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Costa, F.

Publications and source records attributed to Costa, F..

At least 19 recordsLinked to original sources

Large-scale structure prediction of DUF-containing protein-protein interactions

Motivation: Continuing advances in genome and metagenome sequencing expand the number of identified conserved protein families that remain functionally uncharacterized and contain domains of unknown function (DUFs). Functional-association resources such as STRING provide biological context, but mostly do not distinguish indirect association from physical interaction. We assessed whether AlphaFold 3 complex prediction, combined with STRING evidence and domain-level analysis of interfaces and interaction partners, can help identify and characterize DUF-containing proteins. Results: We generated four structural-prediction cohorts from STRING associations involving DUF-containing proteins and evaluated the predicted complexes using interface ipSAE, average pLDDT and buried surface area. An L2-regularized logistic regression model was trained on an initial cohort of predictions from high-confidence STRING associations to prioritize DUF-containing candidates likely to produce structurally confident AlphaFold 3 complexes. The model was then applied across all 12,535 organisms represented in STRING v12.0, followed by grouping into DUF-family and partner-architecture modules, covering 2,076 unique DUF families. The final L2-model screen contained 12,298 successfully modelled protein pairs, including 1,208 (9.82%) complexes meeting a strict-confidence criterion and 2,433 (19.78%) meeting a more liberal confidence criterion. Two examples suggest roles for DUF4130 in nucleic-acid-associated radical-SAM biology and DUF5819 in a bacterial system related to vitamin-K-dependent carboxylation. Availability and implementation: Predicted structures and associated metadata are available through Zenodo at https://doi.org/10.5281/zenodo.21875362. The model implementation and code used to generate the analyses and figures are available at https://github.com/linoriep/Proteome-scale-structure-prediction-of-DUF-containing-protein-protein-interactions.

bioinformatics↗

Conjunctive coding of letter pairs emerges in segregated human medial temporal lobe neurons during working memory

Composing individual memory traces into unified representations is fundamental to encoding of structured relationships and flexible cognition. A central debate in neuroscience concerns the neural mechanisms of these compositions: are these compositions encoded through mixed selectivity, where the same neurons simultaneously represent individual and composed items, or through conjunctive coding within a distinct and specialized neuronal population? Here, we tested these competing hypotheses by recording 996 single units from the medial temporal lobe of 11 epilepsy patients performing a letter-string working memory task. We found neural encoding of both single letters and letter pairs during task related periods, with the latter peaking during the maintenance phase. Crucially, letter pairs were encoded by a sparse subset of neurons that did not show significant selectivity for their constituent letters. This suggests the existence of a segregated population of neurons recruited to compose items independently of single letter neural encoding. These findings provide evidence that the human medial temporal lobe separately encodes compositions within segregated neurons, offering direct neurophysiological support for conjunctive coding at the single-neuron level.

neuroscience↗

Tracing the intruders: a global appraisal of marine invasive species detection through DNA-based approaches

Early detection and monitoring of non-indigenous species (NIS) is crucial to prevent their establishment and to reduce ecological and economic impacts in coastal ecosystems. Traditional monitoring approaches, which rely largely on morphological identification of collected organisms, are often time-consuming and may fail to detect species that occur at low abundance, are morphologically cryptic, or are present in the form of inconspicuous life stages. DNA-based approaches, particularly those resorting to environmental DNA, have demonstrated high aptitude for biodiversity monitoring and biosecurity surveillance. By examining the genetic material from bulk community samples or released into the environment, DNA-based approaches enable the detection of species without the need for direct observation, thereby increasing detection sensitivity and expanding the scope of monitoring programs. Despite the rapid growth of its employment in marine monitoring, a global synthesis of the status and trends of DNA-based approaches for detecting NIS in this environment has been lacking. Here, we present such synthesis, based on 146 published studies employing DNA for NIS detections in coastal environments. Two main methodological approaches were used across the reviewed studies, namely DNA metabarcoding which was applied in 49% of studies, closely followed by targeted single-species PCR assays, used in 42% of the studies. A smaller proportion of studies (10%) combined both approaches, integrating broad community screening with targeted detection to improve surveillance efficiency. Globally, 752 NIS were detected across disparate taxonomic groups, with metazoans representing the largest proportion of detections (464 species), followed by Chromista (210 species) and Plantae (77 species). Among these, the most frequently detected taxonomic groups included Dinophyceae (Dinoflagellata), Teleostei (Chordata), Florideophyceae (Rodophyta), Polychaeta (Annelida), Copepoda and Malacostraca (Arthropoda), and Ascidiacea (Chordata). At the species level, several well-known marine invaders were recurrently reported, including Bugula neritina (Linnaeus, 1758), Styela plicata (Lesueur, 1823), Acartia (Acanthacartia) tonsa Dana, 1849-1852, and Botryllus schlosseri (Pallas, 1766), highlighting the ability of DNA approaches to detect widespread and established invaders across different regions. The mitochondrial cytochrome c oxidase subunit I (COI) gene was the most widely used genetic marker, reflecting its broad taxonomic coverage and extensive representation in reference databases, particularly for targeting Metazoa. Ribosomal RNA genes, particularly 18S and 16S rRNA gene markers, were also frequently employed to target a wider range of eukaryotic taxa. Regarding sampled substrates, water was by far the most analyzed substrate, followed by zooplankton and biofouling communities collected from man-made structures. Notably, approximately 31% of all NIS detections reported in the reviewed studies constituted new regional records. These results highlight the potential of eDNA for coastal monitoring but also underline important limitations. Persistent geographical, taxonomic, and methodological biases can affect detection outcomes, and reliance on single sample types or markers may increase false negatives - particularly critical for NIS early detection. Therefore, multi-marker and multi-substrate approaches are essential to improve detection reliability and support effective biosecurity strategies. As reference databases continue to expand and methodological protocols become increasingly standardized, DNA-based monitoring is likely to play a central role in future management and surveillance of biological invasions in coastal ecosystems. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=133 SRC="FIGDIR/small/722998v1_ufig1.gif" ALT="Figure 1"> View larger version (75K): org.highwire.dtl.DTLVardef@17948b1org.highwire.dtl.DTLVardef@193832dorg.highwire.dtl.DTLVardef@189033dorg.highwire.dtl.DTLVardef@33cddf_HPS_FORMAT_FIGEXP M_FIG C_FIG

ecology↗

Haplotype specific analyses in the phased genomes era: the case of apple cv "Golden Delicious"

Apple is one of the major cultivated fruit crops in temperate regions. To better support breeding programs and facilitate the development of improved cultivars, we generated a new haplotype-resolved version of the Golden Delicious genome, one of the major founders of many modern apple lineages. The assembly features the separation of the two haplotypes, with a total size of 647.3 Mb and 649.2 Mb, respectively. The phasing was accurately validated with 10,321 curated SNPs. Telomere-to-telomere continuity was verified by the analysis of telomeric sequence composition at the end of each chromosome. Gene prediction identified a total of 45,116 genes in haplotype 1 and 45,063 genes in haplotype 2. A pangenome analysis employing 6 haplotype resolved genomes identified both common and unique gene families. The availability of a phased genome enabled the assessment of genome-wide allelic specific expression. Our case study, focusing on Md-PG1 (a key regulator of fruit softening), revealed that the allelic form on haplotype 2 (GDH2-10g24673) was the dominant contributor to total gene expression. In addition, the phased genome also showed specific miRNA chromosomal distribution patterns, as well as a distinct methylation profile. Altogether, these genomic resources provide new insights into the allelic regulation of key agronomic traits and represent a valuable tool to accelerate apple breeding.

genomics↗

KDM7-mediated oxygen sensing reprograms chromatin to enhance hypoxia tolerance in the root

Roots frequently encounter low oxygen (hypoxia) from soil compaction or water saturation and must adapt to this stress. We investigated how root tip cells sense hypoxia and adjust the meristem epigenome to activate genes that promote tolerance and growth under oxygen limitation. In Arabidopsis root tips, hypoxia tolerance was linked to increased trimethylation of histone H3 at lysine 4 (H3K4me3). We also found that group 7 demethylases (KDM7s) are directly inhibited by hypoxia, and that genetic inactivation of KDM7s, like hypoxia, induces expression of genes essential for meristem survival under oxygen deprivation. We propose that KDM7s function as root-specific oxygen sensors that prime and support hypoxia tolerance.

plant biology↗

Large Language Model-assisted text mining reveals bacterial pathogen diversity

Compiling and characterising the diversity of bacterial pathogens of humans is a critical challenge to tackle infection risk, especially in the context of global antimicrobial resistance, climate change, and changing demographics. Here, we present a scalable, automated pipeline that harnesses large language models (LLMs) to systematically mine the biomedical literature for information of human pathogenicity across the bacterial domain. By interrogating tens of thousands of PubMed abstracts we identify 1,222 species with at least one abstract documenting human infection, of which 783 species are supported by 3 or more abstracts and are regarded as "confirmed pathogens". We extract, summarise, visualise and interpret data on infection contexts using both expert-curated LLM prompts and unsupervised text vectorisation. We show that these methods enable fine-grained trait mapping across taxa, including quantifying the degree of specialism or generalism in body site specificity for different taxa and the classification of pathogen species into 75 "pathogen types". An objective measure of the rate at which species are reported in the literature coupled to species clustering offers insights into the drivers of pathogen emergence. Our LLM-driven strategy generates an open, updatable, evidence-based catalogue of bacterial human pathogens and their ecological and clinical traits, providing a foundation for public health surveillance, diagnostics, and predictive modelling. This work demonstrates the potential of AI-assisted literature synthesis to transform our understanding of microbial diversity, including its impact on human health.

microbiology↗

Seroprevalence and risk factors for Toxoplasma gondii transmission in wild, domestic and companion animals in urban informal settlements from Salvador, Brazil

Toxoplasma gondii is a globally neglected zoonotic parasite, particularly prevalent in socioeconomically vulnerable areas. Various animal species serve as reservoirs for T. gondii across different regions, including domestic cats, livestock, and a variety of wild and synanthropic animals. In urban areas, especially informal settlements, the close coexistence of humans, domestic animals, and wildlife may influence local transmission dynamics. This study evaluated the seroprevalence and associated risk factors for T. gondii infection in domestic and synanthropic animals from two low-income communities in Salvador, Brazil. A cross-sectional study was conducted in the neighborhoods of Marechal Rondon and Pau da Lima from October 2021 to February 2023. Blood samples were collected from domestic animals (288 dogs, 112 cats, 27 chickens, and six horses) and synanthropic species (54 brown rats and 75 big-eared opossums). Serological tests were performed using an indirect immunofluorescence antibody test. Questionnaires were used to collect environmental, demographic, and socioeconomic data from households where sampling took place. Generalized linear mixed models were applied to identify predictors of exposure. Seroprevalence was highest in chickens (66.7%), followed by dogs (37.2%), rats (24.1%), cats (22.3%), opossums (20%), and horses (16.7%). No significant factors were found to be associated with T. gondii seroprevalence in chickens, horses, rats, or opossums. Nevertheless, in dogs and cats, homemade diets increased the odds of infection by nearly six times compared to commercial feeding. Dogs from Pau da Lima were twice as likely to be infected as those from Marechal Rondon. These findings underscore the importance of promoting safe pet management, improving sanitation, and monitoring sentinel species to mitigate zoonotic risks in urban informal settlements. Author summaryToxoplasma gondii is a parasite that infects humans and animals worldwide, significantly impacting impoverished areas. In this study, we investigated the prevalence and factors associated with T. gondii infection in domestic and urban wildlife from two low-income communities in Salvador (BA, Brazil). We collected blood samples from dogs, cats, chickens, horses, rats, and opossums to test for antibodies against the parasite and analyzed environmental and lifestyle factors that might influence infection risk. Chickens showed the highest infection rates (67%), followed by dogs (37%), rats (24%), cats (22%), opossums (20%), and horses (17%). We found that homemade diets significantly increased the likelihood of infection in dogs and cats compared to commercial pet food. Additionally, dogs from one neighborhood were twice as likely to be infected as those from the other, likely due to environmental conditions. These findings highlight how diet, environment, and urban living conditions affect the spread of T. gondii. By improving sanitation, promoting responsible pet care, and monitoring animals that share human environments, we can reduce the risk of this parasite in vulnerable communities.

ecology↗

A global survey of intramolecular isopeptide bonds

Many protein domains harbour covalent intramolecular bonds that enhance their stability and resistance to thermal, mechanical and proteolytic insults. Intramolecular isopeptide bonds represent one such covalent interaction, yet their distribution across protein domains and organisms has been largely unexplored. Here, we sought to address this by employing a large-scale prediction of intramolecular isopeptide bonds in the AlphaFold database using the structural template-based software Isopeptor. Our findings reveal an extensive phyletic distribution in surface proteins resembling fibrillar adhesins and pilins. All identified intramolecular isopeptide bonds are found in two structurally distinct folds, CnaA-like or CnaB-like, from a relatively small set of related Pfam families, including ten novel families that we predict to contain intramolecular isopeptide bonds. One CnaA-like domain of unknown function, DUF11 (renamed here to "CLIPPER") is broadly distributed in cell-surface proteins from Gram-positive bacteria, Gram-negative bacteria, and archaea, and is structurally and biophysically characterised in this work. Using X-ray crystallography, we resolve a CLIPPER domain from a Gram-negative fibrillar adhesin that contains an intramolecular isopeptide bond and further demonstrate that it imparts thermostability and resistance to proteolysis. Our findings demonstrate the extensive distribution of intramolecular isopeptide bond-containing protein domains in nature, and structurally resolve the previously cryptic CLIPPER domain.

bioinformatics↗

Isopeptor: a tool for detecting intramolecular isopeptide bonds in protein structures

MotivationIntramolecular isopeptide bonds contribute to the structural stability of proteins, and have primarily been identified in domains of bacterial fibrillar adhesins and pili. At present, there is no systematic method available to detect them in newly determined molecular structures. This can result in mis-annotations and incorrect modelling. ResultsHere, we present Isopeptor, a computational tool designed to predict the presence of intramolecular isopeptide bonds in experimentally determined structures. Isopeptor utilizes structure-guided template matching via the Jess software, combined with a logistic regression classifier that incorporates Root Mean Square Deviation (RMSD) and relative solvent accessible area (rASA) as key features. The tool demonstrates a recall of 1.0 and a precision of 0.95 when tested on a Protein Data Bank (PDB) subset of domains known to contain intramolecular isopeptide bonds that have been deposited with incorrectly modelled geometries. Isopeptors python-based implementation supports integration into bioinformatics workflows, enabling early detection and prediction of isopeptide bonds during protein structure modelling. Availability and implementationIsopeptor is implemented in python and can be accessed via the command line, through a python API or via a Google Colaboratory implementation (https://colab.research.google.com/github/FranceCosta/Isopeptor_development/blob/main/notebooks/Isopeptide_finder.ipynb). Source code is hosted on GitHub (https://github.com/FranceCosta/isopeptor) and can be installed via the python package installation manager PIP.

bioinformatics↗

NERVE 2.0: boosting the New Enhanced Reverse Vaccinology Environment via artificial intelligence and a user-friendly web interface

BackgroundVaccines development in this millennium started by the milestone work on Neisseria meningitidis B, reporting the invention of Reverse Vaccinology (RV), which allows to identify vaccine candidates (VCs) by screening bacterial pathogens genome or proteome through computational analyses. When NERVE (New Enhanced RV Environment), the first RV software integrating tools to perform the selection of VCs, was released, it prompted further development in the field. However, the problem-solving potential of most, if not all, RV programs is still largely unexploited by experimental vaccinologists that impaired by somehow difficult interfaces, requiring bioinformatic skills. ResultsWe report here on the development and release of NERVE 2.0 (available at: https://nerve-bio.org) which keeps the original integrative and modular approach of NERVE, while showing higher predictive performance than its previous version and other web-RV programs (Vaxign and Vaxijen). We renewed some of its modules and added innovative ones, such as Loop Razor, to recover fragments of promising vaccine candidates or epitopepredict for the epitope prediction binding affinities and population coverage. Along with two newly built AI (Artificial Intelligence)-based models: ESPAAN and Virulent. To improve user-friendliness, NERVE was shifted to a tutored, web-based interface, with a noSQL-database to consent the user to submit, obtain and retrieve analysis results at any moment. ConclusionsWith its redesigned and updated environment, NERVE 2.0 allows customisable and refinable bacterial protein vaccine analyses to all different kinds of users.

bioinformatics↗

The Staphylococcus aureus regulatory program in a human skin-like environment

Staphylococcus aureus is a Gram-positive pathogen responsible for the majority of skin and soft tissue infections (SSTIs). S. aureus colonizes the anterior nares of approximately 20-30% of the population and transiently colonizes the skin, thereby increasing the risk of developing SSTIs and more serious infections. Current laboratory models that mimic the skin surface environment are expensive, require substantial infrastructure, and limit the scope of bacterial physiology studies under human skin conditions. To overcome these limitations, we developed a cost-effective, open-source, chemically defined media recipe termed skin-like media (SLM) that incorporates key aspects of the human skin surface environment and supports growth of several Staphylococcal species. We utilized SLM to investigate the transcriptional response of methicillin-resistant S. aureus (MRSA) following growth in SLM compared to a commonly used laboratory media. Through RNA-seq analysis, we observed the upregulation of several virulence factors, including genes encoding functions involved in adhesion, proteolysis, and cytotoxicity. To further explore these findings, we conducted qRT-PCR experiments to determine the influence of media composition, pH, and temperature on the transcriptional response of key factors involved in adhesion and virulence. We also demonstrated that MRSA primed in SLM adhered better to human corneocytes and demonstrated adhesin-specific phenotypes that previously required genetic manipulation. These results support the potential utility of SLM as an in vitro model for assessing Staphylococcal physiology and metabolism on human skin. ImportanceStaphylococcus aureus is the major cause of skin diseases, and its increased prevalence in skin colonization and infections present a need to understand its physiology in this environment. The work presented here outlines S. aureus upregulation of colonization and virulence factors using a newly developed media that strives to replicate the human skin surface environment, and demonstrates roles for adhesins ClfA, SraP, and Fnbps in human corneocyte adherence.

microbiology↗

Unveiling the antiviral capabilities of targeting Human Dihydroorotate Dehydrogenase against SARS-CoV-2

The urgent need for effective treatments against emerging viral diseases, driven by drug-resistant strains and new viral variants, remains critical. We focus on inhibiting the human dihydroorotate dehydrogenase (HsDHODH), one of the enzymes in charge of pyrimidine nucleotide synthesis. This strategy could impede viral replication without provoking resistance. We evaluated quinone-based compounds, discovering potent HsDHODH inhibition (low nanomolar IC50) and promising in vitro anti-SARS-CoV-2 activity (low micromolar EC50). These compounds exhibited low toxicity, indicating potential for further development. Additionally, we employed computational tools like molecular docking and QSAR models to analyze protein-ligand interactions. These findings represent a significant step forward in the search for effective antiviral treatments and have great potential to impact the development of new broad-spectrum antiviral drugs.

biochemistry↗

Fungal Pathogen Gene Selection for Predicting the Onset of Infection Using a Multi-Stage Machine Learning Approach

Phytopathogenic fungi pose a serious threat to global food security. Next-generation sequencing technologies, such as transcriptomics, are increasingly used to profile infection, assess environmental adaptation and gauge host-responses. The accumulation of these large-scale data has created the opportunity to employ new computational methods to gain greater biological insights. Machine learning approaches, that learn to identify patterns in complex data sets, have recently been applied to the field of plant-pathogen interactions. Here, we apply a machine learning approach to transcriptomics data for the fungal pathogen Zymoseptoria tritici, to predict the onset of infection as measured by timing of the appearance of necrosis. We present a method for identifying the most important genes that predict infection timings, accurately classify isolates as early and late infectors and predict the timing of infection of novel isolates using only a subset of the data. These methods and the genes identified further demonstrate the use of these tools in the field of plant-pathogen interactions and have implications for the identification of biomarkers for disease monitoring and forecasting. Fungi that infect plants pose a serious threat to global food security. Methods to study these pathogens generate vast amounts of data that create new opportunities for computational tools to analyse them. Machine learning methods can learn patterns in complex data such as when genes are turned on or off in fungal plant pathogens. In this study we use machine learning approaches to predict the onset of infection in several isolates of an important fungal pathogen. We show that these methods can identify a small group of genes that are predictive of the infection onset. We can even use these methods on novel isolates to infer the likely timing of disease development. Our work has implications for plant disease diagnosis, monitoring and forecasting.

bioinformatics↗

Effect of Socio-environmental Factors on Rat Proliferation and Leptospira Transmission in Suburban Areas of Dungun, Terengganu, Malaysia

BackgroundRats play a significant role as important reservoirs in the transmission of neglected zoonotic diseases, such as leptospirosis, particularly in poor urban and suburban communities of Low and Middle-Income Countries. Their proliferation is influenced by poor socio-environmental conditions like poor housing conditions, improper refuse disposal, and open sewer which generally furnish rats with food, water and harborage sources. Several interventions have been targeted against rats, given their public health menace but with no significant success, probably due to insufficient knowledge about context-specific factors influencing their proliferation in poor environments. Methodology/Principal FindingsWe evaluated the effect of different socio-environmental conditions on rat proliferation and the possible outcome on the transmission of Leptospira in suburban environments. We performed ten trapping sessions between April 2021 and January 2022, and captured 89 small mammals from 1385 trapping efforts (specifically, Rattus norvegicus (n = 39), Rattus rattus (n = 27), Rattus exulans (n = 10), Suncus murinus (n = 11), and Tupaia glis (n = 2)), with a 15.7% (n = 14/89) of the captured animals testing positive for Leptospira bacteria using PCR detection. We used a generalized linear model to evaluate the effect of different socio-environmental variables on household rat infestation and reported residences without paved floors, the presence of animals/pets, residence type and residences with vacant lots as variables positively associated with rat proliferation in the study sites. ConclusionRats are widely and differentially distributed in the poor communities of Dungun. Besides R. norvegicus and R. rattus, S. murinus and T. glis could also maintain and encourage pathogenic Leptospira transmission in the study areas. To adequately control rats proliferation and subsequent human zoonoses transmission, it will be critical to advocate and promote appropriate infrastructure and urban services, together with good hygiene practices to reduce rats access to water, food and harborage. Author SummaryRats are important reservoirs in the transmission of urban and suburban leptospirosis, and their proliferation is generally supported by poor socio-environmental conditions. Despite the strong association between poor socio-environmental conditions, rat proliferation and leptospirosis transmission, few studies have characterized their relationships in many poor suburban environments. Therefore, we evaluated the effect of different socio-environmental conditions on rat proliferation and possible effect on leptospirosis transmission in suburban communities of Dungun, Malaysia. We performed rat trapping and captured 89 small mammals from three orders, with a 15.7% (n = 14/89) of the captured animals testing positive for Leptospira bacteria using PCR detection. Using a generalized linear model, we reported residences without paved floors, the presence of animals/pets, residence type and residences with vacant lots as variables that are positively associated with rat proliferation in the study sites. Our findings show that rats are widely distributed in the study sites, and that in addition to R. norvegicus and R. rattus, S. murinus and T. glis could also maintain and transmit pathogenic Leptospira. Given the variables associated with rats proliferation, it is crucial to promote appropriate infrastructure and good hygiene practices to reduce rats access to water, food and harborage and subsequent rodent-borne diseases.

ecology↗

The spatial network structure of intertidal meiofaunal communities derived from environmental DNA metabarcoding surveys in Northwest Iberia

The identification of the patterns and mechanisms behind species distribution is one of the major challenges in ecology, having also important outcomes for the conservation and management of ecosystems. This is especially true for those components of biodiversity providing essential ecosystem functions and for which standard surveys may underestimate their real taxonomic diversity due to their high degree of cryptic diversity and inherent diagnosis difficulties, such as meiofaunal communities. Environmental DNA (eDNA) metabarcoding may provide a fast and reliable way to refine and scale-up the characterization of biological diversity in complex environmental samples, allowing to bypass such drawbacks and increase the resolution of biodiversity estimates. Moreover, the possibility of integrating eDNA metabarcoding-derived data with tools and methods rooted in network theory would deepen the knowledge of the structuring processes of ecological communities in ways that cannot be predicted from studying individual species/communities in isolation. Here, a sediment eDNA metabarcoding of mitochondrial cytochrome c oxidase I (COI) and the nuclear hypervariable V4 region of the 18S rDNA (18S) was used to reconstruct the spatial networks of intertidal meiofaunal OTUs from three estuaries of North-Western Iberian Peninsula. Null models were used to identify the role of environmental and spatial constraints on the structure of COI- and 18S-derived spatial networks and to characterize the macroecological features of surveyed phyla. Our results show the feasibility of eDNA metabarcoding, not only to capture a fair amount of diversity hard to detect with standard surveys procedures, but also to identify hierarchical spatial structures in intertidal meiofaunal assemblages. This suggests that exclusivity of occurrence rather than pervasiveness appears to be the norm in meiofaunal organisms and that niche-based processes predominantly drive the spatial aggregation and contemporary distribution of meiofaunal phyla within the system.

ecology↗

Prediction of fruit texture with training population optimization for efficient genomic selection in apple

Texture plays a major role in the determination of fruit quality in apple. Due to its physiological and economic relevance, this trait has been largely investigated, leading to the fixation of the major gene PG1 controlling firmness in elite cultivars. To further improve fruit texture, the targeting of an undisclosed reservoir of loci with minor effects is compelling. In this work, we aimed to unlock this potential with a genomic selection approach by predicting fruit acoustic and mechanical features as obtained with a TA.XTplus texture analyzer in 537 individuals genotyped with 8,294 SNP markers. The best prediction accuracies following cross-validations within the training set (TRS) of 259 individuals were obtained for the acoustic linear distance (0.64). Prediction accuracy was further improved through the optimization of TRS size and composition according to the test set. With this strategy, a maximal accuracy of 0.81 was obtained when predicting the synthetic trait PC1 in the family Gala x Pink Lady. We discuss the impact of genetic relatedness and clustering on trait variability and predictability. Moreover, we demonstrated the need for a comprehensive dissection of the complex texture phenotype and the potentiality of using genomic selection to improve fruit quality in apple. HighlightA genomic selection study, together with the optimization of the training set, demonstrated the possibility to accurately predict texture sub-traits valuable for the amelioration of fruit quality in apple.

genetics↗

Rainfall and other meteorological factors as drivers of urban transmission of leptospirosis

BackgroundLeptospirosis is an important public health problem affecting vulnerable urban slum populations in developing country settings. However, the complex interaction of meteorological factors driving the temporal trends of leptospirosis remain incompletely understood.\n\nMethods and findingsFrom 1996 to 2010, we investigated the association between the weekly incidence of leptospirosis and climatic variables in the city of Salvador, Brazil by using a dynamic generalized linear model that accounted for time lags, overall trend and seasonal variation. Our model showed an increase of leptospirosis cases associated with rainfall, lower temperature and higher humidity. There was a lag of one-to-two weeks between weekly values for significant meteorological variables and leptospirosis incidence. Independent of the season, a weekly cumulative rainfall of 20 mm increased the risk of the leptospirosis by 10% compared to a week without rain. Finally, over the 14 year study period the incidence of leptospirosis decreased significantly by four fold (12.8 versus 3.6 per 100,000 people), independently of variations in climate.\n\nConclusionsStrategies to control leptospirosis should focus on avoiding contact with contaminated sources of Leptospira as well as on increasing awareness in the population and health professionals within the short time window after both low-level and extreme rainfall events. Increased leptospirosis incidence was restricted to one-to-two weeks after those events suggesting that infectious Leptospira survival may be limited to short time intervals.\n\nAuthor SummaryTo determine the role of meteorological variables, seasonal variation and temporal trends in the incidence of leptospirosis, we investigated the time series of leptospirosis incidence amongst residents of Salvador, Brazil, from 1996 to 2010. Exploratory and confirmatory statistical methods detected associations between meteorological factors and disease incidence. Results showed the importance of extreme meteorological conditions, particularly rainfall, as short-term predictors of leptospirosis incidence. In addition, we found a long-term decreasing trend of in disease incidence over the observation period.

epidemiology↗

Empowering the annotation and discovery of structured RNAs with scalable and accessible integrative clustering

RNA plays essential regulatory roles in all known forms of life. Clustering RNA sequences with common sequence and structure is an essential step towards studying RNA function. With the advent of high-throughput sequencing techniques, experimental and genomic data are expanding to complement the predictive methods. However, the existing methods do not effectively utilize and cope with the immense amount of data becoming available. Here we present GraphClust2, a comprehensive approach for scalable clustering of RNAs based on sequence and structural similarities. GraphClust2 provides an integrative solution by incorporating diverse types of experimental and genomic data in an accessible fashion via the Galaxy framework. We demonstrate that the tasks of clustering and annotation of structured RNAs can be considerably improved, through a scalable methodology that also supports structure probing data. Based on this, we further introduce an off-the-shelf procedure to identify locally conserved structure candidates in long RNAs. In this way, we suggest the presence and the sparsity of phylogenetically conserved local structures in some long non-coding RNAs. Furthermore, we demonstrate the advantage of a scalable clustering for discovering structured motifs under inherent and experimental biases and uncover prominent targets of the double-stranded RNA binding protein Roquin-1 that are evolutionary conserved.

bioinformatics↗