Protein embedding based alignment.

PURPOSE: Despite the many progresses with alignment algorithms, aligning divergent protein sequences with less than 20-35% pairwise identity (so called "twilight zone") remains a difficult problem. Many alignment algorithms have been using substitution matrices since their creation in the 1970's to generate alignments, however, these matrices do not work well to score alignments within the twilight zone. We developed Protein Embedding based Alignments, or PEbA, to better align sequences with low pairwise identity. Similar to the traditional Smith-Waterman algorithm, PEbA uses a dynamic programming algorithm but the matching score of amino acids is based on the similarity of their embeddings from a protein language model. METHODS: We tested PEbA on over twelve thousand benchmark pairwise alignments from BAliBASE, each one extracted from one of their multiple sequence alignments. Five different BAliBASE references were used, each with different sequence identities, motifs, and lengths, allowing PEbA to showcase how well it aligns under different circumstances. RESULTS: PEbA greatly outperformed BLOSUM substitution matrix-based pairwise alignments, achieving different levels of improvements of the alignment quality for pairs of sequences with different levels of similarity (over four times as well for pairs of sequences with <10% identity). We also compared PEbA with embeddings generated by different protein language models (ProtT5 and ESM-2) and found that ProtT5-XL-U50 produced the most useful embeddings for aligning protein sequences. PEbA also outperformed DEDAL and vcMSA, two recently developed protein language model embedding-based alignment methods. CONCLUSION: Our results suggested that general purpose protein language models provide useful contextual information for generating more accurate protein alignments than typically used methods.

Incorporating metabolic activity, taxonomy and community structure to improve microbiome-based predictive models for host phenotype prediction.

We developed MicroKPNN, a prior-knowledge guided interpretable neural network for microbiome-based human host phenotype prediction. The prior knowledge used in MicroKPNN includes the metabolic activities of different bacterial species, phylogenetic relationships, and bacterial community structure, all in a shallow neural network. Application of MicroKPNN to seven gut microbiome datasets (involving five different human diseases including inflammatory bowel disease, type 2 diabetes, liver cirrhosis, colorectal cancer, and obesity) shows that incorporation of the prior knowledge helped improve the microbiome-based host phenotype prediction. MicroKPNN outperformed fully connected neural network-based approaches in all seven cases, with the most improvement of accuracy in the prediction of type 2 diabetes. MicroKPNN outperformed a recently developed deep-learning based approach DeepMicro, which selects the best combination of autoencoder and machine learning approach to make predictions, in all of the seven cases. Importantly, we showed that MicroKPNN provides a way for interpretation of the predictive models. Using importance scores estimated for the hidden nodes, MicroKPNN could provide explanations for prior research findings by highlighting the roles of specific microbiome components in phenotype predictions. In addition, it may suggest potential future research directions for studying the impacts of microbiome on host health and diseases. MicroKPNN is publicly available at https://github.com/mgtools/MicroKPNN.

Accurate de novo peptide sequencing using fully convolutional neural networks.

De novo peptide sequencing, which does not rely on a comprehensive target sequence database, provides us with a way to identify novel peptides from tandem mass spectra. However, current de novo sequencing algorithms suffer from low accuracy and coverage, which hinders their application in proteomics. In this paper, we present PepNet, a fully convolutional neural network for high accuracy de novo peptide sequencing. PepNet takes an MS/MS spectrum (represented as a high-dimensional vector) as input, and outputs the optimal peptide sequence along with its confidence score. The PepNet model is trained using a total of 3 million high-energy collisional dissociation MS/MS spectra from multiple human peptide spectral libraries. Evaluation results show that PepNet significantly outperforms current best-performing de novo sequencing algorithms (e.g. PointNovo and DeepNovo) in both peptide-level accuracy and positional-level accuracy. PepNet can sequence a large fraction of spectra that were not identified by database search engines, and thus could be used as a complementary tool to database search engines for peptide identification in proteomics. In addition, PepNet runs around 3x and 7x faster than PointNovo and DeepNovo on GPUs, respectively, thus being more suitable for the analysis of large-scale proteomics data.

3DMolMS: prediction of tandem mass spectra from 3D molecular conformations.

MOTIVATION: Tandem mass spectrometry is an essential technology for characterizing chemical compounds at high sensitivity and throughput, and is commonly adopted in many fields. However, computational methods for automated compound identification from their MS/MS spectra are still limited, especially for novel compounds that have not been previously characterized. In recent years, in silico methods were proposed to predict the MS/MS spectra of compounds, which can then be used to expand the reference spectral libraries for compound identification. However, these methods did not consider the compounds' 3D conformations, and thus neglected critical structural information. RESULTS: We present the 3D Molecular Network for Mass Spectra Prediction (3DMolMS), a deep neural network model to predict the MS/MS spectra of compounds from their 3D conformations. We evaluated the model on the experimental spectra collected in several spectral libraries. The results showed that 3DMolMS predicted the spectra with the average cosine similarity of 0.691 and 0.478 with the experimental MS/MS spectra acquired in positive and negative ion modes, respectively. Furthermore, 3DMolMS model can be generalized to the prediction of MS/MS spectra acquired by different labs on different instruments through minor fine-tuning on a small set of spectra. Finally, we demonstrate that the molecular representation learned by 3DMolMS from MS/MS spectra prediction can be adapted to enhance the prediction of chemical properties such as the elution time in the liquid chromatography and the collisional cross section measured by ion mobility spectrometry, both of which are often used to improve compound identification. AVAILABILITY AND IMPLEMENTATION: The codes of 3DMolMS are available at https://github.com/JosieHong/3DMolMS and the web service is at https://spectrumprediction.gnps2.org.

MetaProD: A Highly-Configurable Mass Spectrometry Analyzer for Multiplexed Proteomic and Metaproteomic Data.

The microbiome has been shown to be important for human health because of its influence on disease and the immune response. Mass spectrometry is an important tool for evaluating protein expression and species composition in the microbiome but is technically challenging and time-consuming. Multiplexing has emerged as a way to make spectrometry workflows faster while improving results. Here, we present MetaProD (MetaProteomics in Django) as a highly configurable metaproteomic data analysis pipeline supporting label-free and multiplexed mass spectrometry. The pipeline is open-source, uses fully open-source tools, and is integrated with Django to offer a web-based interface for configuration and data access. Benchmarking of MetaProD using multiple metaproteomics data sets showed that MetaProD achieved fast and efficient identification of peptides and proteins. Application of MetaProD to a multiplexed cancer data set resulted in identification of more differentially expressed human proteins in cancer tissues versus healthy tissues as compared to previous studies; in addition, MetaProD identified bacterial proteins in those samples, some of which are differentially abundant.

Meta-analysis of microbiome association networks reveal patterns of dysbiosis in diseased microbiomes.

The human gut microbiome is composed of a diverse and dynamic population of microbial species which play key roles in modulating host health and physiology. While individual microbial species have been found to be associated with certain disease states, increasing evidence suggests that higher-order microbial interactions may have an equal or greater contribution to host fitness. To better understand microbial community dynamics, we utilize networks to study interactions through a meta-analysis of microbial association networks between healthy and disease gut microbiomes. Taking advantage of the large number of metagenomes derived from healthy individuals and patients with various diseases, together with recent advances in network inference that can deal with sparse compositional data, we inferred microbial association networks based on co-occurrence of gut microbial species and made the networks publicly available as a resource (GitHub repository named GutNet). Through our meta-analysis of inferred networks, we were able to identify network-associated features that help stratify between healthy and disease states such as the differentiation of various bacterial phyla and enrichment of Proteobacteria interactions in diseased networks. Additionally, our findings show that the contributions of taxa in microbial associations are disproportionate to their abundances and that rarer taxa of microbial species play an integral part in shaping dynamics of microbial community interactions. Network-based meta-analysis revealed valuable insights into microbial community dynamics between healthy and disease phenotypes. We anticipate that the healthy and diseased microbiome association networks we inferred will become an important resource for human-related microbiome research.

Strain level microbial detection and quantification with applications to single cell metagenomics.

Computational identification and quantification of distinct microbes from high throughput sequencing data is crucial for our understanding of human health. Existing methods either use accurate but computationally expensive alignment-based approaches or less accurate but computationally fast alignment-free approaches, which often fail to correctly assign reads to genomes. Here we introduce CAMMiQ, a combinatorial optimization framework to identify and quantify distinct genomes (specified by a database) in a metagenomic dataset. As a key methodological innovation, CAMMiQ uses substrings of variable length and those that appear in two genomes in the database, as opposed to the commonly used fixed-length, unique substrings. These substrings allow to accurately decouple mixtures of highly similar genomes resulting in higher accuracy than the leading alternatives, without requiring additional computational resources, as demonstrated on commonly used benchmarking datasets. Importantly, we show that CAMMiQ can distinguish closely related bacterial strains in simulated metagenomic and real single-cell metatranscriptomic data.

Revealing bacteria-phage interactions in human microbiome through the CRISPR-Cas immune systems.

The human gut microbiome is composed of a diverse consortium of microorganisms. Relatively little is known about the diversity of the bacteriophage population and their interactions with microbial organisms in the human microbiome. Due to the persistent rivalry between microbial organisms (hosts) and phages (invaders), genetic traces of phages are found in the hosts' CRISPR-Cas adaptive immune system. Mobile genetic elements (MGEs) found in bacteria include genetic material from phage and plasmids, often resultant from invasion events. We developed a computational pipeline (BacMGEnet), which can be used for inference and exploratory analysis of putative interactions between microbial organisms and MGEs (phages and plasmids) and their interaction network. Given a collection of genomes as the input, BacMGEnet utilizes computational tools we have previously developed to characterize CRISPR-Cas systems in the genomes, which are then used to identify putative invaders from publicly available collections of phage/prophage sequences. In addition, BacMGEnet uses a greedy algorithm to summarize identified putative interactions to produce a bacteria-MGE network in a standard network format. Inferred networks can be utilized to assist further examination of the putative interactions and for discovery of interaction patterns. Here we apply the BacMGEnet pipeline to a few collections of genomic/metagenomic datasets to demonstrate its utilities. BacMGEnet revealed a complex interaction network of the Phocaeicola vulgatus pangenome with its phage invaders, and the modularity analysis of the resulted network suggested differential activities of the different P. vulgatus' CRISPR-Cas systems (Type I-C and Type II-C) against some phages. Analysis of the phage-bacteria interaction network of human gut microbiome revealed a mixture of phages with a broad host range (resulting in large modules with many bacteria and phages), and phages with narrow host range. We also showed that BacMGEnet can be used to infer phages that invade bacteria and their interactions in wound microbiome. We anticipate that BacMGEnet will become an important tool for studying the interactions between bacteria and their invaders for microbiome research.

Diversity and dynamics of the CRISPR-Cas systems associated with Bacteroides fragilis in human population.

BACKGROUND: CRISPR-Cas (clustered regularly interspaced short palindromic repeats-CRISPR-associated proteins) systems are adaptive immune systems commonly found in prokaryotes that provide sequence-specific defense against invading mobile genetic elements (MGEs). The memory of these immunological encounters are stored in CRISPR arrays, where spacer sequences record the identity and history of past invaders. Analyzing such CRISPR arrays provide insights into the dynamics of CRISPR-Cas systems and the adaptation of their host bacteria to rapidly changing environments such as the human gut. RESULTS: In this study, we utilized 601 publicly available Bacteroides fragilis genome isolates from 12 healthy individuals, 6 of which include longitudinal observations, and 222 available B. fragilis reference genomes to update the understanding of B. fragilis CRISPR-Cas dynamics and their differential activities. Analysis of longitudinal genomic data showed that some CRISPR array structures remained relatively stable over time whereas others involved radical spacer acquisition during some periods, and diverse CRISPR arrays (associated with multiple isolates) co-existed in the same individuals with some persisted over time. Furthermore, features of CRISPR adaptation, evolution, and microdynamics were highlighted through an analysis of host-MGE network, such as modules of multiple MGEs and hosts, reflecting complex interactions between B. fragilis and its invaders mediated through the CRISPR-Cas systems. CONCLUSIONS: We made available of all annotated CRISPR-Cas systems and their target MGEs, and their interaction network as a web resource at https://omics.informatics.indiana.edu/CRISPRone/Bfragilis . We anticipate it will become an important resource for studying of B. fragilis, its CRISPR-Cas systems, and its interaction with mobile genetic elements providing insights into evolutionary dynamics that may shape the species virulence and lead to its pathogenicity.

Locality-Sensitive Hashing-Based k-Mer Clustering for Identification of Differential Microbial Markers Related to Host Phenotype.

Microbial organisms play important roles in many aspects of human health and diseases. Encouraged by the numerous studies that show the association between microbiomes and human diseases, computational and machine learning methods have been recently developed to generate and utilize microbiome features for prediction of host phenotypes such as disease versus healthy cancer immunotherapy responder versus nonresponder. We have previously developed a subtractive assembly approach, which focuses on extraction and assembly of differential reads from metagenomic data sets that are likely sampled from differential genomes or genes between two groups of microbiome data sets (e.g., healthy vs. disease). In this article, we further improved our subtractive assembly approach by utilizing groups of k-mers with similar abundance profiles across multiple samples. We implemented a locality-sensitive hashing (LSH)-enabled approach (called kmerLSHSA) to group billions of k-mers into k-mer coabundance groups (kCAGs), which were subsequently used for the retrieval of differential kCAGs for subtractive assembly. Testing of the kmerLSHSA approach on simulated data sets and real microbiome data sets showed that, compared with the conventional approach that utilizes all genes, our approach can quickly identify differential genes that can be used for building promising predictive models for microbiome-based host phenotype prediction. We also discussed other potential applications of LSH-enabled clustering of k-mers according to their abundance profiles across multiple microbiome samples.

Metaproteomics as a tool for studying the protein landscape of human-gut bacterial species.

Host-microbiome interactions and the microbial community have broad impact in human health and diseases. Most microbiome based studies are performed at the genome level based on next-generation sequencing techniques, but metaproteomics is emerging as a powerful technique to study microbiome functional activity by characterizing the complex and dynamic composition of microbial proteins. We conducted a large-scale survey of human gut microbiome metaproteomic data to identify generalist species that are ubiquitously expressed across all samples and specialists that are highly expressed in a small subset of samples associated with a certain phenotype. We were able to utilize the metaproteomic mass spectrometry data to reveal the protein landscapes of these species, which enables the characterization of the expression levels of proteins of different functions and underlying regulatory mechanisms, such as operons. Finally, we were able to recover a large number of open reading frames (ORFs) with spectral support, which were missed by de novo protein-coding gene predictors. We showed that a majority of the rescued ORFs overlapped with de novo predicted protein-coding genes, but on opposite strands or in different frames. Together, these demonstrate applications of metaproteomics for the characterization of important gut bacterial species.

Improvements on physical conditions of bauxite residue following application of organic materials.

Soil formation and ecological rehabilitation is the most promising strategy to eliminate environmental risks of bauxite residue disposal areas. Its poor physical structure is nevertheless a major limitation to plant growth. Organic materials were demonstrated as effective ameliorants to improve the physical conditions of bauxite residue. In this study, three different organic materials including straw (5% W/W), humic acid (5% W/W), and humic acid-acrylamide polymer (0.2% and 0.4%, W/W) were selected to evaluate their effects on physical conditions of bauxite residue pretreated by phosphogypsum following a 120-day incubation experiment. The proportion of 2-1 mm macro-aggregates, mean weight diameter (MWD) and geometric mean diameter (GWD) increased following organic materials addition, which indicated that organic materials could enhance aggregate stability. Compared with straw, and humic acid, humic acid-acrylamide polymer application had improved effects on the formation of water-stable aggregates in the residues. Furthermore, organic materials increased the total porosity, total pore volume and average pore diameter, and reduced the micropore content according to nitrogen gas adsorption (NA) and mercury intrusion porosimetry (MIP) analysis, whilst enhancing water retention of the residues based on water characteristic curves. Compared with traditional organic wastes, humic acid-acrylamide polymer could be regarded as a candidate according to the comprehensive consideration of the additive amount and the effects on physical conditions of bauxite residue. These findings could provide a novel application to both Ca-contained acid solid waste and high-molecular polymers on ecological rehabilitation at disposal areas.

Identification and classification of reverse transcriptases in bacterial genomes and metagenomes.

Reverse transcriptases (RTs) are found in different systems including group II introns, Diversity Generating Retroelements (DGRs), retrons, CRISPR-Cas systems, and Abortive Infection (Abi) systems in prokaryotes. Different classes of RTs can play different roles, such as template switching and mobility in group II introns, spacer acquisition in CRISPR-Cas systems, mutagenic retrohoming in DGRs, programmed cell suicide in Abi systems, and recently discovered phage defense in retrons. While some classes of RTs have been studied extensively, others remain to be characterized. There is a lack of computational tools for identifying and characterizing various classes of RTs. In this study, we built a tool (called myRT) for identification and classification of prokaryotic RTs. In addition, our tool provides information about the genomic neighborhood of each RT, providing potential functional clues. We applied our tool to predict RTs in all complete and draft bacterial genomes, and created a collection that can be used for exploration of putative RTs and their associated protein domains. Application of myRT to metagenomes showed that gut metagenomes encode proportionally more RTs related to DGRs, outnumbering retron-related RTs, as compared to the collection of reference genomes. MyRT is both available as a standalone software (https://github.com/mgtools/myRT) and also through a website (https://omics.informatics.indiana.edu/myRT/).

Msi2-mediated MiR7a-1 processing repression promotes myogenesis.

BACKGROUND: Most of the microRNAs (MiRs) involved in myogenesis are transcriptional regulated. The role of MiR biogenesis in myogenesis has not been characterized yet. RNA-binding protein Musashi 2 (Msi2) is considered to be one of the major drivers for oncogenesis and stem cell proliferation. The functions of Msi2 in myogenesis have not been explored yet. We sought to investigate Msi2-regulated biogenesis of MiRs in myogenesis and muscle stem cell (MuSC) ageing. METHODS: We detected the expression of Msi2 in MuSCs and differentiated myotubes by quantitative reverse transcription PCR (RT-qPCR) and western blot. Msi2-binding partner human antigen R (HuR) was identified by immunoprecipitation followed by mass spectrometry analysis. The cooperative binding of Msi2 and HuR on MiR7a-1 was analysed by RNA immunoprecipitation and electrophoresis mobility shift assays. The inhibition of the processing of pri-MiR7a-1 mediated by Msi2 and HuR was shown by Msi2 and HuR knockdown. Immunofluorescent staining, RT-qPCR and immunoblotting were used to characterize the function of MiR7a-1 in myogenesis. Msi2 and HuR up-regulate cryptochrome circadian regulator 2 (Cry2) via MiR7a-1 was confirmed by the luciferase assay and western blot. The post-transcriptional regulatory cascade was further confirmed by RNAi and overexpressing of Msi2 and HuR in MuSCs, and the in vivo function was characterized by histopathological and molecular biological methods in Msi2 knockout mice. RESULTS: We identified a post-transcription regulatory cascade governed by a pair of RNA-binding proteins Msi2 and HuR. Msi2 is enriched in differentiated muscle cells and promotes MuSC differentiation despite its pro-proliferation functions in other cell types. Msi2 works synergistically with another RNA-binding protein HuR to repress the biogenesis of MiR7a-1 in an Msi2 dose-dependent manner to regulate the translation of the key component of the circadian core oscillator complex Cry2. Down-regulation of Cry2 (0.6-fold, vs. control, P < 0.05) mediated by MiR7a-1 represses MuSC differentiation. The disruption of this cascade leads to differentiation defects of MuSCs. In aged muscles, Msi2 (0.3-fold, vs. control, P < 0.01) expression declined, and the Cry2 protein level also decreases (0.5-fold, vs. control, P < 0.05), suggesting that the disruption of the Msi2-mediated post-transcriptional regulatory cascade could attribute to the declined ability of muscle regeneration in aged skeletal muscle. CONCLUSIONS: Our findings have identified a new post-transcriptional cascade regulating myogenesis. The cascade is disrupted in skeletal muscle ageing, which leads to declined muscle regeneration ability.

Comparison of CRISPR-Cas Immune Systems in Healthcare-Related Pathogens.

The ESKAPE pathogens (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter species) and Clostridium difficile have been identified as the leading global cause of multidrug-resistant bacterial infections in hospitals. CRISPR-Cas systems are bacterial immune systems, empowering the bacteria with defense against invasive mobile genetic elements that may carry the antimicrobial resistance (AMR) genes, among others. On the other hand, the CRISPR-Cas systems are themselves mobile. In this study, we annotated and compared the CRISPR-Cas systems in these pathogens, utilizing their publicly available large numbers of sequenced genomes (e.g., there are more than 12 thousands of S. aureus genomes). The presence of CRISPR-Cas systems showed a very broad spectrum in these pathogens: S. aureus has the least tendency of obtaining the CRISPR-Cas systems with only 0.55% of its isolates containing CRISPR-Cas systems, whereas isolates of C. difficile we analyzed have CRISPR-Cas systems each having multiple CRISPRs. Statistical tests show that CRISPR-Cas containing isolates tend to have more AMRs for four of the pathogens (A. baumannii, E. faecium, P. aeruginosa, and S. aureus). We made available all the annotated CRISPR-Cas systems in these pathogens with visualization at a website (https://omics.informatics.indiana.edu/CRISPRone/pathogen), which we believe will be an important resource for studying the pathogens and their arms-race with invaders mediated through the CRISPR-Cas systems, and for developing potential clinical applications of the CRISPR-Cas systems for battles against the antibiotic resistant pathogens.

Using high-abundance proteins as guides for fast and effective peptide/protein identification from human gut metaproteomic data.

BACKGROUND: A few recent large efforts significantly expanded the collection of human-associated bacterial genomes, which now contains thousands of entities including reference complete/draft genomes and metagenome assembled genomes (MAGs). These genomes provide useful resource for studying the functionality of the human-associated microbiome and their relationship with human health and diseases. One application of these genomes is to provide a universal reference for database search in metaproteomic studies, when matched metagenomic/metatranscriptomic data are unavailable. However, a greater collection of reference genomes may not necessarily result in better peptide/protein identification because the increase of search space often leads to fewer spectrum-peptide matches, not to mention the drastic increase of computation time. Video Abstract METHODS: Here, we present a new approach that uses two steps to optimize the use of the reference genomes and MAGs as the universal reference for human gut metaproteomic MS/MS data analysis. The first step is to use only the high-abundance proteins (HAPs) (i.e., ribosomal proteins and elongation factors) for metaproteomic MS/MS database search and, based on the identification results, to derive the taxonomic composition of the underlying microbial community. The second step is to expand the search database by including all proteins from identified abundant species. We call our approach HAPiID (HAPs guided metaproteomics IDentification). RESULTS: We tested our approach using human gut metaproteomic datasets from a previous study and compared it to the state-of-the-art reference database search method MetaPro-IQ for metaproteomic identification in studying human gut microbiota. Our results show that our two-steps method not only performed significantly faster but also was able to identify more peptides. We further demonstrated the application of HAPiID to revealing protein profiles of individual human-associated bacterial species, one or a few species at a time, using metaproteomic data. CONCLUSIONS: The HAP guided profiling approach presents a novel effective way for constructing target database for metaproteomic data analysis. The HAPiID pipeline built upon this approach provides a universal tool for analyzing human gut-associated metaproteomic data.

Evaluation of aggregate formation, stability and pore characteristics of bauxite residue following polymer materials addition.

Bauxite residue has poor physical conditions, which hinders plant growth and causes potential environmental risks. Polymer materials have broad potential applications for holding water and improving soil aggregation. However, no attempt has been made to assess the effects of polymers on physical structure of bauxite residue. The purpose of this study was to evaluate the effects of polyacrylamide (BP), humic acid (BH), starch-acrylamide polymer (BSA) and humic acid-acrylamide polymer (BHA) on aggregate formation, stability, and pore characteristics in bauxite residue by 60-day pot experiment. Results demonstrated that 0.2% polymer addition increased the proportion of >0.25 mm mechanical-stable aggregates. Under wet sieving, BP and BHA treatments increased the values of mean weight diameter (WMWD) from 0.36 mm to 0.67 mm and 0.68 mm, respectively, which may result in the increase of the percentage of organic functional groups including OCO and CC. Laser diffraction analysis and the visualized 3D surface map revealed that >0.25 mm residue aggregate was more difficult to disintegrate following BHA treatment during 180-min hydraulic circulation. BP and BH treatments elevated <0.5 µm pore size volumes, whilst BHA treatment increased >5 µm pore size volumes and improved the porosity of bauxite residue. Polymer applications indicated that compared with polyacrylamide or humic acid, humic acid-acrylamide polymer could be regarded as an effective ameliorant due to its positive effects on both aggregate stability and pore characteristics. These findings were helpful for understanding the application potential of natural-synthetic polymers on physical conditions of bauxite residue prior to ecological reconstruction on the disposal areas.

Roles of bacteriophages, plasmids and CRISPR immunity in microbial community dynamics revealed using time-series integrated meta-omics.

Viruses and plasmids (invasive mobile genetic elements (iMGEs)) have important roles in shaping microbial communities, but their dynamic interactions with CRISPR-based immunity remain unresolved. We analysed generation-resolved iMGE-host dynamics spanning one and a half years in a microbial consortium from a biological wastewater treatment plant using integrated meta-omics. We identified 31 bacterial metagenome-assembled genomes encoding complete CRISPR-Cas systems and their corresponding iMGEs. CRISPR-targeted plasmids outnumbered their bacteriophage counterparts by at least fivefold, highlighting the importance of CRISPR-mediated defence against plasmids. Linear modelling of our time-series data revealed that the variation in plasmid abundance over time explained more of the observed community dynamics than phages. Community-scale CRISPR-based plasmid-host and phage-host interaction networks revealed an increase in CRISPR-mediated interactions coinciding with a decrease in the dominant 'Candidatus Microthrix parvicella' population. Protospacers were enriched in sequences targeting genes involved in the transmission of iMGEs. Understanding the factors shaping the fitness of specific populations is necessary to devise control strategies for undesirable species and to predict or explain community-wide phenotypes.

Model-based and phylogenetically adjusted quantification of metabolic interaction between microbial species.

Microbial community members exhibit various forms of interactions. Taking advantage of the increasing availability of microbiome data, many computational approaches have been developed to infer bacterial interactions from the co-occurrence of microbes across diverse microbial communities. Additionally, the introduction of genome-scale metabolic models have also enabled the inference of cooperative and competitive metabolic interactions between bacterial species. By nature, phylogenetically similar microbial species are more likely to share common functional profiles or biological pathways due to their genomic similarity. Without properly factoring out the phylogenetic relationship, any estimation of the competition and cooperation between species based on functional/pathway profiles may bias downstream applications. To address these challenges, we developed a novel approach for estimating the competition and complementarity indices for a pair of microbial species, adjusted by their phylogenetic distance. An automated pipeline, PhyloMint, was implemented to construct competition and complementarity indices from genome scale metabolic models derived from microbial genomes. Application of our pipeline to 2,815 human-gut associated bacteria showed high correlation between phylogenetic distance and metabolic competition/cooperation indices among bacteria. Using a discretization approach, we were able to detect pairs of bacterial species with cooperation scores significantly higher than the average pairs of bacterial species with similar phylogenetic distances. A network community analysis of high metabolic cooperation but low competition reveals distinct modules of bacterial interactions. Our results suggest that niche differentiation plays a dominant role in microbial interactions, while habitat filtering also plays a role among certain clades of bacterial species.

Integration of time-series meta-omics data reveals how microbial ecosystems respond to disturbance.

The development of reliable, mixed-culture biotechnological processes hinges on understanding how microbial ecosystems respond to disturbances. Here we reveal extensive phenotypic plasticity and niche complementarity in oleaginous microbial populations from a biological wastewater treatment plant. We perform meta-omics analyses (metagenomics, metatranscriptomics, metaproteomics and metabolomics) on in situ samples over 14 months at weekly intervals. Based on 1,364 de novo metagenome-assembled genomes, we uncover four distinct fundamental niche types. Throughout the time-series, we observe a major, transient shift in community structure, coinciding with substrate availability changes. Functional omics data reveals extensive variation in gene expression and substrate usage amongst community members. Ex situ bioreactor experiments confirm that responses occur within five hours of a pulse disturbance, demonstrating rapid adaptation by specific populations. Our results show that community resistance and resilience are a function of phenotypic plasticity and niche complementarity, and set the foundation for future ecological engineering efforts.