Genetic Model Research Articles

Phylogenetic analysis on 16S rRNA of Pseudomonas Species isolated from clinical specimens in Nigeria and other Regions

This project was to study the clinical isolates of Pseudomonas organisms in Nigeria in comparison with those of other regions of the world, to understand their evolutional relationship, using phylogenetic analysis of 16SrRNA. A total of 51 sequence data files of Pseudomonas species from different countries were selected based on 16S rRNA, using the National Centre for Biotechnology Information (NCBI) webpage. Geneious software (V9.0.5) was employed for sequence alignment. Tamura- Nei was used as the genetic distant model, while the tree build method was Neighbor-Joining with a bootstrap value of 1000 and 100 numbers of replicates to indicate the revolutionary process analyzed over time. Majority of the strains from Nigeria (7) have very little or no mutations in the sequences. These strains with other strains from the other countries were clustered at the 2nd branch of the tree, with a bootstrap value of 96%. Generally, there is similarity in diversity of the Pseudomonas species across the globe. Much diversity was seen in all the strains from Saudi Arabia, as all were found on the 10th branch of the tree. Two Pseudomonas strains previously identified as belonging to other species than aeruginosa were seen to be Pseudomonas aeruginosa strains. The low diversity shown by the strain isolates from Nigeria implies high fidelity to genetic stability with regard to the ancestral origin. The high mutation observed in all the strains from Saudi-Arabia indicates high diversity from the common ancestor and this may likely result to increased virulence and multi-drug resistance.

Differing contributions of the gut microbiota to the blood pressure lowering effects induced by first-line antihypertensive drugs.

This study analyses whether first-line antihypertensive drugs ameliorate the dysbiosis state in hypertension, and to test if this modification contributes to their blood pressure (BP) lowering properties in a genetic model of neurogenic hypertension. Twenty-week-old male Wistar Kyoto rats (WKY) and spontaneously hypertensive rats (SHR) were untreated or treated with captopril, amlodipine or hydrochlorothiazide. A faecal microbiota transplantation (FMT) experiment was also performed by gavage of faecal content from donor SHR-treated groups to SHR recipients for 3 weeks. Faeces from SHR showed gut dysbiosis, characterized by lower acetate- and higher lactate-producing bacteria and lower strict anaerobic bacteria. All three drugs increased the anaerobic bacteria proportion, captopril and amlodipine restored the proportion of acetate-producing bacterial populations to WKY levels, whereas hydrochlorothiazide decreased butyrate-producing bacteria. Captopril and amlodipine decreased gut pathology and permeability and attenuated sympathetic drive in the gut. Both drugs decreased neuroinflammation and oxidative stress in the hypothalamic paraventricular nuclei. Hydrochlorothiazide was unable to reduce neuroinflammation, gut sympathetic tone and gut integrity. FMT from SHR-amlodipine to SHR decreased BP, ameliorated aortic endothelium-dependent relaxation to acetylcholine, lowered NADPH oxidase activity, aortic Th17 infiltration and reduced neuroinflammation, whereas FMT from SHR-hydrochlorothiazide did not have these effects. First-line antihypertensive drugs induced different modifications of gut integrity and gut dysbiosis in SHR, which result in no contribution of microbiota in the BP lowering effects of hydrochlorothiazide, whereas the vasculo-protective effect induced by amlodipine involves gut microbiota reshaping and gut-immune system communication.

Correlation between small-cell lung cancer serum protein/peptides determined by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry and chemotherapy efficacy

BackgroundCurrently, no effective measures are available to predict the curative efficacy of small-cell lung cancer (SCLC) chemotherapy. We expect to develop a method for effectively predicting the SCLC chemotherapy efficacy and prognosis in clinical practice in order to offer more pertinent therapeutic protocols for individual patients.MethodsWe adopted matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) and ClinPro Tools system to detect serum samples from 154 SCLC patients with different curative efficacy of standard chemotherapy and analyze the different peptides/proteins of SCLC patients to discover predictive tumor markers related to chemotherapy efficacy. Ten peptide/protein peaks were significantly different in the two groups.ResultsA genetic algorithm model consisting of four peptides/proteins was developed from the training group to separate patients with different chemotherapy efficacies. Among them, three peptides/proteins (m/z 3323.35, 6649.03 and 6451.08) showed high expression in the disease progression group, whereas the peptide/protein at m/z 4283.18 was highly expressed in the disease response group. The classifier exhibited an accuracy of 91.4% (53/58) in the validation group. The survival analysis showed that the median progression-free survival (PFS) of 30 SCLC patients in disease response group was 9.0 months; in 28 cases in disease progression group, the median PFS was 3.0 months, a statistically significant difference (χ2 = 46.98, P < 0.001). The median overall survival (OS) of the two groups was 13.0 months and 7.0 months, a statistically significant difference (χ2 = 40.64, P < 0.001).ConclusionsThese peptides/proteins may be used as potential biological markers for prediction of the curative efficacy and prognosis for SCLC patients treated with standard regimen chemotherapy.

Large-scale alternative polyadenylation-wide association studies to identify putative cancer susceptibility genes.

Alternative polyadenylation (APA) modulates mRNA processing in the 3' untranslated regions (3' UTR), affecting mRNA stability and translation efficiency. Research into genetically regulated APA has the potential to provide insights into cancer risk. Herein, we conducted large alternative polyadenylation-wide association studies (APA-WAS) to investigate associations of APA levels with cancer risk. Genetic models were built to predict APA levels in multiple tissues using genotype and RNA-sequencing data from 1,337 samples from the Genotype-Tissue Expression Project. Associations of genetically predicted APA levels with cancer risk were assessed by applying the prediction models to data from large genome-wide association studies of six common cancers among European-ancestry populations, including breast, ovary, prostate, colorectum, lung, and pancreas. A total of 58 risk genes (corresponding to 76 APA sites) were associated with at least one type of cancer, including 25 genes previously not linked to cancer susceptibility. Of the identified risk APAs, 97.4% and 26.3% were supported by 3' UTR APA quantitative trait loci and co-localization analyses, respectively. Luciferase reporter assays for four selected putative regulatory 3' UTR variants demonstrated that the risk alleles of 3' UTR variants, rs324015 (STAT6), rs2280503 (DIP2B), rs1128450 (FBXO38), and rs145220637 (LDHA), significantly increased the post-transcriptional activities of their target genes compared to reference alleles. Furthermore, knockdown of the target genes confirmed their ability to promote proliferation and migration. Overall, this study provides insights into the role of APA in the genetic susceptibility to common cancers.

Association between NUDT17 polymorphisms and breast cancer risk.

Breast cancer (BC) is the leading cause of cancer death among women worldwide. The nudix hydrolase 17 (NUDT17) may play notable roles in cancer growth and metastasis. In this study, we explored the importance of NUDT17 gene polymorphism in patients with BC. In our study, 563 BC patients and 552 healthy controls participated. We used logistic regression analysis to calculate odds ratios (OR) and 95% confidence intervals (CI), and multifactor dimension reduction (MDR) analysis of SNP-SNP interactions. Finally, UALCAN and THPA databases were used for bioinformatics analysis. The rs9286836 G allele was associated with a decreased the BC risk (p = 0.022), and the carriers of rs2004659 G allele had a 32% decreased risk of BC than individuals with allele A (p = 0.004). In the four genetic models, rs9286836 and rs2004659 reduced the risk of BC. Additionally, we found that the NUDT17 SNPs were associated with BC risk under age, tumor size, and clinical stage stratification. The MDR analysis showed that the five-locus interaction model was the best in the multi-locus model. Our study found that NUDT17 single nucleotide polymorphisms are associated with BC susceptibility in Chinese Han population.

Abstract IA019: Lessons from glioblastoma diversity, heterogeneity, and beginnings

Abstract Glioblastoma (GBM) is a dire disease with poor prognosis and survival outcomes have changed little over the past two decades. Various genomic studies have emphasized the heterogeneity of GBM yet a consensus on the causes and its role in tumor recurrence has not been reached. Multiple studies using genetic and patient-derived xenograft (PDX) mouse models have demonstrated an inverse relationship between differentiation state and tumor initiation efficiency with neural stem and progenitor cells as the most efficient. However, other studies have proposed that well differentiated cells like neurons and astrocytes can form tumors. In addition, bioinformatic analyses of human primary GBM have reported that plasticity allows for mobility among cell states exist. Regardless of transcriptional modulation in GBM cells, to achieve progress in understanding and addressing clinical recurrence, the outstanding goal remains to identify and isolate the GBM cell type(s) responsible for therapy resistance and recurrence. To achieve this, we have employer autochthonous GBM models as well as orthotopic patient derived xenografts to in parallel examine tumor heterogeneity, cell of origin, and functional consequences. Citation Format: Luis F. Parada. Lessons from glioblastoma diversity, heterogeneity, and beginnings [abstract]. In: Proceedings of the AACR Special Conference on Bladder Cancer: Transforming the Field; 2024 May 17-20; Charlotte, NC. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(10_Suppl):Abstract nr IA019.

Prediction of esophageal cancer risk based on genetic variants and environmental risk factors in Chinese population

BackgroundResults regarding whether it is essential to incorporate genetic variants into risk prediction models for esophageal cancer (EC) are inconsistent due to the different genetic backgrounds of the populations studied. We aimed to identify single-nucleotide polymorphisms (SNPs) associated with EC among the Chinese population and to evaluate the performance of genetic and non-genetic factors in a risk model for developing EC.MethodsA meta-analysis was performed to systematically identify potential SNPs, which were further verified by a case-control study. Three risk models were developed: a genetic model with weighted genetic risk score (wGRS) based on promising SNPs, a non-genetic model with environmental risk factors, and a combined model including both genetic and non-genetic factors. The discrimination ability of the models was compared using the area under the receiver operating characteristic curve (AUC) and the net reclassification index (NRI). The Akaike information criterion (AIC) and Bayesian information criterion (BIC) were used to assess the goodness-of-fit of the models.ResultsFive promising SNPs were ultimately utilized to calculate the wGRS. Individuals in the highest quartile of the wGRS had a 4.93-fold (95% confidence interval [CI]: 2.59 to 9.38) increased risk of EC compared with those in the lowest quartile. The genetic or non-genetic model identified EC patients with AUCs ranging from 0.618 to 0.650. The combined model had an AUC of 0.707 (95% CI: 0.669 to 0.743) and was the best-fitting model (AIC = 750.55, BIC = 759.34). The NRI improved when the wGRS was added to the risk model with non-genetic factors only (NRI = 0.082, P = 0.037).ConclusionsAmong the three risk models for EC, the combined model showed optimal predictive performance and can help to identify individuals at risk of EC for tailored preventive measures.

Comparison of different animal models for estimating genetic parameters for early growth traits and reproductive traits in Tianmu Sainuo sheep

As the economic level of individuals rises, so too does the demand for mutton. Enhancing the breeds of mutton sheep not only boosts production efficiency and economic benefits but also fosters the sustainable growth of the mutton sheep breeding industry. Thus, this study examines the early growth and reproductive traits of Tianmu Sainuo sheep, analyzing the genetic interactions among these traits to furnish a theoretical foundation for refining breeding strategies and expediting the genetic advancement of this breed. The investigation compiled 29,966 data entries, involving 111 sires for birth weight (BWT) and 113 for other metrics. The data encompassed 10,415 BWT records from 1,633 dams, 12,753 weaning weight (WWT) records from 1,570 dams, 12,793 average daily gain (ADG) records from 1,597 dams, and 13,594 litter size (LS) records from 1,499 dams. Utilizing the GLM procedure in SAS 9.2 software, the study analyzed the non-genetic influences on lamb BWT, WWT, ADG, and LS. Concurrently, DMU software estimated the variance components across various animal models for each trait. Employing the Akaike Information Criterion (AIC) and likelihood ratio test (LRT), six models were tested, incorporating or excluding maternal inheritance and environmental impacts, to identify the optimal model for deriving genetic parameters. The findings reveal that birth year (BY), birth quarter (BQ), birth type (BT), age of mother (AM), and birth sex (BS) exerted significant impacts on BWT, WWT, and ADG (p &amp;lt; 0.01). Additionally, BQ and AM significantly influenced LS (p &amp;lt; 0.01). The most accurate genetic evaluation model determined the heritability of BWT, WWT, ADG, and LS to be 0.0695, 0.0849, 0.0777, and 0.1252, respectively.

Almond (Prunus dulcis) varieties classification with genetic designed lightweight CNN architecture

AbstractAlmond (Prunus dulcis) is a nutritious food with a rich content. In addition to consuming as food, it is also used for various purposes in sectors such as medicine, cosmetics and bioenergy. With all these usages, almond has become a globally demanded product. Accurately determining almond variety is crucial for quality assessment and market value. Convolutional Neural Network (CNN) has a great performance in image classification. In this study, a public dataset containing images of four different almond varieties was created. Five well-known and light-weight CNN models (DenseNet121, EfficientNetB0, MobileNet, MobileNet V2, NASNetMobile) were used to classify almond images. Additionally, a model called 'Genetic CNN', which has its hyperparameters determined by Genetic Algorithm, was proposed. Among the well-known and light-weight CNN models, NASNetMobile achieved the most successful result with an accuracy rate of 99.20%, precision of 99.21%, recall of 99.20% and f1-score of 99.19%. Genetic CNN outperformed well-known models with an accuracy rate of 99.55%, precision of 99.56%, recall of 99.55% and f1-score of 99.55%. Furthermore, the Genetic CNN model has a relatively small size and low test time in comparison to other models, with a parameter count of only 1.1 million. Genetic CNN is suitable for embedded and mobile systems and can be used in real-life solutions.

Modelling a foreland fold-and-thrust petroleum system: a Permian example in the NW Sichuan Basin, Southwest China

ABSTRACT Foreland thrust belts are globally significant areas for oil and gas exploration. These belts undergo unique tectonic activity, resulting in distinct variations in the accumulation of oil and gas. Studying the genetic evolution of deep oil and gas in these thrust belts is a crucial means of revealing the patterns of deep oil and gas accumulation. This study utilized a novel approach that combines reservoir geochemistry and basin simulation to determine the migration process of oil and gas in the foreland thrust fault belt of northwestern Sichuan Province. To achieve this goal, we established a thermal evolution model of lower Palaeozoic source rocks through organic chemical analysis. The balanced profile technique was used to determine the paleotectonic evolution pattern of the critical period in this area. Additionally, the mixed flow migration algorithm was employed to simulate the evolution of the Palaeozoic petroleum system. Through basin petroleum system modelling (BPSM), it was discovered that the bitumen found in the thrust nappe belt originated from the lower Cambrian strata. Furthermore, the gas reservoir in the hidden fault belt primarily migrated from the lower Cambrian strata and was also partially mixed with thermogenic gas from Permian source rocks. After establishing the genetic models of various tectonic belts in the study area, it became clear that there are notable differences in hydrocarbon accumulation within these belts. The BPSM results revealed that the oil present in the thrust nappe belt was generated in Lower Cambrian source rocks through early Indosinian hydrocarbon expulsion and continuous charging. The gas found in the thrust nappe belt was produced through the process of oil cracking in the deep reservoir located in the hidden front belt during the late Indosinian. The depth of the thrust nappe belt has helped to preserve hydrocarbon reservoirs. The natural gas present in the hidden fault belt primarily originates from lower Cambrian source rocks, which are combined with gas generated by oil cracking in lower Permian source rocks after the Yanshanian. Therefore, hydrocarbons accumulate deeply in the footwall of the thrust fault belt. The BPSM offers a fresh approach to hydrocarbon genetic models of thrust fault zones in the western Sichuan Basin, and it is beneficial for accurately evaluating the potential for deep hydrocarbon resources in comparable geological settings.

Bulked segregant RNA-seq reveals complex resistance expression profile to powdery mildew in wild emmer wheat W762

Powdery mildew, caused by Blumeria graminis f. sp. tritici (Bgt), is one of the most destructive fungal diseases threatening global wheat production. Exploring powdery mildew resistance (Pm) gene(s) and dissecting the molecular mechanism of the host resistance are critical to effectively and reasonably control this disease. Durum wheat (Triticum turgidum L. var. durumDesf.) is an important gene donor for wheat improvement against powdery mildew. In this study, a resistant durum wheat accession W762 was used to investigate its potential resistance component(s) and profile its expression pattern in responding to Bgt invasion using bulked segregant RNA-Seq (BSR-Seq) and further qRT-PCR verification. Genetic analysis showed that the powdery mildew resistance in W762 did not meet monogenic inheritance and complex genetic model might exist within the population of W762 × Langdon (susceptible durum wheat). After BSR-Seq, 6,196 consistently different single nucleotide polymorphisms (SNPs) were called between resistant and susceptible parents and bulks, and among them, 763 SNPs were assigned to the chromosome arm 7B. Subsequently, 3,653 differentially expressed genes (DEGs) between resistant and susceptible parents and bulks were annotated and analyzed by Gene Ontology (GO), Cluster of Orthologous Groups (COG), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. The potential regulated genes were selected and analyzed their temporal expression patterns following Bgt inoculation. As a result, nine disease-related genes showed distinctive expression profile after Bgt invasion and might serve as potential targets to regulate the resistance against powdery mildew in W762. Our study could lay a foundation for analysis of the molecular mechanism and also provide potential targets for the improvement of durable resistance against powdery mildew.

Sequence and epigenetic variations of R2R3-MYB transcription factors determine the diversity of taproot skin and flesh colors in different cultivated types of radish (Raphanus sativus L.).

This study found that three paralogous R2R3-MYB transcription factors exhibit functional divergence among different subspecies and cultivated types in radish. Cultivated radish taproots exhibit a wide range of color variations due to unique anthocyanin accumulation patterns in various tissues. This study investigated the universal principles of taproot color regulation that developed during domestication of different subspecies and cultivated types. The key candidate genes RsMYB1 and RsMYB2, which control anthocyanin accumulation in radish taproots, were identified using bulked segregant analysis in two genetic populations. We introduced the RsMYB1-RsF3'H-RsMYB1Met genetic model to elucidate the complex and unstable genetic regulation of taproot flesh color in Xinlimei radish. Furthermore, we analyzed the expression patterns of three R2R3-MYB transcription factors in lines with different taproot colors and investigated the relationship between RsMYB haplotypes and anthocyanin accumulation in a natural population of 56 germplasms. The results revealed that three paralogous RsMYBs underwent functional divergence during radish domestication, with RsMYB1 regulating the red flesh of Xinlimei radish, and RsMYB2 and RsMYB3 regulating the red skin of East Asian big long radish (R. sativus var. hortensis) and European small radish (R. sativus var. sativus), respectively. Moreover, RsMYB1-H1, RsMYB2-H10, and RsMYB3-H6 were identified as the primary haplotypes exerting regulatory functions on anthocyanin synthesis. These findings provide an understanding of the genetic mechanisms regulating anthocyanin synthesis in radish and offer a potential strategy for early prediction of color variations in breeding programs.

EphB2 Receptor Promotes Dermal Fibrosis in Systemic Sclerosis.

Erythropoietin-producing hepatocellular (Eph)/Ephrin cell-cell signaling is emerging as a key player in tissue fibrogenesis. The aim of this study was to test the hypothesis that the receptor tyrosine kinase EphB2 mediates dermal fibrosis in systemic sclerosis (SSc). We assessed normal and SSc human skin biopsies for EphB2 expression. The in vivo role of EphB2 in skin fibrosis was investigated by subjecting EphB2-knockout mice to both bleomycin-induced and tight skin (Tsk1/+) genetic mouse models of skin fibrosis. EphB2 kinase-dead and overactive point mutant mice were used to evaluate the role of EphB2 forward signaling in bleomycin-induced dermal fibrosis. In vitro studies were performed on dermal fibroblasts from patients with SSc and healthy controls, which was followed by in vivo analysis of fibroblast-specific Ephb2-deficient mice. Expression of EphB2 is up-regulated in SSc skin tissue and explanted SSc dermal fibroblasts compared with healthy controls. EphB2 expression is elevated in two animal models of dermal fibrosis. In mice, EphB2 drives dermal fibrosis in both the bleomycin and the Tsk1/+ models of skin fibrosis. EphB2 forward signaling is a critical mediator of dermal fibrosis. Transforming growth factor-β (TGF-β) cytokines up-regulate EphB2 in dermal fibroblasts via noncanonical TGF-β/mother against decapentaplegic signaling, and silencing EPHB2 in human dermal fibroblasts is sufficient to dampen TGF-β-induced fibroblast-to-myofibroblast differentiation. Moreover, mice with fibroblast-specific deletion of EphB2 showed impaired fibroblast-to-myofibroblast differentiation and reduced skin fibrosis upon bleomycin challenge. Our data implicate TGF-β regulation of EphB2 overexpression and kinase-mediated forward signaling in the development of dermal fibrosis in SSc. EphB2 thus represents a potential new therapeutic target for SSc.

Jag1/2 maintain esophageal homeostasis and suppress foregut tumorigenesis by restricting the basal progenitor cell pool

Basal progenitor cells are crucial for maintaining foregut (the esophagus and forestomach) homeostasis. When their function is dysregulated, it can promote inflammation and tumorigenesis. However, the mechanisms underlying these processes remain largely unclear. Here, we employ genetic mouse models to reveal that Jag1/2 regulate esophageal homeostasis and foregut tumorigenesis by modulating the function of basal progenitor cells. Deletion of Jag1/2 in mice disrupts esophageal and forestomach epithelial homeostasis. Mechanistically, Jag1/2 deficiency impairs activation of Notch signaling, leading to reduced squamous epithelial differentiation and expansion of basal progenitor cells. Moreover, Jag1/2 deficiency exacerbates the deoxycholic acid (DCA)-induced squamous epithelial injury and accelerates the initiation of squamous cell carcinoma (SCC) in the forestomach. Importantly, expression levels of JAG1/2 are lower in the early stages of human esophageal squamous cell carcinoma (ESCC) carcinogenesis. Collectively, our study demonstrates that Jag1/2 are important for maintaining esophageal and forestomach homeostasis and the onset of foregut SCC.

Genetic Architectures of Adolescent Depression Trajectories in 2 Longitudinal Population Cohorts

Adolescent depression is characterized by diverse symptom trajectories over time and has a strong genetic influence. Research has determined genetic overlap between depression and other psychiatric conditions; investigating the shared genetic architecture of heterogeneous depression trajectories is crucial for understanding disease etiology, prediction, and early intervention. To investigate univariate and multivariate genetic risk for adolescent depression trajectories and assess generalizability across ancestries. This cohort study entailed longitudinal growth modeling followed by polygenic risk score (PRS) association testing for individual and multitrait genetic models. Two longitudinal cohorts from the US and UK were used: the Adolescent Brain and Cognitive Development (ABCD; N = 11 876) study and the Avon Longitudinal Study of Parents and Children (ALSPAC; N = 8787) study. Included were adolescents with genetic information and depression measures at up to 8 and 4 occasions, respectively. Study data were analyzed January to July 2023. Trajectories were derived from growth mixture modeling of longitudinal depression symptoms. PRSs were computed for depression, anxiety, neuroticism, bipolar disorder, schizophrenia, attention-deficit/hyperactivity disorder, and autism in European ancestry. Genomic structural equation modeling was used to build multitrait genetic models of psychopathology followed by multitrait PRS. Depression PRSs were computed in African, East Asian, and Hispanic ancestries in the ABCD cohort only. Association testing was performed between all PRSs and trajectories for both cohorts. A total sample size of 14 112 adolescents (at baseline: mean [SD] age, 10.5 [0.5] years; 7269 male sex [52%]) from both cohorts were included in this analysis. Distinct depression trajectories (stable low, adolescent persistent, increasing, and decreasing) were replicated in the ALSPAC cohort (6096 participants; 3091 female [51%]) and ABCD cohort (8016 participants; 4274 male [53%]) between ages 10 and 17 years. Most univariate PRSs showed significant uniform associations with persistent trajectories, but fewer were significantly associated with intermediate (increasing and decreasing) trajectories. Multitrait PRSs-derived from a hierarchical factor model-showed the strongest associations for persistent trajectories (ABCD cohort: OR, 1.46; 95% CI, 1.26-1.68; ALSPAC cohort: OR, 1.34; 95% CI, 1.20-1.49), surpassing the effect size of univariate PRS in both cohorts. Multitrait PRSs were associated with intermediate trajectories but to a lesser extent (ABCD cohort: hierarchical increasing, OR, 1.27; 95% CI, 1.13-1.43; decreasing, OR, 1.23; 95% CI, 1.09-1.40; ALSPAC cohort: hierarchical increasing, OR, 1.16; 95% CI, 1.04-1.28; decreasing, OR, 1.32; 95% CI, 1.18-1.47). Transancestral genetic risk for depression showed no evidence for association with trajectories. Results of this cohort study revealed a high multitrait genetic loading of persistent symptom trajectories, consistent across traits and cohorts. Variability in univariate genetic association with intermediate trajectories may stem from environmental factors. Multitrait genetics may strengthen depression prediction models, but more diverse data are needed for generalizability.

Association of BDNF polymorphism with gestational diabetes mellitus risk: a novel insight into genetic predisposition.

Gestational diabetes mellitus (GDM) is a prevalent metabolic disorder during pregnancy with potential long-term health implications for the mother and child. The interplay between genetics and GDM susceptibility remains an area of active research. Recently, brain-derived neurotrophic factor (BDNF) was investigated in relation to obesity and impaired glucose metabolism and pathogenesis. We aimed to investigate the association of common BDNF polymorphisms, with GDM risk in Israeli females. A cohort of 4,025 Israeli women data for BDNF common SNPs was analyzed for potential association with GDM using binary logistic regressions analysis (SPSS 29.0 and R) adjusted for confounding variables (age, T1DM, T2DM, PCOS) under different genetic models. The GDM and Non-GDM genetic frequencies for the BDNF rs925946 Tag-SNP were significantly different. The genetic frequencies were 54.16 %, and 66.91 % for the wild type (GG), 38.88 and 29.64 % for the heterozygotes (TC), and 6.94 and 3.48 % for the risk allele homozygotes (TT) for the GDM non-GDM populations, respectively. Carriers of BDNF rs925946 were significantly associated with higher risk for GDM, following the dominant genetic model (OR=1.7, 95 % CI 1.21-2.39, p=0.002), the recessive genetic model (OR=2.05, 95 % CI 1.04-4.03, p=0.03), and the additive genetic model (OR=1.62, 95 % CI 1.13-2.3, p=0.008). This association persisted after adjusting for age, T1DM, T2DM, and polycystic ovary syndrome (PCOS). Carrying BDNF rs925946 polymorphism predisposes to a higher risk of GDM pathogenesis. Its role and implications warrant further investigation, especially when considering preventive measures for GDM development.

Diagenesis and Genetic Model of Calcareous Interbeds in Marine Strata

Diagenesis and Genetic Model of Calcareous Interbeds in Marine Strata

Eomesodermin spatiotemporally orchestrates the early and late stages of NK cell development by targeting KLF2 and T-bet, respectively.

Eomesodermin (Eomes) is a critical factor in the development of natural killer (NK) cells, but its precise role in temporal and spatial coordination during this process remains unclear. Our study revealed that Eomes plays distinct roles during the early and late stages of NK cell development. Specifically, the early deletion of Eomes via the CD122-Cre transgene resulted in significant blockade at the progenitor stage due to the downregulation of KLF2, another important transcription factor. ChIP-seq revealed direct binding of Eomes to the conserved noncoding sequence (CNS) of Klf2. Utilizing the CHimeric IMmune Editing (CHIME) technique, we found that deletion of the CNS region of Klf2 via CRISPRi led to a reduction in the NK cell population and developmental arrest. Moreover, constitutive activation of this specific CNS region through CRISPRa significantly reversed the severe defects in NK cell development caused by Eomes deficiency. Conversely, Ncr1-Cre-mediated terminal deletion of Eomes expedited the transition of NK cell subsets from the CD27+CD11b+ phenotype to the CD27-CD11b+ phenotype. Late-stage deficiency of Eomes led to a significant increase in T-bet expression, which subsequently increased the expression of the transcription factor Zeb2. Genetic deletion of one allele of Tbx21, encoding T-bet, effectively reversed the aberrant differentiation of Eomes-deficient NK cells. In summary, we utilized two innovative genetic models to elucidate the intricate mechanisms underlying Eomes-mediated NK cell commitment and differentiation.

Deep neural network for the prediction of KRAS, NRAS, and BRAF genotypes in left-sided colorectal cancer based on histopathologic images

BackgroundThe KRAS, NRAS, and BRAF genotypes are critical for selecting targeted therapies for patients with metastatic colorectal cancer (mCRC). Here, we aimed to develop a deep learning model that utilizes pathologic whole-slide images (WSIs) to accurately predict the status of KRAS, NRAS, and BRAFV600E. Methods129 patients with left-sided colon cancer and rectal cancer from the Third Affiliated Hospital of Sun Yat-sen University were assigned to the training and testing cohorts. Utilizing three convolutional neural networks (ResNet18, ResNet50, and Inception v3), we extracted 206 pathological features from H&E-stained WSIs, serving as the foundation for constructing specific pathological models. A clinical feature model was then developed, with carcinoembryonic antigen (CEA) identified through comprehensive multiple regression analysis as the key biomarker. Subsequently, these two models were combined to create a clinical-pathological integrated model, resulting in a total of three genetic prediction models. Result103 patients were evaluated in the training cohort (1782,302 image tiles), while the remaining 26 patients were enrolled in the testing cohort (489,481 image tiles). Compared with the clinical model and the pathology model, the combined model which incorporated CEA levels and pathological signatures, showed increased predictive ability, with an area under the curve (AUC) of 0.96 in the training and an AUC of 0.83 in the testing cohort, accompanied by a high positive predictive value (PPV 0.92). ConclusionThe combined model demonstrated a considerable ability to accurately predict the status of KRAS, NRAS, and BRAFV600E in patients with left-sided colorectal cancer, with potential application to assist doctors in developing targeted treatment strategies for mCRC patients, and effectively identifying mutations and eliminating the need for confirmatory genetic testing.

The A2ml1-Knockout mouse as an animal model for non-syndromic otitis media

BackgroundInflammation and infection of the middle ear, known as otitis media (OM), is a leading cause of hearing loss and the most frequently diagnosed disease in children worldwide. Traditionally, mouse models for OM rely on inducing acute infection through inoculation of the middle ear, e.g. with the human otopathogen non-typeable Haemophilus influenzae (NTHi), and with very few genetic models with spontaneous or chronic OM. A2ML1 variants, including loss-of-function variants, were associated with susceptibility to OM in humans, but no animal model has been reported for A2ml1-related OM. Here, we report our middle ear findings in a mouse line with a CRISPR-induced knockout (KO) of A2ml1. MethodsMice were X-rayed prior to harvest to determine if there are craniofacial or skeletal abnormalities. Tissue from mouse middle ears, as well as other upper respiratory mucosal tissues, were harvested. The harvested middle ear bullae were examined under microscope and submitted for histologic preparation to study phenotypic indications of OM. RNA samples isolated from middle ear tissue were assayed for expression of genes correlated with A2ML1 expression in humans. ResultsData from a total of 119 mice (35 wildtype, 40 heterozygous, 44 homozygous) are presented here, with each analyses being performed on subsets of these mice. There were no significant craniofacial differences by genotype (n = 22). Findings in mice with the A2ml1-KO indicated an increased incidence of OM (n=29; odds ratio = 11; CI: 1.1, 573.6; Fisher exact two-sided p = 0.02) with tympanic membrane perforations or thickening, as well as cases of middle ear effusion, inflammatory cells, or fluid from histologic sections. Dsp was upregulated in the middle ear tissues of homozygous mice (Wilcoxon test p = 0.001). ConclusionThus far, our results in this A2ml1-KO mouse line indicate spontaneous occurrence of OM and dysregulation of Dsp in the middle ear as a potential disease mechanism for A2ml1-related OM.