Gene-based Approaches Research Articles

Utilization of an In Silico 16S Ribosomal RNA Gene-Based Restriction Enzyme Approach for Identification of Lactic Acid Bacteria

In silico 16S ribosomal RNA gene-based restriction enzyme approach is a computer-simulated polymerase chain reaction-restriction fragment length polymorphism analysis method, namely customizable in silico sequence evaluation for restriction sites. The purpose of this study was to identify lactic acid bacteria using restriction enzyme analysis of 16S gene (16S ribosomal RNA polymerase chain reaction-restriction fragment length polymorphism). An in silico investigation was conducted to detect species-specific molecular markers in different lactic acid bacteria strains. We selected six out of 20 16S ribosomal RNA gene sequences from probiotic-potential lactic acid bacteria strains. Initially, the corresponding 16S ribosomal RNA gene sequences were selected from the database aligned using the Clustal Omega program and their evolutionary relationships were determined. The results indicated that the studied lactic acid bacteria strains belonged to 6 distinct clades except for the genus of Lactobacillus. Furthermore, the in silico polymerase chain reactionrestriction fragment length polymorphism was conducted for further taxonomic classification. Different restriction enzymes were used to recognize sites present on the lactic acid bacteria 16S ribosomal RNA genes. Customizable in silico sequence evaluation for restriction sites output comprised a predicted agarose gel as virtual polymerase chain reaction-restriction fragment length polymorphism patterns, for proper selection of the appropriate enzyme (s) to be used for polymerase chain reaction-restriction fragment length polymorphism marker generation. Our study demonstrated that ApyPI, CchIII and NheI produced distinct restriction patterns as those of Ku324937_C species. The most abundant species-specific markers were seen for Ku324928_C (10 markers), followed by Ku324908_C (8 markers) of order Hemiptera, then Ku324909_C (5 markers), Ku324898_C (4 markers) and finally, Ku324937_C (3 markers). The results demonstrated that customizable in silico sequence evaluation for restriction sites is a powerful tool to detect species-specific markers to provide better understanding of organisms found in raw and/or fermented milk.

A structure-guided computational screening approach for predicting plant enzyme-metabolite interactions.

Plants are molecular factories that have spent millions of years evolving the enzymes needed to synthesize diverse primary and specialized metabolites. Despite the wealth of metabolites that plants produce, many of the enzymes responsible for generating these molecules have yet to be identified. For enzymes with known substrates, the extent of substrate promiscuity and small-molecule regulation remains unexplored. Many computational methods for identifying metabolic enzymes focus on gene-based approaches that rely on transcriptomics, metabolomics, and comparative genomics. With new AI-based tools for accurate protein structure prediction, protein-based strategies that screen a library of small molecules against a high-quality protein model can facilitate the identification of substrates, products, or inhibitors. Virtual screening has been used for structure-based drug design in the pharmaceutical industry for decades and easily translates to investigating plant metabolic enzymes. Here, we present a method for rapid, user-friendly, and open-source virtual screening using the Arabidopsis thaliana UGT74F2 with a curated library of specialized metabolites and herbicides and AutoDock Vina as an example. This method may be applied broadly to metabolic enzymes, and compound libraries can be easily adapted. Compounds are ranked based on their relative binding affinities and the resulting binding modes are evaluated using a molecular visualization program, like PyMOL. Because this is a computational approach, results from the virtual screen will need to be validated using in vitro or in vivo activity, binding, or inhibition assays. Virtual screening may aid in identifying substrates for enzymes of unknown function, revisiting substrate selectivity, or identifying natural or synthetic inhibitors.

Association of genetic variants in autophagy-lysosome pathway genes with susceptibility and survival to prostate cancer.

Previous studies have indicated the connections between autophagy-lysosome pathway genes dysfunction and prostate cancer, but few studies have investigated whether single nucleotide polymorphisms (SNPs) in autophagy-lysosome pathway genes are implicated in prostate cancer risk and survival. Logistic regression analysis and stepwise Cox regression analysis were conducted in 4,662 cases and 3,114 controls from the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial. The false positive rate probability (FPRP) method was applied to correct for multiple comparisons. Gene-based analysis was calculated by versatile gene-based association study approach. We found that SLC11A1 rs7573065 significantly increased the risk of prostate cancer [adjusted odds ratio (OR)=1.24, 95% confidence interval (CI)=1.06-1.46, P=7.02×10-3, FPRP=0.082]. Furthermore, rs7573065 was confirmed as the independent predicator of overall survival (OS) for prostate cancer patients [Hazard ratio (HR)=1.30, 95% CI=1.01-1.66, P=0.041]. The significant association between SLC11A1 and prostate cancer risk was calculated by gene-based analysis (P=0.030). We also observed that the mRNA of SLC11A1 in prostate tumor tissues was significantly over-expressed than that in normal tissues. This study suggested that rs7573065 in SLC11A1 was associated with an increased risk and poor OS of prostate cancer. Our findings may provide evidence for genetic variants in autophagy-lysosome pathway as the risk and prognostic biomarkers for prostate cancer.

Genetic determinants of telomere length from 109,122 ancestrally diverse whole-genome sequences in TOPMed

Genetic studies on telomere length are important for understanding age-related diseases. Prior GWAS for leukocyte TL have been limited to European and Asian populations. Here, we report the first sequencing-based association study for TL across ancestrally-diverse individuals (European, African, Asian and Hispanic/Latino) from the NHLBI Trans-Omics for Precision Medicine (TOPMed) program. We used whole genome sequencing (WGS) of whole blood for variant genotype calling and the bioinformatic estimation of telomere length in n=109,122 individuals. We identified 59 sentinel variants (p-value <5×10-9) in 36 loci associated with telomere length, including 20 newly associated loci (13 were replicated in external datasets). There was little evidence of effect size heterogeneity across populations. Fine-mapping at OBFC1 indicated the independent signals colocalized with cell-type specific eQTLs for OBFC1 (STN1). Using a multi-variant gene-based approach, we identified two genes newly implicated in telomere length, DCLRE1B (SNM1B) and PARN. In PheWAS, we demonstrated our TL polygenic trait scores (PTS) were associated with increased risk of cancer-related phenotypes.

Epigenomic signatures on paralogous genes reveal underappreciated universality of active histone codes adopted across animals

The results of conventional gene-based analyses which combine epigenome and transcriptome data, including those conducted by the ENCODE/modENCODE projects, suggest various histone modifications performing regulatory functions in controlling mRNA expression (referred to as a histone code) in several model animals. While some histone codes were found to be universally adopted across organisms, “species-specific” histone codes have also been defined. We found that the characterization of these histone codes was confounded by factors (e.g. gene essentiality, expression breadth) that are independent of, but correlated with, gene expression levels. Hence, we attempted to decode histone marks in mouse (Mus musculus), fly (Drosophila melanogaster), and worm (Caenorhabditis elegans) genomes by examining ratios of RNA sequencing (and chromatin immunoprecipitation sequencing) intensities between paralog genes to remove confounding effects that would otherwise be present in a gene-based approach. With this paralog-based approach, associations between four histone modifications (H3K4me3, H3K27ac, H3K9ac, and H3K36me3) and gene expression are substantially revised. For example, we demonstrate that H3K27ac and H3K9ac represent universal active marks in promoters, rather than worm-specific marks as previously reported. Second, acting regions of the studied active marks that are common across species (and across a wide range of tissues at different developmental stages) were found to extend beyond the previously defined regions. Thus, it appears that the active histone codes analyzed have a universality that has previously been underappreciated. Our results suggested that these universal codes, including those previously considered species-specific, could have an ancient origin, and are important in regulating animal gene expression abundance.

Using Drosophila to Identify Naturally-Occurring Modifiers of Alzheimer's Disease

Despite significant progress in identifying risk factors for late-onset Alzheimer’s Disease (LOAD), much of the variance in disease pathogenesis remains unexplained, likely due to the contribution of many genes of small effect size. Model organisms such as Drosophila Melanogaster exhibit conservation in both disease-causing genes and cellular processes implicated in Alzheimer’s Disease (AD), offering a genetically tractable model that can be statistically leveraged to identify causal variants. Here, we combine a Drosophila model of AD with the Drosophila Genetic Reference Panel (DGRP), a model of natural variation consisting of over 200 fully sequenced, isogenic lines derived from a wild-caught population. Expression of two proteins closely associated with AD pathogenesis, A□42 and Tau, in the Drosophila eye results in a “rough eye” phenotype, an easily quantifiable phenotype caused by degeneration of the ommatidial array. By quantifying the degree of A□42- and Tau-mediated degeneration across 164 lines of the DGRP and using a gene-based approach to map associations, we have identified and validated a subset of naturally occurring modifiers of degeneration in Drosophila. Enrichment analysis reveals that the set of genes identified in our screen show significant enrichment for genes identified as significant or suggestive (4x10-6>p>2x10-11) in human GWAS studies. The results presented here provide proof-of-principal for an approach that combines the strengths of forward genetic screens in model organisms with the power of human GWAS studies to identify and validate potential risk factors that have been difficult to detect in human studies alone.

Genetic overlap between mood disorders and Alzheimer's disease: Could it explain the clinical and epidemiological overlap of these disorders?

Mood disorders such as major depressive (MD) and bipolar (BD), are major risk factors for Alzheimer's disease (AD). History of MD and BD can double the risk of developing AD in older adults, and this risk is even higher when depressive symptoms co-occur with mild cognitive impairment. These evidences suggest that mood disorders and AD may share common biological mechanisms, such as genetic risk factors. However, challenges on conducting multi-phenotype long-follow up studies limit the direct assessment of this hypothesis. Recently, bioinformatics methods using Genome-wide association studies (GWAS) results have been developed and applied to assess the existence of genetic overlap among disorders. Herein, we carried out a multilevel approach to investigate the genetic overlap between mood disorders (MD and BD) and AD, by assessing their genetic correlation, polygenic overlap and shared biological pathways. Summary statistics of three large GWAS for MD, BD and AD were assessed. Genetic overlap and correlation were obtained using LDSC and MiXer. ConjFDR was used to identify pleiotropic loci. Finally, a gene-based approach was applied to obtain a list of overlapping genes and pathways for mood disorders and AD. Pathway enrichment analysis and visualization have been carried out using g:Profiler and Enrichment Map App. Each network (MD&AD, B&AD and MD&BD&AD) was visualized using Cytoscape. Positive genetic correlation among both mood disorders and AD (rG=0.162; p=0.012 for MD&AD; and rG=0.162; p=0.018 for BD&AD) was observed, and further confirmed by all approaches. TMEM106B and THSD7A were identified as pleiotropic candidate genes for MD&AD; and MTSS2, VAC14 and FAF1, for BD&AD. Important biological pathways and functions linking these disorders were highlighted, such as acute inflammatory response, programmed cell death, amyloid-beta response, neuronal death, cell growth, respiratory electron transport chain, lipid metabolism and absorption and membrane trafficking. Our results confirm for the first time the existence of genetic correlation between mood disorders and AD, identify candidate pleiotropic genes and reinforce the existence of a structured biological network among mood disorders and AD. The results obtained herein, once confirmed, may contribute to shed light on future efforts for precision medicine applied to neurodegenerative and mood disorders and their comorbidities.

Preclinical assessment of an optimized AAV-FVIII vector in mice and non-human primates for the treatment of hemophilia A

Extensive clinical data from liver-mediated gene therapy trials have shown that dose-dependent immune responses against the vector capsid may impair or even preclude transgene expression if not managed successfully with prompt immune suppression. The goal of this preclinical study was to generate an adeno-associated viral (AAV) vector capable of expressing therapeutic levels of B-domain deleted factor VIII (FVIII) at the lowest possible vector dose to minimize the potential Risk of a capsid-mediated immune response in the clinical setting. Here, we describe the studies that identified the investigational agent SPK-8011, currently being evaluated in a phase 1/2 study (NCT03003533) in individuals with hemophilia A. In particular, the potency of our second-generation expression cassettes was evaluated in mice and in non-human primates using two different bioengineered capsids (AAV-Spark100 and AAV-Spark200). At 2 weeks after gene transfer, primates transduced with 2 × 1012 vg/kg AAV-Spark100-FVIII or AAV-Spark200-FVIII expressed FVIII antigen levels of 13% ± 2% and 22% ± 6% of normal, respectively. Collectively, these preclinical results validate the feasibility of lowering the AAV capsid dose for a gene-based therapeutic approach for hemophilia A to a dose level orders of magnitude lower than the first-generation vectors in the clinic.

Inducing Ito,f and phase 1 repolarization of the cardiac action potential with a Kv4.3/KChIP2.1 bicistronic transgene

The fast transient outward potassium current (Ito,f) plays a key role in phase 1 repolarization of the human cardiac action potential (AP) and its reduction in heart failure (HF) contributes to the loss of contractility. Therefore, restoring Ito,f might be beneficial for treating HF. The coding sequence of a P2A peptide was cloned, in frame, between Kv4.3 and KChIP2.1 genes and ribosomal skipping was confirmed by Western blotting. Typical Ito,f properties with slowed inactivation and accelerated recovery from inactivation due to the association of KChIP2.1 with Kv4.3 was seen in transfected HEK293 cells. Both bicistronic components trafficked to the plasmamembrane and in adenovirus transduced rabbit cardiomyocytes both t-tubular and sarcolemmal construct labelling appeared. The resulting current was similar to Ito,f seen in human ventricular cardiomyocytes and was 50% blocked at ~0.8 mmol/l 4-aminopyridine and increased ~30% by 5 μmol/l NS5806 (an Ito,f agonist). Variation in the density of the expressed Ito,f, in rabbit cardiomyocytes recapitulated typical species-dependent variations in AP morphology. Simultaneous voltage recording and intracellular Ca2+ imaging showed that modification of phase 1 to a non-failing human phenotype improved the rate of rise and magnitude of the Ca2+ transient. Ito,f expression also reduced AP triangulation but did not affect ICa,L and INa magnitudes. This raises the possibility for a new gene-based therapeutic approach to HF based on selective phase 1 modification.

Abstract 12957: Determining Disease Co-Occurrence Architecture of Hypertensive Heart Disease in Penn Medicine Biobank Using Longitudinal EHR Data Linked With PMBB Participants

An estimated 1.2 billion individuals worldwide suffer from hypertension. Hypertensive heart disease (HHD) refers to structural alterations, such as left ventricular hypertrophy in the heart due to prolonged hypertension and can lead to ischemic heart disease, heart failure, and arrhythmias. HHD represents a transitory subclinical condition between the onset of hypertension and cardiovascular disease, typically remaining asymptomatic until the time of adverse cardiac event. The risk of HHD-related diseases is heightened due to hypertension-associated comorbidities such as dyslipidemia and renal failure. The complex etiology underlying HHD makes it challenging to predict outcomes in patients. The goal of this study is to leverage longitudinal electronic health record (EHR) data to identify the conditions and comorbidities that influence HHD outcomes for prognostication. We defined an HHD patient cohort (n = 4,268) in the Penn Medicine BioBank dataset using diagnosis code ICD-10: I11* for HHD. We constructed a disease-disease map of HHD comorbidities using the Ising model, a type of Markov Random Field undirected probabilistic graphical model. We estimated the co-occurrence of disease pairs by calculating pairwise similarities between diseases as relative risk and conducting network analyses. Our results reflect clusters of known disease-disease connections, including interactions between endocrine and nervous system diseases, as well as novel pairwise disease interactions between genitourinary and musculoskeletal diseases. Our findings shed light on the effect of comorbid conditions on the trajectory of HHD adverse outcomes. Using the probabilistic model of disease co-occurrence from these results, we can identify key comorbidities that predict adverse outcomes among HHD patients. This work lays the foundation to integrate rare and common genetic variants into the Ising model using a gene-based binning approach. Incorporating rare loss-of-function variants will enable us to evaluate shared genetic connections between HHD comorbidities. In conclusion, we used EHR data to identify the genetic and non-genetic connections between comorbidities of HHD to develop a predictive metric for the prevention and clinical monitoring of HHD.

Moderate Heat-Assisted Gene Electrotransfer as a Potential Delivery Approach for Protein Replacement Therapy through the Skin.

Gene-based approaches for protein replacement therapies have the potential to reduce the number of administrations. Our previous work demonstrated that expression could be enhanced and/or the applied voltage reduced by preheating the tissue prior to pulse administration. In the current study, we utilized our 16-pin multi-electrode array (MEA) and incorporated nine optical fibers, connected to an infrared laser, between each set of four electrodes to heat the tissue to 43 °C. For proof of principle, a guinea pig model was used to test delivery of reporter genes. We observed that when the skin was preheated, it was possible to achieve the same expression levels as gene electrotransfer without preheating, but with a 23% reduction of applied voltage or a 50% reduction of pulse number. With respect to expression distribution, preheating allowed for delivery to the deep dermis and muscle. This suggested that this cutaneous delivery approach has the potential to achieve expression in the systemic circulation, thus this protocol was repeated using a plasmid encoding Human Factor IX. Elevated Factor IX serum protein levels were detected by ELISA up to 100 days post gene delivery. Further work will involve optimizing protein levels and scalability in an effort to reduce application frequency.

LUBAC: a new player in polyglucosan body disease.

Altered protein ubiquitination is associated with the pathobiology of numerous diseases; however, its involvement in glycogen metabolism and associated polyglucosan body (PB) disease has not been investigated in depth. In PB disease, excessively long and less branched glycogen chains (polyglucosan bodies, PBs) are formed, which precipitate in different tissues causing myopathy, cardiomyopathy and/or neurodegeneration. Linear ubiquitin chain assembly complex (LUBAC) is a multi-protein complex composed of two E3 ubiquitin ligases HOIL-1L and HOIP and an adaptor protein SHARPIN. Together they are responsible for M1-linked ubiquitination of substrates primarily related to immune signaling and cell death pathways. Consequently, severe immunodeficiency is a hallmark of many LUBAC deficient patients. Remarkably, all HOIL-1L deficient patients exhibit accumulation of PBs in different organs especially skeletal and cardiac muscle resulting in myopathy and cardiomyopathy with heart failure. This emphasizes LUBAC's important role in glycogen metabolism. To date, neither a glycogen metabolism-related LUBAC substrate nor the molecular mechanism are known. Hence, current reviews on LUBAC's involvement in glycogen metabolism are lacking. Here, we aim to fill this gap by describing LUBAC's involvement in PB disease. We present a comprehensive review of LUBAC structure, its role in M1-linked and other types of atypical ubiquitination, PB pathology in human patients and findings in new mouse models to study the disease. We conclude the review with recent drug developments and near-future gene-based therapeutic approaches to treat LUBAC related PB disease.

Building Natural Product Libraries Using Quantitative Clade-Based and Chemical Clustering Strategies.

ABSTRACTThe success of natural product-based drug discovery is predicated on having chemical collections that offer broad coverage of metabolite diversity. We propose a simple set of tools combining genetic barcoding and metabolomics to help investigators build natural product libraries aimed at achieving predetermined levels of chemical coverage. It was found that such tools aided in identifying overlooked pockets of chemical diversity within taxa, which could be useful for refocusing collection strategies. We have used fungal isolates identified as Alternaria from a citizen-science-based soil collection to demonstrate the application of these tools for assessing and carrying out predictive measurements of chemical diversity in a natural product collection. Within Alternaria, different subclades were found to contain nonequivalent levels of chemical diversity. It was also determined that a surprisingly modest number of isolates (195 isolates) was sufficient to afford nearly 99% of Alternaria chemical features in the data set. However, this result must be considered in the context that 17.9% of chemical features appeared in single isolates, suggesting that fungi like Alternaria might be engaged in an ongoing process of actively exploring nature’s metabolic landscape. Our results demonstrate that combining modest investments in securing internal transcribed spacer (ITS)-based sequence information (i.e., establishing gene-based clades) with data from liquid chromatography-mass spectrometry (i.e., generating feature accumulation curves) offers a useful route to obtaining actionable insights into chemical diversity coverage trends in a natural product library. It is anticipated that these outcomes could be used to improve opportunities for accessing bioactive molecules that serve as the cornerstone of natural product-based drug discovery.IMPORTANCE Natural product drug discovery efforts rely on libraries of organisms to provide access to diverse pools of compounds. Actionable strategies to rationally maximize chemical diversity, rather than relying on serendipity, can add value to such efforts. Readily implementable biological (i.e., ITS sequence analysis) and chemical (i.e., mass spectrometry-based feature and scaffold measurements) diversity assessment tools can be employed to monitor and adjust library development tactics in real time. In summary, metabolomics-driven technologies and simple gene-based specimen barcoding approaches have broad applicability to building chemically diverse natural product libraries.

What are the roles of global DNA and APC 2 gene promotor hypermethylation in multiple myeloma?

BackgroundIn today's practice, gene-based approaches come to the fore in the determination of prognosis and treatment preferences of multiple myeloma (MM). DNA methylation is one of the new approach parameters. DNA methylation occurs by the addition of a methyl group to cytosines in CpG dinucleotides. In this study, besides comparing the global DNA and APC 2 gene promotor hypermethylation between our patients with MM and healthy control group, we aimed to demonstrate the effect of hypermethylation on MM treatment responses and survival.Methods and results38 patients diagnosed with MM between January 2016 and January 2020 and 50 healthy controls were included in the study. The initial hypermethylation of the patients and the healthy control group were statistically analyzed. In addition, the increase in hypermethylation in the MM group before and after the first series of treatments were analyzed within themselves. There is a significant difference between the patients with MM diagnosis and the healthy control group in terms of the initial global hypermethylation (P = 0.001). In patients with MM, hypermethylation was significantly higher. Global hypermethylation in the post-treatment measurements was significantly increased in comparison to the pre-treatment state (P = 0.012). In terms of APC 2 promotor gene-specific hypermethylation, no significant differences were detected between pre- and post-treatment values (P = 0.368).ConclusionsThis study represents valuable data with the initial global DNA hypermethylation results in the MM patient group and the increase in hypermethylation post-treatment. it will shed light on future studies.

Sex-Specific Cross Tissue Meta-Analysis Identifies Immune Dysregulation in Women With Alzheimer's Disease.

Background: Alzheimer’s disease (AD) is a progressive neurodegenerative disorder and the most common cause of dementia in the United States. In spite of evidence of females having a greater lifetime risk of developing Alzheimer’s Disease (AD) and greater apolipoprotein E4-related (APOE ε4) AD risk compared to males, molecular signatures underlying these differences remain elusive.Methods: We took a meta-analysis approach to study gene expression in the brains of 1,084 AD patients and age-matched controls and whole blood from 645 AD patients and age-matched controls in seven independent datasets. Sex-specific gene expression patterns were investigated through use of gene-based, pathway-based and network-based approaches. The ability of a sex-specific AD gene expression signature to distinguish Alzheimer’s disease from healthy controls was assessed using a linear support vector machine model. Cell type deconvolution from whole blood gene expression data was performed to identify differentially regulated cells in males and females with AD.Results: Strikingly gene-expression, network-based analysis and cell type deconvolution approaches revealed a consistent immune signature in the brain and blood of female AD patients that was absent in males. In females, network-based analysis revealed a coordinated program of gene expression involving several zinc finger nuclease genes related to Herpes simplex viral infection whose expression was modulated by the presence of the APOE ε4 allele. Interestingly, this gene expression program was missing in the brains of male AD patients. Cell type deconvolution identified an increase in neutrophils and naïve B cells and a decrease in M2 macrophages, memory B cells, and CD8+ T cells in AD samples compared to controls in females. Interestingly, among males with AD, no significant differences in immune cell proportions compared to controls were observed. Machine learning-based classification of AD using gene expression from whole blood in addition to clinical features produced an improvement in classification accuracy upon stratifying by sex, achieving an AUROC of 0.91 for females and 0.80 for males.Conclusion: These results help identify sex and APOE ε4 genotype-specific transcriptomic signatures of AD and underscore the importance of considering sex in the development of biomarkers and therapeutic strategies for AD.

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

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.

Detection and evolution of SARS-CoV-2 coronavirus variants of concern with mass spectrometry.

Mass mapping using high-resolution mass spectrometry has been applied to identify and rapidly distinguish SARS-CoV-2 coronavirus strains across five major variants of concern. Deletions or mutations within the surface spike protein across these variants, which originated in the UK, South Africa, Brazil and India (known as the alpha, beta, gamma and delta variants respectively), lead to associated mass differences in the mass maps. Peptides of unique mass have thus been determined that can be used to identify and distinguish the variants. The same mass map profiles are also utilized to construct phylogenetic trees, without the need for protein (or gene) sequences or their alignment, in order to chart and study viral evolution. The combined strategy offers advantages over conventional PCR-based gene-based approaches exploiting the ease with which protein mass maps can be generated and the speed and sensitivity of mass spectrometric analysis.Graphical abstract Supplementary InformationThe online version contains supplementary material available at 10.1007/s00216-021-03649-1.

Effects of organic acids on the chemotaxis profiles and biocontrol traits of antagonistic bacterial endophytes against root-rot disease in Panax notoginseng.

Understanding the role of chemotaxis in ecological interactions between plants and microbes in the rhizosphere is necessary to optimize biocontrol strategies targeting plant soil-borne diseases. Therefore, we examined and profiled the antagonistic endophytic bacteria (AEB) population with chemotaxis potential in the medicinal plant Panax notoginseng using a cheA gene-based approach coupled with 16S rRNA sequencing. Phylogenetic analysis of the chemotactic AEB (CAEB) community in P. notoginseng enabled the identification of 56 CAEB strains affiliated with 30 species of Actinobacteria, Firmicutes, and Proteobacteria; Firmicutes, especially Bacillus, were predominant. We then systematically quantified the chemotactic response profiles of CAEB toward five organic acid (OA) attractants: citric acid, fumaric acid (FA), malic acid, oxalic acid, and succinic acid. Further hierarchical cluster analysis revealed that the chemotaxis of CAEB to the same attractant exhibited different patterns among not only genera but also species and even strains of the same species. Following chemotaxis and hierarchical analysis, we selected the strongest chemoattractant, fumaric acid (FA), as the target for evaluating the effects of OAs on the representative CAEB strain Bacillus amyloliquefaciens subsp. plantarum YP1. Application of FA significantly stimulated the chemotaxis ability and growth of YP1, and increased the transcript levels of cheA and biocontrol-related genes in YP1. This is the first study to characterise the diversity of chemotaxis profiles toward OAs in natural bacterial assemblages of P. notoginseng and to highlight how FA promotes the biocontrol-related traits of P. notoginseng-associated CAEB.

Honey bee-associated bacteria as producers of bioactive compounds for protecting hives. A biosynthetic gene-based approach

Honey bee-associated bacteria are a source of natural compounds of interest for controlling hive decline which is threatening bee health globally. Genes involved in the biosynthesis of a series of extracellular compounds released by bacteria living on the external surface of honey bees were investigated. A biosynthetic gene-based approach was adopted by developing a battery of primers to target the genes involved in the biosynthesis of four groups of bioactive compounds (pyrrolizidine alkaloids, surfactin, 2-heptanone and helveticin J). The primers were tested on 51 bacterial isolates belonging to Bacillus thuringiensis, Acetobacteraceae bacterium, Bifidobacterium asteroides and Apilactobacillus kunkeei. The developed primers led to species-specific detection and characterization of the functional genes involved in the production of three out of four groups of compounds selected for this study. The findings suggest that microbial populations inhabiting apiaries harbor genes involved in the biosynthesis of metabolites linked to the reduction of important honey bee pathogens such as Varroa destructor, Paenibacillus larvae and Nosema ceranae. The gene-based approach adopted for evaluating the biosynthetic potential of bioactive compounds in hives is promising for investigating further compounds for low input control strategies of bee enemies.

EinImpute: a local and gene-based approach to imputation of dropout events in ScRNA-seq data

EinImpute: a local and gene-based approach to imputation of dropout events in ScRNA-seq data