A digital twin model incorporating generalized metabolic fluxes to identify and predict chronic kidney disease in type 2 diabetes mellitus.

We have developed a digital twin-based CKD identification and prediction model that leverages generalized metabolic fluxes (GMF) for patients with Type 2 Diabetes Mellitus (T2DM). GMF digital twins utilized basic clinical and physiological biomarkers as inputs for identification and prediction of CKD. We employed four diverse multi-ethnic cohorts (n = 7072): a Singaporean cohort (EVAS, n = 289) and a North American cohort (NHANES, n = 1044) for baseline CKD identification, and two multi-center Singaporean cohorts (CDMD, n = 2119 and SDR, n = 3627) for 3-year CKD prediction and risk stratification. We subsequently conducted a comprehensive study utilizing a single dataset to evaluate the clinical utility of GMF for CKD prediction. The GMF-based identification model performed strongly, achieving an AUC between 0.80 and 0.82. In prediction, the GMF generated with complete parameters attained high performance with an AUC of 0.86, while with incomplete parameters, it achieved an AUC of 0.75. The GMF-based prediction model utilizing complete inputs is the standard implementation of our algorithm: HealthVector Diabetes®. We have established the GMF digital twin-based model as a robust clinical tool capable of predicting and stratifying the risk of future CKD within a 3-year time horizon. We report the correlation of GMF with basic input parameters, their ability to differentiate between future health states and medication status at baseline, and their capability to quantify CKD progression rates. This holistic methodology provides insights into patients' health states and CKD progression rates based on GMF metabolic profile differences, enabling personalized care plans.

Generalized metabolic flux analysis framework provides mechanism-based predictions of ophthalmic complications in type 2 diabetes patients.

Chronic metabolic diseases arise from changes in metabolic fluxes through biomolecular pathways and gene networks accumulated over the lifetime of an individual. While clinical and biochemical profiles present just real-time snapshots of the patients' health, efficient computation models of the pathological disturbance of biomolecular processes are required to achieve individualized mechanistic insights into disease progression. Here, we describe the Generalized metabolic flux analysis (GMFA) for addressing this gap. Suitably grouping individual metabolites/fluxes into pools simplifies the analysis of the resulting more coarse-grain network. We also map non-metabolic clinical modalities onto the network with additional edges. Instead of using the time coordinate, the system status (metabolite concentrations and fluxes) is quantified as function of a generalized extent variable (a coordinate in the space of generalized metabolites) that represents the system's coordinate along its evolution path and evaluates the degree of change between any two states on that path. We applied GMFA to analyze Type 2 Diabetes Mellitus (T2DM) patients from two cohorts: EVAS (289 patients from Singapore) and NHANES (517) from the USA. Personalized systems biology models (digital twins) were constructed. We deduced disease dynamics from the individually parameterized metabolic network and predicted the evolution path of the metabolic health state. For each patient, we obtained an individual description of disease dynamics and predict an evolution path of the metabolic health state. Our predictive models achieve an ROC-AUC in the range 0.79-0.95 (sensitivity 80-92%, specificity 62-94%) in identifying phenotypes at the baseline and predicting future development of diabetic retinopathy and cataract progression among T2DM patients within 3 years from the baseline. The GMFA method is a step towards realizing the ultimate goal to develop practical predictive computational models for diagnostics based on systems biology. This tool has potential use in chronic disease management in medical practice. Supplementary Information: The online version contains supplementary material available at 10.1007/s13755-023-00218-x.

Coding and noncoding landscape of extracellular RNA released by human glioma stem cells.

Tumor-released RNA may mediate intercellular communication and serve as biomarkers. Here we develop a protocol enabling quantitative, minimally biased analysis of extracellular RNAs (exRNAs) associated with microvesicles, exosomes (collectively called EVs), and ribonucleoproteins (RNPs). The exRNA complexes isolated from patient-derived glioma stem-like cultures exhibit distinct compositions, with microvesicles most closely reflecting cellular transcriptome. exRNA is enriched in small ncRNAs, such as miRNAs in exosomes, and precisely processed tRNA and Y RNA fragments in EVs and exRNPs. EV-enclosed mRNAs are mostly fragmented, and UTRs enriched; nevertheless, some full-length mRNAs are present. Overall, there is less than one copy of non-rRNA per EV. Our results suggest that massive EV/exRNA uptake would be required to ensure functional impact of transferred RNA on brain recipient cells and predict the most impactful miRNAs in such conditions. This study also provides a catalog of diverse exRNAs useful for biomarker discovery and validates its feasibility on cerebrospinal fluid.

A novel community driven software for functional enrichment analysis of extracellular vesicles data.

Bioinformatics tools are imperative for the in depth analysis of heterogeneous high-throughput data. Most of the software tools are developed by specific laboratories or groups or companies wherein they are designed to perform the required analysis for the group. However, such software tools may fail to capture "what the community needs in a tool". Here, we describe a novel community-driven approach to build a comprehensive functional enrichment analysis tool. Using the existing FunRich tool as a template, we invited researchers to request additional features and/or changes. Remarkably, with the enthusiastic participation of the community, we were able to implement 90% of the requested features. FunRich enables plugin for extracellular vesicles wherein users can download and analyse data from Vesiclepedia database. By involving researchers early through community needs software development, we believe that comprehensive analysis tools can be developed in various scientific disciplines.

Fetal Bovine Serum RNA Interferes with the Cell Culture derived Extracellular RNA.

Fetal bovine serum (FBS) has been used in eukaryotic cell cultures for decades. However, little attention has been paid to the biological effects associated with RNA content of FBS on cell cultures. Here, using RNA sequencing, we demonstrate that FBS contains a diverse repertoire of protein-coding and regulatory RNA species, including mRNA, miRNA, rRNA, and snoRNA. The majority of them (>70%) are retained even after extended ultracentrifugation in the preparations of vesicle-depleted FBS (vdFBS) commonly utilized in the studies of extracellular vesicles (EV) and intercellular communication. FBS-associated RNA is co-isolated with cell-culture derived extracellular RNA (exRNA) and interferes with the downstream RNA analysis. Many evolutionally conserved FBS-derived RNA species can be falsely annotated as human or mouse transcripts. Notably, specific miRNAs abundant in FBS, such as miR-122, miR-451a and miR-1246, have been previously reported as enriched in cell-culture derived EVs, possibly due to the confounding effect of the FBS. Analysis of publically available exRNA datasets supports the notion of FBS contamination. Furthermore, FBS transcripts can be taken up by cultured cells and affect the results of highly sensitive gene expression profiling technologies. Therefore, precautions for experimental design are warranted to minimize the interference and misinterpretations caused by FBS-derived RNA.

Genome-wide analysis of mRNAs associated with mouse peroxisomes.

BACKGROUND: RNA is often targeted to be localized to the specific subcellular compartments. Specific localization of mRNA is believed to be an important mechanism for targeting their protein products to the locations, where their function is required. RESULTS: In this study we performed the genome wide transcriptome analysis of peroxisome preparations from the mouse liver using microarrays. We demonstrate that RNA is absent inside peroxisomes, however it is associated at their exterior via the noncovalent contacts with the membrane proteins. We detect enrichment of specific sets of transcripts in two preparations of peroxisomes, purified with different degrees of stringency. Importantly, among these were mRNAs encoding bona fide peroxisomal proteins, such as peroxins and peroxisomal matrix enzymes involved in beta-oxidation of fatty acids and bile acid biosynthesis. The top-most enriched mRNA, whose association with peroxisomes we confirm microscopically was Hmgcs1, encoding 3-hydroxy-3-methylglutaryl-CoA synthase, a crucial enzyme of cholesterol biosynthesis pathway. We observed significant representation of mRNAs encoding mitochondrial and secreted proteins in the peroxisomal fractions. CONCLUSIONS: This is a pioneer genome-wide study of localization of mRNAs to peroxisomes that provides foundation for more detailed dissection of mechanisms of RNA targeting to subcellular compartments.

HOXD-AS1 is a novel lncRNA encoded in HOXD cluster and a marker of neuroblastoma progression revealed via integrative analysis of noncoding transcriptome.

BACKGROUND: Long noncoding RNAs (lncRNAs) constitute a major, but poorly characterized part of human transcriptome. Recent evidence indicates that many lncRNAs are involved in cancer and can be used as predictive and prognostic biomarkers. Significant fraction of lncRNAs is represented on widely used microarray platforms, however they have usually been ignored in cancer studies. RESULTS: We developed a computational pipeline to annotate lncRNAs on popular Affymetrix U133 microarrays, creating a resource allowing measurement of expression of 1581 lncRNAs. This resource can be utilized to interrogate existing microarray datasets for various lncRNA studies. We found that these lncRNAs fall into three distinct classes according to their statistical distribution by length. Remarkably, these three classes of lncRNAs were co-localized with protein coding genes exhibiting distinct gene ontology groups. This annotation was applied to microarray analysis which identified a 159 lncRNA signature that discriminates between localized and metastatic stages of neuroblastoma. Analysis of an independent patient cohort revealed that this signature differentiates also relapsing from non-relapsing primary tumors. This is the first example of the signature developed via the analysis of expression of lncRNAs solely. One of these lncRNAs, termed HOXD-AS1, is encoded in HOXD cluster. HOXD-AS1 is evolutionary conserved among hominids and has all bona fide features of a gene. Studying retinoid acid (RA) response of SH-SY5Y cell line, a model of human metastatic neuroblastoma, we found that HOXD-AS1 is a subject to morphogenic regulation, is activated by PI3K/Akt pathway and itself is involved in control of RA-induced cell differentiation. Knock-down experiments revealed that HOXD-AS1 controls expression levels of clinically significant protein-coding genes involved in angiogenesis and inflammation, the hallmarks of metastatic cancer. CONCLUSIONS: Our findings greatly extend the number of noncoding RNAs functionally implicated in tumor development and patient treatment and highlight their role as potential prognostic biomarkers of neuroblastomas.

Exosomes secreted by human cells transport largely mRNA fragments that are enriched in the 3'-untranslated regions.

UNLABELLED: Small secreted membrane vesicles called exosomes have recently attracted a great interest after the discovery that they transfer mRNA that can be translated into protein in recipient cells. Surprisingly, we found that for the majority of exosomal mRNAs only a fraction of their corresponding probes is detectable on the expression microarrays. Exosomal mRNA fragmentation is characterized with a specific structural pattern. The closer to the 3'-end of the transcript the fragments are localized, the larger fraction among the secreted RNAs they constitute. Since the 3'-ends of transcripts contain elements conferring subcellular localization of mRNA and are rich in miRNA-binding sites, exosomal RNA may act as competing RNA to regulate stability, localization and translation activity of mRNAs in recipient cells. REVIEWERS: This article was reviewed by Neil Smalheiser and Sandor Pongor.

Role of genomic architecture in the expression dynamics of long noncoding RNAs during differentiation of human neuroblastoma cells.

BACKGROUND: Mammalian genomes are extensively transcribed producing thousands of long non-protein-coding RNAs (lncRNAs). The biological significance and function of the vast majority of lncRNAs remain unclear. Recent studies have implicated several lncRNAs as playing important roles in embryonic development and cancer progression. LncRNAs are characterized with different genomic architectures in relationship with their associated protein-coding genes. Our study aimed at bridging lncRNA architecture with dynamical patterns of their expression using differentiating human neuroblastoma cells model. RESULTS: LncRNA expression was studied in a 120-hours timecourse of differentiation of human neuroblastoma SH-SY5Y cells into neurons upon treatment with retinoic acid (RA), the compound used for the treatment of neuroblastoma. A custom microarray chip was utilized to interrogate expression levels of 9,267 lncRNAs in the course of differentiation. We categorized lncRNAs into 19 architecture classes according to their position relatively to protein-coding genes. For each architecture class, dynamics of expression of lncRNAs was studied in association with their protein-coding partners. It allowed us to demonstrate positive correlation of lncRNAs with their associated protein-coding genes at bidirectional promoters and for sense-antisense transcript pairs. In contrast, lncRNAs located in the introns and downstream of the protein-coding genes were characterized with negative correlation modes. We further classified the lncRNAs by the temporal patterns of their expression dynamics. We found that intronic and bidirectional promoter architectures are associated with rapid RA-dependent induction or repression of the corresponding lncRNAs, followed by their constant expression. At the same time, lncRNAs expressed downstream of protein-coding genes are characterized by rapid induction, followed by transcriptional repression. Quantitative RT-PCR analysis confirmed the discovered functional modes for several selected lncRNAs associated with proteins involved in cancer and embryonic development. CONCLUSIONS: This is the first report detailing dynamical changes of multiple lncRNAs during RA-induced neuroblastoma differentiation. Integration of genomic and transcriptomic levels of information allowed us to demonstrate specific behavior of lncRNAs organized in different genomic architectures. This study also provides a list of lncRNAs with possible roles in neuroblastoma.

Beware of moving targets: reference proteome content fluctuates substantially over the years.

Reference proteomes are generated by increasingly sophisticated annotation pipelines as part of regular genome build releases; yet, the corresponding changes in reference proteomes' content are dramatic. In the history of the NCBI-curated human proteome, the total number of entries has remained roughly constant but approximately half of the proteins from the 2003 build 33 are no longer represented by entries in current releases, while about the same number of new proteins have been added (for sequence identity thresholds 50-90%). Although mostly hypothetical proteins are affected, there are also spectacular cases of entry removal/addition of well studied proteins. The changes between the 2003 and recent human proteomes are in a similar order of magnitude as the differences between recent human and chimpanzee proteome releases. As an application example, we show that the proteome fluctuations affect the interpretation (about 74% of hits) of organelle-specific mass-spectrometry data. Although proteome quality tends to improve with more recent releases as, for example, the fraction of proteins with functional annotation has increased over time, existing evidence implies that, apparently, the proteome content still remains incomplete, not just pertaining to isoforms/sequence variants but also to proteins and their families that are clearly distinct.

Ecotopic viral integration site 1 (EVI1) regulates multiple cellular processes important for cancer and is a synergistic partner for FOS protein in invasive tumors.

Ecotropic viral integration site 1 (EVI1) is an oncogenic dual domain zinc finger transcription factor that plays an essential role in the regulation of hematopoietic stem cell renewal, and its overexpression in myeloid leukemia and epithelial cancers is associated with poor patient survival. Despite the discovery of EVI1 in 1988 and its emerging role as a dominant oncogene in various types of cancer, few EVI1 target genes are known. This lack of knowledge has precluded a clear understanding of exactly how EVI1 contributes to cancer. Using a combination of ChIP-Seq and microarray studies in human ovarian carcinoma cells, we show that the two zinc finger domains of EVI1 bind to DNA independently and regulate different sets of target genes. Strikingly, an enriched fraction of EVI1 target genes are cancer genes or genes associated with cancer. We also show that more than 25% of EVI1-occupied genes contain linked EVI1 and activator protein (AP)1 DNA binding sites, and this finding provides evidence for a synergistic cooperative interaction between EVI1 and the AP1 family member FOS in the regulation of cell adhesion, proliferation, and colony formation. An increased number of dual EVI1/AP1 target genes are also differentially regulated in late-stage ovarian carcinomas, further confirming the importance of the functional cooperation between EVI1 and FOS. Collectively, our data indicate that EVI1 is a multipurpose transcription factor that synergizes with FOS in invasive tumors.

Identification of nucleotide patterns enriched in secreted RNAs as putative cis-acting elements targeting them to exosome nano-vesicles.

BACKGROUND: Exosomes are nanoscale membrane vesicles released by most cells. They are postulated to be involved in cell-cell communication and genetic reprogramming of their target cells. In addition to proteins and lipids, they release RNA molecules many of which are not present in the donor cells implying a highly selective mode of their packaging into these vesicles. Sequence motifs targeting RNA to the vesicles are currently unknown. RESULTS: Ab initio approach was applied for computational identification of potential RNA secretory motifs in the primary sequences of exosome-enriched RNAs (eRNAs). Exhaustive motif analysis for the first time revealed unique sequence features of eRNAs. We discovered multiple linear motifs specifically enriched in secreted RNAs. Their potential function as cis-acting elements targeting RNAs to exosomes is proposed. The motifs co-localized in the same transcripts suggesting combinatorial organization of these secretory signals. We investigated associations of the discovered motifs with other RNA parameters. Secreted RNAs were found to have almost twice shorter half-life times on average, in comparison with cytoplasmic RNAs, and the occurrence of some eRNA-specific motifs significantly correlated with this eRNA feature. Also, we found that eRNAs are highly enriched in long noncoding RNAs. CONCLUSIONS: Secreted RNAs share specific sequence motifs that may potentially function as cis-acting elements targeting RNAs to exosomes. Discovery of these motifs will be useful for our understanding the roles of eRNAs in cell-cell communication and genetic reprogramming of the target cells. It will also facilitate nano-scale vesicle engineering and selective targeting of RNAs of interest to these vesicles for gene therapy purposes.

The Sym35 gene required for root nodule development in pea is an ortholog of Nin from Lotus japonicus.

Comparative phenotypic analysis of pea (Pisum sativum) sym35 mutants and Lotus japonicus nin mutants suggested a similar function for the PsSym35 and LjNin genes in early stages of root nodule formation. Both the pea and L. japonicus mutants are non-nodulating but normal in their arbuscular mycorrhizal association. Both are characterized by excessive root hair curling in response to the bacterial microsymbiont, lack of infection thread initiation, and absence of cortical cell divisions. To investigate the molecular basis for the similarity, we cloned and sequenced the PsNin gene, taking advantage of sequence information from the previously cloned LjNin gene. An RFLP analysis on recombinant inbred lines mapped PsNin to the same chromosome arm as the PsSym35 locus and direct evidence demonstrating that PsNin is the PsSym35 gene was subsequently obtained by cosegregation analysis and sequencing of three independent Pssym35 mutant alleles. L. japonicus and pea root nodules develop through different organogenic pathways, so it was of interest to compare the expression of the two orthologous genes during nodule formation. Overall, a similar developmental regulation of the PsNin and LjNin genes was shown by the transcriptional activation in root nodules of L. japonicus and pea. In the indeterminate pea nodules, PsNin is highly expressed in the meristematic cells of zone I and in the cells of infection zone II, corroborating expression of LjNin in determinate nodule primordia. At the protein level, seven domains, including the putative DNA binding/dimerization RWP-RK motif and the PB1 heterodimerization domain, are conserved between the LjNIN and PsNIN proteins.