Exploiting fly models to investigate rare human neurological disorders

Rare neurological diseases, while individually are rare, collectively impact millions globally, leading to diverse and often severe neurological symptoms. Often attributed to genetic mutations that disrupt protein function or structure, understanding their genetic basis is crucial for accurate diagnosis and targeted therapies. To investigate the underlying pathogenesis of these conditions, researchers often use non-mammalian model organisms, such as Drosophila (fruit flies), which is valued for their genetic manipulability, cost-efficiency, and preservation of genes and biological functions across evolutionary time. Genetic tools available in Drosophila, including CRISPR-Cas9, offer a means to manipulate gene expression, allowing for a deep exploration of the genetic underpinnings of rare neurological diseases. Drosophila boasts a versatile genetic toolkit, rapid generation turnover, and ease of large-scale experimentation, making it an invaluable resource for identifying potential drug candidates. Researchers can expose flies carrying disease-associated mutations to various compounds, rapidly pinpointing promising therapeutic agents for further investigation in mammalian models and, ultimately, clinical trials. In this comprehensive review, we explore rare neurological diseases where fly research has significantly contributed to our understanding of their genetic basis, pathophysiology, and potential therapeutic implications. We discuss rare diseases associated with both neuron-expressed and glial-expressed genes. Specific cases include mutations in CDK19 resulting in epilepsy and developmental delay, mutations in TIAM1 leading to a neurodevelopmental disorder with seizures and language delay, and mutations in IRF2BPL causing seizures, a neurodevelopmental disorder with regression, loss of speech, and abnormal movements. And we explore mutations in EMC1 related to cerebellar atrophy, visual impairment, psychomotor retardation, and gain-of-function mutations in ACOX1 causing Mitchell syndrome. Loss-of-function mutations in ACOX1 result in ACOX1 deficiency, characterized by very-long-chain fatty acid accumulation and glial degeneration. Notably, this review highlights how modeling these diseases in Drosophila has provided valuable insights into their pathophysiology, offering a platform for the rapid identification of potential therapeutic interventions. Rare neurological diseases involve a wide range of expression systems, and sometimes common phenotypes can be found among different genes that cause abnormalities in neurons or glia. Furthermore, mutations within the same gene may result in varying functional outcomes, such as complete loss of function, partial loss of function, or gain-of-function mutations. The phenotypes observed in patients can differ significantly, underscoring the complexity of these conditions. In conclusion, Drosophila represents an indispensable and cost-effective tool for investigating rare neurological diseases. By facilitating the modeling of these conditions, Drosophila contributes to a deeper understanding of their genetic basis, pathophysiology, and potential therapies. This approach accelerates the discovery of promising drug candidates, ultimately benefiting patients affected by these complex and understudied diseases.

scIALM: A method for sparse scRNA-seq expression matrix imputation using the Inexact Augmented Lagrange Multiplier with low error

Single-cell RNA sequencing (scRNA-seq) is a high-throughput sequencing technology that quantifies gene expression profiles of specific cell populations at the single-cell level, providing a foundation for studying cellular heterogeneity and patient pathological characteristics. It is effective for developmental, fertility, and disease studies. However, the cell-gene expression matrix of single-cell sequencing data is often sparse and contains numerous zero values. Some of the zero values derive from noise, where dropout noise has a large impact on downstream analysis. In this paper, we propose a method named scIALM for imputation recovery of sparse single-cell RNA data expression matrices, which employs the Inexact Augmented Lagrange Multiplier method to use sparse but clean (accurate) data to recover unknown entries in the matrix. We perform experimental analysis on four datasets, calling the expression matrix after Quality Control (QC) as the original matrix, and comparing the performance of scIALM with six other methods using mean squared error (MSE), mean absolute error (MAE), Pearson correlation coefficient (PCC), and cosine similarity (CS). Our results demonstrate that scIALM accurately recovers the original data of the matrix with an error of 10e-4, and the mean value of the four metrics reaches 4.5072 (MSE), 0.765 (MAE), 0.8701 (PCC), 0.8896 (CS). In addition, at 10%-50% random masking noise, scIALM is the least sensitive to the masking ratio. For downstream analysis, this study uses adjusted rand index (ARI) and normalized mutual information (NMI) to evaluate the clustering effect, and the results are improved on three datasets containing real cluster labels.

Emergent microenvironments of nucleoli

In higher eukaryotes, the nucleolus harbors at least three sub-phases that facilitate multiple functionalities including ribosome biogenesis. The three prominent coexisting sub-phases are the fibrillar center (FC), the dense fibrillar component (DFC), and the granular component (GC). Here, we review recent efforts in profiling sub-phase compositions that shed light on the types of physicochemical properties that emerge from compositional biases and territorial organization of specific types of macromolecules. We highlight roles played by molecular grammars which refers to protein sequence features including the substrate binding domains, the sequence features of intrinsically disordered regions, and the multivalence of these distinct types of domains / regions. We introduce the concept of a barcode of emergent physicochemical properties of nucleoli. Although our knowledge of the full barcode remains incomplete, we hope that the concept prompts investigations into undiscovered emergent properties and engenders an appreciation for how and why unique microenvironments control biochemical reactions.

Deciphering and designing microbial communities by genome-scale metabolic modelling

Microbial communities are shaped by the complex interactions among organisms and the environment. Genome-scale metabolic models (GEMs) can provide deeper insights into the complexity and ecological properties of various microbial communities, revealing their intricate interactions. Many researchers have modified GEMs for the microbial communities based on specific needs. Thus, GEMs need to be comprehensively summarized to better understand the trends in their development. In this review, we summarized the key developments in deciphering and designing microbial communities using different GEMs. A timeline of selected highlights in GEMs indicated that this area is evolving from the single-strain level to the microbial community level. Then, we outlined a framework for constructing GEMs of microbial communities. We also summarized the models and resources of static and dynamic community-level GEMs. We focused on the role of external environmental and intracellular resources in shaping the assembly of microbial communities. Finally, we discussed the key challenges and future directions of GEMs, focusing on the integration of GEMs with quorum sensing mechanisms, microbial ecology interactions, machine learning algorithms, and automatic modeling, all of which contribute to consortia-based applications in different fields.

Comparison of two peroxidases with high potential for biotechnology applications - HRP vs. APEX2

Peroxidases are essential elements in many biotechnological applications. An especially interesting concept involves split enzymes, where the enzyme is separated into two smaller and inactive proteins that can dimerize into a fully active enzyme. Such split forms were developed for the horseradish peroxidase (HRP) and ascorbate peroxidase (APX) already. Both peroxidases have a high potential for biotechnology applications. In the present study, we performed biophysical comparisons of these two peroxidases and their split analogues. The active site availability is similar for all four structures. The split enzymes are comparable in stability with their native analogues, meaning that they can be used for further biotechnology applications. Also, the tertiary structures of the two peroxidases are similar. However, differences that might help in choosing one system over another for biotechnology applications were noticed. The main difference between the two systems is glycosylation which is not present in the case of APX/sAPEX2, while it has a high impact on the HRP/sHRP stability. Further differences are calcium ions and cysteine bridges that are present only in the case of HRP/sHRP. Finally, computational results identified sAPEX2 as the systems with the smallest structural variations during molecular dynamics simulations showing its dominant stability comparing to other simulated proteins. Taken all together, the sAPEX2 system has a high potential for biotechnological applications due to the lack of glycans and cysteines, as well as due to high stability.

High-yield synthesis of 2-O-α-D-glucosyl-D-glycerate by a bifunctional glycoside phosphorylase

Osmolytes are produced by various microorganisms as a defense mechanism to protect cells and macromolecules from damage caused by external stresses in harsh environments. Due to their useful stabilizing properties, these molecules are applied as active ingredients in a wide range of cosmetics and healthcare products. The metabolic pathways and biocatalytic syntheses of glycosidic osmolytes such as 2-O-α-D-glucosyl-D-glycerate often involve the action of a glycoside phosphorylase. Here, we report the discovery of a glucosylglycerate phosphorylase from carbohydrate-active enzyme family GH13 that is also active on sucrose, which contrasts the strict specificity of known glucosylglycerate phosphorylases that can only use α-D-glucose 1-phosphate as glycosyl donor in transglycosylation reactions. The novel enzyme can be distinguished from other phosphorylases from the same family by the presence of an atypical conserved sequence motif at specificity-determining positions in the active site. The promiscuity of the sucrose-active glucosylglycerate phosphorylase can be exploited for the high-yielding and rapid synthesis of 2-O-α-D-glucosyl-D-glycerate from sucrose and D-glycerate. KEY POINTS: • A Xylanimonas protaetiae glycoside phosphorylase can use both d-glycerate and fructose as glucosyl acceptor with high catalytic efficiency • Biocatalytic synthesis of the osmolyte 2-O-α-d-glucosyl-d-glycerate • Positions in the active site of GH13 phosphorylases act as convenient specificity fingerprints.

Cut&tag: a powerful epigenetic tool for chromatin profiling

Analysis of transcription factors and chromatin modifications at the genome-wide level provides insights into gene regulatory processes, such as transcription, cell differentiation and cellular response. Chromatin immunoprecipitation is the most popular and powerful approach for mapping chromatin, and other enzyme-tethering techniques have recently become available for living cells. Among these, Cleavage Under Targets and Tagmentation (CUT&Tag) is a relatively novel chromatin profiling method that has rapidly gained popularity in the field of epigenetics since 2019. It has also been widely adapted to map chromatin modifications and TFs in different species, illustrating the association of these chromatin epitopes with various physiological and pathological processes. Scalable single-cell CUT&Tag can be combined with distinct platforms to distinguish cellular identity, epigenetic features and even spatial chromatin profiling. In addition, CUT&Tag has been developed as a strategy for joint profiling of the epigenome, transcriptome or proteome on the same sample. In this review, we will mainly consolidate the applications of CUT&Tag and its derivatives on different platforms, give a detailed explanation of the pros and cons of this technique as well as the potential development trends and applications in the future.

Gene choice in cancer cells is exclusive in ion transport but concurrent in DNA replication

Cancers share common cellular and physiological features. Little is known about whether distinctive gene expression patterns can be displayed at the single-cell level by gene families in cancer cells. The expression of gene homologs within a family can exhibit concurrence and exclusivity. Concurrence can promote all-or-none expression patterns of related genes and underlie alternative physiological states. Conversely, exclusive gene families express the same or similar number of homologs in each cell, allowing a broad repertoire of cell identities to be generated. We show that gene families involved in the cell-cycle and antigen presentation are expressed concurrently. Concurrence in the DNA replication complex MCM reflects the replicative status of cells, including cell lines and cancer-derived organoids. Exclusive expression requires precise regulatory mechanism, but cancer cells retain this form of control for ion homeostasis and extend it to gene families involved in cell migration. Thus, the cell adhesion-based identity of healthy cells is transformed to an identity based on migration in the population of cancer cells, reminiscent of epithelial-mesenchymal transition.

Identification and validation of protein biomarkers for predicting gastrointestinal stromal tumor recurrence

We conducted a proteomic analysis using mass spectrometry to identify and validate protein biomarkers for accurately predicting recurrence risk in gastrointestinal stromal tumors (GIST) patients, focusing on differentially expressed proteins in metastatic versus primary GIST tissues. We selected five biomarkers-GPX4, RBM4, TPM3, PFKFB2, and PGAM5-and validated their expressions in primary tumors of recurrent and non-recurrent GIST patients via immunohistochemistry. Our analysis of the association between these biomarkers with recurrence-free survival (RFS) and overall survival (OS), along with their interrelationships, revealed that immunohistochemistry confirmed significantly higher expressions of these biomarkers in primary GIST tissues of recurrent patients. Kaplan-Meier survival analysis showed that high expressions of GPX4, RBM4, TPM3, PFKFB2, and PGAM5 correlated with lower RFS, and GPX4 and RBM4 with lower OS. All biomarker pairs showed positive associations, with high expressions correlating with increased recurrence rates, and GPX4 and RBM4 with higher mortality rates. In conclusion, the biomarkers GPX4, RBM4, TPM3, PFKFB2, and PGAM5 are clinically relevant for predicting GIST recurrence, with their high expressions in primary tumors linked to poorer RFS and OS. They serve as potential prognostic indicators, enabling early treatment and improved outcomes. The observed interrelationships among these biomarkers further validate their accuracy in predicting GIST recurrence.

Node features of chromosome structure networks and their connections to genome annotation

The 3D conformations of chromosomes can encode biological significance, and the implications of such structures have been increasingly appreciated recently. Certain chromosome structural features, such as A/B compartmentalization, are frequently extracted from Hi-C pairwise genome contact information (physical association between different regions of the genome) and compared with linear annotations of the genome, such as histone modifications and lamina association. We investigate how additional properties of chromosome structure can be deduced using an abstract graph representation of the contact heatmap, and describe specific network properties that can have a strong connection with some of these biological annotations. We constructed chromosome structure networks (CSNs) from bulk Hi-C data and calculated a set of site-resolved (node-based) network properties. These properties are useful for characterizing certain aspects of chromosomal structure. We examined the ability of network properties to differentiate several scenarios, such as haploid vs diploid cells, partially inverted nuclei vs conventional architecture, depletion of chromosome architectural proteins, and structural changes during cell development. We also examined the connection between network properties and a series of other linear annotations, such as histone modifications and chromatin states including poised promoter and enhancer labels. We found that semi-local network properties exhibit greater capability in characterizing genome annotations compared to diffusive or ultra-local node features. For example, the local square clustering coefficient can be a strong classifier of lamina-associated domains. We demonstrated that network properties can be useful for highlighting large-scale chromosome structure differences that emerge in different biological situations.

The lethal K18-hACE2 knock-in mouse model mimicking the severe pneumonia of COVID-19 is practicable for antiviral development

Animal models of COVID-19 facilitate the development of vaccines and antivirals against SARS-CoV-2. The efficacy of antivirals or vaccines may differ in different animal models with varied degrees of disease. Here, we introduce a mouse model expressing human angiotensin-converting enzyme 2 (ACE2). In this model, ACE2 with the human cytokeratin 18 promoter was knocked into the Hipp11 locus of C57BL/6J mouse by CRISPR - Cas9 (K18-hACE2 KI). Upon intranasal inoculation with high (3 × 105 PFU) or low (2.5 × 102 PFU) dose of SARS-CoV-2 wildtype (WT), Delta, Omicron BA.1, or Omicron BA.2 variants, all mice showed obvious infection symptoms, including weight loss, high viral loads in the lung, and interstitial pneumonia. 100% lethality was observed in K18-hACE2 KI mice infected by variants with a delay of endpoint for Delta and BA.1, and a significantly attenuated pathogenicity was observed for BA.2. The pneumonia of infected mice was accompanied by the infiltration of neutrophils and pulmonary fibrosis in the lung. Compared with K18-hACE2 Tg mice and HFH4-hACE2 Tg mice, K18-hACE2 KI mice are more susceptible to SARS-CoV-2. In the antivirals test, REGN10933 and Remdesivir had limited antiviral efficacies in K18-hACE2 KI mice upon the challenge of SARS-CoV-2 infections, while Nirmatrelvir, monoclonal antibody 4G4, and mRNA vaccines potently protected the mice from death. Our results suggest that the K18-hACE2 KI mouse model is lethal and stable for SARS-CoV-2 infection, and is practicable and stringent to antiviral development.

The astrocyte-enriched gene deathstar plays a crucial role in the development, locomotion, and lifespan of D. melanogaster

The Drosophila melanogaster brain is a complex organ with various cell types, orchestrating the development, physiology, and behaviors of the fly. While each cell type in Drosophila brain is known to express a unique gene set, their complete genetic profile is still unknown. Advances in the RNA sequencing techniques at single-cell resolution facilitate identifying novel cell type markers and/or re-examining the specificity of the available ones. In this study, exploiting a single-cell RNA sequencing data of Drosophila optic lobe, we categorized the cells based on their expression pattern for known markers, then the genes with enriched expression in astrocytes were identified. CG11000 was identified as a gene with a comparable expression profile to the Eaat1 gene, an astrocyte marker, in every individual cell inside the Drosophila optic lobe and midbrain, as well as in the entire Drosophila brain throughout its development. Consistent with our bioinformatics data, immunostaining of the brains dissected from transgenic adult flies showed co-expression of CG11000 with Eaat1 in a set of single cells corresponding to the astrocytes in the Drosophila brain. Physiologically, inhibiting CG11000 through RNA interference disrupted the normal development of male D. melanogaster, while having no impact on females. Expression suppression of CG11000 in adult flies led to decreased locomotion activity and also shortened lifespan specifically in astrocytes, indicating the gene's significance in astrocytes. We designated this gene as 'deathstar' due to its crucial role in maintaining the star-like shape of glial cells, astrocytes, throughout their development into adult stage.

Transcriptional response of Saccharomyces cerevisiae to lactic acid enantiomers

The model yeast, Saccharomyces cerevisiae, is a popular object for both fundamental and applied research, including the development of biosensors and industrial production of pharmaceutical compounds. However, despite multiple studies exploring S. cerevisiae transcriptional response to various substances, this response is unknown for some substances produced in yeast, such as D-lactic acid (DLA). Here, we explore the transcriptional response of the BY4742 strain to a wide range of DLA concentrations (from 0.05 to 45 mM), and compare it to the response to 45 mM L-lactic acid (LLA). We recorded a response to 5 and 45 mM DLA (125 and 113 differentially expressed genes (DEGs), respectively; > 50% shared) and a less pronounced response to 45 mM LLA (63 DEGs; > 30% shared with at least one DLA treatment). Our data did not reveal natural yeast promoters quantitatively sensing DLA but provide the first description of the transcriptome-wide response to DLA and enrich our understanding of the LLA response. Some DLA-activated genes were indeed related to lactate metabolism, as well as iron uptake and cell wall structure. Additional analyses showed that at least some of these genes were activated only by acidic form of DLA but not its salt, revealing the role of pH. The list of LLA-responsive genes was similar to those published previously and also included iron uptake and cell wall genes, as well as genes responding to other weak acids. These data might be instrumental for optimization of lactate production in yeast and yeast co-cultivation with lactic acid bacteria. KEY POINTS: • We present the first dataset on yeast transcriptional response to DLA. • Differential gene expression was correlated with yeast growth inhibition. • The transcriptome response to DLA was richer in comparison to LLA.

Deepening the knowledge of universal stress proteins in Haloferax mediterranei

Haloarchaea, like many other microorganisms, have developed defense mechanisms such as universal stress proteins (USPs) to cope with environmental stresses affecting microbial growth. Despite the wide distribution of these proteins in Archaea, their biochemical characteristics still need to be discovered, and there needs to be more knowledge about them focusing on halophilic Archaea. Therefore, elucidating the role of USPs would provide valuable information to improve future biotechnological applications. Accordingly, transcriptional expression of the 37 annotated USPs in the Haloferax mediterranei genome has been examined under different stress conditions. From a global perspective, finding a clear tendency between particular USPs and specific stress conditions was not possible. Contrary, data analysis indicates that there is a recruitment mechanism of proteins with a similar sequence able to modulate the H. mediterranei growth, accelerating or slowing it, depending on their number. In fact, only three of these USPs were expressed in all the tested conditions, pointing to the cell needing a set of USPs to cope with stress conditions. After analysis of the RNA-Seq data, three differentially expressed USPs were selected and homologously overexpressed. According to the growth data, the overexpression of USPs induces a gain of tolerance in response to stress, as a rule. Therefore, this is the only work that studies all the USPs in an archaeon. It represents a significant first base to continue advancing, not only in this important family of stress proteins but also in the field of biotechnology and, at an industrial level, to improve applications such as designing microorganisms resistant to stress situations. KEY POINTS: • Expression of Haloferax mediterranei USPs has been analyzed in stress conditions. • RNA-seq analysis reveals that most of the USPs in H. mediterranei are downregulated. • Homologous overexpression of USPs results in more stress-tolerant strains.

The potency of mitochondria enlargement for mitochondria-mediated terpenoid production in yeast

Terpenoids are widely used in the food, beverage, cosmetics, and pharmaceutical industries. Microorganisms have been extensively studied for terpenoid production. In yeast, the introduction of the mevalonate (MVA) pathway in organelles in addition to the augmentation of its own MVA pathway have been challenging. Introduction of the MVA pathway into mitochondria is considered a promising approach for terpenoid production because acetyl-CoA, the starting molecule of the MVA pathway, is abundant in mitochondria. However, mitochondria comprise only a small percentage of the entire cell. Therefore, we hypothesized that increasing the total mitochondrial volume per cell would increase terpenoid production. First, we ascertained that the amounts of isopentenyl diphosphate (IPP) and dimethylallyl diphosphate (DMAPP), the final molecules of the MVA pathway, were 15-fold higher of the strain expressing the MVA pathway in mitochondria than in the wild-type yeast strain. Second, we found that different deletion mutants induced different mitochondrial volumes by measuring the mitochondrial volume in various deletion mutants affecting mitochondrial morphology; for example,Δmdm32 increased mitochondrial volume, and Δfzo1 decreased it. Finally, the effects of mitochondrial volume on amounts of IPP/DMAPP and terpenoids (squalene or β-carotene) were investigated using mutants harboring large or small mitochondria expressing the MVA pathway in mitochondria. Amounts of IPP/DMAPP and terpenoids (squalene or β-carotene) increased when the mitochondrial volume expanded. Introducing the MVA pathway into mitochondria for terpenoid production in yeast may become more attractive by enlarging the mitochondrial volume. KEY POINTS: • IPP/DMAPP content increased in the strain expressing the MVA pathway in mitochondria • IPP/DMAPP and terpenoid contents are positively correlated with mitochondrial volume • Enlarging the mitochondria may improve mitochondria-mediated terpenoid production.

Discovery and biocatalytic characterization of opine dehydrogenases by metagenome mining

Enzymatic processes play an increasing role in synthetic organic chemistry which requires the access to a broad and diverse set of enzymes. Metagenome mining is a valuable and efficient way to discover novel enzymes with unique properties for biotechnological applications. Here, we report the discovery and biocatalytic characterization of six novel metagenomic opine dehydrogenases from a hot spring environment (mODHs) (EC 1.5.1.X). These enzymes catalyze the asymmetric reductive amination between an amino acid and a keto acid resulting in opines which have defined biochemical roles and represent promising building blocks for pharmaceutical applications. The newly identified enzymes exhibit unique substrate specificity and higher thermostability compared to known examples. The feature that they preferably utilize negatively charged polar amino acids is so far unprecedented for opine dehydrogenases. We have identified two spatially correlated positions in their active sites that govern this substrate specificity and demonstrated a switch of substrate preference by site-directed mutagenesis. While they still suffer from a relatively narrow substrate scope, their enhanced thermostability and the orthogonality of their substrate preference make them a valuable addition to the toolbox of enzymes for reductive aminations. Importantly, enzymatic reductive aminations with highly polar amines are very rare in the literature. Thus, the preparative-scale enzymatic production, purification, and characterization of three highly functionalized chiral secondary amines lend a special significance to our work in filling this gap. KEY POINTS: • Six new opine dehydrogenases have been discovered from a hot spring metagenome • The newly identified enzymes display a unique substrate scope • Substrate specificity is governed by two correlated active-site residues.

Analysis of Haemonchus embryos at single cell resolution identifies two eukaryotic elongation factors as intervention target candidates

Advances in single cell technologies are allowing investigations of a wide range of biological processes and pathways in animals, such as the multicellular model organism Caenorhabditis elegans - a free-living nematode. However, there has been limited application of such technology to related parasitic nematodes which cause major diseases of humans and animals worldwide. With no vaccines against the vast majority of parasitic nematodes and treatment failures due to drug resistance or inefficacy, new intervention targets are urgently needed, preferably informed by a deep understanding of these nematodes' cellular and molecular biology - which is presently lacking for most worms. Here, we created the first single cell atlas for an early developmental stage of Haemonchus contortus - a highly pathogenic, C. elegans-related parasitic nematode. We obtained and curated RNA sequence (snRNA-seq) data from single nuclei from embryonating eggs of H. contortus (150,000 droplets), and selected high-quality transcriptomic data for > 14,000 single nuclei for analysis, and identified 19 distinct clusters of cells. Guided by comparative analyses with C. elegans, we were able to reproducibly assign seven cell clusters to body wall muscle, hypodermis, neuronal, intestinal or seam cells, and identified eight genes that were transcribed in all cell clusters/types, three of which were inferred to be essential in H. contortus. Two of these genes (i.e. Hc-eef-1A and Hc-eef1G), coding for eukaryotic elongation factors (called Hc-eEF1A and Hc-eEF1G), were also demonstrated to be transcribed and expressed in all key developmental stages of H. contortus. Together with these findings, sequence- and structure-based comparative analyses indicated the potential of Hc-eEF1A and/or Hc-eEF1G as intervention targets within the protein biosynthesis machinery of H. contortus. Future work will focus on single cell studies of all key developmental stages and tissues of H. contortus, and on evaluating the suitability of the two elongation factor proteins as drug targets in H. contortus and related nematodes, with a view to finding new nematocidal drug candidates.

Nuclear pore dysfunction and disease: a complex opportunity

The separation of genetic material from bulk cytoplasm has enabled the evolution of increasingly complex organisms, allowing for the development of sophisticated forms of life. However, this complexity has created new categories of dysfunction, including those related to the movement of material between cellular compartments. In eukaryotic cells, nucleocytoplasmic trafficking is a fundamental biological process, and cumulative disruptions to nuclear integrity and nucleocytoplasmic transport are detrimental to cell survival. This is particularly true in post-mitotic neurons, where nuclear pore injury and errors to nucleocytoplasmic trafficking are strongly associated with neurodegenerative disease. In this review, we summarize the current understanding of nuclear pore biology in physiological and pathological contexts and discuss potential therapeutic approaches for addressing nuclear pore injury and dysfunctional nucleocytoplasmic transport.

Glutaminase inhibition as potential cancer therapeutics: current status and future applications

Alterations in normal metabolic processes are defining features of cancer. Glutamine, an abundant amino acid in the human blood, plays a critical role in regulating several biosynthetic and bioenergetic pathways that support tumour growth. Glutaminolysis is a metabolic pathway that converts glutamine into various metabolites involved in the tricarboxylic acid (TCA) cycle and generates antioxidants that are vital for tumour cell survival. As glutaminase catalyses the initial step of this metabolic pathway, it is of great significance in cancer metabolism and tumour progression. Inhibition of glutaminase and targeting of glutaminolysis have emerged as promising strategies for cancer therapy. This review explores the role of glutaminases in cancer metabolism and discusses various glutaminase inhibitors developed as potential therapies for tumour regression.

Uncovering the potential of APOD as a biomarker in gastric cancer: A retrospective and multi-center study

Gastric cancer (GC) poses a significant health challenge worldwide, necessitating the identification of predictive biomarkers to improve prognosis. Dysregulated lipid metabolism is a well-recognized hallmark of tumorigenesis, prompting investigation into apolipoproteins (APOs). In this study, we focused on apolipoprotein D (APOD) following comprehensive analyses of APOs in pan-cancer. Utilizing data from the TCGA-STAD and GSE62254 cohorts, we elucidated associations between APOD expression and multiple facets of GC, including prognosis, tumor microenvironment (TME), cancer biomarkers, mutations, and immunotherapy response, and identified potential anti-GC drugs. Single-cell analyses and immunohistochemical staining confirmed APOD expression in fibroblasts within the GC microenvironment. Additionally, we independently validated the prognostic significance of APOD in the ZN-GC cohort. Our comprehensive analyses revealed that high APOD expression in GC patients was notably associated with unfavorable clinical outcomes, reduced microsatellite instability and tumor mutation burden, alterations in the TME, and diminished response to immunotherapy. These findings provide valuable insights into the potential prognostic and therapeutic implications of APOD in GC.

drexml: A command line tool and Python package for drug repurposing

We introduce drexml, a command line tool and Python package for rational data-driven drug repurposing. The package employs machine learning and mechanistic signal transduction modeling to identify drug targets capable of regulating a particular disease. In addition, it employs explainability tools to contextualize potential drug targets within the functional landscape of the disease. The methodology is validated in Fanconi Anemia and Familial Melanoma, two distinct rare diseases where there is a pressing need for solutions. In the Fanconi Anemia case, the model successfully predicts previously validated repurposed drugs, while in the Familial Melanoma case, it identifies a promising set of drugs for further investigation.

Microbiota profiling in esophageal diseases: Novel insights into molecular staining and clinical outcomes

Gut microbiota is recognized nowadays as one of the key players in the development of several gastro-intestinal diseases. The first studies focused mainly on healthy subjects with staining of main bacterial species via culture-based techniques. Subsequently, lots of studies tried to focus on principal esophageal disease enlarged the knowledge on esophageal microbial environment and its role in pathogenesis. Gastro Esophageal Reflux Disease (GERD), the most widespread esophageal condition, seems related to a certain degree of mucosal inflammation, via interleukin (IL) 8 potentially enhanced by bacterial components, lipopolysaccharide (LPS) above all. Gram- bacteria, producing LPS), such as Campylobacter genus, have been found associated with GERD. Barrett esophagus (BE) seems characterized by a Gram- and microaerophils-shaped microbiota. Esophageal cancer (EC) development leads to an overturn in the esophageal environment with the shift from an oral-like microbiome to a prevalently low-abundant and low-diverse Gram--shaped microbiome. Although underinvestigated, also changes in the esophageal microbiome are associated with rare chronic inflammatory or neuropathic disease pathogenesis. The paucity of knowledge about the microbiota-driven mechanisms in esophageal disease pathogenesis is mainly due to the scarce sensitivity of sequencing technology and culture methods applied so far to study commensals in the esophagus. However, the recent advances in molecular techniques, especially with the advent of non-culture-based genomic sequencing tools and the implementation of multi-omics approaches, have revolutionized the microbiome field, with promises of implementing the current knowledge, discovering more mechanisms underneath, and giving insights into the development of novel therapies aimed to re-establish the microbial equilibrium for ameliorating esophageal diseases..

Proteomics appending a complementary dimension to precision oncotherapy

Recent advances in high-throughput proteomic profiling technologies have facilitated the precise quantification of numerous proteins across multiple specimens concurrently. Researchers have the opportunity to comprehensively analyze the molecular signatures in plentiful medical specimens or disease pattern cell lines. Along with advances in data analysis and integration, proteomics data could be efficiently consolidated and employed to recognize precise elementary molecular mechanisms and decode individual biomarkers, guiding the precision treatment of tumors. Herein, we review a broad array of proteomics technologies and the progress and methods for the integration of proteomics data and further discuss how to better merge proteomics in precision medicine and clinical settings.

Advanced computational predictive models of miRNA-mRNA interaction efficiency

The modeling of miRNA-mRNA interactions holds significant implications for synthetic biology and human health. However, this research area presents specific challenges due to the multifaceted nature of mRNA downregulation by miRNAs, influenced by numerous factors including competition or synergism among miRNAs and mRNAs. In this study, we present an improved computational model for predicting miRNA-mRNA interactions, addressing aspects not previously modeled. Firstly, we integrated a novel set of features that significantly enhanced the predictor's performance. Secondly, we demonstrated the cell-specific nature of certain aspects of miRNA-mRNA interactions, highlighting the importance of designing models tailored to specific cell types for improved accuracy. Moreover, we introduce a miRNA binding site interaction model (miBSIM) that, for the first time, accounts for both the distribution of miRNA binding sites along the mRNA and their respective strengths in regulating mRNA stability. Our analysis suggests that distant miRNA sites often compete with each other, revealing the intricate interplay of binding site interactions. Overall, our new predictive model shows a significant improvement of up to 6.43% over previous models in the field. The code of our model is available at https://www.cs.tau.ac.il/~tamirtul/miBSIM.

Deep Learning of radiology-genomics integration for computational oncology: A mini review

In the field of computational oncology, patient status is often assessed using radiology-genomics, which includes two key technologies and data, such as radiology and genomics. Recent advances in deep learning have facilitated the integration of radiology-genomics data, and even new omics data, significantly improving the robustness and accuracy of clinical predictions. These factors are driving artificial intelligence (AI) closer to practical clinical applications. In particular, deep learning models are crucial in identifying new radiology-genomics biomarkers and therapeutic targets, supported by explainable AI (xAI) methods. This review focuses on recent developments in deep learning for radiology-genomics integration, highlights current challenges, and outlines some research directions for multimodal integration and biomarker discovery of radiology-genomics or radiology-omics that are urgently needed in computational oncology.

Resistance mechanism of Escherichia coli strains with different ampicillin resistance levels

Antibiotic resistance is an important problem that threatens medical treatment. Differences in the resistance levels of microorganisms cause great difficulties in understanding the mechanisms of antibiotic resistance. Therefore, the molecular reasons underlying the differences in the level of antibiotic resistance need to be clarified. For this purpose, genomic and transcriptomic analyses were performed on three Escherichia coli strains with varying degrees of adaptive resistance to ampicillin. Whole-genome sequencing of strains with different levels of resistance detected five mutations in strains with 10-fold resistance and two additional mutations in strains with 95-fold resistance. Overall, three of the seven mutations occurred as a single base change, while the other four occurred as insertions or deletions. While it was thought that 10-fold resistance was achieved by the effect of mutations in the ftsI, marAR, and rpoC genes, it was found that 95-fold resistance was achieved by the synergistic effect of five mutations and the ampC mutation. In addition, when the general transcriptomic profiles were examined, it was found that similar transcriptomic responses were elicited in strains with different levels of resistance. This study will improve our view of resistance mechanisms in bacteria with different levels of resistance and provide the basis for our understanding of the molecular mechanism of antibiotic resistance in ampicillin-resistant E. coli strains. KEY POINTS: •The mutation of the ampC promoter may act synergistically with other mutations and lead to higher resistance. •Similar transcriptomic responses to ampicillin are induced in strains with different levels of resistance. •Low antibiotic concentrations are the steps that allow rapid achievement of high antibiotic resistance.

Notoginsenoside R1 improves intestinal microvascular functioning in sepsis by targeting Drp1-mediated mitochondrial quality imbalance

CONTEXT

Sepsis can result in critical organ failure, and notoginsenoside R1 (NGR1) offers mitochondrial protection.

OBJECTIVE

To determine whether NGR1 improves organ function and prognosis after sepsis by protecting mitochondrial quality.

MATERIALS AND METHODS

A sepsis model was established in C57BL/6 mice using cecum ligation puncture (CLP) and an in vitro model with lipopolysaccharide (LPS, 10 µg/mL)-stimulated primary intestinal microvascular endothelial cells (IMVECs) and then determine NGR1's safe dosage. Groups for each model were: in vivo-a control group, a CLP-induced sepsis group, and a CLP + NGR1 treatment group (30 mg/kg/d for 3 d); in vitro-a control group, a LPS-induced sepsis group, and a LPS + NGR1 treatment group (4 μM for 30 min). NGR1's effects on survival, intestinal function, mitochondrial quality, and mitochondrial dynamic-related protein (Drp1) were evaluated.

RESULTS

Sepsis resulted in approximately 60% mortality within 7 days post-CLP, with significant reductions in intestinal microvascular perfusion and increases in vascular leakage. Severe mitochondrial quality imbalance was observed in IMVECs. NGR1 (IC50 is 854.1 μM at 30 min) targeted Drp1, inhibiting mitochondrial translocation, preventing mitochondrial fragmentation and restoring IMVEC morphology and function, thus protecting against intestinal barrier dysfunction, vascular permeability, microcirculatory flow, and improving sepsis prognosis.

DISCUSSION AND CONCLUSIONS

Drp1-mediated mitochondrial quality imbalance is a potential therapeutic target for sepsis. Small molecule natural drugs like NGR1 targeting Drp1 may offer new directions for organ protection following sepsis. Future research should focus on clinical trials to evaluate NGR1's efficacy across various patient populations, potentially leading to novel treatments for sepsis.

Added insult to injury? The response of meat-associated pathogens to proposed antimicrobial interventions

Modern requirements for 'green label' meat products have led to the design of novel antimicrobial innovations which prioritise quality, safety and longevity. Plasma-functionalised water (PFW), ultraviolet light and natural antimicrobial compositions have been investigated and optimised for control of foodborne pathogens like Campylobacter jejuni and Salmonella enterica serovar Typhimurium. However, given the adaptive mechanisms present in bacteria under external stresses, it is imperative to understand the effect that sublethal treatment may have on the bacterial transcriptome. In this study, Salmonella Typhimurium and C. jejuni were treated with sublethal doses of ultraviolet light, a citrus juice/essential oil marinade, and 'spark' or 'glow' cold plasma generation system-produced PFW. Immediately after treatment, cells were lysed and RNA was extracted and purified. mRNA was converted to cDNA by reverse transcription-PCR and sequenced by an Illumina MiSeq® system. Sequences were filtered and analysed using the Tuxedo workflow. Sublethal treatment of Campylobacter jejuni and Salmonella Typhimurium led to increased immediate cellular and metabolic activity, as well as diversification in protein and metabolic functioning. There was further expression of pathogenesis and virulence-associated traits associated with spark PFW and marinade treatment of Salmonella Typhimurium. However, similar concerns were not raised with glow PFW or UV-treated samples. This study provides science-based evidence of the efficacy of multi-hurdle antimicrobial system using green-label marinades and PFW or UV to inactivate pathogens without upregulating virulence traits in surviving cells. This study will inform policymakers and food industry stakeholders and reinforces the need to incorporate in-line novel technologies to ensure consumer safety. KEY POINTS: • Salmonella and C. jejuni showed increased cell activity in immediate response to stress. • Virulence genes showed increased expression when treated with natural antimicrobials and sPFW. • Reduced immediate transcriptomic response to gPFW and UV treatment indicates lower risk.

A novel cathelicidin TS-CATH derived from Thamnophis sirtalis combats drug-resistant gram-negative bacteria in vitro and in vivo

Antimicrobial peptides are promising therapeutic agents for treating drug-resistant bacterial disease due to their broad-spectrum antimicrobial activity and decreased susceptibility to evolutionary resistance. In this study, three novel cathelicidin antimicrobial peptides were identified from Thamnophis sirtalis, Balaenoptera musculus, and Lipotes vexillifer by protein database mining and sequence alignment and were subsequently named TS-CATH, BM-CATH, and LV-CATH, respectively. All three peptides exhibited satisfactory antibacterial activity and broad antibacterial spectra against clinically isolated E. coli, P. aeruginosa, K. pneumoniae, and A. baumannii in vitro. Among them, TS-CATH displayed the best antimicrobial/bactericidal activity, with a rapid elimination efficiency against the tested drug-resistant gram-negative bacteria within 20 min, and exhibited the lowest cytotoxicity toward mammalian cells. Furthermore, TS-CATH effectively enhanced the survival rate of mice with ceftazidime-resistant E. coli bacteremia and promoted wound healing in meropenem-resistant P. aeruginosa infection. These results were achieved through the eradication of bacterial growth in target organs and wounds, further inhibiting the systemic dissemination of bacteria and the inflammatory response. TS-CATH exhibited direct antimicrobial activity by damaging the inner and outer membranes, resulting in leakage of the bacterial contents at super-MICs. Moreover, TS-CATH disrupted the bacterial respiratory chain, which inhibited ATP synthesis and induced ROS formation, significantly contributing to its antibacterial efficacy at sub-MICs. Overall, TS-CATH has potential for use as an antibacterial agent.

Cultivation driven transcriptomic changes in the wild-type and mutant strains of Rhodospirillum rubrum

Purple photosynthetic bacteria (PPB) are versatile microorganisms capable of producing various value-added chemicals, e.g., biopolymers and biofuels. They employ diverse metabolic pathways, allowing them to adapt to various growth conditions and even extreme environments. Thus, they are ideal organisms for the Next Generation Industrial Biotechnology concept of reducing the risk of contamination by using naturally robust extremophiles. Unfortunately, the potential of PPB for use in biotechnology is hampered by missing knowledge on regulations of their metabolism. Although Rhodospirillum rubrum represents a model purple bacterium studied for polyhydroxyalkanoate and hydrogen production, light/chemical energy conversion, and nitrogen fixation, little is known regarding the regulation of its metabolism at the transcriptomic level. Using RNA sequencing, we compared gene expression during the cultivation utilizing fructose and acetate as substrates in case of the wild-type strain R. rubrum DSM 467T and its knock-out mutant strain that is missing two polyhydroxyalkanoate synthases PhaC1 and PhaC2. During this first genome-wide expression study of R. rubrum, we were able to characterize cultivation-driven transcriptomic changes and to annotate non-coding elements as small RNAs.

Augusta: From RNA-Seq to gene regulatory networks and Boolean models

Computational models of gene regulations help to understand regulatory mechanisms and are extensively used in a wide range of areas, e.g., biotechnology or medicine, with significant benefits. Unfortunately, there are only a few computational gene regulatory models of whole genomes allowing static and dynamic analysis due to the lack of sophisticated tools for their reconstruction. Here, we describe Augusta, an open-source Python package for Gene Regulatory Network (GRN) and Boolean Network (BN) inference from the high-throughput gene expression data. Augusta can reconstruct genome-wide models suitable for static and dynamic analyses. Augusta uses a unique approach where the first estimation of a GRN inferred from expression data is further refined by predicting transcription factor binding motifs in promoters of regulated genes and by incorporating verified interactions obtained from databases. Moreover, a refined GRN is transformed into a draft BN by searching in the curated model database and setting logical rules to incoming edges of target genes, which can be further manually edited as the model is provided in the SBML file format. The approach is applicable even if information about the organism under study is not available in the databases, which is typically the case for non-model organisms including most microbes. Augusta can be operated from the command line and, thus, is easy to use for automated prediction of models for various genomes. The Augusta package is freely available at github.com/JanaMus/Augusta. Documentation and tutorials are available at augusta.readthedocs.io.

VIBES: A consensus subtyping of the vaginal microbiota reveals novel classification criteria

This study aimed to develop a robust classification scheme for stratifying patients based on vaginal microbiome. By employing consensus clustering analysis, we identified four distinct clusters using a cohort that includes individuals diagnosed with Bacterial Vaginosis (BV) as well as control participants, each characterized by unique patterns of microbiome species abundances. Notably, the consistent distribution of these clusters was observed across multiple external cohorts, such as SRA022855, SRA051298, PRJNA208535, PRJNA797778, and PRJNA302078 obtained from public repositories, demonstrating the generalizability of our findings. We further trained an elastic net model to predict these clusters, and its performance was evaluated in various external cohorts. Moreover, we developed VIBES, a user-friendly R package that encapsulates the model for convenient implementation and enables easy predictions on new data. Remarkably, we explored the applicability of this new classification scheme in providing valuable insights into disease progression, treatment response, and potential clinical outcomes in BV patients. Specifically, we demonstrated that the combined output of VIBES and VALENCIA scores could effectively predict the response to metronidazole antibiotic treatment in BV patients. Therefore, this study's outcomes contribute to our understanding of BV heterogeneity and lay the groundwork for personalized approaches to BV management and treatment selection.

A comprehensive review of the applications of RNA sequencing in celiac disease research

RNA sequencing (RNA-seq) has undergone substantial advancements in recent decades and has emerged as a vital technique for profiling the transcriptome. The transition from bulk sequencing to single-cell and spatial approaches has facilitated the achievement of higher precision at cell resolution. It provides valuable biological knowledge about individual immune cells and aids in the discovery of the molecular mechanisms that contribute to the development of autoimmune diseases. Celiac disease (CeD) is an autoimmune disorder characterized by a strong immune response to gluten consumption. RNA-seq has led to significantly advanced research in multiple fields, particularly in CeD research. It has been instrumental in studies involving comparative transcriptomics, nutritional genomics and wheat research, cancer research in the context of CeD, genetic and noncoding RNA-mediated epigenetic insights, disease monitoring and biomarker discovery, regulation of mitochondrial functions, therapeutic target identification and drug mechanism of action, dietary factors, immune cell profiling and the immune landscape. This review offers a comprehensive examination of recent RNA-seq technology research in the field of CeD, highlighting future challenges and opportunities for its application.

Transcriptome analysis reveals the key network of axillary bud outgrowth modulated by topping in citrus

Topping, an important tree shaping and pruning technique, can promote the outgrowth of citrus axillary buds. However, the underlying molecular mechanism is still unclear. In this study, spring shoots of Citrus reticulata 'Huagan No.2' were topped and transcriptome was compared between axillary buds of topped and untopped shoots at 6 and 11 days after topping (DAT). 1944 and 2394 differentially expressed genes (DEGs) were found at 6 and 11 DAT, respectively. KEGG analysis revealed that many DEGs were related to starch and sucrose metabolism, signal transduction of auxin, cytokinin and abscisic acid. Specially, transcript levels of auxin synthesis, transport, and signaling-related genes (SAURs and ARF5), cytokinin signal transduction related genes (CRE1, AHP and Type-A ARRs), ABA signal responsive genes (PYL and ABF) were up-regulated by topping; while transcript levels of auxin receptor TIR1, auxin responsive genes AUX/IAAs, ABA signal transduction related gene PP2Cs and synthesis related genes NCED3 were down-regulated. On the other hand, the contents of sucrose and fructose in axillary buds of topped shoots were significantly higher than those in untopped shoots; transcript levels of 16 genes related to sucrose synthase, hexokinase, sucrose phosphate synthase, endoglucanase and glucosidase, were up-regulated in axillary buds after topping. In addition, transcript levels of genes related to trehalose 6-phosphate metabolism and glycolysis/tricarboxylic acid (TCA) cycle, as well to some transcription factors including Pkinase, Pkinase_Tyr, Kinesin, AP2/ERF, P450, MYB, NAC and Cyclin_c, significantly responded to topping. Taken together, the present results suggested that topping promoted citrus axillary bud outgrowth through comprehensively regulating plant hormone and carbohydrate metabolism, as well as signal transduction. These results deepened our understanding of citrus axillary bud outgrowth by topping and laid a foundation for further research on the molecular mechanisms of citrus axillary bud outgrowth.

Biological network comparison identifies a novel synergistic mechanism of Ginseng Radix-Astragali Radix herb pair in cancer-related fatigue

ETHNOPHARMACOLOGICAL RELEVANCE

Ginseng Radix and Astragali Radix are commonly combined to tonify Qi and alleviate fatigue. Previous studies have employed biological networks to investigate the mechanisms of herb pairs in treating different diseases. However, these studies have only elucidated a single network for each herb pair, without emphasizing the superiority of the herb combination over individual herbs.

AIM OF THE STUDY

This study proposes an approach of comparing biological networks to highlight the synergistic effect of the pair in treating cancer-related fatigue (CRF).

METHODS

The compounds and targets of Ginseng Radix, Astragali Radix, and CRF diseases were collected and predicted using different databases. Subsequently, the overlapping targets between herbs and disease were imported into the STRING and DAVID tools to build protein-protein interaction (PPI) networks and analyze enriched KEGG pathways. The biological networks of Ginseng Radix and Astragali Radix were compared separately or together using the DyNet application. Molecular docking was used to verify the predicted results. Further, in vitro experiments were conducted to validate the synergistic pathways identified in in silico studies.

RESULTS

In the PPI network comparison, the combination created 89 new interactions and an increased average degree (11.260) when compared to single herbs (10.296 and 9.394). The new interactions concentrated on HRAS, STAT3, JUN, and IL6. The topological analysis identified 20 core targets of the combination, including three Ginseng Radix-specific targets, three Astragali Radix-specific targets, and 14 shared targets. In KEGG enrichment analysis, the combination regulated additional signaling pathways (152) more than Ginseng Radix (146) and Astragali Radix (134) alone. The targets of the herb pair synergistically regulated cancer pathways, specifically hypoxia-inducible factor 1 (HIF-1) signaling pathway. In vitro experiments including enzyme-linked immunosorbent assay and Western blot demonstrated that two herbs combination could up-regulate HIF-1α signaling pathway at different combined concentrations compared to either single herb alone.

CONCLUSION

The herb pair increased protein interactions and adjusted metabolic pathways more than single herbs. This study provides insights into the combination of Ginseng Radix and Astragali Radix in clinical practice.

New challenge: Mitigation and control of antibiotic resistant genes in aquatic environments by biochar

With an increase of diverse contaminants in the environment, particularly antibiotics, the maintenance and propagation of antibiotic resistance genes (ARGs) are promoted by co-selection mechanisms. ARGs are difficult to degrade, cause long-lasting pollution, and are widely transmitted in aquatic environments. Biochar is frequently used to remove various pollutants during environmental remediation. Thus, this review provides a thorough analysis of the current state of ARGs in the aquatic environment as well as their removal by using biochar. This article summarizes the research and application of biochar and modified biochar to remove ARGs in aquatic environments, in order to refine the following contents: 1) fill gaps in the research on the various ARG behaviors mediated by biochar and some influence factors, 2) further investigate the mechanisms involved in effects of biochar on extracellular ARGs (eARGs) and intracellular ARGs (iARGs) in aquatic environments, including direct and the indirect effects, 3) describe the propagation process and resistance mechanisms of ARGs, 4) propose the challenges and prospects of feasibility of application and subsequent treatment in actual aquatic environment. Here we highlight the most recent research on the use of biochar to remove ARGs from aquatic environments and suggest future directions for optimization, as well as current perspectives to guide future studies on the removal of ARGs from aquatic environments.

The role of food preservatives in shaping metabolic profile and chemical sensitivity of fungi - an extensive study on crucial mycological food contaminants from the genus Neosartorya (Aspergillus spp.)

Food preservatives are crucial in fruit production, but fungal resistance is a challenge. The main objective was to compare the sensitivity of Neosartorya spp. isolates to preservatives used in food security applications and to assess the role of metabolic properties in shaping Neosartorya spp. resistance. Sodium metabisulfite, potassium sorbate, sodium bisulfite and sorbic acid showed inhibitory effects, with sodium metabisulfite the most effective. Tested metabolic profiles included fungal growth intensity and utilization of amines and amides, amino acids, polymers, carbohydrates and carboxylic acids. Significant decreases in the utilization of all tested organic compound guilds were observed after fungal exposure to food preservatives compared to the control. Although the current investigation was limited in the number of predominately carbohydrate substrates and the breadth of metabolic responses, extensive sensitivity panels are logical step in establishing a course of action against spoilage agents in food production being important approach for innovative food chemistry.

Quantification of gut microbiome metabolites using chemical isotope derivatization strategy combined with LC-MS/MS: Application in neonatal hypoxic-ischemic encephalopathy rat model

The gut microbiome plays pivotal roles in various physiological and pathological processes, with key metabolites including short chain fatty acids (SCFAs), bile acids (BAs), and tryptophan (TRP) derivatives gaining significant attention for their diverse physiological roles. However, quantifying these metabolites presents challenges due to structural similarity, low abundance, and inherent technical limitations in traditional detection methods. In this study, we developed a precise and sensitive liquid chromatography-tandem mass spectrometry (LC-MS/MS) method utilizing a chemical isotope derivatization technique employing 4-(aminomethyl)-N,N-dimethylaniline-d0/d6 (4-AND-d0/d6) reagents to quantify 37 typical gut microbiome-derived metabolites. This method achieved an impressive 1500-fold enhancement in sensitivity for detecting metabolites, compared to methods using non-derivatized, intact molecules. Moreover, the quantitative accuracy of our chemical isotope derivatization strategy proved comparable to the stable isotope labeled internal standards (SIL-IS) method. Subsequently, we successfully applied this newly developed method to quantify target metabolites in plasma, brain, and fecal samples obtained from a neonatal hypoxic-ischemic encephalopathy (HIE) rat model. The aim was to identify crucial metabolites associated with the progression of HIE. Overall, our sensitive and reliable quantification method holds promise in elucidating the role of gut microbiome metabolites in the pathogenesis of various diseases.

Metal nanoparticles and pesticides under global climate change: Assessing the combined effects of multiple abiotic stressors on soil microbial ecosystems

The soil is a vital resource that hosts many microorganisms crucial in biogeochemical cycles and ecosystem health. However, human activities such as the use of metal nanoparticles (MNPs), pesticides and the impacts of global climate change (GCCh) can significantly affect soil microbial communities (SMC). For many years, pesticides and, more recently, nanoparticles have contributed to sustainable agriculture to ensure continuous food production to sustain the significant growth of the world population and, therefore, the demand for food. Pesticides have a recognized pest control capacity. On the other hand, nanoparticles have demonstrated a high ability to improve water and nutrient retention, promote plant growth, and control pests. However, it has been reported that their accumulation in agricultural soils can also adversely affect the environment and soil microbial health. In addition, climate change, with its variations in temperature and extreme water conditions, can lead to drought and increased soil salinity, modifying both soil conditions and the composition and function of microbial communities. Abiotic stressors can interact and synergistically or additively affect soil microorganisms, significantly impacting soil functioning and the capacity to provide ecosystem services. Therefore, this work reviewed the current scientific literature to understand how multiple stressors interact and affect the SMC. In addition, the importance of molecular tools such as metagenomics, metatranscriptomics, proteomics, or metabolomics in the study of the responses of SMC to exposure to multiple abiotic stressors was examined. Future research directions were also proposed, focusing on exploring the complex interactions between stressors and their long-term effects and developing strategies for sustainable soil management. These efforts will contribute to the preservation of soil health and the promotion of sustainable agricultural practices.

Ecological associations distribution modelling of marine plankton at a global scale

Marine plankton communities form intricate networks of interacting organisms at the base of the food chain, and play a central role in regulating ocean biogeochemical cycles and climate. However, predicting plankton community shifts in response to climate change remains challenging. While species distribution models are valuable tools for predicting changes in species biogeography under climate change scenarios, they generally overlook the key role of biotic interactions, which can significantly shape ecological processes and ecosystem responses. Here, we introduce a novel statistical framework, association distribution modelling (ADM), designed to model and predict ecological associations distribution in space and time. Applied on a Tara Oceans genome-resolved metagenomics dataset, the present-day biogeography of ADM-inferred marine plankton associations revealed four major biogeographic biomes organized along a latitudinal gradient. We predicted the evolution of these biome-specific communities in response to a climate change scenario, highlighting differential responses to environmental change. Finally, we explored the functional potential of impacted plankton communities, focusing on carbon fixation, outlining the predicted evolution of its geographical distribution and implications for ecosystem function.This article is part of the theme issue 'Connected interactions: enriching food web research by spatial and social interactions'.

The type V effectors for CRISPR/Cas-mediated genome engineering in plants

A plethora of CRISPR effectors, such as Cas3, Cas9, and Cas12a, are commonly employed as gene editing tools. Among these, Cas12 effectors developed based on Class II type V proteins exhibit distinct characteristics compared to Class II type VI and type II effectors, such as their ability to generate non-allelic DNA double-strand breaks, their compact structures, and the presence of a single RuvC-like nuclease domain. Capitalizing on these advantages, Cas12 family proteins have been increasingly explored and utilized in recent years. However, the characteristics and applications of different subfamilies within the type V protein family have not been systematically summarized. In this review, we focus on the characteristics of type V effector (CRISPR/Cas12) proteins and the current methods used to discover new effector proteins. We also summarize recent modifications based on engineering of type V effectors. In addition, we introduce the applications of type V effectors for gene editing in animals and plants, including the development of base editors, tools for regulating gene expression, methods for gene targeting, and biosensors. We emphasize the prospects for development and application of CRISPR/Cas12 effectors with the goal of better utilizing toolkits based on this protein family for crop improvement and enhanced agricultural production.

UVB radiation exposure modulates mitophagy in embryonic cells of freshwater prawn Macrobrachium olfersii: Exploring a protective organelle quality control mechanism

Aquatic environments are subject to ultraviolet B (UVB) radiation incidence, and its effects on organisms are dose-dependent. Besides DNA, mitochondria are an important target of this radiation that causes structural damage and impairs its functional dynamics. Here, we hypothesize that mitophagy acts as an organelle quality control mechanism to mitigate UVB impacts in embryonic cells. Then, freshwater prawn Macrobrachium olfersii embryos was used as a model to investigate the effects of UVB on genes (Tomm20, Opa1, Pink, Prkn, Sqstm1, and Map1lc3) and proteins (TOM20, PINK1, p62 and LC3B) involved in mitophagy modulation. The choice of genes and proteins was based on the identification of mitochondrial membrane (Tomm20, Opa1 and TOM20), mediation of mitophagy (Pink1, Prkn and PINK1), and recognition of mitochondria by the autophagosome membrane (Sqstm1, Map1lc3, p62 and LC3B). First, the phylogeny of all genes presented bootstrap values >80 and conserved domains among crustacean species. Gene expression was inherently modulated during development, with transcripts (Tomm20, Opa1, Pink, Prkn, Sqstm1, and Map1lc3) overexpressed in the initial and final stages of development. Moreover, UVB radiation induced upregulation of Tomm20, Opa1, Pink, Prkn, Sqstm1, and Map1lc3 genes at 6 h after exposure. Interestingly, after 12 h, the protein content of PINK1, p62, and LC3B increased, while TOM20 was not responsive. Despite UVB radiation's harmful effects on embryonic cells, the chronology of gene expression and protein content indicates rapid activation of mitophagy, serving as an organelle quality control mechanism, given the analyzed cells' integrity.

Relieving metabolic burden to improve robustness and bioproduction by industrial microorganisms

Metabolic burden is defined by the influence of genetic manipulation and environmental perturbations on the distribution of cellular resources. The rewiring of microbial metabolism for bio-based chemical production often leads to a metabolic burden, followed by adverse physiological effects, such as impaired cell growth and low product yields. Alleviating the burden imposed by undesirable metabolic changes has become an increasingly attractive approach for constructing robust microbial cell factories. In this review, we provide a brief overview of metabolic burden engineering, focusing specifically on recent developments and strategies for diminishing the burden while improving robustness and yield. A variety of examples are presented to showcase the promise of metabolic burden engineering in facilitating the design and construction of robust microbial cell factories. Finally, challenges and limitations encountered in metabolic burden engineering are discussed.

Estrogen receptor beta expression and role in cancers

Estrogen drives the growth of some cancers, such as breast cancer, via estrogen receptor alpha (ERα). Estrogen also activates ERβ, but whether ERβ is expressed and has a role in different cancers is debated. The use of nonspecific antibodies has contributed to the confusion, and this review delves into ERβ's controversial role in cancer and focuses on tumor expression that can be supported by non-antibody-dependent assays. We discuss its expression at the transcript level and focus on its potential role in lymphoma, granulosa cell tumors, testicular, and adrenal cancers, emphasizing recent findings and the complexities that necessitate further research.

WUSCHEL RELATED HOMEOBOX5 and 7 maintain callus development by promoting cell division in Arabidopsis

In tissue culture, a high concentration of auxin in the callus induction medium (CIM) stimulates cell division and subsequent callus formation, which acquires root primordium-like characteristics necessary for cell pluripotency. In Arabidopsis, WUSCHEL-RELATED HOMEOBOX5 (WOX5) and its closest homolog WOX7, which are abundant in the middle cell layer of mature callus, play a crucial role in maintaining pluripotency by promoting auxin accumulation and enhancing cytokinin sensitivity. However, the mechanism by which WOX5/7 regulate callus formation remains unclear. In this study, we found that mutations in WOX5/7 resulted in a significant down-regulation of genes involved in the G2M and S phases during callus induction. Loss-of-function mutants of WOX5/7 exhibited reduced callus formation, which was correlated with decreased expression of CYCB1;1 compared to the wild-type. Furthermore, we provided evidence that WOX5 physically interacts with PHYTOCHROME A SIGNAL TRANSDUCTION1 (PAT1), which spatio-temporally co-expresses with WOX5 in early-induced callus, and up-regulates a subset of cycle-regulating genes targeted by PAT1. Collectively, our findings suggest a critical role for the WOX5-PAT1 protein complex in regulating cell cycle progression, thereby promoting the continuous growth capacity of pluripotent callus.

Metabolic engineering for compartmentalized biosynthesis of the valuable compounds in Saccharomyces cerevisiae

Saccharomyces cerevisiae is commonly used as a microbial cell factory to produce high-value compounds or bulk chemicals due to its genetic operability and suitable intracellular physiological environment. The current biosynthesis pathway for targeted products is primarily rewired in the cytosolic compartment. However, the related precursors, enzymes, and cofactors are frequently distributed in various subcellular compartments, which may limit targeted compounds biosynthesis. To overcome above mentioned limitations, the biosynthesis pathways are localized in different subcellular organelles for product biosynthesis. Subcellular compartmentalization in the production of targeted compounds offers several advantages, mainly relieving competition for precursors from side pathways, improving biosynthesis efficiency in confined spaces, and alleviating the cytotoxicity of certain hydrophobic products. In recent years, subcellular compartmentalization in targeted compound biosynthesis has received extensive attention and has met satisfactory expectations. In this review, we summarize the recent advances in the compartmentalized biosynthesis of the valuable compounds in S. cerevisiae, including terpenoids, sterols, alkaloids, organic acids, and fatty alcohols, etc. Additionally, we describe the characteristics and suitability of different organelles for specific compounds, based on the optimization of pathway reconstruction, cofactor supplementation, and the synthesis of key precursors (metabolites). Finally, we discuss the current challenges and strategies in the field of compartmentalized biosynthesis through subcellular engineering, which will facilitate the production of the complex valuable compounds and offer potential solutions to improve product specificity and productivity in industrial processes.

Achieving robust synthetic tolerance in industrial E. coli through negative auto-regulation of a DsrA-Hfq module

In industrial fermentation processes, microorganisms often encounter acid stress, which significantly impact their productivity. This study focused on the acid-resistant module composed of small RNA (sRNA) DsrA and the sRNA chaperone Hfq. Our previous study had shown that this module improved the cell growth of Escherichia coli MG1655 at low pH, but failed to obtain this desired phenotype in industrial strains. Here, we performed a quantitative analysis of DsrA-Hfq module to determine the optimal expression mode. We then assessed the potential of the CymR-based negative auto-regulation (NAR) circuit for industrial application, under different media, strains and pH levels. Growth assay at pH 4.5 revealed that NAR-05D04H circuit was the best acid-resistant circuit to improve the cell growth of E. coli MG1655. This circuit was robust and worked well in the industrial lysine-producing strain E. coli SCEcL3 at a starting pH of 6.8 and without pH control, resulting in a 250 % increase in lysine titer and comparable biomass in shaking flask fermentation compared to the parent strain. This study showed the practical application of NAR circuit in regulating DsrA-Hfq module, effectively and robustly improving the acid tolerance of industrial strains, which provides a new approach for breeding industrial strains with tolerance phenotype.

Bayesian Simultaneous Factorization and Prediction Using Multi-Omic Data

Integrative factorization methods for multi-omic data estimate factors explaining biological variation. Factors can be treated as covariates to predict an outcome and the factorization can be used to impute missing values. However, no available methods provide a comprehensive framework for statistical inference and uncertainty quantification for these tasks. A novel framework, Bayesian Simultaneous Factorization (BSF), is proposed to decompose multi-omics variation into joint and individual structures simultaneously within a probabilistic framework. BSF uses conjugate normal priors and the posterior mode of this model can be estimated by solving a structured nuclear norm-penalized objective that also achieves rank selection and motivates the choice of hyperparameters. BSF is then extended to simultaneously predict a continuous or binary phenotype while estimating latent factors, termed Bayesian Simultaneous Factorization and Prediction (BSFP). BSF and BSFP accommodate concurrent imputation, i.e., imputation during the model-fitting process, and full posterior inference for missing data, including "blockwise" missingness. It is shown via simulation that BSFP is competitive in recovering latent variation structure, and demonstrate the importance of accounting for uncertainty in the estimated factorization within the predictive model. The imputation performance of BSF is examined via simulation under missing-at-random and missing-not-at-random assumptions. Finally, BSFP is used to predict lung function based on the bronchoalveolar lavage metabolome and proteome from a study of HIV-associated obstructive lung disease, revealing multi-omic patterns related to lung function decline and a cluster of patients with obstructive lung disease driven by shared metabolomic and proteomic abundance patterns.

ECMpy 2.0: A Python package for automated construction and analysis of enzyme-constrained models

Genome-scale metabolic models (GEMs) have been widely employed to predict microorganism behaviors. However, GEMs only consider stoichiometric constraints, leading to a linear increase in simulated growth and product yields as substrate uptake rates rise. This divergence from experimental measurements prompted the creation of enzyme-constrained models (ecModels) for various species, successfully enhancing chemical production. Building upon studies that allocate macromolecule resources, we developed a Python-based workflow (ECMpy) that constructs an enzyme-constrained model. This involves directly imposing an enzyme amount constraint in GEM and accounting for protein subunit composition in reactions. However, this procedure demands manual collection of enzyme kinetic parameter information and subunit composition details, making it rather user-unfriendly. In this work, we've enhanced the ECMpy toolbox to version 2.0, broadening its scope to automatically generate ecGEMs for a wider array of organisms. ECMpy 2.0 automates the retrieval of enzyme kinetic parameters and employs machine learning for predicting these parameters, which significantly enhances parameter coverage. Additionally, ECMpy 2.0 introduces common analytical and visualization features for ecModels, rendering computational results more user accessible. Furthermore, ECMpy 2.0 seamlessly integrates three published algorithms that exploit ecModels to uncover potential targets for metabolic engineering. ECMpy 2.0 is available at https://github.com/tibbdc/ECMpy or as a pip package (https://pypi.org/project/ECMpy/).

Biodegradation of humic acids by Streptomyces rochei to promote the growth and yield of corn

Humic acids (HAs) are organic macromolecules that play an important role in improving soil properties, plant growth and agronomic parameters. However, the feature of relatively complex aromatic structure makes it difficult to be degraded, which restricts the promotion to the crop growth. Thus, exploring microorganisms capable of degrading HAs may be a potential solution. Here, a HAs-degrading strain, Streptomyces rochei L1, and its potential for biodegradation was studied by genomics, transcriptomics, and targeted metabolomics analytical approaches. The results showed that the high molecular weight HAs were cleaved to low molecular aliphatic and aromatic compounds and their derivatives. This cleavage may be associated with the laccase (KatE). In addition, the polysaccharide deacetylase (PdgA) catalyzes the removal of acetyl groups from specific sites on the HAs molecule, resulting in structural changes. The field experiment showed that the degraded HAs significantly promote the growth of corn seedlings and increase the corn yield by 3.6 %. The HAs-degrading products, including aromatic and low molecular weight aliphatic substances as well as secondary metabolites from S. rochei L1, might be the key components responsible for the corn promotion. Our findings will advance the application of HAs as soil nutrients for the green and sustainable agriculture.