Molecular Systems Biology Research Articles

Integration of high-resolution mass spectrometry technology with molecular network analysis and systems biology techniques to elucidate the active ingredients and mechanisms of Shiduqing capsules.

Shiduqing Capsules, a well-known Chinese patent medicine, are widely used clinically for the treatment of pruritus. However, to date, there is a lack of research on its pharmacological substances and mechanisms of action. In the current study, the chemical components of Shiduqing Capsules were identified using UHPLC-QE-Orbitrap-MS technology. Molecular network analysis was employed to identify structurally similar compounds to the known chemical components. The potential molecular targets of the active ingredients were predicted using the SwissTargetPrediction website. The identified targets were further analyzed using gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis through the DAVID database. Molecular docking was used to validate the network pharmacology results. Ultimately, A total of 51 chemical components of Shiduqing Capsules were identified. Molecular network analysis identified 21 flavonoids and 13 terpenoids. The core targets of these ingredients include TP53, AKT1, and STAT3. GO and KEGG enrichment analysis revealed 1,371 different biological functions and 177 signaling pathways. Molecular docking confirmed the high affinity between multiple core active ingredients of Shiduqing Capsules and pruritus targets. In conclusion, the effective ingredients of Shiduqing Capsules exert a multifaceted therapeutic effect on pruritus through multiple targets and pathways.

Evolutionism and intelligent design in the writingsof Józef Życiński (1948 – 2011)

More than 150 years ago, Charles Darwin’s major work, On the Origin of Species,revolutionized science and the approach to explaining the biological complexity of theworld. This happened because Darwin was the first naturalist to propose in a systematic and scientific way a theory intended to explain the creation of all the complexityof the world without resorting to supernatural causes.66 The theory of evolution hassince become a paradigm for practicing natural sciences. However nowadays molecularbiology sheds new light on the issue of the origin of life and the evolution of the naturalworld. Hence at the end of the 20th century, the scientific theory of intelligent designwas developed in the United States, which postulates that there are very serious reasons – related to the very structure of biological molecular systems – to believe that themechanisms of life can never be explained in accordance with Darwinian principles.67The article will present the essence of evolutionism and intelligent design in the lightof Józef Życiński’s writings and their interpretations put forward by some researchers.The last part will attempt to evaluate the final conclusions presented by the philosopher

Exploring Technology-Driven Technology Roadmaps (TRM) for Wearable Biosensors in Healthcare

ObjectivesThis paper is proposed to identify both promising technologies and potential products in the domain of biosensor using patent-based Technology-Driven Technology Roadmaps (TRM). Materials and methodsThe technology-driven TRM with timelines in this study is developed in three layers: technology, function and product. Patent applications are collected and identified to interpret technologies and functions for biosensors in healthcare, and product manuals or releases can be used as product introductions. ResultsMost biosensors in healthcare patents are concentrated in biochemical (T2) and electroencephalography (T5). Glycated hemoglobin (F1), measuring of glucose (F3), and biological process and molecular systems (F6) have a relatively larger patent count. Biochemical (T2) can combine with biological process and molecular systems (F6), and then brain's real-time electrical activity monitoring can be handled. Biochemical (T2) can also devote to glycated hemoglobin (F1), and glucose monitoring (F3), and thus create QCM sensor, CGM and GlucoWatch etc. applications. ConclusionBiochemical (T2) has a wide application among different functions for wearable biosensors in healthcare. This paper identifies and explores new developments biochemical (T2), and electroencephalography (T5) in wearable biosensors are expected to play a significant role over the coming decade in improving the current healthcare infrastructure, and enhancing the democratization of information and allocation of medical resources.

The MSC-EV-microRNAome: A Perspective on Therapeutic Mechanisms of Action in Sepsis and ARDS.

Mesenchymal stromal cells (MSCs) and MSC-derived extracellular vesicles (EVs) have emerged as innovative therapeutic agents for the treatment of sepsis and acute respiratory distress syndrome (ARDS). Although their potential remains undisputed in pre-clinical models, this has yet to be translated to the clinic. In this review, we focused on the role of microRNAs contained in MSC-derived EVs, the EV microRNAome, and their potential contribution to therapeutic mechanisms of action. The evidence that miRNA transfer in MSC-derived EVs has a role in the overall therapeutic effects is compelling. However, several questions remain regarding how to reconcile the stochiometric issue of the low copy numbers of the miRNAs present in the EV particles, how different miRNAs delivered simultaneously interact with their targets within recipient cells, and the best miRNA or combination of miRNAs to use as therapy, potency markers, and biomarkers of efficacy in the clinic. Here, we offer a molecular genetics and systems biology perspective on the function of EV microRNAs, their contribution to mechanisms of action, and their therapeutic potential.

Omics approaches to unravel insecticide resistance mechanism in Bemisia tabaci (Gennadius) (Hemiptera: Aleyrodidae).

Bemisia tabaci (Gennadius) whitefly (BtWf) is an invasive pest that has already spread worldwide and caused major crop losses. Numerous strategies have been implemented to control their infestation, including the use of insecticides. However, prolonged insecticide exposures have evolved BtWf to resist these chemicals. Such resistance mechanism is known to be regulated at the molecular level and systems biology omics approaches could shed some light on understanding this regulation wholistically. In this review, we discuss the use of various omics techniques (genomics, transcriptomics, proteomics, and metabolomics) to unravel the mechanism of insecticide resistance in BtWf. We summarize key genes, enzymes, and metabolic regulation that are associated with the resistance mechanism and review their impact on BtWf resistance. Evidently, key enzymes involved in the detoxification system such as cytochrome P450 (CYP), glutathione S-transferases (GST), carboxylesterases (COE), UDP-glucuronosyltransferases (UGT), and ATP binding cassette transporters (ABC) family played key roles in the resistance. These genes/proteins can then serve as the foundation for other targeted techniques, such as gene silencing techniques using RNA interference and CRISPR. In the future, such techniques will be useful to knock down detoxifying genes and crucial neutralizing enzymes involved in the resistance mechanism, which could lead to solutions for coping against BtWf infestation.

Recent developments in heterologous production of cellulases and xylanases using the Pichia pastoris expression system

AbstractThe yeast expression system, Pichia pastoris, is one of the most robust and versatile expression systems in biotechnology and molecular biology, generally considered as a safe host for heterologous protein expression, especially for producing cellulases and xylanases at the industrial level. Despite the high recombinant protein expression rate, the potential of the P. pastoris expression system has still not been fully explored. Cultivation of P. pastoris under optimized conditions greatly relies on the strain and is associated with certain problems such as promoter strength, sensitivity to methanol, and oxygen demand. To address these issues, different genetic engineering strategies have been employed. Advancements in promoter engineering, optimization of gene dosage and codon usage, recombinant plasmid engineering using CRISPR/Cas9 system, and directed evolution strategies have proven beneficial to the yield of cellulase expression levels. This study will systemically review recent progress in various genetic engineering strategies to enhance cellulase and xylanase expression in the P. pastoris expression system. The utilization of alcohol oxidase 1 promoter (pAOX1), methanol‐free system, and recombinant plasmid engineering for improved production of these enzymes are highlighted. Additionally, we discuss the recent advancements in the P. pastoris expression system toolbox for improved cellulase and xylanase production, thus providing a deep insight into how P. pastoris is becoming the indispensable platform for heterologous protein production. © 2023 Society of Chemical Industry (SCI).

BOSC 2023, the 24th annual Bioinformatics Open Source Conference

The 24th annual Bioinformatics Open Source Conference (BOSC 2023) was part of the 2023i conference on Intelligent Systems for Molecular Biology and the European Conference on Computational Biology (ISMB/ECCB 2023). Launched in 2000 and held yearly since, BOSC is the premier meeting covering open-source bioinformatics and open science. Like ISMB 2022, the 2023 meeting was a hybrid conference, with the in-person component hosted in Lyon, France. ISMB/ECCB attracted a near-record number of attendees, with over 2100 in person and about 900 more online. Approximately 200 people participated in BOSC sessions. In addition to 43 talks and 49 posters, BOSC featured two keynotes: Sara El-Gebali, who spoke about “A New Odyssey: Pioneering the Future of Scientific Progress Through Open Collaboration”, and Joseph Yracheta, who spoke about “The Dissonance between Scientific Altruism & Capitalist Extraction: The Zero Trust and Federated Data Sovereignty Solution.” Once again, a joint session brought together BOSC and the Bio-Ontologies COSI. The conference ended with a panel on Open and Ethical Data Sharing. As in prior years, BOSC was preceded by a CollaborationFest, a collaborative work event that brought together about 40 participants interested in synergistically combining ideas, shaping project plans, developing software, and more.

Advancements in computational modelling of biological systems: seventh annual SysMod meeting

Abstract Summary The Computational Modelling of Systems Biology (SysMod) Community of Special Interest (COSI) convenes annually at the Intelligent Systems for Molecular Biology (ISMB) conference to facilitate knowledge dissemination and exchange of research findings on systems modelling from interdisciplinary domains. The SysMod meeting 2022 was held in a hybrid mode in Madison, Wisconsin, spanning a 1-day duration centred on modelling techniques, applications, and single-cell technology implementations. The meeting showcased innovative approaches to modelling biological systems using cell-specific and multiscale modelling, multiomics data integration, and novel tools to develop systems models using single-cell and multiomics technology. The meeting also recognized outstanding research by awarding the three best posters. This report summarizes the key highlights and outcomes of the meeting. Availability and implementation All resources and further information are freely accessible at https://sysmod.info.

Daily Transfers, Archiving Populations, and Measuring Fitness in the Long-Term Evolution Experiment with Escherichia coli.

The Long-Term Evolution Experiment (LTEE) has followed twelve populations of Escherichia coli as they have adapted to a simple laboratory environment for more than 35 years and 77,000 bacterial generations. The setup and procedures used in the LTEE epitomize reliable and reproducible methods for studying microbial evolution. In this protocol, we first describe how the LTEE populations are transferred to fresh medium and cultured each day. Then, we describe how the LTEE populations are regularly checked for possible signs of contamination and archived to provide a permanent frozen "fossil record" for later study. Multiple safeguards included in these procedures are designed to prevent contamination, detect various problems when they occur, and recover from disruptions without appreciably setting back the progress of the experiment. One way that the overall tempo and character of evolutionary changes are monitored in the LTEE is by measuring the competitive fitness of populations and strains from the experiment. We describe how co-culture competition assays are conducted and provide both a spreadsheet and an R package (fitnessR) for calculating relative fitness from the results. Over the course of the LTEE, the behaviors of some populations have changed in interesting ways, and new technologies like whole-genome sequencing have provided additional avenues for investigating how the populations have evolved. We end by discussing how the original LTEE procedures have been updated to accommodate or take advantage of these changes. This protocol will be useful for researchers who use the LTEE as a model system for studying connections between evolution and genetics, molecular biology, systems biology, and ecology. More broadly, the LTEE provides a tried-and-true template for those who are beginning their own evolution experiments with new microbes, environments, and questions.

Immune gene co-expression signatures implicated in occurence and persistence of cognitive dysfunction in depression

Cognitive dysfunction contributes significantly to the burden caused by Major Depressive Disorder (MDD). Yet, while compelling evidence suggests that different biological processes play a part in both MDD aetiology and the development of cognitive decline more generally, we only begin to understand the molecular underpinnings of depression-related cognitive impairment. Developments in psychometric assessments, molecular high-throughput methods and systems biology derived analysis strategies advance this endeavour.Here, we aim to identify gene expression signatures associated with cognitive dysfunction and cognitive improvement following therapy using RNA sequencing to analyze the whole blood-derived transcriptome of altogether 101 MDD patients who enrolled in the CERT-D study. The mRNA(Nova)Seq based transcriptome was analyzed from whole blood taken at baseline assessment, and patients' cognitive performance was measured twice at baseline and following eight weeks of therapy by means of the THINC integrated tool. Thirty-six patients showed comparatively low cognitive performance at baseline assessment, and 32 patients showed comparatively strong cognitive improvement following therapy. Differential gene expression analysis was performed using limma to a significance threshold of 0.05 and a logFC cutoff of |1.2|. Although we observed some indications for expression differences related to low cognitive performance and cognitive therapy response, signals did not withstand adjustment for multiple testing. Applying WGCNA, we retrieved altogether 25 modules of co-expressed genes and we used a combination of correlational and linear analyses to identify modules related to baseline cognitive performance and cognitive improvement following therapy. Three immune modules reflected distinct but interrelated immune processes (the yellow module: neutrophil-mediated immunity, the darkorange module: interferon signaling, the tan module: platelet activation), and higher expression of the yellow (r = −0.21, p < .05), the dark orange (r = 0.2, p < .05), and the tan (r = −0.23, p < .05) module correlated significantly negatively with patients' cognitive baseline performance. Patients' cognitive baseline performance was a significant predictor of the darkorange module (b = −0.039, p < .05) and the tan module's expression (b = 0.02, p < .05) and was close to becoming a significant predictor of the yellow module's expression (b = −0.02, p = .05). Furthermore, patients characterized by comparatively low cognitive performance at baseline showed significantly higher expression of the tan module when compared to all other patients F(1,97) = 4.32, p < .05, η= 0.04. Following eight weeks of treatment, we observed altogether significant improvement in patients' cognitive performance (b = 0.30, p < .001), and patients with comparatively high cognitive gain showed noticeably lower, but not significantly lower F(1,98) = 3.76, p = .058, expression of a dark turquoise module, which reflects complement and B-cell-associated immune processes. Noteworthy, the relation between cognitive performance and module expression remained observable after controlling for symptom severity and BMI, which partly accounted for variance in module expression. As such, our findings provide further evidence for the involvement of immune processes in MDD related cognitive dysfunction and they suggest that different immune processes contribute to the development and long-term persistence of cognitive dysfunction in the context of depression.

Combining iCn3D and NextStrain to create a novel undergraduate research experience around SARS-CoV-2 variants and commercial antibodies.

Undergraduate research experiences are increasingly important in biology education with efforts underway to provide more projects by embedded them in a course. The shift to online learning at the beginning of the pandemic presented a challenge. How could biology instructors provide research experiences to students who were unable to attend in-person labs? During the 2021 ISMB (Intelligent Systems for Molecular Biology) iCn3D Hackathon-Collaborative Tools for Protein Analysis-we learned about new capabilities in iCn3D for analyzing the interactions between amino acids in the paratopes of antibodies with amino acids in the epitopes of antigens and predicting the effects of mutations on binding. Additionally, new sequence alignment tools in iCn3D support aligning protein sequences with sequences in structure models. We used these methods to create a new undergraduate research project, that students could perform online as part of a course, by combining the use of new features in iCn3D with analysis tools in NextStrain, and a data set of anti-SARS-CoV-2 antibodies. We present results from an example project to illustrate how students would investigate the likelihood of SARS-CoV-2 variants escaping from commercial antibodies and use chemical interaction data to support their hypotheses. We also demonstrate that online tools (iCn3D, NextStrain, and the NCBI databases) can be used to carry out the necessary steps and that this work satisfies the requirements for course-based undergraduate research. This project reinforces major concepts in undergraduate biology-evolution and the relationship between the sequence of a protein, its three-dimensional structure, and its function.

Molecular systems biology approaches to investigate mechanisms of gut-brain communication in neurological diseases.

Whilst the incidence of neurological diseases is increasing worldwide, treatment remains mostly limited to symptom management. The gut-brain axis, which encompasses the communication routes between microbiota, gut and brain, has emerged as a crucial area of investigation for identifying new preventive and therapeutic targets in neurological disease. Due to the inter-organ, systemic nature of the gut-brain axis, together with the multitude of biomolecules and microbial species involved, molecular systems biology approaches are required to accurately investigate the mechanisms of gut-brain communication. High-throughput omics profiling, together with computational methodologies such as dimensionality reduction or clustering, machine learning, network inference and genome-scale metabolic models, allows novel biomarkers to be discovered and elucidates mechanistic insights. In this review, the general concepts of experimental and computational methodologies for gut-brain axis research are introduced and their applications are discussed, mainly in human cohorts. Important aspects are further highlighted concerning rational study design, sampling procedures and data modalities relevant for gut-brain communication, strengths and limitations of methodological approaches and some future perspectives. Multi-omics analyses, together with advanced data mining, are essential to functionally characterize the gut-brain axis and put forward novel preventive or therapeutic strategies in neurological disease.

A simple, dual direct expression plasmid system in prokaryotic and mammalian cells.

We introduce a simple, dual direct cloning plasmid system (pgMAX-II) for gene expression analysis in both prokaryotic (Escherichia coli) and mammalian cells. This system, which uses a prokaryotic expression unit adapted from the pgMAX system and a mammalian promoter, is effective for subcloning using the DNA topoisomerase II toxin CcdB. Given that molecular biological cloning systems broadly rely on E. coli for rapid growth, the proposed concept may have wide applicability beyond mammalian cells.

Growing Directed Acyclic Graphs: Optimization Functions for Pathway Reconstruction Algorithms.

A major challenge in molecular systems biology is to understand how proteins work to transmit external signals to changes in gene expression. Computationally reconstructing these signaling pathways from protein interaction networks can help understand what is missing from existing pathway databases. We formulate a new pathway reconstruction problem, one that iteratively grows directed acyclic graphs (DAGs) from a set of starting proteins in a protein interaction network. We present an algorithm that provably returns the optimal DAGs for two different cost functions and evaluate the pathway reconstructions when applied to six diverse signaling pathways from the NetPath database. The optimal DAGs outperform an existing k-shortest paths method for pathway reconstruction, and the new reconstructions are enriched for different biological processes. Growing DAGs is a promising step toward reconstructing pathways that provably optimize a specific cost function.

Archaea as a Model System for Molecular Biology and Biotechnology.

Archaea represents the third domain of life, displaying a closer relationship with eukaryotes than bacteria. These microorganisms are valuable model systems for molecular biology and biotechnology. In fact, nowadays, methanogens, halophiles, thermophilic euryarchaeota, and crenarchaeota are the four groups of archaea for which genetic systems have been well established, making them suitable as model systems and allowing for the increasing study of archaeal genes' functions. Furthermore, thermophiles are used to explore several aspects of archaeal biology, such as stress responses, DNA replication and repair, transcription, translation and its regulation mechanisms, CRISPR systems, and carbon and energy metabolism. Extremophilic archaea also represent a valuable source of new biomolecules for biological and biotechnological applications, and there is growing interest in the development of engineered strains. In this review, we report on some of the most important aspects of the use of archaea as a model system for genetic evolution, the development of genetic tools, and their application for the elucidation of the basal molecular mechanisms in this domain of life. Furthermore, an overview on the discovery of new enzymes of biotechnological interest from archaea thriving in extreme environments is reported.

Noncovalent tailoring of coordination complexes by resorcin[4]arene-based supramolecular hosts.

Molecular recognition of guest molecules in a confined cavity is one of the important phenomena in biological and artificial molecular systems. When the guest is trapped within an artificial nano-space, its conformation is fixed in an unusual fashion by noncovalent interactions with host frameworks, and also the guest is kept away from the bulk solvent by the steric effect of the host. Therefore, host-guest formations lead to the effective modulation of the chemical and physical properties of guests via noncovalent interactions. In contrast to the many examples of organic guests, the examples of host-guest formation using coordination complex guests have been less explored. This is simply due to the size and shape complementarity problem between small hosts and large coordination complex guests. Resorcin[4]arene-based supramolecular hosts have been shown to provide internal cavities that are large enough to fully accommodate coordination complexes within the internal spaces via effective molecular interactions. In this article, we focus on supramolecular strategies to control the chemical and physical properties of the coordination complex guests within resorcin[4]arene-based supramolecular hosts. By the careful selection of the host and guest complexes, these combinations can produce a new supramolecular system, showing unusual structures, redox, catalytic, and photophysical properties derived from the entrapped coordination complexes.

Affinity-Based Interactome Analysis of Endogenous LINE-1 Macromolecules.

During their proliferation and the host's concomitant attempts to suppress it, LINE-1 (L1) retrotransposons give rise to a collection of heterogeneous ribonucleoproteins (RNPs); their protein and RNA compositions remain poorly defined. The constituents of L1-associated macromolecules can differ depending on numerous factors, including, for example, position within the L1 life cycle, whether the macromolecule is productive or under suppression, and the cell type within which the proliferation is occurring. This chapter describes techniques that aid the capture and characterization of protein and RNA components of L1 macromolecules from tissues that natively express them. The protocols described have been applied to embryonal carcinoma cell lines that are popular model systems for L1 molecular biology (e.g., N2102Ep, NTERA-2, and PA-1 cells), as well as colorectal cancer tissues. N2102Ep cells are given as the use case for this chapter; the protocols should be applicable to essentially any tissue exhibiting endogenous L1 expression with minor modifications.

Immune response in retinal degenerative diseases - Time to rethink?

Retinal degeneration comprises a group of diseases whereby either the retinal neurons or the neurovascular unit degenerates leading to the loss of visual function. Although the initial cause varies in different conditions, inflammation is known to play an important role in disease pathogenesis. Recent advances in molecular and cell biology and systems biology have yielded unexpected findings, including the heterogeneity of immune cells in the degenerative retina, bidirectional neuron-microglia cross talk, and links to the gut microbiome. Here we discuss the immune response in retinal degenerative conditions, taking into account both regional (retinal) and systemic factors. We propose to classify retinal degeneration into dry and wet forms based on whether the blood-retinal barrier (BRB) is breached and fluid is accumulated in retinal parenchyma. The dry form has a relatively intact BRB and is characterised by progressive retinal thinning. Immune response to degenerative insults is dominated by the retinal defence system, which remains to be regulated by neurons. In contrast, the wet form has retinal oedema due to BRB damaged. Inflammation is executed by infiltrating immune cells as well as the retinal defence system. The gut microbiome will have easy access to the retina in wet retinal degeneration and may affect significantly retinal immune response.

CanSAR: update to the cancer translational research and drug discovery knowledgebase.

canSAR (https://cansar.ai) is the largest public cancer drug discovery and translational research knowledgebase. Now hosted in its new home at MD AndersonCancer Center,canSAR integrates billions of experimental measurements from across molecular profiling, pharmacology, chemistry, structural and systems biology. Moreover, canSAR applies a unique suite of machine learning algorithms designed to inform drug discovery. Here, we describe the latest updates to the knowledgebase, including a focus on significant novel data. These include canSAR's ligandability assessment of AlphaFold; mapping of fragment-based screening data; and new chemical bioactivity data for novel targets. We also describe enhancements to the data and interface.

EPCO-16. ESTIMATING THE DIFFERENTIATION POTENTIAL AND PLASTICITY OF GLIOBLASTOMA CELLS USING STATISTICAL MECHANICS

Abstract Glioblastoma (GBM) is the most common form of adult brain tumors and patients diagnosed with GBM face a very dismal prognosis with a median survival rate of 15 months. The challenges in treating GBM are many, there is a complex cellular and molecular heterogeneity, both between patients and within individual tumors. In a single tumor, individual cancer cells adopt a variety of stem-like, progenitor-like, and more differentiated phenotypes (a.k.a. cell states). Recent advances in single-cell technology have made it possible to detect this diversity of cellular states, but yet we lack a good explanation for how the states arise and if transitions between states can be exploited to therapeutic ends. We here propose a new computational strategy to measure the differentiation potential of GBM cells, based on the Ising models from statistical mechanics. Originally used to model coupled magnets on a lattice, the Ising models can be generalized to represent the probability of a particular configuration of any system of n variables. We show that the models can be fitted to high-dimensional single cell data in a matter of seconds by solving a convex optimization problem, and that the models help explain cell differentiation in terms of minimization of an energy function of the system. In our on-going work we have fitted the model to single cell data from our previous work on state transitions in GBM (Larsson, Dalmo et al, Molecular Systems Biology 2021) and shown that the predicted differentiation potential of cell states is consistent with the cell state hierarchies derived from lineage tracing experiments. Moving forward, we are now exploring how the Ising models can be used to model state transitions in GBM and to predict the effect of targeted therapy on the cell’s differentiation potential.