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In this study we aimed to identify the spectrum of affected genes in Ukrainian patients diagnosed with primary myelofibrosis (PMF). DNA samples were obtained from peripheral blood leukocytes of 30 Ukrainian PMF patients. Using Whole Exome Sequencing, we detected previously reported and unreported sequence variants considered as pathogenic or potentially pathogenic. Canonical mutations of usual MPN-driver genes were detected in 70% of PMF patients, including JAK2V617F in 43.3%, MPLW515 in 10% and CALRmutations (type 1-like and 2-like) in 16.7% of patients. Also, a non-canonical MPLP222S variant was detected in one patient negative for these mutations. However, in cell culture assay, MPLP222S expression in Ba/F3 cells did not demonstrate differences in phosphorylation of JAK/STAT signaling proteins in response to TPO stimulation compared to MPLWT expression. Overall, more pathogenic and potentially pathogenic sequence variants were found among PMF patients negative for canonical mutations in three driver genes (JAK2, MPL andCALR), compared to patients, positive for one of these mutations. The mean numbers of variants were 5 (range: 4 7) versus 3.3 (range: 1 9), respectively (p = 0.03). The most frequently affected among Ukrainian PMF patients were genes ASXL1, PEG3, EZH2, ATM, U2AF1, andCDH23in addition to JAK2, MPL andCALR. Pathogenic or potentially pathogenic sequence variants of ASXL1and EZH2genes were identified in 23.3% and 16.7% of cases, respectively. Genes JARID2, RBBP8, RTEL1, SUZ12, BRCA2, LAMB4, NF1, RBM12B, RBM43, and RBP3 were recurrently affected in PMF patients who were also positive for usual mutations in three driver genes. Among PMF patients negative for usual mutations in three driver genes recurrently affected genes were DNMT3Aand TET2in addition to mentioned earlier, and some genetic variants were identified in RTEL1, SUZ12, CBL, CUX1, FLT3LGand UMODL1genes in single cases. Also, we identified several genes affected in single cases (KIT, SF3B1, GNAS) in PMF patients negative for canonical mutations in three driver genes which may be potential MPN drivers.

We identified a predicted compact cysteine-rich sequence in the honey bee genome that we called 'Raalin'. Raalin transcripts are enriched in the brain of adult honey bee workers and drones, with only minimum expression in other tissues or in pre-adult stages. Open-reading frame (ORF) homologues of Raalin were identified in the transcriptomes of fruit flies, mosquitoes and moths. The Raalin-like gene from Drosophila melanogaster encodes for a short secreted protein that is maximally expressed in the adult brain with negligible expression in other tissues or pre-imaginal stages. Raalin-like sequences have also been found in the recently sequenced genomes of six ant species, but not in the jewel wasp Nasonia vitripennis. As in the honey bee, the Raalin-like sequences of ants do not have an ORF. A comparison of the genome region containing Raalin in the genomes of bees, ants and the wasp provides evolutionary support for an extensive genome rearrangement in this sequence. Our analyses identify a new family of ancient cysteine-rich short sequences in insects in which insertions and genome rearrangements may have disrupted this locus in the branch leading to the Hymenoptera. The regulated expression of this transcript suggests that it has a brain-specific function. [ABSTRACT FROM AUTHOR]

Foamy viruses (FV) are the oldest known genus of retroviruses and have persisted in nonhuman primates for over 60 million years. FV are efficiently transmitted, leading to a lifelong nonpathogenic infection. Transmission is thought to occur through saliva, but the detailed mechanism is unknown. Interestingly, this persistent infection contrasts with the rapid cytopathicity caused by FV in vitro, suggesting a host defense against FV. To better understand the tissue specificity of FV replication and host immunologic defense against FV cytopathicity, we quantified FV in tissues of healthy rhesus macaques (RM) and those severely immunosuppressed by simian immunodeficiency virus (SIV). Contrary to earlier findings, we find that all immunocompetent animals consistently have high levels of viral RNA in oral tissues but not in other tissues examined, including the small intestine. Strikingly, abundant viral transcripts were detected in the small intestine of all of the SIV-infected RM, which has been shown to be a major site of SIV (and human immunodeficiency virus)-induced CD4+ T-cell depletion. In contrast, there was a trend to lower viral RNA levels in oropharyngeal tissues of SIV-infected animals. The expansion of FV replication to the small intestine but not to other CD4+ T-cell-depleted tissues suggests that factors other than T-cell depletion, such as dysregulation of the jejunal microenvironment after SIV infection, likely account for the expanded tissue tropism of FV replication. [ABSTRACT FROM AUTHOR]

The Proteome-Wide Association Study (PWAS) is a protein-based genetic association approach designed to complement traditional variant-based methods like GWAS. PWAS operates in two stages: first, machine learning models predict the impact of genetic variants on protein-coding genes, generating effect scores. These scores are then aggregated into a gene-damaging score for each individual. This score is then used in case-control statistical tests to significantly link to specific phenotypes. PWAS Hub (v1.2) is a user-friendly platform that facilitates the exploration of gene-disease associations using clinical and genetic data from the UK Biobank (UKB), encompassing 500k individuals. PWAS Hub reports on 819 diseases and phenotypes determined by PheCode and ICD-10 clinical codes, each with a minimum of 400 affected individuals. PWAS-derived gene associations were reported for 72% of the tested phenotypes. The PWAS Hub also analyzes gene associations separately for males and females, considering sex-specific genetic effects, inheritance patterns (dominant and recessive), and gene pleiotropy. We illustrated the utility of the PWAS Hub for primary (essential) hypertension (I10), type 2 diabetes mellitus (E11), and specified haematuria (R31) that showed sex-dependent genetic signals. The PWAS Hub, available at pwas.huji.ac.il, is a valuable resource for studying genetic contributions to common diseases and sex-specific effects., (© The Author(s) 2024. Published by Oxford University Press on behalf of Nucleic Acids Research.)

PWAS (proteome-wide association study) is an innovative genetic association approach that complements widely used methods like GWAS (genome-wide association study). The PWAS approach involves consecutive phases. Initially, machine learning modeling and probabilistic considerations quantify the impact of genetic variants on protein-coding genes' biochemical functions. Secondly, for each individual, aggregating the variants per gene determines a gene-damaging score. Finally, standard statistical tests are activated in the case-control setting to yield statistically significant genes per phenotype. The PWAS Hub offers a user-friendly interface for an in-depth exploration of gene-disease associations from the UK Biobank (UKB). Results from PWAS cover 99 common diseases and conditions, each with over 10,000 diagnosed individuals per phenotype. Users can explore genes associated with these diseases, with separate analyses conducted for males and females. For each phenotype, the analyses account for sex-based genetic effects, inheritance modes (dominant and recessive), and the pleiotropic nature of associated genes. The PWAS Hub showcases its usefulness for asthma by navigating through proteomic-genetic analyses. Inspecting PWAS asthma-listed genes (a total of 27) provide insights into the underlying cellular and molecular mechanisms. Comparison of PWAS-statistically significant genes for common diseases to the Open Targets benchmark shows partial but significant overlap in gene associations for most phenotypes. Graphical tools facilitate comparing genetic effects between PWAS and coding GWAS results, aiding in understanding the sex-specific genetic impact on common diseases. This adaptable platform is attractive to clinicians, researchers, and individuals interested in delving into gene-disease associations and sex-specific genetic effects., (© 2024 Kelman et al.; Published by Cold Spring Harbor Laboratory Press.)

PARK7, also known as DJ-1, plays a critical role in protecting cells by functioning as a sensitive oxidation sensor and modulator of antioxidants. DJ-1 acts to maintain mitochondrial function and regulate transcription in response to different stressors. In this study, we showed that cell lines vary based on their antioxidation potential under basal conditions. The transcriptome of HEK293 cells was tested following knockdown (KD) of DJ-1 using siRNAs, which reduced the DJ-1 transcripts to only 12% of the original level. We compared the expression levels of 14k protein-coding transcripts and 4.2k non-coding RNAs relative to cells treated with non-specific siRNAs. Among the coding genes, approximately 200 upregulated differentially expressed genes (DEGs) signified a coordinated antiviral innate immune response. Most genes were associated with the regulation of type 1 interferons (IFN) and the induction of inflammatory cytokines. About a quarter of these genes were also induced in cells treated with non-specific siRNAs that were used as a negative control. Beyond the antiviral-like response, 114 genes were specific to the KD of DJ-1 with enrichment in RNA metabolism and mitochondrial functions. A smaller set of downregulated genes (58 genes) was associated with dysregulation in membrane structure, cell viability, and mitophagy. We propose that the KD DJ-1 perturbation diminishes the protective potency against oxidative stress. Thus, it renders the cells labile and responsive to the dsRNA signal by activating a large number of genes, many of which drive apoptosis, cell death, and inflammatory signatures. The KD of DJ-1 highlights its potency in regulating genes associated with antiviral responses, RNA metabolism, and mitochondrial functions, apparently through alteration in STAT activity and downstream signaling. Given that DJ-1 also acts as an oncogene in metastatic cancers, targeting DJ-1 could be a promising therapeutic strategy where manipulation of the DJ-1 level may reduce cancer cell viability and enhance the efficacy of cancer treatments.

Background/objectives: The effects of early life exposures on offspring life-course health are well established. This study assessed whether adding early socio-demographic and perinatal variables to a model based on polygenic risk score (PRS) improves prediction of obesity risk., Methods: We used the Jerusalem Perinatal study (JPS) with data at birth and body mass index (BMI) and waist circumference (WC) measured at age 32. The PRS was constructed using over 2.1M common SNPs identified in genome-wide association study (GWAS) for BMI. Linear and logistic models were applied in a stepwise approach. We first examined the associations between genetic variables and obesity-related phenotypes (e.g., BMI and WC). Secondly, socio-demographic variables were added and finally perinatal exposures, such as maternal pre-pregnancy BMI (mppBMI) and gestational weight gain (GWG) were added to the model. Improvement in prediction of each step was assessed using measures of model discrimination (area under the curve, AUC), net reclassification improvement (NRI) and integrated discrimination improvement (IDI)., Results: One standard deviation (SD) change in PRS was associated with a significant increase in BMI (β = 1.40) and WC (β = 2.45). These associations were slightly attenuated (13.7-14.2%) with the addition of early life exposures to the model. Also, higher mppBMI was associated with increased offspring BMI (β = 0.39) and WC (β = 0.79) (p < 0.001). For obesity (BMI ≥ 30) prediction, the addition of early socio-demographic and perinatal exposures to the PRS model significantly increased AUC from 0.69 to 0.73. At an obesity risk threshold of 15%, the addition of early socio-demographic and perinatal exposures to the PRS model provided a significant improvement in reclassification of obesity (NRI, 0.147; 95% CI 0.068-0.225)., Conclusions: Inclusion of early life exposures, such as mppBMI and maternal smoking, to a model based on PRS improves obesity risk prediction in an Israeli population-sample., (© 2024. The Author(s).)

Hypothyroidism is a common endocrine disorder whose prevalence increases with age. The disease manifests itself when the thyroid gland fails to produce sufficient thyroid hormones. The disorder includes cases of congenital hypothyroidism (CH), but most cases exhibit hormonal feedback dysregulation and destruction of the thyroid gland by autoantibodies. In this study, we sought to identify causal genes for hypothyroidism in large populations. The study used the UK-Biobank (UKB) database, reporting on 13,687 cases of European ancestry. We used GWAS compilation from Open Targets (OT) and tuned protocols focusing on genes and coding regions, along with complementary association methods of PWAS (proteome-based) and TWAS (transcriptome-based). Comparing summary statistics from numerous GWAS revealed a limited number of variants associated with thyroid development. The proteome-wide association study method identified 77 statistically significant genes, half of which are located within the Chr6-MHC locus and are enriched with autoimmunity-related genes. While coding GWAS and PWAS highlighted the centrality of immune-related genes, OT and transcriptome-wide association study mostly identified genes involved in thyroid developmental programs. We used independent populations from Finland (FinnGen) and the Taiwan cohort to validate the PWAS results. The higher prevalence in females relative to males is substantiated as the polygenic risk score prediction of hypothyroidism relied mostly from the female group genetics. Comparing results from OT, TWAS, and PWAS revealed the complementary facets of hypothyroidism's etiology. This study underscores the significance of synthesizing gene-phenotype association methods for this common, intricate disease. We propose that the integration of established association methods enhances interpretability and clinical utility., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Zucker, Kovalerchik, Stern, Kaufman and Linial.)

Background: Distinguishing diseases into distinct subtypes is crucial for study and effective treatment strategies. The Open Targets Platform (OT) integrates biomedical, genetic, and biochemical datasets to empower disease ontologies, classifications, and potential gene targets. Nevertheless, many disease annotations are incomplete, requiring laborious expert medical input. This challenge is especially pronounced for rare and orphan diseases, where resources are scarce., Methods: We present a machine learning approach to identifying diseases with potential subtypes, using the approximately 23,000 diseases documented in OT. We derive novel features for predicting diseases with subtypes using direct evidence. Machine learning models were applied to analyze feature importance and evaluate predictive performance for discovering both known and novel disease subtypes., Results: Our model achieves a high (89.4%) ROC AUC (Area Under the Receiver Operating Characteristic Curve) in identifying known disease subtypes. We integrated pre-trained deep-learning language models and showed their benefits. Moreover, we identify 515 disease candidates predicted to possess previously unannotated subtypes., Conclusions: Our models can partition diseases into distinct subtypes. This methodology enables a robust, scalable approach for improving knowledge-based annotations and a comprehensive assessment of disease ontology tiers. Our candidates are attractive targets for further study and personalized medicine, potentially aiding in the unveiling of new therapeutic indications for sought-after targets., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Inc.)

Breast cancer (BC) is the most common malignancy affecting Western women today. It is estimated that as many as 10% of BC cases can be attributed to germline variants. However, the genetic basis of the majority of familial BC cases has yet to be identified. Discovering predisposing genes contributing to familial BC is challenging due to their presumed rarity, low penetrance, and complex biological mechanisms. Here, we focused on an analysis of rare missense variants in a cohort of 12 families of Middle Eastern origins characterized by a high incidence of BC cases. We devised a novel, high-throughput, variant analysis pipeline adapted for family studies, which aims to analyze variants at the protein level by employing state-of-the-art machine learning models and three-dimensional protein structural analysis. Using our pipeline, we analyzed 1218 rare missense variants that are shared between affected family members and classified 80 genes as candidate pathogenic. Among these genes, we found significant functional enrichment in peroxisomal and mitochondrial biological pathways which segregated across seven families in the study and covered diverse ethnic groups. We present multiple evidence that peroxisomal and mitochondrial pathways play an important, yet underappreciated, role in both germline BC predisposition and BC survival., (© The Author(s) 2024. Published by Oxford University Press.)

Many advances in biomedicine can be attributed to identifying unusual proteins and genes. Many of these proteins' unique properties were discovered by manual inspection, which is becoming infeasible at the scale of modern protein datasets. Here, we propose to tackle this challenge using anomaly detection methods that automatically identify unexpected properties. We adopt a state-of-the-art anomaly detection paradigm from computer vision, to highlight unusual proteins. We generate meaningful representations without labeled inputs, using pretrained deep neural network models. We apply these protein language models (pLM) to detect anomalies in function, phylogenetic families, and segmentation tasks. We compute protein anomaly scores to highlight human prion-like proteins, distinguish viral proteins from their host proteome, and mark non-classical ion/metal binding proteins and enzymes. Other tasks concern segmentation of protein sequences into folded and unstructured regions. We provide candidates for rare functionality (e.g. prion proteins). Additionally, we show the anomaly score is useful in 3D folding-related segmentation. Our novel method shows improved performance over strong baselines and has objectively high performance across a variety of tasks. We conclude that the combination of pLM and anomaly detection techniques is a valid method for discovering a range of global and local protein characteristics., (© The Author(s) 2024. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics.)

Treatment of aging rats for 6 months with ladostigil (1 mg/kg/day) prevented a decline in recognition and spatial memory and suppressed the overexpression of gene-encoding pro-inflammatory cytokines, TNFα, IL1β, and IL6 in the brain and microglial cultures. Primary cultures of mouse microglia stimulated by lipopolysaccharides (LPS, 0.75 µg/mL) and benzoyl ATPs (BzATP) were used to determine the concentration of ladostigil that reduces the secretion of these cytokine proteins. Ladostigil (1 × 10 -11 M), a concentration compatible with the blood of aging rats in, prevented memory decline and reduced secretion of IL1β and IL6 by ≈50%. RNA sequencing analysis showed that BzATP/LPS upregulated 25 genes, including early-growth response protein 1, (Egr1) which increased in the brain of subjects with neurodegenerative diseases. Ladostigil significantly decreased Egr1 gene expression and levels of the protein in the nucleus and increased TNF alpha-induced protein 3 (TNFaIP3), which suppresses cytokine release, in the microglial cytoplasm. Restoration of the aberrant signaling of these proteins in ATP/LPS-activated microglia in vivo might explain the prevention by ladostigil of the morphological and inflammatory changes in the brain of aging rats.

Scientific research trends and interests evolve over time. The ability to identify and forecast these trends is vital for educational institutions, practitioners, investors, and funding organizations. In this study, we predict future trends in scientific publications using heterogeneous sources, including historical publication time series from PubMed, research and review articles, pre-trained language models, and patents. We demonstrate that scientific topic popularity levels and changes (trends) can be predicted five years in advance across 40 years and 125 diverse topics, including life-science concepts, biomedical, anatomy, and other science, technology, and engineering topics. Preceding publications and future patents are leading indicators for emerging scientific topics. We find the ratio of reviews to original research articles informative for identifying increasing or declining topics, with declining topics having an excess of reviews. We find that language models provide improved insights and predictions into temporal dynamics. In temporal validation, our models substantially outperform the historical baseline. Our findings suggest that similar dynamics apply across other scientific and engineering research topics. We present SciTrends, a user-friendly webtool for predicting future publication trends for any topic covered in PubMed., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Michal Linial reports financial support was provided by 10.13039/501100003483Hebrew University of Jerusalem. Michal Linial reports a relationship with Hebrew University of Jerusalem that includes: employment. None., (© 2023 Published by Elsevier Ltd.)

We assessed whether adding early life exposures to a model based on polygenic risk score (PRS) improves prediction of obesity risk. We used a birth cohort with data at birth and BMI and waist circumference (WC) measured at age 32. The PRS was composed of SNPs identified in GWAS for BMI. Linear and logistic models were used to explore associations with obesity-related phenotypes. Improvement in prediction was assessed using measures of model discrimination (AUC), and net reclassification improvement (NRI). One SD change in PRS was associated with a significant increase in BMI and WC. These associations were slightly attenuated (13.7%-14.2%) with the addition of early life exposures to the model. Also, higher maternal pre-pregnancy BMI was associated with increase in offspring BMI and WC (p<0.001). For prediction obesity (BMI ≥ 30), the addition of early life exposures to the PRS model significantly increase the AUC from 0.69 to 0.73. At an obesity risk threshold of 15%, the addition of early life exposures to the PRS model provided a significant improvement in reclassification of obesity (NRI, 0.147; 95% CI 0.068-0.225). We conclude that inclusion of early life exposures to a model based on PRS improves obesity risk prediction in an Israeli population-sample., Competing Interests: Disclosure: The authors declared no conflict of interest.

Hypertension is a polygenic disease that affects over 1.2 billion adults aged 30-79 worldwide. It is a major risk factor for renal, cerebrovascular, and cardiovascular diseases. The heritability of hypertension is estimated to be high; nevertheless, our understanding of its underlying mechanisms remains scarce and incomplete. This study covered the entries from European ancestry from the UK-Biobank (UKB), with 74,090 cases diagnosed with essential (primary) hypertension and 200,734 controls. We compared the findings from large-scale genome-wide association studies (GWAS) to the gene-based method of proteome-wide association studies (PWAS). We focused on 70 statistically significant associated genes, most of which failed to reach significance in variant-based GWAS. A total of 30% of the PWAS-associated genes were validated against independent cohorts, including the Finnish Biobank. Furthermore, gene-based analyses that were performed on both sexes revealed sex-dependent genetics with a stronger genetic component associated with females. Analysis of systolic and diastolic blood pressure measurements confirms a strong genetic effect associated with females. We demonstrated that gene-based approaches provide insight into the underlying biology of hypertension. Specifically, the expression profiles of the identified genes exposed the enrichment of endothelial cells from multiple organs. Furthermore, females' top-ranked significant genes are involved in cellular immunity. We conclude that studying hypertension and blood pressure via gene-based association methods improves interpretability and exposes sex-dependent genetic effects, which enhances clinical utility., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The primary role of microglia is to maintain homeostasis by effectively responding to various disturbances. Activation of transcriptional programs determines the microglia's response to external stimuli. In this study, we stimulated murine neonatal microglial cells with benzoyl ATP (bzATP) and lipopolysaccharide (LPS), and monitored their ability to release pro-inflammatory cytokines. When cells are exposed to bzATP, a purinergic receptor agonist, a short-lived wave of transcriptional changes, occurs. However, only combining bzATP and LPS led to a sustainable and robust response. The transcriptional profile is dominated by induced cytokines (e.g., IL-1α and IL-1β), chemokines, and their membrane receptors. Several abundant long noncoding RNAs (lncRNAs) are induced by bzATP/LPS, including Ptgs2os2, Bc1, and Morrbid, that function in inflammation and cytokine production. Analyzing the observed changes through TNF (Tumor necrosis factor) and NF-κB (nuclear factor kappa light chain enhancer of activated B cells) pathways confirmed that neonatal glial cells exhibit a distinctive expression program in which inflammatory-related genes are upregulated by orders of magnitude. The observed capacity of the microglial culture to activate a robust inflammatory response is useful for studying neurons under stress, brain injury, and aging. We propose the use of a primary neonatal microglia culture as a responsive in vitro model for testing drugs that may interact with inflammatory signaling and the lncRNA regulatory network.

Mature microRNAs (miRNAs) are single-stranded non-coding RNA (ncRNA) molecules that act in post-transcriptional regulation in animals and plants. A mature miRNA is the end product of consecutive, highly regulated processing steps of the primary miRNA transcript. Following base-paring of the mature miRNA with its mRNA target, translation is inhibited, and the targeted mRNA is degraded. There are hundreds of miRNAs in each cell that work together to regulate cellular key processes, including development, differentiation, cell cycle, apoptosis, inflammation, viral infection, and more. In this review, we present an overlooked layer of cellular regulation that addresses cell dynamics affecting miRNA accessibility. We discuss the regulation of miRNA local storage and translocation among cell compartments. The local amounts of the miRNAs and their targets dictate their actual availability, which determines the ability to fine-tune cell responses to abrupt or chronic changes. We emphasize that changes in miRNA storage and compactization occur under induced stress and changing conditions. Furthermore, we demonstrate shared principles on cell physiology, governed by miRNA under oxidative stress, tumorigenesis, viral infection, or synaptic plasticity. The evidence presented in this review article highlights the importance of spatial and temporal miRNA regulation for cell physiology. We argue that limiting the research to mature miRNAs within the cytosol undermines our understanding of the efficacy of miRNAs to regulate cell fate under stress conditions.

Neurodegenerative disorders, brain injury, and the decline in cognitive function with aging are accompanied by a reduced capacity of cells in the brain to cope with oxidative stress and inflammation. In this study, we focused on the response to oxidative stress in SH-SY5Y, a human neuroblastoma cell line. We monitored the viability of the cells in the presence of oxidative stress. Such stress was induced by hydrogen peroxide or by Sin1 (3-morpholinosydnonimine) that generates reactive oxygen and nitrogen species (ROS and RNS). Both stressors caused significant cell death. Our results from the RNA-seq experiments show that SH-SY5Y cells treated with Sin1 for 24 h resulted in 94 differently expressed long non-coding RNAs (lncRNAs), including many abundant ones. Among the abundant lncRNAs that were upregulated by exposing the cells to Sin1 were those implicated in redox homeostasis, energy metabolism, and neurodegenerative diseases (e.g., MALAT1, MIAT, GABPB1-AS1, NEAT1, MIAT, GABPB1-AS1, and HAND2-AS1). Another group of abundant lncRNAs that were significantly altered under oxidative stress included cancer-related SNHG family members. We tested the impact of ladostigil, a bifunctional reagent with antioxidant and anti-inflammatory properties, on the lncRNA expression levels. Ladostigil was previously shown to enhance learning and memory in the brains of elderly rats. In SH-SY5Y cells, several lncRNAs involved in transcription regulation and the chromatin structure were significantly induced by ladostigil. We anticipate that these poorly studied lncRNAs may act as enhancers (eRNA), regulating transcription and splicing, and in competition for miRNA binding (ceRNA). We found that the induction of abundant lncRNAs, such as MALAT1, NEAT-1, MIAT, and SHNG12, by the Sin1 oxidative stress paradigm specifies only the undifferentiated cell state. We conclude that a global alteration in the lncRNA profiles upon stress in SH-SY5Y may shift cell homeostasis and is an attractive in vitro system to characterize drugs that impact the redox state of the cells and their viability.

Post-transcriptional regulation in multicellular organisms is mediated by microRNAs. However, the principles that determine if a gene is regulated by miRNAs are poorly understood. Previous works focused mostly on miRNA seed matches and other features of the 3'-UTR of transcripts. These common approaches rely on knowledge of the miRNA families, and computational approaches still yield poor, inconsistent results, with many false positives. In this work, we present a different paradigm for predicting miRNA-regulated genes based on the encoded proteins. In a novel, automated machine learning framework, we use sequence as well as diverse functional annotations to train models on multiple organisms using experimentally validated data. We present insights from tens of millions of features extracted and ranked from different modalities. We show high predictive performance per organism and in generalization across species. We provide a list of novel predictions including Danio rerio (zebrafish) and Arabidopsis thaliana (mouse-ear cress). We compare genomic models and observe that our protein model outperforms, whereas a unified model improves on both. While most membranous and disease related proteins are regulated by miRNAs, the G-protein coupled receptor (GPCR) family is an exception, being mostly unregulated by miRNAs. We further show that the evolutionary conservation among paralogs does not imply any coherence in miRNA regulation. We conclude that duplicated paralogous genes that often changed their function, also diverse in their tendency to be miRNA regulated. We conclude that protein function is informative across species in predicting post-transcriptional miRNA regulation in living cells., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Ofer and Linial.)