Corrigendum to: Gene Similarity Networks Unveil a Potential Novel Unicellular Group Closely Related to Animals from the Tara Oceans Expedition

ABSTRACT Using automated genome analysis tools, it is often unclear to what degree genetic variability in homologous biosynthetic pathways relates to structural variation. This hampers strain prioritization and compound identification and can lead to overinterpretation of chemical diversity. Here, we assessed the metabolic potential of Nocardia, an underinvestigated actinobacterial genus that is known to comprise opportunistic human pathogens. Our analysis revealed a plethora of putative biosynthetic gene clusters of various classes, including polyketide, nonribosomal peptide, and terpenoid pathways. Furthermore, we used the highly conserved biosynthetic pathway for nocobactin-like siderophores to investigate how gene cluster differences correlate to structural differences in the produced compounds. Sequence similarity networks generated by BiG-SCAPE (Biosynthetic Gene Similarity Clustering and Prospecting Engine) showed the presence of several distinct gene cluster families. Metabolic profiling of selected Nocardia strains using liquid chromatography-mass spectrometry (LC-MS) metabolomics data, nuclear magnetic resonance (NMR) spectroscopy, and GNPS (Global Natural Product Social molecular networking) revealed that nocobactin-like biosynthetic gene cluster (BGC) families above a BiG-SCAPE threshold of 70% can be assigned to distinct structural types of nocobactin-like siderophores. IMPORTANCE Our work emphasizes that Nocardia represent a prolific source for natural products rivaling better-characterized genera such as Streptomyces or Amycolatopsis. Furthermore, we showed that large-scale analysis of biosynthetic gene clusters using similarity networks with high stringency allows the distinction and prediction of natural product structural variations. This will facilitate future genomics-driven drug discovery campaigns.

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Gene similarity networks provide tools for understanding eukaryote origins and evolution

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1211371110 ◽

2013 ◽

Vol 110 (17) ◽

pp. E1594-E1603 ◽

Cited By ~ 41

Author(s):

D. Alvarez-Ponce ◽

P. Lopez ◽

E. Bapteste ◽

J. O. McInerney

Keyword(s):

Similarity Networks ◽

Gene Similarity

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Gene Similarity Networks Unveil a Potential Novel Unicellular Group Closely Related to Animals from the Tara Oceans Expedition

Genome Biology and Evolution ◽

10.1093/gbe/evaa117 ◽

2020 ◽

Vol 12 (9) ◽

pp. 1664-1678 ◽

Cited By ~ 1

Author(s):

Alicia S Arroyo ◽

Romain Iannes ◽

Eric Bapteste ◽

Iñaki Ruiz-Trillo

Keyword(s):

Positive Association ◽

Data Sets ◽

Data Set ◽

Full Diversity ◽

Fresh Perspective ◽

Reference Sequences ◽

Similarity Networks ◽

Environmental Sequences ◽

Network Metrics ◽

Gene Similarity

Abstract The Holozoa clade comprises animals and several unicellular lineages (choanoflagellates, filastereans, and teretosporeans). Understanding their full diversity is essential to address the origins of animals and other evolutionary questions. However, they are poorly known. To provide more insights into the real diversity of holozoans and check for undiscovered diversity, we here analyzed 18S rDNA metabarcoding data from the global Tara Oceans expedition. To overcome the low phylogenetic information contained in the metabarcoding data set (composed of sequences from the short V9 region of the gene), we used similarity networks by combining two data sets: unknown environmental sequences from Tara Oceans and known reference sequences from GenBank. We then calculated network metrics to compare environmental sequences with reference sequences. These metrics reflected the divergence between both types of sequences and provided an effective way to search for evolutionary relevant diversity, further validated by phylogenetic placements. Our results showed that the percentage of unicellular holozoan diversity remains hidden. We found novelties in several lineages, especially in Acanthoecida choanoflagellates. We also identified a potential new holozoan group that could not be assigned to any of the described extant clades. Data on geographical distribution showed that, although ubiquitous, each unicellular holozoan lineage exhibits a different distribution pattern. We also identified a positive association between new animal hosts and the ichthyosporean symbiont Creolimax fragrantissima, as well as for other holozoans previously reported as free-living. Overall, our analyses provide a fresh perspective into the diversity and ecology of unicellular holozoans, highlighting the amount of undescribed diversity.

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Mining Similar Aspects for Gene Similarity Explanation Based on Gene Information Network

IEEE/ACM Transactions on Computational Biology and Bioinformatics ◽

10.1109/tcbb.2020.3041559 ◽

2020 ◽

pp. 1-1

Author(s):

Yidan Zhang ◽

Lei Duan ◽

Huiru Zheng ◽

Jesse Li-Ling ◽

Ruiqi Qin ◽

...

Keyword(s):

Information Network ◽

Gene Similarity ◽

Gene Information

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Clustering and visualizing similarity networks of membrane proteins

Proteins Structure Function and Bioinformatics ◽

10.1002/prot.24832 ◽

2015 ◽

Vol 83 (8) ◽

pp. 1450-1461 ◽

Cited By ~ 6

Author(s):

Geng-Ming Hu ◽

Te-Lun Mai ◽

Chi-Ming Chen

Keyword(s):

Membrane Proteins ◽

Similarity Networks

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Diversity of GPI-anchored fungal adhesins

Biological Chemistry ◽

10.1515/hsz-2020-0199 ◽

2020 ◽

Vol 401 (12) ◽

pp. 1389-1405

Author(s):

Lars-Oliver Essen ◽

Marian Samuel Vogt ◽

Hans-Ulrich Mösch

Keyword(s):

Cell Wall ◽

Cell Surface ◽

Bioinformatics Analysis ◽

Current Knowledge ◽

Sequence Similarity ◽

Growth Forms ◽

Similarity Networks ◽

Fungal Adhesins ◽

Sequence Similarity Networks ◽

Successful Colonization

AbstractSelective adhesion of fungal cells to one another and to foreign surfaces is fundamental for the development of multicellular growth forms and the successful colonization of substrates and host organisms. Accordingly, fungi possess diverse cell wall-associated adhesins, mostly large glycoproteins, which present N-terminal adhesion domains at the cell surface for ligand recognition and binding. In order to function as robust adhesins, these glycoproteins must be covalently linkedto the cell wall via C-terminal glycosylphosphatidylinositol (GPI) anchors by transglycosylation. In this review, we summarize the current knowledge on the structural and functional diversity of so far characterized protein families of adhesion domains and set it into a broad context by an in-depth bioinformatics analysis using sequence similarity networks. In addition, we discuss possible mechanisms for the membrane-to-cell wall transfer of fungal adhesins by membrane-anchored Dfg5 transglycosidases.

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Isolation, Phylogenetic and Gephyromycin Metabolites Characterization of New Exopolysaccharides-Bearing Antarctic Actinobacterium from Feces of Emperor Penguin

Marine Drugs ◽

10.3390/md19080458 ◽

2021 ◽

Vol 19 (8) ◽

pp. 458

Author(s):

Hui-Min Gao ◽

Peng-Fei Xie ◽

Xiao-Ling Zhang ◽

Qiao Yang

Keyword(s):

Genome Mining ◽

Pairwise Comparison ◽

Gene Clusters ◽

Amino Acid Identity ◽

Core Gene ◽

Emperor Penguin ◽

Rrna Gene ◽

Active Metabolites ◽

The Antarctic ◽

Gene Similarity

A new versatile actinobacterium designated as strain NJES-13 was isolated from the feces of the Antarctic emperor penguin. This new isolate was found to produce two active gephyromycin analogues and bioflocculanting exopolysaccharides (EPS) metabolites. Phylogenetic analysis based on pairwise comparison of 16S rRNA gene sequences showed that strain NJES-13 was closely related to Mobilicoccus pelagius Aji5-31T with a gene similarity of 95.9%, which was lower than the threshold value (98.65%) for novel species delineation. Additional phylogenomic calculations of the average nucleotide identity (ANI, 75.9–79.1%), average amino acid identity (AAI, 52.4–66.9%) and digital DNA–DNA hybridization (dDDH, 18.6–21.9%), along with the constructed phylogenomic tree based on the up-to-date bacterial core gene (UBCG) set from the bacterial genomes, unequivocally separated strain NJES-13 from its close relatives within the family Dermatophilaceae. Hence, it clearly indicated that strain NJES-13 represented a putative new actinobacterial species isolated from the gut microbiota of mammals inhabiting the Antarctic. The obtained complete genome of strain NJES-13 consisted of a circular 3.45 Mb chromosome with a DNA G+C content of 67.0 mol%. Furthering genome mining of strain NJES-13 showed the presence of five biosynthetic gene clusters (BGCs) including one type III PKS responsible for the biosynthesis of the core of gephyromycins, and a series of genes encoding for bacterial EPS biosynthesis. Thus, based on the combined phylogenetic and active metabolites characterization presented in this study, we confidently conclude that strain NJES-13 is a novel, fresh actinobacterial candidate to produce active gephyromycins and microbial bioflocculanting EPS, with potential pharmaceutical, environmental and biotechnological implications.

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MeFSAT: A curated natural product database specific to secondary metabolites of medicinal fungi

10.1101/2020.12.04.412502 ◽

2020 ◽

Author(s):

R.P. Vivek-Ananth ◽

Ajaya Kumar Sahoo ◽

Kavyaa Kumaravel ◽

Karthikeyan Mohanraj ◽

Areejit Samal

Keyword(s):

Secondary Metabolites ◽

Physicochemical Properties ◽

Natural Product ◽

Chemical Space ◽

Chemical Structures ◽

Therapeutic Uses ◽

Natural Product Library ◽

Medicinal Fungi ◽

Similarity Networks ◽

Shape Complexity

AbstractFungi are a rich source of secondary metabolites which constitutes a valuable and diverse chemical space of natural products. Medicinal fungi have been used in traditional medicine to treat human ailments for centuries. To date, there is no devoted resource on secondary metabolites and therapeutic uses of medicinal fungi. Such a dedicated resource compiling dispersed information on medicinal fungi across published literature will facilitate ongoing efforts towards natural product based drug discovery. Here, we present the first comprehensive manually curated database on Medicinal Fungi Secondary metabolites And Therapeutics (MeFSAT) that compiles information on 184 medicinal fungi, 1830 secondary metabolites and 149 therapeutics uses. Importantly, MeFSAT contains a non-redundant in silico natural product library of 1830 secondary metabolites along with information on their chemical structures, computed physicochemical properties, drug-likeness properties, predicted ADMET properties, molecular descriptors and predicted human target proteins. By comparing the physicochemical properties of secondary metabolites in MeFSAT with other small molecules collections, we find that fungal secondary metabolites have high stereochemical complexity and shape complexity similar to other natural product libraries. Based on multiple scoring schemes, we have filtered a subset of 228 drug-like secondary metabolites in MeFSAT database. By constructing and analyzing chemical similarity networks, we show that the chemical space of secondary metabolites in MeFSAT is highly diverse. The compiled information in MeFSAT database is openly accessible at: https://cb.imsc.res.in/mefsat/.

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Community detection in sequence similarity networks based on attribute clustering

PLoS ONE ◽

10.1371/journal.pone.0178650 ◽

2017 ◽

Vol 12 (7) ◽

pp. e0178650

Author(s):

Janamejaya Chowdhary ◽

Frank E. Löffler ◽

Jeremy C. Smith

Keyword(s):

Community Detection ◽

Sequence Similarity ◽

Similarity Networks ◽

Sequence Similarity Networks ◽

Attribute Clustering

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Disease Interactome

Epidemiological Research Applications for Public Health Measurement and Intervention - Advances in Human Services and Public Health ◽

10.4018/978-1-7998-4414-3.ch005 ◽

2021 ◽

pp. 69-84

Author(s):

Suma Dawn ◽

Nidhi Jain ◽

Tulika Gangwar

Keyword(s):

Quantitative Analysis ◽

Hierarchical Clustering ◽

Major Part ◽

Visual Representation ◽

Clustering Algorithms ◽

Average Linkage ◽

Co Morbidity ◽

Gene Similarity ◽

The Relationship ◽

Secondary Diseases

The disease interactome is a network of genes that are related to each other through some attributes. These genes, being part of various diseases, show a high correlation among many diseases. Genes being a major part of the interactome thus can be used to determine the relationship between various diseases, their symptoms, clinical similarity, and co-morbidity. Subgraphs and similarity factors such as Jaccardian distance, cosine similarities, and others have been exploited to calculate the relationship between two or more diseases. Many diseases that did not show much resemblance on the basis of gene similarity or symptom similarity were seen to be closely related according to network interactome. The quantitative analysis between disease-disease was also done. Clustering algorithms like hierarchical clustering involving single, complete, and average linkage were applied to get a visual representation in the form of a dendrogram. Thus, disease-disease interactome was created, analyzed for finding related secondary diseases, and their basic nature was understood.

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