Pangenomics in Microbial and Crop Research: Progress, Applications, and Perspectives

Integrated genomics provides insights into the evolution of the polyphosphate accumulation trait of Ca. Accumulibacter

Candidatus Accumulibacter, a prominent polyphosphate-accumulating organism (PAO) in wastewater treatment, plays a crucial role in enhanced biological phosphorus removal (EBPR). The genetic underpinnings of its polyphosphate accumulation capabilities, however, remain largely unknown. Here, we conducted a comprehensive genomic analysis of Ca. Accumulibacter-PAOs and their relatives within the Rhodocyclaceae family, identifying 124 core genes acquired via horizontal gene transfer (HGT) at its least common ancestor. Metatranscriptomic analysis of an enrichment culture of Ca. Accumulibacter revealed active transcription of 44 of these genes during an EBPR cycle, notably including the polyphosphate kinase 2 (PPK2) gene instead of the commonly recognized polyphosphate kinase 1 (PPK1) gene. Intriguingly, the phosphate regulon (Pho) genes showed minimal transcriptions, pointing to a distinctive fact of Pho dysregulation, where PhoU, the phosphate signaling complex protein, was not regulating the high-affinity phosphate transport (Pst) system, resulting in continuous phosphate uptake. To prevent phosphate toxicity, Ca. Accumulibacter utilized the laterally acquired PPK2 to condense phosphate into polyphosphate, resulting in the polyphosphate-accumulating feature. This study provides novel insights into the evolutionary emergence of the polyphosphate-accumulating trait in Ca. Accumulibacter, offering potential advancements in understanding the PAO phenotype in the EBPR process.

Pangenomics: A new era in the field of neurodegenerative diseases

A pangenome is composed of all the genetic variability of a group of individuals, and its application to the study of neurodegenerative diseases may provide valuable insights into the underlying aspects of genetic heterogenetiy for these complex ailments, including gene expression, epigenetics, and translation mechanisms. Furthermore, a reference pangenome allows for the identification of previously undetected structural commonalities and differences among individuals, which may help in the diagnosis of a disease, support the prediction of what will happen over time (prognosis) and aid in developing novel treatments in the perspective of personalized medicine. Therefore, in the present review, the application of the pangenome concept to the study of neurodegenerative diseases will be discussed and analyzed for its potential to enable an improvement in diagnosis and prognosis for these illnesses, leading to the development of tailored treatments for individual patients from the knowledge of the genomic composition of a whole population.

PanKmer: k-mer-based and reference-free pangenome analysis

SUMMARY

Pangenomes are replacing single reference genomes as the definitive representation of DNA sequence within a species or clade. Pangenome analysis predominantly leverages graph-based methods that require computationally intensive multiple genome alignments, do not scale to highly complex eukaryotic genomes, limit their scope to identifying structural variants (SVs), or incur bias by relying on a reference genome. Here, we present PanKmer, a toolkit designed for reference-free analysis of pangenome datasets consisting of dozens to thousands of individual genomes. PanKmer decomposes a set of input genomes into a table of observed k-mers and their presence-absence values in each genome. These are stored in an efficient k-mer index data format that encodes SNPs, INDELs, and SVs. It also includes functions for downstream analysis of the k-mer index, such as calculating sequence similarity statistics between individuals at whole-genome or local scales. For example, k-mers can be "anchored" in any individual genome to quantify sequence variability or conservation at a specific locus. This facilitates workflows with various biological applications, e.g. identifying cases of hybridization between plant species. PanKmer provides researchers with a valuable and convenient means to explore the full scope of genetic variation in a population, without reference bias.

AVAILABILITY AND IMPLEMENTATION

PanKmer is implemented as a Python package with components written in Rust, released under a BSD license. The source code is available from the Python Package Index (PyPI) at https://pypi.org/project/pankmer/ as well as Gitlab at https://gitlab.com/salk-tm/pankmer. Full documentation is available at https://salk-tm.gitlab.io/pankmer/.

Human Pangenomics: Promises and Challenges of a Distributed Genomic Reference

A pangenome is a collection of the common and unique genomes that are present in a given species. It combines the genetic information of all the genomes sampled, resulting in a large and diverse range of genetic material. Pangenomic analysis offers several advantages compared to traditional genomic research. For example, a pangenome is not bound by the physical constraints of a single genome, so it can capture more genetic variability. Thanks to the introduction of the concept of pangenome, it is possible to use exceedingly detailed sequence data to study the evolutionary history of two different species, or how populations within a species differ genetically. In the wake of the Human Pangenome Project, this review aims at discussing the advantages of the pangenome around human genetic variation, which are then framed around how pangenomic data can inform population genetics, phylogenetics, and public health policy by providing insights into the genetic basis of diseases or determining personalized treatments, targeting the specific genetic profile of an individual. Moreover, technical limitations, ethical concerns, and legal considerations are discussed.

Development of rapid and precise approach for quantification of bacterial taxa correlated with soil health

The structure and dynamic of soil bacterial community play a crucial role in soil health and plant productivity. However, there is a gap in studying the un-/or reclaimed soil bacteriome and its impact on future plant performance. The 16S metagenomic analysis is expensive and utilize sophisticated pipelines, making it unfavorable for researchers. Here, we aim to perform (1) in silico and in vitro validation of taxon-specific qPCR primer-panel in the detection of the beneficial soil bacterial community, to ensure its specificity and precision, and (2) multidimensional analysis of three soils/locations in Egypt ('Q', 'B', and 'G' soils) in terms of their physicochemical properties, bacteriome composition, and wheat productivity as a model crop. The in silico results disclosed that almost all tested primers showed high specificity and precision toward the target taxa. Among 17 measured soil properties, the electrical conductivity (EC) value (up to 5 dS/m) of 'Q' soil provided an efficient indicator for soil health among the tested soils. The 16S NGS analysis showed that the soil bacteriome significantly drives future plant performance, especially the abundance of Proteobacteria and Actinobacteria as key indicators. The functional prediction analysis results disclosed a high percentage of N-fixing bacterial taxa in 'Q' soil compared to other soils, which reflects their positive impact on wheat productivity. The taxon-specific qPCR primer-panel results revealed a precise quantification of the targeted taxa compared to the 16S NGS analysis. Moreover, 12 agro-morphological parameters were determined for grown wheat plants, and their results showed a high yield in the 'Q' soil compared to other soils; this could be attributed to the increased abundance of Proteobacteria and Actinobacteria, high enrichment in nutrients (N and K), or increased EC/nutrient availability. Ultimately, the potential use of a taxon-specific qPCR primer-panel as an alternative approach to NGS provides a cheaper, user-friendly setup with high accuracy.