Clinical and Molecular Characterization of Nine Novel Antithrombin Mutations.

Antithrombin (AT) is the major plasma inhibitor of thrombin (FIIa) and activated factor X (FXa), and antithrombin deficiency (ATD) is one of the most severe thrombophilic disorders. In this study, we identified nine novel AT mutations and investigated their genotype-phenotype correlations. Clinical and laboratory data from patients were collected, and the nine mutant AT proteins (p.Arg14Lys, p.Cys32Tyr, p.Arg78Gly, p.Met121Arg, p.Leu245Pro, p.Leu270Argfs*14, p.Asn450Ile, p.Gly456delins_Ala_Thr and p.Pro461Thr) were expressed in HEK293 cells; then, Western blotting, N-Glycosidase F digestion, and ELISA were used to detect wild-type and mutant AT. RT-qPCR was performed to determine the expression of AT mRNA from the transfected cells. Functional studies (AT activity in the presence and in the absence of heparin and heparin-binding studies with the surface plasmon resonance method) were carried out. Mutations were also investigated by in silico methods. Type I ATD caused by altered protein synthesis (p.Cys32Tyr, p.Leu270Argfs*14, p.Asn450Ile) or secretion disorder (p.Met121Arg, p.Leu245Pro, p.Gly456delins_Ala_Thr) was proved in six mutants, while type II heparin-binding-site ATD (p.Arg78Gly) and pleiotropic-effect ATD (p.Pro461Thr) were suggested in two mutants. Finally, the pathogenic role of p.Arg14Lys was equivocal. We provided evidence to understand the pathogenic nature of novel SERPINC1 mutations through in vitro expression studies.

Deep-learning-based removal of autofluorescence and fluorescence quantification in plant-colonizing bacteria in vivo.

Fluorescence microscopy is common in bacteria-plant interaction studies. However, strong autofluorescence from plant tissues impedes in vivo studies on endophytes tagged with fluorescent proteins. To solve this problem, we developed a deep-learning-based approach to eliminate plant autofluorescence from fluorescence microscopy images, tested for the model endophyte Azoarcus olearius BH72 colonizing Oryza sativa roots. Micrographs from three channels (tdTomato for gene expression, green fluorescent protein (GFP) and AutoFluorescence (AF)) were processed by a neural network based approach, generating images that simulate the background autofluorescence in the tdTomato channel. After subtracting the model-generated signals from each pixel in the genuine channel, the autofluorescence in the tdTomato channel was greatly reduced or even removed. The deep-learning-based approach can be applied for fluorescence detection and quantification, exemplified by a weakly expressed, a cell-density modulated and a nitrogen-fixation gene in A. olearius. A transcriptional nifH::tdTomato fusion demonstrated stronger induction of nif genes inside roots than outside, suggesting extension of the rhizosphere effect for diazotrophs into the endorhizosphere. The pre-trained convolutional neural network model is easily applied to process other images of the same plant tissues with the same settings. This study showed the high potential of deep-learning-based approaches in image processing. With proper training data and strategies, autofluorescence in other tissues or materials can be removed for broad applications.

Andrographis paniculata mitigates first-line anti-tubercular drugs-induced nephrotoxicity in Wistar rats.

CONTEXT: Anti-tubercular drugs (ATDs) mediated adverse drug reactions are major concerns for clinicians to treat tuberculosis infection. This study aimed to investigate Andrographis paniculata extract-based phytotherapy to combat the nephrotoxic effects caused by ATDs therapy. METHODS: Reno-protective effect of A. paniculata extract in ATDs-induced rats was evaluated through LPO, GSH, CAT, SOD, GST, urea, creatinine, uric acid, and histopathological studies. Standardization of the extract was performed using RP-HPLC and FTIR analysis. Whereas, the effect of A. paniculata extract on ATDs induced genetic perturbation was analyzed using micronucleus assay. Moreover, the expression level of the xenometabolic gene was investigated using RT-PCR to explore the therapeutic mechanism. RESULTS: The nephrotoxic effect of ATDs was indicated by elevated levels of LPO and renal function markers along with the reduced activity of renal antioxidants. An up-regulated expression profile of NAT gene and histological alterations were observed in renal tissue however, micronucleated PCEs were observed in bone marrow cells. Concomitant treatment with A. paniculata extract revealed a noticeable amelioration of elevated oxidative stress markers, gene expression levels, genotoxic perturbation, and histological alterations in a dose-dependent manner. CONCLUSION: Hence, the present study using A. paniculata leaf extract confirmed to play effective phytotherapy against ATDs induced renal toxicity.

Development and characterization of five novel cell lines from snubnose pompano, Trachinotus blochii (Lacepede, 1801), and their application in gene expression and virological studies.

Five novel permanent cell lines have been established from gill, heart, kidney, eye and fin of snubnose pompano, Trachinotus blochii. They were designated as snubnose pompano gill (SPG), snubnose pompano heart (SPH), snubnose pompano kidney (SPK), snubnose pompano eye (SPE) and snubnose pompano fin (SPF), respectively. All these cell lines were characterized and cryopreserved successfully at different passage levels. Cell lines were passaged every alternate day; SPG, SPH, SPK, SPE and SPF cell lines attained passage levels of 68, 74, 82, 79 and 106, respectively, since the initiation of their development in 2019. The cell lines grew well in Leibovitz's 15 medium containing 15% foetal bovine serum at 28°C. Immunophenotyping of the cell lines revealed the presence of fibronectin and pancytokeratin. No mycoplasma contamination was found. The transfection study revealed the gene expression efficiency of these cell lines by expressing the green fluorescent protein (GFP). The authentication on origin of cell lines from T. blochii was confirmed by amplification of species-specific mitochondrial cytochrome oxidase I gene. The results showed the susceptibility of these cell lines to fish nodavirus (FNV) and tilapia lake virus (TiLV) and resistance to cyprinid herpesvirus 2 (CyHV-2). The FNV infection in the cell lines was confirmed by RT-PCR, Western blot, ELISA and immunocytochemistry, while TiLV infection was confirmed by RT-PCR assay. These results revealed that these cell lines are suitable for virological and foreign gene expression studies.

Distance-correlation based gene set analysis in longitudinal studies.

Longitudinal gene expression profiles of subjects are collected in some clinical studies to monitor disease progression and understand disease etiology. The identification of gene sets that have coordinated changes with relevant clinical outcomes over time from these data could provide significant insights into the molecular basis of disease progression and lead to better treatments. In this article, we propose a Distance-Correlation based Gene Set Analysis (dcGSA) method for longitudinal gene expression data. dcGSA is a non-parametric approach, statistically robust, and can capture both linear and nonlinear relationships between gene sets and clinical outcomes. In addition, dcGSA is able to identify related gene sets in cases where the effects of gene sets on clinical outcomes differ across subjects due to the subject heterogeneity, remove the confounding effects of some unobserved time-invariant covariates, and allow the assessment of associations between gene sets and multiple related outcomes simultaneously. Through extensive simulation studies, we demonstrate that dcGSA is more powerful of detecting relevant genes than other commonly used gene set analysis methods. When dcGSA is applied to a real dataset on systemic lupus erythematosus, we are able to identify more disease related gene sets than other methods.

Clinical and molecular characteristics of patients with Gaucher disease in Southern China.

Gaucher disease (GD) is a common lysosomal storage disorder caused by the deficiency of acid ß-glucosidase, due to mutations in the GBA gene. To explore the clinical and molecular characteristics of GD patients from Southern China, GBA gene were analyzed by nest PCR and direct Sanger-sequencing. Novel missense mutations were transiently transfected in COS-7 cells by plasmid system for functional verification. Among the 22 GD patients, 19 patients were classified as type 1 and three as type 2. Over 60% of the type 1 patient had the onset before two years of age and about 42% of them died before three years of age. Six type 1 patients with L444P homozygous genotype, presented with early onset and severe hepatosplenomegaly. Four novel mutations Y22C, F109L, L149F and c.983_990delCCCACTGG were identified. The GBA activities in vitro of novel mutants Y22C, F109L and L149F were 20.2%, 6.9% and 6.5% of the wild-type, respectively. L444P mutation accounted for 47.7% of the mutant alleles. Our results revealed that type 1 GD tends to present with a severe phenotype among southern Chinese. L444P was the most prevalent mutation and L444P homozygous genotype was associated with severe type 1 GD. Three novel missense mutations identified were pathogenic.

Transcriptomic population markers for human population discrimination.

BACKGROUND: Numerous studies have demonstrated significant differences in the expression level across continental human populations. Most of published results were performed on B-cell lines materials examined under specific laboratory conditions, without further validation in a primary biological material. The goal of our study was to identify mRNA markers characterized by a significant and stable difference in the gene expression profile in Caucasian and Chinese populations, both in the commercially available B-lymphocyte cell lines and in the primary samples of the peripheral blood. RESULTS: The preliminary selection of population-differentiating transcripts was based on Illumina expression microarray analysis of the representative group of ethnically-specified B-lymphocyte cell lines. Twenty genes with the inter-population difference in the mean expression characterized by the at least 1.5-fold change and FDR <  0.05 were identified. Subsequently, a two-step validation procedure was carried out. In the first step, a subset of selected population- differentiating transcripts was tested in the independent set of B-lymphocyte cell lines, using TLDA cards. Based on TLDA analysis, three transcripts representing Fch > 2 were chosen for validation. The differentiating status was confirmed for all of them: UTS2, UGT2B17 and SLC7A7. The mean expression of UTS2 was higher in CHB (25.8-fold change compared to CEU), while the expression of UGT2B17 and SLC7A7 was higher in CEU (3.2- and 2.2-fold change, respectively). In the next validation step, two transcripts were verified in the primary biological material. As an ultimate result of our study, two mRNA markers (UTS2 and UGT2B17) exhibiting population differences in the expression level in both B-cell line and in the blood were identified. Further statistical analysis confirmed the discriminatory potential of these two markers. CONCLUSIONS: An inter-population differences on the level of gene expression were identified in both B-cell lines and peripheral blood samples. These findings may have a practical application in the field of forensic science. In particular, these transcripts, targeted by specific probes, may be used as population-specific targets in the efforts aiming to separate mixture of blood from individuals of different populations. Notwithstanding, these results have to be confirmed on extended population group.

A Bayesian semiparametric factor analysis model for subtype identification.

Disease subtype identification (clustering) is an important problem in biomedical research. Gene expression profiles are commonly utilized to infer disease subtypes, which often lead to biologically meaningful insights into disease. Despite many successes, existing clustering methods may not perform well when genes are highly correlated and many uninformative genes are included for clustering due to the high dimensionality. In this article, we introduce a novel subtype identification method in the Bayesian setting based on gene expression profiles. This method, called BCSub, adopts an innovative semiparametric Bayesian factor analysis model to reduce the dimension of the data to a few factor scores for clustering. Specifically, the factor scores are assumed to follow the Dirichlet process mixture model in order to induce clustering. Through extensive simulation studies, we show that BCSub has improved performance over commonly used clustering methods. When applied to two gene expression datasets, our model is able to identify subtypes that are clinically more relevant than those identified from the existing methods.

A Dirichlet process mixture model for clustering longitudinal gene expression data.

Subgroup identification (clustering) is an important problem in biomedical research. Gene expression profiles are commonly utilized to define subgroups. Longitudinal gene expression profiles might provide additional information on disease progression than what is captured by baseline profiles alone. Therefore, subgroup identification could be more accurate and effective with the aid of longitudinal gene expression data. However, existing statistical methods are unable to fully utilize these data for patient clustering. In this article, we introduce a novel clustering method in the Bayesian setting based on longitudinal gene expression profiles. This method, called BClustLonG, adopts a linear mixed-effects framework to model the trajectory of genes over time, while clustering is jointly conducted based on the regression coefficients obtained from all genes. In order to account for the correlations among genes and alleviate the high dimensionality challenges, we adopt a factor analysis model for the regression coefficients. The Dirichlet process prior distribution is utilized for the means of the regression coefficients to induce clustering. Through extensive simulation studies, we show that BClustLonG has improved performance over other clustering methods. When applied to a dataset of severely injured (burn or trauma) patients, our model is able to identify interesting subgroups. Copyright © 2017 John Wiley & Sons, Ltd.

Similarity of markers identified from cancer gene expression studies: observations from GEO.

Gene expression profiling has been extensively conducted in cancer research. The analysis of multiple independent cancer gene expression datasets may provide additional information and complement single-dataset analysis. In this study, we conduct multi-dataset analysis and are interested in evaluating the similarity of cancer-associated genes identified from different datasets. The first objective of this study is to briefly review some statistical methods that can be used for such evaluation. Both marginal analysis and joint analysis methods are reviewed. The second objective is to apply those methods to 26 Gene Expression Omnibus (GEO) datasets on five types of cancers. Our analysis suggests that for the same cancer, the marker identification results may vary significantly across datasets, and different datasets share few common genes. In addition, datasets on different cancers share few common genes. The shared genetic basis of datasets on the same or different cancers, which has been suggested in the literature, is not observed in the analysis of GEO data.

FAM161A and TTC8 are Differentially Expressed in Non-Allelelic Early Onset Retinal Degeneration.

Ciliary genes FAM161A and TTC8 have been implicated in retinal degeneration (RD) in humans and in dogs. The identification of FAM161A and TTC8 mutations in canine RD is exciting as there is the potential to develop novel large animal models for RD. However, the disease phenotypes in the dog and the roles of abnormal genes in disease pathology have yet to be fully characterized. The present study evaluated the expression patterns of FAM161A and TTC8 during normal retinal development in dogs, and in three non-allelic, early onset canine RD models at critical time points of the disease: RCD1, XLPRA2 and ERD. Both genes were differentially expressed in RCD1 and ERD, but not in XLPRA2. These results add evidence to the hypothesis that (a) mutations in many retinal genes have a cascade effect on the expression of multiple, possibly unrelated genes and (b) a large number and wide range of genes probably contribute to RD in general.

Association between PDE4D gene and ischemic stroke: recent advancements.

Stroke is a severe complication and a leading cause of death worldwide and genetic studies among different ethnicities has provided the basis for involvement of phosphodiesterase 4D (PDE4D) gene in cerebrovascular diseases. Recent advancements have evaluated the role of this gene in stroke and these studies have provided a stronger support for the involvement of this gene in stroke development and few studies also suggest that it may influence outcome. Furthermore, case-control studies and meta-analysis studies have provided strong evidence for certain variants in PDE4D to predispose to stroke only among certain ethnicities. Thus, this review focuses on recent progress made in PDE4D gene research involving genetic, molecular and pharmacological aspect. A strong conclusion has emerged that clearly indicates a pivotal role played by this gene in ischemic stroke globally. Studies have also noticeably highlighted that PDE4D gene/pathway can be a suitable drug target for managing stroke; however, a more comprehensive research is still required to understand the molecular and cellular intricacies this gene plays in stroke development, progression and its outcome.

Decoding heterogeneous and coordinated tissue architecture in glioblastoma using spatial transcriptomics.

Glioblastoma multiforme (GBM) is one of the most lethal brain tumors, characterized by profound heterogeneity. While single-cell transcriptomic studies have revealed extensive intra-tumor heterogeneity, shed light on intra-tumor diversity, spatial intricacies remain largely unexplored. Leveraging clinical GBM specimens, this study employs spatial transcriptomics technology to delve into gene expression heterogeneity. Our investigation unveils a significant enrichment of tissue stem cell signature in regions bordering necrosis and the peritumoral area, positively correlated with the mesenchymal subtype signature. Moreover, upregulated genes in these regions are linked with extracellular matrix (ECM)-receptor interaction, proteoglycans, as well as vascular endothelial growth factor (VEGF) and angiopoietin-Tie (ANGPT) signaling pathways. In contrast, signatures related to glycogen metabolism and oxidative phosphorylation show no relevance to pathological zoning, whereas creatine metabolism signature is notably exclusive to vascular-enriched areas. These spatial profiles not only offer valuable references but also pave the way for future in-depth functional and mechanistic investigations into GBM progression.

Proneural-mesenchymal antagonism dominates the patterns of phenotypic heterogeneity in glioblastoma.

The aggressive nature of glioblastoma (GBM) - one of the deadliest forms of brain tumors - is majorly attributed to underlying phenotypic heterogeneity. Early attempts to classify this heterogeneity at a transcriptomic level in TCGA GBM cohort proposed the existence of four distinct molecular subtypes: Proneural, Neural, Classical, and Mesenchymal. Further, a single-cell RNA sequencing (scRNA-seq) analysis of primary tumors also reported similar four subtypes mimicking neurodevelopmental lineages. However, it remains unclear whether these four subtypes identified via bulk and single-cell transcriptomics are mutually exclusive or not. Here, we perform pairwise correlations among individual genes and gene signatures corresponding to these proposed subtypes and show that the subtypes are not distinctly mutually antagonistic in either TCGA or scRNA-seq data. We observed that the proneural (or neural progenitor-like)-mesenchymal axis is the most prominent antagonistic pair, with the other two subtypes lying on this spectrum. These results are reinforced through a meta-analysis of over 100 single-cell and bulk transcriptomic datasets as well as in terms of functional association with metabolic switching, cell cycle, and immune evasion pathways. Finally, this proneural-mesenchymal antagonistic trend percolates to the association of relevant transcription factors with patient survival. These results suggest rethinking GBM phenotypic characterization for more effective therapeutic targeting efforts.

Elucidating immune-related gene transcriptional programs via factorization of large-scale RNA-profiles.

Recent developments in immunotherapy, including immune checkpoint blockade (ICB) and adoptive cell therapy (ACT), have encountered challenges such as immune-related adverse events and resistance, especially in solid tumors. To advance the field, a deeper understanding of the molecular mechanisms behind treatment responses and resistance is essential. However, the lack of functionally characterized immune-related gene sets has limited data-driven immunological research. To address this gap, we adopted non-negative matrix factorization on 83 human bulk RNA sequencing (RNA-seq) datasets and constructed 28 immune-specific gene sets. After rigorous immunologist-led manual annotations and orthogonal validations across immunological contexts and functional omics data, we demonstrated that these gene sets can be applied to refine pan-cancer immune subtypes, improve ICB response prediction and functionally annotate spatial transcriptomic data. These functional gene sets, informing diverse immune states, will advance our understanding of immunology and cancer research.

Extensive dynamic changes in the human transcriptome and its circadian organization during prolonged bed rest.

Physiological and molecular processes including the transcriptome change across the 24-h day, driven by molecular circadian clocks and behavioral and systemic factors. It is not known how the temporal organization of the human transcriptome responds to a long-lasting challenge. This may, however, provide insights into adaptation, disease, and recovery. We investigated the human 24-h time series transcriptome in 20 individuals during a 90-day constant bed rest protocol. We show that the protocol affected 91% of the transcriptome with 76% of the transcriptome still affected after 10 days of recovery. Dimensionality-reduction approaches revealed that many affected transcripts were associated with mRNA translation and immune function. The number, amplitude, and phase of rhythmic transcripts, including clock genes, varied significantly across the challenge. These findings of long-lasting changes in the temporal organization of the transcriptome have implications for understanding the mechanisms underlying health consequences of conditions such as microgravity and bed rest.

Detailed survey of an in vitro intestinal epithelium model by single-cell transcriptomics.

The co-culture of two adult human colorectal cancer cell lines, Caco-2 and HT29, on Transwell is commonly used as an in vitro gut mimic, yet the translatability of insights from such a system to adult human physiological contexts is not fully characterized. Here, we used single-cell RNA sequencing on the co-culture to obtain a detailed survey of cell type heterogeneity in the system and conducted a holistic comparison with human physiology. We identified the intestinal stem cell-, transit amplifying-, enterocyte-, goblet cell-, and enteroendocrine-like cells in the system. In general, the co-culture was fetal intestine-like, with less variety of gene expression compared to the adult human gut. Transporters for major types of nutrients were found in the majority of the enterocytes-like cells in the system. TLR 4 was not expressed in the sample, indicating that the co-culture model is incapable of mimicking the innate immune aspect of the human epithelium.

Single-cell transcriptional landscapes of bovine peri-implantation development.

Supporting healthy pregnancy outcomes requires a comprehensive understanding of the molecular and cellular programs of peri-implantation development, when most pregnancy failure occurs. Here, we present single-cell transcriptomes of bovine peri-implantation embryo development at day 12, 14, 16, and 18 post-fertilization. We defined the cellular composition and gene expression of embryonic disc, hypoblast, and trophoblast lineages in bovine peri-implantation embryos, and identified markers and pathway signaling that represent distinct stages of bovine peri-implantation lineages; the expression of selected markers was validated in peri-implantation embryos. Using detailed time-course transcriptomic analyses, we revealed a previously unrecognized primitive trophoblast cell lineage. We also characterized conserved and divergence peri-implantation lineage programs between bovine and other mammalian species. Finally, we established cell-cell communication signaling underlies embryonic and extraembryonic cell interaction to ensure proper early development. These data provide foundational information to discover essential biological signaling underpinning bovine peri-implantation development.

Experimental and phylogenetic evidence for correlated gene expression evolution in endometrial and skin fibroblasts.

Gene expression change is a dominant mode of evolution. Mutations, however, can affect gene expression in multiple cell types. Therefore, gene expression evolution in one cell type can lead to similar gene expression changes in another cell type. Here, we test this hypothesis by investigating dermal skin fibroblasts (SFs) and uterine endometrial stromal fibroblasts (ESFs). The comparative dataset consists of transcriptomes from cultured SF and ESF of nine mammalian species. We find that evolutionary changes in gene expression in SF and ESF are highly correlated. The experimental dataset derives from a SCID mouse strain selected for slow cancer growth leading to substantial gene expression changes in SFs. We compared the gene expression profiles of SF with that of ESF and found a significant correlation between them. We discuss the implications of these findings for the evolutionary correlation between placental invasiveness and vulnerability to metastatic cancer.

Reanalysis of primate brain circadian transcriptomics reveals connectivity-related oscillations.

Research shows that brain circuits controlling vital physiological processes are closely linked with endogenous time-keeping systems. In this study, we aimed to examine oscillatory gene expression patterns of well-characterized neuronal circuits by reanalyzing publicly available transcriptomic data from a spatiotemporal gene expression atlas of a non-human primate. Unexpectedly, brain structures known for regulating circadian processes (e.g., hypothalamic nuclei) did not exhibit robust cycling expression. In contrast, basal ganglia nuclei, not typically associated with circadian physiology, displayed the most dynamic cycling behavior of its genes marked by sharp temporally defined expression peaks. Intriguingly, the mammillary bodies, considered hypothalamic nuclei, exhibited gene expression patterns resembling the basal ganglia, prompting reevaluation of their classification. Our results emphasize the potential for high throughput circadian gene expression analysis to deepen our understanding of the functional synchronization across brain structures that influence physiological processes and resulting complex behaviors.