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The number of metagenomes is increasing rapidly. However, current methods for metagenomic analysis are limited by their capability for in-depth data mining among a large number of microbiome each of which carries a complex community structure. Moreover, the complexity of configuring and operating computational pipeline also hinders efficient data processing for the end users. In this work we introduce Parallel-META 3, a comprehensive and fully automatic computational toolkit for rapid data mining among metagenomic datasets, with advanced features including 16S rRNA extraction for shotgun sequences, 16S rRNA copy number calibration, 16S rRNA based functional prediction, diversity statistics, bio-marker selection, interaction network construction, vector-graph-based visualization and parallel computing. Application of Parallel-META 3 on 5,337 samples with 1,117,555,208 sequences from diverse studies and platforms showed it could produce similar results as QIIME and PICRUSt with much faster speed and lower memory usage, which demonstrates its ability to unravel the taxonomical and functional dynamics patterns across large datasets and elucidate ecological links between microbiome and the environment. Parallel-META 3 is implemented in C/C++ and R, and integrated into an executive package for rapid installation and easy access under Linux and Mac OS X. Both binary and source code packages are available at http://bioinfo.single-cell.cn/parallel-meta.html .

Wider application of single-cell analysis has been limited by the lack of an easy-to-use and low-cost strategy for single-cell isolation that can be directly coupled to single-cell sequencing and single-cell cultivation, especially for small-size microbes. Herein, a facile droplet microfluidic platform was developed to dispense individual microbial cells into conventional standard containers for downstream analysis. Functional parts for cell encapsulation, droplet inspection and sorting, as well as a chip-to-tube capillary interface were integrated on one single chip with simple architecture, and control of the droplet sorting was achieved by a low-cost solenoid microvalve. Using microalgal and yeast cells as models, single-cell isolation success rate of over 90% and single-cell cultivation success rate of 80% were demonstrated. We further showed that the individual cells isolated can be used in high-quality DNA and RNA analyses at both gene-specific and whole-genome levels ( i.e. real-time quantitative PCR and genome sequencing). The simplicity and reliability of the method should improve accessibility of single-cell analysis and facilitate its wider application in microbiology researches.

Bacterial communities from freshwater lakes are shaped by various factors such as nutrients, pH value, temperature, etc. Their compositions and relative abundances would undergo changes to adapt the changing environments and in turn could affect the environments of freshwater lakes. Analyses of the freshwater lake’s bacterial communities under different environments would be of pivotal importance to monitor the condition of waterbody. In this study, we have collected freshwater samples from two lakes on Yun-Gui plateau of China, Lake Dianchi and Lake Haixihai and analyzed the bacterial community structures from these samples based on 16S rRNA sequencing. Results have shown that: Firstly, the bacterial community of these samples have very different taxonomical structures, not only between two lakes but also among the intra-groups for samples collected from Dianchi. Secondly, the differences between samples from two lakes are highly associated with the chemical-geographical properties of the two lakes. Thirdly, for samples of Dianchi and Haixihai, analytical results of physicochemical, taxonomical structure and relative abundance of community revealed that extreme physicochemical factors caused by human activities have strongly affected the bacterial ecosystem in Dianchi. These results have clearly indicated the importance of combining biological profiling and chemical-geographical properties for monitoring Chinese plateau freshwater bacterial ecosystem, which could provide clues for Chinese freshwater ecosystem remediation on plateau.

Soil is home to an enormous and complex microbiome that features arguably the highest genomic diversity and metabolic heterogeneity of cells on Earth. Their in situ metabolic activities drive many natural processes of pivotal ecological significance or underlie industrial production of numerous valuable bioactivities. Soil harbors arguably the most metabolically and genetically heterogeneous microbiomes on Earth, yet establishing the link between metabolic functions and genome at the precisely one-cell level has been difficult. Here, for mock microbial communities and then for soil microbiota, we established a Raman-activated gravity-driven single-cell encapsulation and sequencing (RAGE-Seq) platform, which identifies, sorts, and sequences precisely one bacterial cell via its anabolic (incorporating D from heavy water) and physiological (carotenoid-containing) functions. We showed that (i) metabolically active cells from numerically rare soil taxa, such as Corynebacterium spp., Clostridium spp., Moraxella spp., Pantoea spp., and Pseudomonas spp., can be readily identified and sorted based on D 2 O uptake, and their one-cell genome coverage can reach ∼93% to allow high-quality genome-wide metabolic reconstruction; (ii) similarly, carotenoid-containing cells such as Pantoea spp., Legionella spp., Massilia spp., Pseudomonas spp., and Pedobacter spp. were identified and one-cell genomes were generated for tracing the carotenoid-synthetic pathways; and (iii) carotenoid-producing cells can be either metabolically active or inert, suggesting culture-based approaches can miss many such cells. As a Raman-activated cell sorter (RACS) family member that can establish a metabolism-genome link at exactly one-cell resolution from soil, RAGE-Seq can help to precisely pinpoint “who is doing what” in complex ecosystems. IMPORTANCE Soil is home to an enormous and complex microbiome that features arguably the highest genomic diversity and metabolic heterogeneity of cells on Earth. Their in situ metabolic activities drive many natural processes of pivotal ecological significance or underlie industrial production of numerous valuable bioactivities. However, pinpointing “who is doing what” in a soil microbiome, which consists of mainly yet-to-be-cultured species, has remained a major challenge. Here, for soil microbiota, we established a Raman-activated gravity-driven single-cell encapsulation and sequencing (RAGE-Seq) method, which identifies, sorts, and sequences at the resolution of precisely one microbial cell via its catabolic and anabolic functions. As a Raman-activated cell sorter (RACS) family member that can establish a metabolism-genome link at one-cell resolution from soil, RAGE-Seq can help to precisely pinpoint “who is doing what” in complex ecosystems.

Single-cell genomic whole genome amplification (WGA) is a crucial step in single-cell sequencing, yet its low amplification efficiency, incomplete and uneven genome amplification still hinder the throughput and efficiency of single-cell sequencing workflows. Here we introduce a process called Improved Single-cell Genome Amplification (iSGA), in which the whole single-cell sequencing cycle is completed in a high-efficient and high-coverage manner, through phi29 DNA polymerase engineering and process engineering. By establishing a disulfide bond of F137C-A377C, the amplification ability of the enzyme was improved to that of single-cell. By further protein engineering and process engineering, a supreme enzyme named HotJa Phi29 DNA Polymerase was developed and showed significantly better coverage (99.75%) at a higher temperature (40°C). High single-cell genome amplification ability and high coverage (93.59%) were also achieved for commercial probiotic samples. iSGA is more efficient and robust than the wild-type phi29 DNA polymerase, and it is 2.03-fold more efficient and 10.89-fold cheaper than the commercial Thermo Scientific EquiPhi29 DNA Polymerase. These advantages promise its broad applications in large-scale single-cell sequencing.

Background Under nitrogen deficiency situation, Nannochloropsis spp. accumulate large amounts of lipids in the form of triacylglycerides (TAG). Mechanisms of this process from the perspective of transcriptome and metabolome have been obtained previously, yet proteome analysis is still sparse which hinders the analysis of dynamic adaption to nitrogen deficiency. Here, proteomes for 3 h, 6 h, 12 h, 24 h, 48 h and 10th day of nitrogen deplete (N−) and replete (N+) conditions were obtained and integrated with previous transcriptome data for N. oceanica. Results Physiological adaptations to N− not apparent from transcriptome data were unveiled: (a) abundance of proteins related to photosynthesis only slightly decreased in the first 48 h, indicating that photosynthesis is still working efficiently, and protein amounts adjust gradually with reduction in chloroplast size. (b) Most proteins related to the TCA cycle were strongly upregulated after 48 h under N−, suggesting that respiration is enhanced after 48 h and that TCA cycle efflux supports the carbon required for lipid synthesis. (c) Proteins related to lipid accumulation via the Kennedy pathway increased their abundance at 48 h, synchronous with the previously reported diversification of fatty acids after 48 h. Conclusions This study adds a proteome perspective on the major pathways for TAG accumulation in Nannochloropsis spp. Temporal changes of proteome exhibited distinct adaptation phases that are usually delayed relative to transcriptomic responses. Notably, proteome data revealed that photosynthesis and carbon fixation are still ongoing even after 48 h of N−. Moreover, sometimes completely opposite trends in proteome and transcriptome demonstrate the relevance of underexplored post-transcriptional regulation for N− adaptation.

Due to the challenges in detecting in situ activity and cultivating the not-yet-cultured, functional assessment and mining of living microbes from nature has typically followed a ‘culture-first’ paradigm. Here, employing phosphate-solubilizing microbes (PSM) as model, we introduce a ‘screen-first’ strategy that is underpinned by a precisely one-cell-resolution, complete workflow of single-cell Raman-activated Sorting and Cultivation (scRACS-Culture). Directly from domestic sewage, individual cells were screened for in-situ organic-phosphate-solubilizing activity via D2O intake rate, sorted by the function via Raman-activated Gravity-driven Encapsulation (RAGE), and then cultivated from precisely one cell. By scRACS-Culture, pure cultures of strong organic PSM including Comamonas spp., Acinetobacter spp., Enterobacter spp. and Citrobacter spp., were derived, whose phosphate-solubilizing activities in situ are 90–200% higher than in pure culture, underscoring the importance of ‘screen-first’ strategy. Moreover, employing scRACS-Seq for post-RACS cells that remain uncultured, we discovered a previously unknown, low-abundance, strong organic-PSM of Cutibacterium spp. that employs secretary metallophosphoesterase (MPP), cell-wall-anchored 5′-nucleotidase (encoded by ushA) and periplasmic-membrane located PstSCAB-PhoU transporter system for efficient solubilization and scavenging of extracellular phosphate in sewage. Therefore, scRACS-Culture and scRACS-Seq provide an in situ function-based, ‘screen-first’ approach for assessing and mining microbes directly from the environment.

Rapid expansion of the probiotics industry demands fast, sensitive, comprehensive, and low‐cost strategies for quality assessment. Here, we introduce a culture‐free, one‐cell‐resolution, phenome‐genome‐combined strategy called Single‐Cell Identification, Viability and Vitality tests, and Source‐tracking (SCIVVS). For each cell directly extracted from the product, the fingerprint region of D2O‐probed single‐cell Raman spectrum (SCRS) enables species‐level identification with 93% accuracy, based on a reference SCRS database from 21 statutory probiotic species, whereas the C–D band accurately quantifies viability, metabolic vitality plus their intercellular heterogeneity. For source‐tracking, single‐cell Raman‐activated Cell Sorting and Sequencing can proceed, producing indexed, precisely one‐cell‐based genome assemblies that can reach ~99.40% genome‐wide coverage. Finally, we validated an integrated SCIVVS workflow with automated SCRS acquisition where the whole process except sequencing takes just 5 h. As it is >20‐fold faster, >10‐time cheaper, vitality‐revealing, heterogeneity‐resolving, and automation‐prone, SCIVVS is a new technological and data framework for quality assessment of live‐cell products. Highlights We introduce a culture‐free, one‐cell‐resolution, phenome‐genome‐combined strategy called Single‐Cell Identification, Viability and Vitality tests, and Source‐tracking (SCIVVS) for rapid quality assessment of probiotics products. SCIVVS enables species‐level identification with 93% accuracy and quantifies viability, metabolic vitality plus their intercellular heterogeneity. For source‐tracking a cell, SCIVVS can reach ~99.40% genome‐wide coverage for various Lactobacillus, Bifidobacterium, and Streptococcus spp. SCIVVS is >20‐fold faster, >10‐time cheaper, vitality‐revealing, heterogeneity‐resolving, and automation‐prone. SCIVVS is a new technological and data framework that can transform quality evaluation, biomanufacturing‐process monitoring, and intellectual‐property protection of probiotics and other live‐cell products. The rapid expansion of the probiotics industry demands fast, sensitive, comprehensive, and low‐cost methods for quality assessment of such live‐cell products. However current methods are slow, tedious, and incapable of quantifying in situ vitality or its heterogeneity. Here, we introduce a culture‐free, one‐cell‐resolution, phenome‐genome‐combined strategy called Single‐Cell Identification, Viability and Vitality tests, and Source‐tracking (SCIVVS). As it is >20‐fold faster, >10‐time cheaper, vitality‐revealing, heterogeneity‐resolving, and automation‐prone, SCIVVS is a new technological and data framework that can transform quality evaluation, biomanufacturing‐process monitoring, and intellectual‐property protection of probiotics and other live‐cell products. Therefore, we believe this work will attract broad interest and citations.

[This corrects the article DOI: 10.3389/fbioe.2023.1233856.].[This corrects the article DOI: 10.3389/fbioe.2023.1233856.].

Chlorophylls (Chls) are essential cofactors for photosynthesis. One of the least understood steps of Chl biosynthesis is formation of the fifth (E) ring, where the red substrate, magnesium protoporphyrin IX monomethyl ester, is converted to the green product, 3,8-divinyl protochlorophyllide a. In oxygenic phototrophs, this reaction is catalyzed by an oxygen-dependent cyclase, consisting of a catalytic subunit (AcsF/CycI) and an auxiliary protein, Ycf54. Deletion of Ycf54 impairs cyclase activity and results in severe Chl deficiency, but its exact role is not clear. Here, we used a Δycf54 mutant of the model cyanobacterium Synechocystis sp. PCC 6803 to generate suppressor mutations that restore normal levels of Chl. Sequencing Δycf54 revertants identified a single D219G amino acid substitution in CycI and frameshifts in slr1916, which encodes a putative esterase. Introduction of these mutations to the original Δycf54 mutant validated the suppressor effect, especially in combination. However, comprehensive analysis of the Δycf54 suppressor strains revealed that the D219G-substituted CycI is only partially active and its accumulation is misregulated, suggesting that Ycf54 controls both the level and activity of CycI. We also show that Slr1916 has Chl dephytylase activity in vitro and its inactivation up-regulates the entire Chl biosynthetic pathway, resulting in improved cyclase activity. Finally, large-scale bioinformatic analysis indicates that our laboratory evolution of Ycf54-independent CycI mimics natural evolution of AcsF in low-light–adapted ecotypes of the oceanic cyanobacteria Prochlorococcus, which lack Ycf54, providing insight into the evolutionary history of the cyclase enzyme.