Heterogeneity and functions of the 13 IFN-α subtypes - lucky for some?

Type I IFNs are critical for host responses to viral infection and are also implicated in the pathogenesis of multiple autoimmune diseases. Multiple subtypes exist within the type I IFN family, in particular 13 distinct IFN-α genes, which signal through the same heterodimer receptor that is ubiquitously expressed by mammalian cells. Both evolutionary genetic studies and functional antiviral assays strongly suggest differential functions and activity between the 13 IFN-α subtypes, yet we still lack a clear understanding of these different roles. This review summarizes the evidence from studies describing differential functions of IFN-α subtypes and highlights potential reasons for discrepancies between the reports. We examine both acute and chronic viral infection, as well as autoimmunity, and integrate a more recent awareness of the importance of anti-IFN-α autoantibodies in shaping the type I IFN responses in these different conditions.

CRLF3 plays a key role in the final stage of platelet genesis and is a potential therapeutic target for thrombocythemia.

The process of platelet production has so far been understood to be a 2-stage process: megakaryocyte maturation from hematopoietic stem cells followed by proplatelet formation, with each phase regulating the peripheral blood platelet count. Proplatelet formation releases into the bloodstream beads-on-a-string preplatelets, which undergo fission into mature platelets. For the first time, we show that preplatelet maturation is a third, tightly regulated, critical process akin to cytokinesis that regulates platelet count. We show that deficiency in cytokine receptor-like factor 3 (CRLF3) in mice leads to an isolated and sustained 25% to 48% reduction in the platelet count without any effect on other blood cell lineages. We show that Crlf3-/- preplatelets have increased microtubule stability, possibly because of increased microtubule glutamylation via the interaction of CRLF3 with key members of the Hippo pathway. Using a mouse model of JAK2 V617F essential thrombocythemia, we show that a lack of CRLF3 leads to long-term lineage-specific normalization of the platelet count. We thereby postulate that targeting CRLF3 has therapeutic potential for treatment of thrombocythemia.

Pathogenic variants in the NLRP3 LRR domain at position 861 are responsible for a boost-dependent atypical CAPS phenotype.

BACKGROUND: Cryopyrin-associated periodic syndrome (CAPS) is associated with NLRP3 pathogenic variants, mostly located in the NACHT (neuronal apoptosis inhibitor protein, MHC class 2 transcription activator, incompatibility locus protein from Podospora anserina, telomerase-associated protein) domain. Cold-induced urticarial rash is among the main clinical features. However, this study identified a series of 14 patients with pathogenic variants of the Y861 residue (p.Tyr861) of the LRR domain of NLRP3 and minimal prevalence of cold-induced urticarial rash. OBJECTIVES: This study aimed to address a possible genotype/phenotype correlation for patients with CAPS and to investigate at the cellular levels the impact of the Y861C substitution (p.Tyr861Cys) on NLRP3 activation. METHODS: Clinical features of 14 patients with CAPS and heterozygous substitution at position 861 in the LRR domain of NLRP3 were compared to clinical features of 48 patients with CAPS and pathogenic variants outside the LRR domain of NLRP3. IL-1ß secretion by PBMCs and purified monocytes from patients and healthy donors was evaluated following LPS and monosodium urate crystal stimulation. RESULTS: Patients with substitution at position 861 of NLRP3 demonstrated a higher prevalence of sensorineural hearing loss while being less prone to skin urticarial. In contrast to patients with classical CAPS, cells from patients with a pathogenic variant at position 861 required an activation signal to secrete IL-1ß but produced more IL-1ß during the early and late phase of secretion than cells from healthy donors. CONCLUSIONS: Pathogenic variants of Y861 of NLRP3 drive a boost-dependent oversecretion of IL-1ß associated with an atypical CAPS phenotype.

Successful treatment of JAK1-associated inflammatory disease.

BACKGROUND: Gain-of-function variants of JAK1 drive a rare immune dysregulation syndrome associated with atopic dermatitis, allergy, and eosinophilia. OBJECTIVES: This study sought to describe the clinical and immunological characteristics associated with a new gain-of-function variant of JAK1 and report the therapeutic efficacy of Janus kinase (JAK) inhibition. METHODS: The investigators identified a family affected by JAK1-associated autoinflammatory disease and performed clinical assessment and immunological monitoring on 9 patients. JAK1 signaling was studied by flow and mass cytometry in patients' cells at basal state or after immune stimulation. A molecular disease signature in the blood was studied at the transcriptomic level. Patients were treated with 1 of 2 JAK inhibitors: either baricitinib or upadacitinib. Clinical, cellular, and molecular response were evaluated over a 2-year period. RESULTS: Affected individuals displayed a syndromic disease with prominent allergy including atopic dermatitis, ichthyosis, arthralgia, chronic diarrhea, disseminated calcifying fibrous tumors, and elevated whole blood histamine levels. A variant of JAK1 localized in the pseudokinase domain was identified in all 9 affected, tested patients. Hyper-phosphorylation of STAT3 was found in 5 of 6 patients tested. Treatment of patients' cells with baricitinib controlled most of the atypical hyper-phosphorylation of STAT3. Administration of baricitinib to patients led to rapid improvement of the disease in all adults and was associated with reduction of systemic inflammation. CONCLUSIONS: Patients with this new JAK1 gain-of-function pathogenic variant displayed very high levels of blood histamine and showed a variable combination of atopy with articular and gastrointestinal manifestations as well as calcifying fibrous tumors. The disease, which appears to be linked to STAT3 hyperactivation, was well controlled under treatment by JAK inhibitors in adult patients.

Using convolutional dictionary learning to detect task-related neuromagnetic transients and ageing trends in a large open-access dataset.

Human neuromagnetic activity is characterised by a complex combination of transient bursts with varying spatial and temporal characteristics. The characteristics of these transient bursts change during task performance and normal ageing in ways that can inform about underlying cortical sources. Many methods have been proposed to detect transient bursts, with the most successful ones being those that employ multi-channel, data-driven approaches to minimize bias in the detection procedure. There has been little research, however, into the application of these data-driven methods to large datasets for group-level analyses. In the current work, we apply a data-driven convolutional dictionary learning (CDL) approach to detect neuromagnetic transient bursts in a large group of healthy participants from the Cam-CAN dataset. CDL was used to extract repeating spatiotemporal motifs in 538 participants between the ages of 18-88 during a sensorimotor task. Motifs were then clustered across participants based on similarity, and relevant task-related clusters were analysed for age-related trends in their spatiotemporal characteristics. Seven task-related motifs resembling known transient burst types were identified through this analysis, including beta, mu, and alpha type bursts. All burst types showed positive trends in their activation levels with age that could be explained by increasing burst rate with age. This work validated the data-driven CDL approach for transient burst detection on a large dataset and identified robust information about the complex characteristics of human brain signals and how they change with age.

Multivariate semi-blind deconvolution of fMRI time series.

Whole brain estimation of the haemodynamic response function (HRF) in functional magnetic resonance imaging (fMRI) is critical to get insight on the global status of the neurovascular coupling of an individual in healthy or pathological condition. Most of existing approaches in the literature works on task-fMRI data and relies on the experimental paradigm as a surrogate of neural activity, hence remaining inoperative on resting-stage fMRI (rs-fMRI) data. To cope with this issue, recent works have performed either a two-step analysis to detect large neural events and then characterize the HRF shape or a joint estimation of both the neural and haemodynamic components in an univariate fashion. In this work, we express the neural activity signals as a combination of piece-wise constant temporal atoms associated with sparse spatial maps and introduce an haemodynamic parcellation of the brain featuring a temporally dilated version of a given HRF model in each parcel with unknown dilation parameters. We formulate the joint estimation of the HRF shapes and spatio-temporal neural representations as a multivariate semi-blind deconvolution problem in a paradigm-free setting and introduce constraints inspired from the dictionary learning literature to ease its identifiability. A fast alternating minimization algorithm, along with its efficient implementation, is proposed and validated on both synthetic and real rs-fMRI data at the subject level. To demonstrate its significance at the population level, we apply this new framework to the UK Biobank data set, first for the discrimination of haemodynamic territories between balanced groups (n=24 individuals in each) patients with an history of stroke and healthy controls and second, for the analysis of normal aging on the neurovascular coupling. Overall, we statistically demonstrate that a pathology like stroke or a condition like normal brain aging induce longer haemodynamic delays in certain brain areas (e.g. Willis polygon, occipital, temporal and frontal cortices) and that this haemodynamic feature may be predictive with an accuracy of 74 % of the individual's age in a supervised classification task performed on n=459 subjects.

Template-Based Step Detection with Inertial Measurement Units.

This article presents a method for step detection from accelerometer and gyrometer signals recorded with Inertial Measurement Units (IMUs). The principle of our step detection algorithm is to recognize the start and end times of the steps in the signal thanks to a predefined library of templates. The algorithm is tested on a database of 1020 recordings, composed of healthy subjects and patients with various neurological or orthopedic troubles. Simulations on more than 40,000 steps show that the template-based method achieves remarkable results with a 98% recall and a 98% precision. The method adapts well to pathological subjects and can be used in a medical context for robust step estimation and gait characterization.

Brain-derived Neurotrophic Factor in Megakaryocytes.

The biosynthesis of endogenous brain-derived neurotrophic factor (BDNF) has thus far been examined in neurons where it is expressed at very low levels, in an activity-dependent fashion. In humans, BDNF has long been known to accumulate in circulating platelets, at levels far higher than in the brain. During the process of blood coagulation, BDNF is released from platelets, which has led to its extensive use as a readily accessible biomarker, under the assumption that serum levels may somehow reflect brain levels. To identify the cellular origin of BDNF in platelets, we established primary cultures of megakaryocytes, the progenitors of platelets, and we found that human and rat megakaryocytes express the BDNF gene. Surprisingly, the pattern of mRNA transcripts is similar to neurons. In the presence of thapsigargin and external calcium, the levels of the mRNA species leading to efficient BDNF translation rapidly increase. Under these conditions, pro-BDNF, the obligatory precursor of biologically active BDNF, becomes readily detectable. Megakaryocytes store BDNF in α-granules, with more than 80% of them also containing platelet factor 4. By contrast, BDNF is undetectable in mouse megakaryocytes, in line with the absence of BDNF in mouse serum. These findings suggest that alterations of BDNF levels in human serum as reported in studies dealing with depression or physical exercise may primarily reflect changes occurring in megakaryocytes and platelets, including the ability of the latter to retain and release BDNF.

Functional neurological restoration of amputated peripheral nerve using biohybrid regenerative bioelectronics.

The development of neural interfaces with superior biocompatibility and improved tissue integration is vital for treating and restoring neurological functions in the nervous system. A critical factor is to increase the resolution for mapping neuronal inputs onto implants. For this purpose, we have developed a new category of neural interface comprising induced pluripotent stem cell (iPSC)-derived myocytes as biological targets for peripheral nerve inputs that are grafted onto a flexible electrode arrays. We show long-term survival and functional integration of a biohybrid device carrying human iPSC-derived cells with the forearm nerve bundle of freely moving rats, following 4 weeks of implantation. By improving the tissue-electronics interface with an intermediate cell layer, we have demonstrated enhanced resolution and electrical recording in vivo as a first step toward restorative therapies using regenerative bioelectronics.

DiCoDiLe: Distributed Convolutional Dictionary Learning.

Convolutional dictionary learning (CDL) estimates shift invariant basis adapted to represent signals or images. CDL has proven useful for image denoising or inpainting, as well as for pattern discovery on multivariate signals. Contrarily to standard patch-based dictionary learning, patterns estimated by CDL can be positioned anywhere in signals or images. Optimization techniques consequently face the difficulty of working with extremely large inputs with millions of pixels or time samples. To address this optimization problem, we propose a distributed and asynchronous algorithm, employing locally greedy coordinate descent and a soft-locking mechanism that does not require a central server. Computation can be distributed on a number of workers which scales linearly with the size of the data. The parallel computation accelerates the parameter estimation and the distributed setting allows our algorithm to be used with data that do not fit into a single computer's RAM. Experiments confirm the theoretical scaling properties of the algorithm. This allows to demonstrate an improved pattern recovery as images grow in size, and to learn patterns on images from the Hubble Space Telescope containing tens of millions of pixels.

Data augmentation for learning predictive models on EEG: a systematic comparison.

Objective.The use of deep learning for electroencephalography (EEG) classification tasks has been rapidly growing in the last years, yet its application has been limited by the relatively small size of EEG datasets. Data augmentation, which consists in artificially increasing the size of the dataset during training, can be employed to alleviate this problem. While a few augmentation transformations for EEG data have been proposed in the literature, their positive impact on performance is often evaluated on a single dataset and compared to one or two competing augmentation methods. This work proposes to better validate the existing data augmentation approaches through a unified and exhaustive analysis.Approach.We compare quantitatively 13 different augmentations with two different predictive tasks, datasets and models, using three different types of experiments.Main results.We demonstrate that employing the adequate data augmentations can bring up to 45% accuracy improvements in low data regimes compared to the same model trained without any augmentation. Our experiments also show that there is no single best augmentation strategy, as the good augmentations differ on each task.Significance.Our results highlight the best data augmentations to consider for sleep stage classification and motor imagery brain-computer interfaces. More broadly, it demonstrates that EEG classification tasks benefit from adequate data augmentation.

Mapping the biogenesis of forward programmed megakaryocytes from induced pluripotent stem cells.

Platelet deficiency, known as thrombocytopenia, can cause hemorrhage and is treated with platelet transfusions. We developed a system for the production of platelet precursor cells, megakaryocytes, from pluripotent stem cells. These cultures can be maintained for >100 days, implying culture renewal by megakaryocyte progenitors (MKPs). However, it is unclear whether the MKP state in vitro mirrors the state in vivo, and MKPs cannot be purified using conventional surface markers. We performed single-cell RNA sequencing throughout in vitro differentiation and mapped each state to its equivalent in vivo. This enabled the identification of five surface markers that reproducibly purify MKPs, allowing us insight into their transcriptional and epigenetic profiles. Last, we performed culture optimization, increasing MKP production. Together, this study has mapped parallels between the MKP states in vivo and in vitro and allowed the purification of MKPs, accelerating the progress of in vitro-derived transfusion products toward the clinic.

Radiographic-anatomy, natural history and extension pathways of parotid and submandibular gland cancers.

Intensity-modulated radiotherapy has been widely used routinely in recent past years for post-operative radiotherapy of salivary gland cancers Because of the sharp dose fall off outside of target volumes with IMRT, each volume must be strictly and rigorously defined, as the areas not specifically included in the target volume will not be treated to a therapeutic dose. The selection and delineation of these volumes is complex and requires extensive knowledge of parotid and submandibular gland cancer radiographic-anatomy, natural history and extension pathways (including local tumor spread, PNI risks and regional spread), which are detailed in the present article.

Efficacy and Safety of Combined Brain Stereotactic Radiotherapy and Immune Checkpoint Inhibitors in Non-Small-Cell Lung Cancer with Brain Metastases.

BACKGROUND: To analyze the outcomes of patients with brain metastases (BM) from non-small cell lung cancer (NSCLC) treated with immunotherapy (IT) and stereotactic radiotherapy (SRT) and to study the impact of the sequence between the two modalities. METHODS: The authors reviewed the records of 51 patients with 84 BM from NSCLC treated at Institut Curie with IT and SRT. BM were categorized into three groups: 'SRT before IT', 'concurrent SRT and IT', and 'SRT after IT.' Regional progression-free interval (R-PFI) and overall survival (OS) were estimated using the Kaplan-Meier method. RESULTS: After a median follow-up from SRT of 22.5 months (2.7-47.3), the 1-year and 2-year OS were 69.7% (95%CI [58.0-83.8]) and 44.0% [30.6-63.2], respectively. Concerning distant intracranial control, the 1-year and 2-year R-PFI were 40.1% [30.1-53.3] and 35.2% [25.1-49.4], respectively. Moreover, one-year R-PFI in 'SRT before IT', 'concurrent SRT and IT', and 'SRT after IT' groups were 24.1%, 49.6%, and 34.2%, respectively (p = 0.094). The type of therapeutic sequence did not appear to impact the risk of brain necrosis. CONCLUSIONS: The concurrent administration of SRT and IT appeared to offer the best locoregional control, without increasing the risk of toxicity, compared to patients treated with SRT before or after IT.

A rapid and reversible colorimetric assay for the characterization of aminated solid surfaces.

The covalent immobilization of synthetic or natural macromolecular compounds containing amino groups onto polystyrene (PS) solid surfaces is of great interest in diagnostic applications. A sensitive assay allowing the determination of reactive end groups is therefore a powerful tool for predicting the performance of the active surface. Recently, we reported the use of the Coomassie brilliant blue (CBB) colorimetric reagent to quantify protonated groups (N(+)) in linear and dendritic structures in solution (Coussot et al., Polym Int 58(5):511-518, 2009). In this work, a simple method using CBB dye for the characterization of PS aminated solid surfaces is developed. The proposed amino density estimation by colorimetric assay (ADECA) method is based on the reversible complexation of the dye with the N(+) groups on solid surfaces. The assay measures the released dye thanks to the use of a unique sodium carbonate-methanol buffer. Thereby, for the first time, the same surface can be used for characterization and for further coupling applications. A surface density of four N(+) groups per square nanometer can be measured in PS microwell format, the whole characterization being done within 30 min. Performances of this new colorimetric-based method are detailed. The ADECA method is further demonstrated to be useful for the characterization of aminated polypropylene and glass materials with various sizes and shapes.

Process analysis of pluripotent stem cell differentiation to megakaryocytes to make platelets applying European GMP.

Quality, traceability and reproducibility are crucial factors in the reliable manufacture of cellular therapeutics, as part of the overall framework of Good Manufacturing Practice (GMP). As more and more cellular therapeutics progress towards the clinic and research protocols are adapted to comply with GMP standards, guidelines for safe and efficient adaptation have become increasingly relevant. In this paper, we describe the process analysis of megakaryocyte manufacture from induced pluripotent stem cells with a view to manufacturing in vitro platelets to European GMP for transfusion. This process analysis has allowed us an overview of the entire manufacturing process, enabling us to pinpoint the cause and severity of critical risks. Risk mitigations were then proposed for each risk, designed to be GMP compliant. These mitigations will be key in advancing this iPS-derived therapy towards the clinic and have broad applicability to other iPS-derived cellular therapeutics, many of which are currently advancing towards GMP-compliance. Taking these factors into account during protocol design could potentially save time and money, expediting the advent of safe, novel therapeutics from stem cells.

Clinical Characteristics, Care Trajectories and Mortality Rate of SARS-CoV-2 Infected Cancer Patients: A Multicenter Cohort Study.

BACKGROUND: COVID-19 may be more frequent and more severe in cancer patients than in other individuals. Our aims were to assess the rate of COVID-19 in hospitalized cancer patients, to describe their demographic characteristics, clinical features and care trajectories, and to assess the mortality rate. METHODS: This multicenter cohort study was based on the Electronic Health Records of the Assistance Publique-Hôpitaux de Paris (AP-HP). Cancer patients with a diagnosis of COVID-19 between 3 March and 19 May 2020 were included. Main outcome was all-cause mortality within 30 days of COVID-19 diagnosis. RESULTS: A total of 29,141 cancer patients were identified and 7791 (27%) were tested for SARS-CoV-2. Of these, 1359 (17%) were COVID-19-positive and 1148 (84%) were hospitalized; 217 (19%) were admitted to an intensive care unit. The mortality rate was 33% (383 deaths). In multivariate analysis, mortality-related factors were male sex (aHR = 1.39 [95% CI: 1.07-1.81]), advanced age (78-86 y: aHR = 2.83 [95% CI: 1.78-4.51] vs. <66 y; 86-103 y: aHR = 2.61 [95% CI: 1.56-4.35] vs. <66 y), more than two comorbidities (aHR = 2.32 [95% CI: 1.41-3.83]) and C-reactive protein >20 ng/mL (aHR = 2.20 [95% CI: 1.70-2.86]). Primary brains tumors (aHR = 2.19 [95% CI: 1.08-4.44]) and lung cancer (aHR = 1.66 [95% CI: 1.02-2.70]) were associated with higher mortality. Risk of dying was lower among patients with metabolic comorbidities (aHR = 0.65 [95% CI: 0.50-0.84]). CONCLUSIONS: In a hospital-based setting, cancer patients with COVID-19 had a high mortality rate. This mortality was mainly driven by age, sex, number of comorbidities and presence of inflammation. This is the first cohort of cancer patients in which metabolic comorbidities were associated with a better outcome.

Transfer to the clinic: refining forward programming of hPSCs to megakaryocytes for platelet production in bioreactors.

The production of in vitro-derived platelets has great potential for transfusion medicine. Here, we build on our experience in the forward programming (FoP) of human pluripotent stem cells (hPSCs) to megakaryocytes (MKs) and address several aspects of the complex challenges to bring this technology to the bedside. We first identify clinical-grade hPSC lines that generate MKs efficiently. We design a bespoke media to maximize both production and maturity of MKs and improve platelet output. Crucially, we transition the lentiviral-based FoP of hPSCs to a nonviral inducible system. We also show how small molecules promote a definitive hematopoiesis phenotype during the differentiation process, thereby increasing the quality of the final product. Finally, we generate platelets using a bioreactor designed to reproduce the physical cues that promote platelet production in the bone marrow. We show that these platelets are able to contribute to both thrombus formation in vitro and have a hemostatic effect in thrombocytopenic mice in vivo.

Diabetes Increases Severe COVID-19 Outcomes Primarily in Younger Adults.

CONTEXT: Diabetes is reported as a risk factor for severe coronavirus disease 2019 (COVID-19), but whether this risk is similar in all categories of age remains unclear. OBJECTIVE: To investigate the risk of severe COVID-19 outcomes in hospitalized patients with and without diabetes according to age categories. DESIGN SETTING AND PARTICIPANTS: We conducted a retrospective observational cohort study of 6314 consecutive patients hospitalized for COVID-19 between February and 30 June 2020 in the Paris metropolitan area, France; follow-up was recorded until 30 September 2020. MAIN OUTCOME MEASURE(S): The main outcome was a composite outcome of mortality and orotracheal intubation in subjects with diabetes compared with subjects without diabetes, after adjustment for confounding variables and according to age categories. RESULTS: Diabetes was recorded in 39% of subjects. Main outcome was higher in patients with diabetes, independently of confounding variables (hazard ratio [HR] 1.13 [1.03-1.24]) and increased with age in individuals without diabetes, from 23% for those <50 to 35% for those >80 years but reached a plateau after 70 years in those with diabetes. In direct comparison between patients with and without diabetes, diabetes-associated risk was inversely proportional to age, highest in <50 years and similar after 70 years. Similarly, mortality was higher in patients with diabetes (26%) than in those without diabetes (22%, P < 0.001), but adjusted HR for diabetes was significant only in patients younger than age 50 years (HR 1.81 [1.14-2.87]). CONCLUSIONS: Diabetes should be considered as an independent risk factor for the severity of COVID-19 in young adults more so than in older adults, especially for individuals younger than 70 years.

Extraction of Nystagmus Patterns from Eye-Tracker Data with Convolutional Sparse Coding.

The analysis of the Nystagmus waveforms from eye-tracking records is crucial for the clinical interpretation of this pathological movement. A major issue to automatize this analysis is the presence of natural eye movements and eye blink artefacts that are mixed with the signal of interest. We propose a method based on Convolutional Dictionary Learning that is able to automatically highlight the Nystagmus waveforms, separating the natural motion from the pathological movements. We show on simulated signals that our method can indeed improve the pattern recovery rate and provide clinical examples to illustrate how this algorithm performs.