Showing papers in "Frontiers in Genetics in 2021"

Telomere Length as a Marker of Biological Age: State-of-the-Art, Open Issues, and Future Perspectives.

Abstract: Telomere shortening is a well-known hallmark of both cellular senescence and organismal aging. An accelerated rate of telomere attrition is also a common feature of age-related diseases. Therefore, telomere length (TL) has been recognized for a long time as one of the best biomarkers of aging. Recent research findings, however, indicate that TL per se can only allow a rough estimate of aging rate and can hardly be regarded as a clinically important risk marker for age-related pathologies and mortality. Evidence is obtained that other indicators such as certain immune parameters, indices of epigenetic age, etc., could be stronger predictors of the health status and the risk of chronic disease. However, despite these issues and limitations, TL remains to be very informative marker in accessing the biological age when used along with other markers such as indices of homeostatic dysregulation, frailty index, epigenetic clock, etc. This review article is aimed at describing the current state of the art in the field and at discussing recent research findings and divergent viewpoints regarding the usefulness of leukocyte TL for estimating the human biological age.

Nanoparticle Delivery of CRISPR/Cas9 for Genome Editing.

Abstract: The emerging clustered regularly interspaced short palindromic repeat (CRISPR)/CRISPR-associated system (Cas) gene-editing system represents a promising tool for genome manipulation. However, its low intracellular delivery efficiency severely compromises its use and potency for clinical applications. Nanocarriers, such as liposomes, polymers, and inorganic nanoparticles, have shown great potential for gene delivery. The remarkable development of nanoparticles as non-viral carriers for the delivery of the CRISPR/Cas9 system has shown great promise for therapeutic applications. In this review, we briefly summarize the delivery components of the CRISPR/Cas9 system and report on the progress of nano-system development for CRISPR/Cas9 delivery. We also compare the advantages of various nano-delivery systems and their applications to deliver CRISPR/Cas9 for disease treatment. Nano-delivery systems can be modified to fulfill the tasks of targeting cells or tissues. We primarily emphasize the novel exosome-based CRISPR/Cas9 delivery system. Overall, we review the challenges, development trends, and application prospects of nanoparticle-based technology for CRISPR/Cas9 delivery.

ggVennDiagram: An Intuitive, Easy-to-Use, and Highly Customizable R Package to Generate Venn Diagram.

Abstract: Venn diagrams are widely used diagrams to show the set relationships in biomedical studies. In this study, we developed ggVennDiagram, an R package that could automatically generate high-quality Venn diagrams with two to seven sets. The ggVennDiagram is built based on ggplot2, and it integrates the advantages of existing packages, such as venn, RVenn, VennDiagram, and sf. Satisfactory results can be obtained with minimal configurations. Furthermore, we designed comprehensive objects to store the entire data of the Venn diagram, which allowed free access to both intersection values and Venn plot sub-elements, such as set label/edge and region label/filling. Therefore, high customization of every Venn plot sub-element can be fulfilled without increasing the cost of learning when the user is familiar with ggplot2 methods. To date, ggVennDiagram has been cited in more than 10 publications, and its source code repository has been starred by more than 140 GitHub users, suggesting a great potential in applications. The package is an open-source software released under the GPL-3 license, and it is freely available through CRAN (https://cran.r-project.org/package=ggVennDiagram).

Use ggbreak to Effectively Utilize Plotting Space to Deal With Large Datasets and Outliers.

Abstract: With the rapid increase of large-scale datasets, biomedical data visualization is facing challenges. The data may be large, have different orders of magnitude, contain extreme values, and the data distribution is not clear. Here we present an R package ggbreak that allows users to create broken axes using ggplot2 syntax. It can effectively use the plotting area to deal with large datasets (especially for long sequential data), data with different magnitudes, and contain outliers. The ggbreak package increases the available visual space for a better presentation of the data and detailed annotation, thus improves our ability to interpret the data. The ggbreak package is fully compatible with ggplot2 and it is easy to superpose additional layers and applies scale and theme to adjust the plot using the ggplot2 syntax. The ggbreak package is open-source software released under the Artistic-2.0 license, and it is freely available on CRAN (https://CRAN.R-project.org/package=ggbreak) and Github (https://github.com/YuLab-SMU/ggbreak).

Heterosis and Hybrid Crop Breeding: A Multidisciplinary Review.

Abstract: Although hybrid crop varieties are among the most popular agricultural innovations, the rationale for hybrid crop breeding is sometimes misunderstood. Hybrid breeding is slower and more resource-intensive than inbred breeding, but it allows systematic improvement of a population by recurrent selection and exploitation of heterosis simultaneously. Inbred parental lines can identically reproduce both themselves and their F1 progeny indefinitely, whereas outbred lines cannot, so uniform outbred lines must be bred indirectly through their inbred parents to harness heterosis. Heterosis is an expected consequence of whole-genome non-additive effects at the population level over evolutionary time. Understanding heterosis from the perspective of molecular genetic mechanisms alone may be elusive, because heterosis is likely an emergent property of populations. Hybrid breeding is a process of recurrent population improvement to maximize hybrid performance. Hybrid breeding is not maximization of heterosis per se, nor testing random combinations of individuals to find an exceptional hybrid, nor using heterosis in place of population improvement. Though there are methods to harness heterosis other than hybrid breeding, such as use of open-pollinated varieties or clonal propagation, they are not currently suitable for all crops or production environments. The use of genomic selection can decrease cycle time and costs in hybrid breeding, particularly by rapidly establishing heterotic pools, reducing testcrossing, and limiting the loss of genetic variance. Open questions in optimal use of genomic selection in hybrid crop breeding programs remain, such as how to choose founders of heterotic pools, the importance of dominance effects in genomic prediction, the necessary frequency of updating the training set with phenotypic information, and how to maintain genetic variance and prevent fixation of deleterious alleles.

Use of multivariable Mendelian randomization to address biases due to competing risk before recruitment

Abstract: Background: Mendelian randomization (MR) provides unconfounded estimates. MR is open to selection bias when the underlying sample is selected on surviving to recruitment on the genetically instrumented exposure and competing risk of the outcome. Few methods to address this bias exist. Methods: We show that this selection bias can sometimes be addressed by adjusting for common causes of survival and outcome. We use multivariable MR to obtain a corrected MR estimate for statins on stroke. Statins affect survival, and stroke typically occurs later in life than ischemic heart disease (IHD), making estimates for stroke open to bias from competing risk. Results: In univariable MR in the UK Biobank, genetically instrumented statins did not protect against stroke [odds ratio (OR) 1.33, 95% confidence interval (CI) 0.80-2.20] but did in multivariable MR (OR 0.81, 95% CI 0.68-0.98) adjusted for major causes of survival and stroke [blood pressure, body mass index (BMI), and smoking initiation] with a multivariable Q-statistic indicating absence of selection bias. However, the MR estimate for statins on stroke using MEGASTROKE remained positive and the Q statistic indicated pleiotropy. Conclusion: MR studies of harmful exposures on late-onset diseases with shared etiology need to be conceptualized within a mechanistic understanding so as to identify any potential bias due to survival to recruitment on both genetically instrumented exposure and competing risk of the outcome, which may then be investigated using multivariable MR or estimated analytically and results interpreted accordingly.

Graph Neural Networks and Their Current Applications in Bioinformatics

Abstract: Graph neural networks (GNNs), as a branch of deep learning in non-Euclidean space, perform particularly well in various tasks that process graph structure data. With the rapid accumulation of biological network data, GNNs have also become an important tool in bioinformatics. In this research, a systematic survey of GNNs and their advances in bioinformatics is presented from multiple perspectives. We first introduce some commonly used GNN models and their basic principles. Then, three representative tasks are proposed based on the three levels of structural information that can be learned by GNNs: node classification, link prediction, and graph generation. Meanwhile, according to the specific applications for various omics data, we categorize and discuss the related studies in three aspects: disease prediction, drug discovery, and biomedical imaging. Based on the analysis, we provide an outlook on the shortcomings of current studies and point out their developing prospect. Although GNNs have achieved excellent results in many biological tasks at present, they still face challenges in terms of low-quality data processing, methodology, and interpretability and have a long road ahead. We believe that GNNs are potentially an excellent method that solves various biological problems in bioinformatics research.

Th2/Th1 Cytokine Imbalance Is Associated With Higher COVID-19 Risk Mortality

Abstract: A major component of COVID-19 severe respiratory syndrome is the patient's immune response to the SARS-CoV-2 virus and the consequential multi-organ inflammatory response. Several studies suggested a potential role of CD4+ T cells in COVID-19 severe respiratory syndrome. We first hypothesized that there is a type 2 helper (Th2)/type 1 helper (Th1) imbalance in older age, male, asthma, smokers, and high ACE2 expression phenotype in the airway of non-infected patients. Next, we hypothesized that a Th2/Th1 imbalance may predict higher mortality in COVID-19 infected hospitalized patients with and without patient reported current asthma. We first analyzed publicly available gene expression from the sputum of 118 moderate-to-severe asthma patients and 21 healthy controls, and from nasal epithelium of 26 healthy current smokers and 21 healthy never smokers. Secondly, we profiled 288 new serum proteomics samples measured at admission from patients hospitalized within the Mount Sinai Health System with positive SARS-CoV-2 infection. We first computed Th1 and Th2 pathway enrichment scores by gene set variation analysis and then compared the differences in Th2 and Th1 pathway scores between patients that died compared to those that survived, by linear regression. The level of Th2/Th1 imbalance, as determined by the enrichment score, was associated with age, sex, and ACE2 expression in sputum, and with active smoking status in nasal epithelium (p < 0.05). Th2/Th1 imbalance at hospital admission in sera of patients was not significantly associated with death from COVID-19 (p = 0.11), unless evaluated in the asthmatic strata (p = 0.01). Using a similar approach we also observed a higher Th17/Th1 cytokine imbalance in all deceased patients compared to those that survived (p < 0.001), as well as in the asthmatic strata only (p < 0.01). Th2/Th1 imbalance is higher in the sera of asthma patients at admission that do not survive COVID-19, suggesting that the Th2/Th1 interplay may affect patient outcomes in SARS-CoV2 infection. In addition, we report that Th17/Th1 imbalance is increased in all patients that die of COVID-19.

Corrigendum: Multi-Omics Analysis of Fatty Alcohol Production in Engineered Yeasts Saccharomyces cerevisiae and Yarrowia lipolytica (Front Genet, (2019), 10, (747), 10.3389/fgene.2019.00747)

Abstract: Hong-Lei Wang was not included as author in the published article. The corrected Author Contributions Statement appears below. Corrected Author Contributions Statement:.

Network Analysis and Transcriptome Profiling Identify Autophagic and Mitochondrial Dysfunctions in SARS-CoV-2 Infection

Abstract: Analyzing host cells' transcriptional response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection will help delineate biological processes underlying viral pathogenesis. First, analysis of expression profiles of lung cell lines A549 and Calu3 revealed upregulation of antiviral interferon signaling genes in response to all three SARS-CoV-2, MERS-CoV, or influenza A virus (IAV) infections. However, perturbations in expression of genes involved in inflammatory, mitochondrial, and autophagy processes were specifically observed in SARS-CoV-2-infected cells. Next, a validation study in infected human nasopharyngeal samples also revealed perturbations in autophagy and mitochondrial processes. Specifically, mTOR expression, mitochondrial ribosomal, mitochondrial complex I, lysosome acidification, and mitochondrial fission promoting genes were concurrently downregulated in both infected cell lines and human samples. SARS-CoV-2 infection impeded autophagic flux either by upregulating GSK3B in lung cell lines or by downregulating autophagy genes, SNAP29, and lysosome acidification genes in human samples, contributing to increased viral replication. Therefore, drugs targeting lysosome acidification or autophagic flux could be tested as intervention strategies. Finally, age-stratified SARS-CoV-2-positive human data revealed impaired upregulation of chemokines, interferon-stimulated genes, and tripartite motif genes that are critical for antiviral signaling. Together, this analysis has revealed specific aspects of autophagic and mitochondrial function that are uniquely perturbed in SARS-CoV-2-infected host cells.

Gene Size Matters: An Analysis of Gene Length in the Human Genome.

Abstract: While it is expected for gene length to be associated with factors such as intron number and evolutionary conservation, we are yet to understand the connections between gene length and function in the human genome In this study, we show that, as expected, there is a strong positive correlation between gene length, transcript length, and protein size as well as a correlation with the number of genetic variants and introns Among tissue-specific genes, we find that the longest transcripts tend to be expressed in the blood vessels, nerves, thyroid, cervix uteri, and the brain, while the smallest transcripts tend to be expressed in the pancreas, skin, stomach, vagina, and testis We report, as shown previously, that natural selection suppresses changes for genes with longer transcripts and promotes changes for genes with smaller transcripts We also observe that genes with longer transcripts tend to have a higher number of co-expressed genes and protein-protein interactions, as well as more associated publications In the functional analysis, we show that bigger transcripts are often associated with neuronal development, while smaller transcripts tend to play roles in skin development and in the immune system Furthermore, pathways related to cancer, neurons, and heart diseases tend to have genes with longer transcripts, with smaller transcripts being present in pathways related to immune responses and neurodegenerative diseases Based on our results, we hypothesize that longer genes tend to be associated with functions that are important in the early development stages, while smaller genes tend to play a role in functions that are important throughout the whole life, like the immune system, which requires fast responses

COVID-19 and SARS-CoV-2 Variants: Current Challenges and Health Concern

Abstract: The ongoing coronavirus disease 2019 (COVID-19) outbreak in Wuhan, China, was triggered and unfolded quickly throughout the globe by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The new virus, transmitted primarily through inhalation or contact with infected droplets, seems very contagious and pathogenic, with an incubation period varying from 2 to 14 days. The epidemic is an ongoing public health problem that challenges the present global health system. A worldwide social and economic stress has been observed. The transitional source of origin and its transport to humans is unknown, but speedy human transportation has been accepted extensively. The typical clinical symptoms of COVID-19 are almost like colds. With case fatality rates varying from 2 to 3 percent, a small number of patients may experience serious health problems or even die. To date, there is a limited number of antiviral agents or vaccines for the treatment of COVID-19. The occurrence and pathogenicity of COVID-19 infection are outlined and comparatively analyzed, given the outbreak's urgency. The recent developments in diagnostics, treatment, and marketed vaccine are discussed to deal with this viral outbreak. Now the scientist is concerned about the appearance of several variants over the globe and the efficacy of the vaccine against these variants. There is a need for consistent monitoring of the virus epidemiology and surveillance of the ongoing variant and related disease severity.

Adapting CRISPR/Cas9 System for Targeting Mitochondrial Genome.

Abstract: Gene editing of the mitochondrial genome using the CRISPR-Cas9 system is highly challenging mainly due to sub-efficient delivery of guide RNA and Cas9 enzyme complexes into the mitochondria. In this study, we were able to perform gene editing in the mitochondrial DNA by appending an NADH-ubiquinone oxidoreductase chain 4 (ND4) targeting guide RNA to an RNA transport-derived stem loop element (RP-loop) and expressing the Cas9 enzyme with a preceding mitochondrial localization sequence. We observe mitochondrial colocalization of RP-loop gRNA and a marked reduction of ND4 expression in the cells carrying a 11205G variant in their ND4 sequence coincidently decreasing the mtDNA levels. This proof-of-concept study suggests that a stem-loop element added sgRNA can be transported to the mitochondria and functionally interact with Cas9 to mediate sequence-specific mtDNA cleavage. Using this novel approach to target the mtDNA, our results provide further evidence that CRISPR-Cas9-mediated gene editing might potentially be used to treat mitochondrial-related diseases.

Environmental Risk Factors for Schizophrenia and Bipolar Disorder and Their Relationship to Genetic Risk: Current Knowledge and Future Directions.

Abstract: Schizophrenia (SZ) and bipolar disorder (BD) are severe psychiatric disorders which result from complex interplay between genetic and environmental factors. It is well-established that they are highly heritable disorders, and considerable progress has been made identifying their shared and distinct genetic risk factors. However, the 15-40% of risk that is derived from environmental sources is less definitively known. Environmental factors that have been repeatedly investigated and often associated with SZ include: obstetric complications, infections, winter or spring birth, migration, urban living, childhood adversity, and cannabis use. There is evidence that childhood adversity and some types of infections are also associated with BD. Evidence for other risk factors in BD is weaker due to fewer studies and often smaller sample sizes. Relatively few environmental exposures have ever been examined for SZ or BD, and additional ones likely remain to be discovered. A complete picture of how genetic and environmental risk factors confer risk for these disorders requires an understanding of how they interact. Early gene-by-environment interaction studies for both SZ and BD often involved candidate genes and were underpowered. Larger samples with genome-wide data and polygenic risk scores now offer enhanced prospects to reveal genetic interactions with environmental exposures that contribute to risk for these disorders. Overall, although some environmental risk factors have been identified for SZ, few have been for BD, and the extent to which these account for the total risk from environmental sources remains unknown. For both disorders, interactions between genetic and environmental risk factors are also not well understood and merit further investigation. Questions remain regarding the mechanisms by which risk factors exert their effects, and the ways in which environmental factors differ by sex. Concurrent investigations of environmental and genetic risk factors in SZ and BD are needed as we work toward a more comprehensive understanding of the ways in which these disorders arise.

Spliceosomal snRNA Epitranscriptomics.

Abstract: Small nuclear RNAs (snRNAs) are critical components of the spliceosome that catalyze the splicing of pre-mRNA. snRNAs are each complexed with many proteins to form RNA-protein complexes, termed as small nuclear ribonucleoproteins (snRNPs), in the cell nucleus. snRNPs participate in pre-mRNA splicing by recognizing the critical sequence elements present in the introns, thereby forming active spliceosomes. The recognition is achieved primarily by base-pairing interactions (or nucleotide-nucleotide contact) between snRNAs and pre-mRNA. Notably, snRNAs are extensively modified with different RNA modifications, which confer unique properties to the RNAs. Here, we review the current knowledge of the mechanisms and functions of snRNA modifications and their biological relevance in the splicing process.

MeCP2: The Genetic Driver of Rett Syndrome Epigenetics

Abstract: Mutations in methyl CpG binding protein 2 (MeCP2) are the major cause of Rett syndrome (RTT), a rare neurodevelopmental disorder with a notable period of developmental regression following apparently normal initial development. Such MeCP2 alterations often result in changes to DNA binding and chromatin clustering ability, and in the stability of this protein. Among other functions, MeCP2 binds to methylated genomic DNA, which represents an important epigenetic mark with broad physiological implications, including neuronal development. In this review, we will summarize the genetic foundations behind RTT, and the variable degrees of protein stability exhibited by MeCP2 and its mutated versions. Also, past and emerging relationships that MeCP2 has with mRNA splicing, miRNA processing, and other non-coding RNAs (ncRNA) will be explored, and we suggest that these molecules could be missing links in understanding the epigenetic consequences incurred from genetic ablation of this important chromatin modifier. Importantly, although MeCP2 is highly expressed in the brain, where it has been most extensively studied, the role of this protein and its alterations in other tissues cannot be ignored and will also be discussed. Finally, the additional complexity to RTT pathology introduced by structural and functional implications of the two MeCP2 isoforms (MeCP2-E1 and MeCP2-E2) will be described. Epigenetic therapeutics are gaining clinical popularity, yet treatment for Rett syndrome is more complicated than would be anticipated for a purely epigenetic disorder, which should be taken into account in future clinical contexts.

Transmembrane serine protease 2 Polymorphisms and Susceptibility to Severe Acute Respiratory Syndrome Coronavirus Type 2 Infection: A German Case-Control Study

Abstract: The transmembrane serine protease 2 (TMPRSS2) is the major host protease that enables entry of the severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) into host cells by spike (S) protein priming. Single nucleotide polymorphisms (SNPs) in the gene TMPRSS2 have been associated with susceptibility to and severity of H1N1 or H1N9 influenza A virus infections. Functional variants may influence SARS-CoV-2 infection risk and severity of Coronavirus disease 2019 (COVID-19) as well. Therefore, we analyzed the role of SNPs in the gene TMPRSS2 in a German case-control study. We performed genotyping of the SNPs rs2070788, rs383510, and rs12329760 in the gene TMPRSS2 in 239 SARS-CoV-2-positive and 253 SARS-CoV-2-negative patients. We analyzed the association of the SNPs with susceptibility to SARS-CoV-2 infection and severity of COVID-19. SARS-CoV-2-positive and SARS-CoV-2-negative patients did not differ regarding their demographics. The CC genotype of TMPRSS2 rs383510 was associated with a 1.73-fold increased SARS-CoV-2 infection risk, but was not correlated to severity of COVID-19. Neither TMPRSS2 rs2070788 nor rs12329760 polymorphisms were related to SARS-CoV-2 infection risk or severity of COVID-19. In a multivariable analysis (MVA), the rs383510 CC genotype remained an independent predictor for a 2-fold increased SARS-CoV-2 infection risk. In summary, our report appears to be the first showing that the intron variant rs383510 in the gene TMPRSS2 is associated with an increased risk to SARS-CoV-2 infection in a German cohort.

Identification of Protein Subcellular Localization With Network and Functional Embeddings.

Abstract: The functions of proteins are mainly determined by their subcellular localizations in cells. Currently, many computational methods for predicting the subcellular localization of proteins have been proposed. However, these methods require further improvement, especially when used in protein representations. In this study, we present an embedding-based method for predicting the subcellular localization of proteins. We first learn the functional embeddings of KEGG/GO terms, which are further used in representing proteins. Then, we characterize the network embeddings of proteins on a protein-protein network. The functional and network embeddings are combined as novel representations of protein locations for the construction of the final classification model. In our collected benchmark dataset with 4,861 proteins from 16 locations, the best model shows a Matthews correlation coefficient of 0.872 and is thus superior to multiple conventional methods.

Candidate Genes for Age at Menarche Are Associated With Uterine Leiomyoma.

Abstract: Age at menarche (AAM) is an important marker of the pubertal development and function of the hypothalamic-pituitary-ovarian system. It was reported as a possible factor for a risk of uterine leiomyoma (UL). However, while more than 350 loci for AAM have been determined by genome-wide association studies (GWASs) to date, no studies of these loci for their association with UL have been conducted so far. In this study, we analyzed 52 candidate loci for AAM for possible association with UL in a sample of 569 patients and 981 controls. The results of the study suggested that 23 out of the 52 studied polymorphisms had association with UL. Locus rs7759938 LIN28B was individually associated with the disease according to the dominant model. Twenty loci were associated with UL within 11 most significant models of intergenic interactions. Nine loci involved in 16 most significant models of interactions between single-nucleotide polymorphism (SNP), induced abortions, and chronic endometritis were associated with UL. Among the 23 loci associated with UL, 16 manifested association also with either AAM (7 SNPs) or height and/or body mass index (BMI) (13 SNPs). The above 23 SNPs and 514 SNPs linked to them have non-synonymous, regulatory, and expression quantitative trait locus (eQTL) significance for 35 genes, which play roles in the pathways related to development of the female reproductive organs and hormone-mediated signaling [false discovery rate (FDR) ≤ 0.05]. This is the first study reporting associations of candidate genes for AAM with UL.

Metabolomics in the Diagnosis and Prognosis of COVID-19

Abstract: Coronavirus disease 2019 (COVID-19) pandemic triggered an unprecedented global effort in developing rapid and inexpensive diagnostic and prognostic tools. Since the genome of SARS-CoV-2 was uncovered, detection of viral RNA by RT-qPCR has played the most significant role in preventing the spread of the virus through early detection and tracing of suspected COVID-19 cases and through screening of at-risk population. However, a large number of alternative test methods based on SARS-CoV-2 RNA or proteins or host factors associated with SARS-CoV-2 infection have been developed and evaluated. The application of metabolomics in infectious disease diagnostics is an evolving area of science that was boosted by the urgency of COVID-19 pandemic. Metabolomics approaches that rely on the analysis of volatile organic compounds exhaled by COVID-19 patients hold promise for applications in a large-scale screening of population in point-of-care (POC) setting. On the other hand, successful application of mass-spectrometry to detect specific spectral signatures associated with COVID-19 in nasopharyngeal swab specimens may significantly save the cost and turnaround time of COVID-19 testing in the diagnostic microbiology and virology laboratories. Active research is also ongoing on the discovery of potential metabolomics-based prognostic markers for the disease that can be applied to serum or plasma specimens. Several metabolic pathways related to amino acid, lipid and energy metabolism were found to be affected by severe disease with COVID-19. In particular, tryptophan metabolism via the kynurenine pathway were persistently dysregulated in several independent studies, suggesting the roles of several metabolites of this pathway such as tryptophan, kynurenine and 3-hydroxykynurenine as potential prognostic markers of the disease. However, standardization of the test methods and large-scale clinical validation are necessary before these tests can be applied in a clinical setting. With rapidly expanding data on the metabolic profiles of COVID-19 patients with varying degrees of severity, it is likely that metabolomics will play an important role in near future in predicting the outcome of the disease with a greater degree of certainty.

Epigenetic Regulation of Genomic Stability by Vitamin C

Abstract: DNA methylation plays an important role in the maintenance of genomic stability. Ten-eleven translocation proteins (TETs) are a family of iron (Fe2+) and α-KG -dependent dioxygenases that regulate DNA methylation levels by oxidizing 5-methylcystosine (5mC) to generate 5-hydroxymethylcytosine (5hmC), 5-formylcytosine (5fC), and 5-carboxylcytosine (5caC). These oxidized methylcytosines promote passive demethylation upon DNA replication, or active DNA demethylation, by triggering base excision repair and replacement of 5fC and 5caC with an unmethylated cytosine. Several studies over the last decade have shown that loss of TET function leads to DNA hypermethylation and increased genomic instability. Vitamin C, a cofactor of TET enzymes, increases 5hmC formation and promotes DNA demethylation, suggesting that this essential vitamin, in addition to its antioxidant properties, can also directly influence genomic stability. This review will highlight the functional role of DNA methylation, TET activity and vitamin C, in the crosstalk between DNA methylation and DNA repair.

Human Aquaporins: Functional Diversity and Potential Roles in Infectious and Non-infectious Diseases

Abstract: Aquaporins (AQPs) are integral membrane proteins and found in all living organisms from bacteria to human. AQPs mainly involved in the transmembrane diffusion of water as well as various small solutes in a bidirectional manner are widely distributed in various human tissues. Human contains 13 AQPs (AQP0-AQP12) which are divided into three sub-classes namely orthodox aquaporin (AQP0, 1, 2, 4, 5, 6, and 8), aquaglyceroporin (AQP3, 7, 9, and 10) and super or unorthodox aquaporin (AQP11 and 12) based on their pore selectivity. Human AQPs are functionally diverse, which are involved in wide variety of non-infectious diseases including cancer, renal dysfunction, neurological disorder, epilepsy, skin disease, metabolic syndrome, and even cardiac diseases. However, the association of AQPs with infectious diseases has not been fully evaluated. Several studies have unveiled that AQPs can be regulated by microbial and parasitic infections that suggest their involvement in microbial pathogenesis, inflammation-associated responses and AQP-mediated cell water homeostasis. This review mainly aims to shed light on the involvement of AQPs in infectious and non-infectious diseases and potential AQPs-target modulators. Furthermore, AQP structures, tissue-specific distributions and their physiological relevance, functional diversity and regulations have been discussed. Altogether, this review would be useful for further investigation of AQPs as a potential therapeutic target for treatment of infectious as well as non-infectious diseases.

MSU-Net: Multi-Scale U-Net for 2D Medical Image Segmentation.

Abstract: Aiming at the limitation of the convolution kernel with a fixed receptive field and unknown prior to optimal network width in U-Net, multi-scale U-Net (MSU-Net) is proposed by us for medical image segmentation First, multiple convolution sequence is used to extract more semantic features from the images Second, the convolution kernel with different receptive fields is used to make features more diverse The problem of unknown network width is alleviated by efficient integration of convolution kernel with different receptive fields In addition, the multi-scale block is extended to other variants of the original U-Net to verify its universality Five different medical image segmentation datasets are used to evaluate MSU-Net A variety of imaging modalities are included in these datasets, such as electron microscopy, dermoscope, ultrasound, etc Intersection over Union (IoU) of MSU-Net on each dataset are 0771, 0867, 0708, 0900, and 0702, respectively Experimental results show that MSU-Net achieves the best performance on different datasets Our implementation is available at https://githubcom/CN-zdy/MSU_Net

Tumor Microenvironment in Prostate Cancer: Toward Identification of Novel Molecular Biomarkers for Diagnosis, Prognosis, and Therapy Development.

Abstract: Prostate cancer (PCa) is by far the most commonly diagnosed cancer in men worldwide. Despite sensitivity to androgen deprivation, patients with advanced disease eventually develop resistance to therapy and may die of metastatic castration-resistant prostate cancer (mCRPC). A key challenge in the management of PCa is the clinical heterogeneity that is hard to predict using existing biomarkers. Defining molecular biomarkers for PCa that can reliably aid in diagnosis and distinguishing patients who require aggressive therapy from those who should avoid overtreatment is a significant unmet need. Mechanisms underlying the development of PCa are not confined to cancer epithelial cells, but also involve the tumor microenvironment. The crosstalk between epithelial cells and stroma in PCa has been shown to play an integral role in disease progression and metastasis. A number of key markers of reactive stroma has been identified including stem/progenitor cell markers, stromal-derived mediators of inflammation, regulators of angiogenesis, connective tissue growth factors, wingless homologs (Wnts), and integrins. Here, we provide a synopsis of the stromal-epithelial crosstalk in PCa focusing on the relevant molecular biomarkers pertaining to the tumor microenvironment and their role in diagnosis, prognosis, and therapy development.

From GWAS to Gene: Transcriptome-Wide Association Studies and Other Methods to Functionally Understand GWAS Discoveries.

Abstract: Since their inception, genome-wide association studies (GWAS) have identified more than a hundred thousand single nucleotide polymorphism (SNP) loci that are associated with various complex human diseases or traits. The majority of GWAS discoveries are located in non-coding regions of the human genome and have unknown functions. The valley between non-coding GWAS discoveries and downstream affected genes hinders the investigation of complex disease mechanism and the utilization of human genetics for the improvement of clinical care. Meanwhile, advances in high-throughput sequencing technologies reveal important genomic regulatory roles that non-coding regions play in the transcriptional activities of genes. In this review, we focus on data integrative bioinformatics methods that combine GWAS with functional genomics knowledge to identify genetically regulated genes. We categorize and describe two types of data integrative methods. First, we describe fine-mapping methods. Fine-mapping is an exploratory approach that calibrates likely causal variants underneath GWAS signals. Fine-mapping methods connect GWAS signals to potentially causal genes through statistical methods and/or functional annotations. Second, we discuss gene-prioritization methods. These are hypothesis generating approaches that evaluate whether genetic variants regulate genes via certain genetic regulatory mechanisms to influence complex traits, including colocalization, mendelian randomization, and the transcriptome-wide association study (TWAS). TWAS is a gene-based association approach that investigates associations between genetically regulated gene expression and complex diseases or traits. TWAS has gained popularity over the years due to its ability to reduce multiple testing burden in comparison to other variant-based analytic approaches. Multiple types of TWAS methods have been developed with varied methodological designs and biological hypotheses over the past 5 years. We dive into discussions of how TWAS methods differ in many aspects and the challenges that different TWAS methods face. Overall, TWAS is a powerful tool for identifying complex trait-associated genes. With the advent of single-cell sequencing, chromosome conformation capture, gene editing technologies, and multiplexing reporter assays, we are expecting a more comprehensive understanding of genomic regulation and genetically regulated genes underlying complex human diseases and traits in the future.

Regular, Intense Exercise Training as a Healthy Aging Lifestyle Strategy: Preventing DNA Damage, Telomere Shortening and Adverse DNA Methylation Changes Over a Lifetime

Abstract: Exercise training is one of the few therapeutic interventions that improves health span by delaying the onset of age-related diseases and preventing early death. The length of telomeres, the 5'-TTAGGG n -3' tandem repeats at the ends of mammalian chromosomes, is one of the main indicators of biological age. Telomeres undergo shortening with each cellular division. This subsequently leads to alterations in the expression of several genes that encode vital proteins with critical functions in many tissues throughout the body, and ultimately impacts cardiovascular, immune and muscle physiology. The sub-telomeric DNA is comprised of heavily methylated, heterochromatin. Methylation and histone acetylation are two of the most well-studied examples of the epigenetic modifications that occur on histone proteins. DNA methylation is the type of epigenetic modification that alters gene expression without modifying gene sequence. Although diet, genetic predisposition and a healthy lifestyle seem to alter DNA methylation and telomere length (TL), recent evidence suggests that training status or physical fitness are some of the major factors that control DNA structural modifications. In fact, TL is positively associated with cardiorespiratory fitness, physical activity level (sedentary, active, moderately trained, or elite) and training intensity, but is shorter in over-trained athletes. Similarly, somatic cells are vulnerable to exercise-induced epigenetic modification, including DNA methylation. Exercise-training load, however, depends on intensity and volume (duration and frequency). Training load-dependent responses in genomic profiles could underpin the discordant physiological and physical responses to exercise. In the current review, we will discuss the role of various forms of exercise training in the regulation of DNA damage, TL and DNA methylation status in humans, to provide an update on the influence exercise training has on biological aging.

Human Organoids for Predictive Toxicology Research and Drug Development

Abstract: Organoids are three-dimensional structures fabricated in vitro from pluripotent stem cells or adult tissue stem cells via a process of self-organization that results in the formation of organ-specific cell types. Human organoids are expected to mimic complex microenvironments and many of the in vivo physiological functions of relevant tissues, thus filling the translational gap between animals and humans and increasing our understanding of the mechanisms underlying disease and developmental processes. In the last decade, organoid research has attracted increasing attention in areas such as disease modeling, drug development, regenerative medicine, toxicology research, and personalized medicine. In particular, in the field of toxicology, where there are various traditional models, human organoids are expected to blaze a new path in future research by overcoming the current limitations, such as those related to differences in drug responses among species. Here, we discuss the potential usefulness, limitations, and future prospects of human liver, heart, kidney, gut, and brain organoids from the viewpoints of predictive toxicology research and drug development, providing cutting edge information on their fabrication methods and functional characteristics.

GM1 Gangliosidosis—A Mini-Review

Abstract: GM1 gangliosidosis is a progressive, neurosomatic, lysosomal storage disorder caused by mutations in the GLB1 gene encoding the enzyme β-galactosidase. Absent or reduced β-galactosidase activity leads to the accumulation of β-linked galactose-containing glycoconjugates including the glycosphingolipid (GSL) GM1-ganglioside in neuronal tissue. GM1-gangliosidosis is classified into three forms [Type I (infantile), Type II (late-infantile and juvenile), and Type III (adult)], based on the age of onset of clinical symptoms, although the disorder is really a continuum that correlates only partially with the levels of residual enzyme activity. Severe neurocognitive decline is a feature of Type I and II disease and is associated with premature mortality. Most of the disease-causing β-galactosidase mutations reported in the literature are clustered in exons 2, 6, 15, and 16 of the GLB1 gene. So far 261 pathogenic variants have been described, missense/nonsense mutations being the most prevalent. There are five mouse models of GM1-gangliosidosis reported in the literature generated using different targeting strategies of the Glb1 murine locus. Individual models differ in terms of age of onset of the clinical, biochemical, and pathological signs and symptoms, and overall lifespan. However, they do share the major abnormalities and neurological symptoms that are characteristic of the most severe forms of GM1-gangliosidosis. These mouse models have been used to study pathogenic mechanisms, to identify biomarkers, and to evaluate therapeutic strategies. Three GLB1 gene therapy trials are currently recruiting Type I and Type II patients (NCT04273269, NCT03952637, and NCT04713475) and Type II and Type III patients are being recruited for a trial utilizing the glucosylceramide synthase inhibitor, venglustat (NCT04221451).

Transcriptomics of Biostimulation of Plants Under Abiotic Stress

Abstract: Plant biostimulants are compounds, living microorganisms, or their constituent parts that alter plant development programs The impact of biostimulants is manifested in several ways: via morphological, physiological, biochemical, epigenomic, proteomic, and transcriptomic changes For each of these, a response and alteration occur, and these alterations in turn improve metabolic and adaptive performance in the environment Many studies have been conducted on the effects of different biotic and abiotic stimulants on plants, including many crop species However, as far as we know, there are no reviews available that describe the impact of biostimulants for a specific field such as transcriptomics, which is the objective of this review For the commercial registration process of products for agricultural use, it is necessary to distinguish the specific impact of biostimulants from that of other legal categories of products used in agriculture, such as fertilizers and plant hormones For the chemical or biological classification of biostimulants, the classification is seen as a complex issue, given the great diversity of compounds and organisms that cause biostimulation However, with an approach focused on the impact on a particular field such as transcriptomics, it is perhaps possible to obtain a criterion that allows biostimulants to be grouped considering their effects on living systems, as well as the overlap of the impact on metabolism, physiology, and morphology occurring between fertilizers, hormones, and biostimulants

Gene Environment Interactions in the Etiology of Neural Tube Defects

Abstract: Human structural congenital malformations are the leading cause of infant mortality in the United States. Estimates from the United States Center for Disease Control and Prevention (CDC) determine that close to 3% of all United States newborns present with birth defects; the worldwide estimate approaches 6% of infants presenting with congenital anomalies. The scientific community has recognized for decades that the majority of birth defects have undetermined etiologies, although we propose that environmental agents interacting with inherited susceptibility genes are the major contributing factors. Neural tube defects (NTDs) are among the most prevalent human birth defects and as such, these malformations will be the primary focus of this review. NTDs result from failures in embryonic central nervous system development and are classified by their anatomical locations. Defects in the posterior portion of the neural tube are referred to as meningomyeloceles (spina bifida), while the more anterior defects are differentiated as anencephaly, encephalocele, or iniencephaly. Craniorachischisis involves a failure of the neural folds to elevate and thus disrupt the entire length of the neural tube. Worldwide NTDs have a prevalence of approximately 18.6 per 10,000 live births. It is widely believed that genetic factors are responsible for some 70% of NTDs, while the intrauterine environment tips the balance toward neurulation failure in at risk individuals. Despite aggressive educational campaigns to inform the public about folic acid supplementation and the benefits of providing mandatory folic acid food fortification in the United States, NTDs still affect up to 2,300 United States births annually and some 166,000 spina bifida patients currently live in the United States, more than half of whom are now adults. Within the context of this review, we will consider the role of maternal nutritional status (deficiency states involving B vitamins and one carbon analytes) and the potential modifiers of NTD risk beyond folic acid. There are several well-established human teratogens that contribute to the population burden of NTDs, including: industrial waste and pollutants [e.g., arsenic, pesticides, and polycyclic aromatic hydrocarbons (PAHs)], pharmaceuticals (e.g., anti-epileptic medications), and maternal hyperthermia during the first trimester. Animal models for these teratogens are described with attention focused on valproic acid (VPA; Depakote). Genetic interrogation of model systems involving VPA will be used as a model approach to discerning susceptibility factors that define the gene-environment interactions contributing to the etiology of NTDs.