Assessing the reproducibility of machine-learning-based biomarker discovery in Parkinson's disease.

Feature selection and machine learning algorithms can be used to analyze Single Nucleotide Polymorphisms (SNPs) data and identify potential disease biomarkers. Reproducibility of identified biomarkers is critical for them to be useful for clinical research; however, genotyping platforms and selection criteria for individuals to be genotyped affect the reproducibility of identified biomarkers. To assess biomarkers reproducibility, we collected five SNPs datasets from the database of Genotypes and Phenotypes (dbGaP) and explored several data integration strategies. While combining datasets can lead to a reduction in classification accuracy, it has the potential to improve the reproducibility of potential biomarkers. We evaluated the agreement among different strategies in terms of the SNPs that were identified as potential Parkinson's disease (PD) biomarkers. Our findings indicate that, on average, 93% of the SNPs identified in a single dataset fail to be identified in other datasets. However, through dataset integration, this lack of replication is reduced to 62%. We discovered fifty SNPs that were identified at least twice, which could potentially serve as novel PD biomarkers. These SNPs are indirectly linked to PD in the literature but have not been directly associated with PD before. These findings open up new potential avenues of investigation.

Prognostic Outcomes of Cutaneous Squamous Cell Carcinoma in Solid Organ Transplant Recipients: A Retrospective Comparative Cohort Study.

INTRODUCTION: Cutaneous squamous cell carcinoma (cSCC) is the second most common cutaneous neoplasm, and its incidence is on the rise. While most cSCCs have an excellent prognosis, certain risk factors, especially immunosuppression, have been associated with higher rates of local recurrence (LR), metastasis, and poor prognosis. This study aims to assess the risk factors for LR and metastasis development in cSCC among solid organ transplant recipients (SOTRs) and compare these rates with those in immunocompetent patients. MATERIALS AND METHODS: A retrospective observational study included cSCC cases from the University Hospital Reina Sofía in Córdoba, Spain, between 2002 and 2019. Demographic, clinical, and histopathological data were collected. Local recurrence and metastasis rates were analyzed, along with progression-free survival. Univariate analyses were performed to identify prognostic factors in SOTRs. RESULTS: Among 849 cSCC cases, we found higher rates of local recurrence and metastasis in tumors developed by SOTRs compared to those in immunocompetent individuals. However, no significant differences in local recurrence, metastasis, or progression-free survival were observed between the two groups. Risk factors for adverse outcomes in SOTRs included tumor size > 2 cm, depth > 4 mm, and a higher Clark level. A total of 34.4% of SOTRs developed a second primary cSCC during the follow-up. CONCLUSIONS: In our study, cSCCs in SOTRs did not exhibit statistically significant differences in the rates of adverse outcomes compared to immunocompetent patients. The prognosis of cSCCs in SOTRs may be more related to other tumor-dependent risk factors than to the immunosuppression status itself. Future studies are needed to refine risk stratification and follow-up protocols to ensure the optimal management of high-risk cSCC cases, particularly among immunosuppressed patients.

Extracellular vesicle small RNA cargo discriminates non-cancer donors from pediatric B-lymphoblastic leukemia patients.

Pediatric B-acute lymphoblastic leukemia (B-ALL) is a disease of abnormally growing B lymphoblasts. Here we hypothesized that extracellular vesicles (EVs), which are nanosized particles released by all cells (including cancer cells), could be used to monitor B-ALL severity and progression by sampling plasma instead of bone marrow. EVs are especially attractive as they are present throughout the circulation regardless of the location of the originating cell. First, we used nanoparticle tracking analysis to compare EVs between non-cancer donor (NCD) and B-ALL blood plasma; we found that B-ALL plasma contains more EVs than NCD plasma. We then isolated EVs from NCD and pediatric B-ALL peripheral blood plasma using a synthetic peptide-based isolation technique (Vn96), which is clinically amenable and isolates a broad spectrum of EVs. RNA-seq analysis of small RNAs contained within the isolated EVs revealed a signature of differentially packaged and exclusively packaged RNAs that distinguish NCD from B-ALL. The plasma EVs contain a heterogenous mixture of miRNAs and fragments of long non-coding RNA (lncRNA) and messenger RNA (mRNA). Transcripts packaged in B-ALL EVs include those involved in negative cell cycle regulation, potentially suggesting that B-ALL cells may use EVs to discard gene sequences that control growth. In contrast, NCD EVs carry sequences representative of multiple organs, including brain, muscle, and epithelial cells. This signature could potentially be used to monitor B-ALL disease burden in pediatric B-ALL patients via blood draws instead of invasive bone marrow aspirates.

A Study on the Geometric and Kinematic Descriptors of Trajectories in the Classification of Ship Types.

The classification of ships based on their trajectory descriptors is a common practice that is helpful in various contexts, such as maritime security and traffic management. For the most part, the descriptors are either geometric, which capture the shape of a ship's trajectory, or kinematic, which capture the motion properties of a ship's movement. Understanding the implications of the type of descriptor that is used in classification is important for feature engineering and model interpretation. However, this matter has not yet been deeply studied. This article contributes to feature engineering within this field by introducing proper similarity measures between the descriptors and defining sound benchmark classifiers, based on which we compared the predictive performance of geometric and kinematic descriptors. The performance profiles of geometric and kinematic descriptors, along with several standard tools in interpretable machine learning, helped us to provide an account of how different ships differ in movement. Our results indicated that the predictive performance of geometric and kinematic descriptors varied greatly, depending on the classification problem at hand. We also showed that the movement of certain ship classes solely differed geometrically while some other classes differed kinematically and that this difference could be formulated in simple terms. On the other hand, the movement characteristics of some other ship classes could not be delineated along these lines and were more complicated to express. Finally, this study verified the conjecture that the geometric-kinematic taxonomy could be further developed as a tool for more accessible feature selection.

Noninvasive early detection of colorectal cancer by hypermethylation of the LINC00473 promoter in plasma cell-free DNA.

BACKGROUND: Current noninvasive assays have limitations in the early detection of colorectal cancer. We evaluated the clinical utility of promoter methylation of the long noncoding RNA LINC00473 as a noninvasive biomarker to detect colorectal cancer and associated precancerous lesions. METHODS: We evaluated the epigenetic regulation of LINC00473 through promoter hypermethylation in colorectal cancer cell lines using bisulfite genomic sequencing and expression analyses. DNA methylation of LINC00473 was analyzed in primary colorectal tumors using 450K arrays and RNA-seq from The Cancer Genome Atlas (TCGA). Tissue-based findings were validated in several independent cohorts of colorectal cancer and advanced colorectal polyp patients by pyrosequencing. We explored the clinical utility of LINC00473 methylation for the early detection of colorectal cancer in plasma cell-free DNA by quantitative methylation-specific PCR and droplet digital PCR. RESULTS: LINC00473 showed transcriptionally silencing due to promoter hypermethylation in colorectal cancer cell lines and primary tumors. Methylation of the LINC00473 promoter accurately detected primary colorectal tumors in two independent clinical cohorts, with areas under the receiver operating characteristic curves (AUCs) of 0.94 and 0.89. This biomarker also identified advanced colorectal polyps from two other tissue-based clinical cohorts with high diagnostic accuracy (AUCs of 0.99 and 0.78). Finally, methylation analysis of the LINC00473 promoter in plasma cell-free DNA accurately identified patients with colorectal cancer and advanced colorectal polyps (AUCs of 0.88 and 0.84, respectively), which was confirmed in an independent cohort of patients. CONCLUSIONS: Hypermethylation of the LINC00473 promoter is a new promising biomarker for noninvasive early detection of colorectal cancer and related precancerous lesions.

The Campo de Dalias GNSS Network Unveils the Interaction between Roll-Back and Indentation Tectonics in the Gibraltar Arc.

The Gibraltar Arc includes the Betic and Rif Cordilleras surrounding the Alboran Sea; it is formed at the northwest-southeast Eurasia-Nubia convergent plate boundary in the westernmost Mediterranean. Since 2006, the Campo de Dalias GNSS network has monitored active tectonic deformation of the most seismically active area on the north coast of the Alboran Sea. Our results show that the residual deformation rates with respect to Eurasia range from 1.7 to 3.0 mm/year; roughly homogenous west-southwestward displacements of the northern sites occur, while the southern sites evidence irregular displacements towards the west and northwest. This deformation pattern supports simultaneous east-northeast-west-southwest extension, accommodated by normal and oblique faults, and north-northwest-south-southeast shortening that develops east-northeast-west-southwest folds. Moreover, the GNSS results point to dextral creep of the main northwest-southeast Balanegra Fault. These GNNS results thus reveal, for the first time, present-day interaction of the roll-back tectonics of the Rif-Gibraltar-Betic slab in the western part of the Gibraltar Arc with the indentation tectonics affecting the eastern and southern areas, providing new insights for improving tectonic models of arcuate orogens.

sRNARFTarget: a fast machine-learning-based approach for transcriptome-wide sRNA target prediction.

Bacterial small regulatory RNAs (sRNAs) are key regulators of gene expression in many processes related to adaptive responses. A multitude of sRNAs have been identified in many bacterial species; however, their function has yet to be elucidated. A key step to understand sRNAs function is to identify the mRNAs these sRNAs bind to. There are several computational methods for sRNA target prediction, and the most accurate one is CopraRNA which is based on comparative-genomics. However, species-specific sRNAs are quite common and CopraRNA cannot be used for these sRNAs. The most commonly used transcriptome-wide sRNA target prediction method and second-most-accurate method is IntaRNA. However, IntaRNA can take hours to run on a bacterial transcriptome. Here we present sRNARFTarget, a machine-learning-based method for transcriptome-wide sRNA target prediction applicable to any sRNA. We comparatively assessed the performance of sRNARFTarget, CopraRNA and IntaRNA in three bacterial species. Our results show that sRNARFTarget outperforms IntaRNA in terms of accuracy, ranking of true interacting pairs, and running time. However, CopraRNA substantially outperforms the other two programsin terms of accuracy. Thus, we suggest using CopraRNA when homolog sequences of the sRNA are available, and sRNARFTarget for transcriptome-wide prediction or for species-specific sRNAs. sRNARFTarget is available at https://github.com/BioinformaticsLabAtMUN/sRNARFTarget.

Promotech: a general tool for bacterial promoter recognition.

Promoters are genomic regions where the transcription machinery binds to initiate the transcription of specific genes. Computational tools for identifying bacterial promoters have been around for decades. However, most of these tools were designed to recognize promoters in one or few bacterial species. Here, we present Promotech, a machine-learning-based method for promoter recognition in a wide range of bacterial species. We compare Promotech's performance with the performance of five other promoter prediction methods. Promotech outperforms these other programs in terms of area under the precision-recall curve (AUPRC) or precision at the same level of recall. Promotech is available at https://github.com/BioinformaticsLabAtMUN/PromoTech .

Computational Methods for Elucidating Gene Expression Regulation in Bacteria.

Research over the past two decades has uncovered an unexpected complexity and intricacy of gene expression regulation in bacteria. Bacteria have (1) numerous small noncoding RNAs (sRNAs) which are ubiquitous regulators of gene expression, (2) a flexible and diverse promoter structure, and (3) transcription termination as another means of gene expression regulation.To understand bacteria gene expression regulation, one needs to identify promoters, terminators, and sRNAs together with their targets. Here we describe the state of the art in computational methods to perform promoter recognition, sRNA identification, and sRNA target prediction. Additionally, we provide step-by-step instructions to use current approaches to perform these tasks.

Detecting ulcerative colitis from colon samples using efficient feature selection and machine learning.

Ulcerative colitis (UC) is one of the most common forms of inflammatory bowel disease (IBD) characterized by inflammation of the mucosal layer of the colon. Diagnosis of UC is based on clinical symptoms, and then confirmed based on endoscopic, histologic and laboratory findings. Feature selection and machine learning have been previously used for creating models to facilitate the diagnosis of certain diseases. In this work, we used a recently developed feature selection algorithm (DRPT) combined with a support vector machine (SVM) classifier to generate a model to discriminate between healthy subjects and subjects with UC based on the expression values of 32 genes in colon samples. We validated our model with an independent gene expression dataset of colonic samples from subjects in active and inactive periods of UC. Our model perfectly detected all active cases and had an average precision of 0.62 in the inactive cases. Compared with results reported in previous studies and a model generated by a recently published software for biomarker discovery using machine learning (BioDiscML), our final model for detecting UC shows better performance in terms of average precision.

A Two-Component System Acquired by Horizontal Gene Transfer Modulates Gene Transfer and Motility via Cyclic Dimeric GMP.

Bis-(3'-5')-cyclic dimeric guanosine monophosphate (c-di-GMP) is an important intracellular signaling molecule that affects diverse physiological processes in bacteria. The intracellular levels of c-di-GMP are controlled by proteins acting as diguanylate cyclase (DGC) and phosphodiesterase (PDE) enzymes that synthesize and degrade c-di-GMP, respectively. In the alphaproteobacterium Rhodobacter capsulatus, flagellar motility and gene exchange via production of the gene transfer agent RcGTA are regulated by c-di-GMP. One of the R. capsulatus proteins involved in this regulation is Rcc00620, which contains an N-terminal two-component system response regulator receiver (REC) domain and C-terminal DGC and PDE domains. We demonstrate that the enzymatic activity of Rcc00620 is regulated through the phosphorylation status of its REC domain, which is controlled by a cognate histidine kinase protein, Rcc00621. In this system, the phosphorylated form of Rcc00620 is active as a PDE enzyme and stimulates gene transfer and motility. In addition, we discovered that the rcc00620 and rcc00621 genes are present in only one lineage within the genus Rhodobacter and were acquired via horizontal gene transfer from a distantly related alphaproteobacterium in the order Sphingomonadales. Therefore, a horizontally acquired regulatory system regulates gene transfer in the recipient organism.

Learning-induced mRNA alterations in olfactory bulb mitral cells in neonatal rats.

In the olfactory bulb, a cAMP/PKA/CREB-dependent form of learning occurs in the first week of life that provides a unique mammalian model for defining the epigenetic role of this evolutionarily ancient plasticity cascade. Odor preference learning in the week-old rat pup is rapidly induced by a 10-min pairing of odor and stroking. Memory is demonstrable at 24 h, but not 48 h, posttraining. Using this paradigm, pups that showed peppermint preference 30 min posttraining were sacrificed 20 min later for laser microdissection of odor-encoding mitral cells. Controls were given odor only. Microarray analysis revealed that 13 nonprotein-coding mRNAs linked to mRNA translation and splicing and 11 protein-coding mRNAs linked to transcription differed with odor preference training. MicroRNA23b, a translation inhibitor of multiple plasticity-related mRNAs, was down-regulated. Protein-coding transcription was up-regulated for Sec23b, Clic2, Rpp14, Dcbld1, Magee2, Mstn, Fam229b, RGD1566265, and Mgst2. Gng12 and Srcg1 mRNAs were down-regulated. Increases in Sec23b, Clic2, and Dcbld1 proteins were confirmed in mitral cells in situ at the same time point following training. The protein-coding changes are consistent with extracellular matrix remodeling and ryanodine receptor involvement in odor preference learning. A role for CREB and AP1 as triggers of memory-related mRNA regulation is supported. The small number of gene changes identified in the mitral cell input/output link for 24 h memory will facilitate investigation of the nature, and reversibility, of changes supporting temporally restricted long-term memory.

Metagenomic analysis suggests broad metabolic potential in extracellular symbionts of the bivalve Thyasira cf. gouldi.

BACKGROUND: Next-generation sequencing has opened new avenues for studying metabolic capabilities of bacteria that cannot be cultured. Here, we provide a metagenomic description of chemoautotrophic gammaproteobacterial symbionts associated with Thyasira cf. gouldi, a sediment-dwelling bivalve from the family Thyasiridae. Thyasirid symbionts differ from those of other bivalves by being extracellular, and recent work suggests that they are capable of living freely in the environment. RESULTS: Thyasira cf. gouldi symbionts appear to form mixed, non-clonal populations in the host, show no signs of genomic reduction and contain many genes that would only be useful outside the host, including flagellar and chemotaxis genes. The thyasirid symbionts may be capable of sulfur oxidation via both the sulfur oxidation and reverse dissimilatory sulfate reduction pathways, as observed in other bivalve symbionts. In addition, genes for hydrogen oxidation and dissimilatory nitrate reduction were found, suggesting varied metabolic capabilities under a range of redox conditions. The genes of the tricarboxylic acid cycle are also present, along with membrane bound sugar importer channels, suggesting that the bacteria may be mixotrophic. CONCLUSIONS: In this study, we have generated the first thyasirid symbiont genomic resources. In Thyasira cf. gouldi, symbiont populations appear non-clonal and encode genes for a plethora of metabolic capabilities; future work should examine whether symbiont heterogeneity and metabolic breadth, which have been shown in some intracellular chemosymbionts, are signatures of extracellular chemosymbionts in bivalves.

Cyclic di-GMP-Mediated Regulation of Gene Transfer and Motility in Rhodobacter capsulatus.

Gene transfer agents (GTAs) are bacteriophage-like particles produced by several bacterial and archaeal lineages that contain small pieces of the producing cells' genomes that can be transferred to other cells in a process similar to transduction. One well-studied GTA is RcGTA, produced by the alphaproteobacterium Rhodobacter capsulatus RcGTA gene expression is regulated by several cellular regulatory systems, including the CckA-ChpT-CtrA phosphorelay. The transcription of multiple other regulator-encoding genes is affected by the response regulator CtrA, including genes encoding putative enzymes involved in the synthesis and hydrolysis of the second messenger bis-(3'-5')-cyclic dimeric GMP (c-di-GMP). To investigate whether c-di-GMP signaling plays a role in RcGTA production, we disrupted the CtrA-affected genes potentially involved in this process. We found that disruption of four of these genes affected RcGTA gene expression and production. We performed site-directed mutagenesis of key catalytic residues in the GGDEF and EAL domains responsible for diguanylate cyclase (DGC) and c-di-GMP phosphodiesterase (PDE) activities and analyzed the functions of the wild-type and mutant proteins. We also measured RcGTA production in R. capsulatus strains where intracellular levels of c-di-GMP were altered by the expression of either a heterologous DGC or a heterologous PDE. This adds c-di-GMP signaling to the collection of cellular regulatory systems controlling gene transfer in this bacterium. Furthermore, the heterologous gene expression and the four gene disruptions had similar effects on R. capsulatus flagellar motility as found for gene transfer, and we conclude that c-di-GMP inhibits both RcGTA production and flagellar motility in R. capsulatusIMPORTANCE Gene transfer agents (GTAs) are virus-like particles that move cellular DNA between cells. In the alphaproteobacterium Rhodobacter capsulatus, GTA production is affected by the activities of multiple cellular regulatory systems, to which we have now added signaling via the second messenger dinucleotide molecule bis-(3'-5')-cyclic dimeric GMP (c-di-GMP). Similar to the CtrA phosphorelay, c-di-GMP also affects R. capsulatus flagellar motility in addition to GTA production, with lower levels of intracellular c-di-GMP favoring increased flagellar motility and gene transfer. These findings further illustrate the interconnection of GTA production with global systems of regulation in R. capsulatus, providing additional support for the notion that the production of GTAs has been maintained in this and related bacteria because it provides a benefit to the producing organisms.

Burden of influenza-associated respiratory hospitalizations in the Americas, 2010-2015.

BACKGROUND: Despite having influenza vaccination policies and programs, countries in the Americas underutilize seasonal influenza vaccine, in part because of insufficient evidence about severe influenza burden. We aimed to estimate the annual burden of influenza-associated respiratory hospitalizations in the Americas. METHODS: Thirty-five countries in the Americas with national influenza surveillance were invited to provide monthly laboratory data and hospital discharges for respiratory illness (International Classification of Diseases 10th edition J codes 0-99) during 2010-2015. In three age-strata (<5, 5-64, and ≥65 years), we estimated the influenza-associated hospitalizations rate by multiplying the monthly number of respiratory hospitalizations by the monthly proportion of influenza-positive samples and dividing by the census population. We used random effects meta-analyses to pool age-group specific rates and extrapolated to countries that did not contribute data, using pooled rates stratified by age group and country characteristics found to be associated with rates. RESULTS: Sixteen of 35 countries (46%) contributed primary data to the analyses, representing 79% of the America's population. The average pooled rate of influenza-associated respiratory hospitalization was 90/100,000 population (95% confidence interval 61-132) among children aged <5 years, 21/100,000 population (13-32) among persons aged 5-64 years, and 141/100,000 population (95-211) among persons aged ≥65 years. We estimated the average annual number of influenza-associated respiratory hospitalizations in the Americas to be 772,000 (95% credible interval 716,000-829,000). CONCLUSIONS: Influenza-associated respiratory hospitalizations impose a heavy burden on health systems in the Americas. Countries in the Americas should use this information to justify investments in seasonal influenza vaccination-especially among young children and the elderly.

Mantle flow and deep electrical anisotropy in a main gateway: MT study in Tierra del Fuego.

Asthenospheric mantle flow drives lithospheric plate motion and constitutes a relevant feature of Earth gateways. It most likely influences the spatial pattern of seismic velocity and deep electrical anisotropies. The Drake Passage is a main gateway in the global pattern of mantle flow. The separation of the South American and Antarctic plates since the Oligocene produced this oceanic and mantle gateway connecting the Pacific and Atlantic oceans. Here we analyze the deep crustal and upper mantle electrical anisotropy of its northern margin using long period magnetotelluric data from Tierra del Fuego (Argentina). The influence of the surrounding oceans was taken into account to constrain the mantle electrical conductivity features. 3D electrical models were calculated to fit 18 sites responses in this area. The phase tensor pattern for the longest periods reveals the existence of a well-defined NW-SE electrical conductivity anisotropy in the upper mantle. This anisotropy would result from the mantle flow related to the 30 to 6 Ma West Scotia spreading, constricted by the subducted slab orientation of the Pacific plate, rather than the later eastward mantle flow across the Drake Passage. Deep electrical anisotropy proves to be a key tool for a better understanding of mantle flow.

Prioritizing bona fide bacterial small RNAs with machine learning classifiers.

Bacterial small (sRNAs) are involved in the control of several cellular processes. Hundreds of putative sRNAs have been identified in many bacterial species through RNA sequencing. The existence of putative sRNAs is usually validated by Northern blot analysis. However, the large amount of novel putative sRNAs reported in the literature makes it impractical to validate each of them in the wet lab. In this work, we applied five machine learning approaches to construct twenty models to discriminate bona fide sRNAs from random genomic sequences in five bacterial species. Sequences were represented using seven features including free energy of their predicted secondary structure, their distances to the closest predicted promoter site and Rho-independent terminator, and their distance to the closest open reading frames (ORFs). To automatically calculate these features, we developed an sRNA Characterization Pipeline (sRNACharP). All seven features used in the classification task contributed positively to the performance of the predictive models. The best performing model obtained a median precision of 100% at 10% recall and of 64% at 40% recall across all five bacterial species, and it outperformed previous published approaches on two benchmark datasets in terms of precision and recall. Our results indicate that even though there is limited sRNA sequence conservation across different bacterial species, there are intrinsic features in the genomic context of sRNAs that are conserved across taxa. We show that these features are utilized by machine learning approaches to learn a species-independent model to prioritize bona fide bacterial sRNAs.

Fracaso renal agudo posrenal secundario a seudomixoma peritoneal tras apendicectomía; una entidad infrecuente / Post-renal acute renal failure secondary to peritoneal Pseudomyxoma after appendectomy; an uncommon entity

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Complementary chromatic and far-red photoacclimations in Synechococcus ATCC 29403 (PCC 7335). I: The phycobilisomes, a proteomic approach.

Synechococcus ATCC 29403 (PCC 7335) is a unicellular cyanobacterium isolated from Puerto Peñasco, Sonora Mexico. This cyanobacterium performs complementary chromatic acclimation (CCA), far-red light photoacclimation (FaRLiP), and nitrogen fixation. The Synechococcus PCC 7335 genome contains at least 31 genes for proteins of the phycobilisome (PBS). Nine constitutive genes were expressed when cells were grown under white or red lights and the resulting proteins were identified by mass spectrometry in isolated PBS. Five inducible genes were expressed under white light, and phycoerythrin subunits and associated linker proteins were detected. The proteins of five inducible genes expressed under red light were identified, the induced phycocyanin subunits, two rod linkers and the rod-capping linker. The five genes for FaRLiP phycobilisomes were expressed under far-red light together with the apcF gene, and the proteins were identified by mass spectrometry after isoelectric focusing and SDS-PAGE. Based on in silico analysis, Phylogenetic trees, and the observation of a highly conserved amino acid sequence in far-red light absorbing alpha allophycoproteins encoded by FaRLiP gene cluster, we propose a new nomenclature for the genes. Based on a ratio of ApcG2/ApcG3 of six, a model with the arrangement of the allophycocyanin trimers of the core is proposed.

Genetic variation associated with healthy traits and environmental conditions in Vaccinium vitis-idaea.

BACKGROUND: Lingonberry (Vaccinium vitis-idaea L.), one of the least studied fruit crops in the Ericaceae family, has a dramatically increased worldwide demand due to its numerous health benefits. Genetic markers can facilitate the selection of berries with desirable climatic adaptations, agronomic and nutritious characteristics to improve cultivation programs. However, no genomic resources are available for this species. RESULTS: We used Genotyping-by-Sequencing (GBS) to analyze the genetic variation of 56 lingonberry samples from across Newfoundland and Labrador, Canada. To elucidate a potential adaptation to environmental conditions we searched for genotype-environment associations by applying three distinct approaches to screen the identified single nucleotide polymorphisms (SNPs) for correlation with six environmental variables. We also searched for an association between the identified SNPs and two phenotypic traits: the total phenolic content (TPC) and antioxidant capacity (AC) of fruit. We identified 1586 high-quality putative SNPs using the UNEAK pipeline available in TASSEL. We found 132 SNPs likely associated with at least one of the environmental or phenotypic variables. To obtain insights on the function of the genomic sequences containing the SNPs likely to be associated with the environmental or phenotypic variables, we performed a sequence-based functional annotation and identified homologous protein-coding sequences with functional roles related to abiotic stress response, pathogen defense, RNA metabolism, and, most interestingly, phenolic compound biosynthesis. CONCLUSIONS: The putative SNPs discovered are the first genomic resource for lingonberry. This resource might prove useful in high-density quantitative trait locus analysis, and association mapping. The identified candidate genes containing the SNPs need further studies on their potential role in local adaptation of lingonberry. Altogether, the present study provides new resources that can be used to breed for desirable traits in lingonberry.