Kidney360. 2024 Nov 5. doi: 10.34067/KID.0000000620. Online ahead of print.

ABSTRACT

BACKGROUND: The Network for Pancreatic Organ donors with Diabetes-Kidney (nPOD-K) project was initiated to assess the feasibility of using kidneys from organ donors to enhance understanding of diabetic kidney disease (DKD) progression.

METHODS: Traditional and digital pathology approaches were employed to characterize the nPOD-K cohort. Periodic acid-Schiff- and Hematoxylin and Eosin-stained sections were used to manually examine and score each nPOD-K case. Brightfield and fluorescently labelled whole slide images of nPOD-K sections were used to train, validate, and test deep learning compartment segmentation and machine learning image analysis tools within Visiopharm software. These digital pathology tools were subsequently employed to evaluate kidney cell-specific markers and pathological indicators.

RESULTS: Digital quantitation of mesangial expansion, tubular atrophy, kidney injury molecule (KIM)-1 expression, cellular infiltration, and fibrosis index aligned with histological DKD classification, as defined by pathologists' review. Histological quantification confirmed loss of podocyte, endothelial, and tubular markers, correlating with DKD progression. Altered expression patterns of prominin-1, protein-tyrosine phosphatase receptor type O, and coronin 2B were validated, in agreement with reported literature.

CONCLUSIONS: The nPOD-K cohort provides a unique open resource opportunity to not only validate putative drug targets but also better understand DKD pathophysiology. A broad range of pathogenesis can be visualized in each case, providing a simulated timeline of DKD progression. We conclude that organ donor-derived tissues serve as high-quality samples, provide a comprehensive view of tissue pathology, and address the need for human kidney tissues available for research.

PMID:39499578 | DOI:10.34067/KID.0000000620

Elife. 2024 Nov 5;13:RP98489. doi: 10.7554/eLife.98489.

ABSTRACT

Animal behaviour alternates between stochastic exploration and goal-directed actions, which are generated by the underlying neural dynamics. Previously, we demonstrated that the compositional Restricted Boltzmann Machine (cRBM) can decompose whole-brain activity of larval zebrafish data at the neural level into a small number (∼100-200) of assemblies that can account for the stochasticity of the neural activity (van der Plas et al., eLife, 2023). Here, we advance this representation by extending to a combined stochastic-dynamical representation to account for both aspects using the recurrent temporal RBM (RTRBM) and transfer-learning based on the cRBM estimate. We demonstrate that the functional advantage of the RTRBM is captured in the temporal weights on the hidden units, representing neural assemblies, for both simulated and experimental data. Our results show that the temporal expansion outperforms the stochastic-only cRBM in terms of generalization error and achieves a more accurate representation of the moments in time. Lastly, we demonstrate that we can identify the original time-scale of assembly dynamics by estimating multiple RTRBMs at different temporal resolutions. Together, we propose that RTRBMs are a valuable tool for capturing the combined stochastic and time-predictive dynamics of large-scale data sets.

PMID:39499540 | DOI:10.7554/eLife.98489

J Chem Inf Model. 2024 Nov 5. doi: 10.1021/acs.jcim.4c01267. Online ahead of print.

ABSTRACT

The CACHE challenges are a series of prospective benchmarking exercises to evaluate progress in the field of computational hit-finding. Here we report the results of the inaugural CACHE challenge in which 23 computational teams each selected up to 100 commercially available compounds that they predicted would bind to the WDR domain of the Parkinson's disease target LRRK2, a domain with no known ligand and only an apo structure in the PDB. The lack of known binding data and presumably low druggability of the target is a challenge to computational hit finding methods. Of the 1955 molecules predicted by participants in Round 1 of the challenge, 73 were found to bind to LRRK2 in an SPR assay with a KD lower than 150 μM. These 73 molecules were advanced to the Round 2 hit expansion phase, where computational teams each selected up to 50 analogs. Binding was observed in two orthogonal assays for seven chemically diverse series, with affinities ranging from 18 to 140 μM. The seven successful computational workflows varied in their screening strategies and techniques. Three used molecular dynamics to produce a conformational ensemble of the targeted site, three included a fragment docking step, three implemented a generative design strategy and five used one or more deep learning steps. CACHE #1 reflects a highly exploratory phase in computational drug design where participants adopted strikingly diverging screening strategies. Machine learning-accelerated methods achieved similar results to brute force (e.g., exhaustive) docking. First-in-class, experimentally confirmed compounds were rare and weakly potent, indicating that recent advances are not sufficient to effectively address challenging targets.

PMID:39499532 | DOI:10.1021/acs.jcim.4c01267

Plant Physiol. 2024 Nov 5:kiae577. doi: 10.1093/plphys/kiae577. Online ahead of print.

NO ABSTRACT

PMID:39498829 | DOI:10.1093/plphys/kiae577

Front Genet. 2024 Oct 21;15:1458851. doi: 10.3389/fgene.2024.1458851. eCollection 2024.

ABSTRACT

The complex nature of microbiome data has made the differential abundance analysis challenging. Microbiome abundance counts are often skewed to the right and heteroscedastic (also known as overdispersion), potentially leading to incorrect inferences if not properly addressed. In this paper, we propose a simple yet effective framework to tackle the challenges by integrating Poisson (log-linear) regression with standard error estimation through the Bootstrap method and Sandwich robust estimation. Such standard error estimates are accurate and yield satisfactory inference even if the distributional assumption or the variance structure is incorrect. Our approach is validated through extensive simulation studies, demonstrating its effectiveness in addressing overdispersion and improving inference accuracy. Additionally, we apply our approach to two real datasets collected from the human gut and vagina, respectively, demonstrating the wide applicability of our methods. The results highlight the efficacy of our covariance estimators in addressing the challenges of microbiome data analysis. The corresponding software implementation is publicly available at https://github.com/yimshi/robustestimates.

PMID:39498319 | PMC:PMC11532193 | DOI:10.3389/fgene.2024.1458851

Heliyon. 2024 Oct 15;10(20):e39378. doi: 10.1016/j.heliyon.2024.e39378. eCollection 2024 Oct 30.

ABSTRACT

Alzheimer's disease (AD) is a prevalent neurodegenerative disorder characterized by symptoms such as memory loss and impaired learning. This study conducted a cross-transcriptomic analysis of AD using existing microarray datasets from the hippocampus (HC) and entorhinal cortex (EC), comparing them with age-matched non-AD controls. Both of these brain regions are critical for learning and memory processing and are vulnerable areas that exhibit abnormalities in early AD. The cross-transcriptomic analysis identified 564 significantly differentially expressed genes in HC and 479 in EC. Among these, 151 genes were significantly differentially expressed in both tissues, with functions related to synaptic vesicle clustering, synaptic vesicle exocytosis/endocytosis, mitochondrial ATP synthesis, hydrogen ion transmembrane transport, and structural constituent of cytoskeleton, suggesting a potential association between cognitive decline in AD, synaptic vesicle dynamics, dysregulation of cytoskeleton organization, and mitochondrial dysfunction. Further gene ontology analysis specific to the HC revealed the gene ontology enrichment in aerobic respiration, synaptic vesicle cycle, and oxidative phosphorylation. The enrichment analysis in CA1 of HC revealed differentiation in gene expression related to mitochondrial membrane functions involved in bioenergetics, mitochondrial electron transport, and biological processes associated with microtubule-based process, while analysis in the EC region showed enrichment in synaptic vesicle dynamics which is associated with neurotransmitter release and the regulation of postsynaptic membrane potential and synaptic transmission of GABAergic and glutamatergic synapse. Protein-protein interaction analysis highlighted central hub proteins predominantly expressed in mitochondria, involved in regulation of oxidative stress and ATP synthesis. These hub proteins interact not only within the mitochondria but also with proteins in the vesicular membrane and neuronal cytoskeleton, indicating a central role of mitochondria. This finding underscores the association between clinical symptoms and mitochondrial dysregulation of synaptic vesicle dynamics, cytoskeleton organization, and mitochondrial processes in both the HC and EC of AD. Therefore, targeting these dysregulated pathways could provide promising therapeutic targets aimed at cognitive decline and memory impairment in early AD stages.

PMID:39498000 | PMC:PMC11534180 | DOI:10.1016/j.heliyon.2024.e39378

Curr Res Microb Sci. 2024 Oct 11;7:100290. doi: 10.1016/j.crmicr.2024.100290. eCollection 2024.

ABSTRACT

Actinomycetes are a prolific source of bioactive natural compounds many of which are used as antibiotics or other drugs. In this study we investigated the genomic and biochemical diversity of 32 actinobacterial strains that had been deposited at the DSMZ-German Collection of Microorganisms and Cell Cultures decades ago. Genome-based phylogeny and in silico DNA-DNA hybridization supported the assignment of these strains to 26 novel species and two novel subspecies and a reclassification of a Streptomyces species. These results were consistent with the biochemical, enzymatic, and chemotaxonomic features of the strains. Most of the strains showed antimicrobial activities against a range of Gram-positive and Gram-negative bacteria, and against yeast. Genomic analysis revealed the presence of numerous unique biosynthetic gene clusters (BGCs) encoding for potential novel antibiotic and anti-cancer compounds. Strains DSM 41636T and DSM 61640T produced the antibiotic compounds A33853 and SF2768, respectively. Overall, this reflects the significant pharmaceutical and biotechnological potential of the proposed novel type strains and underlines the role of prokaryotic systematics for drug discovery. In order to compensate for the gender gap in naming prokaryotic species, we propose the eponyms for all newly described species to honour female scientists.

PMID:39497933 | PMC:PMC11533595 | DOI:10.1016/j.crmicr.2024.100290

Plant Commun. 2024 Nov 4:101181. doi: 10.1016/j.xplc.2024.101181. Online ahead of print.

ABSTRACT

Brassinosteroids (BRs) are steroidal phytohormones indispensable for plant growth, development, and responses to environmental stresses. The export of bioactive BRs to the apoplast is essential for BR signalling initiation, which requires binding of BR molecule to the extracellular domains of the plasma membrane-localized receptor complex. We have previously shown that the Arabidopsis thaliana ATP-binding cassette (ABC) transporter, ABCB19, functions as a BR exporter, and together with its close homologue, ABCB1, positively regulate BR signalling. Here, we demonstrate that ABCB1 is another BR transporter. The ATP hydrolysis activity of ABCB1 was stimulated by bioactive BRs, and its transport activity was confirmed in proteoliposomes and protoplasts. Structures of ABCB1 in substrate-unbound (apo), brassinolide (BL)-bound, and ATP plus BL-bound states were determined. In the BL-bound structure, BL was bound to the hydrophobic cavity formed by the transmembrane domain, and triggered local conformational changes. Together, our data provide additional insights into the ABC transporter-mediated BR export.

PMID:39497419 | DOI:10.1016/j.xplc.2024.101181

Mol Plant Pathol. 2024 Nov;25(11):e70023. doi: 10.1111/mpp.70023.

ABSTRACT

Ciboria shiraiana is a necrotrophic fungus that causes mulberry sclerotinia disease resulting in huge economic losses in agriculture. During infection, the fungus uses immunity elicitors to induce plant tissue necrosis that could facilitate its colonization on plants. However, the key elicitors and immune mechanisms remain unclear in C. shiraiana. Herein, a novel elicitor Cs08297 secreted by C. shiraiana was identified, and it was found to target the apoplast in plants to induce cell death. Cs08297 is a cysteine-rich protein unique to C. shiraiana, and cysteine residues in Cs08297 were crucial for its ability to induce cell death. Cs08297 induced a series of defence responses in Nicotiana benthamiana, including the burst of reactive oxygen species (ROS), callose deposition, and activation of defence-related genes. Cs08297 induced-cell death was mediated by leucine-rich repeat (LRR) receptor-like kinases BAK1 and SOBIR1. Purified His-tagged Cs08297-thioredoxin fusion protein triggered cell death in different plants and enhanced plant resistance to diseases. Cs08297 was necessary for sclerotial development, oxidative-stress adaptation, and cell wall integrity but negatively regulated virulence of C. shiraiana. In conclusion, our results revealed that Cs08297 is a novel fungal elicitor in fungi inducing plant immunity. Furthermore, its potential to enhance plant resistance provides a new target to control agricultural diseases biologically.

PMID:39497269 | DOI:10.1111/mpp.70023

Microbiome. 2024 Nov 4;12(1):228. doi: 10.1186/s40168-024-01941-7.

ABSTRACT

BACKGROUND: Lifestyle plays an important role in shaping the gut microbiome. However, its contributions to the oral microbiome remain less clear, due to the confounding effects of geography and methodology in investigations of populations studied to date. Furthermore, while the oral microbiome seems to differ between foraging and industrialized populations, we lack insight into whether transitions to and away from agrarian lifestyles shape the oral microbiota. Given the growing interest in so-called "vanishing microbiomes" potentially being a risk factor for increased disease prevalence in industrialized populations, it is important that we distinguish lifestyle from geography in the study of microbiomes across populations.

RESULTS: Here, we investigate salivary microbiomes of 63 Nepali individuals representing a spectrum of lifestyles: foraging, subsistence farming (individuals that transitioned from foraging to farming within the last 50 years), agriculturalists (individuals that have transitioned to farming for at least 300 years), and industrialists (expatriates that immigrated to the USA within the last 20 years). We characterize the role of lifestyle in microbial diversity, identify microbes that differ between lifestyles, and pinpoint specific lifestyle factors that may be contributing to differences in the microbiomes across populations. Contrary to prevailing views, when geography is controlled for, oral microbiome alpha diversity does not differ significantly across lifestyles. Microbiome composition, however, follows the gradient of lifestyles from foraging through agrarianism to industrialism, supporting the notion that lifestyle indeed plays a role in the oral microbiome. Relative abundances of several individual taxa, including Streptobacillus and an unclassified Porphyromonadaceae genus, also mirror lifestyle. Finally, we identify specific lifestyle factors associated with microbiome composition across the gradient of lifestyles, including smoking and grain sources.

CONCLUSION: Our findings demonstrate that by studying populations within Nepal, we can isolate an important role of lifestyle in determining oral microbiome composition. In doing so, we highlight the potential contributions of several lifestyle factors, underlining the importance of carefully examining the oral microbiome across lifestyles to improve our understanding of global microbiomes. Video Abstract.

PMID:39497165 | DOI:10.1186/s40168-024-01941-7

BMC Plant Biol. 2024 Nov 5;24(1):1044. doi: 10.1186/s12870-024-05749-3.

ABSTRACT

BACKGROUND: Groundnut is mainly grown in the semi-arid tropic (SAT) regions worldwide, where abiotic stress like drought is persistent. However, a major research gap exists regarding exploring the genetic and genomic underpinnings of tolerance to drought. In this study, a multi-parent advanced generation inter-cross (MAGIC) population was developed and evaluated for five seasons at two locations for three consecutive years (2018-19, 2019-20 and 2020-21) under drought stress and normal environments.

RESULTS: Phenotyping data of drought tolerance related traits, combined with the high-quality 10,556 polymorphic SNPs, were used to perform multi-locus model genome-wide association study (GWAS) analysis. We identified 37 significant marker-trait associations (MTAs) (Bonferroni-corrected) accounting, 0.91- 9.82% of the phenotypic variance. Intriguingly, 26 significant MTAs overlap on four chromosomes (Ah03, Ah07, Ah10 and Ah18) (harboring 70% of MTAs), indicating genomic hotspot regions governing drought tolerance traits. Furthermore, important candidate genes associated with leaf senescence (NAC transcription factor), flowering (B3 domain-containing transcription factor, Ulp1 protease family, and Ankyrin repeat-containing protein), involved in chlorophyll biosynthesis (FAR1 DNA-binding domain protein), stomatal regulation (Rop guanine nucleotide exchange factor; Galacturonosyltransferases), and associated with yield traits (Fasciclin-like arabinogalactan protein 11 and Fasciclin-like arabinogalactan protein 21) were found in the vicinity of significant MTAs genomic regions.

CONCLUSION: The findings of our investigation have the potential to provide a basis for significant MTAs validation, gene discovery and development of functional markers, which could be employed in genomics-assisted breeding to develop climate-resilient groundnut varieties.

PMID:39497063 | DOI:10.1186/s12870-024-05749-3

Nat Biotechnol. 2024 Nov 4. doi: 10.1038/s41587-024-02462-2. Online ahead of print.

ABSTRACT

The copy number of a plasmid is linked to its functionality, yet there have been few attempts to optimize higher-copy-number mutants for use across diverse origins of replication in different hosts. We use a high-throughput growth-coupled selection assay and a directed evolution approach to rapidly identify origin of replication mutations that influence copy number and screen for mutants that improve Agrobacterium-mediated transformation (AMT) efficiency. By introducing these mutations into binary vectors within the plasmid backbone used for AMT, we observe improved transient transformation of Nicotiana benthamiana in four diverse tested origins (pVS1, RK2, pSa and BBR1). For the best-performing origin, pVS1, we isolate higher-copy-number variants that increase stable transformation efficiencies by 60-100% in Arabidopsis thaliana and 390% in the oleaginous yeast Rhodosporidium toruloides. Our work provides an easily deployable framework to generate plasmid copy number variants that will enable greater precision in prokaryotic genetic engineering, in addition to improving AMT efficiency.

PMID:39496930 | DOI:10.1038/s41587-024-02462-2

PLoS Comput Biol. 2024 Nov 4;20(11):e1012576. doi: 10.1371/journal.pcbi.1012576. Online ahead of print.

ABSTRACT

The increasing availability of enzyme turnover number measurements from experiments and of turnover number predictions from deep learning models prompts the use of these enzyme parameters in precise metabolic engineering. Yet, there is no computational approach that allows the prediction of metabolic engineering strategies that rely on the modification of turnover numbers. It is also unclear if modifications of turnover numbers without alterations in the host's transcriptional regulatory machinery suffice to increase the production of chemicals of interest. Here, we present a constraint-based modeling approach, termed Overcoming Kinetic rate Obstacles (OKO), that uses enzyme-constrained metabolic models to predict in silico strategies to increase the production of a given chemical, while ensuring specified cell growth. We demonstrate that the application of OKO to enzyme-constrained metabolic models of Escherichia coli and Saccharomyces cerevisiae results in strategies that can at least double the production of over 40 compounds with little penalty to growth. Interestingly, we show that the overproduction of compounds of interest does not entail only an increase in the values of turnover numbers. Lastly, we demonstrate that a refinement of OKO, allowing also for manipulation of enzyme abundance, facilitates the usage of the available compendia and deep learning models of turnover numbers in the design of precise metabolic engineering strategies. Our results expand the usage of genome-scale metabolic models toward the identification of targets for protein engineering, allowing their direct usage in the generation of innovative metabolic engineering designs for various biotechnological applications.

PMID:39495797 | DOI:10.1371/journal.pcbi.1012576

J Cell Biol. 2025 Jan 6;224(1):e202403141. doi: 10.1083/jcb.202403141. Epub 2024 Nov 4.

ABSTRACT

In mammalian axon-carrying-dendrite (AcD) neurons, the axon emanates from a basal dendrite, instead of the soma, to create a privileged route for action potential generation at the axon initial segment (AIS). However, it is unclear how such unusual morphology is established and whether the structure and function of the AIS in AcD neurons are preserved. By using dissociated hippocampal cultures as a model, we show that the development of AcD morphology can occur prior to synaptogenesis and independently of the in vivo environment. A single precursor neurite first gives rise to the axon and then to the AcD. The AIS possesses a similar cytoskeletal architecture as the soma-derived AIS and similarly functions as a trafficking barrier to retain axon-specific molecular composition. However, it does not undergo homeostatic plasticity, contains lesser cisternal organelles, and receives fewer inhibitory inputs. Our findings reveal insights into AcD neuron biology and underscore AIS structural differences based on axon onset.

PMID:39495320 | DOI:10.1083/jcb.202403141

Bioinformatics. 2024 Nov 4:btae651. doi: 10.1093/bioinformatics/btae651. Online ahead of print.

ABSTRACT

MOTIVATION: Defective viral genomes (DVGs) are variants of the wild-type (wt) virus that lack the ability to complete autonomously an infectious cycle. However, in the presence of their parental (helper) wt virus, DVGs can interfere with the replication, encapsidation and spread of functional genomes, acting as a significant selective force in viral evolution. DVGs also affect the host's immune responses and are linked to chronic infections and milder symptoms. Thus, identifying and characterizing DVGs is crucial for understanding infection prognosis. Quantifying DVGs is challenging due to their inability to sustain themselves, which makes it difficult to distinguish them from the helper virus, especially using high-throughput RNA sequencing (RNA-seq). An accurate quantification is essential for understanding their very dynamical interactions with the helper virus.

RESULTS: We present a method to simultaneously estimate the abundances of DVGs and wt genomes within a sample by identifying genomic regions with significant deviations from the expected sequencing depth. Our approach involves reconstructing the depth profile through a linear system of equations, which provides an estimate of the number of wt and DVG genomes of each type. Until now, in silico methods have only estimated the DVG-to-wt ratio for localized genomic regions. This is the first method that simultaneously estimates the proportions of wt and DVGs genome wide from short-reads RNA sequencing.

AVAILABILITY AND IMPLEMENTATION: The MATLAB code and the synthetic datasets are freely available at https://github.com/jmusan/wtDVGquantific.

PMID:39495116 | DOI:10.1093/bioinformatics/btae651

Brief Bioinform. 2024 Sep 23;25(6):bbae569. doi: 10.1093/bib/bbae569.

ABSTRACT

Systems biology aims to understand living organisms through mathematically modeling their behaviors at different organizational levels, ranging from molecules to populations. Modeling involves several steps, from determining the model purpose to developing the mathematical model, implementing it computationally, simulating the model's behavior, evaluating, and refining the model. Importantly, model simulation results must be reproducible, ensuring that other researchers can obtain the same results after writing the code de novo and/or using different software tools. Guidelines to increase model reproducibility have been published. However, reproducibility remains a major challenge in this field. In this paper, we tackle this challenge for physiologically-based pharmacokinetic (PBPK) models, which represent the pharmacokinetics of chemicals following exposure in humans or animals. We summarize recommendations for PBPK model reporting that should apply during model development and implementation, in order to ensure model reproducibility and comprehensibility. We make a proposal aiming to harmonize abbreviations used in PBPK models. To illustrate these recommendations, we present an original and reproducible PBPK model code in MATLAB, alongside an example of MATLAB code converted to Systems Biology Markup Language format using MOCCASIN. As directions for future improvement, more tools to convert computational PBPK models from different software platforms into standard formats would increase the interoperability of these models. The application of other systems biology standards to PBPK models is encouraged. This work is the result of an interdisciplinary collaboration involving the ELIXIR systems biology community. More interdisciplinary collaborations like this would facilitate further harmonization and application of good modeling practices in different systems biology fields.

PMID:39494970 | DOI:10.1093/bib/bbae569

J Virol. 2024 Nov 4:e0122324. doi: 10.1128/jvi.01223-24. Online ahead of print.

ABSTRACT

The effectiveness of SARS-CoV-2 therapeutic antibodies targeting the spike (S) receptor-binding domain (RBD) has been hampered by the emergence of variants of concern (VOCs), which have acquired mutations to escape neutralizing antibodies (nAbs). These mutations are not evenly distributed on the RBD surface but cluster on several distinct surfaces, suggesting an influence of the targeted epitope on the capacity to neutralize a broad range of VOCs. Here, we identified a potent nAb from convalescent patients targeting the receptor-binding domain of a broad range of SARS-CoV-2 VOCs. Except for the Lambda and BA.2.86 variants, this nAb efficiently inhibited the entry of most tested VOCs, including Omicron subvariants BA.1, BA.2, XBB.1.5, and EG.5.1 and to a limited extent also BA.4/5, BA.4.6, and BQ.1.1. It bound recombinant S protein with picomolar affinity, reduced the viral load in the lung of infected hamsters, and prevented the severe lung pathology typical for SARS-CoV-2 infections. An X-ray structure of the nAb-RBD complex revealed an epitope that does not fall into any of the conventional classes and provided insights into its broad neutralization properties. Our findings highlight a conserved epitope within the SARS-CoV-2 RBD that should be preferably targeted by therapeutic antibodies and inform rational vaccine development.IMPORTANCETherapeutic antibodies are effective in preventing severe disease from SARS-CoV-2 infection and constitute an important option in pandemic preparedness, but mutations within the S protein of virus variants (e.g., a mutation of L452) confer resistance to many of such antibodies. Here, we identify a human antibody targeting the S protein receptor-binding domain (RBD) with an elevated escape barrier and characterize its interaction with the RBD functionally and structurally at the atomic level. A direct comparison with reported antibodies targeting the same epitope illustrates important differences in the interface, providing insights into the breadth of antibody binding. These findings highlight the relevance of an extended neutralization profiling in combination with biochemical and structural characterization of the antibody-RBD interaction for the selection of future therapeutic antibodies, which may accelerate the control of potential future pandemics.

PMID:39494911 | DOI:10.1128/jvi.01223-24

Appl Environ Microbiol. 2024 Nov 4:e0150524. doi: 10.1128/aem.01505-24. Online ahead of print.

ABSTRACT

Horizontal gene transfer (HGT) is a major process by which genes are transferred between microbes in the rhizosphere. However, examining HGT remains challenging due to the complexity of mimicking conditions within the rhizosphere. Fabricated ecosystems (EcoFABs) have been used to investigate several complex processes in plant-associated environments. Here we show that EcoFABs are efficient tools to examine and measure HGT frequency in the rhizosphere. We provide the first demonstration of gene transfer via a triparental conjugation system in the Brachypodium distachyon rhizosphere in an EcoFAB using Pseudomonas putida KT2440 as both donor and recipient bacterial strain with the donor containing a mobilizable and non-self-transmissible plasmid. We observed that the frequency of plasmid transfer in the rhizosphere is potentially dependent on the plant developmental stage and the composition and amount of root exudates. The frequency of plasmid transfer also increased with higher numbers of donor cells. We demonstrate the transfer of plasmid from P. putida to another B. distachyon root colonizer, Burkholderia sp. OAS925, showing HGT within a rhizosphere microbial community. Environmental stresses also influenced the rate and efficiency of HGT in the rhizosphere between different species and genera. This study provides a robust workflow to evaluate transfer of engineered plasmids in the rhizosphere when such plasmids are potentially introduced in a field or other plant-associated environments.IMPORTANCEWe report the use of EcoFABs to investigate the HGT process in a rhizosphere environment. It highlights the potential of EcoFABs in recapitulating the dynamic rhizosphere conditions as well as their versatility in studying plant-microbe interactions. This study also emphasizes the importance of studying the parameters impacting the HGT frequency. Several factors such as plant developmental stages, nutrient conditions, number of donor cells, and environmental stresses influence gene transfer within the rhizosphere microbial community. This study paves the way for future investigations into understanding the fate and movement of engineered plasmids in a field environment.

PMID:39494898 | DOI:10.1128/aem.01505-24

PeerJ. 2024 Oct 30;12:e18383. doi: 10.7717/peerj.18383. eCollection 2024.

ABSTRACT

Microbial arsenic (As) transformations play a vital role in both driving the global arsenic biogeochemical cycle and determining the mobility and toxicity of arsenic in soils. Due to the complexity of soils, variations in soil characteristics, and the presence and condition of overlying vegetation, soil microbiomes and their functional pathways vary from site to site. Consequently, key arsenic-transforming mechanisms in soil are not well characterized. This study utilized a combination of high-throughput amplicon sequencing and shotgun metagenomics to identify arsenic-transforming pathways in surface agricultural soils. The temporal and successional variations of the soil microbiome and arsenic-transforming bacteria in agricultural soils were examined during tropical monsoonal dry and wet seasons, with a six-month interval. Soil microbiomes of both dry and wet seasons were relatively consistent, particularly the relative abundance of Chloroflexi, Gemmatimonadota, and Bacteroidota. Common bacterial taxa present at high abundance, and potentially capable of arsenic transformations, were Bacillus, Streptomyces, and Microvirga. The resulting shotgun metagenome indicated that among the four key arsenic-functional genes, the arsC gene exhibited the highest relative abundance, followed by the arsM, aioA, and arrA genes, in declining sequence. Gene sequencing data based on 16S rRNA predicted only the arsC and aioA genes. Overall, this study proposed that a cooperative mechanism involving detoxification through arsenate reduction and arsenic methylation was a key arsenic transformation in surface agricultural soils with low arsenic concentration (7.60 to 10.28 mg/kg). This study significantly advances our knowledge of arsenic-transforming mechanisms interconnected with microbial communities in agricultural soil, enhancing pollution control measures, mitigating risks, and promoting sustainable soil management practices.

PMID:39494289 | PMC:PMC11531259 | DOI:10.7717/peerj.18383

iScience. 2024 Oct 4;27(11):110960. doi: 10.1016/j.isci.2024.110960. eCollection 2024 Nov 15.

ABSTRACT

Predicting the phenotypic impact of genetic variants and treatments is crucial in cancer genetics and precision oncology. Here, we have developed a noise decorrelation method that enables quantitative phase imaging (QPI) with the capability for label-free noninvasive mapping of intracellular dry mass fluctuations within the millisecond-to-second timescale regime, previously inaccessible due to temporal phase noise. Applied to breast cancer cells, this method revealed regions driven by thermal forces and regions of intense activity fueled by ATP hydrolysis. Intriguingly, as malignancy increases, the cells strategically expand these active regions to satisfy increasing energy demands. We propose parameters encapsulating key information about the spatiotemporal distribution of intracellular fluctuations, enabling precise phenotyping. This technique addresses the need for accurate, rapid functional screening methods in cancer medicine.

PMID:39493877 | PMC:PMC11530848 | DOI:10.1016/j.isci.2024.110960