Extremophiles in a changing world

Extremophiles and their products have been a major focus of research interest for over 40 years. Through this period, studies of these organisms have contributed hugely to many aspects of the fundamental and applied sciences, and to wider and more philosophical issues such as the origins of life and astrobiology. Our understanding of the cellular adaptations to extreme conditions (such as acid, temperature, pressure and more), of the mechanisms underpinning the stability of macromolecules, and of the subtleties, complexities and limits of fundamental biochemical processes has been informed by research on extremophiles. Extremophiles have also contributed numerous products and processes to the many fields of biotechnology, from diagnostics to bioremediation. Yet, after 40 years of dedicated research, there remains much to be discovered in this field. Fortunately, extremophiles remain an active and vibrant area of research. In the third decade of the twenty-first century, with decreasing global resources and a steadily increasing human population, the world's attention has turned with increasing urgency to issues of sustainability. These global concerns were encapsulated and formalized by the United Nations with the adoption of the 2030 Agenda for Sustainable Development and the presentation of the seventeen Sustainable Development Goals (SDGs) in 2015. In the run-up to 2030, we consider the contributions that extremophiles have made, and will in the future make, to the SDGs.

GPAD: a natural language processing-based application to extract the gene-disease association discovery information from OMIM

BACKGROUND

Thousands of genes have been associated with different Mendelian conditions. One of the valuable sources to track these gene-disease associations (GDAs) is the Online Mendelian Inheritance in Man (OMIM) database. However, most of the information in OMIM is textual, and heterogeneous (e.g. summarized by different experts), which complicates automated reading and understanding of the data. Here, we used Natural Language Processing (NLP) to make a tool (Gene-Phenotype Association Discovery (GPAD)) that could syntactically process OMIM text and extract the data of interest.

RESULTS

GPAD applies a series of language-based techniques to the text obtained from OMIM API to extract GDA discovery-related information. GPAD can inform when a particular gene was associated with a specific phenotype, as well as the type of validation-whether through model organisms or cohort-based patient-matching approaches-for such an association. GPAD extracted data was validated with published reports and was compared with large language model. Utilizing GPAD's extracted data, we analysed trends in GDA discoveries, noting a significant increase in their rate after the introduction of exome sequencing, rising from an average of about 150-250 discoveries each year. Contrary to hopes of resolving most GDAs for Mendelian disorders by now, our data indicate a substantial decline in discovery rates over the past five years (2017-2022). This decline appears to be linked to the increasing necessity for larger cohorts to substantiate GDAs. The rising use of zebrafish and Drosophila as model organisms in providing evidential support for GDAs is also observed.

CONCLUSIONS

GPAD's real-time analyzing capacity offers an up-to-date view of GDA discovery and could help in planning and managing the research strategies. In future, this solution can be extended or modified to capture other information in OMIM and scientific literature.

Discovering and prioritizing candidate resistance genes against soybean pests by integrating GWAS and gene coexpression networks

Soybean is one of the most important legume crops worldwide. Soybean pests have a considerable impact on crop yield. Here, we integrated publicly available genome-wide association studies and transcriptomic data to prioritize candidate resistance genes against the insects Aphis glycines and Spodoptera litura, and the nematode Heterodera glycines. We identified 171, 7, and 228 high-confidence candidate resistance genes against A. glycines, S. litura, and H. glycines, respectively. We found some overlap of candidate genes between insect species, but not between insects and H. glycines. Although 15% of the prioritized candidate genes encode proteins of unknown function, the vast majority of the candidates are related to plant immunity processes, such as transcriptional regulation, signaling, oxidative stress, recognition, and physical defense. Based on the number of resistance alleles, we selected the ten most promising accessions against each pest species in the soybean USDA germplasm. The most resistant accessions do not reach the maximum theoretical resistance potential, indicating that they might be further improved to increase resistance in breeding programs or through genetic engineering. Finally, the coexpression networks we inferred in this work are available in a user-friendly web application (https://soypestgcn.venanciogroup.uenf.br/) and an R/Shiny package (https://github.com/almeidasilvaf/SoyPestGCN) that serve as a public resource to explore soybean-pest interactions at the transcriptional level.

Leveraging synthetic biology and metabolic engineering to overcome obstacles in plant pathway elucidation

Major hurdles in plant biosynthetic pathway elucidation and engineering include the need for rapid testing of enzyme candidates and the lack of complex substrates that are often not accumulated in the plant, amenable to synthesis, or commercially available. Linking metabolic engineering with gene discovery in both yeast and plant holds great promise to expedite the elucidation process and, at the same time, provide a platform for the sustainable production of plant metabolites. In this review, we highlight how synthetic biology and metabolic engineering alleviated longstanding obstacles in plant pathway elucidation. Recent advances in developing these chassis that showcase established and emerging strategies in accelerating biosynthetic gene discovery will also be discussed.

easyMF: A Web Platform for Matrix Factorization-Based Gene Discovery from Large-scale Transcriptome Data

With the development of high-throughput experimental technologies, large-scale RNA sequencing (RNA-Seq) data have been and continue to be produced, but have led to challenges in extracting relevant biological knowledge hidden in the produced high-dimensional gene expression matrices. Here, we develop easyMF ( https://github.com/cma2015/easyMF ), a web platform that can facilitate functional gene discovery from large-scale transcriptome data using matrix factorization (MF) algorithms. Compared with existing MF-based software packages, easyMF exhibits several promising features, such as greater functionality, flexibility and ease of use. The easyMF platform is equipped using the Big-Data-supported Galaxy system with user-friendly graphic user interfaces, allowing users with little programming experience to streamline transcriptome analysis from raw reads to gene expression, carry out multiple-scenario MF analysis, and perform multiple-way MF-based gene discovery. easyMF is also powered with the advanced packing technology to enhance ease of use under different operating systems and computational environments. We illustrated the application of easyMF for seed gene discovery from temporal, spatial, and integrated RNA-Seq datasets of maize (Zea mays L.), resulting in the identification of 3,167 seed stage-specific, 1,849 seed compartment-specific, and 774 seed-specific genes, respectively. The present results also indicated that easyMF can prioritize seed-related genes with superior prediction performance over the state-of-art network-based gene prioritization system MaizeNet. As a modular, containerized and open-source platform, easyMF can be further customized to satisfy users' specific demands of functional gene discovery and deployed as a web service for broad applications.

Whole genome sequencing in the diagnosis of primary ciliary dyskinesia

BACKGROUND

It is estimated that 1-13% of cases of bronchiectasis in adults globally are attributable to primary ciliary dyskinesia (PCD) but many adult patients with bronchiectasis have not been investigated for PCD. PCD is a disorder caused by mutations in genes required for motile cilium structure or function, resulting in impaired mucociliary clearance. Symptoms appear in infancy but diagnosis is often late or missed, often due to the lack of a "gold standard" diagnostic tool and non-specific symptoms. Mutations in > 50 genes account for around 70% of cases, with additional genes, and non-coding, synonymous, missense changes or structural variants (SVs) in known genes presumed to account for the missing heritability.

METHODS

UK patients with no identified genetic confirmation for the cause of their PCD or bronchiectasis were eligible for whole genome sequencing (WGS) in the Genomics England Ltd 100,000 Genomes Project. 21 PCD probands and 52 non-cystic fibrosis (CF) bronchiectasis probands were recruited in Wessex Genome Medicine Centre (GMC). We carried out analysis of single nucleotide variants (SNVs) and SVs in all families recruited in Wessex GMC.

RESULTS

16/21 probands in the PCD cohort received confirmed (n = 9), probable (n = 4) or possible (n = 3) diagnosis from WGS, although 13/16 of these could have been picked up by current standard of care gene panel testing. In the other cases, SVs were identified which were missed by panel testing. We identified variants in novel PCD candidate genes (IFT140 and PLK4) in 2 probands in the PCD cohort. 3/52 probands in the non-CF bronchiectasis cohort received a confirmed (n = 2) or possible (n = 1) diagnosis of PCD. We identified variants in novel PCD candidate genes (CFAP53 and CEP164) in 2 further probands in the non-CF bronchiectasis cohort.

CONCLUSIONS

Genetic testing is an important component of diagnosing PCD, especially in cases of atypical disease history. WGS is effective in cases where prior gene panel testing has found no variants or only heterozygous variants. In these cases it may detect SVs and is a powerful tool for novel gene discovery.

TMEM263: a novel candidate gene implicated in human autosomal recessive severe lethal skeletal dysplasia

INTRODUCTION

Skeletal dysplasia is a common, clinically and genetically heterogeneous disorder in the human population. An increasing number of different genes are being identified causing this disorder. We used whole exome sequencing (WES) for detection of skeletal dysplasia causing mutation in a fetus affected to severe lethal skeletal dysplasia.

PATIENT

Fetus was assessed by ultrasonography in second trimester of pregnancy. He suffers from severe rhizomelic dysplasia and also pathologic shortening of ribs. WES was applied to finding of causal mutation. Furthermore, bioinformatics analysis was performed to predict mutation impact.

RESULTS

Whole exome sequencing (WES) identified a homozygous frameshift mutation in the TMEM263 gene in a fetus with severe lethal skeletal dysplasia. Mutations of this gene have been previously identified in dwarf chickens, but this is the first report of involvement of this gene in human skeletal dysplasia. This gene plays a key role in the growth hormone signaling pathway.

CONCLUSION

TMEM263 can be considered as a new gene responsible for skeletal dysplasia. Given the complications observed in the affected fetus, the mutation of this gene appears to produce much more intense complications than that found in chickens and is likely to play a more important role in bone development in human.

Genetic architecture of autism spectrum disorder: Lessons from large-scale genomic studies

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with a strong genetic component. Recently developed genomic technologies, including microarray and next-generation sequencing (NGS), have enabled researchers to genetic analyses aimed at identifying genetic variations associated with ASD and to elucidate the genetic architecture of the disorder. Large-scale microarray, exome sequencing analyses, and robust statistical methods have resulted in successful gene discovery and identification of high-confidence ASD genes from among de novo and inherited variants. Efforts have been made to understand the genetic architecture of ASD using whole-genome sequencing and genome-wide association studies aimed at identifying noncoding mutations and common variants associated with ASD. In addition, the development of systems biology approaches has resulted in the integration of genetic findings with functional genomic datasets, thereby providing a unique insight into the functional convergence of ASD risk genes and their neurobiology. In this review, we summarize the latest findings of ASD genetic studies involving large cohorts and discuss their implications in ASD neurobiology and in clinical practice.

Rare disease patient matchmaking: development and outcomes of an internet case-finding strategy in the Undiagnosed Diseases Network

Background

Although clinician, researcher, and patient resources for matchmaking exist, finding similar patients remains an obstacle for rare disease diagnosis. The goals of this study were to develop and test the effectiveness of an Internet case-finding strategy and identify factors associated with increased matching within a rare disease population.

Methods

Public web pages were created for consented participants. Matches made, time to each inquiry and match, and outcomes were recorded and analyzed using descriptive statistics. A Poisson regression model was run to identify characteristics associated with matches.

Results

385 participants were referred to the project and 158 had pages posted. 579 inquiries were received; 89.0% were from the general public and 24.7% resulted in a match. 81.6% of pages received at least one inquiry and 15.0% had at least one patient match. Primary symptom category of neurology, diagnosis, gene page, and photo were associated with increased matches (p ≤ 0.05).

Conclusions

This Internet case-finding strategy was of interest to patients, families, and clinicians, and similar patients were identified using this approach. Extending matchmaking efforts to the general public resulted in matches and suggests including this population in matchmaking activities can improve identification of similar patients.

Supplementary information

The online version contains supplementary material available at 10.1186/s13023-021-01825-1.

Xenopus as a platform for discovery of genes relevant to human disease

Congenital birth defects result from an abnormal development of an embryo and have detrimental effects on children's health. Specifically, congenital heart malformations are a leading cause of death among pediatric patients and often require surgical interventions within the first year of life. Increased efforts to navigate the human genome provide an opportunity to discover multiple candidate genes in patients suffering from birth defects. These efforts, however, fail to provide an explanation regarding the mechanisms of disease pathogenesis and emphasize the need for an efficient platform to screen candidate genes. Xenopus is a rapid, cost effective, high-throughput vertebrate organism to model the mechanisms behind human disease. This review provides numerous examples describing the successful use of Xenopus to investigate the contribution of patient mutations to complex phenotypes including congenital heart disease and heterotaxy. Moreover, we describe a variety of unique methods that allow us to rapidly recapitulate patients' phenotypes in frogs: gene knockout and knockdown strategies, the use of fate maps for targeted manipulations, and novel imaging modalities. The combination of patient genomics data and the functional studies in Xenopus will provide necessary answers to the patients suffering from birth defects. Furthermore, it will allow for the development of better diagnostic methods to ensure early detection and intervention. Finally, with better understanding of disease pathogenesis, new treatment methods can be tailored specifically to address patient's phenotype and genotype.

ATAV: a comprehensive platform for population-scale genomic analyses

Background

A common approach for sequencing studies is to do joint-calling and store variants of all samples in a single file. If new samples are continually added or controls are re-used for several studies, the cost and time required to perform joint-calling for each analysis can become prohibitive.

Results

We present ATAV, an analysis platform for large-scale whole-exome and whole-genome sequencing projects. ATAV stores variant and per site coverage data for all samples in a centralized database, which is efficiently queried by ATAV to support diagnostic analyses for trios and singletons, as well as rare-variant collapsing analyses for finding disease associations in complex diseases. Runtime logs ensure full reproducibility and the modularized ATAV framework makes it extensible to continuous development. Besides helping with the identification of disease-causing variants for a range of diseases, ATAV has also enabled the discovery of disease-genes by rare-variant collapsing on datasets containing more than 20,000 samples. Analyses to date have been performed on data of more than 110,000 individuals demonstrating the scalability of the framework.

To allow users to easily access variant-level data directly from the database, we provide a web-based interface, the ATAV data browser (http://atavdb.org/). Through this browser, summary-level data for more than 40,000 samples can be queried by the general public representing a mix of cases and controls of diverse ancestries. Users have access to phenotype categories of variant carriers, as well as predicted ancestry, gender, and quality metrics. In contrast to many other platforms, the data browser is able to show data of newly-added samples in real-time and therefore evolves rapidly as more and more samples are sequenced.

Conclusions

Through ATAV, users have public access to one of the largest variant databases for patients sequenced at a tertiary care center and can look up any genes or variants of interest. Additionally, since the entire code is freely available on GitHub, ATAV can easily be deployed by other groups that wish to build their own platform, database, and user interface.

Effective Mapping by Sequencing to Isolate Causal Mutations in the Tomato Genome

Forward genetic analysis remains as one of the most powerful tools for assessing gene functions, although the identification of the causal mutation responsible for a given phenotype has been a tedious and time-consuming task until recently. Advances in deep sequencing technologies have provided new approaches for the exploitation of natural and artificially induced genetic diversity, thus accelerating the discovery of novel allelic variants. In this chapter, a mapping-by-sequencing forward genetics approach is described to identify causal mutations in tomato (Solanum lycopersicum L.), a major crop species that is also a model species for plant biology and breeding.

Association of RAD51C germline mutations with breast cancer among Bahamians

PURPOSE

RAD51C is known as an ovarian cancer gene; however, its role in breast cancer susceptibility is less clear. As part of a larger study, we assessed the role of germline RAD51C mutations in breast cancer development.

METHODS

We studied 387 unselected, BRCA1- and BRCA2-negative, Bahamian breast cancer cases and 653 controls to search for novel genetic associations with breast cancer development. During the first phase of the study, whole exome sequencing was utilized in 96 cases to identify an association between novel genes and breast cancer susceptibility. In the second phase of the study, targeted gene sequencing was utilized in the entirety of the cases and controls to identify an association between novel genetic mutations and breast cancer development.

RESULTS

A RAD51C mutation was found in five breast cancer cases and in no control (5/387 versus 0/653; p = 0.007). None of the mutation-positive cases reported a family history of ovarian cancer.

CONCLUSIONS

These data support increasing evidence that RAD51C mutations contribute to breast cancer susceptibility, although the impact may vary substantially from country to country.

Full-length transcriptome and targeted metabolome analyses provide insights into defense mechanisms of Malus sieversii against Agrilus mali

Malus sieversii is the wild progenitor for many cultivars of domesticated apple and an important germplasm resource for breeding. However, this valuable species faces a significant threat in the areas north of the Tianshan Mountains in China, by the invasion of Agrilus mali, a destructive pest of apple trees belonging to the family Buprestidae. Our preliminary study has has shown that there may be resistance to this insect in M. sieversii plants in the field, but the corresponding molecular mechanisms remain unclear. In this study, we compared the response of insect-resistant and insect-susceptible plants of M. sieversii to insect feeding using full-length transcriptome and targeted metabolome. 112,103 non-chimeric full-length reads (FLNC) totaling 10.52 Gb of data were generating with Pacific Biosciences SingleMolecule, Real-Time (PacBio SMRT) sequencing. A total of 130.06 Gb data of long reads were acquired with an Illumina HiSeq. Function annotation indicated that the different expressed genes (DEGs) were mainly involved in signal transduction pathway of plant hormones and in the synthesis of compounds such as terpenes, quinones, flavonoids, and jasmonic acid. Through targeted metabolome analysis resistant strains showed higher levels of trans-cinnamic acid, caffeine and ferulic acid after pest infestation. This study helps to decipher the transcriptional changes and related signaling paths in M. sieversii after an insect feeding, which lays a foundation for further research on molecular mechanisms of insect resistance in apples.

De novo Sequencing, Assembly, and Annotation of the Transcriptome for the Free-Living Testate Amoeba Arcella intermedia

Arcella, a diverse understudied genus of testate amoebae is a member of Tubulinea in Amoebozoa group. Transcriptomes are a powerful tool for characterization of these organisms as they are an efficient way of characterizing the protein-coding potential of the genome. In this work, we employed both single-cell and clonal populations transcriptomics to create a reference transcriptome for Arcella. We compared our results with annotations of Dictyostelium discoideum, a model Amoebozoan. We assembled a pool of 38 Arcella intermedia transcriptomes, which after filtering are composed of a total of 14,712 translated proteins. There are GO categories enriched in Arcella including mainly intracellular signal transduction pathways; we also used KEGG to annotate 11,546 contigs, which also have similar distribution to Dictyostelium. A large portion of data is still impossible to assign to a gene family, probably due to a combination of lineage-specific genes, incomplete sequences in the transcriptome and rapidly evolved genes. Some absences in pathways could also be related to low expression of these genes. We provide a reference database for Arcella, and we highlight the emergence of the need for further gene discovery in Arcella.

Global report on primary immunodeficiencies: 2018 update from the Jeffrey Modell Centers Network on disease classification, regional trends, treatment modalities, and physician reported outcomes

Primary immunodeficiencies (PI) are genetic defects of the immune system that result in chronic, serious, and often life-threatening infections, if not diagnosed and treated. Many patients with PI are undiagnosed, underdiagnosed, or misdiagnosed. In fact, recent studies have shown that PI may be more common than previously estimated and that as many as 1% of the population may be affected with a PI when all types and varieties are considered. In order to raise awareness of PI with the overall goal of reducing associated morbidity and mortality, the Jeffrey Modell Foundation (JMF) established a network of specialized centers that could better identify, diagnose, treat, and follow patients with PI disorders. Over the past decade, the Jeffrey Modell Centers Network (JMCN) has provided the infrastructure to accept referrals, provide diagnosis, and offer treatments. Currently, the network consists of 792 Expert Physicians at 358 institutions, in 277 cities, and 86 countries spanning 6 continents. JMF developed an annual survey for physician experts within the JMCN, using the categories and gene defects identified by the International Union of Immunological Societies Expert Committee for the Classification of PI, to report on the number of patients identified with PI; treatment modalities, including immunoglobulins, transplantation, and gene therapy; and data on gender and age. Center Directors also provided physician-reported outcomes and differentials pre- and post-diagnosis. The current physician-reported data reflect an increase in diagnosed patients, as well as those receiving treatment. Suspected patients are being identified and referred so that they can receive early and appropriate diagnosis and treatment. The significant increase in patients identified with a PI is due, in part, to expanding education and awareness initiatives, newborn screening, and the expansion of molecular diagnosis and sequencing. To our knowledge, this is the most extensive single physician report on patients with PI around the world.

Discovering metabolic disease gene interactions by correlated effects on cellular morphology

Objective

Impaired expansion of peripheral fat contributes to the pathogenesis of insulin resistance and Type 2 Diabetes (T2D). We aimed to identify novel disease–gene interactions during adipocyte differentiation.

Methods

Genes in disease-associated loci for T2D, adiposity and insulin resistance were ranked according to expression in human adipocytes. The top 125 genes were ablated in human pre-adipocytes via CRISPR/CAS9 and the resulting cellular phenotypes quantified during adipocyte differentiation with high-content microscopy and automated image analysis. Morphometric measurements were extracted from all images and used to construct morphologic profiles for each gene.

Results

Over 10⁷ morphometric measurements were obtained. Clustering of the morphologic profiles accross all genes revealed a group of 14 genes characterized by decreased lipid accumulation, and enriched for known lipodystrophy genes. For two lipodystrophy genes, BSCL2 and AGPAT2, sub-clusters with PLIN1 and CEBPA identifed by morphological similarity were validated by independent experiments as novel protein–protein and gene regulatory interactions.

Conclusions

A morphometric approach in adipocytes can resolve multiple cellular mechanisms for metabolic disease loci; this approach enables mechanistic interrogation of the hundreds of metabolic disease loci whose function still remains unknown.

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Loss-of-function genetic screen in human adipocytes for 125 genes selected from metabolic disease-associated loci.

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Genetic screen read out by cellular morphometry— 77,000 images taken with 400 morphological features extracted per image.

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Pairwise mechanistic interactions between genes identified by correlations of cellular morphometry—two interactions validated.

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Novel interaction between BSCL2 and PLIN1 from biophysical association of proteins at lipid droplet surface.

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Novel interaction between CEBPA and AGPAT2 from CEBPA dependent transcription of AGPAT2.

Identifying Toxicant-Interacting Genes Using Forward Genetic Screening in Zebrafish

Forward genetic screening is an extremely powerful method for identifying novel genes driving a broad range of phenotypes. This protocol describes the complete process for conducting a forward genetic screen in zebrafish, including mutagenesis with N-ethyl-N-nitrosourea (ENU), mating, phenotypic screening, and genetic mapping.

De novo transcriptome resources of the lichens, Dirinaria sp. UKM-J1 and UKM-K1 collected from Jerantut and Klang, Malaysia

Lichen is a symbiotic organism that exists as a single composite body consisting of a mycobiont (fungus) and a photobiont (algae or a cyanobacterium). Many lichen species are considered as extremophiles due to their tolerance to radiation, desiccation, temperature and pollution. However, not all lichen species are tolerant to harsh environmental conditions as several species are sensitive for example to nitrogen, sulphur, acidity, heavy metals, halogens (e.g. fluoride) and ozone. Thus, to better understand why some lichens can withstand exposure to pollutants as opposed to those that are susceptible, we focused on the lichen species of Dirinaria known for their wide distribution in the tropics, subtropics and pantropical, and moderate tolerance to air pollution. Their moderate tolerance to air pollution affords them to thrive in good air quality environments as well as polluted air environments. Lichen samples of Dirinaria sp., UKM-J1 and UKM-K1, were respectively collected from two areas with different levels of air quality based on Air Pollutant Index or API (with index pollutant criteria of PM₁₀, carbon monoxide, ozone, nitrogen dioxide and sulfur dioxide) in the outskirt of Jerantut (UKM-J1), a rural area in the middle of Peninsular Malaysia and the township of Klang (UKM-K1), in a busy area of the Klang Valley, Malaysia. API was monitored throughout 2012–2013 whereby the sample collection site in Klang showed markedly higher concentrations of pollutants in all the index pollutant criteria as compared to that of Jerantut. We performed transcriptome sequencing using Illumina RNA-seq technology and de novo assembly of the transcripts from the lichen samples. Raw reads from both libraries were deposited in the NCBI database with the accession number SRP138994.

A type 1 diabetes genetic risk score can discriminate monogenic autoimmunity with diabetes from early-onset clustering of polygenic autoimmunity with diabetes

AIMS/HYPOTHESIS

Identifying individuals suitable for monogenic autoimmunity testing and gene discovery studies is challenging: early-onset type 1 diabetes mellitus can cluster with additional autoimmune diseases due to shared polygenic risk and islet- and other organ-specific autoantibodies are present in both monogenic and polygenic aetiologies. We aimed to assess whether a type 1 diabetes genetic risk score (GRS) could identify monogenic autoimmune diabetes and be useful to prioritise individuals for gene discovery studies.

METHODS

We studied 79 individuals with diabetes and at least one additional autoimmune disease diagnosed before the age of 5 years. We screened all participants for the seven genes known to cause monogenic autoimmunity that can include diabetes (AIRE, IL2RA, FOXP3, LRBA, STAT1, STAT3, STAT5B). We genotyped the top ten risk alleles for type 1 diabetes, including HLA and non-HLA loci, to generate a type 1 diabetes GRS.

RESULTS

Of the 79 individuals studied, 37 (47%) had mutations in the monogenic autoimmunity genes. The type 1 diabetes GRS was lower in these individuals than in those without mutations in these genes (median 9th vs 49th centile of type 1 diabetes controls, p < 0.0001). Age of diabetes diagnosis and type 1 diabetes GRS combined to be highly discriminatory of monogenic autoimmunity (receiver operating characteristic AUC: 0.88). Most individuals without a mutation in a known gene had a high type 1 diabetes GRS, suggesting that they have polygenic clustering of type 1 diabetes and additional autoimmunity and should not be included in gene discovery studies.

CONCLUSIONS/INTERPRETATION

We have shown that the type 1 diabetes GRS can identify individuals likely to have monogenic autoimmunity, helping both diagnostic testing and novel monogenic autoimmunity gene discovery. Individuals with monogenic autoimmunity have a different clinical course to those with polygenic type 1 diabetes and can respond well to therapies targeting the underlying genetic defect.

Transposon Insertion Mutagenesis for Archaeal Gene Discovery

Archaea constitute the third domain of life, but studies on their physiology and other features have lagged behind bacteria and eukarya, largely due to the challenging biology of archaea and concomitant difficulties in methods development. The use of genome-wide en masse insertion mutagenesis is one of the most efficient means to discover the genes behind various biological functions, and such a methodology is described in this chapter for a model archaeon Haloferax volcanii. The strategy successfully employs efficient in vitro transposition in combination with gene targeting in vivo via homologous recombination. The methodology is general and should be transferable to other archaeal species.

Mapping a shared genetic basis for neurodevelopmental disorders

Distinct neurodevelopmental disorders have a common genetic etiology that explains the high degree of comorbidity among these disorders. A recent study sought to identify copy number variants across five neurodevelopmental disorders, and detected an enrichment for chromosome 9p24.3 duplication encompassing DOCK8 and KANK1 in affected individuals. Such large-scale studies will help uncover additional causative and modifier loci within common pathways, which will enable the development of therapeutic targets for the treatment of multiple disorders.

See related research 10.1186/s13073-017-0494-1

Primary immunodeficiencies worldwide: an updated overview from the Jeffrey Modell Centers Global Network

Primary immunodeficiencies (PI) are defects of the immune system that cause severe, sometimes life-threatening, infections if not diagnosed and treated appropriately. Many patients with PI are undiagnosed, under-diagnosed, or misdiagnosed. To raise awareness and assure earliest diagnosis, appropriate treatment, and proper care management, the Jeffrey Modell Foundation (JMF) implemented a physician education and public awareness program beginning in 2003. Data are requested annually from physician experts within the Jeffrey Modell Centers Network (JMCN), consisting of 602 expert physicians, at 253 academic institutions, in 206 cities, and 84 countries spanning six continents. Center Directors reported on patients' specific PI defects and treatment modalities including immunoglobulins, transplantation, and gene therapy as well as data on gender and age. Center Directors also provided physician-reported patient outcomes as well as pre- and post-diagnosis differences. Costs were assigned to these factors. In collaboration with the Network, JMF advocated, funded, and implemented population-based newborn screening for severe combined immunodeficiency and T cell lymphopenia, covering 96.2 % of all newborns in the US. Finally, 21 JMF Centers participated in a polio surveillance study of patients with PI who either received or have been exposed to the oral polio vaccine. These initiatives have led to an overall better understanding of the immune system and will continue to improve quality of life for those with PI.

Integrative analysis of DNA methylation and mRNA expression during differentiation of umbilical cord blood derived mononuclear cells to endothelial cells

Differentiation of umbilical cord blood derived mononuclear cells to endothelial cells is accompanied by massive changes in gene expression. Although methylation and demethylation of DNA likely play crucial roles in regulating gene expression, their interplay during differentiation remains elusive. To address this question, we performed deep sequencing of DNA methylation and mRNA expression to profile global changes in promoter methylation and gene expression during differentiation from mononuclear cells to outgrowing cells. We identified 61 downregulated genes with hypermethylation, including CD74, VAV1, TLR8, and NCF4, as well as 21 upregulated genes with hypomethylation, including ECSCR, MCAM, PGF, and ARHGEF15. Interestingly, gene ontology analysis showed that downregulated genes with hypermethylation were enriched in immune-related functions, and upregulated genes with hypomethylation were enriched in the developmental process and angiogenesis, indicating the important roles of DNA methylation in regulating differentiation. We performed polymerase chain reaction analyses and bisulfite sequencing of representative genes (CD74, VAV1, ECSCR, and MCAM) to verify the negative correlation between DNA methylation and gene expression. Further, inhibition of DNA methyltransferase and demethylase activities using 5'-aza-dc and shRNAs, specific for TET1 and TET2 mRNAs, respectively, revealed that DNA methylation was the main regulator of the reversible expression of functionally important genes. Collectively, our findings implicate DNA methylation as a critical regulator of gene expression during umbilical cord blood derived mononuclear cells to endothelial cell differentiation.

Genomics-Based Discovery of Plant Genes for Synthetic Biology of Terpenoid Fragrances: A Case Study in Sandalwood oil Biosynthesis

Terpenoid fragrances are powerful mediators of ecological interactions in nature and have a long history of traditional and modern industrial applications. Plants produce a great diversity of fragrant terpenoid metabolites, which make them a superb source of biosynthetic genes and enzymes. Advances in fragrance gene discovery have enabled new approaches in synthetic biology of high-value speciality molecules toward applications in the fragrance and flavor, food and beverage, cosmetics, and other industries. Rapid developments in transcriptome and genome sequencing of nonmodel plant species have accelerated the discovery of fragrance biosynthetic pathways. In parallel, advances in metabolic engineering of microbial and plant systems have established platforms for synthetic biology applications of some of the thousands of plant genes that underlie fragrance diversity. While many fragrance molecules (eg, simple monoterpenes) are abundant in readily renewable plant materials, some highly valuable fragrant terpenoids (eg, santalols, ambroxides) are rare in nature and interesting targets for synthetic biology. As a representative example for genomics/transcriptomics enabled gene and enzyme discovery, we describe a strategy used successfully for elucidation of a complete fragrance biosynthetic pathway in sandalwood (Santalum album) and its reconstruction in yeast (Saccharomyces cerevisiae). We address questions related to the discovery of specific genes within large gene families and recovery of rare gene transcripts that are selectively expressed in recalcitrant tissues. To substantiate the validity of the approaches, we describe the combination of methods used in the gene and enzyme discovery of a cytochrome P450 in the fragrant heartwood of tropical sandalwood, responsible for the fragrance defining, final step in the biosynthesis of (Z)-santalols.

Trends in Gene Expression Profiling for Prostate Cancer Risk Assessment: A Systematic Review

Objectives

The aim of the study is to review biotechnology advances in gene expression profiling on prostate cancer (PCa), focusing on experimental platform development and gene discovery, in relation to different study designs and outcomes in order to understand how they can be exploited to improve PCa diagnosis and clinical management.

Methods

We conducted a systematic literature review on gene expression profiling studies through PubMed/MEDLINE and Web of Science between 2000 and 2016. Tissue biopsy and clinical gene profiling studies with different outcomes (e.g., recurrence, survival) were included.

Results

Over 3,000 papers were screened and 137 full-text articles were selected. In terms of technology used, microarray is still the most popular technique, increasing from 50 to 70% between 2010 and 2015, but there has been a rise in the number of studies using RNA sequencing (13% in 2015). Sample sizes have increased, as well as the number of genes that can be screened all at once, but we have also observed more focused targeting in more recent studies. Qualitative analysis on the specific genes found associated with PCa risk or clinical outcomes revealed a large variety of gene candidates, with a few consistent cross-studies.

Conclusions

The last 15 years of research in gene expression in PCa have brought a large volume of data and information that has been decoded only in part, but advancements in high-throughput sequencing technology are increasing the amount of data that can be generated. The variety of findings warrants the execution of both validation studies and meta-analyses. Genetic biomarkers have tremendous potential for early diagnosis of PCa and, if coupled with other diagnostics (e.g., imaging), can effectively be used to concretize less-invasive, personalized prediction of PCa risk and progression.

New fungal defensin-like peptides provide evidence for fold change of proteins in evolution

Defensins containing a consensus cystine framework, Cys_[1]…Cys_[2]X₃Cys_[3]…Cys_[4]… Cys_[5]X₁Cys_[6] (X, any amino acid except Cys; …, variable residue numbers), are extensively distributed in a variety of multicellular organisms (plants, fungi and invertebrates) and essentially involved in immunity as microbicidal agents. This framework is a prerequisite for forming the cysteine-stabilized α-helix and β-sheet (CSαβ) fold, in which the two invariant motifs, Cys_[2]X₃Cys_[3]/Cys_[5]X₁Cys_[6], are key determinants of fold formation. By using a computational genomics approach, we identified a large superfamily of fungal defensin-like peptides (fDLPs) in the phytopathogenic fungal genus – Zymoseptoria, which includes 132 structurally typical and 63 atypical members. These atypical fDLPs exhibit an altered cystine framework and accompanying fold change associated with their secondary structure elements and disulfide bridge patterns, as identified by protein structure modelling. Despite this, they definitely are homologous with the typical fDLPs in view of their precise gene structure conservation and identical precursor organization. Sequence and structural analyses combined with functional data suggest that most of Zymoseptoria fDLPs might have lost their antimicrobial activity. The present study provides a clear example of fold change in the evolution of proteins and is valuable in establishing remote homology among peptide superfamily members with different folds.

Developing integrated crop knowledge networks to advance candidate gene discovery

The chances of raising crop productivity to enhance global food security would be greatly improved if we had a complete understanding of all the biological mechanisms that underpinned traits such as crop yield, disease resistance or nutrient and water use efficiency. With more crop genomes emerging all the time, we are nearer having the basic information, at the gene-level, to begin assembling crop gene catalogues and using data from other plant species to understand how the genes function and how their interactions govern crop development and physiology. Unfortunately, the task of creating such a complete knowledge base of gene functions, interaction networks and trait biology is technically challenging because the relevant data are dispersed in myriad databases in a variety of data formats with variable quality and coverage. In this paper we present a general approach for building genome-scale knowledge networks that provide a unified representation of heterogeneous but interconnected datasets to enable effective knowledge mining and gene discovery. We describe the datasets and outline the methods, workflows and tools that we have developed for creating and visualising these networks for the major crop species, wheat and barley. We present the global characteristics of such knowledge networks and with an example linking a seed size phenotype to a barley WRKY transcription factor orthologous to TTG2 from Arabidopsis, we illustrate the value of integrated data in biological knowledge discovery. The software we have developed (www.ondex.org) and the knowledge resources (http://knetminer.rothamsted.ac.uk) we have created are all open-source and provide a first step towards systematic and evidence-based gene discovery in order to facilitate crop improvement.

DNA methylation detection at single base resolution using targeted next generation bisulfite sequencing and cross validation using capillary sequencing

With a purpose of accurate and simultaneous determination of DNA methylation from multiple loci in multiple samples, here, we are demonstrating a method to aid rapid DNA methylation detection of genomic sequences. Using genomic DNA of peripheral blood from 14 healthy individuals, DNA methylation in 465 CpG sites from 12 loci of genes (ADAM22, ATF2, BCR, CD83, CREBBP, IL12B, IL17RA, MAP2K2, RBM38, TGFBR2, TGFBR3, and WNT5A) was analysed by targeted next generation bisulfite sequencing. Analysed region for three genes, BCR, IL17RA and RBM38 showed an absolute mean DNA methylation of 25.6%, 89.2% and 38.9% respectively. Other nine gene loci were unmethylated and exhibited <10% absolute mean DNA methylation. Two genes, IL17RA and RBM38 were technically validated using direct capillary sequencing and results were comparable with positive correlation (P=0.0088 & P<0.0001 respectively) in the CpG sites for DNA methylation. All CpG sites analysed from RBM38 genes locus displayed 95% limits of agreement for DNA methylation measurements from the two methods. The present approach provides a fast and reliable DNA methylation quantitative data at single base resolution with good coverage of the CpG sites under analysis in multiple loci and samples simultaneously. Use of targeted next generation bisulfite sequencing may provide an opportunity to explore genes in the discovery panel for biomarker identification and facilitate functional validation.

Omics in Chlamydomonas for Biofuel Production

In response to demands for sustainable domestic fuel sources, research into biofuels has become increasingly important. Many challenges face biofuels in their effort to replace petroleum fuels, but rational strain engineering of algae and photosynthetic organisms offers a great deal of promise. For decades, mutations and stress responses in photosynthetic microbiota were seen to result in production of exciting high-energy fuel molecules, giving hope but minor capability for design. However, '-omics' techniques for visualizing entire cell processing has clarified biosynthesis and regulatory networks. Investigation into the promising production behaviors of the model organism C. reinhardtii and its mutants with these powerful techniques has improved predictability and understanding of the diverse, complex interactions within photosynthetic organisms. This new equipment has created an exciting new frontier for high-throughput, predictable engineering of photosynthetically produced carbon-neutral biofuels.

Genome, Exome, and Targeted Next-Generation Sequencing in Neonatal Diabetes

The use of targeted gene panels now allows the analysis of all the genes known to cause a disease in a single test. For neonatal diabetes, this has resulted in a paradigm shift with patients receiving a genetic diagnosis early and the genetic results guiding their clinical management. Exome and genome sequencing are powerful tools to identify novel genetic causes of known diseases. For neonatal diabetes, the use of these technologies has resulted in the identification of 2 novel disease genes (GATA6 and STAT3) and a novel regulatory element of PTF1A, in which mutations cause pancreatic agenesis.

Genetic causes of amyotrophic lateral sclerosis: new genetic analysis methodologies entailing new opportunities and challenges

The genetic architecture of amyotrophic lateral sclerosis (ALS) is being increasingly understood. In this far-reaching review, we examine what is currently known about ALS genetics and how these genes were initially identified. We also discuss the various types of mutations that might underlie this fatal neurodegenerative condition and outline some of the strategies that might be useful in untangling them. These include expansions of short repeat sequences, common and low-frequency genetic variations, de novo mutations, epigenetic changes, somatic mutations, epistasis, oligogenic and polygenic hypotheses. This article is part of a Special Issue entitled ALS complex pathogenesis.

Mining the genome for lipid genes

Mining of the genome for lipid genes has since the early 1970s helped to shape our understanding of how triglycerides are packaged (in chylomicrons), repackaged (in very low density lipoproteins; VLDL), and hydrolyzed, and also how remnant and low-density lipoproteins (LDL) are cleared from the circulation. Gene discoveries have also provided insights into high-density lipoprotein (HDL) biogenesis and remodeling. Interestingly, at least half of these key molecular genetic studies were initiated with the benefit of prior knowledge of relevant proteins. In addition, multiple important findings originated from studies in mouse, and from other types of non-genetic approaches. Although it appears by now that the main lipid pathways have been uncovered, and that only modulators or adaptor proteins such as those encoded by LDLRAP1, APOA5, ANGPLT3/4, and PCSK9 are currently being discovered, genome wide association studies (GWAS) in particular have implicated many new loci based on statistical analyses; these may prove to have equally large impacts on lipoprotein traits as gene products that are already known. On the other hand, since 2004 - and particularly since 2010 when massively parallel sequencing has become de rigeur - no major new insights into genes governing lipid metabolism have been reported. This is probably because the etiologies of true Mendelian lipid disorders with overt clinical complications have been largely resolved. In the meantime, it has become clear that proving the importance of new candidate genes is challenging. This could be due to very low frequencies of large impact variants in the population. It must further be emphasized that functional genetic studies, while necessary, are often difficult to accomplish, making it hazardous to upgrade a variant that is simply associated to being definitively causative. Also, it is clear that applying a monogenic approach to dissect complex lipid traits that are mostly of polygenic origin is the wrong way to proceed. The hope is that large-scale data acquisition combined with sophisticated computerized analyses will help to prioritize and select the most promising candidate genes for future research. We suggest that at this point in time, investment in sequence technology driven candidate gene discovery could be recalibrated by refocusing efforts on direct functional analysis of the genes that have already been discovered. This article is part of a Special Issue entitled: From Genome to Function.