Panera: An innovative framework for surmounting uncertainty in microbial community modeling using pan-genera metabolic models.

Utilization of 16S rRNA data in constraint-based modeling to characterize microbial communities confronts a major hurdle of lack of species-level resolution, impeding the construction of community models. We introduce "Panera," an innovative framework designed to model communities under this uncertainty and yet perform metabolic inferences using pan-genus metabolic models (PGMMs). We demonstrated PGMMs' utility for comprehending the metabolic capabilities of a genus and in characterizing community models using amplicon data. The unique, adaptable nature of PGMMs unlocks their potential in building hybrid communities, combining genome-scale metabolic models (GSMMs) and PGMMs. Notably, these models provide predictions comparable to the standard GSMM-based community models, while achieving a nearly 46% reduction in error compared to the genus model-based communities. In essence, "Panera" presents a potent and effective approach to aid in metabolic modeling by enabling robust predictions of community metabolic potential when dealing with amplicon data, and offers insights into genus-level metabolic landscapes.

Designing function-specific minimal microbiomes from large microbial communities.

Microorganisms exist in large communities of diverse species, exhibiting various functionalities. The mammalian gut microbiome, for instance, has the functionality of digesting dietary fibre and producing different short-chain fatty acids. Not all microbes present in a community contribute to a given functionality; it is possible to find a minimal microbiome, which is a subset of the large microbiome, that is capable of performing the functionality while maintaining other community properties such as growth rate and metabolite production. Such a minimal microbiome will also contain keystone species for SCFA production in that community. In this work, we present a systematic constraint-based approach to identify a minimal microbiome from a large community for a user-proposed function. We employ a top-down approach with sequential deletion followed by solving a mixed-integer linear programming problem with the objective of minimising the L1-norm of the membership vector. Notably, we consider quantitative measures of community growth rate and metabolite production rates. We demonstrate the utility of our algorithm by identifying the minimal microbiomes corresponding to three model communities of the gut, and discuss their validity based on the presence of the keystone species in the community. Our approach is generic, flexible and finds application in studying a variety of microbial communities. The algorithm is available from https://github.com/RamanLab/minMicrobiome .

Big Data for a Small World: A Review on Databases and Resources for Studying Microbiomes.

Microorganisms are ubiquitous in nature and form complex community networks to survive in various environments. This community structure depends on numerous factors like nutrient availability, abiotic factors like temperature and pH as well as microbial composition. Categorising accessible biomes according to their habitats would help in understanding the complexity of the environment-specific communities. Owing to the recent improvements in sequencing facilities, researchers have started to explore diverse microbiomes rapidly and attempts have been made to study microbial crosstalk. However, different metagenomics sampling, preprocessing, and annotation methods make it difficult to compare multiple studies and hinder the recycling of data. Huge datasets originating from these experiments demand systematic computational methods to extract biological information beyond microbial compositions. Further exploration of microbial co-occurring patterns across the biomes could help us in designing cross-biome experiments. In this review, we catalogue databases with system-specific microbiomes, discussing publicly available common databases as well as specialised databases for a range of microbiomes. If the new datasets generated in the future could maintain at least biome-specific annotation, then researchers could use those contemporary tools for relevant and bias-free analysis of complex metagenomics data.

Heterologous expression, purification, and functional characterization of recombinant ovine angiotensinogen in the methylotrophic yeast Pichia pastoris.

Angiotensinogen (AGT), a glycosylated plasma noninhibitory serpin, serves as a precursor for angiotensin peptides which regulate blood pressure and electrolyte balance. AGT is specifically cleaved by renin to produce angiotensin-I, the first product of the angiotensin-processing cascade. Ovine angiotensinogen (oAGT) is considered an effective substrate for human renin and consequently finds application in clinical renin assays. In this study, oAGT was cloned into the genome of Pichia pastoris and expressed under the control of alcohol oxidase (AOX1) promoter for high-level production. Compared to the shake flask study, the high cell density cultivation in bioreactor resulted in multifold increase in oAGT titer (420 ± 9.26 mg/L), which is its highest reported titer to date. We purified recombinant oAGT to homogeneity using two chromatography steps. The characterization studies revealed oAGT underwent a two-state transition during thermal denaturation process as assessed by differential scanning fluorimetry, and the melting temperature (Tm ) of the purified oAGT from P. pastoris was 48.3°C. Renin reactivity with recombinant oAGT from P. pastoris (0.51 nM angiotensin-I/min) was slightly lower than the renin reactivity for recombinant oAGT from Escherichia coli (0.67 nM angiotensin-I/min), possibly because of its mannosylated N-glycan content. Enhanced production of functionally active recombinant oAGT using P. pastoris expression system reported in this study envisage the effective utilization of oAGT in clinical studies related to renin in near future.