Toward a common standard for data and specimen provenance in life sciences.

Open and practical exchange, dissemination, and reuse of specimens and data have become a fundamental requirement for life sciences research. The quality of the data obtained and thus the findings and knowledge derived is thus significantly influenced by the quality of the samples, the experimental methods, and the data analysis. Therefore, a comprehensive and precise documentation of the pre-analytical conditions, the analytical procedures, and the data processing are essential to be able to assess the validity of the research results. With the increasing importance of the exchange, reuse, and sharing of data and samples, procedures are required that enable cross-organizational documentation, traceability, and non-repudiation. At present, this information on the provenance of samples and data is mostly either sparse, incomplete, or incoherent. Since there is no uniform framework, this information is usually only provided within the organization and not interoperably. At the same time, the collection and sharing of biological and environmental specimens increasingly require definition and documentation of benefit sharing and compliance to regulatory requirements rather than consideration of pure scientific needs. In this publication, we present an ongoing standardization effort to provide trustworthy machine-actionable documentation of the data lineage and specimens. We would like to invite experts from the biotechnology and biomedical fields to further contribute to the standard.

Mallory-Denk bodies and hepatocellular senescence: a causal relationship?

Mallory-Denk bodies (MDBs) are hepatocellular cytoplasmic inclusions, which occur in certain chronic liver diseases, such as alcohol-related (ASH) and metabolic dysfunction-associated (MASH) steatohepatitis, copper toxicosis, some drug-induced liver disorders, chronic cholangiopathies, and liver tumors. Our study focused on the expression of the senescence markers p21WAF1/cip1 and p16INK4a in hepatocytes containing MDBs in steatohepatitis, chronic cholangiopathies with fibrosis or cirrhosis, Wilson's disease, and hepatocellular carcinomas. Cytoplasm and nuclei of MDB-containing hepatocytes as well as MDB inclusions, except those associated with carcinoma cells, were strongly p16-positive, p21-positive, as well as p21-negative nuclei in MDB-containing hepatocytes which were observed whereas MDBs were p21-negative. Expression of the senescence marker p16 suggests that MDB formation reflects an adaptive response to chronic stress resembling senescence with its consequences, i.e., expression of inflammation- and fibrosis-prone secretome. Thus, senescence can be regarded as "double-edged sword" since, on the one hand, it may be an attempt of cellular defense, but, on the other, also causes further and sustained damage by inducing inflammation and fibrosis related to the senescence-associated secretory phenotype and thus progression of chronic liver disease.

Inter-patient heterogeneity in the hepatic ischemia-reperfusion injury transcriptome: Implications for research and diagnostics.

Cellular responses induced by surgical procedure or ischemia-reperfusion injury (IRI) may severely alter transcriptome profiles and complicate molecular diagnostics. To investigate this effect, we characterized such pre-analytical effects in 143 non-malignant liver samples obtained from 30 patients at different time points of ischemia during surgery from two individual cohorts treated either with the Pringle manoeuvre or total vascular exclusion. Transcriptomics profiles were analyzed by Affymetrix microarrays and expression of selected mRNAs was validated by RT-PCR. We found 179 mutually deregulated genes which point to elevated cytokine signaling with NFκB as a dominant pathway in ischemia responses. In contrast to ischemia, reperfusion induced pro-apoptotic and pro-inflammatory cascades involving TNF, NFκB and MAPK pathways. FOS and JUN were down-regulated in steatosis compared to their up-regulation in normal livers. Surprisingly, molecular signatures of underlying primary and secondary cancers were present in non-tumor tissue. The reported inter-patient variability might reflect differences in individual stress responses and impact of underlying disease conditions. Furthermore, we provide a set of 230 pre-analytically highly robust genes identified from histologically normal livers (<2% covariation across both cohorts) that might serve as reference genes and could be particularly suited for future diagnostic applications.

Pre-analytical properties of different respiratory viruses for PCR-based detection: Comparative analysis of sampling devices and sample stabilization solutions.

After the decline of the COVID-19 pandemic, health systems were challenged by the simultaneous prevalence of different respiratory viruses causing a wide overlap in symptoms. This increased the demand for multi-virus diagnostic tests which require suitable pre-analytical workflow solutions in order to receive valid diagnostic results. In this context, the effects of specimen storage duration and temperature on the RNA/DNA copy number stability of influenza A/B, RSV A/B, SARS-CoV-2 and adenovirus were examined for four commercially available transport swab systems and saliva collection devices. The respiratory viruses were more stable in the saliva collection devices than in the transport swab systems when stored at RT or 37 °C for up to 96 h. Moreover, no differences between viral nucleic acid stability of enveloped and non-enveloped viruses were observed. The infectivity of all enveloped viruses could be inactivated by the saliva collection device from PreAnalytiX. The Norgen saliva device completely inactivated influenza A/B, while RSV A/B were partially inactivated. The non-enveloped adenovirus was inactivated by a reduction factor of 10E+ 4 in both saliva collection devices. All respiratory viruses remained infectious in the transport swab systems. Two possible transport medium additives were tested which inactivated or strongly reduced viral replication of tested enveloped viruses but had no effect on the non-enveloped adenovirus. Finally the implementation of multi-target detection procedures involving a direct amplification approach was successfully tested by spike-in of all enveloped viruses simultaneously into transport swab systems. This fast and reproducible setup presents a valuable solution for future implementations in multi-virus testing strategies.

Colorectal cancer risk stratification on histological slides based on survival curves predicted by deep learning.

Studies have shown that colorectal cancer prognosis can be predicted by deep learning-based analysis of histological tissue sections of the primary tumor. So far, this has been achieved using a binary prediction. Survival curves might contain more detailed information and thus enable a more fine-grained risk prediction. Therefore, we established survival curve-based CRC survival predictors and benchmarked them against standard binary survival predictors, comparing their performance extensively on the clinical high and low risk subsets of one internal and three external cohorts. Survival curve-based risk prediction achieved a very similar risk stratification to binary risk prediction for this task. Exchanging other components of the pipeline, namely input tissue and feature extractor, had largely identical effects on model performance independently of the type of risk prediction. An ensemble of all survival curve-based models exhibited a more robust performance, as did a similar ensemble based on binary risk prediction. Patients could be further stratified within clinical risk groups. However, performance still varied across cohorts, indicating limited generalization of all investigated image analysis pipelines, whereas models using clinical data performed robustly on all cohorts.

Inactivation of Bacteriophage ɸ6 and SARS-CoV-2 in Antimicrobial Surface Tests.

Due to the COVID-19 pandemic, researchers have focused on new preventive measures to limit the spread of SARS-CoV-2. One promising application is the usage of antimicrobial materials on often-touched surfaces to reduce the load of infectious virus particles. Since tests with in vitro-propagated SARS-CoV-2 require biosafety level 3 (BSL-3) laboratories with limited capacities and high costs, experiments with an appropriate surrogate like the bacteriophage ɸ6 are preferred in most studies. The aim of this study was to compare ɸ6 and SARS-CoV-2 within antiviral surface tests. Different concentrations of copper coatings on polyethylene terephthalate (PET) were used to determine their neutralizing activity against ɸ6 and SARS-CoV-2. The incubation on the different specimens led to similar inactivation of both SARS-CoV-2 and ɸ6. After 24 h, no infectious virus particles were evident on any of the tested samples. Shorter incubation periods on specimens with high copper concentrations also showed a complete inactivation. In contrast, the uncoated PET foils resulted only in a negligible reduced inactivation during the one-hour incubation. The similar reduction rate for ɸ6 and SARS-CoV-2 in our experiments provide further evidence that the bacteriophage ɸ6 is an adequate model organism for SARS-CoV-2 for this type of testing.

A critical spotlight on the paradigms of FFPE-DNA sequencing.

In the late 19th century, formalin fixation with paraffin-embedding (FFPE) of tissues was developed as a fixation and conservation method and is still used to this day in routine clinical and pathological practice. The implementation of state-of-the-art nucleic acid sequencing technologies has sparked much interest for using historical FFPE samples stored in biobanks as they hold promise in extracting new information from these valuable samples. However, formalin fixation chemically modifies DNA, which potentially leads to incorrect sequences or misinterpretations in downstream processing and data analysis. Many publications have concentrated on one type of DNA damage, but few have addressed the complete spectrum of FFPE-DNA damage. Here, we review mitigation strategies in (I) pre-analytical sample quality control, (II) DNA repair treatments, (III) analytical sample preparation and (IV) bioinformatic analysis of FFPE-DNA. We then provide recommendations that are tested and illustrated with DNA from 13-year-old liver specimens, one FFPE preserved and one fresh frozen, applying target-enriched sequencing. Thus, we show how DNA damage can be compensated, even when using low quantities (50 ng) of fragmented FFPE-DNA (DNA integrity number 2.0) that cannot be amplified well (Q129 bp/Q41 bp = 5%). Finally, we provide a checklist called 'ERROR-FFPE-DNA' that summarises recommendations for the minimal information in publications required for assessing fitness-for-purpose and inter-study comparison when using FFPE samples.

Glyco engineered pentameric SARS-CoV-2 IgMs show superior activities compared to IgG1 orthologues.

Immunoglobulin M (IgM) is the largest antibody isotype with unique features like extensive glycosylation and oligomerization. Major hurdles in characterizing its properties are difficulties in the production of well-defined multimers. Here we report the expression of two SARS-CoV-2 neutralizing monoclonal antibodies in glycoengineered plants. Isotype switch from IgG1 to IgM resulted in the production of IgMs, composed of 21 human protein subunits correctly assembled into pentamers. All four recombinant monoclonal antibodies carried a highly reproducible human-type N-glycosylation profile, with a single dominant N-glycan species at each glycosite. Both pentameric IgMs exhibited increased antigen binding and virus neutralization potency, up to 390-fold, compared to the parental IgG1. Collectively, the results may impact on the future design of vaccines, diagnostics and antibody-based therapies and emphasize the versatile use of plants for the expression of highly complex human proteins with targeted posttranslational modifications.

Residual Humidity in Paraffin-Embedded Tissue Reduces Nucleic Acid Stability.

Molecular diagnostics in healthcare relies increasingly on genomic and transcriptomic methodologies and requires appropriate tissue specimens from which nucleic acids (NA) of sufficiently high quality can be obtained. Besides the duration of ischemia and fixation type, NA quality depends on a variety of other pre-analytical parameters, such as storage conditions and duration. It has been discussed that the improper dehydration of tissue during processing influences the quality of NAs and the shelf life of fixed tissue. Here, we report on establishing a method for determining the amount of residual water in fixed, paraffin-embedded tissue (fixed by neutral buffered formalin or a non-crosslinking fixative) and its correlation to the performance of NAs in quantitative real-time polymerase chain reaction (qRT-PCR) analyses. The amount of residual water depended primarily on the fixative type and the dehydration protocol and, to a lesser extent, on storage conditions and time. Moreover, we found that these parameters were associated with the qRT-PCR performance of extracted NAs. Besides the cross-linking of NAs and the modification of nucleobases by formalin, the hydrolysis of NAs by residual water was found to contribute to reduced qRT-PCR performance. The negative effects of residual water on NA stability are not only important for the design and interpretation of research but must also be taken into account in clinical diagnostics where the reanalysis of archived tissue from a primary tumor may be required (e.g., after disease recurrence). We conclude that improving the shelf life of fixed tissue requires meticulous dehydration and dry storage to minimize the degradative influence of residual water on NAs.

Antiviral Activity of Zinc Oxide Nanoparticles against SARS-CoV-2.

The highly contagious SARS-CoV-2 virus is primarily transmitted through respiratory droplets, aerosols, and contaminated surfaces. In addition to antiviral drugs, the decontamination of surfaces and personal protective equipment (PPE) is crucial to mitigate the spread of infection. Conventional approaches, including ultraviolet radiation, vaporized hydrogen peroxide, heat and liquid chemicals, can damage materials or lack comprehensive, effective disinfection. Consequently, alternative material-compatible and sustainable methods, such as nanomaterial coatings, are needed. Therefore, the antiviral activity of two novel zinc-oxide nanoparticles (ZnO-NP) against SARS-CoV-2 was investigated in vitro. Each nanoparticle was produced by applying highly efficient "green" synthesis techniques, which are free of fossil derivatives and use nitrate, chlorate and sulfonate salts as starting materials and whey as chelating agents. The two "green" nanomaterials differ in size distribution, with ZnO-NP-45 consisting of particles ranging from 30 nm to 60 nm and ZnO-NP-76 from 60 nm to 92 nm. Human lung epithelial cells (Calu-3) were infected with SARS-CoV-2, pre-treated in suspensions with increasing ZnO-NP concentrations up to 20 mg/mL. Both "green" materials were compared to commercially available ZnO-NP as a reference. While all three materials were active against both virus variants at concentrations of 10-20 mg/mL, ZnO-NP-45 was found to be more active than ZnO-NP-76 and the reference material, resulting in the inactivation of the Delta and Omicron SARS-CoV-2 variants by a factor of more than 106. This effect could be due to its greater total reactive surface, as evidenced by transmission electron microscopy and dynamic light scattering. Higher variations in virus inactivation were found for the latter two nanomaterials, ZnO-NP-76 and ZnO-NP-ref, which putatively may be due to secondary infections upon incomplete inactivation inside infected cells caused by insufficient NP loading of the virions. Taken together, inactivation with 20 mg/mL ZnO-NP-45 seems to have the greatest effect on both SARS-CoV-2 variants tested. Prospective ZnO-NP applications include an antiviral coating of filters or PPE to enhance user protection.

In planta deglycosylation improves the SARS-CoV-2 neutralization activity of recombinant ACE2-Fc.

SARS-CoV-2 infects human cells via binding of the viral spike glycoprotein to its main cellular receptor, angiotensin-converting enzyme 2 (ACE2). The spike protein-ACE2 receptor interaction is therefore a major target for the development of therapeutic or prophylactic drugs to combat coronavirus infections. Various engineered soluble ACE2 variants (decoys) have been designed and shown to exhibit virus neutralization capacity in cell-based assays and in vivo models. Human ACE2 is heavily glycosylated and some of its glycans impair binding to the SARS-CoV-2 spike protein. Therefore, glycan-engineered recombinant soluble ACE2 variants might display enhanced virus-neutralization potencies. Here, we transiently co-expressed the extracellular domain of ACE2 fused to human Fc (ACE2-Fc) with a bacterial endoglycosidase in Nicotiana benthamiana to produce ACE2-Fc decorated with N-glycans consisting of single GlcNAc residues. The endoglycosidase was targeted to the Golgi apparatus with the intention to avoid any interference of glycan removal with concomitant ACE2-Fc protein folding and quality control in the endoplasmic reticulum. The in vivo deglycosylated ACE2-Fc carrying single GlcNAc residues displayed increased affinity to the receptor-binding domain (RBD) of SARS-CoV-2 as well as improved virus neutralization activity and thus is a promising drug candidate to block coronavirus infection.

Privacy risks of whole-slide image sharing in digital pathology.

Access to large volumes of so-called whole-slide images-high-resolution scans of complete pathological slides-has become a cornerstone of the development of novel artificial intelligence methods in pathology for diagnostic use, education/training of pathologists, and research. Nevertheless, a methodology based on risk analysis for evaluating the privacy risks associated with sharing such imaging data and applying the principle "as open as possible and as closed as necessary" is still lacking. In this article, we develop a model for privacy risk analysis for whole-slide images which focuses primarily on identity disclosure attacks, as these are the most important from a regulatory perspective. We introduce a taxonomy of whole-slide images with respect to privacy risks and mathematical model for risk assessment and design . Based on this risk assessment model and the taxonomy, we conduct a series of experiments to demonstrate the risks using real-world imaging data. Finally, we develop guidelines for risk assessment and recommendations for low-risk sharing of whole-slide image data.

Predicting lymph node metastasis from primary tumor histology and clinicopathologic factors in colorectal cancer using deep learning.

BACKGROUND: Presence of lymph node metastasis (LNM) influences prognosis and clinical decision-making in colorectal cancer. However, detection of LNM is variable and depends on a number of external factors. Deep learning has shown success in computational pathology, but has struggled to boost performance when combined with known predictors. METHODS: Machine-learned features are created by clustering deep learning embeddings of small patches of tumor in colorectal cancer via k-means, and then selecting the top clusters that add predictive value to a logistic regression model when combined with known baseline clinicopathological variables. We then analyze performance of logistic regression models trained with and without these machine-learned features in combination with the baseline variables. RESULTS: The machine-learned extracted features provide independent signal for the presence of LNM (AUROC: 0.638, 95% CI: [0.590, 0.683]). Furthermore, the machine-learned features add predictive value to the set of 6 clinicopathologic variables in an external validation set (likelihood ratio test, p < 0.00032; AUROC: 0.740, 95% CI: [0.701, 0.780]). A model incorporating these features can also further risk-stratify patients with and without identified metastasis (p < 0.001 for both stage II and stage III). CONCLUSION: This work demonstrates an effective approach to combine deep learning with established clinicopathologic factors in order to identify independently informative features associated with LNM. Further work building on these specific results may have important impact in prognostication and therapeutic decision making for LNM. Additionally, this general computational approach may prove useful in other contexts.

When colorectal cancers spread to the lymph nodes, it can indicate a poorer prognosis. However, detecting lymph node metastasis (spread) can be difficult and depends on a number of factors such as how samples are taken and processed. Here, we show that machine learning, which involves computer software learning from patterns in data, can predict lymph node metastasis in patients with colorectal cancer from the microscopic appearance of their primary tumor and the clinical characteristics of the patients. We also show that the same approach can predict patient survival. With further work, our approach may help clinicians to inform patients about their prognosis and decide on appropriate treatments.

Identification of a Synthetic Polyhydroxyphenolic Resveratrol Analogue, 3,3',4,4',5,5'-Hexahydroxy-trans-Stilbene with Anti-SARS-CoV-2 Activity.

The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) virus has been causing the COVID-19 pandemic since December 2019, with over 600 million infected persons worldwide and over six million deaths. We investigated the anti-viral effects of polyphenolic green tea ingredients and the synthetic resveratrol analogue 3,3',4,4',5,5'-hexahydroxy-trans-stilbene (HHS), a compound with antioxidant, antitumor and anti-HIV properties. In the TCID50 assay, four out of nine green tea constituents showed minor to modest cell protective effects, whereas HHS demonstrated the highest reduction (1103-fold) of the TCID50, indicating pronounced inhibition of virus replication. HHS was also a highly effective inhibitor of SARS-CoV-2 proliferation in VeroE6 cells with an IC50 value of 31.1 µM. HSS also inhibited the binding of the receptor-binding domain (RBD) of the spike protein to the human angiotensin-converting enzyme 2 (ACE2) receptor (RBD-ACE2) binding with 29% at 100 µM and with 9.2% at 50 µM indicating that the SARS-CoV-2 inhibitory effect might at least in part be attributed to the inhibition of virus binding to ACE2. Based on the chemical similarity to other polyphenols, the oral bioavailability of HHS is likely also very low, resulting in blood levels far below the inhibitory concentration of EGCG against SARS-CoV-2 observed in vitro. However, administration of HHS topically as a nose or throat spray would increase concentrations several-fold above the minimal inhibitory concentration (MIC) in the mucosa and might reduce virus load when administered soon after infection. Due to these promising tissue culture results, further preclinical and clinical studies are warranted to develop HHS as an additional treatment option for SARS-CoV-2 infection to complement vaccines, which is and will be the main pillar to combat the COVID-19 pandemic.

Pathologist Validation of a Machine Learning-Derived Feature for Colon Cancer Risk Stratification.

Importance: Identifying new prognostic features in colon cancer has the potential to refine histopathologic review and inform patient care. Although prognostic artificial intelligence systems have recently demonstrated significant risk stratification for several cancer types, studies have not yet shown that the machine learning-derived features associated with these prognostic artificial intelligence systems are both interpretable and usable by pathologists. Objective: To evaluate whether pathologist scoring of a histopathologic feature previously identified by machine learning is associated with survival among patients with colon cancer. Design, Setting, and Participants: This prognostic study used deidentified, archived colorectal cancer cases from January 2013 to December 2015 from the University of Milano-Bicocca. All available histologic slides from 258 consecutive colon adenocarcinoma cases were reviewed from December 2021 to February 2022 by 2 pathologists, who conducted semiquantitative scoring for tumor adipose feature (TAF), which was previously identified via a prognostic deep learning model developed with an independent colorectal cancer cohort. Main Outcomes and Measures: Prognostic value of TAF for overall survival and disease-specific survival as measured by univariable and multivariable regression analyses. Interpathologist agreement in TAF scoring was also evaluated. Results: A total of 258 colon adenocarcinoma histopathologic cases from 258 patients (138 men [53%]; median age, 67 years [IQR, 65-81 years]) with stage II (n = 119) or stage III (n = 139) cancer were included. Tumor adipose feature was identified in 120 cases (widespread in 63 cases, multifocal in 31, and unifocal in 26). For overall survival analysis after adjustment for tumor stage, TAF was independently prognostic in 2 ways: TAF as a binary feature (presence vs absence: hazard ratio [HR] for presence of TAF, 1.55 [95% CI, 1.07-2.25]; P = .02) and TAF as a semiquantitative categorical feature (HR for widespread TAF, 1.87 [95% CI, 1.23-2.85]; P = .004). Interpathologist agreement for widespread TAF vs lower categories (absent, unifocal, or multifocal) was 90%, corresponding to a κ metric at this threshold of 0.69 (95% CI, 0.58-0.80). Conclusions and Relevance: In this prognostic study, pathologists were able to learn and reproducibly score for TAF, providing significant risk stratification on this independent data set. Although additional work is warranted to understand the biological significance of this feature and to establish broadly reproducible TAF scoring, this work represents the first validation to date of human expert learning from machine learning in pathology. Specifically, this validation demonstrates that a computationally identified histologic feature can represent a human-identifiable, prognostic feature with the potential for integration into pathology practice.

AI for life: Trends in artificial intelligence for biotechnology.

Due to popular successes (e.g., ChatGPT) Artificial Intelligence (AI) is on everyone's lips today. When advances in biotechnology are combined with advances in AI unprecedented new potential solutions become available. This can help with many global problems and contribute to important Sustainability Development Goals. Current examples include Food Security, Health and Well-being, Clean Water, Clean Energy, Responsible Consumption and Production, Climate Action, Life below Water, or protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss. AI is ubiquitous in the life sciences today. Topics include a wide range from machine learning and Big Data analytics, knowledge discovery and data mining, biomedical ontologies, knowledge-based reasoning, natural language processing, decision support and reasoning under uncertainty, temporal and spatial representation and inference, and methodological aspects of explainable AI (XAI) with applications of biotechnology. In this pre-Editorial paper, we provide an overview of open research issues and challenges for each of the topics addressed in this special issue. Potential authors can directly use this as a guideline for developing their paper.

Loss of SQSTM1/p62 Induces Obesity and Exacerbates Alcohol-Induced Liver Injury in Aged Mice.

BACKGROUND: Alcohol-associated liver disease (ALD) is a worldwide health problem, of which the effective treatment is still lacking. Both detrimental and protective roles of adipose tissue have been implicated in ALD. Although alcohol increases adipose tissue lipolysis to promote alcohol-induced liver injury, alcohol also activates brown adipose tissue (BAT) thermogenesis as an adaptive response in protecting against alcohol-induced liver injury. Moreover, aging and obesity are also risk factors for ALD. In the present study, we investigated the effects of autophagy receptor protein SQSTM1/p62 on adipose tissue and obesity in alcohol-induced liver injury in both young and aged mice. METHODS: Young and aged whole-body SQSTM1/p62 knockout (KO) and their age-matched wild-type (WT) mice were subjected to chronic plus binge (Gao-binge) alcohol feeding. Blood, adipose and liver tissues were collected for biochemical and histologic analysis. RESULTS: Aged but not young SQSTM1/p62 KO mice had significantly increased body weight and fat mass compared with the matched WT mice. Gao-binge alcohol feeding induced white adipose atrophy and decreased levels of SQSTM1/p62 levels in adipose tissue in aged WT mice. SQSTM1/p62 KO aged mice were resistant to Gao-binge alcohol-induced white adipose atrophy. Alcohol feeding increased the expression of thermogenic genes in WT mouse BAT, which was significantly blunted in SQSTM1/p62 KO aged mice. Alcohol-fed aged SQSTM1/p62 KO mice showed significantly higher levels of serum alanine aminotransferase, hepatic triglyceride, and inflammation compared with young and aged WT mice fed with alcohol. Alcohol-fed SQSTM1/p62 KO mice also increased secretion of proinflammatory and angiogenic adipokines that may promote alcohol-induced liver injury. CONCLUSIONS: Loss of SQSTM1/p62 in aged mice leads to obesity and impairs alcohol-induced BAT adaptation, resulting in exacerbated alcohol-induced liver injury in mice.

Optimizing variant-specific therapeutic SARS-CoV-2 decoys using deep-learning-guided molecular dynamics simulations.

Treatment of COVID-19 with a soluble version of ACE2 that binds to SARS-CoV-2 virions before they enter host cells is a promising approach, however it needs to be optimized and adapted to emerging viral variants. The computational workflow presented here consists of molecular dynamics simulations for spike RBD-hACE2 binding affinity assessments of multiple spike RBD/hACE2 variants and a novel convolutional neural network architecture working on pairs of voxelized force-fields for efficient search-space reduction. We identified hACE2-Fc K31W and multi-mutation variants as high-affinity candidates, which we validated in vitro with virus neutralization assays. We evaluated binding affinities of these ACE2 variants with the RBDs of Omicron BA.3, Omicron BA.4/BA.5, and Omicron BA.2.75 in silico. In addition, candidates produced in Nicotiana benthamiana, an expression organism for potential large-scale production, showed a 4.6-fold reduction in half-maximal inhibitory concentration (IC50) compared with the same variant produced in CHO cells and an almost six-fold IC50 reduction compared with wild-type hACE2-Fc.

Resilience and Protection of Health Care and Research Laboratory Workers During the SARS-CoV-2 Pandemic: Analysis and Case Study From an Austrian High Security Laboratory.

The SARS-CoV-2 pandemic has highlighted the interdependency of healthcare systems and research organizations on manufacturers and suppliers of personnel protective equipment (PPE) and the need for well-trained personnel who can react quickly to changing working conditions. Reports on challenges faced by research laboratory workers (RLWs) are rare in contrast to the lived experience of hospital health care workers. We report on experiences gained by RLWs (e.g., molecular scientists, pathologists, autopsy assistants) who significantly contributed to combating the pandemic under particularly challenging conditions due to increased workload, sickness and interrupted PPE supply chains. RLWs perform a broad spectrum of work with SARS-CoV-2 such as autopsies, establishment of virus cultures and infection models, development and verification of diagnostics, performance of virus inactivation assays to investigate various antiviral agents including vaccines and evaluation of decontamination technologies in high containment biological laboratories (HCBL). Performance of autopsies and laboratory work increased substantially during the pandemic and thus led to highly demanding working conditions with working shifts of more than eight hours working in PPE that stressed individual limits and also the ergonomic and safety limits of PPE. We provide detailed insights into the challenges of the stressful daily laboratory routine since the pandemic began, lessons learned, and suggest solutions for better safety based on a case study of a newly established HCBL (i.e., BSL-3 laboratory) designed for autopsies and research laboratory work. Reduced personal risk, increased resilience, and stress resistance can be achieved by improved PPE components, better training, redundant safety measures, inculcating a culture of safety, and excellent teamwork.