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Ferroptosis is a type of regulated cell death driven by iron dependent accumulation of cellular reactive oxygen species (ROS) when glutathione (GSH)-dependent lipid peroxidation repair systems are compromised. Nuclear receptor co-activator 4 (NCOA4)-mediated selective autophagy of ferritin, termed ferritinophagy, involves the regulation of ferroptosis. Emerging evidence has revealed that ferritinophagy and ferroptosis exert a significant role in the occurrence and development of cardiovascular disease. In the present review, we aimed to present a brief overview of ferritinophagy and ferroptosis focusing on the underlying mechanism and regulations involved. We summarize and discuss relevant research progress on the role of ferritinophagy and ferroptosis in cardiovascular diseases accompanied with potential applications of ferritinophagy and ferroptosis modulators in the treatment of ferroptosis-associated cardiovascular diseases.

Objective: Understanding clinical variants of motor neuron diseases such as amyotrophic lateral sclerosis (ALS) is critical for discovering disease mechanisms and across-patient differences in therapeutic response. The current work describes two clinical subgroups of patients with ALS that, despite similar levels of bulbar motor involvement, have disparate clinical and functional speech presentations.Methods: Participants included 47 healthy control speakers and 126 speakers with ALS. Participants with ALS were stratified into three clinical subgroups (i.e., bulbar asymptomatic, bulbar symptomatic high speech function, and bulbar symptomatic low speech function) based on clinical metrics of bulbar motor impairment. Acoustic and lip kinematic analytics were derived from each participant's recordings of reading samples and a rapid syllable repetition task. Group differences were reported on clinical scales of ALS and bulbar motor severity and on multiple speech measures.Results: The high and low speech-function subgroups were found to be similar on many of the dependent measures explored. However, these two groups were differentiated on the basis of an acoustic measure used as a proxy for tongue movement.Conclusion: This study supports the hypothesis that high and low speech-function subgroups do not differ solely in overall severity, but rather, constitute two distinct bulbar motor phenotypes. The findings suggest that the low speech-function group exhibited more global involvement of the bulbar muscles than the high speech-function group that had relatively intact lingual function. This work has implications for clinical measures used to grade bulbar motor involvement, suggesting that a single bulbar measure is inadequate for capturing differences among phenotypes.

Motivation Causal biological interaction networks represent cellular regulatory pathways. Their fusion with other biological data enables insights into disease mechanisms and novel opportunities for drug discovery. Results We developed Causal Network of Diseases (CaNDis), a web server for the exploration of a human causal interaction network, which we expanded with data on diseases and FDA-approved drugs, on the basis of which we constructed a disease–disease network in which the links represent the similarity between diseases. We show how CaNDis can be used to identify candidate genes with known and novel roles in disease co-occurrence and drug–drug interactions. Availabilityand implementation CaNDis is freely available to academic users at http://candis.ijs.si and http://candis.insilab.org. Supplementary information Supplementary data are available at Bioinformatics online.

BACKGROUND: African ancestry is associated with higher prevalence and greater severity of asthma. Yet genetic studies of the most significant childhood onset asthma locus on 17q12–21 in African Americans have been equivocal. The goal of this study was to leverage both the careful phenotyping in the CREW birth cohort consortium and the breakdown of linkage disequilibrium in African Americans to fine map this important asthma locus. METHODS: Using nine 17q12–21 tag SNPs, we performed association studies for childhood onset asthma in 1,613 European American and 870 African American CREW children, and meta-analyzed the results with publicly available summary data from the EVE consortium (n=4,343 European American, 3,035 African ancestry). We then performed eQTL studies of the SNPs associated with childhood asthma and the expression of 17q12–21 genes in resting blood-derived and upper airway epithelial cells from 85 and 246, respectively, African American children; and in lower airway epithelial cells from 44 European American and 72 African American adults from Chicago. FINDINGS: 17q12–21 SNPs were broadly associated with asthma in European Americans, but only two SNPs (rs2305480 in GSDMB and rs8076131 in ORDML3) were associated with asthma in African Americans, at a Bonferroni-corrected P

Currently, over 300 million people worldwide have depression, and the socioeconomic burden of this debilitating disorder is anticipated to increase markedly over the coming decades against a background of increasing global turmoil. Despite this impending crisis, we are still waiting for improved therapeutic options for this disorder to emerge, which has led to increasing criticism of the role and value of preclinical models of depression. In this Review, we examine this landscape, focusing firstly on issues related to the terminology used in this context and the myriad of preclinical approaches to modelling and assaying aspects of depression in rodents. We discuss the importance of sex as a biological variable and the controversial idea of intergenerational and transgenerational transmission of depressive-like traits. We then examine the technical strategies available to dissect these models and review emerging evidence for putative druggable disease mechanisms. Finally, we propose a brief framework for future research that makes optimal use of these models and will, we hope, accelerate the discovery of improved antidepressants. Rodent models are essential for characterizing the mechanisms underlying depression as well as for the development of fast-acting and innovative antidepressants. Here, Anand Gururajan and colleagues review strategies for inducing depressive-like behaviours and endophenotypes, and discuss how genetic and circuit-dissection techniques might be used to refine existing models and generate new ones.

Basement membranes (BMs) are specialised extracellular matrix (ECM) structures and collagens are a key component required for BM function. While collagen IV is the major BM collagen, collagens VI, VII, XV, XVII and XVIII are also present. Mutations in these collagens cause rare multi-systemic diseases but these collagens have also been associated with major common diseases including stroke. Developing treatments for these conditions will require a collective effort to increase our fundamental understanding of the biology of these collagens and the mechanisms by which mutations therein cause disease. Novel insights into pathomolecular disease mechanisms and cellular responses to these mutations has been exploited to develop proof-of-concept treatment strategies in animal models. Combined, these studies have also highlighted the complexity of the disease mechanisms and the need to obtain a more complete understanding of these mechanisms. The identification of pathomolecular mechanisms of collagen mutations shared between different disorders represent an attractive prospect for treatments that may be effective across phenotypically distinct disorders.

Diseases such as Alzheimer's disease (AD) and Parkinson's disease (PD) arise from complex interactions of genetic and environmental factors, with genetic variants regulating individual responses to environmental exposures (i.e. gene-by-environment interactions). Identifying gene-by-environment interactions will be critical to fully understanding disease mechanisms and developing personalized therapeutics, though these interactions are still poorly understood and largely under-studied. Candidate gene approaches have shown that known disease risk variants often regulate response to environmental factors. However, recent improvements in exposome- and genome-wide association and interaction studies in humans and mice are enabling discovery of novel genetic variants and pathways that predict response to a variety of environmental factors. Here, we highlight recent approaches and ongoing developments in human and rodent studies to identify genetic modulators of environmental factors using AD and PD as exemplars. Identifying gene-by-environment interactions in disease will be critical to developing personalized intervention strategies and will pave the way for precision medicine.

Purpose of review To discuss the proposed pathophysiology of empty nose syndrome (ENS), summarize and evaluate the role of validated diagnostic tools in the diagnosis of ENS, and review the medical and surgical treatment strategies for patients with ENS. Recent findings Historically, ENS has been associated with a reduction in nasal turbinate size; new data suggest that impaired trigeminal nerve function may also play a role in the pathophysiology of the disease. The newly validated empty nose syndrome 6 item questionnaires and Cotton test are steps forward to standardize the diagnosis of ENS. Finally, there has been a marked increase in surgical treatment strategies to reconstitute turbinate volume with various implant materials. Summary The diagnosis of ENS remains controversial but the last several years have seen a rejuvenation of interest in this disease entity. The validated empty nose syndrome 6 item questionnaires and Cotton test provide a standardized and objective means by which to characterize ENS. Prevention of iatrogenic ENS through avoidance of excessive turbinate reduction remains critical in preventing paradoxical nasal obstruction. Nasal humidification, patient education, and treatment of possible concomitant medical conditions (e.g., depression) constitute first lines of treatment. We support the cautious use of these screening tools as adjuncts to clinical decision-making. Although injectable implants to augment turbinate volume show promise as a therapeutic surgical technique, there is insufficient data to fully support their use at this time.

Cardiorenal syndrome commonly refers to the collective dysfunction of heart and kidney resulting in a cascade of feedback mechanism causing damage to both the organs and is associated with adverse clinical outcomes. The pathophysiology of cardiorenal syndrome is complex, multifactorial, and dynamic. Improving the understanding of disease mechanisms will aid in developing targeted pharmacologic and nonpharmacologic therapies for the management of this syndrome. This article discusses the various mechanisms involved in the pathophysiology of the cardiorenal syndrome.

The past decade has witnessed exponential growth in the generation of high-throughput human data across almost all known dimensions of biological systems. The discipline of network medicine has rapidly evolved in parallel, providing an unbiased, comprehensive biological framework through which to interrogate and integrate systematically these large-scale, multi-omic data to enhance our understanding of disease mechanisms and to design drugs that reflect a deep knowledge of molecular pathobiology. In this review, we discuss the key principles of network medicine and the human disease network and explore the latest applications of network medicine in this multi-omic era. We also highlight the current conceptual and technological challenges, which serve as exciting opportunities by which to improve and expand the network-based applications beyond the artificial boundaries of the current state of human pathobiology.

Contemporary mouse genetic reference populations are a powerful platform to discover complex disease mechanisms. Advanced high-diversity mouse populations include the Collaborative Cross (CC) strains, Diversity Outbred (DO) stock, and their isogenic founder strains. When used in systems genetics and integrative genomics analyses, these populations efficiently harnesses known genetic variation for precise and contextualized identification of complex disease mechanisms. Extensive genetic, genomic, and phenotypic data are already available for these high-diversity mouse populations and a growing suite of data analysis tools have been developed to support research on diverse mice. This integrated resource can be used to discover and evaluate disease mechanisms relevant across species.

Small GTPases are known to have pivotal roles in intracellular trafficking, and several members of the small GTPases superfamily such as Rab10 [1,2•], Rab11 [3-5], Rab34 [6•,7], Rab8 [3,8], Rab23 [9-12], RSG1 [13-15], Arl13b [16-22], and Arl6 [22,23] were recently reported to mediate primary cilia function and/or Hh signalling. Although these functions are implicated in diseases such as ciliopathies, the molecular basis underlying how these small GTPases mediate primary cilia-dependent Hh signalling and pathogenesis of ciliopathies warrants further investigations. Notably, Rab23 and Arl13b have been implicated in ciliopathy-associated human diseases and could regulate Hh signalling cascade in multifaceted manners. This review thus specifically discuss the roles of Rab23 and Arl13b in primary cilia of mammalian systems, their cilia-dependent and cilia-independent modulation of hedgehog signalling pathways and their implications in Carpenter Syndrome and Joubert Syndrome respectively.

Genetic sequencing technologies are evolving at a rapid pace with major implications for research and clinical practice. In this review, the authors provide an updated overview of next-generation sequencing (NGS) and emerging methodologies. NGS has tremendously improved sequencing output while being more time and cost-efficient in comparison to Sanger sequencing. The authors describe short-read sequencing approaches, such as sequencing by synthesis, ion semiconductor sequencing, and nanoball sequencing. Third-generation long-read sequencing now promises to overcome many of the limitations of short-read sequencing, such as the ability to reliably resolve repeat sequences and large genomic rearrangements. By combining complementary methods with massively parallel DNA sequencing, a greater insight into the biological context of disease mechanisms is now possible. Emerging methodologies, such as advances in nanopore technology, in situ nucleic acid sequencing, and microscopy-based sequencing, will continue the rapid evolution of this area. These new technologies hold many potential applications for hematological disorders, with the promise of precision and personalized medical care in the future.

'Time is the best diagnostician': who has not thought this? In clinical practice, presentations are often subtle and decisions made in the face of a 'snapshot.' Crystal balls do not exist; yet, insights from longitudinal studies can help to recognise emerging pictures and anticipate typical trajectories. In the multifactorial, biopsychosocial world of geriatrics, the determinants of those trajectories, and hence opportunities to modify them, can be better understood through careful longitudinal disentangling of the wider determinants of health, and this can be done at multiple levels of analysis, from molecules to society. With this collection and commentary, we highlight the approaches, scope and impacts of a selection of longitudinal studies of ageing published in Age and Ageing within the past 10 years. Longitudinal studies can illuminate disease mechanisms, how declines in multiple domains of intrinsic capacity interact, how losses in one domain may influence the path of another, and in turn, how these changes translate to functional disability, or not. Observing trajectories of geriatric syndromes can suggest opportunities for optimisation and prevention in clinical practice and policy. With global opportunities for harmonising data, longitudinal studies are already offering the opportunity for cross-national comparisons and for developing hypotheses about the relative contributions of time, place and society in the trajectories of frailty, disability and quality of life. We also include studies which show how research-based longitudinal data can be synthesised or be linked to administrative datasets. We hope you find this collection as interesting and encouraging as we have.

ATP-dependent phospholipid flippase activity crucial for generating lipid asymmetry was first detected in red blood cell (RBC) membranes, but the P4-ATPases responsible have not been directly determined. Using affinity-based MS, we show that ATP11C is the only abundant P4-ATPase phospholipid flippase in human RBCs, whereas ATP11C and ATP8A1 are the major P4-ATPasesinmouse RBCs. Wealso found that ATP11A and ATP11B are present at low levels. Mutations in the gene encoding ATP11C are responsible for blood and liver disorders, but the disease mechanisms are not known. Using heterologous expression, we show that the T415N substitution in the phosphorylation motif of ATP11C, responsible for congenital hemolytic anemia, reduces ATP11C expression, increases retention in the endoplasmic reticulum, and decreases ATPase activity by 61% relative to WT ATP11C. The I355K substitution in the transmembrane domain associated with cholestasis and anemia in mice was expressed at WT levels and trafficked to the plasma membrane but was devoid of activity. We conclude that the T415N variant causes significant protein misfolding, resulting in low protein expression, cellular mislocalization, and reduced functional activity. In contrast, the I355K variant folds and traffics normally but lacks key contacts required for activity. We propose that the loss in ATP11C phospholipid flippase activity coupled with phospholipid scramblase activity results in the exposure of phosphatidylserine on the surface of RBCs, decreasing RBC survival and resulting in anemia.

The discoveries of new genes underlying genetic skin diseases have occurred at a rapid pace, supported by advances in DNA sequencing technologies. These discoveries have translated to an improved understanding of disease mechanisms at a molecular level and identified new therapeutic options based on molecular targets. This article highlights just a few of these recent discoveries for a diverse group of skin diseases, including tuberous sclerosis complex, ichthyoses, overgrowth syndromes, interferonopathies, and basal cell nevus syndrome, and how this has translated into novel targeted therapies and improved patient care.

To better understand human health and disease, researchers create a wide variety of mouse models that carry human DNA. With recent advances in genome engineering, the targeted replacement of mouse genomic regions with orthologous human sequences has become increasingly viable, ranging from finely tuned humanisation of individual nucleotides and amino acids to the incorporation of many megabases of human DNA. Here, we examine emerging technologies for targeted genomic humanisation, we review the spectrum of existing genomically humanised mouse models and the insights such models have provided, and consider the lessons learned for designing such models in the future., Generation of transgenic mice has become routine in studying gene function and disease mechanisms, but often this is not enough to fully understand human biology. Here, the authors review the current state of the art of targeted genomic humanisation strategies and their advantages over classic approaches.

Gene expression regulation, including loss-of-function and gain-of-function assays, is a powerful method to study developmental and disease mechanisms. Drosophila melanogaster is an ideal model system particularly well-equipped with many genetic tools. In this review, we describe and discuss the gene expression regulation techniques recently developed and their applications, including the CRISPR/Cas9-triggered heritable mutation system, CRISPR/dCas9-based transcriptional activation (CRISPRa) system, and CRISPR/dCas9-based transcriptional repression (CRISPRi) system, as well as the next-generation transgenic RNAi system. The main purpose of this review is to provide the fly research community with an updated summary of newly developed gene expression regulation techniques and help the community to select appropriate methods and optimize the research strategy.

Determining changes in protein expression and post-translational modifications (PTMs) is crucial for elucidating cellular signal transduction and disease mechanisms. Conventional antibody-based approaches have inherent problems such as the limited availability of high-quality antibodies and batch-to-batch variation. Top-down mass spectrometry (MS)-based proteomics has emerged as the most powerful method for characterization and quantification of protein modifications. Nevertheless, robust methods to simultaneously determine changes in protein expression and PTMs remain lacking. Herein, we have developed a straightforward and robust top-down liquid chromatography (LC)/MS-based targeted proteomics platform for simultaneous quantification of protein expression and PTMs with high throughput and high reproducibility. We employed this method to analyze the sarcomeric subproteome from various muscle types of different species, which successfully revealed skeletal muscle heterogeneity and cardiac developmental changes in sarcomeric protein isoform expression and PTMs. As demonstrated, this targeted top-down proteomics platform offers an excellent 'antibody-independent' alternative for the accurate quantification of sarcomeric protein expression and PTMs concurrently in complex mixtures, which is generally applicable to different species and various tissue types.

Many available drugs have been repurposed as treatments for neurodevelopmental disorders. In the specific case of fragile X syndrome, many clinical trials of available drugs have been conducted with the goal of disease modification. In some cases, detailed understanding of basic disease mechanisms has guided the choice of drugs for clinical trials, and several notable successes in fragile X clinical trials have led to common use of drugs such as minocycline in routine medical practice. Newer technologies like Disease-Gene Expression Matching (DGEM) may allow for more rapid identification of promising repurposing candidates. A DGEM study predicted that sulindac could be therapeutic for fragile X, and subsequent preclinical validation studies have shown promising results. The use of combinations of available drugs and nutraceuticals has the potential to greatly expand the options for repurposing, and may even be a viable business strategy. This article is part of the Special Issue entitled 'Drug Repurposing: old molecules, new ways to fast track drug discovery and development for CNS disorders'.

Cerebral small-vessel disease (cSVD) is a common cause of stroke, functional decline, vascular cognitive impairment, and dementia. Pathological processes in the brain's microcirculation are tightly interwoven with pathology in the brain parenchyma, and this interaction has been conceptualized as the neurovascular unit (NVU). Despite intensive research efforts to decipher the NVU's structure and function to date, molecular mechanisms underlying cSVD remain poorly understood, which hampers the development of cSVD-specific therapies. Important steps forward in understanding the disease mechanisms underlying cSVD have been made using genetic approaches in studies of both monogenic and sporadic SVD. We provide an overview of the NVU's structure and function, the implications for cSVD, and the underlying molecular mechanisms of dysfunction that have emerged from recent genetic studies of both monogenic and sporadic diseases of the small cerebral vasculature.

The feasibility, safety, and efficacy of a high-intensity multimodal exercise program (aerobic, strengthening, and balance training) have not been well vetted in persons with Parkinson disease (PD). Thus, the primary aim was to determine whether a high-intensity multimodal exercise boot camp (HIBC) was both feasible and safe in persons with PD. The secondary aim was to determine whether the program would produce greater benefit than a usual care, low-intensity exercise program (UC). An exploratory aim was to determine whether these programs affected putative disease-modifying mechanisms.Twenty-seven participants (19 men and 8 women) were randomized into 8 weeks of either the HIBC or UC supervised by physical therapists. For feasibility, participation, and meeting, Centers for Disease Control and Prevention (CDC) exercise guidelines were assessed. For safety, adverse events were monitored. For efficacy, the following outcome domains were assessed before and after participation: balance, motor activity, endurance and fatigue, strength, mental health, and quality of life. For disease-modifying mechanisms, circulating brain-derived neurotrophic factor (BDNF) and its genotype, superoxide dismutase, and cytokines (tumor necrosis factor-α, interleukin-6, and interleukin-10) were monitored.The HIBC was better at attaining CDC guidelines (P = 0.013) and spent more minutes in higher-intensity exercise per week (P0.001). There were no differences in adverse events (P = 0.419). The HIBC experienced significant improvements in 7/31 outcomes versus 3/31 in the UC arm. BDNF improved significantly for both groups from pre- to posttests (Ps ≤ 0.041) and an improved anti-inflammatory was observed for both groups.A high-intensity multimodal exercise boot camp was feasible and safe in persons with PD. Compared with usual care, there were no differences in adverse events. Moreover, the high-intensity multimodal exercise program produced more improvement across more domains than usual care. Our results also suggest a possible link between improvement in outcomes and an improved anti-inflammatory milieu.Video Abstract available for more insights from the authors (see Video, Supplemental Digital Content 1, available at: http://links.lww.com/JNPT/A244).

Most pathologists will encounter deaths in patients with epilepsy in their practice, including sudden deaths. A systematic approach to these post-mortem examinations is required, including gathering relevant clinical details and around the circumstances of death, in order to correctly categorise these cases. Macroscopic and histological examination of the brain can reveal (i) the underlying cause of the epilepsy, as cortical dysplasia or tumours, (ii) sequel of previous seizures, including hippocampal sclerosis and contusions and (iii) potentially the cause of death. Sudden and unexpected death in epilepsy (SUDEP) is still under-reported as a cause of death. Although by definition there is no structural or toxicological cause of death at post-mortem, clinical and experimental studies together with neuropathological findings are beginning to identify alterations in central autonomic regions, including the brainstem, critical for cardio-respiratory regulation. Neuropathology investigations following post-mortem examinations form an essential component in the identification of disease mechanisms in epilepsy-related deaths and their future prevention.

Although computed tomography (CT) and MR imaging alone have been used extensively to evaluate various musculoskeletal disorders, hybrid imaging modalities of PET-CT and PET-MR imaging were recently developed, combining the advantages of each method: molecular information from PET and anatomical information from CT or MR imaging. Furthermore, different radiotracers can be used in PET to uncover different disease mechanisms. In this article, potential applications of PET-CT and PET-MR imaging for benign musculoskeletal disorders are organized by benign cell proliferation/dysplasia, diabetic foot complications, joint prostheses, degeneration, inflammation, and trauma, metabolic bone disorders, and pain (acute and chronic) and peripheral nerve imaging.

Patients carrying an inactive NF1 allele develop tumors of Schwann cell origin called neurofibromas (NF). Genetically engineered mouse models have significantly enriched our understanding of plexiform forms of NFs (pNF). However, this has not been the case for cutaneous neurofibromas (cNF), observed in all NF1 patients, as no previous model recapitulates their development. Here, we show that conditional Nf1 inactivation in Prss56-positive boundary cap cells leads to bona fide pNFs and cNFs. This work identifies subepidermal glia as a likely candidate for the cellular origin of cNFs and provides insights on disease mechanisms, revealing a long, multistep pathologic process in which inflammation-related signals play a pivotal role. This new mouse model is an important asset for future clinical and therapeutic investigations of NF1-associated neurofibromas. Significance: Patients affected by NF1 develop numerous cNFs. We present a mouse model that faithfully recapitulates cNFs, identify a candidate cell type at their origin, analyze the steps involved in their formation, and show that their development is dramatically accelerated by skin injury. These findings have important clinical/therapeutic implications. This article is highlighted in the In This Issue feature, p. 1

Asthma, characterized by airway hyperresponsiveness, inflammation and remodeling, is a chronic airway disease with complex etiology. Severe asthma is characterized by frequent exacerbations and poor therapeutic response to conventional asthma therapy. A clear understanding of cellular and molecular mechanisms of asthma is critical for the discovery of novel targets for optimal therapeutic control of asthma. Metabolomics is emerging as a powerful tool to elucidate novel disease mechanisms in a variety of diseases. In this review, we summarize the current status of knowledge in asthma metabolomics at systemic and cellular levels. The findings demonstrate that various metabolic pathways, related to energy metabolism, macromolecular biosynthesis and redox signaling, are differentially modulated in asthma. Airway smooth muscle cell plays pivotal roles in asthma by contributing to airway hyperreactivity, inflammatory mediator release and remodeling. We posit that metabolomic profiling of airway structural cells, including airway smooth muscle cells, will shed light on molecular mechanisms of asthma and airway hyperresponsiveness and help identify novel therapeutic targets.

Author(s): Nallamilli, Babi Ramesh Reddy; Chakravorty, Samya; Kesari, Akanchha; Tanner, Alice; Ankala, Arunkanth; Schneider, Thomas; da Silva, Cristina; Beadling, Randall; Alexander, John J; Askree, Syed Hussain; Whitt, Zachary; Bean, Lora; Collins, Christin; Khadilkar, Satish; Gaitonde, Pradnya; Dastur, Rashna; Wicklund, Matthew; Mozaffar, Tahseen; Harms, Matthew; Rufibach, Laura; Mittal, Plavi; Hegde, Madhuri | Abstract: ObjectiveLimb-girdle muscular dystrophies (LGMDs), one of the most heterogeneous neuromuscular disorders (NMDs), involves predominantly proximal-muscle weakness with g30 genes associated with different subtypes. The clinical-genetic overlap among subtypes and with other NMDs complicate disease-subtype identification lengthening diagnostic process, increases overall costs hindering treatment/clinical-trial recruitment. Currently seven LGMD clinical trials are active but still no gene-therapy-related treatment is available. Till-date no nation-wide large-scale LGMD sequencing program was performed. Our objectives were to understand LGMD genetic basis, different subtypes' relative prevalence across US and investigate underlying disease mechanisms.MethodsA total of 4656 patients with clinically suspected-LGMD across US were recruited to conduct next-generation sequencing (NGS)-based gene-panel testing during June-2015 to June-2017 in CLIA-CAP-certified Emory-Genetics-Laboratory. Thirty-five LGMD-subtypes-associated or LGMD-like other NMD-associated genes were investigated. Main outcomes were diagnostic yield, gene-variant spectrum, and LGMD subtypes' prevalence in a large US LGMD-suspected population.ResultsMolecular diagnosis was established in 27% (1259 cases; 95% CI, 26-29%) of the patients with major contributing genes to LGMD phenotypes being: CAPN3(17%), DYSF(16%), FKRP(9%) and ANO5(7%). We observed an increased prevalence of genetically confirmed late-onset Pompe disease, DNAJB6-associated LGMD subtype1E and CAPN3-associated autosomal-dominant LGMDs. Interestingly, we identified a high prevalence of patients with pathogenic variants in more than one LGMD gene suggesting possible synergistic heterozygosity/digenic/multigenic contribution to disease presentation/progression that needs consideration as a part of diagnostic modality.InterpretationOverall, this study has improved our understanding of the relative prevalence of different LGMD subtypes, their respective genetic etiology, and the changing paradigm of their inheritance modes and novel mechanisms that will allow for improved timely treatment, management, and enrolment of molecularly diagnosed individuals in clinical trials.

The aim of this article is to introduce transcranial magnetic stimulation (TMS) to dental practitioners and to explain its use in obstructive sleep apnea (OSA) and sleep bruxism (SB). In these 2 sleep disorders, TMS has proven to be a useful tool to explore pathophysiological pathways and disease mechanisms, and in a more limited way, to recruit upper airway muscles in OSA and reduce muscle activity and pain intensity in SB. Although promising, research using TMS in these conditions is still very limited and future investigations should be conducted before its clinical application can be considered.

Polyglutamine (polyQ) diseases are a group of clinically and genetically heterogeneous neurodegenerative diseases. Expansion size correlates with age-at-onset (AO) and severity, and shows a critical threshold for each polyQ disease. Although an expanded CAG tract is sufficient to trigger disease, not all variation in AO is explained by (CAG)n length, which suggests the contribution of other modifying factors. Methods used to identify genetic modifiers in polyQ diseases have progressed from candidate genes to unbiased genome-wide searches. Inconsistency of results from candidate-genes studies are partly explained by sample size, study design and variable population frequency of "polymorphisms"; a genome-wide search may help elucidating more precise disease mechanisms underlying specific interaction networks. We review known genetic modifiers for polyQ diseases, and discuss developing strategies to find modulation, from common variants to networks disclosing small cumulative effects of key genes and modifying pathways. This may lead to a better understanding of genotype-phenotype correlation and the proposal of new potential targets for therapeutical interventions.

Age-related macular degeneration (AMD) showed several processes and risk factors in common with neurodegenerative disorders (NDDs). The present work explored the existence of genetic determinants associated with AMD, which may provide insightful clues concerning its relationship with NDDs and their possible application into the clinical practice. In this study, 400 AMD patients were subjected to the genotyping analysis of 120 genetic variants by OpenArray technology. As the reference group, 503 samples representative of the European general population were utilized. Statistical analysis revealed the association of 23 single-nucleotide polymorphisms (SNPs) with AMD risk. The analysis of epistatic effects revealed that ARMS2, IL6, APOE, and IL2RA could contribute to AMD and neurodegenerative processes by synergistic modulation of the expression of disease-relevant genes. In addition, the bioinformatic analysis of the associated miRNA variants highlighted miR-196a, miR-6796, miR-6499, miR-6810, miR-499, and miR-7854 as potential candidates for counteracting AMD and neurodegenerative processes. Finally, this work highlighted the existence of shared disease mechanisms (oxidative stress, immune-inflammatory response, mitochondrial dysfunction, axonal guidance pathway, and synaptogenesis) between AMD and NDDs and described the associated SNPs as candidate biomarkers for developing novel strategies for early diagnosis, monitoring, and treatment of such disorders in a progressive aging population.

Introduction: The discovery of causal mechanisms underlying diseases enables better diagnosis, prognosis and treatment selection. Clinical trials have been the gold standard for determining causality, but they are resource intensive, sometimes infeasible or unethical. Electronic Health Records (EHR) contain a wealth of real-world data that holds promise for the discovery of disease mechanisms, yet the existing causal structure discovery (CSD) methods fall short on leveraging them due to the special characteristics of the EHR data. We propose a new data transformation method and a novel CSD algorithm to overcome the challenges posed by these characteristics. Materials and methods: We demonstrated the proposed methods on an application to type-2 diabetes mellitus. We used a large EHR data set from Mayo Clinic to internally evaluate the proposed transformation and CSD methods and used another large data set from an independent health system, Fairview Health Services, as external validation. We compared the performance of our proposed method to Fast Greedy Equivalence Search (FGES), a state-of-the-art CSD method in terms of correctness, stability and completeness. We tested the generalizability of the proposed algorithm through external validation. Results and conclusions: The proposed method improved over the existing methods by successfully incorporating study design considerations, was robust in face of unreliable EHR timestamps and inferred causal effect directions more correctly and reliably. The proposed data transformation successfully improved the clinical correctness of the discovered graph and the consistency of edge orientation across bootstrap samples. It resulted in superior accuracy, stability, and completeness., Comment: 20 pages, 2 figures

The study of multi-type Protein-Protein Interaction (PPI) is fundamental for understanding biological processes from a systematic perspective and revealing disease mechanisms. Existing methods suffer from significant performance degradation when tested in unseen dataset. In this paper, we investigate the problem and find that it is mainly attributed to the poor performance for inter-novel-protein interaction prediction. However, current evaluations overlook the inter-novel-protein interactions, and thus fail to give an instructive assessment. As a result, we propose to address the problem from both the evaluation and the methodology. Firstly, we design a new evaluation framework that fully respects the inter-novel-protein interactions and gives consistent assessment across datasets. Secondly, we argue that correlations between proteins must provide useful information for analysis of novel proteins, and based on this, we propose a graph neural network based method (GNN-PPI) for better inter-novel-protein interaction prediction. Experimental results on real-world datasets of different scales demonstrate that GNN-PPI significantly outperforms state-of-the-art PPI prediction methods, especially for the inter-novel-protein interaction prediction., 10 pages(3 pages appendix), 2 figures, Accepted by Conference IJCAI2021, which is its extended version

A hexanucleotide repeat expansion mutation in the first intron of C9orf72 is the most common known genetic cause of amyotrophic lateral sclerosis and frontotemporal dementia. Since the discovery in 2011, numerous pathogenic mechanisms, including both loss and gain of function, have been proposed. The body of work overall suggests that toxic gain of function arising from bidirectionally transcribed repeat RNA is likely to be the primary driver of disease. In this review, we outline the key pathogenic mechanisms that have been proposed to date and discuss some of the novel therapeutic approaches currently in development.

The coronavirus disease 2019 (COVID-19) pandemic has had a major impact on education at all age levels, including professional schools and health professions programs. We describe the experience of adapting preclinical medical school courses within an integrated curriculum to virtual instruction. A major feature of two of the courses were pathology small groups adapted from pathology courses in the previous medical school curriculum. These small groups were designed to use facilitated groups of 8 to 10 students. With a sudden change to virtual learning, these small groups were shifted to large group virtual sessions. In general, the conversion went well, with ongoing optimization of the format of the large group sessions mainly occurring over the first several sessions. End-of-course student evaluations were generally positive, but with a preference toward returning to live sessions in the future. Scores on 5 multiple choice examinations in the spring 2020 course were essentially identical in mean, standard deviation, and distribution to examinations in the previous 2 years of the course that had similar layout and topic organization. We discuss the challenges and successes of the switch to virtual instruction and of teaching pathology content within an integrated medical school curriculum.

Funding Acknowledgements Type of funding sources: Public grant(s) – EU funding. Main funding source(s): European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement 764738. British Heart Foundation Intermediate Basic Science Fellowship (FS/17/22/32644). Background The pathogenic TNNI3R21C/+ variant causes malignant hypertrophic cardiomyopathy (HCM) with high incidence of sudden cardiac death, even in individuals absent of hypertrophy. There is evidence to support a known biophysical defect in the protein, yet the cellular mechanisms that precipitate adverse clinical outcomes remain unclear. Purpose We aim to computationally model and map the TNNI3R21C/+ cellular phenotype observed in induced pluripotent stem cell derived cardiomyocytes (iPSC-CMs) to human disease, thereby explaining the key mechanisms driving HCM in TNNI3R21C/+ variant carriers. Methods Wild-type (WT) and TNNI3R21C/+ iPSC-CMs were characterised by calcium transient analysis and direct sarcomere tracking to assess cellular contraction and relaxation. In-vitro data was used to inform the in-silico modelling of human cardiomyocytes. We constructed an in-silico population of WT adult cardiomyocytes and used it to transform the in-vitro data into corresponding adult phenotypes by means of a novel iPSC-to-adult data mapping. We tested the hypothesis that the abnormal TNNI3R21C/+ phenotype observed in iPSC-CMs would be explained by alterations in calcium affinity of troponin and increased myofilament calcium sensitivity. Results Analysis of in-vitro iPSC-CM data showed that TNNI3R21C/+ cells exhibit increased contractility with slowed relaxation when compared to WT. They also exhibited a faster rise in the calcium transient with a slowed calcium decay in comparison to WT. The in-silico adult TNNI3R21C/+ phenotype from the iPSC-to-adult mapping replicated the abnormalities observed in iPSC-CMs. The WT in-silico population accurately covered the ranges of electromechanical biomarkers providing a representative cohort of physiological variability. The TNNI3R21C/+ calcium phenotype could be recovered by our in-silico mutant models. Simulation results suggest that calcium abnormalities in TNNI3R21C/+ are a direct consequence of abnormal calcium buffering by thin filaments, mediated by increases in calcium affinity of troponin and myofilament calcium sensitivity. The TNNI3R21C/+ phenotype could not be recovered if these two factors were considered in isolation. Corresponding contractility analyses of in-silico models showed that the calcium level changes caused by the TNNI3R21C/+ phenotype are associated with hypercontractility and diastolic dysfunction, well-known hallmarks of HCM, which were also observed in the iPSC-CM model of disease. Conclusions This study showcases human-based computational and experimental methodologies that unearth direct mechanistic explanations of phenotypes driven by the TNNI3R21C/+ HCM variant. We show that the TNNI3R21C/+ HCM-causing mutation exhibits multifactorial remodelling of troponin calcium affinity and myofilament calcium sensitivity. Unearthing mechanistic pathways in mutation-specific HCM will be key to develop effective pharmacological interventions for a wide variety of understudied variants.

Induced pluripotent stem cells (iPSCs) have emerged as a key component of cardiac tissue engineering, enabling studies of cardiovascular disease mechanisms, drug responses, and developmental processes in human 3D tissue models assembled from isogenic cells. Since the very first engineered heart tissues were introduced more than two decades ago, a wide array of iPSC-derived cardiac spheroids, organoids, and heart-on-a-chip models have been developed incorporating the latest available technologies and materials. In this review, we will first outline the fundamental biological building blocks required to form a functional unit of cardiac muscle, including iPSC-derived cells differentiated by soluble factors (e.g., small molecules), extracellular matrix scaffolds, and exogenous biophysical maturation cues. We will then summarize the different fabrication approaches and strategies employed to reconstruct the heart in vitro at varying scales and geometries. Finally, we will discuss how these platforms, with continued improvements in scalability and tissue maturity, can contribute to both basic cardiovascular research and clinical applications in the future.

Accurate assessment of protein-protein interactions (PPIs) is critical to deciphering disease mechanisms and developing novel drugs, and with rapidly growing PPI data, the need for more efficient predictive methods is emerging. We propose here a symmetric logistic matrix factorization (symLMF)-based approach to predict PPIs, especially useful for large PPI networks. Benchmarked against two widely used datasets (Saccharomyces cerevisiae and Homo sapiens benchmarks) and their extended versions, the symLMF-based method proves to outperform most of the state-of-the-art data-driven methods applied to human PPIs, and it shows a performance comparable to those of deep learning methods despite its conceptual and technical simplicity and efficiency. Tests performed on humans, yeast, and tissue (brain and liver)- and disease (neurodegenerative and metabolic disorders)-specific datasets further demonstrate the high capability to capture the hidden interactions. Notably, many "de novo predictions" made by symLMF are verified to exist in PPI databases other than those used for training/testing the method, indicating that the method could be of broad utility as a simple, yet efficient and accurate, tool applicable to PPI datasets.

The nervous system consists of neurons, glial cells, blood vessels, and extracellular matrix. Neurons are electrically excitable cells and are primarily responsible for initiation, processing, and transmission of information. However, their function is affected by their reciprocal interactions with glial cells, which contribute to development, survival, and plasticity of synaptic connections and shape the activity of neuronal ensembles and systems critical for cognition and behavior. Advances in molecular, cellular, and electrophysiological approaches have provided major insight not only in normal function of neurons and glial cells but also in the pathophysiology of neurologic diseases at the molecular, synaptic, cellular network, and system levels.

The aim of this book is to provide the clinician with a comprehensive and clinical relevant survey of emerging concepts on the organization and function of the nervous system and neurologic disease mechanisms, at the molecular, cellular, and system levels. The content of is based on the review of information obtained from recent advances in genetic, molecular, and cell biology techniques; electrophysiological recordings; brain mapping; and mouse models, emphasizing the clinical and possible therapeutic implications. Many chapters of this book contain information that will be relevant not only to clinical neurologists but also to psychiatrists and physical therapists. The scope includes the mechanisms and abnormalities of DNA/RNA metabolism, proteostasis, vesicular biogenesis, and axonal transport and mechanisms of neurodegeneration; the role of the mitochondria in cell function and death mechanisms; ion channels, neurotransmission and mechanisms of channelopathies and synaptopathies; the functions of astrocytes, oligodendrocytes, and microglia and their involvement in disease; the local circuits and synaptic interactions at the level of the cerebral cortex, thalamus, basal ganglia, cerebellum, brainstem, and spinal cord transmission regulating sensory processing, behavioral state, and motor functions; the peripheral and central mechanisms of pain and homeostasis; and networks involved in emotion, memory, language, and executive function.

Neurodegenerative diseases are gradually becoming the main burden of society. The morbidity and mortality caused by neurodegenerative diseases remain significant health-care concerns. For most neurodegenerative diseases, there are no effective treatments. Over the past few decades, in a quest to exploit efficacious disease-modifying therapies for the treatment of neurodegenerative diseases, disease mechanisms, reliable biomarkers and therapeutic targets have become a research priority. At present, lncRNA is an area with potential research value. In this article, we first summarize some of the existing results of research into lncRNAs, including origin, molecular characteristics, location types, and functional types. We then introduce the possible functions of lncRNAs in different neurodegenerative diseases. Furthermore, some lncRNAs which show promise as biomarkers or potential therapeutic targets are systematically summarized.

Autism is a spectrum disorder with wide variation in type and severity of symptoms. Understanding gene–phenotype associations is vital to unravel the disease mechanisms and advance its diagnosis and treatment. To date, several databases have stored a large portion of gene–phenotype associations which are mainly obtained from genetic experiments. However, a large proportion of gene–phenotype associations are still buried in the autism-related literature and there are limited resources to investigate autism-associated gene-phenotype associations. Given the abundance of the autism-related literature, we were thus motivated to develop Autism_genepheno, a text mining pipeline to identify sentence-level mentions of autism-associated genes and phenotypes in literature through natural language processing methods. We have generated a comprehensive database of gene-phenotype associations in the last five years’ autism-related literature that can be easily updated as new literature becomes available. We have evaluated our pipeline through several different approaches, and we are able to rank and select top autism-associated genes through their unique and wide spectrum of phenotypic profiles, which could provide a unique resource for the diagnosis and treatment of autism. The data resources and the Autism_genpheno pipeline are available at: https://github.com/maiziezhoulab/Autism_genepheno.

Mathematical biology and pharmacology models have a long and rich history in the fields of medicine and physiology, impacting our understanding of disease mechanisms and the development of novel therapeutics. With an increased focus on the pharmacology application of system models and the advances in data science spanning mechanistic and empirical approaches, there is a significant opportunity and promise to leverage these advancements to enhance the development and application of the systems pharmacology field. In this paper, we will review milestones in the evolution of mathematical biology and pharmacology models, highlight some of the gaps and challenges in developing and applying systems pharmacology models, and provide a vision for an integrated strategy that leverages advances in adjacent fields to overcome these challenges.

Purpose of review In this review, we synthesise recent research on the association between loneliness and cardiovascular disease (CVD). We present evidence for mechanisms underlying this association and propose directions for future research. Recent findings Loneliness is related to increased risk of early mortality and CVD comparable to other well-established risk factors such as obesity or smoking. Summary Loneliness has been linked to higher rates of incident CVD, poorer CVD patient outcomes, and early mortality from CVD. Loneliness likely affects risk for these outcomes via health-related behaviours (e.g. physical inactivity and smoking), biological mechanisms (e.g. inflammation, stress reactivity), and psychological factors (e.g. depression) to indirectly damage health.

Background: Variants in the LMNA gene, encoding lamins A/C, are responsible for a growing number of diseases, all of which complying with the definition of rare diseases. LMNA-related disorders have a varied phenotypic expression with more than 15 syndromes described, belonging to five phenotypic groups: Muscular Dystrophies, Neuropathies, Cardiomyopathies, Lipodystrophies and Progeroid Syndromes. Overlapping phenotypes are also reported. Linking gene and variants with phenotypic expression, disease mechanisms, and corresponding treatments is particularly challenging in laminopathies. Treatment recommendations are limited, and very few are variant-based. Objective: The Treatabolome initiative aims to provide a shareable dataset of existing variant-specific treatment for rare diseases within the Solve-RD EU project. As part of this project, we gathered evidence of specific treatments for laminopathies via a systematic literature review adopting the FAIR (Findable, Accessible, Interoperable, and Reusable) guidelines for scientific data production. Methods: Treatments for LMNA-related conditions were systematically collected from MEDLINE and Embase bibliographic databases and clinical trial registries (Cochrane Central Registry of Controlled Trials, clinicaltrial.gov and EudraCT). Two investigators extracted and analyzed the literature data independently. The included papers were assessed using the Oxford Centre for Evidence-Based Medicine 2011 Levels of Evidence. Results: From the 4783 selected articles by a systematic approach, we identified 78 papers for our final analysis that corresponded to the profile of data defined in the inclusion and exclusion criteria. These papers include 2 guidelines/consensus papers, 4 meta-analyses, 14 single-arm trials, 15 case series, 13 cohort studies, 21 case reports, 8 expert reviews and 1 expert opinion. The treatments were summarized electronically according to significant phenome-genome associations. The specificity of treatments according to the different laminopathic phenotypical presentations is variable. Conclusions: We have extracted Treatabolome-worthy treatment recommendations for patients with different forms of laminopathies based on significant phenome-genome parings. This dataset will be available on the Treatabolome website and, through interoperability, on genetic diagnosis and treatment support tools like the RD-Connect’s Genome Phenome Analysis Platform.

From the first illustrations of neuronal morphology by Ramon y Cajal to the recent three-dimensional reconstruction of synaptic connections, the development of modern neuroscience has greatly benefited from breakthroughs in imaging technology. This also applies specifically to the study of neurodegenerative diseases. Much of the research into these diseases relies on the direct visualisation of intracellular structures and their dynamics in degenerating neural cells, which cannot be fully resolved by diffraction-limited microscopes. Progress in the field has therefore been closely linked to the development of super-resolution imaging methods. Their application has greatly advanced our understanding of disease mechanisms, ranging from the structural progression of protein aggregates to defects in organelle morphology. Recent super-resolution studies have specifically implicated the disruption of inter-organelle interactions in multiple neurodegenerative diseases. In this article, we describe some of the key super-resolution techniques that have contributed to this field. We then discuss work to visualise changes in the structure and dynamics of organelles and associated dysfunctions. Finally, we consider what future developments in imaging technology may further our knowledge of these processes.

Paroxysmal movement disorders include paroxysmal kinesigenic dyskinesia, paroxysmal non-kinesigenic dyskinesia, paroxysmal exercise-induced dyskinesia, and episodic ataxias. In recent years, there has been renewed interest and recognition of these disorders and their intersection with epilepsy, at the molecular and pathophysiological levels. In this review, we discuss how these distinct phenotypes were constructed from a historical perspective and discuss how they are currently coalescing into established genetic etiologies with extensive pleiotropy, emphasizing clinical phenotyping important for diagnosis and for interpreting results from genetic testing. We discuss insights on the pathophysiology of select disorders and describe shared mechanisms that overlap treatment principles in some of these disorders. In the near future, it is likely that a growing number of genes will be described associating movement disorders and epilepsy, in parallel with improved understanding of disease mechanisms leading to more effective treatments.

Unraveling molecular regulatory networks underlying disease progression is critically important for understanding disease mechanisms and identifying drug targets. The existing methods for inferring gene regulatory networks (GRNs) rely mainly on time-course gene expression data. However, most available omics data from cross-sectional studies of cancer patients often lack sufficient temporal information, leading to a key challenge for GRN inference. Through quantifying the latent progression using random walks-based manifold distance, we propose a latent-temporal progression-based Bayesian method, PROB, for inferring GRNs from the cross-sectional transcriptomic data of tumor samples. The robustness of PROB to the measurement variabilities in the data is mathematically proved and numerically verified. Performance evaluation on real data indicates that PROB outperforms other methods in both pseudotime inference and GRN inference. Applications to bladder cancer and breast cancer demonstrate that our method is effective to identify key regulators of cancer progression or drug targets. The identified ACSS1 is experimentally validated to promote epithelial-to-mesenchymal transition of bladder cancer cells, and the predicted FOXM1-targets interactions are verified and are predictive of relapse in breast cancer. Our study suggests new effective ways to clinical transcriptomic data modeling for characterizing cancer progression and facilitates the translation of regulatory network-based approaches into precision medicine., Author summary Reconstructing gene regulatory network (GRN) is an essential question in systems biology. The lack of temporal information in sample-based transcriptomic data leads to a major challenge for inferring GRN and its translation to precision medicine. To address the above challenge, we propose to decode the latent temporal information underlying cancer progression via ordering patient samples based on transcriptomic similarity, and design a latent-temporal progression-based Bayesian method to infer GRNs from sample-based transcriptomic data of cancer patients. The advantages of our method include its capability to infer causal GRNs (with directed and signed edges) and its robustness to the measurement variability in the data. Performance evaluation using both simulated data and real data demonstrate that our method outperforms other existing methods in both pseudotime inference and GRN inference. Our method is then applied to reconstruct EMT regulatory networks in bladder cancer and to identify key regulators underlying progression of breast cancer. Importantly, the predicted key regulators/interactions are experimentally validated. Our study suggests that inferring dynamic progression trajectory from static expression data of tumor samples helps to uncover regulatory mechanisms underlying cancer progression and to discovery key regulators which may be used as candidate drug targets.