Int J Mol Sci. 2023 Aug 31;24(17):13540. doi: 10.3390/ijms241713540.

ABSTRACT

Type II diabetes mellitus (T2DM) accounts for approximately 90% of all diabetes mellitus cases in the world. Glucagon-like peptide-1 receptor (GLP-1R) agonists have established an increased capability to target directly or indirectly six core defects associated with T2DM, while the underlying molecular mechanisms of these pharmacological effects are not fully known. This exploratory study was conducted to analyze the effect of treatment with GLP-1R agonists on the urinary peptidome of T2DM patients. Urine samples of thirty-two T2DM patients from the PROVALID study ("A Prospective Cohort Study in Patients with T2DM for Validation of Biomarkers") collected pre- and post-treatment with GLP-1R agonist drugs were analyzed by CE-MS. In total, 70 urinary peptides were significantly affected by GLP-1R agonist treatment, generated from 26 different proteins. The downregulation of MMP proteases, based on the concordant downregulation of urinary collagen peptides, was highlighted. Treatment also resulted in the downregulation of peptides from SERPINA1, APOC3, CD99, CPSF6, CRNN, SERPINA6, HBA2, MB, VGF, PIGR, and TTR, many of which were previously found to be associated with increased insulin resistance and inflammation. The findings indicate potential molecular mechanisms of GLP-1R agonists in the context of the management of T2DM and the prevention or delaying of the progression of its associated diseases.

PMID:37686344 | DOI:10.3390/ijms241713540

Int J Mol Sci. 2023 Aug 29;24(17):13406. doi: 10.3390/ijms241713406.

ABSTRACT

Metabolic-dysfunction-associated steatotic liver disease (MASLD), which affects 30 million people in the US and is anticipated to reach over 100 million by 2030, places a significant financial strain on the healthcare system. There is presently no FDA-approved treatment for MASLD despite its public health significance and financial burden. Understanding the connection between point mutations, liver enzymes, and MASLD is important for comprehending drug toxicity in healthy or diseased individuals. Multiple genetic variations have been linked to MASLD susceptibility through genome-wide association studies (GWAS), either increasing MASLD risk or protecting against it, such as PNPLA3 rs738409, MBOAT7 rs641738, GCKR rs780094, HSD17B13 rs72613567, and MTARC1 rs2642438. As the impact of genetic variants on the levels of drug-metabolizing cytochrome P450 (CYP) enzymes in human hepatocytes has not been thoroughly investigated, this study aims to describe the analysis of metabolic functions for selected phase I and phase II liver enzymes in human hepatocytes. For this purpose, fresh isolated primary hepatocytes were obtained from healthy liver donors (n = 126), and liquid chromatography-mass spectrometry (LC-MS) was performed. For the cohorts, participants were classified into minor homozygotes and nonminor homozygotes (major homozygotes + heterozygotes) for five gene polymorphisms. For phase I liver enzymes, we found a significant difference in the activity of CYP1A2 in human hepatocytes carrying MBOAT7 (p = 0.011) and of CYP2C8 in human hepatocytes carrying PNPLA3 (p = 0.004). It was also observed that the activity of CYP2C9 was significantly lower in human hepatocytes carrying HSD17B13 (p = 0.001) minor homozygous compared to nonminor homozygous. No significant difference in activity of CYP2E1, CYP2C8, CYP2D6, CYP2E1, CYP3A4, ECOD, FMO, MAO, AO, and CES2 and in any of the phase II liver enzymes between human hepatocytes carrying genetic variants for PNPLA3 rs738409, MBOAT7 rs641738, GCKR rs780094, HSD17B13 rs72613567, and MTARC1 rs2642438 were observed. These findings offer a preliminary assessment of the influence of genetic variations on drug-metabolizing cytochrome P450 (CYP) enzymes in healthy human hepatocytes, which may be useful for future drug discovery investigations.

PMID:37686209 | DOI:10.3390/ijms241713406

Int J Mol Sci. 2023 Aug 22;24(17):13068. doi: 10.3390/ijms241713068.

ABSTRACT

Head and neck squamous cell carcinoma (HNSC) exhibits genetic heterogeneity in etiologies, tumor sites, and biological processes, which significantly impact therapeutic strategies and prognosis. While the influence of human papillomavirus on clinical outcomes is established, the molecular subtypes determining additional treatment options for HNSC remain unclear and inconsistent. This study aims to identify distinct HNSC molecular subtypes to enhance diagnosis and prognosis accuracy. In this study, we collected three HNSC microarrays (n = 306) from the Gene Expression Omnibus (GEO), and HNSC RNA-Seq data (n = 566) from The Cancer Genome Atlas (TCGA) to identify differentially expressed genes (DEGs) and validate our results. Two scoring methods, representative score (RS) and perturbative score (PS), were developed for DEGs to summarize their possible activation functions and influence in tumorigenesis. Based on the RS and PS scoring, we selected candidate genes to cluster TCGA samples for the identification of molecular subtypes in HNSC. We have identified 289 up-regulated DEGs and selected 88 genes (called HNSC88) using the RS and PS scoring methods. Based on HNSC88 and TCGA samples, we determined three HNSC subtypes, including one HPV-associated subtype, and two HPV-negative subtypes. One of the HPV-negative subtypes showed a relationship to smoking behavior, while the other exhibited high expression in tumor immune response. The Kaplan-Meier method was used to compare overall survival among the three subtypes. The HPV-associated subtype showed a better prognosis compared to the other two HPV-negative subtypes (log rank, p = 0.0092 and 0.0001; hazard ratio, 1.36 and 1.39). Additionally, within the HPV-negative group, the smoking-related subgroup exhibited worse prognosis compared to the subgroup with high expression in immune response (log rank, p = 0.039; hazard ratio, 1.53). The HNSC88 not only enables the identification of HPV-associated subtypes, but also proposes two potential HPV-negative subtypes with distinct prognoses and molecular signatures. This study provides valuable strategies for summarizing the roles and influences of genes in tumorigenesis for identifying molecular signatures and subtypes of HNSC.

PMID:37685875 | DOI:10.3390/ijms241713068

J Anim Sci Biotechnol. 2023 Sep 9;14(1):119. doi: 10.1186/s40104-023-00921-5.

ABSTRACT

BACKGROUND: Many phenotypes in animal breeding are derived from incomplete measures, especially if they are challenging or expensive to measure precisely. Examples include time-dependent traits such as reproductive status, or lifespan. Incomplete measures for these traits result in phenotypes that are subject to left-, interval- and right-censoring, where phenotypes are only known to fall below an upper bound, between a lower and upper bound, or above a lower bound respectively. Here we compare three methods for deriving phenotypes from incomplete data using age at first elevation (> 1 ng/mL) in blood plasma progesterone (AGEP4), which generally coincides with onset of puberty, as an example trait.

METHODS: We produced AGEP4 phenotypes from three blood samples collected at about 30-day intervals from approximately 5,000 Holstein-Friesian or Holstein-Friesian × Jersey cross-bred dairy heifers managed in 54 seasonal-calving, pasture-based herds in New Zealand. We used these actual data to simulate 7 different visit scenarios, increasing the extent of censoring by disregarding data from one or two of the three visits. Three methods for deriving phenotypes from these data were explored: 1) ordinal categorical variables which were analysed using categorical threshold analysis; 2) continuous variables, with a penalty of 31 d assigned to right-censored phenotypes; and 3) continuous variables, sampled from within a lower and upper bound using a data augmentation approach.

RESULTS: Credibility intervals for heritability estimations overlapped across all methods and visit scenarios, but estimated heritabilities tended to be higher when left censoring was reduced. For sires with at least 5 daughters, the correlations between estimated breeding values (EBVs) from our three-visit scenario and each reduced data scenario varied by method, ranging from 0.65 to 0.95. The estimated breed effects also varied by method, but breed differences were smaller as phenotype censoring increased.

CONCLUSION: Our results indicate that using some methods, phenotypes derived from one observation per offspring for a time-dependent trait such as AGEP4 may provide comparable sire rankings to three observations per offspring. This has implications for the design of large-scale phenotyping initiatives where animal breeders aim to estimate variance parameters and estimated breeding values (EBVs) for phenotypes that are challenging to measure or prohibitively expensive.

PMID:37684681 | DOI:10.1186/s40104-023-00921-5

Commun Biol. 2023 Sep 8;6(1):920. doi: 10.1038/s42003-023-05265-4.

ABSTRACT

Burkholderia pseudomallei is a highly versatile pathogen with ~25% of its genome annotated to encode hypothetical proteins. One such hypothetical protein, BPSL1038, is conserved across seven bacterial genera and 654 Burkholderia spp. Here, we present a 1.55 Å resolution crystal structure of BPSL1038. The overall structure folded into a modified βαββαβα ferredoxin fold similar to known Cas2 nucleases. The Cas2 equivalent catalytic aspartate (D11) pairs are conserved in BPSL1038 although B. pseudomallei has no known CRISPR associated system. Functional analysis revealed that BPSL1038 is a nuclease with endonuclease activity towards double-stranded DNA. The DNase activity is divalent ion independent and optimum at pH 6. The concentration of monovalent ions (Na+ and K+) is crucial for nuclease activity. An active site with a unique D11(X20)SST motif was identified and proposed for BPSL1038 and its orthologs. Structure modelling indicates the catalytic role of the D11(X20)SST motif and that the arginine residues R10 and R30 may interact with the nucleic acid backbone. The structural similarity of BPSL1038 to Cas2 proteins suggests that BPSL1038 may represent a sub-family of nucleases that share a common ancestor with Cas2.

PMID:37684342 | DOI:10.1038/s42003-023-05265-4

Sci Rep. 2023 Sep 8;13(1):14830. doi: 10.1038/s41598-023-39217-z.

ABSTRACT

Arbuscular mycorrhizas (AM) are one of the most widespread symbiosis on earth. This plant-fungus interaction involves around 72% of plant species, including most crops. AM symbiosis improves plant nutrition and tolerance to biotic and abiotic stresses. The fungus, in turn, receives carbon compounds derived from the plant photosynthetic process, such as sugars and lipids. Most studies investigating AM and their applications in agriculture requires a precise quantification of the intensity of plant colonization. At present, the majority of researchers in the field base AM quantification analyses on manual visual methods, prone to operator errors and limited reproducibility. Here we propose a novel semi-automated approach to quantify AM fungal root colonization based on digital image analysis comparing three methods: (i) manual quantification (ii) image thresholding, (iii) machine learning. We recognize machine learning as a very promising tool for accelerating, simplifying and standardizing critical steps in analysing AM quantification, answering to an urgent need by the scientific community studying this symbiosis.

PMID:37684263 | DOI:10.1038/s41598-023-39217-z

Nat Commun. 2023 Sep 8;14(1):5537. doi: 10.1038/s41467-023-41264-z.

ABSTRACT

Bats, recognized as considerable reservoirs for coronaviruses (CoVs), serve as natural hosts for several highly pathogenic CoVs, including SARS-CoV and SARS-CoV-2. Investigating the bat CoV community provides insights into the origin for highly pathogenic CoVs and highlights bat CoVs with potential spillover risks. This study probes the evolution, recombination, host range, geographical distribution, and cross-species transmission characteristics of bat CoVs across China and its associated CoVs in other regions. Through detailed research on 13,064 bat samples from 14 provinces of China, 1141 CoV strains are found across 10 subgenera and one unclassified Alpha-CoV, generating 399 complete genome sequences. Within bat CoVs, 11 new CoV species are identified and 425 recombination events are detected. Bats in southern China, particularly in Yunnan province, exhibit a pronounced diversity of CoVs. Limited sampling and low detection rates exist for CoVs in Myotacovirus, Nyctacovirus, Hibecovirus, Nobecovirus in China. The genus Myotis is highlighted as a potential ancestral host for Alpha-CoV, with the genus Hipposideros suggested as a likely progenitor host for bat-associated Beta-CoV, indicating the complexity of cross-species transmission dynamics. Through the comprehensive analysis, this study enriches the understanding of bat CoVs and offers a valuable resource for future research.

PMID:37684236 | DOI:10.1038/s41467-023-41264-z

Nat Commun. 2023 Sep 8;14(1):5520. doi: 10.1038/s41467-023-41281-y.

ABSTRACT

Many eukaryotic receptors and enzymes rely on glycosylphosphatidylinositol (GPI) anchors for membrane localization and function. The transmembrane complex GPI-T recognizes diverse proproteins at a signal peptide region that lacks consensus sequence and replaces it with GPI via a transamidation reaction. How GPI-T maintains broad specificity while preventing unintentional cleavage is unclear. Here, substrates- and products-bound human GPI-T structures identify subsite features that enable broad proprotein specificity, inform catalytic mechanism, and reveal a multilevel safeguard mechanism against its promiscuity. In the absence of proproteins, the catalytic site is invaded by a locally stabilized loop. Activation requires energetically unfavorable rearrangements that transform the autoinhibitory loop into crucial catalytic cleft elements. Enzyme-proprotein binding in the transmembrane and luminal domains respectively powers the conformational rearrangement and induces a competent cleft. GPI-T thus integrates various weak specificity regions to form strong selectivity and prevent accidental activation. These findings provide important mechanistic insights into GPI-anchored protein biogenesis.

PMID:37684232 | DOI:10.1038/s41467-023-41281-y

Metab Eng. 2023 Sep 6:S1096-7176(23)00123-4. doi: 10.1016/j.ymben.2023.09.003. Online ahead of print.

ABSTRACT

DHA is a marine PUFA of commercial value, given its multiple health benefits. The worldwide emerging shortage in DHA supply has increased interest in microbial cell factories that can provide the compound de novo. In this regard, the present work aimed to improve DHA production in the oleaginous yeast strain Y. lipolytica Af4, which synthetized the PUFA via a heterologous myxobacterial polyketide synthase (PKS)-like gene cluster. As starting point, we used transcriptomics, metabolomics, and 13C-based metabolic pathway profiling to study the cellular dynamics of Y. lipolytica Af4. The shift from the growth to the stationary DHA-production phase was associated with fundamental changes in carbon core metabolism, including a strong upregulation of the PUFA gene cluster, as well as an increase in citrate and fatty acid degradation. At the same time, the intracellular levels of the two DHA precursors acetyl-CoA and malonyl-CoA dropped by up to 98% into the picomolar range. Interestingly, the degradation pathways for the ketogenic amino acids l-lysine, l-leucine, and l-isoleucine were transcriptionally activated, presumably to provide extra acetyl-CoA. Supplementation with small amounts of these amino acids at the beginning of the DHA production phase beneficially increased the intracellular CoA-ester pools and boosted the DHA titer by almost 40%. Isotopic 13C-tracer studies revealed that the supplements were efficiently directed toward intracellular CoA-esters and DHA. Hereby, l-lysine was found to be most efficient, as it enabled long-term activation, due to storage within the vacuole and continuous breakdown. The novel strategy enabled DHA production in Y. lipolytica at the gram scale for the first time. DHA was produced at a high selectivity (27% of total fatty acids) and free of the structurally similar PUFA DPA, which facilitates purification for high-value medical applications that require API-grade DHA. The assembled multi-omics picture of the central metabolism of Y. lipolytica provides valuable insights into this important yeast. Beyond our work, the enhanced catabolism of ketogenic amino acids seems promising for the overproduction of other compounds in Y. lipolytica, whose synthesis is limited by the availability of CoA ester precursors.

PMID:37683719 | DOI:10.1016/j.ymben.2023.09.003

Curr Biol. 2023 Aug 29:S0960-9822(23)01074-6. doi: 10.1016/j.cub.2023.08.030. Online ahead of print.

ABSTRACT

Plant roots originated independently in lycophytes and euphyllophytes, whereas early vascular plants were rootless. The organization of the root apical meristem in euphyllophytes is well documented, especially in the model plant Arabidopsis. However, little is known about lycophyte roots and their molecular innovations during evolution. In this study, spatial transcriptomics was used to detect 97 root-related genes in the roots of the lycophyte Selaginella moellendorffii. A high number of genes showed expression patterns similar to what has been reported for seed plants, supporting the idea of a highly convergent evolution of mechanisms to control root development. Interaction and complementation data of SHORTROOT (SHR) and SCARECROW (SCR) homologs, furthermore, support a comparable regulation of the ground tissue (GT) between euphyllophytes and lycophytes. Root cap formation, in contrast, appears to be differently regulated. Several experiments indicated an important role of the WUSCHEL-RELATED HOMEOBOX13 gene SmWOX13a in Selaginella root cap formation. In contrast to multiple Arabidopsis WOX paralogs, SmWOX13a is able to induce root cap cells in Arabidopsis and has functionally conserved homologs in the fern Ceratopteris richardii. Lycophytes and a part of the euphyllophytes, therefore, may share a common mechanism regulating root cap formation, which was diversified or lost during seed plant evolution. In summary, we here provide a new spatial data resource for the Selaginella root, which in general advocates for conserved mechanisms to regulate root development but shows a clear divergence in the control of root cap formation, with a novel putative role of WOX genes in root cap formation in non-seed plants.

PMID:37683643 | DOI:10.1016/j.cub.2023.08.030

Cell. 2023 Aug 30:S0092-8674(23)00906-6. doi: 10.1016/j.cell.2023.08.017. Online ahead of print.

ABSTRACT

Patescibacteria, also known as the candidate phyla radiation (CPR), are a diverse group of bacteria that constitute a disproportionately large fraction of microbial dark matter. Its few cultivated members, belonging mostly to Saccharibacteria, grow as epibionts on host Actinobacteria. Due to a lack of suitable tools, the genetic basis of this lifestyle and other unique features of Patescibacteira remain unexplored. Here, we show that Saccharibacteria exhibit natural competence, and we exploit this property for their genetic manipulation. Imaging of fluorescent protein-labeled Saccharibacteria provides high spatiotemporal resolution of phenomena accompanying epibiotic growth, and a transposon-insertion sequencing (Tn-seq) genome-wide screen reveals the contribution of enigmatic Saccharibacterial genes to growth on their hosts. Finally, we leverage metagenomic data to provide cutting-edge protein structure-based bioinformatic resources that support the strain Southlakia epibionticum and its corresponding host, Actinomyces israelii, as a model system for unlocking the molecular underpinnings of the epibiotic lifestyle.

PMID:37683634 | DOI:10.1016/j.cell.2023.08.017

Methods Mol Biol. 2023;2698:351-360. doi: 10.1007/978-1-0716-3354-0_20.

ABSTRACT

Gene regulatory networks (GRNs) are important for determining how an organism develops and how it responds to external stimuli. In the case of Arabidopsis thaliana, several GRNs have been identified covering many important biological processes. We present AGENT, the Arabidopsis GEne Network Tool, for exploring and analyzing published GRNs. Using tools in AGENT, regulatory motifs such as feed-forward loops can be easily identified. Nodes with high centrality-and hence importance-can likewise be identified. Gene expression data can also be overlaid onto GRNs to help discover subnetworks acting in specific tissues or under certain conditions.

PMID:37682484 | DOI:10.1007/978-1-0716-3354-0_20

Methods Mol Biol. 2023;2698:323-349. doi: 10.1007/978-1-0716-3354-0_19.

ABSTRACT

Gene regulatory networks (GRNs) represent the regulatory links between transcription factors (TF) and their target genes. In plants, they are essential to understand transcriptional programs that control important agricultural traits such as yield or (a)biotic stress response. Although several high- and low-throughput experimental methods have been developed to map GRNs in plants, these are sometimes expensive, come with laborious protocols, and are not always optimized for tomato, one of the most important horticultural crops worldwide. In this chapter, we present a computational method that covers two protocols: one protocol to map gene identifiers between two different tomato genome assemblies, and another protocol to predict putative regulators and delineate GRNs given a set of functionally related or coregulated genes by exploiting publicly available TF-binding information. As an example, we applied the motif enrichment protocol on tomato using upregulated genes in response to jasmonate, as well as upregulated and downregulated genes in plants with genotypes OENAM1 and nam1, respectively. We found that our protocol accurately infers the expected TFs as top enriched regulators and identifies GRNs functionally enriched in biological processes related with the experimental context under study.

PMID:37682483 | DOI:10.1007/978-1-0716-3354-0_19

Methods Mol Biol. 2023;2698:195-220. doi: 10.1007/978-1-0716-3354-0_13.

ABSTRACT

Many methods are now available to identify or predict the target genes of transcription factors (TFs) in plants. These include experimental approaches such as in vivo or in vitro TF-target gene-binding assays and various methods for identifying regulated targets in mutants, transgenics, or isolated plant cells. In addition, computational approaches are used to infer TF-target gene interactions from the regulatory elements or gene expression changes across treatments. While each of these approaches has now been applied to a large number of TFs from many species, each method has its own limitations which necessitates that multiple data types are integrated to build the most accurate representation of the gene regulatory networks operating in plants. To make the analyses of TF-target interaction datasets available to the broader research community, we have developed the ConnecTF web platform ( https://connectf.org/ ). In this chapter, we describe how ConnecTF can be used to integrate validated and predicted TF-target gene interactions in order to dissect the regulatory role of TFs in developmental and stress response pathways. Using as our examples KN1 and RA1, two well-characterized maize TFs involved in developing floral tissue, we demonstrate how ConnecTF can be used to (1) compare the target genes between TFs, (2) identify direct vs. indirect targets by combining TF-binding and TF-regulation datasets, (3) chart and visualize network paths between TFs and their downstream targets, and (4) prune inferred user networks for high-confidence predicted interactions using validated TF-target gene data. Finally, we provide instructions for setting up a private version of ConnecTF that enables research groups to store and analyze their own TF-target gene interaction datasets.

PMID:37682477 | DOI:10.1007/978-1-0716-3354-0_13

Methods Mol Biol. 2023;2698:87-107. doi: 10.1007/978-1-0716-3354-0_7.

ABSTRACT

Capturing the dynamic and transient interactions of a transcription factor (TF) with its genome-wide targets whose regulation leads to plants' adaptation to their changing environment is a major technical challenge. This is a widespread problem with biochemical methods such as chromatin immunoprecipitation-sequencing (ChIP-seq) which are biased towards capturing stable TF-target gene interactions. Herein, we describe how DNA adenine methyltransferase identification and sequencing (DamID-seq) can be used to capture both transient and stable TF-target interactions by DNA methylation. The DamID technique uses a TF protein fused to a DNA adenine methyltransferase (Dam) from E. coli. When expressed in a plant cell, the Dam-TF fusion protein will methylate adenine (A) bases near the sites of TF-DNA interactions. In this way, DamID results in a permanent, stable DNA methylation mark on TF-target gene promoters, even if the target gene is only transiently "touched" by the Dam-TF fusion protein. Here we provide a step-by-step protocol to perform DamID-seq experiments in isolated plant cells for any Dam-TF fusion protein of interest. We also provide information that will enable researchers to analyze DamID-seq data to identify TF-binding sites in the genome. Our protocol includes instructions for vector cloning of the Dam-TF fusion proteins, plant cell protoplast transfections, DamID preps, library preparation, and sequencing data analysis. The protocol outlined in this chapter is performed in Arabidopsis thaliana, however, the DamID-seq workflow developed in this guide is broadly applicable to other plants and organisms.

PMID:37682471 | DOI:10.1007/978-1-0716-3354-0_7

Methods Mol Biol. 2023;2698:41-56. doi: 10.1007/978-1-0716-3354-0_4.

ABSTRACT

Droplet-based single-cell RNA-sequencing (scRNA-seq) empowers transcriptomic profiling with an unprecedented resolution, facilitating insights into the cellular heterogeneity of tissues, developmental progressions, stress-response dynamics, and more at single-cell level. In this chapter, we describe the experimental workflow of processing Arabidopsis root tissue into protoplasts and generating single-cell transcriptomes. We also describe the general computational workflow of visualizing and utilizing scRNA-seq data. This protocol can be used as a starting point for establishing a scRNA-seq workflow.

PMID:37682468 | DOI:10.1007/978-1-0716-3354-0_4

Methods Mol Biol. 2023;2698:1-11. doi: 10.1007/978-1-0716-3354-0_1.

ABSTRACT

A major question in plant biology is to understand how plant growth, development, and environmental responses are controlled and coordinated by the activities of regulatory factors. Gene regulatory network (GRN) analyses require integrated approaches that combine experimental approaches with computational analyses. A wide range of experimental approaches and tools are now available, such as targeted perturbation of gene activities, quantitative and cell-type specific measurements of dynamic gene activities, and systematic analysis of the molecular 'hard-wiring' of the systems. At the computational level, different tools and databases are available to study regulatory sequences, including intuitive visualizations to explore data-driven gene regulatory networks in different plant species. Furthermore, advanced data integration approaches have recently been developed to efficiently leverage complementary regulatory data types and learn context-specific networks.

PMID:37682465 | DOI:10.1007/978-1-0716-3354-0_1

Cancer Invest. 2023 Sep 8:1-25. doi: 10.1080/07357907.2023.2255672. Online ahead of print.

ABSTRACT

This study aimed to reveal the drug repurposing candidates for colorectal cancer (CRC) via drug repurposing methods and network biology approaches. A novel, differentially co-expressed, highly interconnected, and co-regulated prognostic gene module was identified for CRC. Based on the gene module, polyethylene glycol, gallic acid, pyrazole, cordycepin, phenothiazine, pantoprazole, cysteamine, indisulam, valinomycin, trametinib, BRD-K81473043, AZD8055, dovitinib, BRD-A17065207, and tyrphostin AG1478 presented as drugs and small molecule candidates previously studied in the CRC. Lornoxicam, suxamethonium, oprelvekin, sirukumab, levetiracetam, sulpiride, NVP-TAE684, AS605240, 480743.cdx, HDAC6 inhibitor ISOX, BRD-K03829970, and L-6307 are proposed as novel drugs and small molecule candidates for CRC.

PMID:37682113 | DOI:10.1080/07357907.2023.2255672

J Hypertens. 2023 Sep 1. doi: 10.1097/HJH.0000000000003522. Online ahead of print.

ABSTRACT

BACKGROUND: Various sequelae have been described after nonsevere coronavirus disease 2019 (COVID-19), but knowledge on postacute effects on blood pressure is limited.

METHODS: This is a cross-sectional analysis of blood pressure profiles in individuals after nonsevere COVID-19 compared with matched population-based individuals without prior COVID-19. Data were derived from the ongoing and prospective Hamburg City Health Study, a population-based study in Hamburg, Germany, and its associated COVID-19 program, which included individuals at least 4 months after COVID-19. Matching was performed by age, sex, education, and preexisting hypertension in a 1 : 4 ratio.

RESULTS: Four hundred and thirty-two individuals after COVID-19 (mean age 56.1 years) were matched to 1728 controls without prior COVID-19 (56.2 years). About 92.8% of COVID-19 courses were mild or moderate, only 7.2% were hospitalized, and no individual had been treated on an intensive care unit. Even after adjustment for relevant competing risk factors, DBP [+4.7 mmHg, 95% confidence interval (95% CI) 3.97-5.7, P < 0.001] was significantly higher in individuals after COVID-19. For SBP, a trend towards increased values was observed (+1.4 mmHg, 95% CI -0.4 to 3.2, P = 0.120). Hypertensive blood pressures at least 130/80 mmHg (according to the ACC/AHA guideline) and at least 140/90 mmHg (ESC/ESH guideline) occurred significantly more often in individuals after COVID-19 than matched controls (odds ratio 2.0, 95% CI 1.5-2.7, P < 0.001 and odds ratio 1.6, 95% CI 1.3-2.0, P < 0.001, respectively), mainly driven by changes in DBP.

CONCLUSION: Blood pressure is higher in individuals after nonsevere COVID-19 compared with uninfected individuals suggesting a significant hypertensive sequela.

PMID:37682048 | DOI:10.1097/HJH.0000000000003522

Plant J. 2023 Sep 8. doi: 10.1111/tpj.16459. Online ahead of print.

ABSTRACT

Soybean is a crucial crop worldwide, used as a source of food, feed, and industrial products due to its high protein and oil content. Previously, the rapid accumulation of soybean RNA-seq data in public databases and the computational challenges of processing raw RNA-seq data motivated us to develop the Soybean Expression Atlas, a gene expression database of over a thousand RNA-seq samples. Over the past few years, our database has allowed researchers to explore the expression profiles of important gene families, discover genes associated with agronomic traits, and understand the transcriptional dynamics of cellular processes. Here, we present the Soybean Expression Atlas v2, an updated version of our database with a fourfold increase in the number of samples, featuring transcript- and gene-level transcript abundance matrices for 5481 publicly available RNA-seq samples. New features in our database include the availability of transcript-level abundance estimates and equivalence classes to explore differential transcript usage, abundance estimates in bias-corrected counts to increase the accuracy of differential gene expression analyses, a new web interface with improved data visualization and user experience, and a reproducible and scalable pipeline available as an R package. The Soybean Expression Atlas v2 is available at https://soyatlas.venanciogroup.uenf.br/, and it will accelerate soybean research, empowering researchers with high-quality and easily accessible gene expression data.

PMID:37681739 | DOI:10.1111/tpj.16459