Draft Genome Sequence of Sphingobacterium sp. CZ-UAM, Isolated from a Methanotrophic Consortium

Efficient methane production from agro-industrial residues using anaerobic fungal-rich consortia

Anaerobic digestion (AD) emerges as a pivotal technique in climate change mitigation, transforming organic materials into biogas, a renewable energy form. This process significantly impacts energy production and waste management, influencing greenhouse gas emissions. Traditional research has largely focused on anaerobic bacteria and methanogens for methane production. However, the potential of anaerobic lignocellulolytic fungi for degrading lignocellulosic biomass remains less explored. In this study, buffalo rumen inocula were enriched and acclimatized to improve lignocellulolytic hydrolysis activity. Two consortia were established: the anaerobic fungi consortium (AFC), selectively enriched for fungi, and the anaerobic lignocellulolytic microbial consortium (ALMC). The consortia were utilized to create five distinct microbial cocktails-AF0, AF20, AF50, AF80, and AF100. These cocktails were formulated based on varying of AFC and ALMC by weights (w/w). Methane production from each cocktail of lignocellulosic biomasses (cassava pulp and oil palm residues) was evaluated. The highest methane yields of CP, EFB, and MFB were obtained at 337, 215, and 54 mL/g VS, respectively. Cocktails containing a mix of anaerobic fungi, hydrolytic bacteria (Sphingobacterium sp.), syntrophic bacteria (Sphaerochaeta sp.), and hydrogenotrophic methanogens produced 2.1-2.6 times higher methane in cassava pulp and 1.1-1.2 times in oil palm empty fruit bunch compared to AF0. All cocktails effectively produced methane from oil palm empty fruit bunch due to its lipid content. However, methane production ceased after 3 days when oil palm mesocarp fiber was used, due to long-chain fatty acid accumulation. Anaerobic fungi consortia showed effective lignocellulosic and starchy biomass degradation without inhibition due to organic acid accumulation. These findings underscore the potential of tailored microbial cocktails for enhancing methane production from diverse lignocellulosic substrates.

Comparative analysis of draft genome assemblies developed from whole genome sequences of two Hyaloperonospora brassicae isolate samples differing in field virulence on Brassica napus

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We report first draft genome assemblies for two isolates Hyaloperonospora brassicae, differing for their virulence. These revealed genome sizes of genome sizes of 72.762 and 76.950Mb and 6,438 and 6,470 scaffolds respectively.

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In silico annotation allowed understanding of the genome architecture of H. brassicae  in terms of genes for pathogenicity and virulence.

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The observed reduction in virulence or loss of pathogenicity in a larger number of genes in the sample with low virulence in comparison to sample with high virulence may reflect differential rates of mutation and selection during host–parasite co‐evolution.

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Genomic resources develop will aid in monitoring field prevalence of H. brassicae pathotypes and to detect early any virulence changes within pathogen populations.

Hyaloperonospora brassicae causes downy mildew, a major disease of Brassicaceae species. We sequenced the genomes of two H. brassicae isolates of high (Sample B) and low (Sample C) virulence. Sequencing reads were first assembled de novo with software's SOAPdenovo2, ABySS V2.1 and Velvet V1.1 and later combined to create meta-assemblies with genome sizes of 72.762 and 76.950Mb and predicted gene densities of 1628 and 1644 /Mb, respectively. We could annotate 12.255 and 13,030 genes with high proportions (91-92%) of complete BUSCOs for Sample B and C, respectively. Comparative analysis revealed conserved and varied molecular machinery underlying the physiological specialisation and infection capabilities. BLAST analysis against PHI gene database suggested a relatively higher loss of genes for virulence and pathogenicity in Sample C compared to Sample B, reflecting pathogen evolution through differential rates of mutation and selection. These studies will enable identification and monitoring of H. brassicae virulence factors prevailing in-field.

A systematic comparison of two empirical gas-liquid mass transfer determination methodologies to characterize methane biodegradation in stirred tank bioreactors

This study aimed at systematically comparing the potential of two empirical methods for the estimation of the volumetric CH₄ mass transfer coefficient (k_la_CH4), namely gassing-out and oxygen transfer rate (OTR), to describe CH₄ biodegradation in a fermenter operated with a methanotrophic consortium at 400, 600 and 800 rpm. The k_la_CH4 estimated from the OTR methodology accurately predicted the CH₄ elimination capacity (EC) under CH₄ mass transfer limiting conditions regardless of the stirring rate (∼9% of average error between empirical and estimated ECs). Thus, empirical CH₄-ECs of 37.8 ± 5.8, 42.5 ± 5.4 and 62.3 ± 5.2 g CH₄ m^-3 h^-1vs predicted CH₄-ECs of 35.6 ± 2.2, 50.1 ± 2.3 and 59.6 ± 3.4 g CH₄ m^-3 h^-1 were recorded at 400, 600 and 800 rpm, respectively. The rapid Co²⁺-catalyzed reaction of O₂ with SO₃^-2 in the vicinity of the gas-liquid interphase during OTR determinations, mimicking microbial CH₄ uptake in the biotic experiments, was central to accurately describe the k_la_CH4.

Predicting Dynamic Metabolic Demands in the Photosynthetic Eukaryote Chlorella vulgaris

Phototrophic organisms exhibit a highly dynamic proteome, adapting their biomass composition in response to diurnal light/dark cycles and nutrient availability. Here, we used experimentally determined biomass compositions over the course of growth to determine and constrain the biomass objective function (BOF) in a genome-scale metabolic model of Chlorella vulgaris UTEX 395 over time. Changes in the BOF, which encompasses all metabolites necessary to produce biomass, influence the state of the metabolic network thus directly affecting predictions. Simulations using dynamic BOFs predicted distinct proteome demands during heterotrophic or photoautotrophic growth. Model-driven analysis of extracellular nitrogen concentrations and predicted nitrogen uptake rates revealed an intracellular nitrogen pool, which contains 38% of the total nitrogen provided in the medium for photoautotrophic and 13% for heterotrophic growth. Agreement between flux and gene expression trends was determined by statistical comparison. Accordance between predicted flux trends and gene expression trends was found for 65% of multisubunit enzymes and 75% of allosteric reactions. Reactions with the highest agreement between simulations and experimental data were associated with energy metabolism, terpenoid biosynthesis, fatty acids, nucleotides, and amino acid metabolism. Furthermore, predicted flux distributions at each time point were compared with gene expression data to gain new insights into intracellular compartmentalization, specifically for transporters. A total of 103 genes related to internal transport reactions were identified and added to the updated model of C. vulgaris, iCZ946, thus increasing our knowledgebase by 10% for this model green alga.

Elucidation of complexity and prediction of interactions in microbial communities

Microorganisms engage in complex interactions with other members of the microbial community, higher organisms as well as their environment. However, determining the exact nature of these interactions can be challenging due to the large number of members in these communities and the manifold of interactions they can engage in. Various omic data, such as 16S rRNA gene sequencing, shotgun metagenomics, metatranscriptomics, metaproteomics and metabolomics, have been deployed to unravel the community structure, interactions and resulting community dynamics in situ. Interpretation of these multi-omic data often requires advanced computational methods. Modelling approaches are powerful tools to integrate, contextualize and interpret experimental data, thus shedding light on the underlying processes shaping the microbiome. Here, we review current methods and approaches, both experimental and computational, to elucidate interactions in microbial communities and to predict their responses to perturbations.