Chem-CRISPR/dCas9FCPF: a platform for chemically induced epigenome editing.

Epigenetic aberration is one of the major driving factors in human cancer, often leading to acquired resistance to chemotherapies. Various small molecule epigenetic modulators have been reported. Nonetheless, outcomes from animal models and clinical trials have underscored the substantial setbacks attributed to pronounced on- and off-target toxicities. To address these challenges, CRISPR/dCas9 technology is emerging as a potent tool for precise modulation of epigenetic mechanism. However, this technology involves co-expressing exogenous epigenetic modulator proteins, which presents technical challenges in preparation and delivery with potential undesirable side effects. Recently, our research demonstrated that Cas9 tagged with the Phe-Cys-Pro-Phe (FCPF)-peptide motif can be specifically targeted by perfluorobiphenyl (PFB) derivatives. Here, we integrated the FCPF-tag into dCas9 and established a chemically inducible platform for epigenome editing, called Chem-CRISPR/dCas9FCPF. We designed a series of chemical inhibitor-PFB conjugates targeting various epigenetic modulator proteins. Focusing on JQ1, a panBET inhibitor, we demonstrate that c-MYC-sgRNA-guided JQ1-PFB specifically inhibits BRD4 in close proximity to the c-MYC promoter/enhancer, thereby effectively repressing the intricate transcription networks orchestrated by c-MYC as compared with JQ1 alone. In conclusion, our Chem-CRISPR/dCas9FCPF platform significantly increased target specificity of chemical epigenetic inhibitors, offering a viable alternative to conventional fusion protein systems for epigenome editing.

Predicting the involvement of polyQ- and polyA in protein-protein interactions by their amino acid context.

Homorepeats, specifically polyglutamine (polyQ) and polyalanine (polyA), are often implicated in protein-protein interactions (PPIs). So far, a method to predict the participation of homorepeats in protein interactions is lacking. We propose a machine learning approach to identify PPI-involved polyQ and polyA regions within the human proteome based on known interacting regions. Using the dataset of human homorepeats, we identified 157 polyQ and 745 polyA regions potentially involved in PPIs. Machine learning models, trained on amino acid context and homorepeat length, demonstrated high precision (0.90-0.98) but variable recall (0.42-0.85). Random forest outperformed other models (AUC polyQ = 0.686, AUC polyA = 0.732) using the positions surrounding the homorepeat -10 to +10. Integrating paralog information marginally improved predictions but was excluded for model simplicity. Further optimization revealed that for polyQ, using amino acid surrounding positions from -6 to +6 increased AUC to 0.715. For polyA, no improvement was found. Incorporating coiled coil overlap information enhanced polyA predictions (AUC = 0.745) but not polyQ. Finally, we applied these models to predict PPI involvement across all polyQ and polyA regions, identifying potential interactions. Case studies illustrated the method's predictive capacity, highlighting known interacting regions with high scores and elucidating potential false negatives.

SEOM-GETNE-TTCC Clinical guideline thyroid cancer (2023).

Thyroid cancer (TC) represents 3% of global cancer incidence. Recent changes have optimized treatment decisions based on risk assessment, molecular profiling, and imaging assessment, leading the development of targeted agents that have modified the natural history of this disease. This increasing complexity on treatment options requires careful assessment at the different stages of the disease to provide the most suitable approach from diagnosis to long-term follow-up. This guideline aims to offer a comprehensive and practical overview on the current status and last updates of TC management.

Overlooked poor-quality patient samples in sequencing data impair reproducibility of published clinically relevant datasets.

BACKGROUND: Reproducibility is a major concern in biomedical studies, and existing publication guidelines do not solve the problem. Batch effects and quality imbalances between groups of biological samples are major factors hampering reproducibility. Yet, the latter is rarely considered in the scientific literature. RESULTS: Our analysis uses 40 clinically relevant RNA-seq datasets to quantify the impact of quality imbalance between groups of samples on the reproducibility of gene expression studies. High-quality imbalance is frequent (14 datasets; 35%), and hundreds of quality markers are present in more than 50% of the datasets. Enrichment analysis suggests common stress-driven effects among the low-quality samples and highlights a complementary role of transcription factors and miRNAs to regulate stress response. Preliminary ChIP-seq results show similar trends. Quality imbalance has an impact on the number of differential genes derived by comparing control to disease samples (the higher the imbalance, the higher the number of genes), on the proportion of quality markers in top differential genes (the higher the imbalance, the higher the proportion; up to 22%) and on the proportion of known disease genes in top differential genes (the higher the imbalance, the lower the proportion). We show that removing outliers based on their quality score improves the resulting downstream analysis. CONCLUSIONS: Thanks to a stringent selection of well-designed datasets, we demonstrate that quality imbalance between groups of samples can significantly reduce the relevance of differential genes, consequently reducing reproducibility between studies. Appropriate experimental design and analysis methods can substantially reduce the problem.

Pharmacophore Identification and Structure-Activity Relationship Analysis of a Series of Substituted Azaindoles as Inhibitors of Trypanosoma brucei.

Human African trypanosomiasis is among the World Health Organization's designated neglected tropical diseases. Repurposing strategies are often employed in academic drug discovery programs due to financial limitations, and in this instance, we used human kinase inhibitor chemotypes to identify substituted 4-aminoazaindoles, exemplified by 1. Structure-activity and structure-property relationship analysis, informed by cheminformatics, identified 4s as a potent inhibitor of Trypanosoma brucei growth. While 4s appeared to be fast acting and cidal in the in vitro assays, it failed to cure a murine model of infection. Preliminary efforts to identify the potential mechanism of action of the series pointed to arginine kinase, though, as we demonstrate, this does not appear to be the sole target of our compounds. This comprehensive approach to drug discovery, encompassing cheminformatics, structure-potency and structure-property analysis, and pharmacophore identification, highlights our multipronged efforts to identify novel lead compounds for this deadly disease.

CT-based radiomics for predicting breast cancer radiotherapy side effects.

Skin inflammation with the potential sequel of moist epitheliolysis and edema constitute the most frequent breast radiotherapy (RT) acute side effects. The aim of this study was to compare the predictive value of tissue-derived radiomics features to the total breast volume (TBV) for the moist cells epitheliolysis as a surrogate for skin inflammation, and edema. Radiomics features were extracted from computed tomography (CT) scans of 252 breast cancer patients from two volumes of interest: TBV and glandular tissue (GT). Machine learning classifiers were trained on radiomics and clinical features, which were evaluated for both side effects. The best radiomics model was a least absolute shrinkage and selection operator (LASSO) classifier, using TBV features, predicting moist cells epitheliolysis, achieving an area under the receiver operating characteristic (AUROC) of 0.74. This was comparable to TBV breast volume (AUROC of 0.75). Combined models of radiomics and clinical features did not improve performance. Exclusion of volume-correlated features slightly reduced the predictive performance (AUROC 0.71). We could demonstrate the general propensity of planning CT-based radiomics models to predict breast RT-dependent side effects. Mammary tissue was more predictive than glandular tissue. The radiomics features performance was influenced by their high correlation to TBV volume.

GPS Suitability for Physical Frailty Assessment.

The ageing of the population needs the automation of patient monitoring. The objective of this is twofold: to improve care and reduce costs. Frailty, as a state of increased vulnerability resulting from several diseases, can be seen as a pandemic for older people. One of the most common detection tests is gait speed. This article compares the gait speed measured outdoors using smartphones with that measured using manual tests conducted in medical centres. In the experiments, the walking speed was measured over a straight path of 80 m. Additionally, the speed was measured over 2.4 m in the middle of the path, given that this is the minimum distance used in medical frailty tests. To eliminate external factors, the participants were healthy individuals, the weather was good, and the path was flat and free of obstacles. The results obtained are promising. The measurements taken with common smartphones over a straight path of 80 m are within the same order of error as those observed in the manual tests conducted by practitioners.

A dynamic in vitro model of Down syndrome neurogenesis with trisomy 21 gene dosage correction.

Excess gene dosage from chromosome 21 (chr21) causes Down syndrome (DS), spanning developmental and acute phenotypes in terminal cell types. Which phenotypes remain amenable to intervention after development is unknown. To address this question in a model of DS neurogenesis, we derived trisomy 21 (T21) human induced pluripotent stem cells (iPSCs) alongside, otherwise, isogenic euploid controls from mosaic DS fibroblasts and equipped one chr21 copy with an inducible XIST transgene. Monoallelic chr21 silencing by XIST is near-complete and irreversible in iPSCs. Differential expression reveals that T21 neural lineages and iPSCs share suppressed translation and mitochondrial pathways and activate cellular stress responses. When XIST is induced before the neural progenitor stage, T21 dosage correction suppresses a pronounced skew toward astrogenesis in neural differentiation. Because our transgene remains inducible in postmitotic T21 neurons and astrocytes, we demonstrate that XIST efficiently represses genes even after terminal differentiation, which will empower exploration of cell type-specific T21 phenotypes that remain responsive to chr21 dosage.

Homorepeat variability within the human population.

Genetic variation within populations plays a crucial role in driving evolution. Unlike the average protein sequence, the evolution of homorepeats can be influenced by DNA replication slippage, when DNA polymerases either add or skip repeats of nucleotides. While there are some diseases known to be caused by abnormal changes in the length of amino acid homorepeats, naturally occurring variations in homorepeat length remain relatively unexplored. In our study, we examined the variation in amino acid homorepeat length of human individuals by analyzing 125 748 exomes, as well as 15 708 whole genomes. Our analyses revealed significant variability in homorepeat length across the human population, indicating that these motifs are prone to mutations at higher rates than non repeat sequences. We focused our study on glutamine homorepeats, also known as polyQ sequences, and found that shorter polyQ sequences tend to exhibit greater length variation, while longer ones primarily undergo deletions. Notably, polyQ sequencesthat are more conserved across primates tend to show less variation within the human population, indicating stronger selective pressure to maintain their length. Overall, our results demonstrate that there is large natural variation in the length of homorepeats within the human population, with no apparent impact on observable traits.

Depolymerization of SUMO chains induces slender to stumpy differentiation in T. brucei bloodstream parasites.

Trypanosoma brucei are protozoan parasites that cause sleeping sickness in humans and nagana in cattle. Inside the mammalian host, a quorum sensing-like mechanism coordinates its differentiation from a slender replicative form into a quiescent stumpy form, limiting growth and activating metabolic pathways that are beneficial to the parasite in the insect host. The post-translational modification of proteins with the Small Ubiquitin-like MOdifier (SUMO) enables dynamic regulation of cellular metabolism. SUMO can be conjugated to its targets as a monomer but can also form oligomeric chains. Here, we have investigated the role of SUMO chains in T. brucei by abolishing the ability of SUMO to polymerize. We have found that parasites able to conjugate only SUMO monomers are primed for differentiation. This was demonstrated for monomorphic lines that are normally unable to produce stumpy forms in response to quorum sensing signaling in mice, and also for pleomorphic cell lines in which stumpy cells were observed at unusually low parasitemia levels. SUMO chain mutants showed a stumpy compatible transcriptional profile and better competence to differentiate into procyclics. Our study indicates that SUMO depolymerization may represent a coordinated signal triggered during stumpy activation program.

Structured Tandem Repeats in Protein Interactions.

Tandem repeats (TRs) in protein sequences are consecutive, highly similar sequence motifs. Some types of TRs fold into structural units that pack together in ensembles, forming either an (open) elongated domain or a (closed) propeller, where the last unit of the ensemble packs against the first one. Here, we examine TR proteins (TRPs) to see how their sequence, structure, and evolutionary properties favor them for a function as mediators of protein interactions. Our observations suggest that TRPs bind other proteins using large, structured surfaces like globular domains; in particular, open-structured TR ensembles are favored by flexible termini and the possibility to tightly coil against their targets. While, intuitively, open ensembles of TRs seem prone to evolve due to their potential to accommodate insertions and deletions of units, these evolutionary events are unexpectedly rare, suggesting that they are advantageous for the emergence of the ancestral sequence but are early fixed. We hypothesize that their flexibility makes it easier for further proteins to adapt to interact with them, which would explain their large number of protein interactions. We provide insight into the properties of open TR ensembles, which make them scaffolds for alternative protein complexes to organize genes, RNA and proteins.

The lncRNA Snhg11, a new candidate contributing to neurogenesis, plasticity, and memory deficits in Down syndrome.

Down syndrome (DS) stands as the prevalent genetic cause of intellectual disability, yet comprehensive understanding of its cellular and molecular underpinnings remains limited. In this study, we explore the cellular landscape of the hippocampus in a DS mouse model, the Ts65Dn, through single-nuclei transcriptional profiling. Our findings demonstrate that trisomy manifests as a highly specific modification of the transcriptome within distinct cell types. Remarkably, we observed a significant shift in the transcriptomic profile of granule cells in the dentate gyrus (DG) associated with trisomy. We identified the downregulation of a specific small nucleolar RNA host gene, Snhg11, as the primary driver behind this observed shift in the trisomic DG. Notably, reduced levels of Snhg11 in this region were also observed in a distinct DS mouse model, the Dp(16)1Yey, as well as in human postmortem brain tissue, indicating its relevance in Down syndrome. To elucidate the function of this long non-coding RNA (lncRNA), we knocked down Snhg11 in the DG of wild-type mice. Intriguingly, this intervention alone was sufficient to impair synaptic plasticity and adult neurogenesis, resembling the cognitive phenotypes associated with trisomy in the hippocampus. Our study uncovers the functional role of Snhg11 in the DG and underscores the significance of this lncRNA in intellectual disability. Furthermore, our findings highlight the importance of DG in the memory deficits observed in Down syndrome.

Plantar keratoderma-like crusted scabies in an immunocompetent infant after topical steroids.

A healthy 6-month-old girl presented with plantar keratoderma-like lesions unresponsive to topical corticosteroids. Nocturnal pruritus in 13 relatives, presence of burrows on clinical exam, and the positive scabies preparation led to the diagnosis of crusted scabies. She was successfully treated with topical and oral scabicides. Crusted scabies is a severe form of Sarcoptes scabiei infection uncommon in immunocompetent subjects, in whom previous corticosteroid use may favor its occurrence.

Intranuclear inclusions of polyQ-expanded ATXN1 sequester RNA molecules.

Spinocerebellar ataxia type 1 (SCA1) is an autosomal dominant neurodegenerative disease caused by a trinucleotide (CAG) repeat expansion in the ATXN1 gene. It is characterized by the presence of polyglutamine (polyQ) intranuclear inclusion bodies (IIBs) within affected neurons. In order to investigate the impact of polyQ IIBs in SCA1 pathogenesis, we generated a novel protein aggregation model by inducible overexpression of the mutant ATXN1(Q82) isoform in human neuroblastoma SH-SY5Y cells. Moreover, we developed a simple and reproducible protocol for the efficient isolation of insoluble IIBs. Biophysical characterization showed that polyQ IIBs are enriched in RNA molecules which were further identified by next-generation sequencing. Finally, a protein interaction network analysis indicated that sequestration of essential RNA transcripts within ATXN1(Q82) IIBs may affect the ribosome resulting in error-prone protein synthesis and global proteome instability. These findings provide novel insights into the molecular pathogenesis of SCA1, highlighting the role of polyQ IIBs and their impact on critical cellular processes.

A Systematic Review of Lipid-Focused Cardiovascular Disease Research: Trends and Opportunities.

Lipids are important modifiers of protein function, particularly as parts of lipoproteins, which transport lipophilic substances and mediate cellular uptake of circulating lipids. As such, lipids are of particular interest as blood biological markers for cardiovascular disease (CVD) as well as for conditions linked to CVD such as atherosclerosis, diabetes mellitus, obesity and dietary states. Notably, lipid research is particularly well developed in the context of CVD because of the relevance and multiple causes and risk factors of CVD. The advent of methods for high-throughput screening of biological molecules has recently resulted in the generation of lipidomic profiles that allow monitoring of lipid compositions in biological samples in an untargeted manner. These and other earlier advances in biomedical research have shaped the knowledge we have about lipids in CVD. To evaluate the knowledge acquired on the multiple biological functions of lipids in CVD and the trends in their research, we collected a dataset of references from the PubMed database of biomedical literature focused on plasma lipids and CVD in human and mouse. Using annotations from these records, we were able to categorize significant associations between lipids and particular types of research approaches, distinguish non-biological lipids used as markers, identify differential research between human and mouse models, and detect the increasingly mechanistic nature of the results in this field. Using known associations between lipids and proteins that metabolize or transport them, we constructed a comprehensive lipid-protein network, which we used to highlight proteins strongly connected to lipids found in the CVD-lipid literature. Our approach points to a series of proteins for which lipid-focused research would bring insights into CVD, including Prostaglandin G/H synthase 2 (PTGS2, a.k.a. COX2) and Acylglycerol kinase (AGK). In this review, we summarize our findings, putting them in a historical perspective of the evolution of lipid research in CVD.

Corrigendum: Identification of novel anti-amoebic pharmacophores from kinase inhibitor chemotypes.

[This corrects the article DOI: 10.3389/fmicb.2023.1149145.].

Structure-function relationships in protein homorepeats.

Homorepeats (or polyX), protein segments containing repetitions of the same amino acid, are abundant in proteomes from all kingdoms of life and are involved in crucial biological functions as well as several neurodegenerative and developmental diseases. Mainly inserted in disordered segments of proteins, the structure/function relationships of homorepeats remain largely unexplored. In this review, we summarize present knowledge for the most abundant homorepeats, highlighting the role of the inherent structure and the conformational influence exerted by their flanking regions. Recent experimental and computational methods enable residue-specific investigations of these regions and promise novel structural and dynamic information for this elusive group of proteins. This information should increase our knowledge about the structural bases of phenomena such as liquid-liquid phase separation and trinucleotide repeat disorders.

The nucleotide landscape of polyXY regions.

PolyXY regions are compositionally biased regions composed of two different amino acids. They are classified according to the arrangement of the two amino acid types 'X' and 'Y' into direpeats (composed of alternating amino acids, e.g. 'XYXYXY'), joined (composed of two consecutive stretches of each amino acid, e.g. 'XXXYYY') and shuffled (other arrangements, e.g., 'XYXXYY'). They have been characterized at the amino acid level in all domains of life, and are described as often found within intrinsically disordered regions. Since DNA replication slippage has been proposed as a driver of repeat variation, and given that some polyXY have a repetitive nature, we hypothesized that characterizing the nucleotide coding of various types of polyXY could give hints about their origin and evolution. To test this, we obtained all polyXY regions in the human transcriptome, categorized them, and studied their coding nucleotide sequences. We observed that polyXY exacerbates the codon biases, and that the similarity between the X and Y codons is higher than in the background proteome. Our results support a general mechanism of emergence and evolution of polyXY from single-codon polyX. PolyXY are revealed as hotspots for replication slippage, particularly those composed of repeats: joined and direpeat polyXY. Inter-conversion to shuffled polyXY disrupts nucleotide repeats and restricts further evolution by replication slippage, a mechanism that we previously observed in polyX. Our results shed light on polyXY composition and should simplify the determination of their functions.

The importance of planning CT-based imaging features for machine learning-based prediction of pain response.

Patients suffering from painful spinal bone metastases (PSBMs) often undergo palliative radiation therapy (RT), with an efficacy of approximately two thirds of patients. In this exploratory investigation, we assessed the effectiveness of machine learning (ML) models trained on radiomics, semantic and clinical features to estimate complete pain response. Gross tumour volumes (GTV) and clinical target volumes (CTV) of 261 PSBMs were segmented on planning computed tomography (CT) scans. Radiomics, semantic and clinical features were collected for all patients. Random forest (RFC) and support vector machine (SVM) classifiers were compared using repeated nested cross-validation. The best radiomics classifier was trained on CTV with an area under the receiver-operator curve (AUROC) of 0.62 ± 0.01 (RFC; 95% confidence interval). The semantic model achieved a comparable AUROC of 0.63 ± 0.01 (RFC), significantly below the clinical model (SVM, AUROC: 0.80 ± 0.01); and slightly lower than the spinal instability neoplastic score (SINS; LR, AUROC: 0.65 ± 0.01). A combined model did not improve performance (AUROC: 0,74 ± 0,01). We could demonstrate that radiomics and semantic analyses of planning CTs allowed for limited prediction of therapy response to palliative RT. ML predictions based on established clinical parameters achieved the best results.