Cell Rep. 2024 Jul 4;43(7):114436. doi: 10.1016/j.celrep.2024.114436. Online ahead of print.

ABSTRACT

Single-gene missense mutations remain challenging to interpret. Here, we deploy scalable functional screening by sequencing (SEUSS), a Perturb-seq method, to generate mutations at protein interfaces of RUNX1 and quantify their effect on activities of downstream cellular programs. We evaluate single-cell RNA profiles of 115 mutations in myelogenous leukemia cells and categorize them into three functionally distinct groups, wild-type (WT)-like, loss-of-function (LoF)-like, and hypomorphic, that we validate in orthogonal assays. LoF-like variants dominate the DNA-binding site and are recurrent in cancer; however, recurrence alone does not predict functional impact. Hypomorphic variants share characteristics with LoF-like but favor protein interactions, promoting gene expression indicative of nerve growth factor (NGF) response and cytokine recruitment of neutrophils. Accessible DNA near differentially expressed genes frequently contains RUNX1-binding motifs. Finally, we reclassify 16 variants of uncertain significance and train a classifier to predict 103 more. Our work demonstrates the potential of targeting protein interactions to better define the landscape of phenotypes reachable by missense mutations.

PMID:38968069 | DOI:10.1016/j.celrep.2024.114436

J Math Biol. 2024 Jul 5;89(2):26. doi: 10.1007/s00285-024-02118-4.

ABSTRACT

Models of biochemical networks are often large intractable sets of differential equations. To make sense of the complexity, relationships between genes/proteins are presented as connected graphs, the edges of which are drawn to indicate activation or inhibition relationships. These diagrams are useful for drawing qualitative conclusions in many cases by the identifying recurring of topological motifs, for example positive and negative feedback loops. These topological features are usually classified under the presumption that activation and inhibition are inverse relationships. For example, inhibition of an inhibitor is often classified the same as activation of an activator within a motif classification, effectively treating them as equivalent. Whilst in many contexts this may not lead to catastrophic errors, drawing conclusions about the behavior of motifs, pathways or networks from these broad classes of topological feature without adequate mathematical descriptions can lead to obverse outcomes. We investigate the extent to which a biochemical pathway/network will behave quantitatively dissimilar to pathway/ networks with similar typologies formed by swapping inhibitors as the inverse of activators. The purpose of the study is to determine under what circumstances rudimentary qualitative assessment of network structure can provide reliable conclusions as to the quantitative behaviour of the network. Whilst there are others, We focus on two main mathematical qualities which may cause a divergence in the behaviour of two pathways/networks which would otherwise be classified as similar; (i) a modelling feature we label 'bias' and (ii) the precise positioning of activators and inhibitors within simple pathways/motifs.

PMID:38967811 | DOI:10.1007/s00285-024-02118-4

Genome Biol. 2024 Jul 4;25(1):177. doi: 10.1186/s13059-024-03320-9.

ABSTRACT

Identifying viruses from metagenomes is a common step to explore the virus composition in the human gut. Here, we introduce VirRep, a hybrid language representation learning framework, for identifying viruses from human gut metagenomes. VirRep combines a context-aware encoder and an evolution-aware encoder to improve sequence representation by incorporating k-mer patterns and sequence homologies. Benchmarking on both simulated and real datasets with varying viral proportions demonstrates that VirRep outperforms state-of-the-art methods. When applied to fecal metagenomes from a colorectal cancer cohort, VirRep identifies 39 high-quality viral species associated with the disease, many of which cannot be detected by existing methods.

PMID:38965579 | DOI:10.1186/s13059-024-03320-9

J Transl Med. 2024 Jul 4;22(1):626. doi: 10.1186/s12967-024-05342-0.

ABSTRACT

The persistence of coronavirus disease 2019 (COVID-19)-related hospitalization severely threatens medical systems worldwide and has increased the need for reliable detection of acute status and prediction of mortality. We applied a systems biology approach to discover acute-stage biomarkers that could predict mortality. A total 247 plasma samples were collected from 103 COVID-19 (52 surviving COVID-19 patients and 51 COVID-19 patients with mortality), 51 patients with other infectious diseases (IDCs) and 41 healthy controls (HCs). Paired plasma samples were obtained from survival COVID-19 patients within 1 day after hospital admission and 1-3 days before discharge. There were clear differences between COVID-19 patients and controls, as well as substantial differences between the acute and recovery phases of COVID-19. Samples from patients in the acute phase showed suppressed immunity and decreased steroid hormone biosynthesis, as well as elevated inflammation and proteasome activation. These findings were validated by enzyme-linked immunosorbent assays and metabolomic analyses in a larger cohort. Moreover, excessive proteasome activity was a prominent signature in the acute phase among patients with mortality, indicating that it may be a key cause of poor prognosis. Based on these features, we constructed a machine learning panel, including four proteins [C-reactive protein (CRP), proteasome subunit alpha type (PSMA)1, PSMA7, and proteasome subunit beta type (PSMB)1)] and one metabolite (urocortisone), to predict mortality among COVID-19 patients (area under the receiver operating characteristic curve: 0.976) on the first day of hospitalization. Our systematic analysis provides a novel method for the early prediction of mortality in hospitalized COVID-19 patients.

PMID:38965561 | DOI:10.1186/s12967-024-05342-0

Nat Methods. 2024 Jul 4. doi: 10.1038/s41592-024-02333-3. Online ahead of print.

ABSTRACT

Dynamic imaging of genomic loci is key for understanding gene regulation, but methods for imaging genomes, in particular non-repetitive DNAs, are limited. We developed CRISPRdelight, a DNA-labeling system based on endonuclease-deficient CRISPR-Cas12a (dCas12a), with an engineered CRISPR array to track DNA location and motion. CRISPRdelight enables robust imaging of all examined 12 non-repetitive genomic loci in different cell lines. We revealed the confined movement of the CCAT1 locus (chr8q24) at the nuclear periphery for repressed expression and active motion in the interior nucleus for transcription. We uncovered the selective repositioning of HSP gene loci to nuclear speckles, including a remarkable relocation of HSPH1 (chr13q12) for elevated transcription during stresses. Combining CRISPR-dCas12a and RNA aptamers allowed multiplex imaging of four types of satellite DNA loci with a single array, revealing their spatial proximity to the nucleolus-associated domain. CRISPRdelight is a user-friendly and robust system for imaging and tracking genomic dynamics and regulation.

PMID:38965442 | DOI:10.1038/s41592-024-02333-3

Nat Genet. 2024 Jul 4. doi: 10.1038/s41588-024-01824-5. Online ahead of print.

NO ABSTRACT

PMID:38965415 | DOI:10.1038/s41588-024-01824-5

Sci Rep. 2024 Jul 4;14(1):15408. doi: 10.1038/s41598-024-66079-w.

ABSTRACT

Chemosensory impairment is an outstanding symptom of SARS-CoV-2 infections. We hypothesized that measured sensory impairments are accompanied by transcriptomic changes in the foliate papillae area of the tongue. Hospital personnel with known SARS-CoV-2 immunoglobulin G (IgG) status completed questionnaires on sensory perception (n = 158). A subcohort of n = 141 participated in forced choice taste tests, and n = 43 participants consented to donate tongue swabs of the foliate papillae area for whole transcriptome analysis. The study included four groups of participants differing in IgG levels (≥ 10 AU/mL = IgG+; < 10 AU/mL = IgG-) and self-reported sensory impairment (SSI±). IgG+ subjects not detecting metallic taste had higher IgG+ levels than IgG+ participants detecting iron gluconate (p = 0.03). Smell perception was the most impaired biological process in the transcriptome data from IgG+/SSI+ participants subjected to gene ontology enrichment. IgG+/SSI+ subjects demonstrated lower expression levels of 166 olfactory receptors (OR) and 9 taste associated receptors (TAS) of which OR1A2, OR2J2, OR1A1, OR5K1 and OR1G1, as well as TAS2R7 are linked to metallic perception. The question raised by this study is whether odorant receptors on the tongue (i) might play a role in metal sensation, and (ii) are potential targets for virus-initiated sensory impairments, which needs to be investigated in future functional studies.

PMID:38965271 | DOI:10.1038/s41598-024-66079-w

Nat Commun. 2024 Jul 4;15(1):5611. doi: 10.1038/s41467-024-49985-5.

ABSTRACT

Mitotic errors generate micronuclei entrapping mis-segregated chromosomes, which are susceptible to catastrophic fragmentation through chromothripsis. The reassembly of fragmented chromosomes by error-prone DNA double-strand break (DSB) repair generates diverse genomic rearrangements associated with human diseases. How specific repair pathways recognize and process these lesions remains poorly understood. Here we use CRISPR/Cas9 to systematically inactivate distinct DSB repair pathways and interrogate the rearrangement landscape of fragmented chromosomes. Deletion of canonical non-homologous end joining (NHEJ) components substantially reduces complex rearrangements and shifts the rearrangement landscape toward simple alterations without the characteristic patterns of chromothripsis. Following reincorporation into the nucleus, fragmented chromosomes localize within sub-nuclear micronuclei bodies (MN bodies) and undergo ligation by NHEJ within a single cell cycle. In the absence of NHEJ, chromosome fragments are rarely engaged by alternative end-joining or recombination-based mechanisms, resulting in delayed repair kinetics, persistent 53BP1-labeled MN bodies, and cell cycle arrest. Thus, we provide evidence supporting NHEJ as the exclusive DSB repair pathway generating complex rearrangements from mitotic errors.

PMID:38965240 | DOI:10.1038/s41467-024-49985-5

Cancer Lett. 2024 Jul 2:217089. doi: 10.1016/j.canlet.2024.217089. Online ahead of print.

ABSTRACT

Glutamine is a conditionally essential amino acid for the growth and survival of rapidly proliferating cancer cells. Many cancers are addicted to glutamine, and as a result, targeting glutamine metabolism has been explored clinically as a therapeutic approach. Glutamine-catalyzing enzymes are highly expressed in primary and metastatic head and neck squamous cell carcinoma (HNSCC). However, the nature of the glutamine-associated pathways in this aggressive cancer type has not been elucidated. Here, we explored the therapeutic potential of a broad glutamine antagonist, DRP-104 (sirpiglenastat), in HNSCC tumors and aimed at shedding light on glutamine-dependent pathways in this disease. We observed a potent antitumoral effect of sirpiglenastat in HPV- and HPV+ HNSCC xenografts. We conducted a whole-genome CRISPR screen and metabolomics analyses to identify mechanisms of sensitivity and resistance to glutamine metabolism blockade. These approaches revealed that glutamine metabolism blockade results in the rapid buildup of polyunsaturated fatty acids (PUFAs) via autophagy nutrient-sensing pathways. Finally, our analysis demonstrated that GPX4 mediates the protection of HNSCC cells from accumulating toxic lipid peroxides; hence, glutamine blockade sensitizes HNSCC cells to ferroptosis cell death upon GPX4 inhibition. These findings demonstrate the therapeutic potential of sirpiglenastat in HNSCC and establish a novel link between glutamine metabolism and ferroptosis, which may be uniquely translated into targeted glutamine-ferroptosis combination therapies.

PMID:38964731 | DOI:10.1016/j.canlet.2024.217089

Mol Cell. 2024 Jul 2:S1097-2765(24)00514-8. doi: 10.1016/j.molcel.2024.06.011. Online ahead of print.

ABSTRACT

DNA repair is directly performed by hundreds of core factors and indirectly regulated by thousands of others. We massively expanded a CRISPR inhibition and Cas9-editing screening system to discover factors indirectly modulating homology-directed repair (HDR) in the context of ∼18,000 individual gene knockdowns. We focused on CCAR1, a poorly understood gene that we found the depletion of reduced both HDR and interstrand crosslink repair, phenocopying the loss of the Fanconi anemia pathway. CCAR1 loss abrogated FANCA protein without substantial reduction in the level of its mRNA or that of other FA genes. We instead found that CCAR1 prevents inclusion of a poison exon in FANCA. Transcriptomic analysis revealed that the CCAR1 splicing modulatory activity is not limited to FANCA, and it instead regulates widespread changes in alternative splicing that would damage coding sequences in mouse and human cells. CCAR1 therefore has an unanticipated function as a splicing fidelity factor.

PMID:38964321 | DOI:10.1016/j.molcel.2024.06.011

Biomed Pharmacother. 2024 Jul 3;177:117070. doi: 10.1016/j.biopha.2024.117070. Online ahead of print.

ABSTRACT

Predicting drug responses based on individual transcriptomic profiles holds promise for refining prognosis and advancing precision medicine. Although many studies have endeavored to predict the responses of known drugs to novel transcriptomic profiles, research into predicting responses for newly discovered drugs remains sparse. In this study, we introduce scDrug+, a comprehensive pipeline that seamlessly integrates single-cell analysis with drug-response prediction. Importantly, scDrug+ is equipped to predict the response of new drugs by analyzing their molecular structures. The open-source tool is available as a Docker container, ensuring ease of deployment and reproducibility. It can be accessed at https://github.com/ailabstw/scDrugplus.

PMID:38964180 | DOI:10.1016/j.biopha.2024.117070

Curr Opin Biotechnol. 2024 Jul 3;88:103168. doi: 10.1016/j.copbio.2024.103168. Online ahead of print.

ABSTRACT

Agricultural systems are under increasing pressure from declining environmental conditions, a growing population, and changes in consumer preferences, resulting in widespread malnutrition-related illnesses. Improving plant nutritional content through biotechnology techniques such as synthetic biology is a promising strategy to help combat hidden hunger caused by the lack of affordable and healthy foods in human diets. Production of compounds usually found in animal-rich diets, such as vitamin D or omega-3 fatty acids, has been recently demonstrated in planta. Here, we review recent biotechnological approaches to biofortifying plants with vitamins, minerals, and other metabolites, and summarise synthetic biology advances that offer the opportunity to build on these early biofortification efforts.

PMID:38964080 | DOI:10.1016/j.copbio.2024.103168

Glycobiology. 2024 Jul 4:cwae046. doi: 10.1093/glycob/cwae046. Online ahead of print.

ABSTRACT

Heparan sulfate (HS) is a linear polysaccharide with high structural and functional diversity. Detection and localization of HS in tissues can be performed using single chain variable fragment (scFv) antibodies. Although several anti-HS antibodies recognizing different sulfation motifs have been identified, little is known about their interaction with HS. In this study the interaction between the scFv antibody HS4C3 and heparin was investigated. Heparin-binding lysine and arginine residues were identified using a protect and label methodology. Site-directed mutagenesis was applied to further identify critical heparin-binding lysine/arginine residues using immunohistochemical and biochemical assays. In addition, computational docking of a heparin tetrasaccharide towards a 3-D homology model of HS4C3 was applied to identify potential heparin-binding sites. Of the 12 lysine and 15 arginine residues within the HS4C3 antibody, 6 and 9, respectively, were identified as heparin-binding. Most of these residues are located within one of the complementarity determining regions (CDR) or in their proximity. All basic amino acid residues in the CDR3 region of the heavy chain were involved in binding. Computational docking showed a heparin tetrasaccharide close to these regions. Mutagenesis of heparin-binding residues reduced or altered reactivity towards HS and heparin. Identification of heparin-binding arginine and lysine residues in HS4C3 allows for better understanding of the interaction with HS and creates a framework to rationally design antibodies targeting specific HS motifs.

PMID:38963938 | DOI:10.1093/glycob/cwae046

Microrna. 2024 Jul 3. doi: 10.2174/0122115366294831240606115216. Online ahead of print.

ABSTRACT

BACKGROUND: Hydrolethalus Syndrome 1 (HYDS1) is a rare disorder that occurs commonly in Finnish infants but originates from the mother. This autosomal recessive syn-drome is associated with the FBF1, which is usually expressed in the centriole. The FBF1 is an inheritable arthritis disease phenotype that includes rheumatoid arthritis. Several studies have investigated males with FBF1 mutation carriers also related to arthritis diseases, including those under rheumatoid arthritis conditions, which revealed the possibility of conferring the gene mutation to the next generation of offspring. Nonetheless, there are some complications of FBF1 mutation with target miRNAs that can be affected by exercise.

OBJECTIVE: The objective of this study was to evaluate the different exercises that can be utilized to suppress the FBF1 mutation targeted by Novel-rno-miRNAs-1135 as a biomarker and assess the effectiveness of exercise in mitigating the FBF1 mutation.

METHODS: Four exercise interventional groups were divided into exercise and non-exercise groups. One hundred microliter pristane-induced arthritis (PIA) was injected at the dorsal re-gion of the tails of rodents and introduced to the two PIA interventional groups. On day forty-five, all animals were euthanized, and total RNA was extracted from the blood samples of ro-dents, while polymerase chain reaction (PCR) was amplified by using 5-7 primers. Computeri-zation was used for miRNA regulation and analysis of target gene candidates.

RESULTS: The novel-rno-miRNA-1135 was downregulated to FBF1 in exercise groups. The exercise was found to have no significant impact in terms of change in novel-rno-miRNA-1135 regulation of FBF1 expression.

CONCLUSION: Exercise has no impact on novel-rno-miRNA-1135 targeted for FBF1 in autoso-mal recessive disease.

PMID:38963098 | DOI:10.2174/0122115366294831240606115216

Front Med (Lausanne). 2024 Jun 18;11:1380210. doi: 10.3389/fmed.2024.1380210. eCollection 2024.

ABSTRACT

Sarcopenia, a geriatric syndrome characterized by progressive loss of muscle mass and strength, and osteoarthritis, a common degenerative joint disease, are both prevalent in elderly individuals. However, the relationship and molecular mechanisms underlying these two diseases have not been fully elucidated. In this study, we screened microarray data from the Gene Expression Omnibus to identify associations between sarcopenia and osteoarthritis. We employed multiple statistical methods and bioinformatics tools to analyze the shared DEGs (differentially expressed genes). Additionally, we identified 8 hub genes through functional enrichment analysis, protein-protein interaction analysis, transcription factor-gene interaction network analysis, and TF-miRNA coregulatory network analysis. We also discovered potential shared pathways between the two diseases, such as transcriptional misregulation in cancer, the FOXO signalling pathway, and endometrial cancer. Furthermore, based on common DEGs, we found that strophanthidin may be an optimal drug for treating sarcopenia and osteoarthritis, as indicated by the Drug Signatures database. Immune infiltration analysis was also performed on the sarcopenia and osteoarthritis datasets. Finally, receiver operating characteristic (ROC) curves were plotted to verify the reliability of our results. Our findings provide a theoretical foundation for future research on the potential common pathogenesis and molecular mechanisms of sarcopenia and osteoarthritis.

PMID:38962732 | PMC:PMC11221828 | DOI:10.3389/fmed.2024.1380210

Nat Rev Bioeng. 2024 Feb;2(2):120-135. doi: 10.1038/s44222-023-00119-4. Epub 2023 Oct 10.

ABSTRACT

Bacteria are emerging as living drugs to treat a broad range of disease indications. However, the inherent advantages of these replicating and immunostimulatory therapies also carry the potential for toxicity. Advances in synthetic biology and the integration of nanomedicine can address this challenge through the engineering of controllable systems that regulate spatial and temporal activation for improved safety and efficacy. Here, we review recent progress in nanobiotechnology-driven engineering of bacteria-based therapies, highlighting limitations and opportunities that will facilitate clinical translation.

PMID:38962719 | PMC:PMC11218715 | DOI:10.1038/s44222-023-00119-4

ISME Commun. 2024 Jun 10;4(1):ycae077. doi: 10.1093/ismeco/ycae077. eCollection 2024 Jan.

ABSTRACT

Staphylococcus aureus (Sa) and Acinetobacter baumannii (Ab) are frequently co-isolated from polymicrobial infections that are severe and refractory to therapy. Here, we apply a combination of wet-lab experiments and in silico modeling to unveil the intricate nature of the Ab/Sa interaction using both, representative laboratory strains and strains co-isolated from clinical samples. This comprehensive methodology allowed uncovering Sa's capability to exert a partial interference on Ab by the expression of phenol-soluble modulins. In addition, we observed a cross-feeding mechanism by which Sa supports the growth of Ab by providing acetoin as an alternative carbon source. This study is the first to dissect the Ab/Sa interaction dynamics wherein competitive and cooperative strategies can intertwine. Through our findings, we illuminate the ecological mechanisms supporting their coexistence in the context of polymicrobial infections. Our research not only enriches our understanding but also opens doors to potential therapeutic avenues in managing these challenging infections.

PMID:38962494 | PMC:PMC11221087 | DOI:10.1093/ismeco/ycae077

Front Bioinform. 2024 Jun 19;4:1390607. doi: 10.3389/fbinf.2024.1390607. eCollection 2024.

ABSTRACT

BACKGROUND: Complex disorders, such as Alzheimer's disease (AD), result from the combined influence of multiple biological and environmental factors. The integration of high-throughput data from multiple omics platforms can provide system overviews, improving our understanding of complex biological processes underlying human disease. In this study, integrated data from four omics platforms were used to characterise biological signatures of AD.

METHOD: The study cohort consists of 455 participants (Control:148, Cases:307) from the Religious Orders Study and Memory and Aging Project (ROSMAP). Genotype (SNP), methylation (CpG), RNA and proteomics data were collected, quality-controlled and pre-processed (SNP = 130; CpG = 83; RNA = 91; Proteomics = 119). Using a diagnosis of Mild Cognitive Impairment (MCI)/AD combined as the target phenotype, we first used Partial Least Squares Regression as an unsupervised classification framework to assess the prediction capabilities for each omics dataset individually. We then used a variation of the sparse generalized canonical correlation analysis (sGCCA) to assess predictions of the combined datasets and identify multi-omics signatures characterising each group of participants.

RESULTS: Analysing datasets individually we found methylation data provided the best predictions with an accuracy of 0.63 (95%CI = [0.54-0.71]), followed by RNA, 0.61 (95%CI = [0.52-0.69]), SNP, 0.59 (95%CI = [0.51-0.68]) and proteomics, 0.58 (95%CI = [0.51-0.67]). After integration of the four datasets, predictions were dramatically improved with a resulting accuracy of 0.95 (95% CI = [0.89-0.98]).

CONCLUSION: The integration of data from multiple platforms is a powerful approach to explore biological systems and better characterise the biological signatures of AD. The results suggest that integrative methods can identify biomarker panels with improved predictive performance compared to individual platforms alone. Further validation in independent cohorts is required to validate and refine the results presented in this study.

PMID:38962175 | PMC:PMC11219798 | DOI:10.3389/fbinf.2024.1390607

Front Microbiol. 2024 Jun 19;15:1368377. doi: 10.3389/fmicb.2024.1368377. eCollection 2024.

ABSTRACT

Microbiomes, comprised of diverse microbial species and viruses, play pivotal roles in human health, environmental processes, and biotechnological applications and interact with each other, their environment, and hosts via ecological interactions. Our understanding of microbiomes is still limited and hampered by their complexity. A concept improving this understanding is systems biology, which focuses on the holistic description of biological systems utilizing experimental and computational methods. An important set of such experimental methods are metaomics methods which analyze microbiomes and output lists of molecular features. These lists of data are integrated, interpreted, and compiled into computational microbiome models, to predict, optimize, and control microbiome behavior. There exists a gap in understanding between microbiologists and modelers/bioinformaticians, stemming from a lack of interdisciplinary knowledge. This knowledge gap hinders the establishment of computational models in microbiome analysis. This review aims to bridge this gap and is tailored for microbiologists, researchers new to microbiome modeling, and bioinformaticians. To achieve this goal, it provides an interdisciplinary overview of microbiome modeling, starting with fundamental knowledge of microbiomes, metaomics methods, common modeling formalisms, and how models facilitate microbiome control. It concludes with guidelines and repositories for modeling. Each section provides entry-level information, example applications, and important references, serving as a valuable resource for comprehending and navigating the complex landscape of microbiome research and modeling.

PMID:38962127 | PMC:PMC11220171 | DOI:10.3389/fmicb.2024.1368377

Am J Physiol Renal Physiol. 2024 Jul 4. doi: 10.1152/ajprenal.00050.2024. Online ahead of print.

ABSTRACT

Chronic kidney disease mineral bone disorder (CKD-MBD) is a complex clinical syndrome responsible for the accelerated cardiovascular mortality seen in individuals afflicted with CKD. Current approaches to therapy have failed to improve clinical outcomes adequately, likely due to targeting surrogate biochemical parameters as articulated by the guideline developer, KDIGO (Kidney Disease: Improving Global Outcomes). We hypothesized that using a Systems Biology Approach combining machine learning with mathematical modeling, we could test a novel approach to therapy targeting the abnormal movement of mineral out of bone and into soft tissue that is characteristic of CKD-MBD. The mathematical model describes the movement of calcium and phosphate between body compartments in response to standard therapeutic agents. The machine learning technique we applied is Reinforcement Learning (RL). We compared calcium, phosphate, PTH, and mineral movement out of bone and into soft tissue under four scenarios: standard approach (KDIGO), achievement of KDIGO guidelines using RL (RLKDIGO), targeting abnormal mineral flux (RLFLUX), and combining achievement of KDIGO guidelines with minimization of abnormal mineral flux (RLKDIGOFLUX). We demonstrate through simulations that explicitly targeting abnormal mineral flux significantly decreases abnormal mineral movement compared to standard approach while achieving acceptable biochemical outcomes. These investigations highlight the limitations of current therapeutic targets, primarily secondary hyperparathyroidism, and emphasize the central role of deranged phosphate homeostasis in the genesis of the CKD-MBD syndrome.

PMID:38961848 | DOI:10.1152/ajprenal.00050.2024