A novel application of Gini coefficient for the quantitative measurement of bacterial aggregation

Dispersion indices for universal quantification of fluorescently-labelled subcellular structure spatial distributions

In biology, accurate and robust quantification of biological images is critical for understanding distribution patterns and heterogeneity of subcellular structures within a cell. While various methods tailored to specific biological contexts have been employed for image analysis, there is a need for versatile approaches that transcend the constraints imposed by the intricacies of different biological systems. Here we report the application of dispersion indices - a statistical concept widely used to measure the income distribution within a population by economists - as a powerful and agnostic tool for quantifying biological images, which offers distinct advantages over traditional methods. In our approach, we substitute pixel intensity for income and number of pixels for population. We demonstrate the utility of dispersion indices in quantifying autophagic puncta, mitochondrial clustering, and microtubule dynamics, all of which are key measures relevant for maladies ranging from metabolic and neuronal diseases to cancer. Further, we show utility in 2D cell cultures and a 3D multicellular midbrain culture as well as measurement of a performance metric such as a half maximal effective concentration value (EC50).

Multicellular dynamics and wealth distribution in bacteria

KR Allison discusses a dynamic model of multicellular “patches” of bacteria upon antibiotic treatment to show beneficial community interactions support their collective survival as reported by Şimşek et al in this issue of Molecular Systems Biology .

Transcriptomic and metabolomic analyses to study the key role by which Ralstonia insidiosa induces Listeria monocytogenes to form suspended aggregates

Ralstonia insidiosa can survive in a wide range of aqueous environments, including food processing areas, and is harmful to humans. It can induce Listeria monocytogenes to form suspended aggregates, resulting from the co-aggregation of two bacteria, which allows for more persistent survival and increases the risk of L. monocytogenes contamination. In our study, different groups of aggregates were analyzed and compared using Illumina RNA sequencing technology. These included R. insidiosa under normal and barren nutrient conditions and in the presence or absence of L. monocytogenes as a way to screen for differentially expressed genes (DEGs) in the process of aggregate formation. In addition, sterile supernatants of R. insidiosa were analyzed under different nutrient conditions using metabolomics to investigate the effect of nutrient-poor conditions on metabolite production by R. insidiosa. We also undertook a combined analysis of transcriptome and metabolome data to further investigate the induction effect of R. insidiosa on L. monocytogenes in a barren environment. The results of the functional annotation analysis on the surface of DEGs and qPCR showed that under nutrient-poor conditions, the acdx, puuE, and acs genes of R. insidiosa were significantly upregulated in biosynthetic processes such as carbon metabolism, metabolic pathways, and biosynthesis of secondary metabolites, with Log2FC reaching 4.39, 3.96, and 3.95 respectively. In contrast, the Log2FC of cydA, cyoB, and rpsJ in oxidative phosphorylation and ribosomal pathways reached 3.74, 3.87, and 4.25, respectively. Thirty-one key components were identified while screening for differential metabolites, which mainly included amino acids and their metabolites, enriched to the pathways of biosynthesis of amino acids, phenylalanine metabolism, and methionine metabolism. Of these, aminomalonic acid and Proximicin B were the special components of R. insidiosa that were metabolized under nutrient-poor conditions.

The buffering effect of social capital for daily mental stress in an unequal society: a lesson from Seoul

This study attempted to illustrate whether mental health deterioration could be alleviated by high social capital in an environment with high economic inequality. Daily mental stress was employed as a mental health factor when analyzing the association with economic inequality in the Seoul Survey data. Regarding social capital, community trust and altruism were included as cognitive dimensions, and participation and cooperation were included as structural dimensions in each model. The first finding showed a significantly positive relationship between economic inequality and daily stress, meaning that, like other mental health problems, daily mental stress is also high in regions with high economic inequality. Second, the slope of the daily stress increased in respondents with high social trust and participation was alleviated in an economically unequal environment. This indicates that social trust and participation have a buffering effect by moderating the slope of daily stress in societies with high inequality. Third, the buffering effect differs depending on the social capital factor. The buffering effect of trust and participation showed in an unequal environment, while the buffering effect of cooperation showed regardless of the unequal environment. In summary, social capital factors showed the effect of relieving daily mental stress in the relationship with economic inequality. Also, the buffering effect of social capital on mental health may show different aspects for each element.

Micron-Scale Biogeography of Seawater Biofilm Colonies at Submersed Solid Substrata Affected by Organic Matter and Microbiome Transformation in the Baltic Sea

The aim of this research was to determine temporal and spatial evolution of biofilm architecture formed at model solid substrata submersed in Baltic sea coastal waters in relation to organic matter transformation along a one-year period. Several materials (metals, glass, plastics) were deployed for a certain time, and the collected biofilm-covered samples were studied with a confocal microscopy technique using the advanced programs of image analysis. The geometric and structural biofilm characteristics: biovolume, coverage fraction, mean thickness, spatial heterogeneity, roughness, aggregation coefficient, etc., turned out to evolve in relation to organic matter transformation trends, trophic water status, microbiome evolution, and biofilm micro-colony transition from the heterotrophic community (mostly bacteria) to autotrophic (diatom-dominated) systems. The biofilm morphology parameters allowed the substratum roughness, surface wettability, chromatic organisms colony adaptation to substrata, and quorum sensing or cell to cell signaling effects to be quantitatively evaluated. In addition to the previous work, the structural biofilm parameters could become further novel trophic state indicators.

The c-di-GMP Phosphodiesterase PipA (PA0285) Regulates Autoaggregation and Pf4 Bacteriophage Production in Pseudomonas aeruginosa PAO1

In Pseudomonas aeruginosa PAO1, 41 genes encode proteins predicted to be involved in the production or degradation of c-di-GMP, a ubiquitous secondary messenger that regulates a variety of physiological behaviors closely related to biofilm and aggregate formation. Despite extensive effort, the entire picture of this important signaling network is still unclear, with one-third of these proteins remaining uncharacterized. Here, we show that the deletion of pipA, which produces a protein containing two PAS domains upstream of a GGDEF-EAL tandem, significantly increased the intracellular c-di-GMP level and promoted the formation of aggregates both on surfaces and in planktonic cultures. However, this regulatory effect was not contributed by either of the two classic pathways modulating biofilm formation, exopolysaccharide (EPS) overproduction or motility inhibition. Transcriptome sequencing (RNA-Seq) data revealed that the expression levels of 361 genes were significantly altered in a ΔpipA mutant strain compared to the wild type (WT), indicating the critical role of PipA in PAO1. The most remarkably downregulated genes were located on the Pf4 bacteriophage gene cluster, which corresponded to a 2-log reduction in the Pf4 phage production in the ΔpipA mutant. The sizes of aggregates in ΔpipA cultures were affected by exogenously added Pf4 phage in a concentration-dependent manner, suggesting the quantity of phage plays a part in regulating the formation of aggregates. Further analysis demonstrated that PipA is highly conserved across 83 P. aeruginosa strains. Our work therefore for the first time showed that a c-di-GMP phosphodiesterase can regulate bacteriophage production and provided new insights into the relationship between bacteriophage and bacterial aggregation. IMPORTANCE The c-di-GMP signaling pathways in P. aeruginosa are highly organized and well coordinated, with different diguanylate cyclases and phosphodiesterases playing distinct roles in a complex network. Understanding the function of each enzyme and the underlying regulatory mechanisms not only is crucial for revealing how bacteria decide the transition between motile and sessile lifestyles, but also greatly facilitates the development of new antibiofilm strategies. This work identified bacteriophage production as a novel phenotypic output controlled transcriptionally by a phosphodiesterase, PipA. Further analysis suggested that the quantity of phage may be important in regulating autoaggregation, as either a lack of phage or overproduction was associated with higher levels of aggregation. Our study therefore extended the scope of c-di-GMP-controlled phenotypes and discovered a potential signaling circuit that can be target for biofilm treatment.

Exploiting cheminformatic and machine learning to navigate the available chemical space of potential small molecule inhibitors of SARS-CoV-2

The current life-threatening and tenacious pandemic eruption of coronavirus disease in 2019 (COVID-19) has posed a significant global hazard concerning high mortality rate, economic meltdown, and everyday life distress. The rapid spread of COVID-19 demands countermeasures to combat this deadly virus. Currently, there are no drugs approved by the FDA to treat COVID-19. Therefore, discovering small molecule therapeutics for treating COVID-19 infection is essential. So far, only a few small molecule inhibitors are reported for coronaviruses. There is a need to expand the small chemical space of coronaviruses inhibitors by adding potent and selective scaffolds with anti-COVID activity. In this context, the huge antiviral chemical space already available can be analysed using cheminformatic and machine learning to unearth new scaffolds. We created three specific datasets called "antiviral dataset" (N = 38,428) "drug-like antiviral dataset" (N = 20,963) and "anticorona dataset" (N = 433) for this purpose. We analyzed the 433 molecules of "anticorona dataset" for their scaffold diversity, physicochemical distributions, principal component analysis, activity cliffs, R-group decomposition, and scaffold mapping. The scaffold diversity of the "anticorona dataset" in terms of Murcko scaffold analysis demonstrates a thorough representation of diverse chemical scaffolds. However, physicochemical descriptor analysis and principal component analysis demonstrated negligible drug-like features for the "anticorona dataset" molecules. The "antiviral dataset" and "drug-like antiviral dataset" showed low scaffold diversity as measured by the Gini coefficient. The hierarchical clustering of the "antiviral dataset" against the "anticorona dataset" demonstrated little molecular similarity. We generated a library of frequent fragments and polypharmacological ligands targeting various essential viral proteins such as main protease, helicase, papain-like protease, and replicase polyprotein 1ab. Further structural and chemical features of the "anticorona dataset" were compared with SARS-CoV-2 repurposed drugs, FDA-approved drugs, natural products, and drugs currently in clinical trials. Using machine learning tool DCA (DMax Chemistry Assistant), we converted the "anticorona dataset" into an elegant hypothesis with significant functional biological relevance. Machine learning analysis uncovered that FDA approved drugs, Tizanidine HCl, Cefazolin, Raltegravir, Azilsartan, Acalabrutinib, Luliconazole, Sitagliptin, Meloxicam (Mobic), Succinyl sulfathiazole, Fluconazole, and Pranlukast could be repurposed as effective drugs for COVID-19. Fragment-based scaffold analysis and R-group decomposition uncovered pyrrolidine and the indole molecular scaffolds as the potent fragments for designing and synthesizing the novel drug-like molecules for targeting SARS-CoV-2. This comprehensive and systematic assessment of small-molecule viral therapeutics' entire chemical space realised critical insights to potentially privileged scaffolds that could aid in enrichment and rapid discovery of efficacious antiviral drugs for COVID-19.

Non-surface Attached Bacterial Aggregates: A Ubiquitous Third Lifestyle

Bacteria are now generally believed to adopt two main lifestyles: planktonic individuals, or surface-attached biofilms. However, in recent years medical microbiologists started to stress that suspended bacterial aggregates are a major form of bacterial communities in chronic infection sites. Despite sharing many similarities with surface-attached biofilms and are thus generally defined as biofilm-like aggregates, these non-attached clumps of cells in vivo show much smaller sizes and different formation mechanisms. Furthermore, ex vivo clinical isolates were frequently reported to be less attached to abiotic surfaces when compared to standard type strains. While this third lifestyle is starting to draw heavy attention in clinical studies, it has a long history in natural and environmental sciences. For example, marine gel particles formed by bacteria attachment to phytoplankton exopolymers have been well documented in oceans; large river and lake snows loaded with bacterial aggregates are frequently found in freshwater systems; multispecies bacterial "flocs" have long been used in wastewater treatment. This review focuses on non-attached aggregates found in a variety of natural and clinical settings, as well as some recent technical developments facilitating aggregate research. The aim is to summarise the characteristics of different types of bacterial aggregates, bridging the knowledge gap, provoking new perspectives for researchers from different fields, and highlighting the importance of more research input in this third lifestyle of bacteria closely relevant to our daily life.

Gini Coefficients as a Single Value Metric to Define Chemical Probe Selectivity

Selectivity is a key requirement of high-quality chemical probes and lead medicines; however, methods to quantify and compare the selectivity of small molecules have not been standardized across the field. Herein, we discuss the origins and use of a comprehensive, single value term to quantify selectivity, the Gini coefficient. Case studies presented include compounds that target protein kinases, small molecules that bind RNA structures, and small molecule chimeras that bind to and degrade the target RNA. With an increasing number of transcriptome- and proteome-wide studies, we submit that reporting Gini coefficients as a quantitative descriptor of selectivity should be used broadly.