ChemistryOpen. 2024 Mar 1:e202300263. doi: 10.1002/open.202300263. Online ahead of print.

ABSTRACT

Organophosphates (OPs) are a class of neurotoxic acetylcholinesterase inhibitors including widely used pesticides as well as nerve agents such as VX and VR. Current treatment of these toxins relies on reactivating acetylcholinesterase, which remains ineffective. Enzymatic scavengers are of interest for their ability to degrade OPs systemically before they reach their target. Here we describe a library of computationally designed variants of phosphotriesterase (PTE), an enzyme that is known to break down OPs. The mutations G208D, F104A, K77A, A80V, H254G, and I274N broadly improve catalytic efficiency of VX and VR hydrolysis without impacting the structure of the enzyme. The mutation I106 A improves catalysis of VR and L271E abolishes activity, likely due to disruptions of PTE's structure. This study elucidates the importance of these residues and contributes to the design of enzymatic OP scavengers with improved efficiency.

PMID:38426687 | DOI:10.1002/open.202300263

Mol Med Rep. 2024 Apr;29(4):65. doi: 10.3892/mmr.2024.13189. Epub 2024 Mar 1.

ABSTRACT

Subsequently to the publication of the above article, an interested reader drew to the authors' attention that two pairs of data panels featured in Figs. 2E and 6D, portraying the results from cell invasion and migration assay experiments, appeared to contain overlapping sections, such that data which were intended to show the results from differently performed experiments had apparently been derived from a smaller number of original sources. The authors were able to re‑examine their original data (which was also presented to the Editorial Office), and realized that errors has been made in the compilation of Fig. 2. The proposed revised version of Fig. 2, now showing the results from the 'field 1' view of the data, is shown on the next page. Note that these errors did not significantly affect either the results or the conclusions reported in this paper,.All the authors agree to the publication of this Corrigendum, and are grateful to the Editor of Molecular Medicine Reports for allowing them the opportunity to correct this error; furthermore, they apologize to the readership for any inconvenience caused. [Molecular Medicine Reports 25: 71, 2022; DOI: 10.3892/mmr.2022.12587].

PMID:38426568 | DOI:10.3892/mmr.2024.13189

Obesity (Silver Spring). 2024 Mar 1. doi: 10.1002/oby.23989. Online ahead of print.

ABSTRACT

OBJECTIVE: Big Data are increasingly used in obesity and nutrition research to gain new insights and derive personalized guidance; however, this data in raw form are often not usable. Substantial preprocessing, which requires machine learning (ML), human judgment, and specialized software, is required to transform Big Data into artificial intelligence (AI)- and ML-ready data. These preprocessing steps are the most complex part of the entire modeling pipeline. Understanding the complexity of these steps by the end user is critical for reducing misunderstanding, faulty interpretation, and erroneous downstream conclusions.

METHODS: We reviewed three popular obesity/nutrition Big Data sources: microbiome, metabolomics, and accelerometry. The preprocessing pipelines, specialized software, challenges, and how decisions impact final AI- and ML-ready products were detailed.

RESULTS: Opportunities for advances to improve quality control, speed of preprocessing, and intelligent end user consumption were presented.

CONCLUSIONS: Big Data have the exciting potential for identifying new modifiable factors that impact obesity research. However, to ensure accurate interpretation of conclusions arising from Big Data, the choices involved in preparing AI- and ML-ready data need to be transparent to investigators and clinicians relying on the conclusions.

PMID:38426232 | DOI:10.1002/oby.23989

JACS Au. 2024 Feb 13;4(2):760-770. doi: 10.1021/jacsau.3c00781. eCollection 2024 Feb 26.

ABSTRACT

We reported over 20 years ago MNS-4.1, the first DNA aptamer with a micromolar affinity for cocaine. MNS-4.1 is based on a structural motif that is very common in any random pool of oligonucleotides, and it is actually a nonspecific hydrophobic receptor with wide cross-reactivity with alkaloids and steroids. Despite such weaknesses preventing broad applications, this aptamer became widely used in proof-of-concept demonstrations of new formats of biosensors. We now report a series of progressively improved DNA aptamers recognizing cocaine, with the final optimized receptors having low nanomolar affinity and over a thousand-fold selectivity over the initial cross-reactants. In the process of optimization, we tested different methods to eliminate cross-reactivities and improve affinity, eventually achieving properties that are comparable to those of the reported monoclonal antibody candidates for the therapy of overdose. Multiple aptamers that we now report share structural motifs with the previously reported receptor for serotonin. Further mutagenesis studies revealed a palindromic, highly adaptable, broadly cross-reactive hydrophobic motif that could be rebuilt through mutagenesis, expansion of linker regions, and selections into receptors with exceptional affinities and varying specificities.

PMID:38425914 | PMC:PMC10900216 | DOI:10.1021/jacsau.3c00781

J Am Soc Nephrol. 2024 Mar 1. doi: 10.1681/ASN.0000000000000330. Online ahead of print.

NO ABSTRACT

PMID:38424674 | DOI:10.1681/ASN.0000000000000330

EBioMedicine. 2024 Feb 28;101:105028. doi: 10.1016/j.ebiom.2024.105028. Online ahead of print.

ABSTRACT

BACKGROUND: Understanding formation of the human tissue resident memory T cell (TRM) repertoire requires longitudinal access to human non-lymphoid tissues.

METHODS: By applying flow cytometry and next generation sequencing to serial blood, lymphoid tissue, and gut samples from 16 intestinal transplantation (ITx) patients, we assessed the origin, distribution, and specificity of human TRMs at phenotypic and clonal levels.

FINDINGS: Donor age ≥1 year and blood T cell macrochimerism (peak level ≥4%) were associated with delayed establishment of stable recipient TRM repertoires in the transplanted ileum. T cell receptor (TCR) overlap between paired gut and blood repertoires from ITx patients was significantly greater than that in healthy controls, demonstrating increased gut-blood crosstalk after ITx. Crosstalk with the circulating pool remained high for years of follow-up. TCR sequences identifiable in pre-Tx recipient gut but not those in lymphoid tissues alone were more likely to populate post-Tx ileal allografts. Clones detected in both pre-Tx gut and lymphoid tissue had distinct transcriptional profiles from those identifiable in only one tissue. Recipient T cells were distributed widely throughout the gut, including allograft and native colon, which had substantial repertoire overlap. Both alloreactive and microbe-reactive recipient T cells persisted in transplanted ileum, contributing to the TRM repertoire.

INTERPRETATION: Our studies reveal human intestinal TRM repertoire establishment from the circulation, preferentially involving lymphoid tissue counterparts of recipient intestinal T cell clones, including TRMs. We have described the temporal and spatial dynamics of this active crosstalk between the circulating pool and the intestinal TRM pool.

FUNDING: This study was funded by the National Institute of Allergy and Infectious Diseases (NIAID) P01 grant AI106697.

PMID:38422982 | DOI:10.1016/j.ebiom.2024.105028

Comput Biol Med. 2024 Feb 21;171:108182. doi: 10.1016/j.compbiomed.2024.108182. Online ahead of print.

ABSTRACT

Cell-type-Specific Chromatin Loops (CSCLs) are crucial for gene regulation and cell fate determination. However, the mechanisms governing their establishment remain elusive. Here, we present SpecLoop, a network regularization-based machine learning framework, to investigate the role of transcription factors (TFs) cooperation in CSCL formation. SpecLoop integrates multi-omics data, including gene expression, chromatin accessibility, sequence, protein-protein interaction, and TF binding motif data, to predict CSCLs and identify TF cooperations. Using high resolution Hi-C data as the gold standard, SpecLoop accurately predicts CSCL in GM12878, IMR90, HeLa-S3, K562, HUVEC, HMEC, and NHEK seven cell types, with the AUROC values ranging from 0.8645 to 0.9852 and AUPR values ranging from 0.8654 to 0.9734. Notably SpecLoop demonstrates improved accuracy in predicting long-distance CSCLs and identifies TF complexes with strong predictive ability. Our study systematically explores the TFs and TF pairs associated with CSCL through effective integration of diverse omics data. SpecLoop is freely available at https://github.com/AMSSwanglab/SpecLoop.

PMID:38422958 | DOI:10.1016/j.compbiomed.2024.108182

BioData Min. 2024 Feb 29;17(1):8. doi: 10.1186/s13040-024-00359-z.

ABSTRACT

BACKGROUND: Breast cancer is the most common malignancy among women worldwide. Despite advances in treating breast cancer over the past decades, drug resistance and adverse effects remain challenging. Recent therapeutic progress has shifted toward using drug combinations for better treatment efficiency. However, with a growing number of potential small-molecule cancer inhibitors, in silico strategies to predict pharmacological synergy before experimental trials are required to compensate for time and cost restrictions. Many deep learning models have been previously proposed to predict the synergistic effects of drug combinations with high performance. However, these models heavily relied on a large number of drug chemical structural fingerprints as their main features, which made model interpretation a challenge.

RESULTS: This study developed a deep neural network model that predicts synergy between small-molecule pairs based on their inhibitory activities against 13 selected key proteins. The synergy prediction model achieved a Pearson correlation coefficient between model predictions and experimental data of 0.63 across five breast cancer cell lines. BT-549 and MCF-7 achieved the highest correlation of 0.67 when considering individual cell lines. Despite achieving a moderate correlation compared to previous deep learning models, our model offers a distinctive advantage in terms of interpretability. Using the inhibitory activities against key protein targets as the main features allowed a straightforward interpretation of the model since the individual features had direct biological meaning. By tracing the synergistic interactions of compounds through their target proteins, we gained insights into the patterns our model recognized as indicative of synergistic effects.

CONCLUSIONS: The framework employed in the present study lays the groundwork for future advancements, especially in model interpretation. By combining deep learning techniques and target-specific models, this study shed light on potential patterns of target-protein inhibition profiles that could be exploited in breast cancer treatment.

PMID:38424554 | DOI:10.1186/s13040-024-00359-z

Nat Chem Biol. 2024 Feb 29. doi: 10.1038/s41589-024-01568-7. Online ahead of print.

ABSTRACT

Organisms use organic molecules called osmolytes to adapt to environmental conditions. In vitro studies indicate that osmolytes thermally stabilize proteins, but mechanisms are controversial, and systematic studies within the cellular milieu are lacking. We analyzed Escherichia coli and human protein thermal stabilization by osmolytes in situ and across the proteome. Using structural proteomics, we probed osmolyte effects on protein thermal stability, structure and aggregation, revealing common mechanisms but also osmolyte- and protein-specific effects. All tested osmolytes (trimethylamine N-oxide, betaine, glycerol, proline, trehalose and glucose) stabilized many proteins, predominantly via a preferential exclusion mechanism, and caused an upward shift in temperatures at which most proteins aggregated. Thermal profiling of the human proteome provided evidence for intrinsic disorder in situ but also identified potential structure in predicted disordered regions. Our analysis provides mechanistic insight into osmolyte function within a complex biological matrix and sheds light on the in situ prevalence of intrinsically disordered regions.

PMID:38424171 | DOI:10.1038/s41589-024-01568-7

Sci Rep. 2024 Feb 29;14(1):4972. doi: 10.1038/s41598-024-55270-8.

ABSTRACT

The 21-residue PGLa peptide is well known for antimicrobial activity attributed to its ability to compromize bacterial membranes. Using all-atom explicit solvent replica exchange molecular dynamics with solute tempering, we studied PGLa binding to a model anionic DMPC/DMPG bilayer at the high peptide:lipid ratio that promotes PGLa dimerization (a two peptides per leaflet system). As a reference we used our previous simulations at the low peptide:lipid ratio (a one peptide per leaflet system). We found that the increase in the peptide:lipid ratio suppresses PGLa helical propensity, tilts the bound peptide toward the bilayer hydrophobic core, and forces it deeper into the bilayer. Surprisingly, at the high peptide:lipid ratio PGLa binding induces weaker bilayer thinning, but deeper water permeation. We explain these effects by the cross-correlations between lipid shells surrounding PGLa that leads to a much diminished efflux of DMPC lipids from the peptide proximity at the high peptide:lipid ratio. Consistent with the experimental data the propensity for PGLa dimerization was found to be weak resulting in coexistence of monomers and dimers with distinctive properties. PGLa dimers assemble via apolar criss-cross interface and become partially expelled from the bilayer residing at the bilayer-water boundary. We rationalize their properties by the dimer tendency to preserve favorable electrostatic interactions between lysine and phosphate lipid groups as well as to avoid electrostatic repulsion between lysines in the low dielectric environment of the bilayer core. PGLa homedimer interface is predicted to be distinct from that involved in PGLa-magainin heterodimers.

PMID:38424117 | DOI:10.1038/s41598-024-55270-8

Annu Rev Plant Biol. 2024 Feb 29. doi: 10.1146/annurev-arplant-070523-041445. Online ahead of print.

ABSTRACT

Understanding the mechanistic basis of epigenetic memory has proven to be a difficult task due to the underlying complexity of the systems involved in its establishment and maintenance. Here, we review the role of computational modeling in helping to unlock this complexity, allowing the dissection of intricate feedback dynamics. We focus on three forms of epigenetic memory encoded in gene regulatory networks, DNA methylation, and histone modifications and discuss the important advantages offered by plant systems in their dissection. We summarize the main modeling approaches involved and highlight the principal conceptual advances that the modeling has enabled through iterative cycles of predictive modeling and experiments. Lastly, we discuss remaining gaps in our understanding and how intertwined theory and experimental approaches might help in their resolution. Expected final online publication date for the Annual Review of Plant Biology, Volume 75 is May 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

PMID:38424070 | DOI:10.1146/annurev-arplant-070523-041445

Science. 2024 Feb 29:eabn3263. doi: 10.1126/science.abn3263. Online ahead of print.

ABSTRACT

Vocal production learning is a convergently evolved trait in vertebrates. To identify brain genomic elements associated with mammalian vocal learning, we integrated genomic, anatomical and neurophysiological data from the Egyptian fruit-bat with analyses of the genomes of 215 placental mammals. First, we identified a set of proteins evolving more slowly in vocal learners. Then, we discovered a vocal-motor cortical region in the Egyptian fruit-bat, an emergent vocal learner, and leveraged that knowledge to identify active cis-regulatory elements in the motor cortex of vocal learners. Machine learning methods applied to motor cortex open chromatin revealed 50 enhancers robustly associated with vocal learning whose activity tended to be lower in vocal learners. Our research implicates convergent losses of motor cortex regulatory elements in mammalian vocal learning evolution.

PMID:38422184 | DOI:10.1126/science.abn3263

Science. 2024 Mar;383(6686):eabm9903. doi: 10.1126/science.abm9903. Epub 2024 Mar 1.

ABSTRACT

All living organisms deploy cell-autonomous defenses to combat infection. In plants and animals, large supramolecular complexes often activate immune proteins for protection. In this work, we resolved the native structure of a massive host-defense complex that polymerizes 30,000 guanylate-binding proteins (GBPs) over the surface of gram-negative bacteria inside human cells. Construction of this giant nanomachine took several minutes and remained stable for hours, required guanosine triphosphate hydrolysis, and recruited four GBPs plus caspase-4 and Gasdermin D as a cytokine and cell death immune signaling platform. Cryo-electron tomography suggests that GBP1 can adopt an extended conformation for bacterial membrane insertion to establish this platform, triggering lipopolysaccharide release that activated coassembled caspase-4. Our "open conformer" model provides a dynamic view into how the human GBP1 defense complex mobilizes innate immunity to infection.

PMID:38422126 | DOI:10.1126/science.abm9903

PLoS Comput Biol. 2024 Feb 29;20(2):e1011779. doi: 10.1371/journal.pcbi.1011779. Online ahead of print.

ABSTRACT

Recent studies have established that the circadian clock influences onset, progression and therapeutic outcomes in a number of diseases including cancer and heart diseases. Therefore, there is a need for tools to measure the functional state of the molecular circadian clock and its downstream targets in patients. Moreover, the clock is a multi-dimensional stochastic oscillator and there are few tools for analysing it as a noisy multigene dynamical system. In this paper we consider the methodology behind TimeTeller, a machine learning tool that analyses the clock as a noisy multigene dynamical system and aims to estimate circadian clock function from a single transcriptome by modelling the multi-dimensional state of the clock. We demonstrate its potential for clock systems assessment by applying it to mouse, baboon and human microarray and RNA-seq data and show how to visualise and quantify the global structure of the clock, quantitatively stratify individual transcriptomic samples by clock dysfunction and globally compare clocks across individuals, conditions and tissues thus highlighting its potential relevance for advancing circadian medicine.

PMID:38422117 | DOI:10.1371/journal.pcbi.1011779

Microb Genom. 2024 Feb;10(2). doi: 10.1099/mgen.0.001203.

ABSTRACT

Molecular profiling techniques such as metagenomics, metatranscriptomics or metabolomics offer important insights into the functional diversity of the microbiome. In contrast, 16S rRNA gene sequencing, a widespread and cost-effective technique to measure microbial diversity, only allows for indirect estimation of microbial function. To mitigate this, tools such as PICRUSt2, Tax4Fun2, PanFP and MetGEM infer functional profiles from 16S rRNA gene sequencing data using different algorithms. Prior studies have cast doubts on the quality of these predictions, motivating us to systematically evaluate these tools using matched 16S rRNA gene sequencing, metagenomic datasets, and simulated data. Our contribution is threefold: (i) using simulated data, we investigate if technical biases could explain the discordance between inferred and expected results; (ii) considering human cohorts for type two diabetes, colorectal cancer and obesity, we test if health-related differential abundance measures of functional categories are concordant between 16S rRNA gene-inferred and metagenome-derived profiles and; (iii) since 16S rRNA gene copy number is an important confounder in functional profiles inference, we investigate if a customised copy number normalisation with the rrnDB database could improve the results. Our results show that 16S rRNA gene-based functional inference tools generally do not have the necessary sensitivity to delineate health-related functional changes in the microbiome and should thus be used with care. Furthermore, we outline important differences in the individual tools tested and offer recommendations for tool selection.

PMID:38421266 | DOI:10.1099/mgen.0.001203

Aging (Albany NY). 2024 Feb 27;16. doi: 10.18632/aging.205604. Online ahead of print.

ABSTRACT

The search for interventions to slow down and even reverse aging is a burgeoning field. The literature cites hundreds of supposedly beneficial pharmacological and genetic interventions in model organisms: mice, rats, flies and worms, where research into physiology is routinely accompanied by lifespan data. However, when experimental animals from one article live as long as controls from another article, comparing the results of interventions across studies can yield misleading outcomes. Theoretically, all lifespan data are ripe for re-analysis: we could contrast the molecular targets and pathways across studies and help focus the further search for interventions. Alas, the results of most longevity studies are difficult to compare. This is in part because there are no clear, universally accepted standards for conducting such experiments or even for reporting such data. The situation is worsened by the fact that the authors often do not describe experimental conditions completely. As a result, works on longevity make up a set of precedents, each of which might be interesting in its own right, yet incoherent and incomparable at least for the reason that in a general context, it may indicate, for example, not prolonging the life of an average organism, but compensating for any genetic abnormalities of a particular sample or inappropriate living conditions. Here we point out specific issues and propose solutions for quality control by checking both inter- and intra-study consistency of lifespan data.

PMID:38421245 | DOI:10.18632/aging.205604

Psychiatry Clin Neurosci. 2024 Feb 29. doi: 10.1111/pcn.13655. Online ahead of print.

ABSTRACT

AIM: The gut microbiota can influence human behavior. However, due to the massive multiple-testing problem, research into the relationship between microbiome ecosystems and the human brain faces drawbacks. This problem arises when attempting to correlate thousands of gut bacteria with thousands of brain voxels.

METHODS: We performed brain magnetic resonance imaging (MRI) scans on 133 participants and applied machine-learning algorithms (Ridge regressions) combined with permutation tests. Using this approach, we were able to correlate specific gut bacterial families with brain MRI signals, circumventing the difficulties of massive multiple testing while considering sex, age, and body mass index as confounding factors.

RESULTS: The relative abundance (RA) of the Selenomonadaceae, Clostridiaceae, and Veillonellaceae families in the gut was associated with altered cerebellar, visual, and frontal T2-mapping and diffusion tensor imaging measures. Conversely, decreased relative abundance of the Eubacteriaceae family was also linked to T2-mapping values in the cerebellum. Significantly, the brain regions associated with the gut microbiome were also correlated with depressive symptoms and attentional deficits.

CONCLUSIONS: Our analytical strategy offers a promising approach for identifying potential brain biomarkers influenced by gut microbiota. By gathering a deeper understanding of the microbiota-brain connection, we can gain insights into the underlying mechanisms and potentially develop targeted interventions to mitigate the detrimental effects of dysbiosis on brain function and mental health.

PMID:38421082 | DOI:10.1111/pcn.13655

Mol Biol Evol. 2024 Feb 29:msae048. doi: 10.1093/molbev/msae048. Online ahead of print.

ABSTRACT

Errors in protein translation can lead to non-genetic, phenotypic mutations, including amino acid misincorporations. While phenotypic mutations can increase protein diversity, the systematic characterization of their proteome-wide frequencies and their evolutionary impact has been lacking. Here, we developed a mechanistic model of translation errors to investigate how selection acts on protein populations produced by amino acid misincorporations. We fitted the model to empirical observations of misincorporations obtained from over a hundred mass spectrometry datasets of E. coli and S. cerevisiae. We found that on average 20-23% of proteins synthesized in the cell are expected to harbour at least one amino acid misincorporation, and that deleterious misincorporations are less likely to occur. Combining misincorporation probabilities and the estimated fitness effects of amino acid substitutions in a population genetics framework, we found 74% of mistranslation events in E. coli and 94% in S. cerevisiae to be neutral. We further show that the set of available synonymous tRNAs is subject to evolutionary pressure, as the presence of missing tRNAs would increase codon-anticodon cross-reactivity and misincorporation error rates. Overall, we find that the translation machinery is likely optimal in E. coli and S. cerevisiae and that both local solutions at the level of codons and a global solution such as the tRNA pool can mitigate the impact of translation errors. We provide a framework to study the evolutionary impact of codon specific translation errors and a method for their proteome-wide detection across organisms and conditions.

PMID:38421032 | DOI:10.1093/molbev/msae048

Elife. 2024 Feb 29;12:RP84141. doi: 10.7554/eLife.84141.

ABSTRACT

An animal entering a new environment typically faces three challenges: explore the space for resources, memorize their locations, and navigate towards those targets as needed. Here we propose a neural algorithm that can solve all these problems and operates reliably in diverse and complex environments. At its core, the mechanism makes use of a behavioral module common to all motile animals, namely the ability to follow an odor to its source. We show how the brain can learn to generate internal "virtual odors" that guide the animal to any location of interest. This endotaxis algorithm can be implemented with a simple 3-layer neural circuit using only biologically realistic structures and learning rules. Several neural components of this scheme are found in brains from insects to humans. Nature may have evolved a general mechanism for search and navigation on the ancient backbone of chemotaxis.

PMID:38420996 | DOI:10.7554/eLife.84141

ACS Synth Biol. 2024 Feb 29. doi: 10.1021/acssynbio.3c00631. Online ahead of print.

ABSTRACT

Studies of quantitative systems and synthetic biology have extensively utilized models to interpret data, make predictions, and guide experimental designs. However, models often simplify complex biological systems and lack experimentally validated parameters, making their reliability in perturbed systems unclear. Here, we developed a droplet-based synthetic cell system to continuously tune parameters at the single-cell level in multiple dimensions with full dynamic ranges, providing an experimental framework for global parameter space scans. We systematically perturbed a cell-cycle oscillator centered on cyclin-dependent kinase (Cdk1), enabling comprehensive mapping of period landscapes in response to network perturbations. The data allowed us to challenge existing models and refine a new model that matches the observed response. Our analysis demonstrated that Cdk1 positive feedback inhibition restricts the cell cycle frequency range, confirming model predictions; furthermore, it revealed new cellular responses to the inhibition of the Cdk1-counteracting phosphatase PP2A: monomodal or bimodal distributions across varying inhibition levels, underscoring the complex nature of cell cycle regulation that can be explained by our model. This comprehensive perturbation platform may be generalizable to exploring other complex dynamic systems.

PMID:38420905 | DOI:10.1021/acssynbio.3c00631