An Integrated Program in Microbial Genome Sequencing and Analysis

Genome analysis of microbial pathogens has provided unique insights into their virulence, host adaptation and evolution. Common themes have emerged, including lateral gene transfer among enteric pathogens, genome decay among obligate intracellular pathogens and antigenic variation among mucosal pathogens. The advent of post-genomic approaches and the sequencing of the human genome will enable scientists to investigate the complex and dynamic interplay between host and pathogen. This wealth of information will catalyse the development of new intervention strategies to reduce the burden of microbial-related disease.

With the rapid development of genomic sequencing technologies, there is an increasing demand for efficient and accurate sequence analysis methods. However, existing methods face challenges in handling long, variable-length sequences and large-scale datasets. To address these issues, we propose a novel encoding method—Energy Entropy Vector (EEV). This method encodes gene sequences of arbitrary length into fixed-dimensional vector representations by modeling nucleotide energy characteristics based on information entropy. Experiments conducted on five microbial datasets demonstrate that, compared to traditional alignment-free methods, EEV achieves higher accuracy in convex hull classification and species classification tasks, with improvements of 15% to 30% in family-level classification. In phylogenetic tree construction, EEV significantly accelerates the process relative to multiple sequence alignment methods while maintaining high tree quality, enabling rapid and accurate phylogenetic reconstruction. Moreover, EEV supports flexible dimensional expansion by superimposing nucleotide energies, enhancing its ability to represent complex genomic sequences while effectively alleviating sparsity issues in high-dimensional representations. This study provides an efficient gene encoding strategy for large-scale genomic analysis and evolutionary research.

As the price of genome sequencing has been rapidly decreasing and can be expected to keep on doing so in the next 10 years, the speed at which new microbial genome sequences become available will increase accordingly. In most genome projects, the first step after acquiring a genome sequence is predicting protein‐encoding open reading frames (pORFs). Small proteins or peptides, loosely defined as less than 50 amino acids, encoded in microbial genomes have been largely underestimated. Recent focused functional genomics efforts have led to the identification of a number of new small proteins encoded in genomes of both Gram‐negative and Gram‐positive bacteria, and fungi (Kastenmayer et al., 2006; Li et al., 2008; Hemm et al., 2010; Hobbs et al., 2010; Bitton et al., 2011). Increasing evidence demonstrates that small proteins participate in a wide array of cellular processes and exhibit great diversity in their mechanisms of action. A recent review (Hobbs et al., 2011) highlights examples of small proteins that, in addition to the well‐conserved small ribosomal proteins, participate in cell signalling or regulation, act as antibiotics and toxins/anti‐toxins, alter membrane features, act as chaperones, stabilize protein complexes or serve as structural proteins (Table 1) (Fig. 1). Table 1 Types and functions of small proteins. Figure 1 Structure of small proteins. Small secreted proteins exhibit diversity in their three‐dimensional structures and can contain unique intramolecular linkages or modified amino acids. For example, the mature form of (A) subtilosin (PDB: 1PXQ) is ... Failure to recognize a pORF encoding a small protein means that these important cell constituents will be missed. Here, we give a brief summary of which problems arise in searching for such encoded small proteins, and what we could do to improve the search process.

Reconstruction of ancient genome and gene order from complete microbial genome sequences

The analysis of microbial genomes has long been recognised as a complex and data-rich domain where artificial intelligence (AI) can assist. As AI technologies have matured and expanded, pathogen genomics has also contended with exponentially larger datasets and an expanding role in clinical and public health practice. In this mini-review, we discuss examples of emerging applications of AI to address challenges in pathogen genomics for precision medicine and public health. These include models for genotyping whole genome sequences, identifying novel pathogens in metagenomic next generation sequencing, modelling genomic information using approaches from computational linguistics, phylodynamic estimation, and using large language models to make bioinformatics more accessible to non-experts. We also examine factors affecting the adoption of AI into routine laboratory and public health practice and the need for a renewed vision for the potential of AI to assist pathogen genomics practice.

B. licheniformis DSM13 is a Gram-positive, ubiquitous, endospore forming bacterium that is extensively used for industrial production of exoenzymes as it can secrete high amounts of protein. The genome was sequenced in 2004 at the Goettingen Genomics Laboratory. Since the sequencing of the model organism B. subtilis 168 in 1997 and its resequencing and -annotation in 2009, numerous other representatives of the species Bacillus have been sequenced. The aim of the present dissertation is a detailed genomic understanding of B. licheniformis DSM13 and the generation of predictions about which parts of the sequence are essential and non-essential for the enormous production of proteases by bioinformatic comparison of protein sequences. Therefore, the focus lies on the analysis of the B. licheniformis DSM13 competence system since this strain s genetic transformation is difficult compared to the laboratory-optimised B. subtilis 168. A softwaretool for the comparative whole genome analysis was implemented, which automatically identifies shared proteins in different organisms. Local bidirectional best hits for the orthologues identification, the evaluation with a global alignment algorithm and the adequate output for further analyses make the pipeline a widespread tool in microbial genome analysis. It was used in a broad comparative analysis of B. licheniformis DSM13. The combination of in silico predictions and experimental micro-array analyses generated deletion targets for further biological analysis. The present work provides a contribution to the design of an optimal genome of B. licheniformis DSM13.

It is probable that, increasingly, genome investigations are going to be based on statistical formalization. This review summarizes the state of art and potentiality of using statistics in microbial genome analysis. First, I focus on recent advances in functional genomics, such as finding genes and operons, identifying gene conversion events, detecting DNA replication origins and analysing regulatory sites. Then I describe how to use phylogenetic methods in genome analysis and methods for genome-wide scanning for positively selected amino acids. I conclude with speculations on the future course of genome statistical modeling.

7 - Analysis of Microbial Genomic Macrorestriction Patterns by Pulsed-Field Gel Electrophoresis (PFGE) Typing

For the past 20 years, the Clusters of Orthologous Genes (COG) database had been a popular tool for microbial genome annotation and comparative genomics. Initially created for the purpose of evolutionary classification of protein families, the COG have been used, apart from straightforward functional annotation of sequenced genomes, for such tasks as (i) unification of genome annotation in groups of related organisms; (ii) identification of missing and/or undetected genes in complete microbial genomes; (iii) analysis of genomic neighborhoods, in many cases allowing prediction of novel functional systems; (iv) analysis of metabolic pathways and prediction of alternative forms of enzymes; (v) comparison of organisms by COG functional categories; and (vi) prioritization of targets for structural and functional characterization. Here we review the principles of the COG approach and discuss its key advantages and drawbacks in microbial genome analysis.

Linking biosynthetic genes to natural products using inverse stable isotopic labeling (InverSIL).

Chapter Eighteen - Use and Discovery of Chemical Elicitors That Stimulate Biosynthetic Gene Clusters in Streptomyces Bacteria

Genome and Transcriptome Sequence Resources and Effector Repertoire of Pythium myriotylum Drechsler.

Novel antibiotics are urgently needed to address the looming global crisis of antibiotic resistance. Historically, the primary source of clinically used antibiotics has been microbial secondary metabolism. Microbial genome sequencing has revealed a plethora of uncharacterized natural antibiotics that remain to be discovered. However, the isolation of these molecules is hindered by the challenge of linking sequence information to the chemical structures of the encoded molecules. Here, we present PRISM 4, a comprehensive platform for prediction of the chemical structures of genomically encoded antibiotics, including all classes of bacterial antibiotics currently in clinical use. The accuracy of chemical structure prediction enables the development of machine-learning methods to predict the likely biological activity of encoded molecules. We apply PRISM 4 to chart secondary metabolite biosynthesis in a collection of over 10,000 bacterial genomes from both cultured isolates and metagenomic datasets, revealing thousands of encoded antibiotics. PRISM 4 is freely available as an interactive web application at http://prism.adapsyn.com.

Microbial genome sequencing has uncovered a myriad of natural products (NPs) that have yet to be explored. Bacteria in the genus Pseudomonas serve as pathogens, plant growth promoters, and therapeutically, industrially, and environmentally important microorganisms. Though most species of Pseudomonas have a large number of NP biosynthetic gene clusters (BGCs) in their genomes, it is difficult to link many of these BGCs with products under current laboratory conditions. In order to gain new insights into the diversity, distribution, and evolution of these BGCs in Pseudomonas for the discovery of unexplored NPs, we applied several bioinformatic programming approaches to characterize BGCs from Pseudomonas reference genome sequences available in public databases along with phylogenetic and genomic comparison. Our research revealed that most BGCs in the genomes of Pseudomonas species have a high diversity for NPs at the species and subspecies levels and built the correlation of species with BGC taxonomic ranges. These data will pave the way for the algorithmic detection of species- and subspecies-specific pathways for NP development.

Classification and phylogeny of hydrogenases.