MAPPS: A Web-Based Tool for Metabolic Pathway Prediction and Network Analysis in the Postgenomic Era

Metabolic Connectome and Its Role in the Prediction, Diagnosis, and Treatment of Complex Diseases

The interconnectivity of advanced biological systems is essential for their proper functioning. In modern connectomics, biological entities such as proteins, genes, RNA, DNA, and metabolites are often represented as nodes, while the physical, biochemical, or functional interactions between them are represented as edges. Among these entities, metabolites are particularly significant as they exhibit a closer relationship to an organism's phenotype compared to genes or proteins. Moreover, the metabolome has the ability to amplify small proteomic and transcriptomic changes, even those from minor genomic changes. Metabolic networks, which consist of complex systems comprising hundreds of metabolites and their interactions, play a critical role in biological research by mediating energy conversion and chemical reactions within cells. This review provides an introduction to common metabolic network models and their construction methods. It also explores the diverse applications of metabolic networks in elucidating disease mechanisms, predicting and diagnosing diseases, and facilitating drug development. Additionally, it discusses potential future directions for research in metabolic networks. Ultimately, this review serves as a valuable reference for researchers interested in metabolic network modeling, analysis, and their applications.

A smart RBS library and its prediction model for robust and accurate fine-tuning of gene expression in Bacillus species

Bacillus species, such as Bacillus subtilis and Bacillus licheniformis, are important industrial bacteria. However, there is a lack of standardized and predictable genetic tools for convenient and reproducible assembly of genetic modules in Bacillus species to realize their full potential. In this study, we constructed a Ribosome Binding Site (RBS) library in B. licheniformis, which provides incremental regulation of expression levels over a 104-fold range. Additionally, we developed a model to quantify the resulting translation rates. We successfully demonstrated the robust expression of various target genes using the RBS library and showed that the model accurately predicts the translation rates of arbitrary coding genes. Importantly, we also extended the use of the RBS library and prediction model to B. subtilis, B. thuringiensis, and B. amyloliquefacie. The versatility of the RBS library and its prediction model enables quantification of biological behavior, facilitating reliable forward engineering of gene expression.

Computational medicinal chemistry applications to target Asian-prevalent strain of hepatitis C virus

Hepatitis C Virus (HCV), affecting millions of people worldwide, is the leading cause of liver disorder, cirrhosis, and hepatocellular carcinoma. HCV is genetically diverse having eight genotypes and several subtypes predominant in different regions of the globe. The HCV NS3/4A protease is a primary therapeutic target for HCV with various FDA-approved antivirals and several clinical developments. However, available protease inhibitors (PIs) have lower potency against HCV genotype 3 (GT3), prevalent in South Asia. In this study, the incumbent computational tools were utilized to understand and explore interactions of the HCV GT3 receptor with the potential inhibitors after the virtual screening of one million compounds retrieved from the ZINC database. The molecular dynamics, pharmacological studies, and experimental studies uncovered the potential PIs as ZINC000224449889, ZINC000224374291, and ZINC000224374456 and the derivative of ZINC000224374456 from the ZINC library. The study revealed that these top-hit compounds exhibited good binding and better pharmacokinetics properties that might be considered the most promising compound against HCV GT3 protease. Viability test, on primary healthy Human Gingival Fibroblasts (HGFs) and cancerous AGS cell line, was also carried out to assess their safety profile after administration. In addition, Surface Plasmon Resonance (SPR) was also performed for the determination of affinity and kinetics of synthesized compounds with target proteins.

piTracer - Automatic reconstruction of molecular cascades for the identification of synergistic drug targets

Cancer cells frequently undergo metabolic reprogramming as a mechanism of resistance against chemotherapeutic drugs. Metabolomic profiling provides a direct readout of metabolic changes and can thus be used to identify these tumor escape mechanisms. Here, we introduce piTracer, a computational tool that uses multi-scale molecular networks to identify potential combination therapies from pre- and post-treatment metabolomics data. We first demonstrate piTracer’s core ability to reconstruct cellular cascades by inspecting well-characterized molecular pathways and previously studied associations between genetic variants and metabolite levels. We then apply a new gene ranking algorithm on differential metabolomic profiles from human breast cancer cells after glutaminase inhibition. Four of the automatically identified gene targets were experimentally tested by simultaneous inhibition of the respective targets and glutaminase. Of these combination treatments, two were be confirmed to induce synthetic lethality in the cell line. In summary, piTracer integrates the molecular monitoring of escape mechanisms into comprehensive pathway networks to accelerate drug target identification. The tool is open source and can be accessed at https://github.com/krumsieklab/pitracer .

From single- to multi-omics: future research trends in medicinal plants

Medicinal plants are the main source of natural metabolites with specialised pharmacological activities and have been widely examined by plant researchers. Numerous omics studies of medicinal plants have been performed to identify molecular markers of species and functional genes controlling key biological traits, as well as to understand biosynthetic pathways of bioactive metabolites and the regulatory mechanisms of environmental responses. Omics technologies have been widely applied to medicinal plants, including as taxonomics, transcriptomics, metabolomics, proteomics, genomics, pangenomics, epigenomics and mutagenomics. However, because of the complex biological regulation network, single omics usually fail to explain the specific biological phenomena. In recent years, reports of integrated multi-omics studies of medicinal plants have increased. Until now, there have few assessments of recent developments and upcoming trends in omics studies of medicinal plants. We highlight recent developments in omics research of medicinal plants, summarise the typical bioinformatics resources available for analysing omics datasets, and discuss related future directions and challenges. This information facilitates further studies of medicinal plants, refinement of current approaches and leads to new ideas.

Yeast synthetic biology advances biofuel production

Increasing concerns of environmental impacts and global warming calls for urgent need to switch from use of fossil fuels to renewable technologies. Biofuels represent attractive alternatives of fossil fuels and have gained continuous attentions. Through the use of synthetic biology it has become possible to engineer microbial cell factories for efficient biofuel production in a more precise and efficient manner. Here, we review advances on yeast-based biofuel production. Following an overview of synthetic biology impacts on biofuel production, we review recent advancements on the design, build, test, learn steps of yeast-based biofuel production, and end with discussion of challenges associated with use of synthetic biology for developing novel processes for biofuel production.

Bioremediation of lignin derivatives and phenolics in wastewater with lignin modifying enzymes: Status, opportunities and challenges

Lignin modifying enzymes from fungi and bacteria are potential biocatalysts for sustainable mitigation of different potentially toxic pollutants in wastewater. Notably, the paper and pulp industry generates enormous amounts of wastewater containing high amounts of complex lignin-derived chlorinated phenolics and sulfonated pollutants. The presence of these compounds in wastewater is a critical issue from environmental and toxicological perspectives. Some chloro-phenols are harmful to the environment and human health, as they exert carcinogenic, mutagenic, cytotoxic, and endocrine-disrupting effects. In order to address these most urgent concerns, the use of oxidative lignin modifying enzymes for bioremediation has come into focus. These enzymes catalyze modification of phenolic and non-phenolic lignin-derived substances, and include laccase and a range of peroxidases, specifically lignin peroxidase (LiP), manganese peroxidase (MnP), versatile peroxidase (VP), and dye-decolorizing peroxidase (DyP). In this review, we explore the key pollutant-generating steps in paper and pulp processing, summarize the most recently reported toxicological effects of industrial lignin-derived phenolic compounds, especially chlorinated phenolic pollutants, and outline bioremediation approaches for pollutant mitigation in wastewater from this industry, emphasizing the oxidative catalytic potential of oxidative lignin modifying enzymes in this regard. We highlight other emerging biotechnical approaches, including phytobioremediation, bioaugmentation, Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-based technology, protein engineering, and degradation pathways prediction, that are currently gathering momentum for the mitigation of wastewater pollutants. Finally, we address current research needs and options for maximizing sustainable biobased and biocatalytic degradation of toxic industrial wastewater pollutants.

Network Analysis in Systems Epidemiology

Traditional epidemiological studies have identified a number of risk factors for various diseases using regression-based methods that examine the association between an exposure and an outcome (i.e., one-to-one correspondences). One of the major limitations of this approach is the "black-box" aspect of the analysis, in the sense that this approach cannot fully explain complex relationships such as biological pathways. With high-throughput data in current epidemiology, comprehensive analyses are needed. The network approach can help to integrate multi-omics data, visualize their interactions or relationships, and make inferences in the context of biological mechanisms. This review aims to introduce network analysis for systems epidemiology, its procedures, and how to interpret its findings.

Harnessing the natural pool of polyketide and non-ribosomal peptide family: A route map towards novel drug development

Emergence of communicable and non-communicable diseases possess health challenge to millions of people worldwide and is a major threat to the economic and social development in the coming century. The occurrence of recent pandemic, SARS-CoV-2 caused by lethal severe acute respiratory syndrome coronavirus 2 is one such example. Rapid research and development of drugs for the treatment and management of these diseases has been an incredibly challenging task for the pharmaceutical industry. Although, substantial focus has been made in the discovery of therapeutic compounds from natural sources having significant medicinal potential, their synthesis has shown a slow progress. Hence, the discovery of new targets by the application of the latest biotechnological and synthetic biology approaches is very much the need of the hour. Polyketides (PKs) and non-ribosomal peptides (NRPs) found in bacteria, fungi and plants are a large diverse family of natural products synthesized by two classes of enzymes: polyketide synthases (PKS) and non-ribosomal peptide synthetases (NRPS). These enzymes possess immense biomedical potential due to their simple architecture, catalytic capacity, as well as diversity. With the advent of latest in-silico and in-vitro strategies, these enzymes and their related metabolic pathways, if targeted, can contribute highly towards the biosynthesis of an array of potentially natural drug leads that have antagonist effects on biopolymers associated with various human diseases. In the face of the rising threat from the multidrug-resistant pathogens, this will further open new avenues for the discovery of novel and improved drugs by combining the natural and the synthetic approaches. This review discusses the relevance of polyketides and non-ribosomal peptides and the improvement strategies for the development of their derivatives and scaffolds, and how they will be beneficial to the future bioprospecting and drug discovery.

Automated engineering of synthetic metabolic pathways for efficient biomanufacturing

Metabolic engineering involves the engineering and optimization of processes from single-cell to fermentation in order to increase production of valuable chemicals for health, food, energy, materials and others. A systems approach to metabolic engineering has gained traction in recent years thanks to advances in strain engineering, leading to an accelerated scaling from rapid prototyping to industrial production. Metabolic engineering is nowadays on track towards a truly manufacturing technology, with reduced times from conception to production enabled by automated protocols for DNA assembly of metabolic pathways in engineered producer strains. In this review, we discuss how the success of the metabolic engineering pipeline often relies on retrobiosynthetic protocols able to identify promising production routes and dynamic regulation strategies through automated biodesign algorithms, which are subsequently assembled as embedded integrated genetic circuits in the host strain. Those approaches are orchestrated by an experimental design strategy that provides optimal scheduling planning of the DNA assembly, rapid prototyping and, ultimately, brings forward an accelerated Design-Build-Test-Learn cycle and the overall optimization of the biomanufacturing process. Achieving such a vision will address the increasingly compelling demand in our society for delivering valuable biomolecules in an affordable, inclusive and sustainable bioeconomy.