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The impact of variations in the three-dimensional structure of the genome has been recognized, but solid cancer tissue studies are limited. Here, we performed integrated deep Hi-C sequencing with matched whole-genome sequencing, whole-genome bisulfite sequencing, 5-hydroxymethylcytosine (5hmC) sequencing and RNA sequencing across a cohort of 80 biopsy samples from patients with metastatic castration-resistant prostate cancer. Dramatic differences were present in gene expression, 5-methylcytosine/5hmC methylation and in structural variation versus mutation rate between A and B (open and closed) chromatin compartments. A subset of tumors exhibited depleted regional chromatin contacts at the AR locus, linked to extrachromosomal circular DNA (ecDNA) and worse response to AR signaling inhibitors. We also identified topological subtypes associated with stark differences in methylation structure, gene expression and prognosis. Our data suggested that DNA interactions may predispose to structural variant formation, exemplified by the recurrent TMPRSS2-ERG fusion. This comprehensive integrated sequencing effort represents a unique clinical tumor resource.

PurposeAlthough numerous biology-driven subtypes have been described previously in metastatic castration-resistant prostate cancer (mCRPC), unsupervised molecular subtyping based on gene expression has been less studied, especially using large cohorts. Thus, we sought to identify the intrinsic molecular subtypes of mCRPC and assess molecular and clinical correlates in the largest combined cohort of mCRPC samples with gene expression data available to date.Experimental designWe combined and batch-effect corrected gene expression data from four mCRPC cohorts from the Fred Hutchinson Cancer Research Center (N = 157), a small-cell neuroendocrine (NE) prostate cancer (SCNC)-enriched cohort from Weill Cornell Medicine (N = 49), and cohorts from the Stand Up 2 Cancer/Prostate Cancer Foundation East Coast Dream Team (N = 266) and the West Coast Dream Team (N = 162).ResultsHierarchical clustering of RNA-sequencing data from these 634 mCRPC samples identified two distinct adenocarcinoma subtypes, one of which (adeno-immune) was characterized by higher gene expression of immune pathways, higher CIBERSORTx immune scores, diminished ASI benefit, and non-lymph node metastasis tropism compared with an adeno-classic subtype. We also identified two distinct subtypes with enrichment for an NE phenotype, including an NE-liver subgroup characterized by liver metastasis tropism, PTEN loss, and APC and SPOP mutations compared with an NE-classic subgroup.ConclusionsOur results emphasize the heterogeneity of mCRPC beyond currently accepted molecular phenotypes, and suggest that future studies should consider incorporating transcriptome-wide profiling to better understand how these differences impact treatment responses and outcomes.

Abstract Histopathologic diagnosis and classification of cancer plays a critical role in guiding treatment. Advances in next-generation sequencing have ushered in new complementary molecular frameworks. However, existing approaches do not independently assess both site-of-origin (e.g. prostate) and lineage (e.g. adenocarcinoma) and have minimal validation in metastatic disease, where classification is more difficult. Utilizing gradient-boosted machine learning, we developed ATLAS, a pair of separate AI Tumor Lineage and Site-of-origin models from RNA expression data on 8249 tumor samples. We assessed performance independently in 10,376 total tumor samples, including 1490 metastatic samples, achieving an accuracy of 91.4% for cancer site-of-origin and 97.1% for cancer lineage. High confidence predictions (encompassing the majority of cases) were accurate 98–99% of the time in both localized and remarkably even in metastatic samples. We also identified emergent properties of our lineage scores for tumor types on which the model was never trained (zero-shot learning). Adenocarcinoma/sarcoma lineage scores differentiated epithelioid from biphasic/sarcomatoid mesothelioma. Also, predicted lineage de-differentiation identified neuroendocrine/small cell tumors and was associated with poor outcomes across tumor types. Our platform-independent single-sample approach can be easily translated to existing RNA-seq platforms. ATLAS can complement and guide traditional histopathologic assessment in challenging situations and tumors of unknown primary.

BackgroundCoronavirus disease 2019 (COVID-19)-related short-term mortality is high in dialysis patients, but longer-term outcomes are largely unknown. We therefore assessed patient recovery in a large cohort of dialysis patients 3 months after their COVID-19 diagnosis.MethodsWe analyzed data on dialysis patients diagnosed with COVID-19 from 1 February 2020 to 31 March 2021 from the European Renal Association COVID-19 Database (ERACODA). The outcomes studied were patient survival, residence and functional and mental health status (estimated by their treating physician) 3 months after COVID-19 diagnosis. Complete follow-up data were available for 854 surviving patients. Patient characteristics associated with recovery were analyzed using logistic regression.ResultsIn 2449 hemodialysis patients (mean ± SD age 67.5 ± 14.4 years, 62% male), survival probabilities at 3 months after COVID-19 diagnosis were 90% for nonhospitalized patients (n = 1087), 73% for patients admitted to the hospital but not to an intensive care unit (ICU) (n = 1165) and 40% for those admitted to an ICU (n = 197). Patient survival hardly decreased between 28 days and 3 months after COVID-19 diagnosis. At 3 months, 87% functioned at their pre-existent functional and 94% at their pre-existent mental level. Only few of the surviving patients were still admitted to the hospital (0.8-6.3%) or a nursing home (∼5%). A higher age and frailty score at presentation and ICU admission were associated with worse functional outcome.ConclusionsMortality between 28 days and 3 months after COVID-19 diagnosis was low and the majority of patients who survived COVID-19 recovered to their pre-existent functional and mental health level at 3 months after diagnosis.

This article analyzes the process of Nasal Assimilation in English.1 The approach to Nasal Assimilation in a standard rule-based framework can be conducted in two ways: by assuming an underlying alveolar nasal or by employing underspecification. The article contributes to the ongoing debate regarding underspecification in phonology and focuses on employing underspecified representations in Optimality Theory. First, it is argued that in such words as, for instance, somber, Nasal Assimilation is best analyzed in terms of positional faithfulness in the form of prevocalic faithfulness. Second, as the analyses show, positional faithfulness does not provide a workable scenario for all the data, and it is necessary to use underspecification to satisfactorily analyze English words which lack the context for positional faithfulness, for example, swamp. Nevertheless, subsequent evaluations demonstrate that in certain phrases, for instance, sing boys, employing underspecification is not sufficient either, and level distinction is necessary. Therefore, the article also offers an argument in favour of levels in Optimality Theory. * This paper develops the analysis originally conducted in Rubach (2019) by extending the empirical coverage.

BackgroundPatients on kidney replacement therapy (KRT) are at very high risk of coronavirus disease 2019 (COVID-19). The triage pathway for KRT patients presenting to hospitals with varying severity of COVID-19 illness remains ill-defined. We studied the clinical characteristics of patients at initial and subsequent hospital presentations and the impact on patient outcomes.MethodsThe European Renal Association COVID-19 Database (ERACODA) was analysed for clinical and laboratory features of 1423 KRT patients with COVID-19 either hospitalized or non-hospitalized at initial triage and those re-presenting a second time. Predictors of outcomes (hospitalization, 28-day mortality) were then determined for all those not hospitalized at initial triage.ResultsAmong 1423 KRT patients with COVID-19 [haemodialysis (HD), n = 1017; transplant, n = 406), 25% (n = 355) were not hospitalized at first presentation due to mild illness (30% HD, 13% transplant). Of the non-hospitalized patients, only 10% (n = 36) re-presented a second time, with a 5-day median interval between the two presentations (interquartile range 2-7 days). Patients who re-presented had worsening respiratory symptoms, a decrease in oxygen saturation (97% versus 90%) and an increase in C-reactive protein (26 versus 73 mg/L) and were older (72 vs 63 years) compared with those who did not return a second time. The 28-day mortality between early admission (at first presentation) and deferred admission (at second presentation) was not significantly different (29% versus 25%; P = 0.6). Older age, prior smoking history, higher clinical frailty score and self-reported shortness of breath at first presentation were identified as risk predictors of mortality when re-presenting after discharge at initial triage.ConclusionsThis study provides evidence that KRT patients with COVID-19 and mild illness can be managed effectively with supported outpatient care and with vigilance of respiratory symptoms, especially in those with risk factors for poor outcomes. Our findings support a risk-stratified clinical approach to admissions and discharges of KRT patients presenting with COVID-19 to aid clinical triage and optimize resource utilization during the ongoing pandemic.

This article reviews studies on SARS-CoV-2 antibodies, including neutralizing antibodies (NAbs) and post-vaccination response. The SARS-CoV-2 virus is closely related to SARS-CoV and MERS-CoV, sharing 79% and 50% of its genome sequence with them, respectively. Antibodies are crucial in identifying and destroying pathogens such as viruses, and understanding virus-specific antibodies can help end pandemics quickly. The SARS-CoV-2 virus uses the spike protein to enter human cells via the angiotensin-converting enzyme 2 receptor (ACE2). The immune response to the virus involves various components, including dendritic cells, T cells, B cells, and NAbs. Seroconversion, antibody kinetics, and immune response after COVID-19 vaccination are essential factors in understanding the virus and developing treatments. Neutralizing antibody therapies have been explored as potential treatments, but the emergence of viral variants challenges their long-term development. Lessons learned from antibody-based therapies can influence future strategies for treating emerging infectious diseases.

We are now in an era of molecular medicine, where specific DNA alterations can be used to identify patients who will respond to specific drugs. However, there are only a handful of clinically used predictive biomarkers in oncology. Herein, we describe an approach utilizing in vitro DNA and RNA sequencing and drug response data to create TreAtment Response Generalized Elastic-neT Signatures (TARGETS). We trained TARGETS drug response models using Elastic-Net regression in the publicly available Genomics of Drug Sensitivity in Cancer (GDSC) database. Models were then validated on additional in-vitro data from the Cancer Cell Line Encyclopedia (CCLE), and on clinical samples from The Cancer Genome Atlas (TCGA) and Stand Up to Cancer/Prostate Cancer Foundation West Coast Prostate Cancer Dream Team (WCDT). First, we demonstrated that all TARGETS models successfully predicted treatment response in the separate in-vitro CCLE treatment response dataset. Next, we evaluated all FDA-approved biomarker-based cancer drug indications in TCGA and demonstrated that TARGETS predictions were concordant with established clinical indications. Finally, we performed independent clinical validation in the WCDT and found that the TARGETS AR signaling inhibitors (ARSI) signature successfully predicted clinical treatment response in metastatic castration-resistant prostate cancer with a statistically significant interaction between the TARGETS score and PSA response (p = 0.0252). TARGETS represents a pan-cancer, platform-independent approach to predict response to oncologic therapies and could be used as a tool to better select patients for existing therapies as well as identify new indications for testing in prospective clinical trials.

The long-time behavior of many complex molecular systems can often be described by Markovian dynamics in a slow subspace spanned by a few reaction coordinates referred to as collective variables (CVs). However, determining CVs poses a fundamental challenge in chemical physics. Depending on intuition or trial and error to construct CVs can lead to non-Markovian dynamics with long memory effects, hindering analysis. To address this problem, we continue to develop a recently introduced deep-learning technique called spectral map [J. Rydzewski, J. Phys. Chem. Lett. 14, 5216–5220 (2023)]. Spectral map learns slow CVs by maximizing a spectral gap of a Markov transition matrix describing anisotropic diffusion. Here, to represent heterogeneous and multiscale free-energy landscapes with spectral map, we implement an adaptive algorithm to estimate transition probabilities. Through a Markov state model analysis, we validate that spectral map learns slow CVs related to the dominant relaxation timescales and discerns between long-lived metastable states. [ABSTRACT FROM AUTHOR]

Abstract DNA mutations in specific genes can confer preferential benefit from drugs targeting those genes. However, other molecular perturbations can “phenocopy” pathogenic mutations, but would not be identified using standard clinical sequencing, leading to missed opportunities for other patients to benefit from targeted treatments. We hypothesized that RNA phenocopy signatures of key cancer driver gene mutations could improve our ability to predict response to targeted therapies, despite not being directly trained on drug response. To test this, we built gene expression signatures in tissue samples for specific mutations and found that phenocopy signatures broadly increased accuracy of drug response predictions in-vitro compared to DNA mutation alone, and identified additional cancer cell lines that respond well with a positive/negative predictive value on par or better than DNA mutations. We further validated our results across four clinical cohorts. Our results suggest that routine RNA sequencing of tumors to identify phenocopies in addition to standard targeted DNA sequencing would improve our ability to accurately select patients for targeted therapies in the clinic.

IntroductionTularemia is mainly caused by Francisella tularensis (Ft) subsp. tularensis (Ftt) and Ft subsp. holarctica (Ftt) in humans and in more than 200 animal species including rabbits and hares. Human clinical manifestations depend on the route of infection and range from flu-like symptoms to severe pneumonia with a mortality rate up to 60% without treatment. So far, only 2D cell culture and animal models are used to study Francisella virulence, but the gained results are transferable to human infections only to a certain extent.MethodIn this study, we firstly established an ex vivo human lung tissue infection model using different Francisella strains: Ftt Life Vaccine Strain (LVS), Ftt LVS ΔiglC, Ftt human clinical isolate A-660 and a German environmental Francisella species strain W12-1067 (F-W12). Human lung tissue was used to determine the colony forming units and to detect infected cell types by using spectral immunofluorescence and electron microscopy. Chemokine and cytokine levels were measured in culture supernatants.ResultsOnly LVS and A-660 were able to grow within the human lung explants, whereas LVS ΔiglC and F-W12 did not replicate. Using human lung tissue, we observed a greater increase of bacterial load per explant for patient isolate A-660 compared to LVS, whereas a similar replication of both strains was observed in cell culture models with human macrophages. Alveolar macrophages were mainly infected in human lung tissue, but Ftt was also sporadically detected within white blood cells. Although Ftt replicated within lung tissue, an overall low induction of pro-inflammatory cytokines and chemokines was observed. A-660-infected lung explants secreted slightly less of IL-1β, MCP-1, IP-10 and IL-6 compared to Ftt LVS-infected explants, suggesting a more repressed immune response for patient isolate A-660. When LVS and A-660 were used for simultaneous co-infections, only the ex vivo model reflected the less virulent phenotype of LVS, as it was outcompeted by A-660.ConclusionWe successfully implemented an ex vivo infection model using human lung tissue for Francisella. The model delivers considerable advantages and is able to discriminate virulent Francisella from less- or non-virulent strains and can be used to investigate the role of specific virulence factors.

Francisella tularensis is the causative agent of tularemia, a zoonotic disease with a wide host range. F. tularensis ssp. holarctica (Fth) is of clinical relevance for European countries, including Germany. Whole genome sequencing methods, including canonical Single Nucleotide Polymorphism (canSNP) typing and whole genome SNP typing, have revealed that European Fth strains belong to a few monophyletic populations. The majority of German Fth isolates belong to two basal phylogenetic clades B.6 (biovar I) and B.12 (biovar II). Strains of B.6 and B.12 seem to differ in their pathogenicity, and it has been shown that strains of biovar II are resistant against erythromycin. In this study, we present data corroborating our previous data demonstrating that basal clade B.12 can be divided into clades B.71 and B.72. By applying phylogenetic whole genome analysis as well as proteome analysis, we could verify that strains of these two clades are distinct from one another. This was confirmed by measuring the intensity of backscatter light on bacteria grown in liquid media. Strains belonging to clades B.6, B.71 or B.72 showed clade-specific backscatter growth curves. Furthermore, we present the whole genome sequence of strain A-1341, as a reference genome of clade B.71, and whole proteomes comparison of Fth strains belonging to clades B.6, B.71 and B.72. Further research is necessary to investigate phenotypes and putative differences in pathogenicity of the investigated different clades of Fth to better understand the relationship between observed phenotypes, pathogenicity and distribution of Fth strains.

The PLUMED consortium unifies developers and contributors to PLUMED, an open-source library for enhanced-sampling, free-energy calculations and the analysis of molecular dynamics simulations. Here, we outline our efforts to promote transparency and reproducibility by disseminating protocols for enhanced-sampling molecular simulations.

Glioblastoma (GBM) is the most malignant type of glial tumor associated with a very unfavorable prognosis. Typical radiological features of GBM include the presence of a tumor with irregular contrast-enhancing margins and central necrosis surrounded by a wide area of vasogenic edema. Here, we presented an atypical clinical presentation of GBM mimicking autoimmune meningitis. A 69-years-old previously healthy male was admitted to the emergency room due to signs of increasing cognitive impairment, weight loss, changes in behavior, difficulty in walking, and prolonged episodes of nausea over the past month. An magnetic resonance imaging (MRI) brain scan revealed hyperintense changes of the periventricular area surrounding brain ventricles in T2 and FLAIR, and post-contrast leptomeningeal enhancement and thickening of meninges involving cerebellar sulci. An additional MRI scan of the cervical spine showed an in-core contrastenhancing lesion on the C7-Th1 level as well as leptomeningeal thickening and post-contrast-enhancement around the spinal cord. Various laboratory tests and two stereotactic biopsies were performed with no essential to diagnosis clinical findings. A couple of months after first hospital admission, the patient died. Post-mortem examination of the brain revealed numerous foci of abnormal tissue inside the subarachnoid space, lateral ventricles, and cerebral aqueduct. Histological examination showed diffuse malignant astroglial neoplasm, and diagnosis of glioblastoma NOS WHO G IV was established. Even though the appearance of usual GBM is widely recognizable, one must bear in mind the possibility of unusual presentation. The presented case highlights the diagnostic difficulties of diffuse glioblastoma with atypical clinical presentation.

Introduction: The course of the branches of the thyrocervical trunk (TT) such as the inferior thyroid artery (ITA), suprascapular artery (SSA), transverse cervical artery (TCA) and ascending cervical artery (ACA) has a range of anatomical variations. The available literature distinguishes between 4 to 9 types of TT variation and its branches. Aim: The following paper presents an anatomical variation that was not included in any known classifications. Case study: During routine dissection of a body of a 84-year-old woman located in the Department of Anatomy of the University of Warmia and Mazury in Olsztyn, a following course of TT was observed. In the described case the SSA derives directly from the subclavian artery (SA), while the TT gives away ITA, ACA and TCA, which further bifurcates into two dorsal scapular arteries (DSA). Results and discussion: The analysis of the described cadaver showed that this anatomical variation of TT cannot be included in any known classification. This indicates, that it is a very rare anatomical variation of the TT. Conclusions: Knowledge of the course of the branches of TT has some significant clinical importance, inter alia during endovascular procedures, during which any mistakes may result in serious consequences for the operated patient. The most accurate understanding of the course of TT vessels and the dissemination of knowledge on this subject may reduce the risk of complications after the treatment. [ABSTRACT FROM AUTHOR]

Biofilms are a matrix-associated lifestyle of microbial communities, often enabling survivability and persistence of such bacteria. The objective of this study was to investigate the survival of the wild-type strain A-271 of Francisella tularensis subsp. holarctica (Fth) in a natural aquatic ex vivo biofilm. To that purpose, we allowed Fth A-271 to produce its own biofilm on solid surfaces but also to colonize naturally formed biofilms from aquatic habitats, which were infected with Francisella in the laboratory. The survival rates of the bacteria in biofilms were compared to those of planktonic bacteria as a function of the employed culture condition. It could be shown by light- and electron microscopy that Fth is able to form a complex, matrix-associated biofilm. The biofilm form of Francisella showed longer cultivability on agar plates in natural water when compared to planktonic (free-living) bacteria. Be it as a part of the existing ex vivo biofilm or free-floating above as planktonic bacteria, more than 80% of Francisella were not only able to survive under these conditions for 28 days, but even managed to establish microcolonies and areas with their own exclusive biofilm architecture within the ex vivo biofilm. Here, we can demonstrate for the first time that a Francisella tularensis wild-type strain (Type B) is able to successfully colonize an aquatic multi-species ex vivo biofilm. It is worthwhile to speculate that Fth might become more persistent in the environment when it forms its own biofilm or integrates in an existing one. Multi-species biofilms have been shown to be more resistant against stress compared to single-species biofilms. This may have an important impact on the long-term survival of Francisella in aquatic habitats and infection cycles in nature.