Vaginal microbial profile of cervical cancer patients receiving chemoradiotherapy: the potential involvement of Lactobacillus iners in recurrence.

The vaginal microbiome is an immune defense against reproductive diseases and can serve as an important biomarker for cervical cancer. However, the intrinsic relationship between the recurrence and the vaginal microbiome in patients with cervical cancer before and after concurrent chemoradiotherapy is poorly understood. Here, we analyzed 125 vaginal microbial profiles from a patient cohort of stage IB-IVB cervical cancer using 16S metagenomic sequencing and deciphered the microbial composition and functional characteristics of the recurrent and non-recurrent both before and after chemoradiotherapy. We demonstrated that the abundance of beneficial bacteria and stability of the microbial community in the vagina decreased in the recurrence group, implying the unique characteristics of the vaginal microbiome for recurrent cervical cancer. Moreover, using machine learning, we identified Lactobacillus iners as the most important biomarker, combined with age and other biomarkers (such as Ndongobacter massiliensis, Corynebacterium pyruviciproducens ATCC BAA-1742, and Prevotella buccalis), and could predict cancer recurrence phenotype before chemoradiotherapy. This study prospectively employed rigorous bioinformatics analysis and highlights the critical role of vaginal microbiota in post-treatment cervical cancer recurrence, identifying promising biomarkers with prognostic significance in the context of concurrent chemoradiotherapy for cervical cancer. The role of L. iners in determining chemoradiation resistance in cervical cancer warrants further detailed investigation. Our results expand our understanding of cervical cancer recurrence and help develop better strategies for prognosis prediction and personalized therapy.

Alterations of the bile microbiome is associated with progression-free survival in pancreatic ductal adenocarcinoma patients.

BACKGROUND: Patients with pancreatic ductal adenocarcinoma (PDAC) display an altered oral, gastrointestinal, and intra-pancreatic microbiome compared to healthy individuals. However, knowledge regarding the bile microbiome and its potential impact on progression-free survival in PDACs remains limited. METHODS: Patients with PDAC (n = 45), including 20 matched pairs before and after surgery, and benign controls (n = 16) were included prospectively. The characteristics of the microbiomes of the total 81 bile were revealed by 16  S-rRNA gene sequencing. PDAC patients were divided into distinct groups based on tumor marker levels, disease staging, before and after surgery, as well as progression free survival (PFS) for further analysis. Disease diagnostic model was formulated utilizing the random forest algorithm. RESULTS: PDAC patients harbor a unique and diverse bile microbiome (PCoA, weighted Unifrac, p = 0.038), and the increasing microbial diversity is correlated with dysbiosis according to key microbes and microbial functions. Aliihoeflea emerged as the genus displaying the most significant alteration among two groups (p < 0.01). Significant differences were found in beta diversity of the bile microbiome between long-term PFS and short-term PFS groups (PCoA, weighted Unifrac, p = 0.005). Bacillota and Actinomycetota were identified as altered phylum between two groups associated with progression-free survival in all PDAC patients. Additionally, we identified three biomarkers as the most suitable set for the random forest model, which indicated a significantly elevated likelihood of disease occurrence in the PDAC group (p < 0.0001). The area under the receiver operating characteristic (ROC) curve reached 80.8% with a 95% confidence interval ranging from 55.0 to 100%. Due to the scarcity of bile samples, we were unable to conduct further external verification. CONCLUSION: PDAC is characterized by an altered microbiome of bile ducts. Biliary dysbiosis is linked with progression-free survival in all PDACs. This study revealed the alteration of the bile microbiome in PDACs and successfully developed a diagnostic model for PDAC.

Effects of different assembly strategies on gene annotation in activated sludge.

Activated sludge comprises diverse bacteria, fungi, and other microorganisms, featuring a rich repertoire of genes involved in antibiotic resistance, pollutant degradation, and elemental cycling. In this regard, hybrid assembly technology can revolutionize metagenomics by detecting greater gene diversity in environmental samples. Nonetheless, the optimal utilization and comparability of genomic information between hybrid assembly and short- or long-read technology remain unclear. To address this gap, we compared the performance of the hybrid assembly, short- and long-read technologies, abundance and diversity of annotated genes, and taxonomic diversity by analysing 46, 161, and 45 activated sludge metagenomic datasets, respectively. The results revealed that hybrid assembly technology exhibited the best performance, generating the most contiguous and longest contigs but with a lower proportion of high-quality metagenome-assembled genomes than short-read technology. Compared with short- or long-read technologies, hybrid assembly technology can detect a greater diversity of microbiota and antibiotic resistance genes, as well as a wider range of potential hosts. However, this approach may yield lower gene abundance and pathogen detection. Our study revealed the specific advantages and disadvantages of hybrid assembly and short- and long-read applications in wastewater treatment plants, and our approach could serve as a blueprint to be extended to terrestrial environments.

HPV-related cervical diseases: Alteration of vaginal microbiotas and promising potential for diagnosis.

Vaginal microbiota is closely associated with women's health, however, the correlation between HPV-related cervical disease (HRCD) and vaginal microbiota is still obscure. In this study, patients with HRCD (n = 98) and healthy controls (n = 58) in Hangzhou were recruited, and their vaginal microbiota were collected and analyzed. The composition of the vaginal microbial community was explored, and a disease classification model was developed by random forest algorithm. The results suggested that the diversity of vaginal microbiota was significantly higher in HRCDs than that in healthy controls (p < 0.05). Firmicutes is the dominant phylum in vaginal microbiota, and Lactobacillus was identified as the most altered genus between two groups (p < 0.01). Kyoto Encyclopedia of Genes and Genomes analysis suggested the difference in vaginal microbial community functions between two groups. Furthermore, we identified 10 biomarkers as the optimal marker sets for the random forest model and found a higher probability of disease values in HRCD group in discovery cohort (p < 0.0001), with an area under the receiver operating characteristic curve reaching 89.7% (95% confidence interval: 78.3%-100%). We further validated the model in both validation and independent diagnosis cohorts, confirming its accuracy in the prediction of HRCD. In conclusion, this study revealed the community composition of vaginal microbiota in HRCDs and successfully constructed a diagnostic model for HRCD.

Antimicrobial Peptides in the Global Microbiome: Biosynthetic Genes and Resistance Determinants.

Antimicrobial peptides are a promising new class of antimicrobials that could address the antibiotic resistance crisis, which poses a major threat to human health. These peptides are present in all kingdoms of life, but especially in microorganisms, having multiple origins in diverse taxa. To date, there has been no global study on the diversity of antimicrobial peptides, the hosts in which these occur, and the potential for resistance to these agents. Here, we investigated the diversity and number of antimicrobial peptides in four main habitats (aquatic, terrestrial, human, and engineered) by analyzing 52,515 metagenome-assembled genomes. The number of antimicrobial peptides was higher in the human gut microbiome than in other habitats, and most hosts of antimicrobial peptides were habitat-specific. The relative abundance of genes that confer resistance to antimicrobial peptides varied between habitats and was generally low, except for the built environment and on human skin. The horizontal transfer of potential resistance genes among these habitats was probably constrained by ecological barriers. We systematically quantified the risk of each resistance determinant to human health and found that nearly half of them pose a threat, especially those that confer resistance to multiple AMPs and polymyxin B. Our results help identify the biosynthetic potential of antimicrobial peptides in the global microbiome, further identifying peptides with a low risk of developing resistance.

Development of a machine-learning model to identify the impacts of pesticides characteristics on soil microbial communities from high-throughput sequencing data.

High-throughput sequencing (HTS) of soil environmental DNA provides an advanced insight into the effects of pesticides on soil microbial systems. However, the association between the properties of the pesticide and its ecological impact remains methodically challenging. Risks associated with pesticide use can be minimized if pesticides with optimal structural traits were applied. For this purpose, we merged the 20 independent HTS studies, to reveal that pesticides significantly reduced beneficial bacteria associated with soil and plant immunity, enhanced the human pathogen and weaken the soil's ecological stability. Through the machine-learning approach, correlating these impacts with the physicochemical properties of the pesticides yielded a random forest model with good predictive capabilities. The models revealed that physical pesticide properties such as the dissociation constant (pKa), the molecular weight and water solubility, determined the ecological impact of pesticides to a large extent. Moreover, this study identified that eco-friendly pesticides should possess a value of pKa > 5 and a molecular weight in the range of 200-300 g/mol, which were found to be conducive to bacteria related to plant immunity promotion and exerted the lowest fluctuation of human opportunistic pathogen and keystone species. This guides the design of pesticides for which the impacts on soil biota are minimized.

Potential applications of deep learning in single-cell RNA sequencing analysis for cell therapy and regenerative medicine.

When used in cell therapy and regenerative medicine strategies, stem cells have potential to treat many previously incurable diseases. However, current application methods using stem cells are underdeveloped, as these cells are used directly regardless of their culture medium and subgroup. For example, when using mesenchymal stem cells (MSCs) in cell therapy, researchers do not consider their source and culture method nor their application angle and function (soft tissue regeneration, hard tissue regeneration, suppression of immune function, or promotion of immune function). By combining machine learning methods (such as deep learning) with data sets obtained through single-cell RNA sequencing (scRNA-seq) technology, we can discover the hidden structure of these cells, predict their effects more accurately, and effectively use subpopulations with differentiation potential for stem cell therapy. scRNA-seq technology has changed the study of transcription, because it can express single-cell genes with single-cell anatomical resolution. However, this powerful technology is sensitive to biological and technical noise. The subsequent data analysis can be computationally difficult for a variety of reasons, such as denoising single cell data, reducing dimensionality, imputing missing values, and accounting for the zero-inflated nature. In this review, we discussed how deep learning methods combined with scRNA-seq data for research, how to interpret scRNA-seq data in more depth, improve the follow-up analysis of stem cells, identify potential subgroups, and promote the implementation of cell therapy and regenerative medicine measures.

PmiRDiscVali: an integrated pipeline for plant microRNA discovery and validation.

BACKGROUND: MicroRNAs (miRNAs) constitute a well-known small RNA (sRNA) species with important regulatory roles. To date, several bioinformatics tools have been developed for large-scale prediction of miRNAs based on high-throughput sequencing data. However, some of these tools become invalid without reference genomes, while some tools cannot supply user-friendly outputs. Besides, most of the current tools focus on the importance of secondary structures and sRNA expression patterns for miRNA prediction, while they do not pay attention to miRNA processing for reliability check. RESULTS: Here, we reported a pipeline PmiRDiscVali for plant miRNA discovery and partial validation. This pipeline integrated the popular tool miRDeep-P for plant miRNA prediction, making PmiRDiscVali compatible for both reference-based and de novo predictions. To check the prediction reliability, we adopted the concept that the miRNA processing intermediates could be tracked by degradome sequencing (degradome-seq) during the development of PmiRDiscVali. A case study was performed by using the public sequencing data of Dendrobium officinale, in order to show the clear and concise presentation of the prediction results. CONCLUSION: Summarily, the integrated pipeline PmiRDiscVali, featured with degradome-seq data-based validation and vivid result presentation, should be useful for large-scale identification of plant miRNA candidates.

Development of an InDel polymorphism database for jute via comparative transcriptome analysis.

Jute (Corchorus spp.) is one of the most commercially important bast fiber crops in the world. However, molecular markers and high-density genetic maps are still lacking on jute compared with other crops. Insertion/deletion (InDel) markers, one of the most abundant sources of DNA/RNA variations in plant genomes, can easily be distinguished among different accessions using high-throughput sequencing. Using three transcriptome datasets, we identified and developed InDel markers. Altogether, 51 172 InDel sites in 18 800 unigenes were discovered, and the number of InDel loci per unigene varied from 1 to 31. Further, we found 94 InDel types, varying from 1 to 159 bp; the most common were single-nucleotide (23 028), binucleotide (9824), and trinucleotide (9182). In total, 49 563 InDels in 18 445 transcripts were discovered in the comparison between TC and YG, followed by 48 934 InDels in 18 408 transcripts between NY and YG, and 3570 InDels in 2701 unigenes between NY and TC. Additionally, there were 1273 InDel sites in 1129 unigenes with polymorphisms between any two of the three accessions. Twenty-nine (58%) primer pairs represented polymorphisms when compared to the jute accessions, and PIC varied from 0.340 to 0.680, with an average of 0.491.

Dynamics of chloroplast genomes in green plants.

Chloroplasts are essential organelles, in which genes have widely been used in the phylogenetic analysis of green plants. Here, we took advantage of the breadth of plastid genomes (cpDNAs) sequenced species to investigate their dynamic changes. Our study showed that gene rearrangements occurred more frequently in the cpDNAs of green algae than in land plants. Phylogenetic trees were generated using 55 conserved protein-coding genes including 33 genes for photosynthesis, 16 ribosomal protein genes and 6 other genes, which supported the monophyletic evolution of vascular plants, land plants, seed plants, and angiosperms. Moreover, we could show that seed plants were more closely related to bryophytes rather than pteridophytes. Furthermore, the substitution rate for cpDNA genes was calculated to be 3.3×10(-10), which was almost 10 times lower than genes of nuclear genomes, probably because of the plastid homologous recombination machinery.

Expression profile analysis of microRNAs in prostate cancer by next-generation sequencing.

BACKGROUND: Prostate cancer (PCa) is the second leading cause of tumor mortality among males in western societies. In China, the diagnostic and fatality rate of PCa is increasing yearly. METHODS: To characterize underlying molecular mechanisms, the microRNA (miRNA) profile of high-grade PCa, low-grade PCa, and benign prostate hyperplasia (BPH) were compared using high-throughput Illumina sequencing and quantitative real-time PCR (qRT-PCR) methods. Moreover, a variety of biological information softwares and databases were applied to predict the target genes of miRNA, molecular functions, and signal pathways. RESULTS: Eighteen miRNAs were differentially expressed (fold change ≥ 2, P < 0.05), of which thirteen were upregulated and five were downregulated by sequencing. This was confirmed by qRT-PCR in more clinical tissue samples. In the tumors, miRNAs (miR-125b-5p, miR-126-5p, miR-151a-5p, miR-221-3p, and miR-222-3p) were significantly upregulated with downregulation of miR-486-5p. In addition, 13 novel miRNAs were identified from three prostate tissue libraries, with 12 of them assayed in 21 human normal tissues by qRT-PCR. Multiple databases indicated target genes for these differentially expressed miRNAs. Function annotation of target genes indicated that most of them tend to target genes involved in signal transduction and cell communication, especially cancer-related PI3K-Akt and p53 signaling pathway. CONCLUSIONS: The small RNA transcriptomes obtained in this study uncovers six differentially expressed miRNAs and 12 novel miRNAs, and provides a better understanding of the expression and function of miRNAs in the development of PCa and reveals several miRNAs in PCa that may have biomarker and therapeutic potentials.

MicroRNA mediates DNA methylation of target genes.

Small RNAs represented by microRNA (miRNA) plays important roles in plant development and responds to biotic and abiotic stresses. Previous studies have placed special emphasis on gene-repression mediated by miRNA. In this work, the DNA methylation pattern of microRNA genes (MIRs) was interrogated. Full-length cDNA and EST were used to confirm the entity of pri-miRNA. In parallel, miRNA in 24 nucleotides (nt) was pooled to detect chromatin modification effect by using bisulfite sequencing data. 97 MIRs were supported by full-length cDNA and 30 more were hit by EST. Notably, methylation levels of conserved MIRs were significantly lower than the non-conserved at all contexts (CG, CHG, and CHH). Additionally, a substantial part of 24-nt miRNA was able to induce target site methylation, providing a broader perspective for researchers.

CPuORF correlates with miRNA responsive elements on protein evolutionary rates.

miRNA is increasingly being recognized as a key regulator of metabolism in animals. A wealth of evidence has suggested that miRNA mainly binds 3' UTR of mRNA and modulates the cell activities via repressing the mRNA translation. However, as the translation initiates at 5' UTR, cis elements like upstream open reading frame (uORF) resided in 5' UTR may also affect the translation efficiency or elongation. In this study, we performed a systematic analysis of miRNA responsive elements (MREs) and uORF of the same transcript in three model organisms (human, mouse, and Drosophila). Intriguingly, we found that the 3' UTR length grew with the complexity of species (human>mouse>Drosophila), in sharp contrast with the invariability of 5' UTR. Additionally, MRE number correlated well with the 3' UTR length, while uORF number showed a weak correlation with the 5' UTR length. Further, we found that human genes with conserved peptide upstream open reading frame (CPuORF) tend to have more MREs and lower evolutionary rates, which provides new insights into the correlation between UTR properties and translational control in animals.

Flexible microRNA arm selection in rice.

MicroRNAs act at the post-transcriptional level and guide Argonaute proteins to cleave their corresponding target transcripts. However, little attention has been paid to arm selection in miRNA precursors. In this study, small RNA high-throughput sequencing data from 29 different rice libraries were pooled to investigate tissue- and abiotic stress-specific dynamic expression of miRNAs. We found that more than half of pre-miRNAs showed changes in arm selection in different tissues and/or under different abiotic stresses. Our findings suggest that miRNA selection is remarkably prevalent in plants, providing new insights into the role of miRNAs in plant growth and development.

Global insights into endophytic bacterial communities of terrestrial plants: Exploring the potential applications of endophytic microbiota in sustainable agriculture.

Endophytic microorganisms are indispensable symbionts during plant growth and development and often serve functions such as growth promotion and stress resistance in plants. Therefore, an increasing number of researchers have applied endophytes for multifaceted phytoremediation (e.g., organic pollutants and heavy metals) in recent years. With the availability of next-generation sequencing technologies, an increasing number of studies have shifted the focus from culturable bacteria to total communities. However, information on the composition, structure, and function of bacterial endophytic communities is still not widely synthesized. To explore the general patterns of variation in bacterial communities between plant niches, we reanalyzed data from 1499 samples in 30 individual studies from different continents and provided comprehensive insights. A group of bacterial genera were commonly found in most plant roots and shoots. Our analysis revealed distinct variations in the diversity, composition, structure, and function of endophytic bacterial communities between plant roots and shoots. These variations underscore the sophisticated mechanisms by which plants engage with their endophytic microbiota, optimizing these interactions to bolster growth, health, and resilience against stress. Highlighting the strategic role of endophytic bacteria in promoting sustainable agricultural practices and environmental stewardship, our study not only offers global insights into the endophytic bacterial communities of terrestrial plants but also underscores the untapped potential of these communities as invaluable resources for future research.

Phages in different habitats and their ability to carry antibiotic resistance genes.

As the most abundant organisms on Earth, phages play a key role in the evolution of bacterial antibiotic resistance. Although previous studies have demonstrated the molecular mechanisms of horizontal gene transfer mediated by mobile genetic elements, our understanding of the intertwined relationships between antibiotic resistance genes (ARGs) and phages is limited. In this study, we analysed 2781 metagenomic samples to reveal the composition and species interactions of phage communities in different habitats as well as their capacity to carry ARGs with health risks. The composition of phage communities varies in different habitats and mainly depends on environmental conditions. Terrestrial habitats display more complex and robust interactions between phages than aquatic and human-associated habitats, resulting in the highest biodiversity of phages. Several types of phages in certain taxa (4.95-7.67%, mainly belonging to Caudoviricetes) have the capacity to carry specific ARGs and display a high potential risk to human health, especially in human-associated habitats. Overall, our results provide insights into the assembly mechanisms of phage communities and their effects on the dissemination of antibiotic resistance.

Global biogeography of microbes driving ocean ecological status under climate change.

Microbial communities play a crucial role in ocean ecology and global biogeochemical processes. However, understanding the intricate interactions among diversity, taxonomical composition, functional traits, and how these factors respond to climate change remains a significant challenge. Here, we propose seven distinct ecological statuses by systematically considering the diversity, structure, and biogeochemical potential of the ocean microbiome to delineate their biogeography. Anthropogenic climate change is expected to alter the ecological status of the surface ocean by influencing environmental conditions, particularly nutrient and oxygen contents. Our predictive model, which utilizes machine learning, indicates that the ecological status of approximately 32.44% of the surface ocean may undergo changes from the present to the end of this century, assuming no policy interventions. These changes mainly include poleward shifts in the main taxa, increases in photosynthetic carbon fixation and decreases in nutrient metabolism. However, this proportion can decrease significantly with effective control of greenhouse gas emissions. Our study underscores the urgent necessity for implementing policies to mitigate climate change, particularly from an ecological perspective.

[Genome-scale sequence data processing and epigenetic analysis of DNA methylation].

A new approach recently developed for detecting cytosine DNA methylation (mC) and analyzing the genome-scale DNA methylation profiling, is called BS-Seq which is based on bisulfite conversion of genomic DNA combined with next-generation sequencing. The method can not only provide an insight into the difference of genome-scale DNA methylation among different organisms, but also reveal the conservation of DNA methylation in all contexts and nucleotide preference for different genomic regions, including genes, exons, and repetitive DNA sequences. It will be helpful to under-stand the epigenetic impacts of cytosine DNA methylation on the regulation of gene expression and maintaining silence of repetitive sequences, such as transposable elements. In this paper, we introduce the preprocessing steps of DNA methylation data, by which cytosine (C) and guanine (G) in the reference sequence are transferred to thymine (T) and adenine (A), and cytosine in reads is transferred to thymine, respectively. We also comprehensively review the main content of the DNA methylation analysis on the genomic scale: (1) the cytosine methylation under the context of different sequences; (2) the distribution of genomic methylcytosine; (3) DNA methylation context and the preference for the nucleotides; (4) DNA- protein interaction sites of DNA methylation; (5) degree of methylation of cytosine in the different structural elements of genes. DNA methylation analysis technique provides a powerful tool for the epigenome study in human and other species, and genes and environment interaction, and founds the theoretical basis for further development of disease diagnostics and therapeutics in human.

Mutational Bias and Natural Selection Driving the Synonymous Codon Usage of Single-Exon Genes in Rice (Oryza sativa L.).

The relative abundance of single-exon genes (SEGs) in higher plants is perplexing. Uncovering the synonymous codon usage pattern of SEGs will benefit for further understanding their underlying evolutionary mechanism in plants. Using internal correspondence analysis (ICA), we reveal a significant difference in synonymous codon usage between SEGs and multiple-exon genes (MEGs) in rice. But the effect is weak, accounting for only 2.61% of the total codon usage variability. SEGs and MEGs contain remarkably different base compositions, and are under clearly differential selective constraints, with the former having higher GC content, and evolving relatively faster during evolution. In the group of SEGs, the variability in synonymous codon usage among genes is partially due to the variations in GC content, gene function, and gene expression level, which accounts for 22.03%, 5.99%, and 3.32% of the total codon usage variability, respectively. Therefore, mutational bias and natural selection should work on affecting the synonymous codon usage of SEGs in rice. These findings may deepen our knowledge for the mechanisms of origination, differentiation and regulation of SEGs in plants.