Melt analysis of mismatch amplification mutation assays (melt-MAMA): a functional study of a cost-effective SNP genotyping assay in bacterial models.

Single nucleotide polymorphisms (SNPs) are abundant in genomes of all species and biologically informative markers extensively used across broad scientific disciplines. Newly identified SNP markers are publicly available at an ever-increasing rate due to advancements in sequencing technologies. Efficient, cost-effective SNP genotyping methods to screen sample populations are in great demand in well-equipped laboratories, but also in developing world situations. Dual Probe TaqMan assays are robust but can be cost-prohibitive and require specialized equipment. The Mismatch Amplification Mutation Assay, coupled with melt analysis (Melt-MAMA), is flexible, efficient and cost-effective. However, Melt-MAMA traditionally suffers from high rates of assay design failures and knowledge gaps on assay robustness and sensitivity. In this study, we identified strategies that improved the success of Melt-MAMA. We examined the performance of 185 Melt-MAMAs across eight different pathogens using various optimization parameters. We evaluated the effects of genome size and %GC content on assay development. When used collectively, specific strategies markedly improved the rate of successful assays at the first design attempt from ~50% to ~80%. We observed that Melt-MAMA accurately genotypes across a broad DNA range (~100 ng to ~0.1 pg). Genomic size and %GC content influence the rate of successful assay design in an independent manner. Finally, we demonstrated the versatility of these assays by the creation of a duplex Melt-MAMA real-time PCR (two SNPs) and conversion to a size-based genotyping system, which uses agarose gel electrophoresis. Melt-MAMA is comparable to Dual Probe TaqMan assays in terms of design success rate and accuracy. Although sensitivity is less robust than Dual Probe TaqMan assays, Melt-MAMA is superior in terms of cost-effectiveness, speed of development and versatility. We detail the parameters most important for the successful application of Melt-MAMA, which should prove useful to the wider scientific community

Understanding the evolution of immune genes in jawed vertebrates

Driven by co-evolution with pathogens, host immunity continuously adapts to optimize defence against pathogens within a given environment. Recent advances in genetics, genomics and transcriptomics have enabled a more detailed investigation into how immunogenetic variation shapes the diversity of immune responses seen across domestic and wild animal species. However, a deeper understanding of the diverse molecular mechanisms that shape immunity within and among species is still needed to gain insight into-and generate evolutionary hypotheses on-the ultimate drivers of immunological differences. Here, we discuss current advances in our understanding of molecular evolution underpinning jawed vertebrate immunity. First, we introduce the immunome concept, a framework for characterizing genes involved in immune defence from a comparative perspective, then we outline how immune genes of interest can be identified. Second, we focus on how different selection modes are observed acting across groups of immune genes and propose hypotheses to explain these differences. We then provide an overview of the approaches used so far to study the evolutionary heterogeneity of immune genes on macro and microevolutionary scales. Finally, we discuss some of the current evidence as to how specific pathogens affect the evolution of different groups of immune genes. This review results from the collective discussion on the current key challenges in evolutionary immunology conducted at the ESEB 2021 Online Satellite Symposium: Molecular evolution of the vertebrate immune system, from the lab to natural populations

Phylogeography of Francisella tularensis subspecies holarctica from the country of Georgia

<p>Abstract</p> <p>Background</p> <p><it>Francisella tularensis</it>, the causative agent of tularemia, displays subspecies-specific differences in virulence, geographic distribution, and genetic diversity. <it>F. tularensis </it>subsp. <it>holarctica </it>is widely distributed throughout the Northern Hemisphere. In Europe, <it>F. tularensis </it>subsp. <it>holarctica </it>isolates have largely been assigned to two phylogenetic groups that have specific geographic distributions. Most isolates from Western Europe are assigned to the B.Br.FTNF002-00 group, whereas most isolates from Eastern Europe are assigned to numerous lineages within the B.Br.013 group. The eastern geographic extent of the B.Br.013 group is currently unknown due to a lack of phylogenetic knowledge about populations at the European/Asian juncture and in Asia. In this study, we address this knowledge gap by describing the phylogenetic structure of <it>F. tularensis </it>subsp. <it>holarctica </it>isolates from the country of Georgia, and by placing these isolates into a global phylogeographic context.</p> <p>Results</p> <p>We identified a new genetic lineage of <it>F. tularensis </it>subsp. <it>holarctica </it>from Georgia that belongs to the B.Br.013 group. This new lineage is genetically and geographically distinct from lineages previously described from the B.Br.013 group from Central-Eastern Europe. Importantly, this new lineage is basal within the B.Br.013 group, indicating the Georgian lineage diverged before the diversification of the other known B.Br.013 lineages. Although two isolates from the Georgian lineage were collected nearby in the Ukrainian region of Crimea, all other global isolates assigned to this lineage were collected in Georgia. This restricted geographic distribution, as well as the high levels of genetic diversity within the lineage, is consistent with a relatively older origin and localized differentiation.</p> <p>Conclusions</p> <p>We identified a new lineage of <it>F. tularensis </it>subsp. <it>holarctica </it>from Georgia that appears to have an older origin than any other diversified lineages previously described from the B.Br.013 group. This finding suggests that additional phylogenetic studies of <it>F. tularensis </it>subsp. <it>holarctica </it>populations in Eastern Europe and Asia have the potential to yield important new insights into the evolutionary history and phylogeography of this broadly dispersed <it>F. tularensis </it>subspecies.</p

Identification of microRNA-mRNA modules using microarray data

<p>Abstract</p> <p>Background</p> <p>MicroRNAs (miRNAs) are post-transcriptional regulators of mRNA expression and are involved in numerous cellular processes. Consequently, miRNAs are an important component of gene regulatory networks and an improved understanding of miRNAs will further our knowledge of these networks. There is a many-to-many relationship between miRNAs and mRNAs because a single miRNA targets multiple mRNAs and a single mRNA is targeted by multiple miRNAs. However, most of the current methods for the identification of regulatory miRNAs and their target mRNAs ignore this biological observation and focus on miRNA-mRNA pairs.</p> <p>Results</p> <p>We propose a two-step method for the identification of many-to-many relationships between miRNAs and mRNAs. In the first step, we obtain miRNA and mRNA clusters using a combination of miRNA-target mRNA prediction algorithms and microarray expression data. In the second step, we determine the associations between miRNA clusters and mRNA clusters based on changes in miRNA and mRNA expression profiles. We consider the miRNA-mRNA clusters with statistically significant associations to be potentially regulatory and, therefore, of biological interest.</p> <p>Conclusions</p> <p>Our method reduces the interactions between several hundred miRNAs and several thousand mRNAs to a few miRNA-mRNA groups, thereby facilitating a more meaningful biological analysis and a more targeted experimental validation.</p

SARS-CoV-2 breakthrough infections among vaccinated individuals with rheumatic disease : Results from the COVID-19 Global Rheumatology Alliance provider registry

Funding Information: members of the COVID-19 Global Rheumatology Alliance and do not necessarily represent the views of the American College of Rheumatology (ACR), EULAR, the UK National Health Service (NHS), the National Institute for Health Research (NIHR), the UK Department of Health or any other organisation. Competing interests KLH reports she has received non-personal speaker’s fees from AbbVie and grant income from BMS, UCB and Pfizer, all unrelated to this manuscript; KLH is supported by the NIHR Manchester Biomedical Research Centre. LG reports personal consultant fees from AbbVie, Amgen, BMS, Biogen, Celgene, Gilead, Janssen, Lilly, Novartis, Pfizer, Samsung Bioepis, Sanofi-Aventis and UCB, and grants from Amgen, Lilly, Janssen, Pfizer, Sandoz, Sanofi and Galapagos, all unrelated to this manuscript. AS reports research grants from a consortium of 14 companies (among them AbbVie, BMS, Celltrion, Fresenius Funding Information: Kabi, Gilead/Galapagos, Lilly, Mylan/Viatris, Hexal, MSD, Pfizer, Roche, Samsung, Sanofi-Aventis and UCB) supporting the German RABBIT register and personal fees from lectures for AbbVie, MSD, Roche, BMS, Lilly and Pfizer, all unrelated to this manuscript. LC has not received fees or personal grants from any laboratory, but her institute works by contract for laboratories among other institutions, such as AbbVie Spain, Eisai, Gebro Pharma, Merck Sharp & Dohme España, Novartis Farmaceutica, Pfizer, Roche Farma, Sanofi-Aventis, Astellas Pharma, Actelion Pharmaceuticals España, Grünenthal and UCB Pharma. EF-M reports personal consultant fees from Boehringer Ingelheim Portugal and that LPCDR received support for specific activities: grants from AbbVie, Novartis, Janssen-Cilag, Lilly Portugal, Sanofi, Grünenthal, MSD, Celgene, Medac, Pharmakern and GAfPA; grants and non-financial support from Pfizer; and non-financial support from Grünenthal, outside the submitted work. IB reports personal consultant fees from AbbVie, Novartis, Pfizer and Janssen, all unrelated to this manuscript. JZ reports speaker fees from AbbVie, Novartis and Janssen/Johnson & Johnson, all unrelated to this manuscript. GR-C reports personal consultant fees from Eli Lilly and Novartis, all unrelated to this manuscript. JS is supported by the National Institute of Arthritis and Musculoskeletal and Skin Diseases (grant numbers: R01 AR077607, P30 AR070253 and P30 AR072577), and the R Bruce and Joan M Mickey Research Scholar Fund. JS has received research support from Amgen and Bristol Myers Squibb and performed consultancy for Bristol Myers Squibb, Gilead, Inova, Janssen and Optum, unrelated to this work. LW receives speaker’s bureau fees from Aurinia Pharma, unrelated to this manuscript. SB reports no competing interests related to this work. He reports non-branded consulting fees for AbbVie, Horizon and Novartis (all <$10 000). MGM has no competing interests related to this work. She serves as a patient consultant for BMS, BI JNJ and Aurinia (all <$10 000). RG reports no competing interests related to this work. Outside of this work she reports personal and/or speaking fees from AbbVie, Janssen, Novartis, Pfizer and Cornerstones and travel assistance from Pfizer (all <$10 000). JH reports no competing interests related to this work. He is supported by grants from the Rheumatology Research Foundation and has salary support from the Childhood Arthritis and Rheumatology Research Alliance. He has performed consulting for Novartis, Sobi and Biogen, all unrelated to this work (<$10 000). ESi reports non-financial support from Canadian Arthritis Patient Alliance, outside the submitted work. PS reports personal fees from the American College of Rheumatology/Wiley Publishing, outside the submitted work. ZW reports grant support from Bristol Myers Squibb and Principia/Sanofi and performed consultancy for Viela Bio and MedPace, outside the submitted work. His work is supported by grants from the National Institutes of Health. PMM has received consulting/speaker’s fees from AbbVie, BMS, Celgene, Eli Lilly, Galapagos, Janssen, MSD, Novartis, Orphazyme, Pfizer, Roche and UCB, all unrelated to this study. PMM is supported by the National Institute for Health Research (NIHR) University College London Hospitals (UCLH) Biomedical Research Centre (BRC). PCR reports no competing interests related to this work. Outside of this work PCR reports personal fees from AbbVie, Atom Bioscience, Eli Lilly, Gilead, GlaxoSmithKline, Janssen, Kukdong, Novartis, UCB, Roche and Pfizer; meeting attendance support from BMS, Pfizer and UCB; and grant funding from Janssen, Novartis, Pfizer and UCB Pharma (all <$10 000). JY reports no competing interests related to this work. Her work is supported by grants from the National Institutes of Health (K24 AR074534 and P30 AR070155). Outside of this work, she has received research grants or performed consulting for Gilead, BMS Foundation, Pfizer, Aurinia and AstraZeneca. Funding Information: Twitter Jean Liew @rheum_cat, Loreto Carmona @carmona_loreto, Pedro M Machado @pedrommcmachado and Philip C Robinson @philipcrobinson Contributors All authors contributed to the study design, data collection, interpretation of results and review/approval of the final submitted manuscript. JL and MG are guarantors for this manuscript. Funding MG reports grants from the National Institutes of Health, NIAMS, outside the submitted work. KLH is supported by the NIHR Manchester Biomedical Research Centre. JS is supported by the National Institute of Arthritis and Musculoskeletal and Skin Diseases (grant numbers: R01 AR077607, P30 AR070253 and P30 AR072577), and the R Bruce and Joan M Mickey Research Scholar Fund. JH is supported by grants from the Rheumatology Research Foundation. ZW is supported by grants from the National Institutes of Health. PMM is supported by the National Institute for Health Research (NIHR) University College London Hospitals (UCLH) Biomedical Research Centre (BRC). JY is supported by grants from the National Institutes of Health (K24 AR074534 and P30 AR070155). Publisher Copyright: ©Objective. While COVID-19 vaccination prevents severe infections, poor immunogenicity in immunocompromised people threatens vaccine effectiveness. We analysed the clinical characteristics of patients with rheumatic disease who developed breakthrough COVID-19 after vaccination against SARS-CoV-2.  Methods. We included people partially or fully vaccinated against SARS-CoV-2 who developed COVID-19 between 5 January and 30 September 2021 and were reported to the Global Rheumatology Alliance registry. Breakthrough infections were defined as occurring ≥14 days after completion of the vaccination series, specifically 14 days after the second dose in a two-dose series or 14 days after a single-dose vaccine. We analysed patients' demographic and clinical characteristics and COVID-19 symptoms and outcomes. Results SARS-CoV-2 infection was reported in 197 partially or fully vaccinated people with rheumatic disease (mean age 54 years, 77% female, 56% white). The majority (n=140/197, 71%) received messenger RNA vaccines. Among the fully vaccinated (n=87), infection occurred a mean of 112 (±60) days after the second vaccine dose. Among those fully vaccinated and hospitalised (n=22, age range 36-83 years), nine had used B cell-depleting therapy (BCDT), with six as monotherapy, at the time of vaccination. Three were on mycophenolate. The majority (n=14/22, 64%) were not taking systemic glucocorticoids. Eight patients had pre-existing lung disease and five patients died. Conclusion. More than half of fully vaccinated individuals with breakthrough infections requiring hospitalisation were on BCDT or mycophenolate. Further risk mitigation strategies are likely needed to protect this selected high-risk population.publishersversionPeer reviewe

Early disease onset is predicted by a higher genetic risk for lupus and is associated with a more severe phenotype in lupus patients

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/94407/1/Webb_2011_Early_disease_onset.pdf165

Assessment of Brain Age in Posttraumatic Stress Disorder: Findings from the ENIGMA PTSD and Brain Age Working Groups

Background Posttraumatic stress disorder (PTSD) is associated with markers of accelerated aging. Estimates of brain age, compared to chronological age, may clarify the effects of PTSD on the brain and may inform treatment approaches targeting the neurobiology of aging in the context of PTSD. Method Adult subjects (N = 2229; 56.2% male) aged 18–69 years (mean = 35.6, SD = 11.0) from 21 ENIGMA-PGC PTSD sites underwent T1-weighted brain structural magnetic resonance imaging, and PTSD assessment (PTSD+, n = 884). Previously trained voxel-wise (brainageR) and region-of-interest (BARACUS and PHOTON) machine learning pipelines were compared in a subset of control subjects (n = 386). Linear mixed effects models were conducted in the full sample (those with and without PTSD) to examine the effect of PTSD on brain predicted age difference (brain PAD; brain age − chronological age) controlling for chronological age, sex, and scan site. Results BrainageR most accurately predicted brain age in a subset (n = 386) of controls (brainageR: ICC = 0.71, R = 0.72, MAE = 5.68; PHOTON: ICC = 0.61, R = 0.62, MAE = 6.37; BARACUS: ICC = 0.47, R = 0.64, MAE = 8.80). Using brainageR, a three-way interaction revealed that young males with PTSD exhibited higher brain PAD relative to male controls in young and old age groups; old males with PTSD exhibited lower brain PAD compared to male controls of all ages. Discussion Differential impact of PTSD on brain PAD in younger versus older males may indicate a critical window when PTSD impacts brain aging, followed by age-related brain changes that are consonant with individuals without PTSD. Future longitudinal research is warranted to understand how PTSD impacts brain aging across the lifespan