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BMC Genomics May 2024The colonization of land and the diversification of terrestrial plants is intimately linked to the evolutionary history of their symbiotic fungal partners. Extant...
BACKGROUND
The colonization of land and the diversification of terrestrial plants is intimately linked to the evolutionary history of their symbiotic fungal partners. Extant representatives of these fungal lineages include mutualistic plant symbionts, the arbuscular mycorrhizal (AM) fungi in Glomeromycota and fine root endophytes in Endogonales (Mucoromycota), as well as fungi with saprotrophic, pathogenic and endophytic lifestyles. These fungal groups separate into three monophyletic lineages but their evolutionary relationships remain enigmatic confounding ancestral reconstructions. Their taxonomic ranks are currently fluid.
RESULTS
In this study, we recognize these three monophyletic linages as phyla, and use a balanced taxon sampling and broad taxonomic representation for phylogenomic analysis that rejects a hard polytomy and resolves Glomeromycota as sister to a clade composed of Mucoromycota and Mortierellomycota. Low copy numbers of genes associated with plant cell wall degradation could not be assigned to the transition to a plant symbiotic lifestyle but appears to be an ancestral phylogenetic signal. Both plant symbiotic lineages, Glomeromycota and Endogonales, lack numerous thiamine metabolism genes but the lack of fatty acid synthesis genes is specific to AM fungi. Many genes previously thought to be missing specifically in Glomeromycota are either missing in all analyzed phyla, or in some cases, are actually present in some of the analyzed AM fungal lineages, e.g. the high affinity phosphorus transporter Pho89.
CONCLUSION
Based on a broad taxon sampling of fungal genomes we present a well-supported phylogeny for AM fungi and their sister lineages. We show that among these lineages, two independent evolutionary transitions to mutualistic plant symbiosis happened in a genomic background profoundly different from that known from the emergence of ectomycorrhizal fungi in Dikarya. These results call for further reevaluation of genomic signatures associated with plant symbiosis.
Topics: Mycorrhizae; Symbiosis; Phylogeny; Genomics; Evolution, Molecular; Genome, Fungal; Glomeromycota; Plants
PubMed: 38811885
DOI: 10.1186/s12864-024-10391-2 -
BMC Plant Biology May 2024Carbon nano sol (CNS) can markedly affect the plant growth and development. However, few systematic analyses have been conducted on the underlying regulatory mechanisms...
Integrated analyses of ionomics, phytohormone profiles, transcriptomics, and metabolomics reveal a pivotal role of carbon-nano sol in promoting the growth of tobacco plants.
BACKGROUND
Carbon nano sol (CNS) can markedly affect the plant growth and development. However, few systematic analyses have been conducted on the underlying regulatory mechanisms in plants, including tobacco (Nicotiana tabacum L.).
RESULTS
Integrated analyses of phenome, ionome, transcriptome, and metabolome were performed in this study to elucidate the physiological and molecular mechanisms underlying the CNS-promoting growth of tobacco plants. We found that 0.3% CNS, facilitating the shoot and root growth of tobacco plants, significantly increased shoot potassium concentrations. Antioxidant, metabolite, and phytohormone profiles showed that 0.3% CNS obviously reduced reactive oxygen species production and increased antioxidant enzyme activity and auxin accumulation. Comparative transcriptomics revealed that the GO and KEGG terms involving responses to oxidative stress, DNA binding, and photosynthesis were highly enriched in response to exogenous CNS application. Differential expression profiling showed that NtNPF7.3/NtNRT1.5, potentially involved in potassium/auxin transport, was significantly upregulated under the 0.3% CNS treatment. High-resolution metabolic fingerprints showed that 141 and 163 metabolites, some of which were proposed as growth regulators, were differentially accumulated in the roots and shoots under the 0.3% CNS treatment, respectively.
CONCLUSIONS
Taken together, this study revealed the physiological and molecular mechanism underlying CNS-mediated growth promotion in tobacco plants, and these findings provide potential support for improving plant growth through the use of CNS.
Topics: Nicotiana; Carbon; Plant Growth Regulators; Transcriptome; Metabolomics; Gene Expression Profiling; Metabolome; Plant Roots; Plant Shoots
PubMed: 38811869
DOI: 10.1186/s12870-024-05195-1 -
Scientific Reports May 2024Time-stamped cross-sectional data, which lack linkage across time points, are commonly generated in single-cell transcriptional profiling. Many previous methods for...
Time-stamped cross-sectional data, which lack linkage across time points, are commonly generated in single-cell transcriptional profiling. Many previous methods for inferring gene regulatory networks (GRNs) driving cell-state transitions relied on constructing single-cell temporal ordering. Introducing COSLIR (COvariance restricted Sparse LInear Regression), we presented a direct approach to reconstructing GRNs that govern cell-state transitions, utilizing only the first and second moments of samples between two consecutive time points. Simulations validated COSLIR's perfect accuracy in the oracle case and demonstrated its robust performance in real-world scenarios. When applied to single-cell RT-PCR and RNAseq datasets in developmental biology, COSLIR competed favorably with existing methods. Notably, its running time remained nearly independent of the number of cells. Therefore, COSLIR emerges as a promising addition to GRN reconstruction methods under cell-state transitions, bypassing the single-cell temporal ordering to enhance accuracy and efficiency in single-cell transcriptional profiling.
Topics: Gene Regulatory Networks; Single-Cell Analysis; Gene Expression Profiling; Humans; Computational Biology; Algorithms
PubMed: 38811747
DOI: 10.1038/s41598-024-62850-1 -
Scientific Reports May 2024Triple-negative breast cancer (TNBC) has high heterogeneity, poor prognosis, and limited treatment success. Recently, an immunohistochemistry-based surrogate...
Triple-negative breast cancer (TNBC) has high heterogeneity, poor prognosis, and limited treatment success. Recently, an immunohistochemistry-based surrogate classification for the "Fudan University Shanghai Cancer Center (FUSCC) subtyping" has been developed and is considered more suitable for clinical application. Seventy-one paraffin-embedded sections of surgically resected TNBC were classified into four molecular subtypes using the IHC-based surrogate classification. Genomic analysis was performed by targeted next-generation sequencing and the specificity of the subtypes was explored by bioinformatics, including survival analysis, multivariate Cox regression, pathway enrichment, Pyclone analysis, mutational signature analysis and PHIAL analysis. AKT1 and BRCA1 mutations were identified as independent prognostic factors in TNBC. TNBC molecular subtypes encompass distinct genomic landscapes that show specific heterogeneities. The luminal androgen receptor (LAR) subtype was associated with mutations in PIK3CA and PI3K pathways, which are potentially sensitive to PI3K pathway inhibitors. The basal-like immune-suppressed (BLIS) subtype was characterized by high genomic instability and the specific possession of signature 19 while patients in the immunomodulatory (IM) subtype belonged to the PD-L1 ≥ 1% subgroup with enrichment in Notch signaling, suggesting a possible benefit of immune checkpoint inhibitors and Notch inhibitors. Moreover, mesenchymal-like (MES) tumors displayed enrichment in the receptor tyrosine kinase (RTK)-RAS pathway and potential sensitivity to RTK pathway inhibitors. The findings suggest potential treatment targets and prognostic factors, indicating the possibility of TNBC stratified therapy in the future.
Topics: Humans; Triple Negative Breast Neoplasms; Female; Mutation; Middle Aged; Proto-Oncogene Proteins c-akt; Prognosis; Class I Phosphatidylinositol 3-Kinases; Genomics; BRCA1 Protein; Adult; Biomarkers, Tumor; Aged; High-Throughput Nucleotide Sequencing; B7-H1 Antigen
PubMed: 38811720
DOI: 10.1038/s41598-024-62991-3 -
Scientific Data May 2024Proteins are often referred to as the workhorses of cells, and their interactions are necessary to facilitate specific cellular functions. Despite the recognition that...
Proteins are often referred to as the workhorses of cells, and their interactions are necessary to facilitate specific cellular functions. Despite the recognition that protein-protein interactions, and thus protein functions, are determined by proteoform states, such as mutations and post-translational modifications (PTMs), methods for determining the differential abundance of proteoforms across conditions are very limited. Classically, immunoprecipitation coupled with mass spectrometry (IP-MS) has been used to understand how the interactome (preys) of a given protein (bait) changes between conditions to elicit specific cellular functions. Reversing this concept, we present here a new workflow for IP-MS data analysis that focuses on identifying the differential peptidoforms of the bait protein between conditions. This method can provide detailed information about specific bait proteoforms, potentially revealing pathogenic protein states that can be exploited for the development of targeted therapies.
Topics: Data Analysis; Immunoprecipitation; Mass Spectrometry; Protein Processing, Post-Translational; Proteomics
PubMed: 38811611
DOI: 10.1038/s41597-024-03394-x -
Nature Communications May 2024Molecular computing is an emerging paradigm that plays an essential role in data storage, bio-computation, and clinical diagnosis with the future trends of more...
Molecular computing is an emerging paradigm that plays an essential role in data storage, bio-computation, and clinical diagnosis with the future trends of more efficient computing scheme, higher modularity with scaled-up circuity and stronger tolerance of corrupted inputs in a complex environment. Towards these goals, we construct a spatially localized, DNA integrated circuits-based classifier (DNA IC-CLA) that can perform neuromorphic architecture-based computation at a molecular level for medical diagnosis. The DNA-based classifier employs a two-dimensional DNA origami as the framework and localized processing modules as the in-frame computing core to execute arithmetic operations (e.g. multiplication, addition, subtraction) for efficient linear classification of complex patterns of miRNA inputs. We demonstrate that the DNA IC-CLA enables accurate cancer diagnosis in a faster (about 3 h) and more effective manner in synthetic and clinical samples compared to those of the traditional freely diffusible DNA circuits. We believe that this all-in-one DNA-based classifier can exhibit more applications in biocomputing in cells and medical diagnostics.
Topics: Humans; Neoplasms; DNA; MicroRNAs; Computers, Molecular; Algorithms; Computational Biology
PubMed: 38811607
DOI: 10.1038/s41467-024-48869-y -
NPJ Systems Biology and Applications May 2024Under ideal conditions, Escherichia coli cells divide after adding a fixed cell size, a strategy known as the adder. This concept applies to various microbes and is...
Under ideal conditions, Escherichia coli cells divide after adding a fixed cell size, a strategy known as the adder. This concept applies to various microbes and is often explained as the division that occurs after a certain number of stages, associated with the accumulation of precursor proteins at a rate proportional to cell size. However, under poor media conditions, E. coli cells exhibit a different size regulation. They are smaller and follow a sizer-like division strategy where the added size is inversely proportional to the size at birth. We explore three potential causes for this deviation: degradation of the precursor protein and two models where the propensity for accumulation depends on the cell size: a nonlinear accumulation rate, and accumulation starting at a threshold size termed the commitment size. These models fit the mean trends but predict different distributions given the birth size. To quantify the precision of the models to explain the data, we used the Akaike information criterion and compared them to open datasets of slow-growing E. coli cells in different media. We found that none of the models alone can consistently explain the data. However, the degradation model better explains the division strategy when cells are larger, whereas size-related models (power-law and commitment size) account for smaller cells. Our methodology proposes a data-based method in which different mechanisms can be tested systematically.
Topics: Escherichia coli; Models, Biological; Cell Division; Cell Size; Escherichia coli Proteins
PubMed: 38811603
DOI: 10.1038/s41540-024-00383-z -
NPJ Systems Biology and Applications May 2024The discovery of upstream regulatory genes of a gene of interest still remains challenging. Here we applied a scalable computational method to unbiasedly predict...
The discovery of upstream regulatory genes of a gene of interest still remains challenging. Here we applied a scalable computational method to unbiasedly predict candidate regulatory genes of critical transcription factors by searching the whole genome. We illustrated our approach with a case study on the master regulator FOXP3 of human primary regulatory T cells (Tregs). While target genes of FOXP3 have been identified, its upstream regulatory machinery still remains elusive. Our methodology selected five top-ranked candidates that were tested via proof-of-concept experiments. Following knockdown, three out of five candidates showed significant effects on the mRNA expression of FOXP3 across multiple donors. This provides insights into the regulatory mechanisms modulating FOXP3 transcriptional expression in Tregs. Overall, at the genome level this represents a high level of accuracy in predicting upstream regulatory genes of key genes of interest.
Topics: Humans; Forkhead Transcription Factors; T-Lymphocytes, Regulatory; Transcriptome; Computational Biology; Gene Expression Regulation; Gene Expression Profiling; Genes, Regulator
PubMed: 38811598
DOI: 10.1038/s41540-024-00387-9 -
NPJ Systems Biology and Applications May 2024The amazing complexity of gene regulatory circuits, and biological systems in general, makes mathematical modeling an essential tool to frame and develop our... (Review)
Review
The amazing complexity of gene regulatory circuits, and biological systems in general, makes mathematical modeling an essential tool to frame and develop our understanding of their properties. Here, we present some fundamental considerations to develop and analyze a model of a gene regulatory circuit of interest, either representing a natural, synthetic, or theoretical system. A mathematical model allows us to effectively evaluate the logical implications of our hypotheses. Using our models to systematically perform in silico experiments, we can then propose specific follow-up assessments of the biological system as well as to reformulate the original assumptions, enriching both our knowledge and our understanding of the system. We want to invite the community working on different aspects of gene regulatory circuits to explore the power and benefits of mathematical modeling in their system.
Topics: Gene Regulatory Networks; Models, Genetic; Computer Simulation; Systems Biology; Humans; Gene Expression Regulation; Computational Biology
PubMed: 38811585
DOI: 10.1038/s41540-024-00380-2 -
Nature Communications May 2024Gene drive systems could be a viable strategy to prevent pathogen transmission or suppress vector populations by propagating drive alleles with super-Mendelian...
Gene drive systems could be a viable strategy to prevent pathogen transmission or suppress vector populations by propagating drive alleles with super-Mendelian inheritance. CRISPR-based homing gene drives convert wild type alleles into drive alleles in heterozygotes with Cas9 and gRNA. It is thus desirable to identify Cas9 promoters that yield high drive conversion rates, minimize the formation rate of resistance alleles in both the germline and the early embryo, and limit somatic Cas9 expression. In Drosophila, the nanos promoter avoids leaky somatic expression, but at the cost of high embryo resistance from maternally deposited Cas9. To improve drive efficiency, we test eleven Drosophila melanogaster germline promoters. Some achieve higher drive conversion efficiency with minimal embryo resistance, but none completely avoid somatic expression. However, such somatic expression often does not carry detectable fitness costs for a rescue homing drive targeting a haplolethal gene, suggesting somatic drive conversion. Supporting a 4-gRNA suppression drive, one promoter leads to a low drive equilibrium frequency due to fitness costs from somatic expression, but the other outperforms nanos, resulting in successful suppression of the cage population. Overall, these Cas9 promoters hold advantages for homing drives in Drosophila species and may possess valuable homologs in other organisms.
Topics: Animals; Promoter Regions, Genetic; Drosophila melanogaster; Drosophila Proteins; Gene Drive Technology; CRISPR-Cas Systems; Germ Cells; RNA, Guide, CRISPR-Cas Systems; Animals, Genetically Modified; CRISPR-Associated Protein 9; Alleles; Female; Male; RNA-Binding Proteins
PubMed: 38811556
DOI: 10.1038/s41467-024-48874-1