Solving genomic puzzles: computational methods for metagenomic binning.

Metagenomics involves the study of genetic material obtained directly from communities of microorganisms living in natural environments. The field of metagenomics has provided valuable insights into the structure, diversity and ecology of microbial communities. Once an environmental sample is sequenced and processed, metagenomic binning clusters the sequences into bins representing different taxonomic groups such as species, genera, or higher levels. Several computational tools have been developed to automate the process of metagenomic binning. These tools have enabled the recovery of novel draft genomes of microorganisms allowing us to study their behaviors and functions within microbial communities. This review classifies and analyzes different approaches of metagenomic binning and different refinement, visualization, and evaluation techniques used by these methods. Furthermore, the review highlights the current challenges and areas of improvement present within the field of research.

Antiviral and Anti-Inflammatory Therapeutic Effect of RAGE-Ig Protein against Multiple SARS-CoV-2 Variants of Concern Demonstrated in K18-hACE2 Mouse and Syrian Golden Hamster Models.

SARS-CoV-2 variants of concern (VOCs) continue to evolve and reemerge with chronic inflammatory long COVID sequelae, necessitating the development of anti-inflammatory therapeutic molecules. Therapeutic effects of the receptor for advanced glycation end products (RAGE) were reported in many inflammatory diseases. However, a therapeutic effect of RAGE in COVID-19 has not been reported. In the present study, we investigated whether and how the RAGE-Ig fusion protein would have an antiviral and anti-inflammatory therapeutic effect in the COVID-19 system. The protective therapeutic effect of RAGE-Ig was determined in vivo in K18-hACE2 transgenic mice and Syrian golden hamsters infected with six VOCs of SARS-CoV-2. The underlying antiviral mechanism of RAGE-Ig was determined in vitro in SARS-CoV-2-infected human lung epithelial cells (BEAS-2B). Following treatment of K18-hACE2 mice and hamsters infected with various SARS-CoV-2 VOCs with RAGE-Ig, we demonstrated (1) significant dose-dependent protection (i.e., greater survival, less weight loss, lower virus replication in the lungs); (2) a reduction of inflammatory macrophages (F4/80+/Ly6C+) and neutrophils (CD11b+/Ly6G+) infiltrating the infected lungs; (3) a RAGE-Ig dose-dependent increase in the expression of type I IFNs (IFN-α and IFN-ß) and type III IFN (IFNλ2) and a decrease in the inflammatory cytokines (IL-6 and IL-8) in SARS-CoV-2-infected human lung epithelial cells; and (4) a dose-dependent decrease in the expression of CD64 (FcgR1) on monocytes and lung epithelial cells from symptomatic COVID-19 patients. Our preclinical findings revealed type I and III IFN-mediated antiviral and anti-inflammatory therapeutic effects of RAGE-Ig protein against COVID-19 caused by multiple SARS-CoV-2 VOCs.

PRFect: a tool to predict programmed ribosomal frameshifts in prokaryotic and viral genomes.

BACKGROUND: One of the stranger phenomena that can occur during gene translation is where, as a ribosome reads along the mRNA, various cellular and molecular properties contribute to stalling the ribosome on a slippery sequence and shifting the ribosome into one of the other two alternate reading frames. The alternate frame has different codons, so different amino acids are added to the peptide chain. More importantly, the original stop codon is no longer in-frame, so the ribosome can bypass the stop codon and continue to translate the codons past it. This produces a longer version of the protein, a fusion of the original in-frame amino acids, followed by all the alternate frame amino acids. There is currently no automated software to predict the occurrence of these programmed ribosomal frameshifts (PRF), and they are currently only identified by manual curation. RESULTS: Here we present PRFect, an innovative machine-learning method for the detection and prediction of PRFs in coding genes of various types. PRFect combines advanced machine learning techniques with the integration of multiple complex cellular properties, such as secondary structure, codon usage, ribosomal binding site interference, direction, and slippery site motif. Calculating and incorporating these diverse properties posed significant challenges, but through extensive research and development, we have achieved a user-friendly approach. The code for PRFect is freely available, open-source, and can be easily installed via a single command in the terminal. Our comprehensive evaluations on diverse organisms, including bacteria, archaea, and phages, demonstrate PRFect's strong performance, achieving high sensitivity, specificity, and an accuracy exceeding 90%. The code for PRFect is freely available and installs with a single terminal command. CONCLUSION: PRFect represents a significant advancement in the field of PRF detection and prediction, offering a powerful tool for researchers and scientists to unravel the intricacies of programmed ribosomal frameshifting in coding genes.

Prophages: an integral but understudied component of the human microbiome.

Phages integrated into a bacterial genome - called prophages - continuously monitor the vigour of the host bacteria to determine when to escape the genome and to protect their host from other phage infections, and they may provide genes that promote bacterial growth. Prophages are essential to almost all microbiomes, including the human microbiome. However, most human microbiome studies have focused on bacteria, ignoring free and integrated phages, so we know little about how these prophages affect the human microbiome. To address this gap in our knowledge, we compared the prophages identified in 14 987 bacterial genomes isolated from human body sites to characterize prophage DNA in the human microbiome. Here, we show that prophage DNA is ubiquitous, comprising on average 1-5 % of each bacterial genome. The prophage content per genome varies with the isolation site on the human body, the health of the human and whether the disease was symptomatic. The presence of prophages promotes bacterial growth and sculpts the microbiome. However, the disparities caused by prophages vary throughout the body.

Driving through stop signs: predicting stop codon reassignment improves functional annotation of bacteriophages.

The majority of bacteriophage diversity remains uncharacterized, and new intriguing mechanisms of their biology are being continually described. Members of some phage lineages, such as the Crassvirales, repurpose stop codons to encode an amino acid by using alternate genetic codes. Here, we investigated the prevalence of stop codon reassignment in phage genomes and its subsequent impacts on functional annotation. We predicted 76 genomes within INPHARED and 712 vOTUs from the Unified Human Gut Virome Catalogue (UHGV) that repurpose a stop codon to encode an amino acid. We re-annotated these sequences with modified versions of Pharokka and Prokka, called Pharokka-gv and Prokka-gv, to automatically predict stop codon reassignment prior to annotation. Both tools significantly improved the quality of annotations, with Pharokka-gv performing best. For sequences predicted to repurpose TAG to glutamine (translation table 15), Pharokka-gv increased the median gene length (median of per genome median) from 287 to 481 bp for UHGV sequences (67.8% increase) and from 318 to 550 bp for INPHARED sequences (72.9% increase). The re-annotation increased median coding capacity from 66.8% to 90.0% and from 69.0% to 89.8% for UHGV and INPHARED sequences predicted to use translation table 15. Furthermore, the proportion of genes that could be assigned functional annotation increased, including an increase in the number of major capsid proteins that could be identified. We propose that automatic prediction of stop codon reassignment before annotation is beneficial to downstream viral genomic and metagenomic analyses.

How low can you go? Short-read polishing of Oxford Nanopore bacterial genome assemblies.

It is now possible to assemble near-perfect bacterial genomes using Oxford Nanopore Technologies (ONT) long reads, but short-read polishing is usually required for perfection. However, the effect of short-read depth on polishing performance is not well understood. Here, we introduce Pypolca (with default and careful parameters) and Polypolish v0.6.0 (with a new careful parameter). We then show that: (1) all polishers other than Pypolca-careful, Polypolish-default and Polypolish-careful commonly introduce false-positive errors at low read depth; (2) most of the benefit of short-read polishing occurs by 25× depth; (3) Polypolish-careful almost never introduces false-positive errors at any depth; and (4) Pypolca-careful is the single most effective polisher. Overall, we recommend the following polishing strategies: Polypolish-careful alone when depth is very low (<5×), Polypolish-careful and Pypolca-careful when depth is low (5-25×), and Polypolish-default and Pypolca-careful when depth is sufficient (>25×).

The macroecology of butyrate-producing bacteria via metagenomic assessment of butyrate production capacity.

Butyrate-producing bacteria are found in many outdoor ecosystems and host organisms, including humans, and are vital to ecosystem functionality and human health. These bacteria ferment organic matter, producing the short-chain fatty acid butyrate. However, the macroecological influences on their biogeographical distribution remain poorly resolved. Here we aimed to characterise their global distribution together with key explanatory climatic, geographical and physicochemical variables. We developed new normalised butyrate production capacity (BPC) indices derived from global metagenomic (n = 13,078) and Australia-wide soil 16S rRNA (n = 1331) data, using Geographic Information System (GIS) and modelling techniques to detail their ecological and biogeographical associations. The highest median BPC scores were found in anoxic and fermentative environments, including the human (BPC = 2.99) and non-human animal gut (BPC = 2.91), and in some plant-soil systems (BPC = 2.33). Within plant-soil systems, roots (BPC = 2.50) and rhizospheres (BPC = 2.34) had the highest median BPC scores. Among soil samples, geographical and climatic variables had the strongest overall effects on BPC scores (variable importance score range = 0.30-0.03), with human population density also making a notable contribution (variable importance score = 0.20). Higher BPC scores were in soils from seasonally productive sandy rangelands, temperate rural residential areas and sites with moderate-to-high soil iron concentrations. Abundances of butyrate-producing bacteria in outdoor soils followed complex ecological patterns influenced by geography, climate, soil chemistry and hydrological fluctuations. These new macroecological insights further our understanding of the ecological patterns of outdoor butyrate-producing bacteria, with implications for emerging microbially focused ecological and human health policies.

Pre-Bleaching Coral Microbiome Is Enriched in Beneficial Taxa and Functions.

Coral reef health is tightly connected to the coral holobiont, which is the association between the coral animal and a diverse microbiome functioning as a unit. The coral holobiont depends on key services such as nitrogen and sulfur cycling mediated by the associated bacteria. However, these microbial services may be impaired in response to environmental changes, such as thermal stress. A perturbed microbiome may lead to coral bleaching and disease outbreaks, which have caused an unprecedented loss in coral cover worldwide, particularly correlated to a warming ocean. The response mechanisms of the coral holobiont under high temperatures are not completely understood, but the associated microbial community is a potential source of acquired heat-tolerance. Here we investigate the effects of increased temperature on the taxonomic and functional profiles of coral surface mucous layer (SML) microbiomes in relationship to coral-algal physiology. We used shotgun metagenomics in an experimental setting to understand the dynamics of microbial taxa and genes in the SML microbiome of the coral Pseudodiploria strigosa under heat treatment. The metagenomes of corals exposed to heat showed high similarity at the level of bacterial genera and functional genes related to nitrogen and sulfur metabolism and stress response. The coral SML microbiome responded to heat with an increase in the relative abundance of taxa with probiotic potential, and functional genes for nitrogen and sulfur acquisition. Coral-algal physiology significantly explained the variation in the microbiome at taxonomic and functional levels. These consistent and specific microbial taxa and gene functions that significantly increased in proportional abundance in corals exposed to heat are potentially beneficial to coral health and thermal resistance.

Hybracter: enabling scalable, automated, complete and accurate bacterial genome assemblies.

Improvements in the accuracy and availability of long-read sequencing mean that complete bacterial genomes are now routinely reconstructed using hybrid (i.e. short- and long-reads) assembly approaches. Complete genomes allow a deeper understanding of bacterial evolution and genomic variation beyond single nucleotide variants. They are also crucial for identifying plasmids, which often carry medically significant antimicrobial resistance genes. However, small plasmids are often missed or misassembled by long-read assembly algorithms. Here, we present Hybracter which allows for the fast, automatic and scalable recovery of near-perfect complete bacterial genomes using a long-read first assembly approach. Hybracter can be run either as a hybrid assembler or as a long-read only assembler. We compared Hybracter to existing automated hybrid and long-read only assembly tools using a diverse panel of samples of varying levels of long-read accuracy with manually curated ground truth reference genomes. We demonstrate that Hybracter as a hybrid assembler is more accurate and faster than the existing gold standard automated hybrid assembler Unicycler. We also show that Hybracter with long-reads only is the most accurate long-read only assembler and is comparable to hybrid methods in accurately recovering small plasmids.

Hybracter: Enabling Scalable, Automated, Complete and Accurate Bacterial Genome Assemblies.

Improvements in the accuracy and availability of long-read sequencing mean that complete bacterial genomes are now routinely reconstructed using hybrid (i.e. short- and long-reads) assembly approaches. Complete genomes allow a deeper understanding of bacterial evolution and genomic variation beyond single nucleotide variants (SNVs). They are also crucial for identifying plasmids, which often carry medically significant antimicrobial resistance (AMR) genes. However, small plasmids are often missed or misassembled by long-read assembly algorithms. Here, we present Hybracter which allows for the fast, automatic, and scalable recovery of near-perfect complete bacterial genomes using a long-read first assembly approach. Hybracter can be run either as a hybrid assembler or as a long-read only assembler. We compared Hybracter to existing automated hybrid and long-read only assembly tools using a diverse panel of samples of varying levels of long-read accuracy with manually curated ground truth reference genomes. We demonstrate that Hybracter as a hybrid assembler is more accurate and faster than the existing gold standard automated hybrid assembler Unicycler. We also show that Hybracter with long-reads only is the most accurate long-read only assembler and is comparable to hybrid methods in accurately recovering small plasmids.

Ecological phage therapy: Can bacteriophages help rapidly restore the soil microbiome?

Soil microbiota underpin ecosystem functionality yet are rarely targeted during ecosystem restoration. Soil microbiota recovery following native plant revegetation can take years to decades, while the effectiveness of soil inoculation treatments on microbiomes remains poorly explored. Therefore, innovative restoration treatments that target soil microbiota represent an opportunity to accelerate restoration outcomes. Here, we introduce the concept of ecological phage therapy-the application of phage for the targeted reduction of the most abundant and dominant bacterial taxa present in degraded ecosystems. We propose that naturally occurring bacteriophages-viruses that infect bacteria-could help rapidly shift soil microbiota towards target communities. Bacteriophages sculpt the microbiome by lysis of specific bacteria, and if followed by the addition of reference soil microbiota, such treatments could facilitate rapid reshaping of soil microbiota. Here, we experimentally tested this concept in a pilot study. We collected five replicate pre-treatment degraded soil samples, then three replicate soil samples 48 hours after phage, bacteria, and control treatments. Bacterial 16S rDNA sequencing showed that phage-treated soils had reduced bacterial diversity; however, when we combined ecological phage therapy with reference soil inoculation, we did not see a shift in soil bacterial community composition from degraded soil towards a reference-like community. Our pilot study provides early evidence that ecological phage therapy could help accelerate the reshaping of soil microbiota with the ultimate aim of reducing timeframes for ecosystem recovery. We recommend the next steps for ecological phage therapy be (a) developing appropriate risk assessment and management frameworks, and (b) focussing research effort on its practical application to maximise its accessibility to restoration practitioners.

Colorectal Cancer Stem Cell Subtypes Orchestrate Distinct Tumor Microenvironments.

Several classification systems have been developed to define tumor subtypes in colorectal cancer (CRC). One system proposes that tumor heterogeneity derives in part from distinct cancer stem cell populations that co-exist as admixtures of varying proportions. However, the lack of single cell resolution has prohibited a definitive identification of these types of stem cells and therefore any understanding of how each influence tumor phenotypes. Here were report the isolation and characterization of two cancer stem cell subtypes from the SW480 CRC cell line. We find these cancer stem cells are oncogenic versions of the normal Crypt Base Columnar (CBC) and Regenerative Stem Cell (RSC) populations from intestinal crypts and that their gene signatures are consistent with the "Admixture" and other CRC classification systems. Using publicly available single cell RNA sequencing (scRNAseq) data from CRC patients, we determine that RSC and CBC cancer stem cells are commonly co-present in human CRC. To characterize influences on the tumor microenvironment, we develop subtype-specific xenograft models and we define their tumor microenvironments at high resolution via scRNAseq. RSCs create differentiated, inflammatory, slow growing tumors. CBCs create proliferative, undifferentiated, invasive tumors. With this enhanced resolution, we unify current CRC patient classification schema with TME phenotypes and organization.

Hecatomb: an integrated software platform for viral metagenomics.

BACKGROUND: Modern sequencing technologies offer extraordinary opportunities for virus discovery and virome analysis. Annotation of viral sequences from metagenomic data requires a complex series of steps to ensure accurate annotation of individual reads and assembled contigs. In addition, varying study designs will require project-specific statistical analyses. FINDINGS: Here we introduce Hecatomb, a bioinformatic platform coordinating commonly used tasks required for virome analysis. Hecatomb means "a great sacrifice." In this setting, Hecatomb is "sacrificing" false-positive viral annotations using extensive quality control and tiered-database searches. Hecatomb processes metagenomic data obtained from both short- and long-read sequencing technologies, providing annotations to individual sequences and assembled contigs. Results are provided in commonly used data formats useful for downstream analysis. Here we demonstrate the functionality of Hecatomb through the reanalysis of a primate enteric and a novel coral reef virome. CONCLUSION: Hecatomb provides an integrated platform to manage many commonly used steps for virome characterization, including rigorous quality control, host removal, and both read- and contig-based analysis. Each step is managed using the Snakemake workflow manager with dependency management using Conda. Hecatomb outputs several tables properly formatted for immediate use within popular data analysis and visualization tools, enabling effective data interpretation for a variety of study designs. Hecatomb is hosted on GitHub (github.com/shandley/hecatomb) and is available for installation from Bioconda and PyPI.

Cross-protection induced by highly conserved human B, CD4+, and CD8+ T-cell epitopes-based vaccine against severe infection, disease, and death caused by multiple SARS-CoV-2 variants of concern.

Background: The coronavirus disease 2019 (COVID-19) pandemic has created one of the largest global health crises in almost a century. Although the current rate of Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections has decreased significantly, the long-term outlook of COVID-19 remains a serious cause of morbidity and mortality worldwide, with the mortality rate still substantially surpassing even that recorded for influenza viruses. The continued emergence of SARS-CoV-2 variants of concern (VOCs), including multiple heavily mutated Omicron sub-variants, has prolonged the COVID-19 pandemic and underscores the urgent need for a next-generation vaccine that will protect from multiple SARS-CoV-2 VOCs. Methods: We designed a multi-epitope-based coronavirus vaccine that incorporated B, CD4+, and CD8+ T- cell epitopes conserved among all known SARS-CoV-2 VOCs and selectively recognized by CD8+ and CD4+ T-cells from asymptomatic COVID-19 patients irrespective of VOC infection. The safety, immunogenicity, and cross-protective immunity of this pan-variant SARS-CoV-2 vaccine were studied against six VOCs using an innovative triple transgenic h-ACE-2-HLA-A2/DR mouse model. Results: The pan-variant SARS-CoV-2 vaccine (i) is safe , (ii) induces high frequencies of lung-resident functional CD8+ and CD4+ TEM and TRM cells , and (iii) provides robust protection against morbidity and virus replication. COVID-19-related lung pathology and death were caused by six SARS-CoV-2 VOCs: Alpha (B.1.1.7), Beta (B.1.351), Gamma or P1 (B.1.1.28.1), Delta (lineage B.1.617.2), and Omicron (B.1.1.529). Conclusion: A multi-epitope pan-variant SARS-CoV-2 vaccine bearing conserved human B- and T- cell epitopes from structural and non-structural SARS-CoV-2 antigens induced cross-protective immunity that facilitated virus clearance, and reduced morbidity, COVID-19-related lung pathology, and death caused by multiple SARS-CoV-2 VOCs.

High frequencies of alpha common cold coronavirus/SARS-CoV-2 cross-reactive functional CD4+ and CD8+ memory T cells are associated with protection from symptomatic and fatal SARS-CoV-2 infections in unvaccinated COVID-19 patients.

Background: Cross-reactive SARS-CoV-2-specific memory CD4+ and CD8+ T cells are present in up to 50% of unexposed, pre-pandemic, healthy individuals (UPPHIs). However, the characteristics of cross-reactive memory CD4+ and CD8+ T cells associated with subsequent protection of asymptomatic coronavirus disease 2019 (COVID-19) patients (i.e., unvaccinated individuals who never develop any COVID-19 symptoms despite being infected with SARS-CoV-2) remains to be fully elucidated. Methods: This study compares the antigen specificity, frequency, phenotype, and function of cross-reactive memory CD4+ and CD8+ T cells between common cold coronaviruses (CCCs) and SARS-CoV-2. T-cell responses against genome-wide conserved epitopes were studied early in the disease course in a cohort of 147 unvaccinated COVID-19 patients who were divided into six groups based on the severity of their symptoms. Results: Compared to severely ill COVID-19 patients and patients with fatal COVID-19 outcomes, the asymptomatic COVID-19 patients displayed significantly: (i) higher rates of co-infection with the 229E alpha species of CCCs (α-CCC-229E); (ii) higher frequencies of cross-reactive functional CD134+CD137+CD4+ and CD134+CD137+CD8+ T cells that cross-recognized conserved epitopes from α-CCCs and SARS-CoV-2 structural, non-structural, and accessory proteins; and (iii) lower frequencies of CCCs/SARS-CoV-2 cross-reactive exhausted PD-1+TIM3+TIGIT+CTLA4+CD4+ and PD-1+TIM3+TIGIT+CTLA4+CD8+ T cells, detected both ex vivo and in vitro. Conclusions: These findings (i) support a crucial role of functional, poly-antigenic α-CCCs/SARS-CoV-2 cross-reactive memory CD4+ and CD8+ T cells, induced following previous CCCs seasonal exposures, in protection against subsequent severe COVID-19 disease and (ii) provide critical insights into developing broadly protective, multi-antigen, CD4+, and CD8+ T-cell-based, universal pan-Coronavirus vaccines capable of conferring cross-species protection.

Bioenergetic mapping of 'healthy microbiomes' via compound processing potential imprinted in gut and soil metagenomes.

Despite mounting evidence of their importance in human health and ecosystem functioning, the definition and measurement of 'healthy microbiomes' remain unclear. More advanced knowledge exists on health associations for compounds used or produced by microbes. Environmental microbiome exposures (especially via soils) also help shape, and may supplement, the functional capacity of human microbiomes. Given the synchronous interaction between microbes, their feedstocks, and micro-environments, with functional genes facilitating chemical transformations, our objective was to examine microbiomes in terms of their capacity to process compounds relevant to human health. Here we integrate functional genomics and biochemistry frameworks to derive new quantitative measures of in silico potential for human gut and environmental soil metagenomes to process a panel of major compound classes (e.g., lipids, carbohydrates) and selected biomolecules (e.g., vitamins, short-chain fatty acids) linked to human health. Metagenome functional potential profile data were translated into a universal compound mapping 'landscape' based on bioenergetic van Krevelen mapping of function-level meta-compounds and corresponding functional relative abundances, reflecting imprinted genetic capacity of microbiomes to metabolize an array of different compounds. We show that measures of 'compound processing potential' associated with human health and disease (examining atherosclerotic cardiovascular disease, colorectal cancer, type 2 diabetes and anxious-depressive behavior case studies), and displayed seemingly predictable shifts along gradients of ecological disturbance in plant-soil ecosystems (three case studies). Ecosystem quality explained 60-92 % of variation in soil metagenome compound processing potential measures in a post-mining restoration case study dataset. With growing knowledge of the varying proficiency of environmental microbiota to process human health associated compounds, we might design environmental interventions or nature prescriptions to modulate our exposures, thereby advancing microbiota-oriented approaches to human health. Compound processing potential offers a simplified, integrative approach for applying metagenomics in ongoing efforts to understand and quantify the role of microbiota in environmental- and human-health.

Predicting stop codon reassignment improves functional annotation of bacteriophages.

The majority of bacteriophage diversity remains uncharacterised, and new intriguing mechanisms of their biology are being continually described. Members of some phage lineages, such as the Crassvirales, repurpose stop codons to encode an amino acid by using alternate genetic codes. Here, we investigated the prevalence of stop codon reassignment in phage genomes and subsequent impacts on functional annotation. We predicted 76 genomes within INPHARED and 712 vOTUs from the Unified Human Gut Virome catalogue (UHGV) that repurpose a stop codon to encode an amino acid. We re-annotated these sequences with modified versions of Pharokka and Prokka, called Pharokka-gv and Prokka-gv, to automatically predict stop codon reassignment prior to annotation. Both tools significantly improved the quality of annotations, with Pharokka-gv performing best. For sequences predicted to repurpose TAG to glutamine (translation table 15), Pharokka-gv increased the median gene length (median of per genome medians) from 287 to 481 bp for UHGV sequences (67.8% increase) and from 318 to 550 bp for INPHARED sequences (72.9% increase). The re-annotation increased mean coding density from 66.8% to 90.0%, and from 69.0% to 89.8% for UHGV and INPHARED sequences. Furthermore, the proportion of genes that could be assigned functional annotation increased, including an increase in the number of major capsid proteins that could be identified. We propose that automatic prediction of stop codon reassignment before annotation is beneficial to downstream viral genomic and metagenomic analyses.