Biophys J. 2023 Feb 10;122(3S1):247a. doi: 10.1016/j.bpj.2022.11.1438.

NO ABSTRACT

PMID:36783216 | DOI:10.1016/j.bpj.2022.11.1438

Biophys J. 2023 Feb 10;122(3S1):203a. doi: 10.1016/j.bpj.2022.11.1227.

NO ABSTRACT

PMID:36782985 | DOI:10.1016/j.bpj.2022.11.1227

Biophys J. 2023 Feb 10;122(3S1):187a. doi: 10.1016/j.bpj.2022.11.1150.

NO ABSTRACT

PMID:36782889 | DOI:10.1016/j.bpj.2022.11.1150

Biophys J. 2023 Feb 10;122(3S1):175a. doi: 10.1016/j.bpj.2022.11.1089.

NO ABSTRACT

PMID:36782827 | DOI:10.1016/j.bpj.2022.11.1089

Nat Microbiol. 2023 Feb 13. doi: 10.1038/s41564-023-01328-8. Online ahead of print.

ABSTRACT

Although virulence is typically attributed to single pathogenic strains, here we investigated whether effectors secreted by a population of non-virulent strains could function as public goods to enable the emergence of collective virulence. We disaggregated the 36 type III effectors of the phytopathogenic bacterium Pseudomonas syringae strain PtoDC3000 into a 'metaclone' of 36 coisogenic strains, each carrying a single effector in an effectorless background. Each coisogenic strain was individually unfit, but the metaclone was collectively as virulent as the wild-type strain on Arabidopsis thaliana, suggesting that effectors can drive the emergence of cooperation-based virulence through their public action. We show that independently evolved effector suits can equally drive this cooperative behaviour by transferring the effector alleles native to the strain PmaES4326 into the conspecific but divergent strain PtoDC3000. Finally, we transferred the disaggregated PtoDC3000 effector arsenal into Pseudomonas fluorescens and show that their cooperative action was sufficient to convert this rhizosphere-inhabiting beneficial bacterium into a phyllosphere pathogen. These results emphasize the importance of microbial community interactions and expand the ecological scale at which disease may be attributed.

PMID:36782026 | DOI:10.1038/s41564-023-01328-8

Sci Rep. 2023 Feb 13;13(1):2533. doi: 10.1038/s41598-023-28551-x.

ABSTRACT

Tongkat ali commonly known as Malaysian Ginseng (Eurycoma longifolia) is a herbal root worldwide available in nutraceuticals, either as a crude powder or capsules blended with other herbal products. Herein, a multiplexed metabolomics approach based on nuclear magnetic resonance (NMR) and solid-phase microextraction combined with gas chromatography-mass spectrometry (SPME-GC-MS) was applied for authentic tongkat ali extract vs some commercial products quality control analysis. NMR metabolite fingerprinting identified 15 major metabolites mostly ascribed to sugars, organic and fatty acids in addition to quassinoids and cinnamates. Following that, multivariate analysis as the non-supervised principal component analysis (PCA) and supervised orthogonal partial least squares-discriminant analysis (OPLS-DA) were applied revealing that differences were related to fatty acids and 13,21-dihydroeurycomanone being more enriched in authentic root. SPME-GC-MS aroma profiling led to the identification of 59 volatiles belonging mainly to alcohols, aldehydes/furans and sesquiterpene hydrocarbons. Results revealed that aroma of commercial products showed relatively different profiles being rich in vanillin, maltol, and methyl octanoate. Whereas E-cinnamaldehyde, endo-borneol, terpinen-4-ol, and benzaldehyde were more associated to the authentic product. The present study shed the light for the potential of metabolomics in authentication and standardization of tongkat ali and identification of its true flavor composition.

PMID:36781893 | DOI:10.1038/s41598-023-28551-x

Nat Commun. 2023 Feb 13;14(1):812. doi: 10.1038/s41467-023-36354-x.

ABSTRACT

Unlike PIWI-interacting RNA (piRNA) in other species that mostly target transposable elements (TEs), >80% of piRNAs in adult mammalian testes lack obvious targets. However, mammalian piRNA sequences and piRNA-producing loci evolve more rapidly than the rest of the genome for unknown reasons. Here, through comparative studies of chickens, ducks, mice, and humans, as well as long-read nanopore sequencing on diverse chicken breeds, we find that piRNA loci across amniotes experience: (1) a high local mutation rate of structural variations (SVs, mutations ≥ 50 bp in size); (2) positive selection to suppress young and actively mobilizing TEs commencing at the pachytene stage of meiosis during germ cell development; and (3) negative selection to purge deleterious SV hotspots. Our results indicate that genetic instability at pachytene piRNA loci, while producing certain pathogenic SVs, also protects genome integrity against TE mobilization by driving the formation of rapid-evolving piRNA sequences.

PMID:36781861 | DOI:10.1038/s41467-023-36354-x

Methods Mol Biol. 2023;2628:353-364. doi: 10.1007/978-1-0716-2978-9_23.

ABSTRACT

Mass spectrometry (MS)-based protein quantitation is an attractive means for research and diagnostics due to its high specificity, precision, sensitivity, versatility, and the ability to develop multiplexed assays for the "absolute" quantitation of virtually any protein target. However, due to the large dynamic range of protein concentrations in blood, high abundance proteins in blood plasma hinder the detectability and quantification of lower-abundance proteins which are often relevant in the context of different diseases. Here we outline a streamlined method involving offline high-pH reversed-phase fractionation of human plasma samples followed by the quantitative analysis of specific fractions using nanoLC-parallel reaction monitoring (PRM) on a Q Exactive Plus mass spectrometer for peptide detection and quantitation with increased sensitivity. Because we use a set of synthetic peptide standards, we can more efficiently determine the precise retention times of the target peptides in the first-dimensional separation and specifically collect eluting fractions of interest for the subsequent targeted MS quantitation, making the analysis faster and easier. An eight-point standard curve was generated by serial dilution of a mixture of previously validated unlabeled ("light") synthetic peptides of interest at known concentrations. The corresponding heavy stable-isotope-labeled standard (SIS) analogues were used as normalizers to account for losses during sample processing and analysis. Using this method, we were able to improve the sensitivity of plasma protein quantitation by up to 50-fold compared to using nanoLC-PRM alone.

PMID:36781797 | DOI:10.1007/978-1-0716-2978-9_23

Clin Transl Med. 2023 Feb;13(2):e1179. doi: 10.1002/ctm2.1179.

ABSTRACT

BACKGROUND: The biguanide drug metformin is a safe and widely prescribed drug for type 2 diabetes. Interestingly, hundreds of clinical trials have been set to evaluate the potential role of metformin in the prevention and treatment of cancer including colorectal cancer (CRC). However, the "metformin signaling" remains controversial.

AIMS AND METHODS: To interrogate cell signaling induced by metformin in CRC and explore the druggability of the metformin-rewired phosphorylation network, we performed integrative analysis of phosphoproteomics, bioinformatics, and cell proliferation assays on a panel of 12 molecularly heterogeneous CRC cell lines. Using the high-resolute data-independent analysis mass spectrometry (DIA-MS), we monitored a total of 10,142 proteins and 56,080 phosphosites (P-sites) in CRC cells upon a short- and a long-term metformin treatment.

RESULTS AND CONCLUSIONS: We found that metformin tended to primarily remodel cell signaling in the long-term and only minimally regulated the total proteome expression levels. Strikingly, the phosphorylation signaling response to metformin was highly heterogeneous in the CRC panel, based on a network analysis inferring kinase/phosphatase activities and cell signaling reconstruction. A "MetScore" was determined to assign the metformin relevance of each P-site, revealing new and robust phosphorylation nodes and pathways in metformin signaling. Finally, we leveraged the metformin P-site signature to identify pharmacodynamic interactions and confirmed a number of candidate metformin-interacting drugs, including navitoclax, a BCL-2/BCL-xL inhibitor. Together, we provide a comprehensive phosphoproteomic resource to explore the metformin-induced cell signaling for potential cancer therapeutics. This resource can be accessed at https://yslproteomics.shinyapps.io/Metformin/.

PMID:36781298 | DOI:10.1002/ctm2.1179

Brief Bioinform. 2023 Feb 13:bbad052. doi: 10.1093/bib/bbad052. Online ahead of print.

ABSTRACT

Post-translational modifications (PTMs) fine-tune various signaling pathways not only by the modification of a single residue, but also by the interplay of different modifications on residue pairs within or between proteins, defined as PTM cross-talk. As a challenging question, less attention has been given to PTM dynamics underlying cross-talk residue pairs and structural information underlying protein-protein interaction (PPI) graph, limiting the progress in this PTM functional research. Here we propose a novel integrated deep neural network PPICT (Predictor for PTM Inter-protein Cross-Talk), which predicts PTM cross-talk by combining protein sequence-structure-dynamics information and structural information for PPI graph. We find that cross-talk events preferentially occur among residues with high co-evolution and high potential in allosteric regulation. To make full use of the complex associations between protein evolutionary and biophysical features, and protein pair features, a heterogeneous feature combination net is introduced in the final prediction of PPICT. The comprehensive test results show that the proposed PPICT method significantly improves the prediction performance with an AUC value of 0.869, outperforming the existing state-of-the-art methods. Additionally, the PPICT method can capture the potential PTM cross-talks involved in the functional regulatory PTMs on modifying enzymes and their catalyzed PTM substrates. Therefore, PPICT represents an effective tool for identifying PTM cross-talk between proteins at the proteome level and highlights the hints for cross-talk between different signal pathways introduced by PTMs.

PMID:36781207 | DOI:10.1093/bib/bbad052

Proteomics. 2023 Feb 13:e2200104. doi: 10.1002/pmic.202200104. Online ahead of print.

ABSTRACT

Plant metabolites are mainly produced through chemical reactions catalysed by enzymes encoded in the genome. Mutations in enzyme-encoding or transcription factor-encoding genes can alter the metabolome by changing the enzyme's catalytic activity or abundance, respectively. Insertion of transposable elements into non-coding regions has also been reported to affect transcription and ultimately metabolite content. In addition to genetic mutations, transgenerational epigenetic variations have also been found to affect metabolic content by controlling transcription of metabolism related genes. However, the majority of cases reported so far, in which epigenetic mechanisms are associated to metabolism, are non-transgenerational and are triggered by developmental signals or environmental stress. Although, accumulating research has provided evidence of strong genetic control of the metabolome, epigenetic control has been largely untouched. Here, we provide a review of the genetic and epigenetic control of metabolism with a focus on epigenetics. We discuss both transgenerational and non-transgenerational epigenetic marks regulating metabolism as well as prospects of the field of metabolic control where intricate interactions between genetics and epigenetics are involved. This article is protected by copyright. All rights reserved.

PMID:36781168 | DOI:10.1002/pmic.202200104

Prog Biophys Mol Biol. 2023 Feb 11:S0079-6107(23)00014-7. doi: 10.1016/j.pbiomolbio.2023.02.002. Online ahead of print.

ABSTRACT

Tuberculosis (TB) is a pervasive and devastating air-borne disease caused by the organisms belonging to the Mycobacterium tuberculosis (Mtb) complex. Currently, it is the global leader in infectious disease-related death in adults. The proclivity of TB to enter the latent state has become a significant impediment to the global effort to eradicate TB. Despite decades of research, latent tuberculosis (LTB) mechanisms remain poorly understood, making it difficult to develop efficient treatment methods. In this review, we seek to shed light on the current understanding of the mechanism of LTB, with an accentuation on the insights gained through computational biology. We have outlined various well-established computational biology components, such as omics, network-based techniques, mathematical modelling, artificial intelligence, and molecular docking, to disclose the crucial facets of LTB. Additionally, we highlighted important tools and software that may be used to conduct a variety of systems biology assessments. Finally, we conclude the article by addressing the possible future directions in this field, which might help a better understanding of LTB progression.

PMID:36781150 | DOI:10.1016/j.pbiomolbio.2023.02.002

J Struct Biol. 2023 Feb 11:107942. doi: 10.1016/j.jsb.2023.107942. Online ahead of print.

ABSTRACT

Small GTPase RhoA switches from GTP-bound state to GDP-bound state by hydrolyzing GTP, which is accelerated by GTPases activating proteins (GAPs). However, less study of RhoA structural dynamic changes was conducted during this process, which is essential for understanding the molecular mechanism of GAP dissociation. Here, we solved a RhoA structure in GDP-bound state with switch II flipped outward. Because lacking the intermolecular interactions with guanine nucleotide, we proposed this conformation of RhoA could be an intermediate after GAP dissociation. Further molecular dynamics simulations found the conformational changes of switch regions are indeed existing in RhoA and involved in the regulation of GAP dissociation and GEF recognition. Besides, the guanine nucleotide binding pocket extended to switch II region, indicating a potential "druggable" cavity for RhoA. Taken together, our study provides a deeper understanding of the dynamic properties of RhoA switch regions and highlights the direction for future drug development.

PMID:36781028 | DOI:10.1016/j.jsb.2023.107942

Am J Respir Cell Mol Biol. 2023 Feb 13. doi: 10.1165/rcmb.2022-0302OC. Online ahead of print.

ABSTRACT

The integration of transcriptomic and proteomic data from lung tissue with chronic obstructive pulmonary disease (COPD)-associated genetic variants could provide insight into the biological mechanisms for COPD. Here, we assessed associations between lung transcriptomics and proteomics with COPD in 98 subjects from the Lung Tissue Research Consortium. Low correlations between transcriptomics and proteomics were generally observed, but higher correlations were found for COPD-associated proteins. We integrated COPD risk SNPs or SNPs near COPD-associated proteins with lung transcripts and proteins to identify regulatory cis quantitative trait loci (QTLs). Significant expression QTLs (eQTLs) and protein QTLs (pQTLs) were found regulating multiple COPD-associated biomarkers. We investigated mediated associations from significant protein quantitative trait loci through transcripts to protein levels of COPD-associated proteins. We also attempted to identify colocalized effects between GWAS, eQTL, and pQTL signals. Evidence for colocalization between COPD GWAS signals and pQTL for RHOB and eQTL for DSP was found. We applied Weighted Gene Co-Expression Network Analysis (WGCNA) to find consensus COPD-associated network modules. Two network modules generated by consensus WGCNA were associated with COPD with FDR < 0.05. One network module is related to the catenin complex, and the other module is related to plasma membrane components. In summary, multiple cis-acting effects for transcripts and proteins associated with COPD were identified. Colocalization analysis, mediation analysis, and correlation-based network analysis of multiple Omics data may identify key genes and proteins that work together to influence COPD pathogenesis.

PMID:36780661 | DOI:10.1165/rcmb.2022-0302OC

Proc Natl Acad Sci U S A. 2023 Feb 21;120(8):e2211091120. doi: 10.1073/pnas.2211091120. Epub 2023 Feb 13.

ABSTRACT

Microbes in the wild face highly variable and unpredictable environments and are naturally selected for their average growth rate across environments. Apart from using sensory regulatory systems to adapt in a targeted manner to changing environments, microbes employ bet-hedging strategies where cells in an isogenic population switch stochastically between alternative phenotypes. Yet, bet-hedging suffers from a fundamental trade-off: Increasing the phenotype-switching rate increases the rate at which maladapted cells explore alternative phenotypes but also increases the rate at which cells switch out of a well-adapted state. Consequently, it is currently believed that bet-hedging strategies are effective only when the number of possible phenotypes is limited and when environments last for sufficiently many generations. However, recent experimental results show that gene expression noise generally decreases with growth rate, suggesting that phenotype-switching rates may systematically decrease with growth rate. Such growth rate dependent stability (GRDS) causes cells to be more explorative when maladapted and more phenotypically stable when well-adapted, and we show that GRDS can almost completely overcome the trade-off that limits bet-hedging, allowing for effective adaptation even when environments are diverse and change rapidly. We further show that even a small decrease in switching rates of faster-growing phenotypes can substantially increase long-term fitness of bet-hedging strategies. Together, our results suggest that stochastic strategies may play an even bigger role for microbial adaptation than hitherto appreciated.

PMID:36780518 | DOI:10.1073/pnas.2211091120

Curr Protoc. 2023 Feb;3(2):e664. doi: 10.1002/cpz1.664.

ABSTRACT

RNA-sequencing (RNA-seq) is a gold-standard method to profile genome-wide changes in gene expression. RNA-seq uses high-throughput sequencing technology to quantify the amount of RNA in a biological sample. With the increasing popularity of RNA-seq, many variations on the protocol have been proposed to extract unique and relevant information from biological samples. 3' Tag-Seq (also called TagSeq, 3' Tag-RNA-Seq, and Quant-Seq 3' mRNA-Seq) is one RNA-seq variation where the 3' end of the transcript is selected and amplified to yield one copy of cDNA from each transcript in the biological sample. We present a simple, easy-to-use, and publicly available computational workflow to analyze 3' Tag-Seq data. The workflow begins by trimming sequence adapters from raw FASTQ files. The trimmed sequence reads are checked for quality using FastQC and aligned to the reference genome, and then read counts are obtained using STAR. Differential gene expression analysis is performed using DESeq2, based on differential analysis of gene count data. The outputs of this workflow are MA plots, tables of differentially expressed genes, and UpSet plots. This protocol is intended for users specifically interested in analyzing 3' Tag-Seq data, and thus normalizations based on transcript length are not performed within the workflow. Future updates to this workflow could include custom analyses based on the gene counts table as well as data visualization enhancements. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol: Running the 3' Tag-Seq workflow Support Protocol: Generating genome indices.

PMID:36779816 | DOI:10.1002/cpz1.664

Microbiol Resour Announc. 2023 Feb 13:e0101522. doi: 10.1128/mra.01015-22. Online ahead of print.

ABSTRACT

Many modern farming practices negatively impact ecosystems on the local and global scales. Here, we assessed the taxonomic structures of 48 soil microbial communities along an agricultural transect using 16S rRNA and internal transcribed spacer (ITS) amplicon sequencing. We further characterized the functional structures of a subsample of 12 microbiomes using whole-genome sequencing.

PMID:36779724 | DOI:10.1128/mra.01015-22

Elife. 2023 Feb 13;12:e81939. doi: 10.7554/eLife.81939. Online ahead of print.

ABSTRACT

Humans make a number of choices when they walk, such as how fast and for how long. The preferred steady walking speed seems chosen to minimize energy expenditure per distance traveled. But the speed of actual walking bouts is not only steady, but rather a time-varying trajectory, which can also be modulated by task urgency or an individual's movement vigor. Here we show that speed trajectories and durations of human walking bouts are explained better by an objective to minimize Energy and Time, meaning the total work or energy to reach destination, plus a cost proportional to bout duration. Applied to a computational model of walking dynamics, this objective predicts dynamic speed vs. time trajectories with inverted U shapes. Model and human experiment (&#x1D441; = 10) show that shorter bouts are unsteady and dominated by the time and effort of accelerating, and longer ones are steadier and faster and dominated by steady-state time and effort. Individual-dependent vigor may be characterized by the energy one is willing to spend to save a unit of time, which explains why some may walk faster than others, but everyone may have similar-shaped trajectories due to similar walking dynamics. Tradeoffs between energy and time costs can predict transient, steady, and vigor-related aspects of walking.

PMID:36779697 | DOI:10.7554/eLife.81939

Elife. 2023 Feb 13;12:e81188. doi: 10.7554/eLife.81188. Online ahead of print.

ABSTRACT

Recent studies have suggested that the human germline mutation rate and spectrum evolve rapidly. Variation in generation time has been linked to these changes, though its contribution remains unclear. We develop a framework to characterize temporal changes in polymorphisms within and between populations, while controlling for the effects of natural selection and biased gene conversion. Application to the 1000 Genomes Project dataset reveals multiple independent changes that arose after the split of continental groups, including a previously reported, transient elevation in TCC>TTC mutations in Europeans and novel signals of divergence in C>G and T>A mutation rates among population samples. We also find a significant difference between groups sampled in and outside of Africa, in old T>C polymorphisms that predate the out-of-Africa migration. This surprising signal is driven by TpG>CpG mutations, and stems in part from mis-polarized CpG transitions, which are more likely to undergo recurrent mutations. Finally, by relating the mutation spectrum of polymorphisms to parental age effects on de novo mutations, we show that plausible changes in the generation time cannot explain the patterns observed for different mutation types jointly. Thus, other factors--genetic modifiers or environmental exposures--must have had a non-negligible impact on the human mutation landscape.

PMID:36779395 | DOI:10.7554/eLife.81188

Front Epidemiol. 2022;2:899655. doi: 10.3389/fepid.2022.899655. Epub 2022 Sep 13.

ABSTRACT

As the cost of high-throughput genomic sequencing technology declines, its application in clinical research becomes increasingly popular. The collected datasets often contain tens or hundreds of thousands of biological features that need to be mined to extract meaningful information. One area of particular interest is discovering underlying causal mechanisms of disease outcomes. Over the past few decades, causal discovery algorithms have been developed and expanded to infer such relationships. However, these algorithms suffer from the curse of dimensionality and multicollinearity. A recently introduced, non-orthogonal, general empirical Bayes approach to matrix factorization has been demonstrated to successfully infer latent factors with interpretable structures from observed variables. We hypothesize that applying this strategy to causal discovery algorithms can solve both the high dimensionality and collinearity problems, inherent to most biomedical datasets. We evaluate this strategy on simulated data and apply it to two real-world datasets. In a breast cancer dataset, we identified important survival-associated latent factors and biologically meaningful enriched pathways within factors related to important clinical features. In a SARS-CoV-2 dataset, we were able to predict whether a patient (1) had Covid-19 and (2) would enter the ICU. Furthermore, we were able to associate factors with known Covid-19 related biological pathways.

PMID:36778756 | PMC:PMC9910507 | DOI:10.3389/fepid.2022.899655