COVID-19 patient transcriptomic and genomic profiling reveals comorbidity interactions with psychiatric disorders

Physical and cognitive correlates, inflammatory levels, and treatment response in post-COVID-19 first-onset vs. recurrent depressive episodes

Psychiatric symptoms have been frequently reported in patients affected by COVID-19, both as new occurring and recurrences of pre-existing diseases. Depressive symptoms are estimated to affect at least 30% of patients following infection, with specific physical and cognitive features and relevant immune-inflammatory alterations. This study aimed to retrospectively characterize post-COVID-19 first-onset and recurrent major depressive episodes (MDE) and to evaluate the effects of antidepressants on physical and cognitive correlates of depression, in addition to mood, anxiety, and underlying inflammatory status. We evaluated 116 patients (44.8% males, 51.1 ± 17 years) with post-COVID-19 first-onset (38.8%) and recurrent (61.2%) MDE at baseline and after one- and three-month treatment with antidepressants (31% SSRIs, 25.9% SNRIs, 43.1% others). We assessed sociodemographic and clinical features and psychopathological dimensions through: Hamilton Depression and Anxiety Rating Scales; Short Form-36 Health Survey Questionnaire; Perceived Deficits Questionnaire-Depression 5-items. The systemic immune-inflammatory index was calculated to measure inflammation levels. Alongside the reduction of depression and anxiety (p < 0.001), physical and cognitive symptoms improved (p < 0.001) and inflammatory levels decreased (p < 0.001) throughout treatment in both groups. Post-COVID-19 recurrent MDE showed a significantly more severe course of physical and cognitive symptoms and persistently higher levels of inflammation than first-onset episodes. Antidepressants proved to be effective in both post-COVID-19 first-onset and recurrent MDE. However, a sustained inflammatory status might blunt treatment response in patients with recurrent depression in terms of physical correlates and cognition. Therefore, personalized approaches, possibly involving combinations with anti-inflammatory compounds, could promote better outcomes in this clinical population.

Prevalence of COVID-19 and Psychotropic Drug Treatment in Psychiatric In-patients in Germany in 2020: Results from a Nationwide Pilot Survey

INTRODUCTION

In patients with a pre-existing mental disorder, an increased risk for a first manifestation of a psychiatric disorder in COVID-19 patients, a more severe course of COVID-19 and an increased mortality have been described. Conversely, observations of lower COVID-19 incidences in psychiatric in-patients suggested protective effects of psychiatric treatment and/or psychotropic drugs against COVID-19.

METHODS

A retrospective multi-center study was conducted in 24 German psychiatric university hospitals. Between April and December 2020 (the first and partly second wave of COVID-19), the effects of COVID-19 were assessed on psychiatric in-patient care, the incidence and course of a SARS-CoV-2 infection, and treatment with psychotropic drugs.

RESULTS

Patients (n=36,322) were admitted to the hospitals. Mandatory SARS-CoV-2 tests before/during admission were reported by 23 hospitals (95.8%), while 18 (75%) conducted regular testing during the hospital stay. Two hundred thirty-two (0.6%) patients were tested SARS-CoV-2-positive. Thirty-seven (16%) patients were receiving medical treatment for COVID-19 at the psychiatric hospital, ten (4.3%) were transferred to an intermediate/intensive care unit, and three (1.3%) died. The most common prescription for SARS-CoV-2-positive patients was for second-generation antipsychotics (n=79, 28.2%) and antidepressants (SSRIs (n=38, 13.5%), mirtazapine (n=36, 12.9%) and SNRIs (n=29, 10.4%)).

DISCUSSION

Contrary to previous studies, our results showed a low number of infections and mortality in SARS-CoV-2-positive psychiatric patients. Several preventive measures seem effective to protect this vulnerable group. Our observations are compatible with the hypothesis of a protective effect of psychotropic drugs against COVID-19 as the overall mortality and need for specific medical treatment was low.

Neurological manifestations of SARS-CoV-2: complexity, mechanism and associated disorders

BACKGROUND

Coronaviruses such as Severe Acute Respiratory Syndrome coronavirus (SARS), Middle Eastern Respiratory Syndrome (MERS) and Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) are associated with critical illnesses, including severe respiratory disorders. SARS-CoV-2 is the causative agent of the deadly COVID-19 illness, which has spread globally as a pandemic. SARS-CoV-2 may enter the human body through olfactory lobes and interact with the angiotensin-converting enzyme2 (ACE2) receptor, further facilitating cell binding and entry into the cells. Reports have shown that the virus can pass through the blood-brain barrier (BBB) and enter the central nervous system (CNS), resulting in various disorders. Cell entry by SARS-CoV-2 largely relies on TMPRSS2 and cathepsin L, which activate S protein. TMPRSS2 is found on the cell surface of respiratory, gastrointestinal and urogenital epithelium, while cathepsin-L is a part of endosomes.

AIM

The current review aims to provide information on how SARS-CoV-2 infection affects brain function.. Furthermore, CNS disorders associated with SARS-CoV-2 infection, including ischemic stroke, cerebral venous thrombosis, Guillain-Barré syndrome, multiple sclerosis, meningitis, and encephalitis, are discussed. The many probable mechanisms and paths involved in developing cerebrovascular problems in COVID patients are thoroughly detailed.

MAIN BODY

There have been reports that the SARS-CoV-2 virus can cross the blood-brain barrier (BBB) and enter the central nervous system (CNS), where it could cause a various illnesses. Patients suffering from COVID-19 experience a range of neurological complications, including sleep disorders, viral encephalitis, headaches, dysgeusia, and cognitive impairment. The presence of SARS-CoV-2 in the cerebrospinal fluid (CSF) of COVID-19 patients has been reported. Health experts also reported its presence in cortical neurons and human brain organoids. The possible mechanism of virus infiltration into the brain can be neurotropic, direct infiltration and cytokine storm-based pathways. The olfactory lobes could also be the primary pathway for the entrance of SARS-CoV-2 into the brain.

CONCLUSIONS

SARS-CoV-2 can lead to neurological complications, such as cerebrovascular manifestations, motor movement complications, and cognitive decline. COVID-19 infection can result in cerebrovascular symptoms and diseases, such as strokes and thrombosis. The virus can affect the neural system, disrupt cognitive function and cause neurological disorders. To combat the epidemic, it is crucial to repurpose drugs currently in use quickly and develop novel therapeutics.

Schizophrenia and the COVID-19 pandemic: A narrative review from the biomedical perspective

The outbreak of the Coronavirus Disease 2019 (COVID-19) pandemic in 2020 caused a rapid worsening of global mental health. Patients with severe mental disorders, including schizophrenia, are at higher risk of being infected. The neuroinvasive potential of the severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) has been confirmed. The aim of this article was to present a narrative and comprehensive review of multidimensional associations between schizophrenia and COVID-19 with special emphasis on common biological pathways. Online searches were performed in the PubMed database and covered the publication period until September 17, 2022. Search terms included "psychosis", "schizophrenia", "inflammation" and "COVID-19". Viewed as a neuroinflammatory state, schizophrenia shares several neurobiological mechanisms with the COVID-19. Environmental stress, common comorbidities of schizophrenia and adverse effects of antipsychotic treatment are associated with the higher severity and mortality of the COVID-19. Additionally, more frequent relapses of psychosis have been observed, and might be related to lower treatment adherence. In the context of clinical manifestation, higher level of negative symptoms has been identified among patients with schizophrenia during the pandemic. Improvements in mental health care policy and treatment adjustment are necessary to protect people with schizophrenia who are the population that is particularly vulnerable to the consequences of the COVID-19 pandemic. Future research will show if prenatal infection with the SARS-CoV-2 increases a risk of psychosis.

Psychiatric polygenic risk as a predictor of COVID-19 risk and severity: insight into the genetic overlap between schizophrenia and COVID-19

Despite the high contagion and mortality rates that have accompanied the coronavirus disease-19 (COVID-19) pandemic, the clinical presentation of the syndrome varies greatly from one individual to another. Potential host factors that accompany greater risk from COVID-19 have been sought and schizophrenia (SCZ) patients seem to present more severe COVID-19 than control counterparts, with certain gene expression similarities between psychiatric and COVID-19 patients reported. We used summary statistics from the last SCZ, bipolar disorder (BD), and depression (DEP) meta-analyses available on the Psychiatric Genomics Consortium webpage to calculate polygenic risk scores (PRSs) for a target sample of 11,977 COVID-19 cases and 5943 subjects with unknown COVID-19 status. Linkage disequilibrium score (LDSC) regression analysis was performed when positive associations were obtained from the PRS analysis. The SCZ PRS was a significant predictor in the case/control, symptomatic/asymptomatic, and hospitalization/no hospitalization analyses in the total and female samples; and of symptomatic/asymptomatic status in men. No significant associations were found for the BD or DEP PRS or in the LDSC regression analysis. SNP-based genetic risk for SCZ, but not for BD or DEP, may be associated with higher risk of SARS-CoV-2 infection and COVID-19 severity, especially among women; however, predictive accuracy barely exceeded chance level. We believe that the inclusion of sexual loci and rare variations in the analysis of genomic overlap between SCZ and COVID-19 will help to elucidate the genetic commonalities between these conditions.

Role of different types of RNA molecules in the severity prediction of SARS-CoV-2 patients

SARS-CoV-2 pandemic is the current threat of the world with enormous number of deceases. As most of the countries have constraints on resources, particularly for intensive care and oxygen, severity prediction with high accuracy is crucial. This prediction will help the medical society in the selection of patients with the need for these constrained resources. Literature shows that using clinical data in this study is the common trend and molecular data is rarely utilized in this prediction. As molecular data carry more disease related information, in this study, three different types of RNA molecules ( lncRNA, miRNA and mRNA) of SARS-COV-2 patients are used to predict the severity stage and treatment stage of those patients. Using seven different machine learning algorithms along with several feature selection techniques shows that in both phenotypes, feature importance selected features provides the best accuracy along with random forest classifier. Further to this, it shows that in the severity stage prediction miRNA and lncRNA give the best performance, and lncRNA data gives the best in treatment stage prediction. As most of the studies related to molecular data uses mRNA data, this is an interesting finding.

Does COVID-19 related symptomatology indicate a transdiagnostic neuropsychiatric disorder? - Multidisciplinary implications

The clinical presentation that emerges from the extensive coronavirus disease 2019 (COVID-19) mental health literature suggests high correlations among many conventional psychiatric diagnoses. Arguments against the use of multiple comorbidities for a single patient have been published long before the pandemic. Concurrently, diagnostic recommendations for use of transdiagnostic considerations for improved treatment have been also published in recent years. In this review, we pose the question of whether a transdiagnostic mental health disease, including psychiatric and neuropsychiatric symptomology, has emerged since the onset of the pandemic. There are many attempts to identify a syndrome related to the pandemic, but none of the validated scales is able to capture the entire psychiatric and neuropsychiatric clinical presentation in infected and non-infected individuals. These scales also only marginally touch the issue of etiology and prevalence. We suggest a working hypothesis termed Complex Stress Reaction Syndrome (CSRS) representing a global psychiatric reaction to the pandemic situation in the general population (Type A) and a neuropsychiatric reaction in infected individuals (Type B) which relates to neurocognitive and psychiatric features which are part (excluding systemic and metabolic dysfunctions) of the syndrome termed in the literature as long COVID. We base our propositions on multidisciplinary scientific data regarding mental health during the global pandemic situation and the effects of viral infection reviewed from Google Scholar and PubMed between February 1, 2022 and March 10, 2022. Search in-clusion criteria were “mental health”, “COVID-19” and “Long COVID”, English language and human studies only. We suggest that this more comprehensive way of understanding COVID-19 complex mental health reactions may promote better prevention and treatment and serve to guide implementation of recommended administrative regulations that were recently published by the World Psychiatric Association. This review may serve as a call for an international investigation of our working hypothesis.

Network based systems biology approach to identify diseasome and comorbidity associations of Systemic Sclerosis with cancers

Systemic Sclerosis (SSc) is an autoimmune disease associated with changes in the skin's structure in which the immune system attacks the body. A recent meta-analysis has reported a high incidence of cancer prognosis including lung cancer (LC), leukemia (LK), and lymphoma (LP) in patients with SSc as comorbidity but its underlying mechanistic details are yet to be revealed. To address this research gap, bioinformatics methodologies were developed to explore the comorbidity interactions between a pair of diseases. Firstly, appropriate gene expression datasets from different repositories on SSc and its comorbidities were collected. Then the interconnection between SSc and its cancer comorbidities was identified by applying the developed pipelines. The pipeline was designed as a generic workflow to demonstrate a premise comorbid condition that integrate regarding gene expression data, tissue/organ meta-data, Gene Ontology (GO), Molecular pathways, and other online resources, and analyze them with Gene Set Enrichment Analysis (GSEA), Pathway enrichment and Semantic Similarity (SS). The pipeline was implemented in R and can be accessed through our Github repository: https://github.com/hiddenntreasure/comorbidity. Our result suggests that SSc and its cancer comorbidities share differentially expressed genes, functional terms (gene ontology), and pathways. The findings have led to a better understanding of disease pathways and our developed methodologies may be applied to any set of diseases for finding any association between them. This research may be used by physicians, researchers, biologists, and others.

Neuropsychiatric Drugs Against COVID-19: What is the Clinical Evidence?

Since the beginning of the coronavirus disease (COVID)-19 pandemic, the need for effective treatments for COVID-19 led to the idea of “repurposing” drugs for antiviral treatment. Several antipsychotics and antidepressants have been tested for in vitro activity against the severe acute respiratory syndrome coronavirus 2. Chlorpromazine, other phenothiazine antipsychotics, and the antidepressant fluoxetine were found to be rather potent in these studies. However, whether effective plasma concentrations can be obtained with clinically accepted doses of these drugs is not clear. Data of COVID-19 patients are not yet available but several clinical studies are currently underway.

The specific serotonin reuptake inhibitor fluvoxamine is a potent Sigma-1 receptor agonist and reduces inflammation in animal models of cytokine-stress. Accordingly, fluvoxamine treatment was superior to placebo in reducing impaired respiratory function and other symptoms of inflammation in COVID-19 patients in a placebo-controlled clinical study and another open clinical trial. The beneficial effects of fluvoxamine on the course of COVID-19 were recently confirmed in a large placebo-controlled double-blind trial with several hundred patients.

Inflammation represents a major risk factor for many psychiatric disorders which explains the high susceptibilitiy of COVID-19 patients for psychiatric diseases. Many antidepressants and antipsychotics possess anti-inflammatory properties independent of sigma-1 activity which might be important to reduce psychiatric symptoms of COVID-19 patients and to improve respiratory dysfunction and other consequences of inflammation. This might explain the rather unspecific benefit which has been reported for several cohorts of COVID-19 patients treated with different psychotropic drugs.

Mental health and COVID-19 in a general population cohort in Spain (COVICAT study)

PURPOSE

Mental health conditions may affect outcome of COVID-19 disease, while exposure to stressors during the pandemic may impact mental health. The purpose of this study was to examine these factors in relation to ocurrence of depression and anxiety after the first outbreak in Spain.

METHODS

We contacted 9515 participants from a population-based cohort study in Catalonia between May and October 2020. We drew blood samples to establish infection to the virus. Pre-pandemic mental health conditions were confirmed through Electronic Health Registries. We used the Hospital Anxiety and Depression Scale to assess severe depression and anxiety post-pandemic. Exposure to proximal, financial and wider environment stressors during the lockdown were collected. We calculated Relative Risks (RR), adjusting for individual- and contextual covariates.

RESULTS

Pre-pandemic mental health disorders were not associated with SARS-CoV-2 infection , but were associated with severity of COVID-19 disease. People with pre-existing mental health disorders showed higher prevalence of severe depression (25.4%) and anxiety (37.8%) than those without prior mental disorders (4.9% and 10.1%). Living alone was a strong predictor of severe depression among mental health patients (RR = 1.6, 95% CI 1.2-2.2). Among those without prior mental health disorders, post-lockdown depression and anxiety were associated with household interpersonal conflicts (RR = 2.6, 95% CI 2.1-3.1; RR = 2.1, 95% CI 1.9-2.4) and financial instability (RR = 2.2, 95% CI 1.8-2.9; 1.9, 95% CI 1.6-2.2).

CONCLUSIONS

The COVID-19 pandemic and the lockdown were associated with increased post-lockdown depression and anxiety. Patients with pre-existing mental health conditions are a vulnerable group for severe COVID-19 disease.

A deep learning approach using effective preprocessing techniques to detect COVID-19 from chest CT-scan and X-ray images

Coronavirus disease-19 (COVID-19) is a severe respiratory viral disease first reported in late 2019 that has spread worldwide. Although some wealthy countries have made significant progress in detecting and containing this disease, most underdeveloped countries are still struggling to identify COVID-19 cases in large populations. With the rising number of COVID-19 cases, there are often insufficient COVID-19 diagnostic kits and related resources in such countries. However, other basic diagnostic resources often do exist, which motivated us to develop Deep Learning models to assist clinicians and radiologists to provide prompt diagnostic support to the patients. In this study, we have developed a deep learning-based COVID-19 case detection model trained with a dataset consisting of chest CT scans and X-ray images. A modified ResNet50V2 architecture was employed as deep learning architecture in the proposed model. The dataset utilized to train the model was collected from various publicly available sources and included four class labels: confirmed COVID-19, normal controls and confirmed viral and bacterial pneumonia cases. The aggregated dataset was preprocessed through a sharpening filter before feeding the dataset into the proposed model. This model attained an accuracy of 96.452% for four-class cases (COVID-19/Normal/Bacterial pneumonia/Viral pneumonia), 97.242% for three-class cases (COVID-19/Normal/Bacterial pneumonia) and 98.954% for two-class cases (COVID-19/Viral pneumonia) using chest X-ray images. The model acquired a comprehensive accuracy of 99.012% for three-class cases (COVID-19/Normal/Community-acquired pneumonia) and 99.99% for two-class cases (Normal/COVID-19) using CT-scan images of the chest. This high accuracy presents a new and potentially important resource to enable radiologists to identify and rapidly diagnose COVID-19 cases with only basic but widely available equipment.

Gene-Environment Interactions in Schizophrenia: A Literature Review

Schizophrenia is a devastating mental illness with a strong genetic component that is the subject of extensive research. Despite the high heritability, it is well recognized that non-genetic factors such as certain infections, cannabis use, psychosocial stress, childhood adversity, urban environment, and immigrant status also play a role. Whenever genetic and non-genetic factors co-exist, interaction between the two is likely. This means that certain exposures would only be of consequence given a specific genetic makeup. Here, we provide a brief review of studies reporting evidence of such interactions, exploring genes and variants that moderate the effect of the environment to increase risk of developing psychosis. Discovering these interactions is crucial to our understanding of the pathogenesis of complex disorders. It can help in identifying individuals at high risk, in developing individualized treatments and prevention plans, and can influence clinical management.

Effects of Bacille Calmette Guerin (BCG) vaccination during COVID-19 infection

The coronavirus disease 2019 (COVID-19) is caused by the infection of highly contagious severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), also known as the novel coronavirus. In most countries, the containment of this virus spread is not controlled, which is driving the pandemic towards a more difficult phase. In this study, we investigated the impact of the Bacille Calmette Guerin (BCG) vaccination on the severity and mortality of COVID-19 by performing transcriptomic analyses of SARS-CoV-2 infected and BCG vaccinated samples in peripheral blood mononuclear cells (PBMC). A set of common differentially expressed genes (DEGs) were identified and seeded into their functional enrichment analyses via Gene Ontology (GO)-based functional terms and pre-annotated molecular pathways databases, and their Protein-Protein Interaction (PPI) network analysis. We further analysed the regulatory elements, possible comorbidities and putative drug candidates for COVID-19 patients who have not been BCG-vaccinated. Differential expression analyses of both BCG-vaccinated and COVID-19 infected samples identified 62 shared DEGs indicating their discordant expression pattern in their respected conditions compared to control. Next, PPI analysis of those DEGs revealed 10 hub genes, namely ITGB2, CXCL8, CXCL1, CCR2, IFNG, CCL4, PTGS2, ADORA3, TLR5 and CD33. Functional enrichment analyses found significantly enriched pathways/GO terms including cytokine activities, lysosome, IL-17 signalling pathway, TNF-signalling pathways. Moreover, a set of identified TFs, miRNAs and potential drug molecules were further investigated to assess their biological involvements in COVID-19 and their therapeutic possibilities. Findings showed significant genetic interactions between BCG vaccination and SARS-CoV-2 infection, suggesting an interesting prospect of the BCG vaccine in relation to the COVID-19 pandemic. We hope it may potentially trigger further research on this critical phenomenon to combat COVID-19 spread.

Application of omics technology to combat the COVID-19 pandemic

As of August 27, 2021, the ongoing pandemic of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has spread to over 220 countries, areas, and territories. Thus far, 214,468,601 confirmed cases, including 4,470,969 deaths, have been reported to the World Health Organization. To combat the COVID-19 pandemic, multiomics-based strategies, including genomics, transcriptomics, proteomics, and metabolomics, have been used to study the diagnosis methods, pathogenesis, prognosis, and potential drug targets of COVID-19. In order to help researchers and clinicians to keep up with the knowledge of COVID-19, we summarized the most recent progresses reported in omics-based research papers. This review discusses omics-based approaches for studying COVID-19, summarizing newly emerged SARS-CoV-2 variants as well as potential diagnostic methods, risk factors, and pathological features of COVID-19. This review can help researchers and clinicians gain insight into COVID-19 features, providing direction for future drug development and guidance for clinical treatment, so that patients can receive appropriate treatment as soon as possible to reduce the risk of disease progression.

A Framework-Based Approach to Assessing Mental Health Impacts of the COVID-19 Pandemic on Children and Adolescents

The COVID-19 pandemic has yielded extensive impacts globally in the year of 2020. Although the mental health of children and adolescents may be particularly susceptible to stressors stemming from the pandemic and anti-contagion policies, most ongoing efforts are geared toward curbing the viral spread. In the current perspective, we have identified four domains of factors corresponding to an ecological framework that may directly or indirectly influence the mental health of children and adolescents during the pandemic. The evidence suggests that anti-contagion policies might trigger cascades that impact the mental health of children and their families through multiple different sectors that used to form a safety net for youths. Additionally, children with neuropsychiatric disorders could experience exacerbated symptoms during the pandemic. Furthermore, the risk of domestic violence has surged during the pandemic, which further compounds the imminent mental health crisis. A mental health pandemic could be inevitable if no proactive prevention strategies were in place. Therefore, we recommend understanding each individual mental health risk pathway via the ecological framework in order to develop integrative prevention and intervention strategies.