Temporal and fertilizer-dependent dynamics of soil bacterial communities in buckwheat fields under long-term management.

This study integrated bacterial community and soil chemicals to characterize the soil ecosystem in an open upland field managed by six controlled fertilizer programs using the minimum amount of pesticides. Amplicon sequencing the 16S rRNA gene revealed that inorganic nitrogen fertilizer and compost altered the diversity and structure of the soil bacterial community throughout buckwheat (Fagopyrum esculentum Moench 'Hitachiakisoba') cultivation. The bacterial community comprised three clusters that contained bacteria that are prevalent in soils fertilized with nitrogen (cluster 1, 340 taxa), without nitrogen and compost (cluster 2, 234 taxa), and with compost-fertilized (cluster 3, 296 taxa). Cluster 2 contained more taxa in Actinobacteriota and less in Acidobacteriota, and cluster 3 contained more taxa in Gemmatimonadota compared with the other clusters. The most frequent taxa in cluster 1 were within the Chloroflexi phylum. The bacterial community structure correlated with soil chemical properties including pH, total organic carbon, SO42-, soluble Ca2+. A co-occurrence network of bacterial taxa and chemicals identified key bacterial groups comprising the center of a community network that determined topology and dynamics of the network. Temporal dynamics of the bacterial community structure indicated that Burkholderiales were associated with buckwheat ripening, indicating plant-bacteria interaction in the ecosystem.

Nondestructive microbial discrimination using single-cell Raman spectra and random forest machine learning algorithm.

Raman microspectroscopy is a powerful tool for obtaining biomolecular information from single microbial cells in a nondestructive manner. Here, we detail steps to discriminate prokaryotic species using single-cell Raman spectra acquisitions followed by data preprocessing and random forest model tuning. In addition, we describe the steps required to evaluate the model. This protocol requires minimal preprocessing of Raman spectral data, making it accessible to non-spectroscopists, yet allows intuitive visualization of feature importance. For complete details on the use and execution of this protocol, please refer to Kanno et al. (2021).

Lifestyle Evolution Analysis by Binary-State Speciation and Extinction (BiSSE) Model.

Phylogenetic comparative methods (PCMs) combine statistics and evolutionary models to infer the dynamics of trait evolution and diversification that underlie the observed phylogeny. While PCMs have been used to study macro-evolutionary processes and evolutionary transitions of macroorganisms, their application to microbes is still limited. With the abundance of publicly available genomic and trait character data for diverse microbes nowadays, applications of PCMs on these data can provide insights into the fundamental principles that govern microbial evolution. Here, we introduce the Binary-State Speciation and Extinction (BiSSE) model, which is a relatively simple yet powerful approach for analyzing trait evolution. We begin by explaining the theoretical background and intuition behind the BiSSE model. Then, R commands for running the BiSSE model are presented. Finally, we introduce a case study that successfully applied the BiSSE model to investigate generalist and specialist microbial lifestyle evolution.

Machine learning-assisted single-cell Raman fingerprinting for in situ and nondestructive classification of prokaryotes.

Accessing enormous uncultivated microorganisms (microbial dark matter) in various Earth environments requires accurate, nondestructive classification, and molecular understanding of the microorganisms in in situ and at the single-cell level. Here we demonstrate a combined approach of random forest (RF) machine learning and single-cell Raman microspectroscopy for accurate classification of phylogenetically diverse prokaryotes (three bacterial and three archaeal species from different phyla). Our RF classifier achieved a 98.8 ± 1.9% classification accuracy among the six species in pure populations and 98.4% for three species in an artificially mixed population. Feature importance scores against each wavenumber reveal that the presence of carotenoids and structure of membrane lipids play key roles in distinguishing the prokaryotic species. We also find unique Raman markers for an ammonia-oxidizing archaeon. Our approach with moderate data pretreatment and intuitive visualization of feature importance is easy to use for non-spectroscopists, and thus offers microbiologists a new single-cell tool for shedding light on microbial dark matter.

Graph Splitting: A Graph-Based Approach for Superfamily-Scale Phylogenetic Tree Reconstruction.

A protein superfamily contains distantly related proteins that have acquired diverse biological functions through a long evolutionary history. Phylogenetic analysis of the early evolution of protein superfamilies is a key challenge because existing phylogenetic methods show poor performance when protein sequences are too diverged to construct an informative multiple sequence alignment (MSA). Here, we propose the Graph Splitting (GS) method, which rapidly reconstructs a protein superfamily-scale phylogenetic tree using a graph-based approach. Evolutionary simulation showed that the GS method can accurately reconstruct phylogenetic trees and be robust to major problems in phylogenetic estimation, such as biased taxon sampling, heterogeneous evolutionary rates, and long-branch attraction when sequences are substantially diverge. Its application to an empirical data set of the triosephosphate isomerase (TIM)-barrel superfamily suggests rapid evolution of protein-mediated pyrimidine biosynthesis, likely taking place after the RNA world. Furthermore, the GS method can also substantially improve performance of widely used MSA methods by providing accurate guide trees.

Collaborative environmental DNA sampling from petal surfaces of flowering cherry Cerasus × yedoensis 'Somei-yoshino' across the Japanese archipelago.

Recent studies have shown that environmental DNA is found almost everywhere. Flower petal surfaces are an attractive tissue to use for investigation of the dispersal of environmental DNA in nature as they are isolated from the external environment until the bud opens and only then can the petal surface accumulate environmental DNA. Here, we performed a crowdsourced experiment, the "Ohanami Project", to obtain environmental DNA samples from petal surfaces of Cerasus × yedoensis 'Somei-yoshino' across the Japanese archipelago during spring 2015. C. × yedoensis is the most popular garden cherry species in Japan and clones of this cultivar bloom simultaneously every spring. Data collection spanned almost every prefecture and totaled 577 DNA samples from 149 collaborators. Preliminary amplicon-sequencing analysis showed the rapid attachment of environmental DNA onto the petal surfaces. Notably, we found DNA of other common plant species in samples obtained from a wide distribution; this DNA likely originated from the pollen of the Japanese cedar. Our analysis supports our belief that petal surfaces after blossoming are a promising target to reveal the dynamics of environmental DNA in nature. The success of our experiment also shows that crowdsourced environmental DNA analyses have considerable value in ecological studies.

Comprehensive computational analysis of bacterial CRP/FNR superfamily and its target motifs reveals stepwise evolution of transcriptional networks.

The cAMP receptor protein (CRP)/fumarate and nitrate reduction regulatory protein (FNR)-type transcription factors (TFs) are members of a well-characterized global TF family in bacteria and have two conserved domains: the N-terminal ligand-binding domain for small molecules (e.g., cAMP, NO, or O(2)) and the C-terminal DNA-binding domain. Although the CRP/FNR-type TFs recognize very similar consensus DNA target sequences, they can regulate different sets of genes in response to environmental signals. To clarify the evolution of the CRP/FNR-type TFs throughout the bacterial kingdom, we undertook a comprehensive computational analysis of a large number of annotated CRP/FNR-type TFs and the corresponding bacterial genomes. Based on the amino acid sequence similarities among 1,455 annotated CRP/FNR-type TFs, spectral clustering classified the TFs into 12 representative groups, and stepwise clustering allowed us to propose a possible process of protein evolution. Although each cluster mainly consists of functionally distinct members (e.g., CRP, NTC, FNR-like protein, and FixK), FNR-related TFs are found in several groups and are distributed in a wide range of bacterial phyla in the sequence similarity network. This result suggests that the CRP/FNR-type TFs originated from an ancestral FNR protein, involved in nitrogen fixation. Furthermore, a phylogenetic profiling analysis showed that combinations of TFs and their target genes have fluctuated dynamically during bacterial evolution. A genome-wide analysis of TF-binding sites also suggested that the diversity of the transcriptional regulatory system was derived by the stepwise adaptation of TF-binding sites to the evolution of TFs.

Deep sequencing reveals as-yet-undiscovered small RNAs in Escherichia coli.

BACKGROUND: In Escherichia coli, approximately 100 regulatory small RNAs (sRNAs) have been identified experimentally and many more have been predicted by various methods. To provide a comprehensive overview of sRNAs, we analysed the low-molecular-weight RNAs (< 200 nt) of E. coli with deep sequencing, because the regulatory RNAs in bacteria are usually 50-200 nt in length. RESULTS: We discovered 229 novel candidate sRNAs (≥ 50 nt) with computational or experimental evidence of transcription initiation. Among them, the expression of seven intergenic sRNAs and three cis-antisense sRNAs was detected by northern blot analysis. Interestingly, five novel sRNAs are expressed from prophage regions and we note that these sRNAs have several specific characteristics. Furthermore, we conducted an evolutionary conservation analysis of the candidate sRNAs and summarised the data among closely related bacterial strains. CONCLUSIONS: This comprehensive screen for E. coli sRNAs using a deep sequencing approach has shown that many as-yet-undiscovered sRNAs are potentially encoded in the E. coli genome. We constructed the Escherichia coli Small RNA Browser (ECSBrowser; http://rna.iab.keio.ac.jp/), which integrates the data for previously identified sRNAs and the novel sRNAs found in this study.

Bioinformatic analysis of post-transcriptional regulation by uORF in human and mouse.

RNA decay is thought to exert an important influence on gene expression by maintaining a steady-state level of transcripts and/or by eliminating aberrant transcripts. However, the sequence elements which control such processes have not been determined. Upstream open reading frames (uORFs) in the transcripts of several genes are reported to control translational initiation by stalling ribosomes and thereby promote RNA decay. We therefore performed bioinformatic analysis of the tissue-wide expression profiles and mRNA half-life of transcripts containing uORFs in humans and mice to assess the relationship between RNA decay and the presence of uORFs in transcripts. The expression levels of transcripts containing uORF were markedly lower than those not containing uORF. Moreover, the half-life of the uORF-containing transcripts was also shorter. These results suggest that uORFs are sequence elements that down-regulate RNA transcripts via RNA decay mechanisms.

SPLITS: a new program for predicting split and intron-containing tRNA genes at the genome level.

In the archaea, some tRNA precursors contain intron(s) not only in the anticodon loop region but also in diverse sites of the gene (intron-containing tRNA or cis-spliced tRNA). The parasite Nanoarchaeum equitans, a member of the Nanoarchaeota kingdom, creates functional tRNA from separate genes, one encoding the 5'-half and the other the 3'-half (split tRNA or trans-spliced tRNA). Although recent genome projects have revealed a huge amount of nucleotide sequence data in the archaea, a comprehensive methodology for intron-containing and split tRNA searching is yet to be established. We therefore developed SPLITS, which is aimed at searching for any type of tRNA gene and is especially focused on intron-containing tRNAs or split tRNAs at the genome level. SPLITS initially predicts the bulge-helix-bulge splicing motif (a well-known, required structure in archaeal pre-tRNA introns) to determine and remove the intronic regions of tRNA genes. The intron-removed DNA sequences are automatically queried to tRNAscan-SE. SPLITS can predict known tRNAs with single introns located at unconventional sites on the genes (100%), tRNAs with double introns (85.7%), and known split tRNAs (100%). Our program will be very useful for identifying novel tRNA genes after completion of genome projects. The SPLITS source code is freely downloadable at http://splits.iab.keio.ac.jp/.

A comprehensive software suite for the analysis of cDNAs.

We have developed a comprehensive software suite for bioinformatics research of cDNAs; it is aimed at rapid characterization of the features of genes and the proteins they code. Methods implemented include the detection of translation initiation and termination signals, statistical analysis of codon usage, comparative study of amino acid composition, comparative modeling of the structures of product proteins, prediction of alternative splice forms, and metabolic pathway reconstruction.