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Ecology and Evolution May 2024termite mounds in the Kruger National Park occupy a significant part of the savanna landscapes, occurring at densities of up to 70 km and often exceeding 10 m in...
termite mounds in the Kruger National Park occupy a significant part of the savanna landscapes, occurring at densities of up to 70 km and often exceeding 10 m in width and 4 m in height. The mounds are usually devoid of trees, but have dense grass cover in wet years. As a result, these mounds form large patches of grassland amongst the wooded savanna. To our knowledge, it is not known why trees are largely excluded from the mounds. We analysed soil surface nutrient concentrations on and off mounds (0-2 cm deep, = 80) to ascertain whether the availability of nutrients could be influencing competition between grasses and tree seedlings. The results showed that potential deficiencies in P, Ca, Cu, Zn and B in soils off the mounds are likely to be constraining plant growth. Notably, only B, with an average concentration of 0.19 mg kg, was likely to be limiting plant growth on the mounds. Notwithstanding likely interactions with herbivory and fire, we hypothesise that because grasses are far less susceptible to deficiencies of B than dicotyledonous trees, it is likely that grass competition with tree seedlings is considerably greater on mounds than off mounds.
PubMed: 38756685
DOI: 10.1002/ece3.11348 -
Rare microbial taxa as the major drivers of nutrient acquisition under moss biocrusts in karst area.Frontiers in Microbiology 2024Karst rocky desertification refers to the process of land degradation caused by various factors such as climate change and human activities including deforestation and...
Karst rocky desertification refers to the process of land degradation caused by various factors such as climate change and human activities including deforestation and agriculture on a fragile karst substrate. Nutrient limitation is common in karst areas. Moss crust grows widely in karst areas. The microorganisms associated with bryophytes are vital to maintaining ecological functions, including climate regulation and nutrient circulation. The synergistic effect of moss crusts and microorganisms may hold great potential for restoring degraded karst ecosystems. However, our understanding of the responses of microbial communities, especially abundant and rare taxa, to nutrient limitations and acquisition in the presence of moss crusts is limited. Different moss habitats exhibit varying patterns of nutrient availability, which also affect microbial diversity and composition. Therefore, in this study, we investigated three habitats of mosses: autochthonal bryophytes under forest, lithophytic bryophytes under forest and on cliff rock. We measured soil physicochemical properties and enzymatic activities. We conducted high-throughput sequencing and analysis of soil microorganisms. Our finding revealed that autochthonal moss crusts under forest had higher nutrient availability and a higher proportion of copiotrophic microbial communities compared to lithophytic moss crusts under forest or on cliff rock. However, enzyme activities were lower in autochthonal moss crusts under forest. Additionally, rare taxa exhibited distinct structures in all three habitats. Analysis of co-occurrence network showed that rare taxa had a relatively high proportion in the main modules. Furthermore, we found that both abundant and rare taxa were primarily assembled by stochastic processes. Soil properties significantly affected the community assembly of the rare taxa, indirectly affecting microbial diversity and complexity and finally nutrient acquisition. These findings highlight the importance of rare taxa under moss crusts for nutrient acquisition. Addressing this knowledge gap is essential for guiding ongoing ecological restoration projects in karst rocky desertification regions.
PubMed: 38751717
DOI: 10.3389/fmicb.2024.1384367 -
Nature Communications May 2024The availability of protein measurements and whole exome sequence data in the UK Biobank enables investigation of potential observational and genetic protein-cancer risk...
The availability of protein measurements and whole exome sequence data in the UK Biobank enables investigation of potential observational and genetic protein-cancer risk associations. We investigated associations of 1463 plasma proteins with incidence of 19 cancers and 9 cancer subsites in UK Biobank participants (average 12 years follow-up). Emerging protein-cancer associations were further explored using two genetic approaches, cis-pQTL and exome-wide protein genetic scores (exGS). We identify 618 protein-cancer associations, of which 107 persist for cases diagnosed more than seven years after blood draw, 29 of 618 were associated in genetic analyses, and four had support from long time-to-diagnosis ( > 7 years) and both cis-pQTL and exGS analyses: CD74 and TNFRSF1B with NHL, ADAM8 with leukemia, and SFTPA2 with lung cancer. We present multiple blood protein-cancer risk associations, including many detectable more than seven years before cancer diagnosis and that had concordant evidence from genetic analyses, suggesting a possible role in cancer development.
Topics: Humans; United Kingdom; Neoplasms; Risk Factors; Biological Specimen Banks; Male; Female; Exome; Proteomics; Prospective Studies; Middle Aged; Blood Proteins; Aged; Exome Sequencing; Genetic Predisposition to Disease; Incidence; UK Biobank
PubMed: 38750076
DOI: 10.1038/s41467-024-48017-6 -
Science Advances May 2024Forest canopy structural complexity (CSC) plays a crucial role in shaping forest ecosystem productivity and stability, but the precise nature of their relationships...
Forest canopy structural complexity (CSC) plays a crucial role in shaping forest ecosystem productivity and stability, but the precise nature of their relationships remains controversial. Here, we mapped the global distribution of forest CSC and revealed the factors influencing its distribution using worldwide light detection and ranging data. We find that forest CSC predominantly demonstrates significant positive relationships with forest ecosystem productivity and stability globally, although substantial variations exist among forest ecoregions. The effects of forest CSC on productivity and stability are the balanced results of biodiversity and resource availability, providing valuable insights for comprehending forest ecosystem functions. Managed forests are found to have lower CSC but more potent enhancing effects of forest CSC on ecosystem productivity and stability than intact forests, highlighting the urgent need to integrate forest CSC into the development of forest management plans for effective climate change mitigation.
Topics: Forests; Ecosystem; Biodiversity; Climate Change; Conservation of Natural Resources; Trees
PubMed: 38748796
DOI: 10.1126/sciadv.adl1947 -
Science Advances May 2024Reliable prediction of T cell specificity against antigenic signatures is a formidable task, complicated by the immense diversity of T cell receptor and antigen sequence...
Reliable prediction of T cell specificity against antigenic signatures is a formidable task, complicated by the immense diversity of T cell receptor and antigen sequence space and the resulting limited availability of training sets for inferential models. Recent modeling efforts have demonstrated the advantage of incorporating structural information to overcome the need for extensive training sequence data, yet disentangling the heterogeneous TCR-antigen interface to accurately predict MHC-allele-restricted TCR-peptide interactions has remained challenging. Here, we present RACER-m, a coarse-grained structural model leveraging key biophysical information from the diversity of publicly available TCR-antigen crystal structures. Explicit inclusion of structural content substantially reduces the required number of training examples and maintains reliable predictions of TCR-recognition specificity and sensitivity across diverse biological contexts. Our model capably identifies biophysically meaningful point-mutant peptides that affect binding affinity, distinguishing its ability in predicting TCR specificity of point-mutants from alternative sequence-based methods. Its application is broadly applicable to studies involving both closely related and structurally diverse TCR-peptide pairs.
Topics: Receptors, Antigen, T-Cell; T-Lymphocytes; Humans; Protein Binding; Models, Molecular; Peptides; T-Cell Antigen Receptor Specificity; Protein Conformation
PubMed: 38748791
DOI: 10.1126/sciadv.adl0161 -
Anais Da Academia Brasileira de Ciencias 2024Mangroves buffer metals transfer to coastal areas though strong accumulation in sediments making necessary to investigate metals' bioavailability to plants at the...
Mangroves buffer metals transfer to coastal areas though strong accumulation in sediments making necessary to investigate metals' bioavailability to plants at the rhizosphere. This work evaluates the effect of mangrove root activity, through iron plaque formation, on the mobility of iron and copper its influence on metals' uptake, and translocation through simultaneous histochemical analysis. The Fe2+ and Fe3+ contents in porewaters ranged from 0.02 to 0.11 µM and 1.0 to 18.3 µg.l-1, respectively, whereas Cu concentrations were below the method's detection limit (<0.1 µM). In sediments, metal concentrations ranged from 12,800 to 39,500 µg.g-1 for total Fe and from 10 to 24 µg.g-1 for Cu. In iron plaques, Cu concentrations ranged from 1.0 to 160 µg.g-1, and from 19.4 to 316 µg.g-1 in roots. Fe concentrations were between 605 to 36,000 µg.g-1 in the iron plaques and from 2,100 to 62,400 µg.g-1 in roots. Histochemical characterization showed Fe3+ predominance at the tip of roots and Fe2+ in more internal tissues. A. schaueriana showed significant amounts of Fe in pneumatophores and evident translocation of this metal to leaves and excretion through salt glands. Iron plaques formation was essential to the Fe and Cu regulation and translocation in tissues of mangrove plants.
Topics: Rhizophoraceae; Iron; Brazil; Copper; Avicennia; Plant Roots; Geologic Sediments; Biological Availability; Water Pollutants, Chemical; Environmental Monitoring
PubMed: 38747797
DOI: 10.1590/0001-3765202420231075 -
BioRxiv : the Preprint Server For... Apr 2024The advent of long-read single-cell transcriptome sequencing (lr-scRNA-Seq) represents a significant leap forward in single-cell genomics. With the recent introduction...
The advent of long-read single-cell transcriptome sequencing (lr-scRNA-Seq) represents a significant leap forward in single-cell genomics. With the recent introduction of R10 flowcells by Oxford Nanopore, we propose that previous computational methods designed to handle high sequencing error rates are no longer relevant, and that the prevailing approach using short reads to compile "barcode space" (candidate barcode list) to de-multiplex long reads are no longer necessary. Instead, computational methods should now shift focus on harnessing the unique benefits of long reads to analyze transcriptome complexity. In this context, we introduce a comprehensive suite of computational methods named Single-Cell Omics for Transcriptome CHaracterization (SCOTCH). Our method is compatible with the single-cell library preparation platform from both 10X Genomics and Parse Biosciences, facilitating the analysis of special cell populations, such as neurons, hepatocytes and developing cardiomyocytes. We specifically re-formulated the transcript mapping problem with a compatibility matrix and addressed the multiple-mapping issue using probabilistic inference, which allows the discovery of novel isoforms as well as the detection of differential isoform usage between cell populations. We evaluated SCOTCH through analysis of real data across different combinations of single-cell libraries and sequencing technologies (10X + Illumina, Parse + Illumina, 10X + Nanopore_R9, 10X + Nanopore_R10, Parse + Nanopore_R10), and showed its ability to infer novel biological insights on cell type-specific isoform expression. These datasets enhance the availability of publicly available data for continued development of computational approaches. In summary, SCOTCH allows extraction of more biological insights from the new advancements in single-cell library construction and sequencing technologies, facilitating the examination of transcriptome complexity at the single-cell level.
PubMed: 38746128
DOI: 10.1101/2024.04.29.590597 -
Bioinformatics Advances 2024MerCat2 ("Mer-Catenate2") is a versatile, parallel, scalable and modular property software package for robustly analyzing features in omics data. Using massively...
MOTIVATION
MerCat2 ("Mer-Catenate2") is a versatile, parallel, scalable and modular property software package for robustly analyzing features in omics data. Using massively parallel sequencing raw reads, assembled contigs, and protein sequences from any platform as input, MerCat2 performs -mer counting of any length , resulting in feature abundance counts tables, quality control reports, protein feature metrics, and graphical representation (i.e. principal component analysis (PCA)).
RESULTS
MerCat2 allows for direct analysis of data properties in a database-independent manner that initializes all data, which other profilers and assembly-based methods cannot perform. MerCat2 represents an integrated tool to illuminate omics data within a sample for rapid cross-examination and comparisons.
AVAILABILITY AND IMPLEMENTATION
MerCat2 is written in Python and distributed under a BSD-3 license. The source code of MerCat2 is freely available at https://github.com/raw-lab/mercat2. MerCat2 is compatible with Python 3 on Mac OS X and Linux. MerCat2 can also be easily installed using bioconda: mamba create -n mercat2 -c conda-forge -c bioconda mercat2.
PubMed: 38745763
DOI: 10.1093/bioadv/vbae061 -
BMC Bioinformatics May 2024Long non-coding RNAs (lncRNAs) are a class of molecules involved in important biological processes. Extensive efforts have been provided to get deeper understanding of...
MOTIVATION
Long non-coding RNAs (lncRNAs) are a class of molecules involved in important biological processes. Extensive efforts have been provided to get deeper understanding of disease mechanisms at the lncRNA level, guiding towards the detection of biomarkers for disease diagnosis, treatment, prognosis and prevention. Unfortunately, due to costs and time complexity, the number of possible disease-related lncRNAs verified by traditional biological experiments is very limited. Computational approaches for the prediction of disease-lncRNA associations allow to identify the most promising candidates to be verified in laboratory, reducing costs and time consuming.
RESULTS
We propose novel approaches for the prediction of lncRNA-disease associations, all sharing the idea of exploring associations among lncRNAs, other intermediate molecules (e.g., miRNAs) and diseases, suitably represented by tripartite graphs. Indeed, while only a few lncRNA-disease associations are still known, plenty of interactions between lncRNAs and other molecules, as well as associations of the latters with diseases, are available. A first approach presented here, NGH, relies on neighborhood analysis performed on a tripartite graph, built upon lncRNAs, miRNAs and diseases. A second approach (CF) relies on collaborative filtering; a third approach (NGH-CF) is obtained boosting NGH by collaborative filtering. The proposed approaches have been validated on both synthetic and real data, and compared against other methods from the literature. It results that neighborhood analysis allows to outperform competitors, and when it is combined with collaborative filtering the prediction accuracy further improves, scoring a value of AUC equal to 0966.
AVAILABILITY
Source code and sample datasets are available at: https://github.com/marybonomo/LDAsPredictionApproaches.git.
Topics: RNA, Long Noncoding; Humans; Computational Biology; Algorithms; MicroRNAs; Genetic Predisposition to Disease
PubMed: 38741200
DOI: 10.1186/s12859-024-05777-8 -
Briefings in Bioinformatics Mar 2024Proteins interact with diverse ligands to perform a large number of biological functions, such as gene expression and signal transduction. Accurate identification of... (Review)
Review
Proteins interact with diverse ligands to perform a large number of biological functions, such as gene expression and signal transduction. Accurate identification of these protein-ligand interactions is crucial to the understanding of molecular mechanisms and the development of new drugs. However, traditional biological experiments are time-consuming and expensive. With the development of high-throughput technologies, an increasing amount of protein data is available. In the past decades, many computational methods have been developed to predict protein-ligand interactions. Here, we review a comprehensive set of over 160 protein-ligand interaction predictors, which cover protein-protein, protein-nucleic acid, protein-peptide and protein-other ligands (nucleotide, heme, ion) interactions. We have carried out a comprehensive analysis of the above four types of predictors from several significant perspectives, including their inputs, feature profiles, models, availability, etc. The current methods primarily rely on protein sequences, especially utilizing evolutionary information. The significant improvement in predictions is attributed to deep learning methods. Additionally, sequence-based pretrained models and structure-based approaches are emerging as new trends.
Topics: Nucleic Acids; Proteins; Computational Biology; Ligands; Protein Binding; Humans
PubMed: 38739759
DOI: 10.1093/bib/bbae162