Genomic Findings Research Articles

OTHR-45. Kids First Variant WorkBench: application to germline genomic discoveries in the Children's Brain Tumor Network

The Gabriella Miller Kids First Pediatric Research Program (Kids First) aims at facilitating researchers to uncover new insights into the biology of childhood cancer (CC) and structural birth defects (SBD). Kids First has two initiatives, i) whole genome sequencing of biospecimens from families with CC/SBD, and ii) establishing Kids First Data Resources. Kids First Data Resource Center developed the Kids First Data Resource Portal (KFDRP), a centralized platform to search, view, analyze, and identify currently accessible data from both Kids First and collaborative cohorts, incorporating omics and phenotypic information of 30 studies and 26,300 participants. A recently released KFDRP component is Variant WorkBench (VWB), enabling users to query, mangle, analyze and visualize genomic variants from participating cohorts, with the Children’s Brain Tumor Network (CBTN) being one of the cohorts. VWB supports programming languages such as Python, Spark, SQL and R for in-depth analysis in Apache Zeppelin notebooks. In addition to variant calls and phenotypic information, VWB hosts rich external variant annotations in the public domain, such as Cancer Hotspots, COSMIC and ClinVar. Users can also load additional databases (e.g. Human Gene Mutation Database/HGMD) within a notebook, import custom datasets as temporary query tables, export analysis outputs to local drives, visualize analysis results in multiple chart styles, display local figures, and save notebooks for sharing, further use and Cavatica projects. In an effort to screen tier 1 genes (n=578) from the most recent Cancer Gene Census provided by COSMIC in CBTN, we identified ~127,500 germline variants that are both rare and damaging, or that are already cataloged in the most recent version of ClinVar/HGMD. The whole process took less than one hour which is much faster than conventional methods. VWB enables efficient genomic variant analysis and discoveries in pediatric neuro-oncology research with advanced big data technology.

Development and validation of Duoseq as a novel diagnostic and companion assay for lymphoma and other cancers.

3074 Background: While NGS applications of DNAseq and RNAseq have proven to be powerful tools for genomic discovery, NGS remains underutilized in the clinic. We hypothesized that the clinical translation of NGS would be greatly improved by addressing two critical issues: first, having a single assay for both DNA and RNA sequencing, and second, including all the bioinformatics to enable rapid analysis and reporting of clinically relevant findings within 2 days rather than 2-3 weeks which remains the widespread standard. We developed Duoseq to address these issues. Methods: In most cancer biopsies, RNA constitutes nearly 80% of the total nucleic acid and DNA only 20%. Duoseq incorporates interfering factors that make the ligation of sequencing barcodes to RNA much less efficient than that to DNA to, in effect, invert the proportion of DNA and RNA from the sample. This enables the efficient generation of high quality DNA and RNA libraries. The assay targets over 450 recurrently altered genes in hematologic cancers. We developed secure bioinformatics software that connects to an Illumina sequencer to render four major classes of clinical measurements: mutations, gene expression (cell of origin in DLBCL), Epstein Barr virus (EBV) status and and common translocations (BCL2, BCL6, MYC, IRF4, CCND1). Results: Technicians at three different CLIA labs were trained to perform Duoseq at the respective sites using a local Illumina sequencer. The assay was performed on 111 FFPE lymphoma cases including diffuse large B cell lymphoma, and other B and T cell lymphomas that were characterized using clinical standard assays. The standards included Sanger sequencing or Foundation One testing (mutations), in situ hybridization (EBV), immunohistochemistry or Nanostring (expression) and fluorescent in situ hybridization (translocation). The included Duoseq software provided the bioinformatics results as well as annotations. With a minimum input of 50ng of nucleic acid/sample, Duoseq generated a mean depth of coverage of 438X. We found that, compared to clinical standards, Duoseq performed accurately for mutations (95.9%), EBV status (96.5%), translocations (94.1%) and expression (100%). In addition, Duoseq provided information on clonality, specific translocation partners and identified novel fusions that were not available from standard assays. Conclusions: Duoseq accurately recapitulates the clinical workup of lymphomas replacing the need for single analyte tests such as FISH, while providing a wealth of additional data. The simplicity of the assay and the included bioinformatics software enables its performance at any clinical lab without the need for specialized personnel or computing infrastructure. We anticipate the application of Duoseq as a diagnostic assay, as well as in clinical trial selection and companion diagnostic applications owing to its high accuracy and short turnaround time.

Increasing genomic discovery in newly diagnosed multiple myeloma: defining disease biology and its correlation to risk.

Our understanding of MM genomics has expanded rapidly in the past 5-10 years as a consequence of cytogenetic analyses obtained in routine clinical practice as well as the ability to perform whole-exome/genome sequencing and gene expression profiling on large patient data sets. This knowledge has offered new insights into disease biology and is increasingly defining high-risk genomic patterns. In this manuscript, we present a thorough review of our current knowledge of MM genomics. The epidemiology and biology of chromosomal abnormalities including both copy number abnormalities and chromosomal translocation are described in full with a focus on those most clinically impactful such as 1q amplification and del(17p) as well as certain chromosome 14 translocations. A review of our ever-expanding knowledge of genetic mutations derived from recent whole-genome/exome data sets is then reviewed including those that drive disease pathogenesis from precursor states as well as those that may impact clinical outcomes. We then transition and attempt to elucidate how both chromosomal abnormalities and gene mutations are evolving our understanding of disease risk. We conclude by offering our perspectives moving forward as to how we might apply whole-genome/exome-level data in addition to routine cytogenetic analyses to improve patient outcomes as well as further knowledge gaps that must be addressed.

Downregulated ADAMTS1 Incorporating A2M Contributes to Tumorigenesis and Alters Tumor Immune Microenvironment in Lung Adenocarcinoma.

Simple SummaryLung cancer is the most dreadful cancer type and has the worst cancer-related clinical outcomes. This study used specimens from the in-house lung cancer cohort and public cohort to verify the roles of downregulated ADAMTS1, a protease remodeling extracellular matrix, to facilitate cancer promotion and progress. Based on the clinical specimens, cell and animal study with the aid of the public databases, we concluded that downregulated expression of ADAMTS1 might promote tumor progression and metastasis and modify the tumor microenvironment in lung cancer. Further investigation would be required for its application in treating lung cancer.Lung adenocarcinoma (LUAD) still holds the most dreadful clinical outcomes worldwide. Despite advanced treatment strategies, there are still some unmet needs. Next-generation sequencing of large-scale cancer genomics discovery projects combined with bioinformatics provides the opportunity to take a step forward in meeting clinical conditions. Based on in-house and The Cancer Genome Atlas (TCGA) cohorts, the results showed decreased levels of ADAMTS1 conferred poor survival compared with normal parts. Gene set enrichment analyses (GSEA) indicated the negative correlation between ADAMTS1 and the potential roles of epithelial–mesenchymal transition (EMT), metastasis, and poor prognosis in LUAD patients. With the knockdown of ADAMTS1, A549 lung cancer cells exhibited more aggressive behaviors such as EMT and increased migration, resulting in cancer metastasis in a mouse model. The pathway interaction network disclosed the linkage of downregulated α2-macroglobulin (A2M), which regulates EMT and metastasis. Furthermore, immune components analysis indicated a positive relationship between ADAMTS1 and the infiltrating levels of multiple immune cells, especially anticancer CD4+ T cells in LUAD. Notably, ADAMTS1 expression was also inversely correlated with the accumulation of immunosuppressive myeloid-derived suppressor cells and regulatory T cells, implying the downregulated ADAMTS1 mediated immune adjustment to fit the tumor survival disadvantages in LUAD patients. In conclusion, our study indicates that ADAMTS1 interacts with A2M in regulating EMT and metastasis in LUAD. Additionally, ADAMTS1 contributes to poor prognosis and immune infiltration in LUAD patients

Health Disparities in Cardiometabolic Traits: Genomics, Gaps, and Paradoxes

Cardiometabolic traits vary considerably across ancestry groups, creating interest in disentangling the contributing factors to minimize health disparities. While environmental factors, such as socioeconomic status, access to healthcare, and lifestyle, are expected to play a large role in these differences, the contribution of genomics is less clear. This presentation will consider health disparities in cardiometabolic traits and the role of genomics can play in disentangling or exacerbating these disparities, with examples drawn from serum lipids and African ancestry populations. Serum lipids (especially HDL cholesterol and triglycerides) manifest clear differences in distribution across ancestries. While individuals of African ancestry often experience worse cardiometabolic outcomes (e.g., higher rates of hypertension, obesity, and diabetes), healthier mean serum lipid concentrations are generally observed among those of African ancestry. This lipid profile persists in those of West African ancestry across the African Diaspora, despite differences in social factors and lifestyle, lending support to genomic influences. This distribution, however, does not translate into a reduced rate of lipids‐associated cardiometabolic diseases, for reasons which are unclear, and this discrepancy has implications for use of lipid biomarkers for identifying individuals with disease risk. Well‐powered genomic studies of serum lipids in African ancestry individuals are emerging, revealing novel loci along with some evidence of gene‐lifestyle interactions and differing associations among subcontinental groups. These studies, along with increasing publications of other cardiometabolic traits in African and other non‐European ancestry individuals, are of great importance in a field plagued by underrepresentation of non‐European ancestry individuals. This underrepresentation has profound implications for the clinical utility of genomic discoveries. Without attention to this issue, genomics is poised to exacerbate health disparities in non‐EUR ancestry groups. African ancestry individuals are poorly represented in key genomic databases, yet their profound genetic diversity makes it likely to find variants among them that are not present in other populations. This imbalance can lead to clinical care that is sub‐optimal for those of African ancestry compared to better‐represented groups, a rising concern with increasing use of genomics in clinical decisions. For example, polygenic risk scores, derived from large, well‐powered GWAS, can predict disease risk for some traits of a magnitude that rivals Mendelian risk transmission, growing closer to clinical utility. Unfortunately, because of the comparatively limited representation of non‐European ancestry individuals in the underlying GWAS, these tools perform more poorly in these individuals. While these findings call for a greater number of genomic studies in diverse populations, attention also must be paid to the way individuals of non‐European ancestry are included and presented to ensure that the results facilitate the advancements their inclusion can address. Genomics has the potential to promote understanding of health disparities, but care must be given that they do not play a role in exacerbating them.

DEPhT: a novel approach for efficient prophage discovery and precise extraction.

Advances in genome sequencing have produced hundreds of thousands of bacterial genome sequences, many of which have integrated prophages derived from temperate bacteriophages. These prophages play key roles by influencing bacterial metabolism, pathogenicity, antibiotic resistance, and defense against viral attack. However, they vary considerably even among related bacterial strains, and they are challenging to identify computationally and to extract precisely for comparative genomic analyses. Here, we describe DEPhT, a multimodal tool for prophage discovery and extraction. It has three run modes that facilitate rapid screening of large numbers of bacterial genomes, precise extraction of prophage sequences, and prophage annotation. DEPhT uses genomic architectural features that discriminate between phage and bacterial sequences for efficient prophage discovery, and targeted homology searches for precise prophage extraction. DEPhT is designed for prophage discovery in Mycobacterium genomes but can be adapted broadly to other bacteria. We deploy DEPhT to demonstrate that prophages are prevalent in Mycobacterium strains but are absent not only from the few well-characterized Mycobacterium tuberculosis strains, but also are absent from all ∼30 000 sequenced M. tuberculosis strains.

Beacon v2 and Beacon networks: A "lingua franca" for federated data discovery in biomedical genomics, and beyond.

Beacon is a basic data discovery protocol issued by the Global Alliance for Genomics and Health (GA4GH). The main goal addressed by version 1 of the Beacon protocol was to test the feasibility of broadly sharing human genomic data, through providing simple “yes” or “no” responses to queries about the presence of a given variant in datasets hosted by Beacon providers. The popularity of this concept has fostered the design of a version 2, that better serves real‐world requirements and addresses the needs of clinical genomics research and healthcare, as assessed by several contributing projects and organizations. Particularly, rare disease genetics and cancer research will benefit from new case level and genomic variant level requests and the enabling of richer phenotype and clinical queries as well as support for fuzzy searches. Beacon is designed as a “lingua franca” to bridge data collections hosted in software solutions with different and rich interfaces. Beacon version 2 works alongside popular standards like Phenopackets, OMOP, or FHIR, allowing implementing consortia to return matches in beacon responses and provide a handover to their preferred data exchange format. The protocol is being explored by other research domains and is being tested in several international projects.

From telomere to telomere: The transcriptional and epigenetic state of human repeat elements.

Mobile elements and repetitive genomic regions are sources of lineage-specific genomic innovation and uniquely fingerprint individual genomes. Comprehensive analyses of such repeat elements, including those found in more complex regions of the genome, require a complete, linear genome assembly. We present a de novo repeat discovery and annotation of the T2T-CHM13 human reference genome. We identified previously unknown satellite arrays, expanded the catalog of variants and families for repeats and mobile elements, characterized classes of complex composite repeats, and located retroelement transduction events. We detected nascent transcription and delineated CpG methylation profiles to define the structure of transcriptionally active retroelements in humans, including those in centromeres. These data expand our insight into the diversity, distribution, and evolution of repetitive regions that have shaped the human genome.

Integrated analysis of the lncRNA/circRNA-miRNA-mRNA expression profiles reveals novel insights into potential mechanisms in response to root-knot nematodes in peanut

BackgroundPeanut is the most essential oil and food crop globally due to its high oil and protein content. Root-knot nematode infects peanut roots, causing poor development and severely limiting peanut yields worldwide. The discovery of peanut genome identified a considerable number of genetic loci controlling the peanut root-knot nematode; however, the molecular mechanism of root-knot nematode remains unknown.ResultsThe heterogeneous response to root-knot nematode stress in peanut roots was identified using whole-transcriptome RNA-seq. A total of 430 mRNAs, 111 miRNAs, 4453 lncRNAs, and 123 circRNAs were found to have differential expression between infected and non-infected peanuts. The expression profiles of the lncRNA/circRNA-miRNA-mRNA network were developed to understand the potential pathways that lead to root-knot nematodes in peanut roots. During root-knot nematodes stress, a total of 10 lncRNAs, 4 circRNAs, 5 miRNAs, and 13 mRNAs can create competing endogenous RNA and participate in the oxidation–reduction process as well as other biological metabolism processes in peanuts. The findings will highlight the role of peanut ceRNAs in response to root-knot nematodes.ConclusionThe GO classification and KEGG pathway enrichment study of core regulatory networks revealed that ceRNAs are involved in oxidation–reduction, peroxidase activity, lignin synthesis in the xylem, and flavonoid synthesis. Overall, these findings may help researchers better understand the role of non-coding RNAs in response to root-knot nematodes.

Genes To Mental Health (G2MH): A Framework to Map the Combined Effects of Rare and Common Variants on Dimensions of Cognition and Psychopathology.

Rare genomic disorders (RGDs) confer elevated risk for neurodevelopmental psychiatric disorders. In this era of intense genomics discoveries, the landscape of RGDs is rapidly evolving. However, there has not been comparable progress to date in scalable, harmonized phenotyping methods. As a result, beyond associations with categorical diagnoses, the effects on dimensional traits remain unclear for many RGDs. The nature and specificity of RGD effects on cognitive and behavioral traits is an area of intense investigation: RGDs are frequently associated with more than one psychiatric condition, and those studied to date affect, to varying degrees, a broad range of developmental and cognitive functions. Although many RGDs have large effects, phenotypic expression is typically influenced by additional genomic and environmental factors. There is emerging evidence that using polygenic risk scores in individuals with RGDs offers opportunities to refine prediction, thus allowing for the identification of those at greatest risk of psychiatric illness. However, translation into the clinic is hindered by roadblocks, which include limited genetic testing in clinical psychiatry, and the lack of guidelines for following individuals with RGDs, who are at high risk of developing psychiatric symptoms. The Genes to Mental Health Network (G2MH) is a newly funded National Institute of Mental Health initiative that will collect, share, and analyze large-scale data sets combining genomics and dimensional measures of psychopathology spanning diverse populations and geography. The authors present here the most recent understanding of the effects of RGDs on dimensional behavioral traits and risk for psychiatric conditions and discuss strategies that will be pursued within the G2MH network, as well as how expected results can be translated into clinical practice to improve patient outcomes.

Effectiveness of educational interventions to improve genomic competency in non-­‐geneticist medical doctors: a systematic review of the literature


 
 
 Background. Given rapid advances in genomics, continuing medical education of medical doctors (MDs) is crucial for appropriate integration of human genome discoveries into healthcare and disease prevention. This work presents a systematic review of educational interventions aimed at improving genomic competency in non‐geneticist MDs, with consideration of how outcomes are affected by intervention type (face-to-face learning, distance learning, decision aids and information provision).
 Methods. We performed a systematic search of MEDLINE, ISI Web of Science, and SCOPUS databases for the relevant studies on educational interventions and genomic competency, published in English language between January, 1st, 2001 and March, 31th, 2017. In order to synthesize the results of the eligible studies, we evaluated the effect of education on knowledge, management and confidence, and we compared the outcomes between different intervention types.
 Results. We included sixteen studies including different educational interventions. The majority of studies reported significant effects on at least one component of genomic competency. When the improvement in clinical application of genomic knowledge was evaluated, moderate or null effect has been reported for the different intervention types.
 Conclusions. The majority of studies included reported a positive impact of genomic education on non‐geneticist MDs genomic competency. However, no clear conclusion can be drawn given the heterogeneity of the studies in terms of methods, intervention types and outcome measurements. Future studies considering effectiveness of interventions with a focus on long-term outcomes are recommended.
 
 

Gene Editing: The Next Breakthrough Technology in Our 10,000-Year Journey of Crop Improvement

Humans have improved plants for their utility through selective self-pollination, crossing, and progeny selection for >10,000 years, largely based on physical characteristics. Less than 200 years ago, the genetic basis of heritability in selection was revealed, enabling breeders to accelerate genetic gain. Breakthroughs in genomics and molecular markers for the past century have enabled breeders to locate and select genomic regions affecting desirable traits, improving breeding precision. Transgenesis has enabled crop insertion of desirable exogenous genes, enabling de novo functionality. These technologies, along with agronomic practices, have generated more than sixfold yield improvements in crops such as corn in the past century. Gene editing, with its unique ability to precisely edit, change expression, and move genes within a crop's genome, has the potential to be the next breakthrough technology. For this to come to fruition, it is critical to take a holistic view considering perspectives of scientists, farmers, regulators, and consumers.

Pancreatic Ductal Adenocarcinoma Cortical Mechanics and Clinical Implications.

Pancreatic ductal adenocarcinoma (PDAC) remains one of the deadliest cancers due to low therapeutic response rates and poor prognoses. Majority of patients present with symptoms post metastatic spread, which contributes to its overall lethality as the 4th leading cause of cancer-related deaths. Therapeutic approaches thus far target only one or two of the cancer specific hallmarks, such as high proliferation rate, apoptotic evasion, or immune evasion. Recent genomic discoveries reveal that genetic heterogeneity, early micrometastases, and an immunosuppressive tumor microenvironment contribute to the inefficacy of current standard treatments and specific molecular-targeted therapies. To effectively combat cancers like PDAC, we need an innovative approach that can simultaneously impact the multiple hallmarks driving cancer progression. Here, we present the mechanical properties generated by the cell’s cortical cytoskeleton, with a spotlight on PDAC, as an ideal therapeutic target that can concurrently attack multiple systems driving cancer. We start with an introduction to cancer cell mechanics and PDAC followed by a compilation of studies connecting the cortical cytoskeleton and mechanical properties to proliferation, metastasis, immune cell interactions, cancer cell stemness, and/or metabolism. We further elaborate on the implications of these findings in disease progression, therapeutic resistance, and clinical relapse. Manipulation of the cancer cell’s mechanical system has already been shown to prevent metastasis in preclinical models, but it has greater potential for target exploration since it is a foundational property of the cell that regulates various oncogenic behaviors.

NetSeekR: a network analysis pipeline for RNA-Seq time series data

BackgroundRecent development of bioinformatics tools for Next Generation Sequencing data has facilitated complex analyses and prompted large scale experimental designs for comparative genomics. When combined with the advances in network inference tools, this can lead to powerful methodologies for mining genomics data, allowing development of pipelines that stretch from sequence reads mapping to network inference. However, integrating various methods and tools available over different platforms requires a programmatic framework to fully exploit their analytic capabilities. Integrating multiple genomic analysis tools faces challenges from standardization of input and output formats, normalization of results for performing comparative analyses, to developing intuitive and easy to control scripts and interfaces for the genomic analysis pipeline.ResultsWe describe here NetSeekR, a network analysis R package that includes the capacity to analyze time series of RNA-Seq data, to perform correlation and regulatory network inferences and to use network analysis methods to summarize the results of a comparative genomics study. The software pipeline includes alignment of reads, differential gene expression analysis, correlation network analysis, regulatory network analysis, gene ontology enrichment analysis and network visualization of differentially expressed genes. The implementation provides support for multiple RNA-Seq read mapping methods and allows comparative analysis of the results obtained by different bioinformatics methods.ConclusionOur methodology increases the level of integration of genomics data analysis tools to network inference, facilitating hypothesis building, functional analysis and genomics discovery from large scale NGS data. When combined with network analysis and simulation tools, the pipeline allows for developing systems biology methods using large scale genomics data.

Identification of the occurrence and potential mechanisms of heterotopic ossification associated with 17-beta-estradiol targeting MKX by bioinformatics analysis and cellular experiments.

BackgroundTendon heterotopic ossification (HO) is a common condition occurring secondary to tendon injury or surgical trauma that significantly affects the patient’s quality of life. The treatment of tendon HO remains challenging due to a lack of clarity regarding the pathological mechanism. Mohawk (MKX) is a key factor in preventing tendon HO; however, its upstream regulatory mechanism remains to be understood. This study aimed to identify potential compounds that target and regulate MKX and explore their functional mechanisms.MethodsBioinformatics analysis of MKX-related compounds and proteins was performed based on data from the STITCH and OncoBinder databases. Subsequently, the SymMap database was used to study MKX-related traditional Chinese medicine drugs and symptoms. Next, the OncoBinder genomic and proteomic discovery model was applied to identify potential regulators of MKX. The analytical tool Expert Protein Analysis System for proteomics was used to predict the three-dimensional structure of MKX, and the AutoDockTools software was used to identify pockets of activity at potential sites for molecular docking. Furthermore, we evaluated the effect of different doses of 17-beta-estradiol on bone marrow-derived mesenchymal stem cells (BM-MSCs).ResultsBy predicting the three-dimensional structure of MKX and simulating molecular docking, Pro-Tyr and 17-beta-Estradiol were found to target and bind to MKX. Analysis of the STITCH and OncoBinder databases showed that MKX had a significant regulatory correlation with suppressor interacting 3 A/histone deacetylase 1 (SIN3A/HDAC1). The GO and KEGG pathway enrichment analysis revealed that the functions of MKX and its associated proteins were mainly enriched in osteogenic-related pathways. Assessment of the proliferation of BM-MSCs revealed that 17-beta-estradiol possibly upregulated the mRNA expression of the HDAC1-SIN3A/BMP pathway-related RUNX2, thereby promoting the proliferation of BM-MSCs.ConclusionsThe compounds Pro-Tyr and 17-beta-Estradiol may bind to MKX and thus affect the interaction of MKX with SIN3A/HDAC1.

Genomic discovery and structural dissection of a novel type of polymorphic toxin system in gram-positive bacteria

Bacteria have developed several molecular conflict systems to facilitate kin recognition and non-kin competition to gain advantages in the acquisition of growth niches and of limited resources. One such example is a large class of so-called polymorphic toxin systems (PTSs), which comprise a variety of the toxin proteins secreted via T2SS, T5SS, T6SS, T7SS and many others. These systems are highly divergent in terms of sequence/structure, domain architecture, toxin-immunity association, and organization of the toxin loci, which makes it difficult to identify and characterize novel systems using traditional experimental and bioinformatic strategies. In recent years, we have been developing and utilizing unique genome-mining strategies and pipelines, based on the organizational principles of both domain architectures and genomic loci of PTSs, for an effective and comprehensive discovery of novel PTSs, dissection of their components, and prediction of their structures and functions. In this study, we present our systematic discovery of a new type of PTS (S8-PTS) in several gram-positive bacteria. We show that the S8-PTS contains three components: a peptidase of the S8 family (subtilases), a polymorphic toxin, and an immunity protein. We delineated the typical organization of these polymorphic toxins, in which a N-terminal signal peptide is followed by a potential receptor binding domain, BetaH, and one of 16 toxin domains. We classified each toxin domain by the distinct superfamily to which it belongs, identifying nine BECR ribonucleases, one Restriction Endonuclease, one HNH nuclease, two novel toxin domains homologous to the VOC enzymes, one toxin domain with the Frataxin-like fold, and several other unique toxin families such as Ntox33 and HicA. Accordingly, we identified 20 immunity families and classified them into different classes of folds. Further, we show that the S8-PTS-associated peptidases are analogous to many other processing peptidases found in T5SS, T7SS, T9SS, and many proprotein-processing peptidases, indicating that they function to release the toxin domains during secretion. The S8-PTSs are mostly found in animal and plant-associated bacteria, including many pathogens. We propose S8-PTSs will facilitate the competition of these bacteria with other microbes or contribute to the pathogen-host interactions.

Racial Bias Can Confuse AI for Genomic Studies

Large-scale genomic studies are important ways to comprehensively decode the human genomics, and provide valuable insights to human disease causalities and phenotype developments. Genomic studies are in need of high throughput bioinformatics analyses to harness and integrate such big data. It is in this overarching context that artificial intelligence (AI) offers enormous potentials to advance genomic studies. However, racial bias is always an important issue in the data. It is usually due to the accumulation process of the dataset that inevitability involved diverse subjects with different races. How can race bias affect the outcomes of AI methods? In this work, we performed comprehensive analyses taking The Cancer Genome Atlas (TCGA) project as a case study. We construct a survival model as well as multiple artificial intelligence prediction models to analyze potential confusion caused by racial bias. From the genomic discovery, we demonstrated cancer associated genes identified from the major race hardly overlap with the discoveries from minor races from the same causal gene discovery model. We demonstrated that the biased racial distribution will greatly affect the cancer-associated genes, even taking the racial identity as a confounding factor in the model. The prediction models will be potentially risky and less accurate due to the existence of racial bias in projects. Cancer genes from the overall patient model with strong racial bias will be less informative to the minor races. Meanwhile, when the racial bias is less severe, the major conclusion from the overall analysis can be less useful even for the major group.

A Summary of the Inaugural WHO Classification of Pediatric Tumors: Transitioning from the Optical into the Molecular Era.

Pediatric tumors are uncommon, yet are the leading cause of cancer-related death in childhood. Tumor types, molecular characteristics, and pathogenesis are unique, often originating from a single genetic driver event. The specific diagnostic challenges of childhood tumors led to the development of the first World Health Organization (WHO) Classification of Pediatric Tumors. The classification is rooted in a multilayered approach, incorporating morphology, IHC, and molecular characteristics. The volume is organized according to organ sites and provides a single, state-of-the-art compendium of pediatric tumor types. A special emphasis was placed on "blastomas," which variably recapitulate the morphologic maturation of organs from which they originate. SIGNIFICANCE: In this review, we briefly summarize the main features and updates of each chapter of the inaugural WHO Classification of Pediatric Tumors, including its rapid transition from a mostly microscopic into a molecularly driven classification systematically taking recent discoveries in pediatric tumor genomics into account.

Plant Copper Metalloenzymes As Prospects for New Metabolism Involving Aromatic Compounds.

Copper is an important transition metal cofactor in plant metabolism, which enables diverse biocatalysis in aerobic environments. Multiple classes of plant metalloenzymes evolved and underwent genetic expansions during the evolution of terrestrial plants and, to date, several representatives of these copper enzyme classes have characterized mechanisms. In this review, we give an updated overview of chemistry, structure, mechanism, function and phylogenetic distribution of plant copper metalloenzymes with an emphasis on biosynthesis of aromatic compounds such as phenylpropanoids (lignin, lignan, flavonoids) and cyclic peptides with macrocyclizations via aromatic amino acids. We also review a recent addition to plant copper enzymology in a copper-dependent peptide cyclase called the BURP domain. Given growing plant genetic resources, a large pool of copper biocatalysts remains to be characterized from plants as plant genomes contain on average more than 70 copper enzyme genes. A major challenge in characterization of copper biocatalysts from plant genomes is the identification of endogenous substrates and catalyzed reactions. We highlight some recent and future trends in filling these knowledge gaps in plant metabolism and the potential for genomic discovery of copper-based enzymology from plants.

Monoplex and multiplex immunoassays: approval, advancements, and alternatives

Immunoassays are a powerful diagnostic tool and are widely used for the quantification of proteins and biomolecules in medical diagnosis and research. Enzyme-linked immunosorbent assay (ELISA) is the most commonly used immunoassay format and allows the detection of biomarkers at a very low concentration. The diagnostic platforms such as enzyme immunoassay (EIA), chemiluminescence (CL) assay, polymerase chain reaction (PCR), flow cytometry (FC), and mass spectrometry (MS) have been used to identify molecular biomarkers. However, these diagnostic tools requiring expensive equipment, long testing time, and qualified personnel that is not always available in small local hospitals with limited resources. The lateral flow immunoassay (LFIA) platform was developed for rapidly obtaining laboratory results and to make urgent decisions in emergency medicine, as well as the recently introduced concept of testing at the site of care (point-of-care, POC). The simultaneous measurement of different substances from a single sample called multiplex assays have become increasingly significant for in vitro quantification of multiple analytes in a single sample, thereby minimising cost, time, and volume. In multiplex immunoassays, the ligands are immobilized either in planar format (flat surface) or on microspheres in suspension that binds to target analytes in sample. The multiplex technology has established itself in proteomic networks and pathways, validation of genomic discoveries, and in the development of clinical biomarkers. In the present review article, various types of monoplex/simplex and complex/multiplex immunoassays have been analysed that are increasingly being applied in laboratory medicine. Also, some advantages and disadvantages of these multiplex assays have also been included such as experimental animals, in vitro tests using cell lines and tissue samples, 3-dimensional modelling and bioprinting, in silico tests, organ-on-chip, and computer modelling.