Drosophila that modestly reduces all miRNAs by, on average, ∼20%. The defining characteristic of stabilizers is that, when their capacity is compromised, GRNs do not change their short-term behaviors. Indeed, even with such broad reductions across all miRNAs, the changes in the transcriptome are very modest during development in stable environment. By comparison, broad knockdowns of other regulatory genes (esp. transcription factors) by the same method should lead to drastic changes in the GRNs. The consequence of destabilization may thus be in long-term development as postulated by the theory of canalization. Flies with modest miRNA reductions may gradually deviate from the developmental norm, resulting in late-stage failures such as shortened longevity. In the optimal culture condition, the survival to adulthood is indeed normal in the dcr-1 KD line but, importantly, adult longevity is reduced by ∼90%. When flies are stressed by high temperature, dcr-1 KD induces lethality earlier in late pupation and, as the perturbations are shifted earlier, the affected stages are shifted correspondingly. Hence, in late stages of development with deviations piling up, GRN would be increasingly in need of stabilization. In conclusion, miRNAs appear to be a solution to weak but constant environmental perturbations.

Intra- vs. Interhost Evolution of SARS-CoV-2 Driven by Uncorrelated Selection-The Evolution Thwarted.

In viral evolution, a new mutation has to proliferate within the host (Stage I) in order to be transmitted and then compete in the host population (Stage II). We now analyze the intrahost single nucleotide variants (iSNVs) in a set of 79 SARS-CoV-2 infected patients with most transmissions tracked. Here, every mutation has two measures: 1) iSNV frequency within each individual host in Stage I; 2) occurrence among individuals ranging from 1 (private), 2-78 (public), to 79 (global) occurrences in Stage II. In Stage I, a small fraction of nonsynonymous iSNVs are sufficiently advantageous to rise to a high frequency, often 100%. However, such iSNVs usually fail to become public mutations. Thus, the selective forces in the two stages of evolution are uncorrelated and, possibly, antagonistic. For that reason, successful mutants, including many variants of concern, have to avoid being eliminated in Stage I when they first emerge. As a result, they may not have the transmission advantage to outcompete the dominant strains and, hence, are rare in the host population. Few of them could manage to slowly accumulate advantageous mutations to compete in Stage II. When they do, they would appear suddenly as in each of the six successive waves of SARS-CoV-2 strains. In conclusion, Stage I evolution, the gate-keeper, may contravene the long-term viral evolution and should be heeded in viral studies.

The Ecology and Evolution of Cancer: The Ultra-Microevolutionary Process.

Although tumorigenesis has been accepted as an evolutionary process ( 20 , 102 ), many forces may operate differently in cancers than in organisms, as they evolve at vastly different time scales. Among such forces, natural selection, here defined as differential cellular proliferation among distinct somatic cell genotypes, is particularly interesting because its action might be thwarted in multicellular organisms ( 20 , 29 ). In this review, selection is analyzed in two stages of cancer evolution: Stage I is the evolution between tumors and normal tissues, and Stage II is the evolution within tumors. The Cancer Genome Atlas (TCGA) data show a low degree of convergent evolution in Stage I, where genetic changes are not extensively shared among cases. An equally important, albeit much less highlighted, discovery using TCGA data is that there is almost no net selection in cancer evolution. Both positive and negative selection are evident but they neatly cancel each other out, rendering total selection ineffective in the absence of recombination. The efficacy of selection is even lower in Stage II, where neutral (non-Darwinian) evolution is increasingly supported by high-density sampling studies ( 81 , 123 ). Because natural selection is not a strong deterministic force, cancers usually evolve divergently even in similar tissue environments.

Somatic mutation effects diffused over microRNA dysregulation.

MOTIVATION: As an important player in transcriptome regulation, microRNAs may effectively diffuse somatic mutation impacts to broad cellular processes and ultimately manifest disease and dictate prognosis. Previous studies that tried to correlate mutation with gene expression dysregulation neglected to adjust for the disparate multitudes of false positives associated with unequal sample sizes and uneven class balancing scenarios. RESULTS: To properly address this issue, we developed a statistical framework to rigorously assess the extent of mutation impact on microRNAs in relation to a permutation-based null distribution of a matching sample structure. Carrying out the framework in a pan-cancer study, we ascertained 9008 protein-coding genes with statistically significant mutation impacts on miRNAs. Of these, the collective miRNA expression for 83 genes showed significant prognostic power in nine cancer types. For example, in lower-grade glioma, 10 genes' mutations broadly impacted miRNAs, all of which showed prognostic value with the corresponding miRNA expression. Our framework was further validated with functional analysis and augmented with rich features including the ability to analyze miRNA isoforms; aggregative prognostic analysis; advanced annotations such as mutation type, regulator alteration, somatic motif, and disease association; and instructive visualization such as mutation OncoPrint, Ideogram, and interactive mRNA-miRNA network. AVAILABILITY AND IMPLEMENTATION: The data underlying this article are available in MutMix, at http://innovebioinfo.com/Database/TmiEx/MutMix.php.

Photo-protection and photo-inhibition during light induction in Barbula indica and Conocephalum conicum under different light gradients.

The objectives of this study were to measure the chlorophyll fluorescence (ChlF) parameters of Barbula indica (Hook.) Spreng and Conocephalum conicum (L.) Dumort subjected to various light intensities (LI) as a reflection of their adaptability to their habitats. The electron transport rate (ETR) of all plants under 500 µmol m-2 s-1 photosynthetic photon flux density (PPFD) was significantly higher than other LI treatments, implying that these plants could be grown under a specific and optimal light intensity adapted to 500 PPFD conditions. As LI increased from 50 to 2,000 PPFD, we observed in all plants increased non-photochemical quenching (NPQ) and photo-inhibitory quenching (qI) and decreased photosystem II efficiency (ΦPSII), potential quantum efficiency of PSII (Fv/Fm), actual PSII efficiency (ΔF/Fm'%), and Fv/Fm%. In addition, energy-dependent quenching (qE), the light protection system (qE + qZ + qT), and qI increased as ΦPSII decreased and photo-inhibition% increased under 1000, 1500, and 2000 PPFD conditions, suggesting that these plants had higher photo-protective ability under high LI treatments to maintain higher photosynthetic system performance. B. indica plants remained photochemically active and maintained higher qE under 300, 500, and 1000 PPFD, whereas C. conicum qZ + qT exhibited higher photo-protection under 500, 1000, and 1500 PPFD conditions. These ChlF indices can be used for predicting photosynthetic responses to light induction in different bryophytes and provide a theoretical basis for ecological monitoring.

Associations between the artificial intelligence scoring system and live birth outcomes in preimplantation genetic testing for aneuploidy cycles.

BACKGROUND: Several studies have demonstrated that iDAScore is more accurate in predicting pregnancy outcomes in cycles without preimplantation genetic testing for aneuploidy (PGT-A) compared to KIDScore and the Gardner criteria. However, the effectiveness of iDAScore in cycles with PGT-A has not been thoroughly investigated. Therefore, this study aims to assess the association between artificial intelligence (AI)-based iDAScore (version 1.0) and pregnancy outcomes in single-embryo transfer (SET) cycles with PGT-A. METHODS: This retrospective study was approved by the Institutional Review Board of Chung Sun Medical University, Taichung, Taiwan. Patients undergoing SET cycles (n = 482) following PGT-A at a single reproductive center between January 2017 and June 2021. The blastocyst morphology and morphokinetics of all embryos were evaluated using a time-lapse system. The blastocysts were ranked based on the scores generated by iDAScore, which were defined as AI scores, or by KIDScore D5 (version 3.2) following the manufacturer's protocols. A single blastocyst without aneuploidy was transferred after examining the embryonic ploidy status using a next-generation sequencing-based PGT-A platform. Logistic regression analysis with generalized estimating equations was conducted to assess whether AI scores are associated with the probability of live birth (LB) while considering confounding factors. RESULTS: Logistic regression analysis revealed that AI score was significantly associated with LB probability (adjusted odds ratio [OR] = 2.037, 95% confidence interval [CI]: 1.632-2.542) when pulsatility index (PI) level and types of chromosomal abnormalities were controlled. Blastocysts were divided into quartiles in accordance with their AI score (group 1: 3.0-7.8; group 2: 7.9-8.6; group 3: 8.7-8.9; and group 4: 9.0-9.5). Group 1 had a lower LB rate (34.6% vs. 59.8-72.3%) and a higher rate of pregnancy loss (26% vs. 4.7-8.9%) compared with the other groups (p < 0.05). The receiver operating characteristic curve analysis verified that the iDAScore had a significant but limited ability to predict LB (area under the curve [AUC] = 0.64); this ability was significantly weaker than that of the combination of iDAScore, type of chromosomal abnormalities, and PI level (AUC = 0.67). In the comparison of the LB groups with the non-LB groups, the AI scores were significantly lower in the non-LB groups, both for euploid (median: 8.6 vs. 8.8) and mosaic (median: 8.0 vs. 8.6) SETs. CONCLUSIONS: Although its predictive ability can be further enhanced, the AI score was significantly associated with LB probability in SET cycles. Euploid or mosaic blastocysts with low AI scores (≤ 7.8) were associated with a lower LB rate, indicating the potential of this annotation-free AI system as a decision-support tool for deselecting embryos with poor pregnancy outcomes following PGT-A.

Investigating developmental characteristics of biopsied blastocysts stratified by mitochondrial copy numbers using time-lapse monitoring.

BACKGROUND: For in vitro fertilization (IVF), mitochondrial DNA (mtDNA) levels in the trophectodermal (TE) cells of biopsied blastocysts have been suggested to be associated with the cells' developmental potential. However, scholars have reached differing opinions regarding the use of mtDNA levels as a reliable biomarker for predicting IVF outcomes. Therefore, this study aims to assess the association of mitochondrial copy number measured by mitoscore associated with embryonic developmental characteristics and ploidy. METHODS: This retrospective study analyzed the developmental characteristics of embryos and mtDNA levels in biopsied trophectodermal cells. The analysis was carried out using time-lapse monitoring and next-generation sequencing from September 2021 to September 2022. Five hundred and fifteen blastocysts were biopsied from 88 patients undergoing IVF who met the inclusion criteria. Embryonic morphokinetics and morphology were evaluated at 118 h after insemination using all recorded images. Blastocysts with appropriate morphology on day 5 or 6 underwent TE biopsy and preimplantation genetic testing for aneuploidy (PGT-A). Statistical analysis involved generalized estimating equations, Pearson's chi-squared test, Fisher's exact test, and Kruskal-Wallis test, with a significance level set at P < 0.05. RESULTS: To examine differences in embryonic characteristics between blastocysts with low versus high mitoscores, the blastocysts were divided into quartiles based on their mitoscore. Regarding morphokinetic characteristics, no significant differences in most developmental kinetics and observed cleavage dysmorphisms were discovered. However, blastocysts in mitoscore group 1 had a longer time for reaching 3-cell stage after tPNf (t3; median: 14.4 h) than did those in mitoscore group 2 (median: 13.8 h) and a longer second cell cycle (CC2; median: 11.7 h) than did blastocysts in mitoscore groups 2 (median: 11.3 h) and 4 (median: 11.4 h; P < 0.05). Moreover, blastocysts in mitoscore group 4 had a lower euploid rate (22.6%) and a higher aneuploid rate (59.1%) than did those in the other mitoscore groups (39.6-49.3% and 30.3-43.2%; P < 0.05). The rate of whole-chromosomal alterations in mitoscore group 4 (63.4%) was higher than that in mitoscore groups 1 (47.3%) and 2 (40.1%; P < 0.05). A multivariate logistic regression model was used to analyze associations between the mitoscore and euploidy of elective blastocysts. After accounting for factors that could potentially affect the outcome, the mitoscore still exhibited a negative association with the likelihood of euploidy (adjusted OR = 0.581, 95% CI: 0.396-0.854; P = 0.006). CONCLUSIONS: Blastocysts with varying levels of mitochondrial DNA, identified through biopsies, displayed similar characteristics in their early preimplantation development as observed through time-lapse imaging. However, the mitochondrial DNA level determined by the mitoscore can be used as a standalone predictor of euploidy.

Controlling the confounding effect of metabolic gene expression to identify actual metabolite targets in microsatellite instability cancers.

BACKGROUND: The metabolome is the best representation of cancer phenotypes. Gene expression can be considered a confounding covariate affecting metabolite levels. Data integration across metabolomics and genomics to establish the biological relevance of cancer metabolism is challenging. This study aimed to eliminate the confounding effect of metabolic gene expression to reflect actual metabolite levels in microsatellite instability (MSI) cancers. METHODS: In this study, we propose a new strategy using covariate-adjusted tensor classification in high dimensions (CATCH) models to integrate metabolite and metabolic gene expression data to classify MSI and microsatellite stability (MSS) cancers. We used datasets from the Cancer Cell Line Encyclopedia (CCLE) phase II project and treated metabolomic data as tensor predictors and data on gene expression of metabolic enzymes as confounding covariates. RESULTS: The CATCH model performed well, with high accuracy (0.82), sensitivity (0.66), specificity (0.88), precision (0.65), and F1 score (0.65). Seven metabolite features adjusted for metabolic gene expression, namely, 3-phosphoglycerate, 6-phosphogluconate, cholesterol ester, lysophosphatidylethanolamine (LPE), phosphatidylcholine, reduced glutathione, and sarcosine, were found in MSI cancers. Only one metabolite, Hippurate, was present in MSS cancers. The gene expression of phosphofructokinase 1 (PFKP), which is involved in the glycolytic pathway, was related to 3-phosphoglycerate. ALDH4A1 and GPT2 were associated with sarcosine. LPE was associated with the expression of CHPT1, which is involved in lipid metabolism. The glycolysis, nucleotide, glutamate, and lipid metabolic pathways were enriched in MSI cancers. CONCLUSIONS: We propose an effective CATCH model for predicting MSI cancer status. By controlling the confounding effect of metabolic gene expression, we identified cancer metabolic biomarkers and therapeutic targets. In addition, we provided the possible biology and genetics of MSI cancer metabolism.

Impact of the pulmonary venous entry site morphology on postoperative pulmonary vein stenosis in total anomalous pulmonary venous connection patients.

BACKGROUND: To evaluate the association between the pulmonary vein (PV) entry site morphology after total anomalous pulmonary vein repair (TAPVC) and postoperative pulmonary vein stenosis (PVS). METHODS: Computed tomography (CT) examination was performed to determine the PV entry site morphology. The width of the PV confluence was divided by the width of the left atrium (LA) to obtain the cPV/LA index. The cPV/LA index was compared between patients with and without postoperative PVS. RESULTS: Fifty-one patients who had undergone CT after TAPVC repair were included, with a median cPV/LA index of 0.5 (interquartile range (IQR) = 0.349-0.654). Among them, 27 patients developed postoperative PVS. The median cPV/LA index after primary TAPVC repair was significantly lower in patients with PVS compared to those without PVS (0.367, IQR = 0.308-0.433 vs. 0.657, IQR = 0.571-0.783, P < 0.0001). Additionally, the cPV/LA index after surgical re-intervention for PVS was significantly smaller in patients who developed recurrent stenosis compared to those who remained free-from re-stenosis after surgical relief (0.459, IQR = 0.349-0.556; vs. 0.706, IQR = 0.628-0.810, P = 0.0045). CONCLUSION: A small PV confluence width is associated with the development of postoperative PVS and recurrent stenosis after surgical relief of PVS. Our results suggest that adequate bilateral pulmonary vein lateralization during TAPVC surgery is crucial.

Adaptation to a new environment with pre-adaptive genomic features - Evidence from woody plants colonizing the land-sea interface.

Adaptation to new environments is a key evolutionary process which presumably involves complex genomic changes. Mangroves, a collection of approximately 80 woody plants that have independently invaded intertidal zones >20 times, are ideal for studying this process. We assembled near-chromosome-scale genomes of three Xylocarpus species as well as an outgroup species using single-molecule real-time sequencing. Phylogenomic analysis reveals two separate lineages, one with the mangrove Xylocarpus granatum and the other comprising a mangrove Xylocarpus moluccensis and a terrestrial Xylocarpus rumphii. In conjunction with previous studies, we identified several genomic features associated with mangroves: (i) signals of positive selection in genes related to salt tolerance and root development; (ii) genome-wide elevated ratios of non-synonymous to synonymous substitution relative to terrestrial relatives; and (iii) active elimination of long terminal repeats. These features are found in the terrestrial X. rumphii in addition to the two mangroves. These genomic features, not being strictly mangrove-specific, are hence considered pre-adaptive. We infer that the coastal but non-intertidal habitat of X. rumphii may have predisposed the common ancestor to invasion of true mangrove habitats. Other features including the preferential retention of duplicated genes and intolerance to pseudogenization are not found in X. rumphii and are likely true adaptive features in mangroves. In conclusion, by studying adaptive shift and partial shifts among closely related species, we set up a framework to study genomic features that are acquired at different stages of the pre-adaptation and adaptation to new environments.

The Runaway Evolution of SARS-CoV-2 Leading to the Highly Evolved Delta Strain.

In new epidemics after the host shift, the pathogens may experience accelerated evolution driven by novel selective pressures. When the accelerated evolution enters a positive feedback loop with the expanding epidemics, the pathogen's runaway evolution may be triggered. To test this possibility in coronavirus disease 2019 (COVID-19), we analyze the extensive databases and identify five major waves of strains, one replacing the previous one in 2020-2021. The mutations differ entirely between waves and the number of mutations continues to increase, from 3-4 to 21-31. The latest wave in the fall of 2021 is the Delta strain which accrues 31 new mutations to become highly prevalent. Interestingly, these new mutations in Delta strain emerge in multiple stages with each stage driven by 6-12 coding mutations that form a fitness group. In short, the evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) from the oldest to the youngest wave, and from the earlier to the later stages of the Delta wave, is a process of acceleration with more and more mutations. The global increase in the viral population size (M(t), at time t) and the mutation accumulation (R(t)) may have indeed triggered the runaway evolution in late 2020, leading to the highly evolved Alpha and then Delta strain. To suppress the pandemic, it is crucial to break the positive feedback loop between M(t) and R(t), neither of which has yet to be effectively dampened by late 2021. New waves after Delta, hence, should not be surprising.

Evolution under Spatially Heterogeneous Selection in Solid Tumors.

Spatial genetic and phenotypic diversity within solid tumors has been well documented. Nevertheless, how this heterogeneity affects temporal dynamics of tumorigenesis has not been rigorously examined because solid tumors do not evolve as the standard population genetic model due to the spatial constraint. We therefore, propose a neutral spatial (NS) model whereby the mutation accumulation increases toward the periphery; the genealogical relationship is spatially determined and the selection efficacy is blunted (due to kin competition). In this model, neutral mutations are accrued and spatially distributed in manners different from those of advantageous mutations. Importantly, the distinctions could be blurred in the conventional model. To test the NS model, we performed a three-dimensional multiple microsampling of two hepatocellular carcinomas. Whole-genome sequencing (WGS) revealed a 2-fold increase in mutations going from the center to the periphery. The operation of natural selection can then be tested by examining the spatially determined clonal relationships and the clonal sizes. Due to limited migration, only the expansion of highly advantageous clones can sweep through a large part of the tumor to reveal the selective advantages. Hence, even multiregional sampling can only reveal a fraction of fitness differences in solid tumors. Our results suggest that the NS patterns are crucial for testing the influence of natural selection during tumorigenesis, especially for small solid tumors.

Deep neural network based tissue deconvolution of circulating tumor cell RNA.

Prior research has shown that the deconvolution of cell-free RNA can uncover the tissue origin. The conventional deconvolution approaches rely on constructing a reference tissue-specific gene panel, which cannot capture the inherent variation present in actual data. To address this, we have developed a novel method that utilizes a neural network framework to leverage the entire training dataset. Our approach involved training a model that incorporated 15 distinct tissue types. Through one semi-independent and two complete independent validations, including deconvolution using a semi in silico dataset, deconvolution with a custom normal tissue mixture RNA-seq data, and deconvolution of longitudinal circulating tumor cell RNA-seq (ctcRNA) data from a cancer patient with metastatic tumors, we demonstrate the efficacy and advantages of the deep-learning approach which were exerted by effectively capturing the inherent variability present in the dataset, thus leading to enhanced accuracy. Sensitivity analyses reveal that neural network models are less susceptible to the presence of missing data, making them more suitable for real-world applications. Moreover, by leveraging the concept of organotropism, we applied our approach to trace the migration of circulating tumor cell-derived RNA (ctcRNA) in a cancer patient with metastatic tumors, thereby highlighting the potential clinical significance of early detection of cancer metastasis.

Monoclonal enolase-1 blocking antibody ameliorates pulmonary inflammation and fibrosis.

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a chronic fatal disease with limited therapeutic options. The infiltration of monocytes and fibroblasts into the injured lungs is implicated in IPF. Enolase-1 (ENO1) is a cytosolic glycolytic enzyme which could translocate onto the cell surface and act as a plasminogen receptor to facilitate cell migration via plasmin activation. Our proprietary ENO1 antibody, HL217, was screened for its specific binding to ENO1 and significant inhibition of cell migration and plasmin activation (patent: US9382331B2). METHODS: In this study, effects of HL217 were evaluated in vivo and in vitro for treating lung fibrosis. RESULTS: Elevated ENO1 expression was found in fibrotic lungs in human and in bleomycin-treated mice. In the mouse model, HL217 reduced bleomycin-induced lung fibrosis, inflammation, body weight loss, lung weight gain, TGF-ß upregulation in bronchial alveolar lavage fluid (BALF), and collagen deposition in lung. Moreover, HL217 reduced the migration of peripheral blood mononuclear cells (PBMC) and the recruitment of myeloid cells into the lungs. In vitro, HL217 significantly reduced cell-associated plasmin activation and cytokines secretion from primary human PBMC and endothelial cells. In primary human lung fibroblasts, HL217 also reduced cell migration and collagen secretion. CONCLUSIONS: These findings suggest multi-faceted roles of cell surface ENO1 and a potential therapeutic approach for pulmonary fibrosis.

Whole Genome Sequencing Revealed Inherited Rare Oligogenic Variants Contributing to Schizophrenia and Major Depressive Disorder in Two Families.

Schizophrenia and affective disorder are two major complex mental disorders with high heritability. Evidence shows that rare variants with significant clinical impacts contribute to the genetic liability of these two disorders. Also, rare variants associated with schizophrenia and affective disorders are highly personalized; each patient may carry different variants. We used whole genome sequencing analysis to study the genetic basis of two families with schizophrenia and major depressive disorder. We did not detect de novo, autosomal dominant, or recessive pathogenic or likely pathogenic variants associated with psychiatric disorders in these two families. Nevertheless, we identified multiple rare inherited variants with unknown significance in the probands. In family 1, with singleton schizophrenia, we detected four rare variants in genes implicated in schizophrenia, including p.Arg1627Trp of LAMA2, p.Pro1338Ser of CSMD1, p.Arg691Gly of TLR4, and Arg182X of AGTR2. The p.Arg691Gly of TLR4 was inherited from the father, while the other three were inherited from the mother. In family 2, with two affected sisters diagnosed with major depressive disorder, we detected three rare variants shared by the two sisters in three genes implicated in affective disorders, including p.Ala4551Gly of FAT1, p.Val231Leu of HOMER3, and p.Ile185Met of GPM6B. These three rare variants were assumed to be inherited from their parents. Prompted by these findings, we suggest that these rare inherited variants may interact with each other and lead to psychiatric conditions in these two families. Our observations support the conclusion that inherited rare variants may contribute to the heritability of psychiatric disorders.

Molecular basis for ATPase-powered substrate translocation by the Lon AAA+ protease.

The Lon AAA+ (adenosine triphosphatases associated with diverse cellular activities) protease (LonA) converts ATP-fuelled conformational changes into sufficient mechanical force to drive translocation of a substrate into a hexameric proteolytic chamber. To understand the structural basis for the substrate translocation process, we determined the cryo-electron microscopy (cryo-EM) structure of Meiothermus taiwanensis LonA (MtaLonA) in a substrate-engaged state at 3.6 Å resolution. Our data indicate that substrate interactions are mediated by the dual pore loops of the ATPase domains, organized in spiral staircase arrangement from four consecutive protomers in different ATP-binding and hydrolysis states. However, a closed AAA+ ring is maintained by two disengaged ADP-bound protomers transiting between the lowest and highest position. This structure reveals a processive rotary translocation mechanism mediated by LonA-specific nucleotide-dependent allosteric coordination among the ATPase domains, which is induced by substrate binding.

OrchidBase 5.0: updates of the orchid genome knowledgebase.

Containing the largest number of species, the orchid family provides not only materials for studying plant evolution and environmental adaptation, but economically and culturally important ornamental plants for human society. Previously, we collected genome and transcriptome information of Dendrobium catenatum, Phalaenopsis equestris, and Apostasia shenzhenica which belong to two different subfamilies of Orchidaceae, and developed user-friendly tools to explore the orchid genetic sequences in the OrchidBase 4.0. The OrchidBase 4.0 offers the opportunity for plant science community to compare orchid genomes and transcriptomes and retrieve orchid sequences for further study.In the year 2022, two whole-genome sequences of Orchidoideae species, Platanthera zijinensis and Platanthera guangdongensis, were de novo sequenced, assembled and analyzed. In addition, systemic transcriptomes from these two species were also established. Therefore, we included these datasets to develop the new version of OrchidBase 5.0. In addition, three new functions including synteny, gene order, and miRNA information were also developed for orchid genome comparisons and miRNA characterization.OrchidBase 5.0 extended the genetic information to three orchid subfamilies (including five orchid species) and provided new tools for orchid researchers to analyze orchid genomes and transcriptomes. The online resources can be accessed at https://cosbi.ee.ncku.edu.tw/orchidbase5/.