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The small GTPases H, K, and NRAS are molecular switches that are indispensable for proper regulation of cellular proliferation and growth. Mutations in this family of proteins are associated with cancer and result in aberrant activation of signaling processes caused by a deregulated recruitment of downstream effector proteins. In this study, we engineered novel variants of the Ras-binding domain (RBD) of the kinase CRAF. These variants bound with high affinity to the effector binding site of active Ras. Structural characterization showed how the newly identified mutations cooperate to enhance affinity to the effector binding site compared to RBDwt. The engineered RBD variants closely mimic the interaction mode of naturally occurring Ras effectors and as dominant negative affinity reagent block their activation. Experiments with cancer cells showed that expression of these RBD variants inhibits Ras signaling leading to a reduced growth and inductions of apoptosis. Using the optimized RBD variants, we stratified patient-derived colorectal cancer organoids according to Ras dependency, which showed that the presence of Ras mutations was insufficient to predict sensitivity to Ras inhibition.
We provide in this paper a comprehensive comparison of various transfer learning strategies and deep learning architectures for computer-aided classification of adult-type diffuse gliomas. We evaluate the generalizability of out-of-domain ImageNet representations for a target domain of histopathological images, and study the impact of in-domain adaptation using self-supervised and multi-task learning approaches for pretraining the models using the medium-to-large scale datasets of histopathological images. A semi-supervised learning approach is furthermore proposed, where the fine-tuned models are utilized to predict the labels of unannotated regions of the whole slide images (WSI). The models are subsequently retrained using the ground-truth labels and weak labels determined in the previous step, providing superior performance in comparison to standard in-domain transfer learning with balanced accuracy of 96.91% and F1-score 97.07%, and minimizing the pathologist's efforts for annotation. Finally, we provide a visualization tool working at WSI level which generates heatmaps that highlight tumor areas; thus, providing insights to pathologists concerning the most informative parts of the WSI.
Attention-Deficit/Hyperactivity Disorder (ADHD) is frequently comorbid with other psychiatric disorders and also with somatic conditions, such as obesity. In addition to the clinical overlap, significant genetic correlations have been found between ADHD and obesity as well as body mass index (BMI). The biological mechanisms driving this association are largely unknown, but some candidate systems, like dopaminergic neurotransmission and circadian rhythm, have been suggested. Our aim was to identify the biological mechanisms underpinning the link between ADHD and obesity measures. Using the largest GWAS summary statistics currently available for ADHD (N=53,293), BMI (N=681,275), and obesity (N=98,697), we first tested the association of dopaminergic and circadian rhythm gene sets with each phenotype. This hypothesis-driven approach showed that the dopaminergic gene set was associated with both ADHD (P=5.81×10−3) and BMI (P=1.63×10−5), while the circadian rhythm gene set was associated with BMI only (P=1.28×10−3). We then took a data-driven approach by conducting genome-wide ADHD-BMI and ADHD-obesity gene-based meta-analyses, followed by pathway enrichment analyses. This approach further supported the implication of dopaminergic signaling in the link between ADHD and obesity measures, as the Dopamine-DARPP32 Feedback in cAMP Signaling pathway was significantly enriched in both the ADHD-BMI and ADHD-obesity gene-based meta-analysis results. Our findings suggest that dopaminergic neurotransmission, partially through DARPP-32-dependent signaling, is a key player underlying the genetic overlap between ADHD and obesity measures. Uncovering the shared etiological factors underlying the frequently observed ADHD-obesity comorbidity may have important implications in terms of preventive interventions and/or efficient treatment of these conditions.
Attention-Deficit/Hyperactivity Disorder (ADHD) and obesity are frequently comorbid, genetically correlated, and share brain substrates. The biological mechanisms driving this association are unclear, but candidate systems, like dopaminergic neurotransmission and circadian rhythm, have been suggested. Our aim was to identify the biological mechanisms underpinning the genetic link between ADHD and obesity measures and investigate associations of overlapping genes with brain volumes. We tested the association of dopaminergic and circadian rhythm gene sets with ADHD, body mass index (BMI), and obesity (using GWAS data of N=53,293, N=681,275, and N=98,697, respectively). We then conducted genome-wide ADHD-BMI and ADHD-obesity gene-based meta-analyses, followed by pathway enrichment analyses. Finally, we tested the association of ADHD-BMI overlapping genes with brain volumes (primary GWAS data N=10,720–10,928; replication data N=9,428). The dopaminergic gene set was associated with both ADHD (P=5.81×10−3) and BMI (P=1.63×10−5), the circadian rhythm was associated with BMI (P=1.28×10−3). The genome-wide approach also implicated the dopaminergic system, as the Dopamine-DARPP32 Feedback in cAMP Signaling pathway was enriched in both ADHD-BMI and ADHD-obesity results. The ADHD-BMI overlapping genes were associated with putamen volume (P=7.7×10−3; replication data P=3.9×10−2) – a brain region with volumetric reductions in ADHD and BMI and linked to inhibitory control. Our findings suggest that dopaminergic neurotransmission, partially through DARPP-32-dependent signaling and involving the putamen, is a key player underlying the genetic overlap between ADHD and obesity measures. Uncovering shared etiological factors underlying the frequently observed ADHD-obesity comorbidity may have important implications in terms of prevention and/or efficient treatment of these conditions.
Targeted protein degradation (TPD) has recently emerged as an exciting new drug modality. However, the strategy of developing small molecule-based protein degraders has evolved over the past two decades and has now established molecular tags that are already in clinical use, as well as chimeric molecules, PROteolysis TArgeting Chimeras (PROTACs), based mainly on ligand systems developed for the two E3 ligases CRBN and VHL. The large size of the human E3 ligase family suggests that PROTACs can be developed by targeting a large diversity of E3 ligases, some of which have restricted expression patterns with the potential to design disease- or tissue-specific degraders. Indeed, many new E3 ligands have been published recently, confirming the druggability of E3 ligases. This review summarises recent data on E3 ligases and highlights the challenges in developing these molecules into efficient PROTACs rivalling the established degrader systems.
Highlights
• Artificial intelligence systems for mechanically ventilated patients are increasing.
• The clinical and financial impact of these models are often unexamined.
• We developed a generic health-economic model for artificial intelligence systems.
• This model assesses the cost-effectiveness for many different scenarios.
• The developed framework is easily adjustable to other (clinical) situations.
Abstract
Purpose: The health and economic consequences of artificial intelligence (AI) systems for mechanically ventilated intensive care unit patients often remain unstudied. Early health technology assessments (HTA) can examine the potential impact of AI systems by using available data and simulations. Therefore, we developed a generic health-economic model suitable for early HTA of AI systems for mechanically ventilated patients.
Materials and methods: Our generic health-economic model simulates mechanically ventilated patients from their hospitalisation until their death. The model simulates two scenarios, care as usual and care with the AI system, and compares these scenarios to estimate their cost-effectiveness.
Results: The generic health-economic model we developed is suitable for estimating the cost-effectiveness of various AI systems. By varying input parameters and assumptions, the model can examine the cost-effectiveness of AI systems across a wide range of different clinical settings.
Conclusions: Using the proposed generic health-economic model, investors and innovators can easily assess whether implementing a certain AI system is likely to be cost-effective before an exact clinical impact is determined. The results of the early HTA can aid investors and innovators in deployment of AI systems by supporting development decisions, informing value-based pricing, clinical trial design, and selection of target patient groups.
Highlights
• Deletion of SPPL3 promotes resistance of malignant B cells to NK cell cytotoxicity
• Loss of SPPL3 blocks ligand binding to NK receptors via increased N-glycosylation
• B3GNT2 deletion reduces LacNAc addition and restores SPPL3-KO cell sensitivity to NK cells
• SPPL3-deficient cells are enriched in tetra-antennary N-glycans with LacNAc elongations
Summary
Natural killer (NK) cells are primary defenders against cancer precursors, but cancer cells can persist by evading immune surveillance. To investigate the genetic mechanisms underlying this evasion, we perform a genome-wide CRISPR screen using B lymphoblastoid cells. SPPL3, a peptidase that cleaves glycosyltransferases in the Golgi, emerges as a top hit facilitating evasion from NK cytotoxicity. SPPL3-deleted cells accumulate glycosyltransferases and complex N-glycans, disrupting not only binding of ligands to NK receptors but also binding of rituximab, a CD20 antibody approved for treating B cell cancers. Notably, inhibiting N-glycan maturation restores receptor binding and sensitivity to NK cells. A secondary CRISPR screen in SPPL3-deficient cells identifies B3GNT2, a transferase-mediating poly-LacNAc extension, as crucial for resistance. Mass spectrometry confirms enrichment of N-glycans bearing poly-LacNAc upon SPPL3 loss. Collectively, our study shows the essential role of SPPL3 and poly-LacNAc in cancer immune evasion, suggesting a promising target for cancer treatment.
Die chronische myeloische Leukämie (CML) ist eine klonale myeloproliferative Neoplasie und hat ihren Ursprung in transformierten pluripotenten Stammzellen im Knochenmark. Der Krankheitsentstehung liegt eine reziproke chromosomale Translokation zugrunde, in deren Folge ein neues Fusionsgen, das sogenannte Philadelphia-Chromosom, entsteht. Das hiervon codierte Genprodukt ist eine Tyrosinkinase mit konstitutiver Aktivität mit resultierender unkontrollierter Signaltransduktion. Gegen diese Tyrosinkinase existiert eine molekular zielgerichtete Therapie, die Tyrosinkinase-Inhibitoren (TKI).
Den Therapiestandard stellt die Behandlung mit einem TKI dar. Seit einigen Jahren existiert jedoch das Therapieziel der therapiefreien Remission (TFR), bei dem CML-Patienten mit einem guten molekularen Ansprechen den TKI nach einiger Zeit absetzen und unter engmaschigen Kontrollen therapiefrei bleiben. Ungefähr die Hälfte dieser Patienten erleidet kein molekulares Rezidiv und bleibt langfristig in TFR. Zu der Thematik der TFR existieren zahlreiche klinische Studien, die die Umsetzbarkeit und die Sicherheit eines Absetzversuchs belegen und Kriterien definiert haben, die für einen Absetzversuch erfüllt sein sollten. In dieser Dissertation wird die Umsetzung der TFR im klinischen Alltag onkologischer Praxen untersucht. Es wird untersucht, ob die Studienergebnisse mit den TFR-Raten und den identifizierten Einflussfaktoren auf den Praxisalltag übertragbar sind und ob das Therapieziel TFR in den klinischen Alltag integrierbar ist. Hierfür werden die Daten von 61 CML-Patienten mit einem Absetzversuch aus fünf onkologischen Praxen retrospektiv ausgewertet. Erhoben werden Parameter der Routineversorgung und die Ergebnisse der Kontrolluntersuchungen während der TFR werden dokumentiert und ausgewertet. Die TFR-Raten von ca. 50%, die in den Absetz-Studien beobachtet wurden, finden sich im hier untersuchten Patientenkollektiv wieder. Mithilfe der binären logistischen Regression wird getestet, ob bestimmte Faktoren, wie die Therapiedauer und das verwendete Medikament vor dem Absetzen, einen signifikanten Einfluss auf den Verlauf der TFR haben. Hier kann kein signifikanter Einflussfaktor identifiziert werden. Es lässt sich eine signifikant längere therapiefreie Überlebensdauer bei den Patienten zeigen, die mit einem TKI der zweiten Generation vor dem Absetzen behandelt wurden.
Zusammenfassend lässt sich sagen, dass die TFR bei geeigneten CML-Patienten in der Praxis umsetzbar und sicher ist und fest im klinischen Alltag onkologischer Praxen verankert sein sollte.
MicroRNAs (miRNAs) are critical post-transcriptional regulators in many biological processes. They act by guiding RNA-induced silencing complexes to miRNA response elements (MREs) in target mRNAs, inducing translational inhibition and/or mRNA degradation. Functional MREs are expected to predominantly occur in the 3’ untranslated region and involve perfect base-pairing of the miRNA seed. Here, we generate a high-resolution map of miR-181a/b-1 (miR-181) MREs to define the targeting rules of miR-181 in developing murine T-cells. By combining a multi-omics approach with computational high-resolution analyses, we uncover novel miR-181 targets and demonstrate that miR-181 acts predominantly through RNA destabilization. Importantly, we discover an alternative seed match and identify a distinct set of targets with repeat elements in the coding sequence which are targeted by miR-181 and mediate translational inhibition. In conclusion, deep profiling of MREs in primary cells is critical to expand physiologically relevant targetomes and establish context-dependent miRNA targeting rules.
Key Points:
* Deep profiling identifies novel targets of miR-181 associated with global gene regulation.
* miR-181 MREs in repeat elements in the coding sequence act through translational inhibition.
* High-resolution analysis reveals an alternative seed match in functional MREs.