PStrain: An Iterative Microbial Strains Profiling Algorithm for Shotgun Metagenomic Sequencing Data

CyanoStrainChip: A Novel DNA Microarray Tool for High-Throughput Detection of Environmental Cyanobacteria at the Strain Level

Detecting cyanobacteria in environments is an important concern due to their crucial roles in ecosystems, and they can form blooms with the potential to harm humans and nonhuman entities. However, the most widely used methods for high-throughput detection of environmental cyanobacteria, such as 16S rRNA sequencing, typically provide above-species-level resolution, thereby disregarding intraspecific variation. To address this, we developed a novel DNA microarray tool, termed the CyanoStrainChip, that enables strain-level comprehensive profiling of environmental cyanobacteria. The CyanoStrainChip was designed to target 1277 strains; nearly all major groups of cyanobacteria are included by implementing 43,666 genome-wide, strain-specific probes. It demonstrated strong specificity by in vitro mock community experiments. The high correlation (Pearson's R > 0.97) between probe fluorescence intensities and the corresponding DNA amounts (ranging from 1-100 ng) indicated excellent quantitative capability. Consistent cyanobacterial profiles of field samples were observed by both the CyanoStrainChip and next-generation sequencing methods. Furthermore, CyanoStrainChip analysis of surface water samples in Lake Chaohu uncovered a high intraspecific variation of abundance change within the genus Microcystis between different severity levels of cyanobacterial blooms, highlighting two toxic Microcystis strains that are of critical concern for Lake Chaohu harmful blooms suppression. Overall, these results suggest a potential for CyanoStrainChip as a valuable tool for cyanobacterial ecological research and harmful bloom monitoring to supplement existing techniques.

Uncovering the complexity of childhood undernutrition through strain-level analysis of the gut microbiome

BACKGROUND

Undernutrition (UN) is a critical public health issue that threatens the lives of children under five in developing countries. While evidence indicates the crucial role of the gut microbiome (GM) in UN pathogenesis, the strain-level inspection and bacterial co-occurrence network investigation in the GM of UN children are lacking.

RESULTS

This study examines the strain compositions of the GM in 61 undernutrition patients (UN group) and 36 healthy children (HC group) and explores the topological features of GM co-occurrence networks using a complex network strategy. The strain-level annotation reveals that the differentially enriched species between the UN and HC groups are due to discriminated strain compositions. For example, Prevotella copri is mainly composed of P. copri ASM1680343v1 and P. copri ASM345920v1 in the HC group, but it is composed of P. copri ASM346549v1 and P. copri ASM347465v1 in the UN group. In addition, the UN-risk model constructed at the strain level demonstrates higher accuracy (AUC = 0.810) than that at the species level (AUC = 0.743). With complex network analysis, we further discovered that the UN group had a more complex GM co-occurrence network, with more hub bacteria and a higher clustering coefficient but lower information transfer efficiencies. Moreover, the results at the strain level suggested the inaccurate and even false conclusions obtained from species level analysis.

CONCLUSIONS

Overall, this study highlights the importance of examining the GM at the strain level and investigating bacterial co-occurrence networks to advance our knowledge of UN pathogenesis.

Inspecting mother-to-infant microbiota transmission: disturbance of strain inheritance by cesarian section

Introduction

The initial acquisition and subsequent development of the microbiota in early life is crucial to future health. Cesarean-section (CS) birth is considered to affect early microbial transmission from mother to infant.

Methods

In this study, we collected fecal samples from 34 CS infants and their mothers from West China Second Hospital, Sichuan University to assess the microbiota developmental trajectory of mothers and infants. We explored mother-infant gut microbiome transmission via comparison with corresponding Finnish data.

Results

Metagenomic analysis of gut microbiota profiles indicated that the communities of mothers and infants were distinct. The composition of the infant gut microbiome was highly variable but also followed predictable patterns in the early stages of life. Maternal communities were stable and mainly dominated by species from Bacteroidacea spp. We used PStrain to analyze and visualize strain transmission in each mother-infant pair. Excluding missing data, we included 32 mother-infant pairs for analysis of strain transmission. Most CS deliveries (65.6%, 21/32) did not demonstrate transmission of strains from mother to infant. To further explore the mother-infant strain transmission, we analyzed metagenomics data from Finnish mother-infant pairs. A total of 32 mother-infant pairs were included in the analysis, including 28 vaginal delivery (VD) infants and four CS infants. Strain transmission was observed in 30 infants, including 28 VD infants and two CS infants. All VD infants received transmitted stains from their mothers. Finally, a total of 193 strain transmission events were observed, comprising 131 strains and 45 species.

Discussion

Taken together, our data suggested that delivery mode was an important factor influencing the mother-infant strain transmission.

SpecHLA enables full-resolution HLA typing from sequencing data

Reconstructing diploid sequences of human leukocyte antigen (HLA) genes, i.e., full-resolution HLA typing, from sequencing data is challenging. The high homogeneity across HLA genes and the high heterogeneity within HLA alleles complicate the identification of genomic source loci for sequencing reads. Here, we present SpecHLA, which utilizes fine-tuned reads binning and local assembly to achieve accurate full-resolution HLA typing. SpecHLA accepts sequencing data from paired-end, 10×-linked-reads, high-throughput chromosome conformation capture (Hi-C), Pacific Biosciences (PacBio), and Oxford Nanopore Technology (ONT). It can also incorporate pedigree data and genotype frequency to refine typing. In 32 Human Genome Structural Variation Consortium, Phase 2 (HGSVC2) samples, SpecHLA achieved 98.6% accuracy for G-group-resolution HLA typing, inferring entire HLA alleles with an average of three mismatches fewer, ten gaps fewer, and 590 bp less edit distance than HISAT-genotype per allele. Additionally, SpecHLA exhibited a 2-field typing accuracy of 98.6% in 875 real samples. Finally, SpecHLA detected HLA loss of heterozygosity with 99.7% specificity and 96.8% sensitivity in simulated samples of cancer cell lines.

Orally administrated Lactobacillus gasseri TM13 and Lactobacillus crispatus LG55 can restore the vaginal health of patients recovering from bacterial vaginosis

Bacterial vaginosis (BV) is a common infection of the lower genital tract with a vaginal microbiome dysbiosis caused by decreasing of lactobacilli. Previous studies suggested that supplementation with live Lactobacillus may benefit the recovery of BV, however, the outcomes vary in people from different regions. Herein, we aim to evaluate the effectiveness of oral Chinese-origin Lactobacillus with adjuvant metronidazole (MET) on treating Chinese BV patients. In total, 67 Chinese women with BV were enrolled in this parallel controlled trial and randomly assigned to two study groups: a control group treated with MET vaginal suppositories for 7 days and a probiotic group treated with oral Lactobacillus gasseri TM13 and Lactobacillus crispatus LG55 as an adjuvant to MET for 30 days. By comparing the participants with Nugent Scores ≥ 7 and < 7 on days 14, 30, and 90, we found that oral administration of probiotics did not improve BV cure rates (72.73% and 84.00% at day 14, 57.14% and 60.00% at day 30, 32.14% and 48.39% at day 90 for probiotic and control group respectively). However, the probiotics were effective in restoring vaginal health after cure by showing higher proportion of participants with Nugent Scores < 4 in the probiotic group compared to the control group (87.50% and 71.43% on day 14, 93.75% and 88.89% on day 30, and 77.78% and 66.67% on day 90). The relative abundance of the probiotic strains was significantly increased in the intestinal microbiome of the probiotic group compared to the control group at day 14, but no significance was detected after 30 and 90 days. Also, the probiotics were not detected in vaginal microbiome, suggesting that L. gasseri TM13 and L. crispatus LG55 mainly acted through the intestine. A higher abundance of Prevotella timonensis at baseline was significantly associated with long-term cure failure of BV and greatly contributed to the enrichment of the lipid IVA synthesis pathway, which could aggravate inflammation response. To sum up, L. gasseri TM13 and L. crispatus LG55 can restore the vaginal health of patients recovering from BV, and individualized intervention mode should be developed to restore the vaginal health of patients recovering from BV.

Clinical trial registration

https://classic.clinicaltrials.gov/ct2/show/, identifier NCT04771728.

Statistical and Computational Methods for Microbial Strain Analysis

Microbial strains are interpreted as a lineage derived from a recent ancestor that have not experienced "too many" recombination events and can be successfully retrieved with culture-independent techniques using metagenomic sequencing. Such a strain variability has been increasingly shown to display additional phenotypic heterogeneities that affect host health, such as virulence, transmissibility, and antibiotics resistance. New statistical and computational methods have recently been developed to track the strains in samples based on shotgun metagenomics data either based on reference genome sequences or Metagenome-assembled genomes (MAGs). In this paper, we review some recent statistical methods for strain identifications based on frequency counts at a set of single nucleotide variants (SNVs) within a set of single-copy marker genes. These methods differ in terms of whether reference genome sequences are needed, how SNVs are called, what methods of deconvolution are used and whether the methods can be applied to multiple samples. We conclude our review with areas that require further research.

Best practice for wildlife gut microbiome research: A comprehensive review of methodology for 16S rRNA gene investigations

Extensive research in well-studied animal models underscores the importance of commensal gastrointestinal (gut) microbes to animal physiology. Gut microbes have been shown to impact dietary digestion, mediate infection, and even modify behavior and cognition. Given the large physiological and pathophysiological contribution microbes provide their host, it is reasonable to assume that the vertebrate gut microbiome may also impact the fitness, health and ecology of wildlife. In accordance with this expectation, an increasing number of investigations have considered the role of the gut microbiome in wildlife ecology, health, and conservation. To help promote the development of this nascent field, we need to dissolve the technical barriers prohibitive to performing wildlife microbiome research. The present review discusses the 16S rRNA gene microbiome research landscape, clarifying best practices in microbiome data generation and analysis, with particular emphasis on unique situations that arise during wildlife investigations. Special consideration is given to topics relevant for microbiome wildlife research from sample collection to molecular techniques for data generation, to data analysis strategies. Our hope is that this article not only calls for greater integration of microbiome analyses into wildlife ecology and health studies but provides researchers with the technical framework needed to successfully conduct such investigations.

StrainPanDA: Linked reconstruction of strain composition and gene content profiles via pangenome-based decomposition of metagenomic data

Microbial strains of variable functional capacities coexist in microbiomes. Current bioinformatics methods of strain analysis cannot provide the direct linkage between strain composition and their gene contents from metagenomic data. Here we present Strain-level Pangenome Decomposition Analysis (StrainPanDA), a novel method that uses the pangenome coverage profile of multiple metagenomic samples to simultaneously reconstruct the composition and gene content variation of coexisting strains in microbial communities. We systematically validate the accuracy and robustness of StrainPanDA using synthetic data sets. To demonstrate the power of gene-centric strain profiling, we then apply StrainPanDA to analyze the gut microbiome samples of infants, as well as patients treated with fecal microbiota transplantation. We show that the linked reconstruction of strain composition and gene content profiles is critical for understanding the relationship between microbial adaptation and strain-specific functions (e.g., nutrient utilization and pathogenicity). Finally, StrainPanDA has minimal requirements for computing resources and can be scaled to process multiple species in a community in parallel. In short, StrainPanDA can be applied to metagenomic data sets to detect the association between molecular functions and microbial/host phenotypes to formulate testable hypotheses and gain novel biological insights at the strain or subspecies level.

Deciphering mechanisms and implications of bacterial translocation in human health and disease

Significant increases in potential microbial translocation, especially along the oral-gut axis, have been identified in many immune-related and inflammatory diseases, such as inflammatory bowel disease, colorectal cancer, rheumatoid arthritis, and liver cirrhosis, for which we currently have no cure or long-term treatment options. Recent advances in computational and experimental omics approaches now enable strain tracking, functional profiling, and strain isolation in unprecedented detail, which has the potential to elucidate the causes and consequences of microbial translocation. In this review, we discuss current evidence for the detection of bacterial translocation, examine different translocation axes with a primary focus on the oral-gut axis, and outline currently known translocation mechanisms and how they adversely affect the host in disease. Finally, we conclude with an overview of state-of-the-art computational and experimental tools for strain tracking and highlight the required next steps to elucidate the role of bacterial translocation in human health.

Species-level gut microbiota analysis in ovariectomized osteoporotic rats by Shallow shotgun sequencing

Gut microbiota was verified to regulate bone metabolism and was closely associated with osteoporosis. Using 16S rRNA sequencing, gut microbiota at genus level such as Helicobacter, Bacteroides, and Prevotella were found to increase in the osteoporotic animals and people. However, the changes of species-level gut microbiota and related functional alterations were still unknown. Female SD rats were divided into the ovariectomized (OVX) group and the control group, and the fecal samples were collected at 4, 8, and 12 weeks to analyze the information of gut microbiota. Using Shallow shotgun sequencing, we compared the species-level gut microbiota structure, composition, and functional pathways of the OVX group with the control group. Alpha diversity of the OVX rats were significantly decreased than those in the control group. Beta diversity showed that samples in the two groups could be distinguished in each coordinate at different time points. Furthermore, the relative abundance of gut microbiota at species-level and differential analysis found that bacteria species such as Helicobacter rodentium, Lachnospiraceae bacterium 10 1, and Lachnospiraceae bacterium A4 were markedly increased in the OVX rats. Furthermore, differential analysis of KEGG functional pathway revealed that lysine metabolism was enriched in the OVX group.In conclusion, gut microbiota were significantly altered in structure and composition estrogen-deficiency osteoporotic rats at the species level. Functional metabolism of gut microbiota was also changed in osteoporotic group. These changes in gut microbiota at the species level might be closely associated with osteoporosis caused by estrogen deficiency.

Computational analyses of bacterial strains from shotgun reads

Shotgun sequencing is routinely employed to study bacteria in microbial communities. With the vast amount of shotgun sequencing reads generated in a metagenomic project, it is crucial to determine the microbial composition at the strain level. This study investigated 20 computational tools that attempt to infer bacterial strain genomes from shotgun reads. For the first time, we discussed the methodology behind these tools. We also systematically evaluated six novel-strain-targeting tools on the same datasets and found that BHap, mixtureS and StrainFinder performed better than other tools. Because the performance of the best tools is still suboptimal, we discussed future directions that may address the limitations.