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Allogeneic hematopoietic cell transplantation (HCT) is used to treat many blood-based disorders and malignancies, however it can also result in serious adverse events, such as the development of acute graft-versus-host disease (aGVHD). This study aimed to develop a donor-specific epigenetic classifier to reduce incidence of aGVHD by improving donor selection. Genome-wide DNA methylation was assessed in a discovery cohort of 288 HCT donors selected based on recipient aGVHD outcome; this cohort consisted of 144 cases with aGVHD grades III-IV and 144 controls with no aGVHD. We applied a machine learning algorithm to identify CpG sites predictive of aGVHD. Receiver operating characteristic (ROC) curve analysis of these sites resulted in a classifier with an encouraging area under the ROC curve (AUC) of 0.91. To test this classifier, we used an independent validation cohort (n = 288) selected using the same criteria as the discovery cohort. Attempts to validate the classifier failed with the AUC falling to 0.51. These results indicate that donor DNA methylation may not be a suitable predictor of aGVHD in an HCT setting involving unrelated donors, despite the initial promising results in the discovery cohort. Our work highlights the importance of independent validation of machine learning classifiers, particularly when developing classifiers intended for clinical use., 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 Webster, Ecker, Moghul, Liu, Dhami, Marzi, Paul, Kuxhausen, Lee, Spellman, Wang, Feber, Rakyan, Peggs and Beck.)

Purpose: To evaluate the ability of preoperative MRI-based measurements to predict the pathological T (pT) stage and cervical lymph node metastasis (CLNM) via machine learning (ML)-driven models trained in oral tongue squamous cell carcinoma (OTSCC)., Materials and Methods: 108 patients with a new diagnosis of OTSCC were enrolled. The preoperative MRI study included post-contrast high-resolution T1-weighted images acquired in all patients. MRI-based depth of invasion (DOI) and tumor dimension-together with shape-based and intensity-based features-were extracted from the lesion volume segmentation. The entire dataset was randomly divided into a training set and a validation set, and the performances of different types of ML algorithms were evaluated and compared., Results: MRI-based DOI and tumor dimension together with several shape-based and intensity-based signatures significantly discriminated the pT stage and LN status. The overall accuracy of the model for predicting the pT stage was 0.86 (95%CI, 0.78-0.92) and 0.81 (0.64-0.91) in the training and validation sets, respectively. There was no improvement in the model performance upon including shape-based and intensity-based features. The model for predicting CLNM based on DOI and tumor dimensions had a fair accuracy of 0.68 (0.57-0.78) and 0.69 (0.51-0.84) in the training and validation sets, respectively. The shape-based and intensity-based signatures have shown potential for improving the model sensitivity, with a comparable accuracy., Conclusion: MRI-based models driven by ML algorithms could stratify patients with OTSCC according to the pT stages. They had a moderate ability to predict cervical lymph node metastasis., Competing Interests: None of the authors have potential conflict of interest including any financial, personal or other relationships with other people or organizations that could inappropriately influence this work., (© 2023 The Authors.)

Progress in dementia research has been limited, with substantial gaps in our knowledge of targets for prevention, mechanisms for disease progression, and disease-modifying treatments. The growing availability of multimodal data sets opens possibilities for the application of machine learning and artificial intelligence (AI) to help answer key questions in the field. We provide an overview of the state of the science, highlighting current challenges and opportunities for utilisation of AI approaches to move the field forward in the areas of genetics, experimental medicine, drug discovery and trials optimisation, imaging, and prevention. Machine learning methods can enhance results of genetic studies, help determine biological effects and facilitate the identification of drug targets based on genetic and transcriptomic information. The use of unsupervised learning for understanding disease mechanisms for drug discovery is promising, while analysis of multimodal data sets to characterise and quantify disease severity and subtype are also beginning to contribute to optimisation of clinical trial recruitment. Data-driven experimental medicine is needed to analyse data across modalities and develop novel algorithms to translate insights from animal models to human disease biology. AI methods in neuroimaging outperform traditional approaches for diagnostic classification, and although challenges around validation and translation remain, there is optimism for their meaningful integration to clinical practice in the near future. AI-based models can also clarify our understanding of the causality and commonality of dementia risk factors, informing and improving risk prediction models along with the development of preventative interventions. The complexity and heterogeneity of dementia requires an alternative approach beyond traditional design and analytical approaches. Although not yet widely used in dementia research, machine learning and AI have the potential to unlock current challenges and advance precision dementia medicine., (© 2023. The Author(s).)

Escherichia coli CspA is an RNA binding protein that accumulates during cold-shock and stimulates translation of several mRNAs-including its own. Translation in the cold of cspA mRNA involves a cis-acting thermosensor element, which enhances ribosome binding, and the trans-acting action of CspA. Using reconstituted translation systems and probing experiments we show that, at low temperature, CspA specifically promotes the translation of the cspA mRNA folded in the conformation less accessible to the ribosome, which is formed at 37°C but is retained upon cold shock. CspA interacts with its mRNA without inducing large structural rearrangements, but allowing the progression of the ribosomes during the transition from translation initiation to translation elongation. A similar structure-dependent mechanism may be responsible for the CspA-dependent translation stimulation observed with other probed mRNAs, for which the transition to the elongation phase is progressively facilitated during cold acclimation with the accumulation of CspA., 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. The reviewer RE declared a shared affiliation with the author VH to the handling editor at the time of review., (Copyright © 2023 Giuliodori, Belardinelli, Duval, Garofalo, Schenckbecher, Hauryliuk, Ennifar and Marzi.)

Background: In this prospective study, we hypothesized that magnetic resonance imaging (MRI) may represent not only the tumor but also the microenvironment, reflecting the heterogeneity and microstructural complexity of neoplasms. We investigated the correlation between both diffusion kurtosis imaging (DKI) and dynamic contrast-enhanced (DCE)-MRI with the pathological factors in oral cavity squamous cell carcinomas (OSCCs)., Methods: A total of 37 patients with newly diagnosed OSCCs underwent an MR examination on a 3T system. The diffusion coefficient (D), the kurtosis parameter (K), the transfer constants K trans and K ep and the volume of extravascular extracellular space v e were quantified. A histogram-based approach was proposed to investigate the associations between the imaging and the pathological factors based on the histology and immunochemistry., Results: Significant differences in the DCE-MRI and DKI parameters were found in relation to the inflammatory infiltrate, tumor grading, keratinization and desmoplastic reaction. Relevant relationships emerged between tumor-infiltrating lymphocytes (TILs) and DKI, with lower D and higher K values being associated with increased TILs., Conclusion: Although a further investigation is needed, these findings provide a more comprehensive biological characterization of OSCCs and may contribute to a better understanding of DKI-derived parameters, whose biophysical meaning is still not well-defined., 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. The funders had no role in the design of the study; in the collection, analyses or interpretation of data; in the writing of the manuscript or in the decision to publish the results.

The ribosome is a complex cellular machinery whose solved structure allowed for an incredible leap in structural biology research. Different ions bind to the ribosome, stabilizing inter-subunit interfaces and structurally linking rRNAs, proteins, and ligands. Besides cations such as K + and Mg 2+ , polyamines are known to stabilize the folding of RNA and overall structure. The bacterial ribosome is composed of a small (30S) subunit containing the decoding center and a large (50S) subunit devoted to peptide bond formation. We have previously shown that the small ribosomal subunit of Staphylococcus aureus is sensitive to changes in ionic conditions and polyamines concentration. In particular, its decoding center, where mRNA codons and tRNA anticodons interact, is prone to structural deformations in the absence of spermidine. Here, we report a detailed protocol for the purification of the intact and functional 30S, achieved through specific ionic conditions and the addition of spermidine. Using this protocol, we obtained the cryo-electron microscopy (cryo-EM) structure of the 30S-mRNA complex from S. aureus at 3.6 Å resolution. The 30S-mRNA complex formation was verified by a toeprinting assay. In this article, we also include a description of toeprinting and cryo-EM protocols. The described protocols can be further used to study the process of translation regulation. Graphical abstract., Competing Interests: Competing interests The authors declare no competing interests., (Copyright © 2022 The Authors; exclusive licensee Bio-protocol LLC.)

The prognosis of a subset of patients with locally advanced oropharyngeal cancer (LA-OPC) is still poor despite improvements in patient selection and treatment. Identifying specific patient- and tumor-related factors can help to select those patients who need intensified treatment. We aimed to assess the role of historical risk factors and novel magnetic resonance imaging (MRI) biomarkers in predicting outcomes in these patients. Patients diagnosed with LA-OPC were studied with diffusion-weighted imaging (DWI) and dynamic-contrast enhanced MRI at baseline and at the 10th radiotherapy (RT) fraction. Clinical information was collected as well. The endpoint of the study was the development of disease progression, locally or distantly. Of the 97 patients enrolled, 68 were eligible for analysis. Disease progression was recorded in 21 patients (11 had loco-regional progression, 10 developed distant metastases). We found a correlation between N diameter and disease control ( p = 0.02); features such as p16 status and extranodal extension only showed a trend towards statistical significance. Among perfusion MRI features, higher median values of K ep both in primary tumor (T, p = 0.016) and lymph node (N, p = 0.003) and lower median values of v e ( p = 0.018 in T, p = 0.004 in N) correlated with better disease control. K ep P90 and N diameter were identified by MRMR algorithm as the best predictors of outcome. In conclusion, the association of non-invasive MRI biomarkers and patients and tumor characteristics may help in predicting disease behavior and patient outcomes in order to ensure a more customized treatment.

Staphylococcus aureus RsaG is a 3'-untranslated region (3'UTR) derived sRNA from the conserved uhpT gene encoding a glucose-6-phosphate (G6P) transporter expressed in response to extracellular G6P. The transcript uhpT-RsaG undergoes degradation from 5'- to 3'-end by the action of the exoribonucleases J1/J2, which are blocked by a stable hairpin structure at the 5'-end of RsaG, leading to its accumulation. RsaG together with uhpT is induced when bacteria are internalized into host cells or in the presence of mucus-secreting cells. Using MS2-affinity purification coupled with RNA sequencing, several RNAs were identified as targets including mRNAs encoding the transcriptional factors Rex, CcpA, SarA, and the sRNA RsaI. Our data suggested that RsaG contributes to the control of redox homeostasis and adjusts metabolism to changing environmental conditions. RsaG uses different molecular mechanisms to stabilize, degrade, or repress the translation of its mRNA targets. Although RsaG is conserved only in closely related species, the uhpT 3'UTR of the ape pathogen S. simiae harbors an sRNA, whose sequence is highly different, and which does not respond to G6P levels. Our results hypothesized that the 3'UTRs from UhpT transporter encoding mRNAs could have rapidly evolved to enable adaptation to host niches., (© 2021 John Wiley & Sons Ltd.)

The advent of quantitative imaging in personalized radiotherapy (RT) has offered the opportunity for a better understanding of individual variations in intrinsic radiosensitivity. We aimed to assess the role of magnetic resonance imaging (MRI) biomarkers, patient-related factors, and treatment-related factors in predicting xerostomia 12 months after RT (XER 12 ) in patients affected by oropharyngeal squamous cell carcinoma (OSCC). Patients with locally advanced OSCC underwent diffusion-weighted imaging (DWI) and dynamic-contrast enhanced MRI at baseline; DWI was repeated at the 10th fraction of RT. The Radiation Therapy Oncology Group (RTOG) toxicity scale was used to evaluate salivary gland toxicity. Xerostomia-related questionnaires (XQs) were administered weekly during and after RT. RTOG toxicity ≥ grade 2 at XER 12 was considered as endpoint to build prediction models. A Decision Tree classification learner was applied to build the prediction models following a five-fold cross-validation. Of the 89 patients enrolled, 63 were eligible for analysis. Thirty-six (57.1%) and 21 (33.3%) patients developed grade 1 and grade 2 XER 12 , respectively. Including only baseline variables, the model based on DCE-MRI and V65 (%) (volume of both glands receiving doses ≥ 65 Gy) had a fair accuracy (77%, 95% CI: 66.5-85.4%). The model based on V65 (%) and XQ-Int mid (integral of acute XQ scores from the start to the middle of RT) reached the best accuracy (81%, 95% CI: 71-88.7%). In conclusion, non-invasive biomarkers from DCE-MRI, in combination with dosimetric variables and self-assessed acute XQ scores during treatment may help predict grade 2 XER 12 with a fair to good accuracy.

Cryo-electron microscopy is now used as a method of choice in structural biology for studying protein synthesis, a process mediated by the ribosome machinery. In order to achieve high-resolution structures using this approach, one needs to obtain homogeneous and stable samples, which requires optimization of ribosome purification in a species-dependent manner. This is especially critical for the bacterial small ribosomal subunit that tends to be unstable in the absence of ligands. Here, we report a protocol for purification of stable 30 S from the Gram-positive bacterium Staphylococcus aureus and its cryo-EM structures: in presence of spermidine at a resolution ranging between 3.4 and 3.6 Å and in its absence at 5.3 Å. Using biochemical characterization and cryo-EM, we demonstrate the importance of spermidine for stabilization of the 30 S via preserving favorable conformation of the helix 44., 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 © 2021 Belinite, Khusainov, Soufari, Marzi, Romby, Yusupov and Hashem.)

The aim of this paper is to define the pre-treatment radiological characteristics of oropharyngeal squamous cell carcinoma (OPSCC) using morphological and non-morphological magnetic resonance imaging (MRI), based on HPV status, in a single-institution cohort. In total, 100 patients affected by OPSCC were prospectively enrolled in the present study. All patients underwent 1.5T MR with standard sequences, including diffusion-weighted imaging with and intravoxel incoherent motion (IVIM-DWI) technique and a dynamic contrast-enhanced (DCE) MRI. For all patients, human papillomavirus (HPV) status was available. No statistically significant differences in the volume of primary tumors (PTs) and lymph nodes (LNs) were observed based on HPV status. When comparing the two patient groups, no significant differences were found for the PT radiologic characteristics (presence of well-defined borders, exophytic growth, ulceration, and necrosis) and LN morphology (solid/cystic/necrotic). Tumor subsite, smoking status, and alcohol intake significantly differed based on HPV status, as well as ADC and D t values of both PTs and LNs. We detected no significant difference in DCE-MRI parameters by HPV status. Based on a multivariate logistic regression model, the combination of clinical factors, such as tumor subsite and alcohol habits, with the perfusion-free diffusion coefficient D t of LNs, may help to accurately discriminate OPSCC by HPV status.