Novel sequencing technologies to support industrial biotechnology

BioProtIS: Streamlining protein-ligand interaction pipeline for analysis in genomic and transcriptomic exploration

The identification of protein-ligand interactions plays a pivotal role in elucidating biological processes and discovering potential bioproducts. Harnessing the capabilities of computational methods in drug discovery, we introduce an innovative Inverted Virtual Screening (IVS) pipeline. This pipeline Integrated molecular dynamics and docking analyses to ensure that protein structures are not only energetically favorable but also representative of stable conformations. The primary objective of this pipeline is to automate and streamline the analysis of protein-ligand interactions at both genomic and transcriptomic scales. In the contemporary post-genomic era, high-throughput computational screening for bioproducts, biological systems, and therapeutic drugs has become a cornerstone practice. This approach offers the promise of cost-effectiveness, time efficiency, and optimization of laboratory work. Nevertheless, a notable deficiency persists in the availability of efficient pipelines capable of automating the virtual screening process, seamlessly integrating input and output, and leveraging the full potential of open-source tools. To bridge this critical gap, we have developed a versatile pipeline known as BioProtIS. This tool seamlessly integrates a suite of state-of-the-art tools, including Modeller, AlphaFold, Gromacs, FPOCKET, and AutoDock Vina, thus facilitating the streamlined docking of ligands with an expansive repertoire of proteins sourced from genomes and transcriptomes, and substrates. To assess the pipeline's performance, we employed the transcriptomes of Cereus jamacaru (a cactus species) and Aspisoma lineatum (firefly), along with the genome of Homo sapiens. This integration not only improves the accuracy of ligand-protein interactions by minimizing replicability deviations but also optimizes the discovery process by enabling the simultaneous evaluation of multiple substrates. Furthermore, our pipeline accommodates distinct testing scenarios, such as blind docking or site-specific targeting, which are invaluable in applications ranging from drug repositioning to the exploration of new allosteric binding sites and toxicity assessments. BioProtIS has been designed with modularity at its core. This inherent flexibility empowers users to make custom modifications directly within the source code, tailoring the pipeline to their specific research needs. Moreover, it lays the foundation for seamless integration of diverse docking algorithms in future iterations, promising ongoing advancements in the field of computational biology. This pipeline is available for free distribution and can be download at: https://github.com/BBMDO/BioProtIS.

Genomic Analysis of Surfactant-Producing Bacillus vallismortis TIM68: First Glimpse at Species Pangenome and Prediction of New Plipastatin-Like Lipopeptide

Surfactants are applied in several industrial processes when the modification of interface activity and the stability of colloidal systems are required. Lipopeptides are a class of microbial biosurfactants produced by species of the Bacillus genus. The present study aimed at assembling and analyzing the genome of a new Bacillus vallismortis strain, TIM68, that was shown to produce surfactant lipopeptides. The draft genome was also screened for common virulence factors and antibiotics resistance genes to investigate the strain biosafety. Comparative genomics analyses, i.e., synteny, average nucleotide identity (ANI), and pangenome, were also carried out using strain TIM68 and publicly available B. vallismortis complete and partial genomes. Three peptide synthetase operons were found in TIM68 genome, and they were surfactin A, mojavensin, and a novel plipastatin-like lipopeptide named vallisin. No virulence factors that render pathogenicity to the strain have been identified, but a region of prophage, that may contain unknown pathogenic factors, has been predicted. The pangenome of the species was characterized as closed, with 57% of genes integrating the core genome. The results obtained here on the genetic potential of TIM68 strain should contribute to its exploration in biotechnological applications.

Metagenomics: An Approach for Unraveling the Community Structure and Functional Potential of Activated Sludge of a Common Effluent Treatment Plant

The common effluent treatment plant (CETP) located at Baddi treats the industrial effluent from various industries, leading to the pooling of a diverse range of substrates and metabolites. The nutrient loading and its availability decide the balance of the microbial community and its diversity. The samples thus collected from the activated sludge (BS14) of CETP and Sirsa river (SR1) from the vicinity of CETP effluent discharge were processed for the whole metagenome analysis to reveal the microbial community and its functional potential. The taxonomic classification of the BS14 sample showed the dominance of the bacterial community with 96% of abundance, whereas the SR1 was populated by eukaryotes representing 50.4% of the community of SR1. The bacterial community of SR1 was constituted of 47.2%. The functional analysis of BS14 and SR1 with GhostKOALA against the KEGG database assigned 43.7% and 27.8% of the open reading frames (ORFs) with functions. It revealed the xenobiotic degradation modules with complete pathways along with resistance against the beta-lactams. The analysis with the comprehensive antibiotic resistance database (CARD) revealed 33 and 32 unique types of antimicrobial resistance in BS14 and SR1, respectively. Both the samples were dominated by the beta-lactam resistance genes. The carbohydrate-active enzyme (CAZy) database assigned a total of 6,611 and 2,941 active enzymes to BS14 and SR1, respectively. In contrast, the glycosyl hydrolases (GH) and glycosyltransferases (GT) class of enzymes were found to be abundant in both the samples as compared with polysaccharide lyases (PL), auxiliary activities (AA), carbohydrate esterases (CE), and carbohydrate-binding module (CBM).

Genomic analysis uncovers laccase-coding genes and biosynthetic gene clusters encoding antimicrobial compounds in laccase-producing Acinetobacter baumannii

Laccases are multicopper oxidase family enzymes that can oxidize various substrates. In this study, we isolated laccase-producing Acinetobacter spp. from the environment, and one isolate of laccase-producing Acinetobacter baumannii, designated NI-65, was identified. The NI-65 strain exhibited constitutive production of extracellular laccase in a crude extract using 2,6-dimethoxyphenol as a substrate when supplemented with 2 mM CuSO₄. Whole-genome sequencing of the NI-65 strain revealed a genome size of 3.6 Mb with 3,471 protein-coding sequences. The phylogenetic analysis showed high similarity to the genome of A. baumannii NCIMB8209. Three laccase proteins, PcoA and CopA, that belong to bacterial CopA superfamilies, and LAC-AB, that belongs to the I-bacterial bilirubin oxidase superfamily, were identified. These proteins were encoded by three laccase-coding genes (pcoA, copA, and lac-AB). The lac-AB gene showed a sequence similar to that of polyphenol oxidase (PPO). Gene clusters encoding the catabolized compounds involved in the utilization of plant substances and secondary metabolite biosynthesis gene clusters encoding antimicrobial compounds were identified. This is the first report of whole-genome sequencing of laccase-producing A. baumannii, and the data from this study help to elucidate the genome of A. baumannii to facilitate its application in synthetic biology for enzyme production.

Metagenome-assembled genome of a Chitinophaga sp. and its potential in plant biomass degradation, as well of affiliated Pandoraea and Labrys species

The prospection of new degrading enzymes of the plant cell wall has been the subject of many studies and is fundamental for industries, due to the great biotechnological importance of achieving a more efficient depolymerization conversion from plant polysaccharides to fermentable sugars, which are useful not only for biofuel production but also for various bioproducts. Thus, we explored the shotgun metagenome data of a bacterial community (CB10) isolated from sugarcane bagasse and recovered three metagenome-assembled genomes (MAGs). The genomic distance analyses, along with phylogenetic analysis, revealed the presence of a putative novel Chitinophaga species, a Pandoraea nosoerga, and Labrys sp. isolate. The isolation process for each one of these bacterial lineages from the community was carried out in order to relate them with the MAGs. The recovered draft genomes have reasonable completeness (72.67-100%) and contamination (0.26-2.66%) considering the respective marker lineage for Chitinophaga (Bacteroidetes), Pandoraea (Burkholderiales), and Labrys (Rhizobiales). The in-vitro assay detected cellulolytic activity (endoglucanases) only for the isolate Chitinophaga, and its genome analysis revealed 319 CAZymes, of which 115 are classified as plant cell wall degrading enzymes, which can act in fractions of hemicellulose and pectin. Our study highlights the potential of this Chitinophaga isolate provides several plant-polysaccharide-degrading enzymes.

KITSUNE: A Tool for Identifying Empirically Optimal K-mer Length for Alignment-Free Phylogenomic Analysis

Genomic DNA is the best “unique identifier” for organisms. Alignment-free phylogenomic analysis, simple, fast, and efficient method to compare genome sequences, relies on looking at the distribution of small DNA sequence of a particular length, referred to as k-mer. The k-mer approach has been explored as a basis for sequence analysis applications, including assembly, phylogenetic tree inference, and classification. Although this approach is not novel, selecting the appropriate k-mer length to obtain the optimal resolution is rather arbitrary. However, it is a very important parameter for achieving the appropriate resolution for genome/sequence distances to infer biologically meaningful phylogenetic relationships. Thus, there is a need for a systematic approach to identify the appropriate k-mer from whole-genome sequences. We present K-mer–length Iterative Selection for UNbiased Ecophylogenomics (KITSUNE), a tool for assessing the empirically optimal k-mer length of any given set of genomes of interest for phylogenomic analysis via a three-step approach based on (1) cumulative relative entropy (CRE), (2) average number of common features (ACF), and (3) observed common features (OCF). Using KITSUNE, we demonstrated the feasibility and reliability of these measurements to obtain empirically optimal k-mer lengths of 11, 17, and ∼34 from large genome datasets of viruses, bacteria, and fungi, respectively. Moreover, we demonstrated a feature of KITSUNE for accurate species identification for the two de novo assembled bacterial genomes derived from error-prone long-reads sequences, and for a published yeast genome. In addition, KITSUNE was used to identify the shortest species-specific k-mer accurately identifying viruses. KITSUNE is freely available at https://github.com/natapol/kitsune.

Decoding the ocean's microbiological secrets for marine enzyme biodiscovery

A global census of marine microbial life has been underway over the past several decades. During this period, there have been scientific breakthroughs in estimating microbial diversity and understanding microbial functioning and ecology. It is estimated that the ocean, covering 71% of the earth's surface with its estimated volume of about 2 × 10¹⁸ m³ and an average depth of 3800 m, hosts the largest population of microbes on Earth. More than 2 million eukaryotic and prokaryotic species are thought to thrive both in the ocean and on its surface. Prokaryotic cell abundances can reach densities of up to 10¹² cells per millilitre, exceeding eukaryotic densities of around 10⁶ cells per millilitre of seawater. Besides their large numbers and abundance, marine microbial assemblages and their organic catalysts (enzymes) have a largely underestimated value for their use in the development of industrial products and processes. In this perspective article, we identified critical gaps in knowledge and technology to fast-track this development. We provided a general overview of the presumptive microbial assemblages in oceans, and an estimation of what is known and the enzymes that have been currently retrieved. We also discussed recent advances made in this area by the collaborative European Horizon 2020 project ‘INMARE’.

Changes in the Substrate Source Reveal Novel Interactions in the Sediment-Derived Methanogenic Microbial Community

Methanogenesis occurs in many natural environments and is used in biotechnology for biogas production. The efficiency of methane production depends on the microbiome structure that determines interspecies electron transfer. In this research, the microbial community retrieved from mining subsidence reservoir sediment was used to establish enrichment cultures on media containing different carbon sources (tryptone, yeast extract, acetate, CO₂/H₂). The microbiome composition and methane production rate of the cultures were screened as a function of the substrate and transition stage. The relationships between the microorganisms involved in methane formation were the major focus of this study. Methanogenic consortia were identified by next generation sequencing (NGS) and functional genes connected with organic matter transformation were predicted using the PICRUSt approach and annotated in the KEGG. The methane production rate (exceeding 12.8 mg CH₄ L⁻¹ d⁻¹) was highest in the culture grown with tryptone, yeast extract, and CO₂/H_2. The analysis of communities that developed on various carbon sources casts new light on the ecophysiology of the recently described bacterial phylum Caldiserica and methanogenic Archaea representing the genera Methanomassiliicoccus and Methanothrix. Furthermore, it is hypothesized that representatives of Caldiserica may support hydrogenotrophic methanogenesis.

On the Role of Bioinformatics and Data Science in Industrial Microbiome Applications

Advances in sequencing and computational biology have drastically increased our capability to explore the taxonomic and functional compositions of microbial communities that play crucial roles in industrial processes. Correspondingly, commercial interest has risen for applications where microbial communities make important contributions. These include food production, probiotics, cosmetics, and enzyme discovery. Other commercial applications include software that takes the user's gut microbiome data as one of its inputs and outputs evidence-based, automated, and personalized diet recommendations for balanced blood sugar levels. These applications pose several bioinformatic and data science challenges that range from requiring strain-level resolution in community profiles to the integration of large datasets for predictive machine learning purposes. In this perspective, we provide our insights on such challenges by touching upon several industrial areas, and briefly discuss advances and future directions of bioinformatics and data science in microbiome research.