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

Bioinformatic approaches to blood and tissue microbiome analyses: challenges and perspectives

Advances in next-generation sequencing have resulted in a growing understanding of the microbiome and its role in human health. Unlike traditional microbiome analysis, blood and tissue microbiome analyses focus on the detection and characterization of microbial DNA in blood and tissue, previously considered a sterile environment. In this review, we discuss the challenges and methodologies associated with analyzing these samples, particularly emphasizing blood and tissue microbiome research. Key preprocessing steps-including the removal of ribosomal RNA, host DNA, and other contaminants-are critical to reducing noise and accurately capturing microbial evidence. We also explore how taxonomic profiling tools, machine learning, and advanced normalization techniques address contamination and low microbial biomass, thereby improving reliability. While it offers the potential for identifying microbial involvement in systemic diseases previously undetectable by traditional methods, this methodology also carries risks and lacks universal acceptance due to concerns over reliability and interpretation errors. This paper critically reviews these factors, highlighting both the promise and pitfalls of using blood and tissue microbiome analyses as a tool for biomarker discovery.

MeStanG-Resource for High-Throughput Sequencing Standard Data Sets Generation for Bioinformatic Methods Evaluation and Validation

Metagenomics analysis has enabled the measurement of the microbiome diversity in environmental samples without prior targeted enrichment. Functional and phylogenetic studies based on microbial diversity retrieved using HTS platforms have advanced from detecting known organisms and discovering unknown species to applications in disease diagnostics. Robust validation processes are essential for test reliability, requiring standard samples and databases deriving from real samples and in silico generated artificial controls. We propose a MeStanG as a resource for generating HTS Nanopore data sets to evaluate present and emerging bioinformatics pipelines. MeStanG allows samples to be designed with user-defined organism abundances expressed as number of reads, reference sequences, and predetermined or custom errors by sequencing profiles. The simulator pipeline was evaluated by analyzing its output mock metagenomic samples containing known read abundances using read mapping, genome assembly, and taxonomic classification on three scenarios: a bacterial community composed of nine different organisms, samples resembling pathogen-infected wheat plants, and a viral pathogen serial dilution sampling. The evaluation was able to report consistently the same organisms, and their read abundances as provided in the mock metagenomic sample design. Based on this performance and its novel capacity of generating exact number of reads, MeStanG can be used by scientists to develop mock metagenomic samples (artificial HTS data sets) to assess the diagnostic performance metrics of bioinformatic pipelines, allowing the user to choose predetermined or customized models for research and training.

kMetaShot: a fast and reliable taxonomy classifier for metagenome-assembled genomes

The advent of high-throughput sequencing (HTS) technologies unlocked the complexity of the microbial world through the development of metagenomics, which now provides an unprecedented and comprehensive overview of its taxonomic and functional contribution in a huge variety of macro- and micro-ecosystems. In particular, shotgun metagenomics allows the reconstruction of microbial genomes, through the assembly of reads into MAGs (metagenome-assembled genomes). In fact, MAGs represent an information-rich proxy for inferring the taxonomic composition and the functional contribution of microbiomes, even if the relevant analytical approaches are not trivial and still improvable. In this regard, tools like CAMITAX and GTDBtk have implemented complex approaches, relying on marker gene identification and sequence alignments, requiring a large processing time. With the aim of deploying an effective tool for fast and reliable MAG taxonomic classification, we present here kMetaShot, a taxonomy classifier based on k-mer/minimizer counting. We benchmarked kMetaShot against CAMITAX and GTDBtk by using both in silico and real mock communities and demonstrated how, while implementing a fast and concise algorithm, it outperforms the other tools in terms of classification accuracy. Additionally, kMetaShot is an easy-to-install and easy-to-use bioinformatic tool that is also suitable for researchers with few command-line skills. It is available and documented at https://github.com/gdefazio/kMetaShot.

Beyond the Gut: The intratumoral microbiome's influence on tumorigenesis and treatment response

The intratumoral microbiome (TM) refers to the microorganisms in the tumor tissues, including bacteria, fungi, viruses, and so on, and is distinct from the gut microbiome and circulating microbiota. TM is strongly associated with tumorigenesis, progression, metastasis, and response to therapy. This paper highlights the current status of TM. Tract sources, adjacent normal tissue, circulatory system, and concomitant tumor co-metastasis are the main origin of TM. The advanced techniques in TM analysis are comprehensively summarized. Besides, TM is involved in tumor progression through several mechanisms, including DNA damage, activation of oncogenic signaling pathways (phosphoinositide 3-kinase [PI3K], signal transducer and activator of transcription [STAT], WNT/β-catenin, and extracellular regulated protein kinases [ERK]), influence of cytokines and induce inflammatory responses, and interaction with the tumor microenvironment (anti-tumor immunity, pro-tumor immunity, and microbial-derived metabolites). Moreover, promising directions of TM in tumor therapy include immunotherapy, chemotherapy, radiotherapy, the application of probiotics/prebiotics/synbiotics, fecal microbiome transplantation, engineered microbiota, phage therapy, and oncolytic virus therapy. The inherent challenges of clinical application are also summarized. This review provides a comprehensive landscape for analyzing TM, especially the TM-related mechanisms and TM-based treatment in cancer.

Identification of intracellular bacteria from multiple single-cell RNA-seq platforms using CSI-Microbes

The study of the tumor microbiome has been garnering increased attention. We developed a computational pipeline (CSI-Microbes) for identifying microbial reads from single-cell RNA sequencing (scRNA-seq) data and for analyzing differential abundance of taxa. Using a series of controlled experiments and analyses, we performed the first systematic evaluation of the efficacy of recovering microbial unique molecular identifiers by multiple scRNA-seq technologies, which identified the newer 10x chemistries (3' v3 and 5') as the best suited approach. We analyzed patient esophageal and colorectal carcinomas and found that reads from distinct genera tend to co-occur in the same host cells, testifying to possible intracellular polymicrobial interactions. Microbial reads are disproportionately abundant within myeloid cells that up-regulate proinflammatory cytokines like IL1Β and CXCL8, while infected tumor cells up-regulate antigen processing and presentation pathways. These results show that myeloid cells with bacteria engulfed are a major source of bacterial RNA within the tumor microenvironment (TME) and may inflame the TME and influence immunotherapy response.

Fast, parallel, and cache-friendly suffix array construction

PURPOSE

String indexes such as the suffix array (SA) and the closely related longest common prefix (LCP) array are fundamental objects in bioinformatics and have a wide variety of applications. Despite their importance in practice, few scalable parallel algorithms for constructing these are known, and the existing algorithms can be highly non-trivial to implement and parallelize.

METHODS

In this paper we present CAPS-SA, a simple and scalable parallel algorithm for constructing these string indexes inspired by samplesort and utilizing an LCP-informed mergesort. Due to its design, CAPS-SA has excellent memory-locality and thus incurs fewer cache misses and achieves strong performance on modern multicore systems with deep cache hierarchies.

RESULTS

We show that despite its simple design, CAPS-SA outperforms existing state-of-the-art parallel SA and LCP-array construction algorithms on modern hardware. Finally, motivated by applications in modern aligners where the query strings have bounded lengths, we introduce the notion of a bounded-context SA and show that CAPS-SA can easily be extended to exploit this structure to obtain further speedups. We make our code publicly available at https://github.com/jamshed/CaPS-SA .

Isolation and Cultivation of Human Gut Microorganisms: A Review

Microbial resources from the human gut may find use in various applications, such as empirical research on the microbiome, the development of probiotic products, and bacteriotherapy. Due to the development of "culturomics", the number of pure bacterial cultures obtained from the human gut has significantly increased since 2012. However, there is still a considerable number of human gut microbes to be isolated and cultured. Thus, to improve the efficiency of obtaining microbial resources from the human gut, some constraints of the current methods, such as labor burden, culture condition, and microbial targetability, still need to be optimized. Here, we overview the general knowledge and recent development of culturomics for human gut microorganisms. Furthermore, we discuss the optimization of several parts of culturomics including sample collection, sample processing, isolation, and cultivation, which may improve the current strategies.

Sketching and sampling approaches for fast and accurate long read classification

BACKGROUND

In modern sequencing experiments, quickly and accurately identifying the sources of the reads is a crucial need. In metagenomics, where each read comes from one of potentially many members of a community, it can be important to identify the exact species the read is from. In other settings, it is important to distinguish which reads are from the targeted sample and which are from potential contaminants. In both cases, identification of the correct source of a read enables further investigation of relevant reads, while minimizing wasted work. This task is particularly challenging for long reads, which can have a substantial error rate that obscures the origins of each read.

RESULTS

Existing tools for the read classification problem are often alignment or index-based, but such methods can have large time and/or space overheads. In this work, we investigate the effectiveness of several sampling and sketching-based approaches for read classification. In these approaches, a chosen sampling or sketching algorithm is used to generate a reduced representation (a "screen") of potential source genomes for a query readset before reads are streamed in and compared against this screen. Using a query read's similarity to the elements of the screen, the methods predict the source of the read. Such an approach requires limited pre-processing, stores and works with only a subset of the input data, and is able to perform classification with a high degree of accuracy.

CONCLUSIONS

The sampling and sketching approaches investigated include uniform sampling, methods based on MinHash and its weighted and order variants, a minimizer-based technique, and a novel clustering-based sketching approach. We demonstrate the effectiveness of these techniques both in identifying the source microbial genomes for reads from a metagenomic long read sequencing experiment, and in distinguishing between long reads from organisms of interest and potential contaminant reads. We then compare these approaches to existing alignment, index and sketching-based tools for read classification, and demonstrate how such a method is a viable alternative for determining the source of query reads. Finally, we present a reference implementation of these approaches at https://github.com/arun96/sketching .