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Alzheimer’s disease (AD) is a debilitating neurodegenerative disorder affecting 50 million people globally. It is characterized by the presence of extracellular senile plaques and intracellular neurofibrillary tangles, consisting of amyloid-β and hyperphosphorylated tau proteins, respectively. Despite global research efforts, there is currently no cure available, due in part to an incomplete understanding of the disease pathogenesis. Numerous possible mechanisms, or hypotheses, explaining the origins of sporadic or late-onset AD have been proposed, including the amyloid-β, inflammatory, vascular, and infectious hypotheses. However, despite ample evidence, the failure of multiple trial drugs at the clinical stage illuminates the possible pitfalls of these hypotheses. Systems biology is a strategy which aims to elucidate the interactions between parts of a whole. Using this approach, the current paper shows how the four previously mentioned hypotheses of AD pathogenesis can be intricately connected. This approach allows for seemingly contradictory evidence to be unified in a system-focused explanation of sporadic AD development. Within this view, it is seen that infectious agents, such as P. gingivalis, may play a central role. The data presented here shows that when present, P. gingivalis or its virulence factors, such as gingipains, may induce or exacerbate pathologies underlying sporadic AD. This evidence supports the view that infectious agents, and specifically P. gingivalis, may be suitable treatment targets in AD.

‘Long COVID’ is the term used to describe the phenomenon in which patients who have survived a COVID-19 infection continue to experience prolonged SARS-CoV-2 symptoms. Millions of people across the globe are affected by Long COVID. Solving the Long COVID conundrum will require drawing upon the lessons of the genomics, medicine, data science, and computer science collaborated, sharing data and pooling resources to attack the problem from multiple angles. Thus far, there has been no global consensus on the definition, diagnosis, and most effective treatment of Long COVID. In this work, we examine the possible applications of data sharing and data science in general with a view to, ultimately, understand Long COVID in greater detail and hasten relief for the millions of people experiencing it. We examine the literature and investigate the current state, challenges, and opportunities of data sharing in Long COVID research. Significance:Although millions of people across the globe have been diagnosed with Long COVID, there still exist many research gaps in our understanding of the condition and its underlying causes. This work aims to elevate the discussion surrounding data sharing and data science in the research community and to engage data sharing as an enabler to fast-track the process of finding effective treatment for Long COVID.

Background: Fibrin(ogen) amyloid microclots and platelet hyperactivation are key pathological findings in patients with acute COVID-19 infection and also in those with Long COVID/Post-Acute Sequelae of COVID-19 (PASC). These pathologies may represent a suitable target for pharmacological treatment of Long COVID. Methods: Here we report on the symptoms displayed by a cohort of 91 South African Long COVID patients at baseline and after a clinician-initiated anticoagulant regime was completed. For laboratory analysis, patients provided a blood sample before and after treatment. Fibrinaloid microclot presence was studied by adding thioflavin T to platelet poor plasma (PPP), whilst platelet hyperactivation was studied using two platelet markers- PAC1 and CD62P (P-selectin). The anticoagulant regime included dual antiplatelet therapy (DAPT- Clopidogrel 75mg + Aspirin 75mg) once a day, and a direct oral anticoagulant (DOAC- Apixaban) 5mg twice a day. A proton pump inhibitor (PPI) pantoprazole 40 mg/day was also prescribed for gastric protection. Each of the treated cases reported their main Long COVID symptoms, and whether their symptoms resolved following treatment or not. Results: In our cohort a most participants did not report any comorbidities before acute COVID-19 infection. Hypertension and dyslipidaemia were the commonest underlying illnesses, whilst the most commonly reported Long COVID symptoms included fatigue, cognitive dysfunction, shortness of breath, and joint and muscle pains. Following completion of treatment, each of the different symptoms resolved in the majority of patients. This was also reflected in the laboratory analysis, where a decrease in the severity of fibrin amyloid microclotting and the degree of platelet pathology was noted. No serious adverse bleeding events were reported. Conclusions: Fibrin amyloid microclots, platelet hyperactivation/ aggregation, and widespread endothelialitis inhibit the transport of oxygen at a capillary/cellular level. This provides a ready explanation for the symptoms of Long COVID. By normalizing the failed clotting physiology and reversal of the endothelialitis, triple anticoagulant therapy represents a promising treatment option that appears to be highly efficacious, and warrants controlled clinical studies. We caution that such a regime must only be followed under expert medical supervision in view of the risk of bleeding.

INTRODUCTIONSince the beginning of the SARS-CoV-2 pandemic in December 2019 multiple metabolomics studies have proposed predictive biomarkers of infection severity and outcome. Whilst some trends have emerged, the findings remain intangible and uninformative when it comes to new patients.OBJECTIVESIn this study, we accurately quantitate a subset of compounds in patient serum that were found predictive of severity and outcome.METHODSA targeted LC-MS method was used in 46 control and 95 acute COVID-19 patient samples to quantitate the selected metabolites. These compounds included tryptophan and its degradation products kynurenine and kynurenic acid (reflective of immune response), butyrylcarnitine and its isomer (reflective of energy metabolism) and finally 3’,4’-didehydro-3’-deoxycytidine, a deoxycytidine analogue, (reflective of host viral defence response). We subsequently examine changes in those markers by disease severity and outcome relative to those of control patients’ levels.RESULTS & CONCLUSIONFinally, we demonstrate the added value of the kynurenic acid / tryptophan ratio for severity and outcome prediction and highlight the viral detection potential of ddhC.

Ergothioneine is a naturally occurring antioxidant that has shown potential in ameliorating neurodegenerative and cardiovascular diseases. In this study, we investigated the potential of the Crabtree-negative, oleaginous yeast Yarrowia lipolytica as an alternative host for ergothioneine production. We expressed the biosynthetic enzymes EGT1 from Neurospora crassa and EGT2 from Claviceps purpurea to obtain 158 mg/L of ergothioneine in small-scale cultivation, with an additional copy of each gene improving the titer to 205 mg/L. The effect of phosphate limitation on ergothioneine production was studied, and finally, a phosphate-limited fed-batch fermentation in 1 L bioreactors yielded 1.63 ± 0.04 g/L ergothioneine in 220 hours, corresponding to an overall volumetric productivity of 7.41 mg L-1 h-1 , showing that Yarrowia lipolytica is a promising host for ergothioneine production.

Long COVID has become a significant global health and economic burden, yet there are currently no established diagnostic tools to identify which patients might benefit from specific treatments. One of the major pathophysiological factors contributing to Long COVID is the presence of hypercoagulability; this results in insoluble amyloid microclots that are resistant to fibrinolysis. Our previous research using fluorescence microscopy has demonstrated a significant amyloid microclot load in Long COVID patients. However, this approach lacked statistical robustness, objectivity, and rapid throughput. In the current study, we have used imaging flow cytometry for the first time to show significantly increased concentration and size of these microclots. We identified notable variations in size and fluorescence between microclots in Long COVID and those of controls even using a 20x objective. By combining cell imaging and the high-event-rate nature of a conventional flow cytometer, imaging flow cytometry can eliminate erroneous results and increase accuracy in gating and analysis beyond what pure quantitative measurements from conventional flow cytometry can provide. Although imaging flow cytometry was used in our study, our results suggest that the signals indicating the presence of microclots should be easily detectable using a conventional flow cytometer. Flow cytometry is a more widely available technique which has been used in pathology laboratories for decades, rendering it a potentially more suitable and accessible method for detecting microclots in individuals suffering from both Long COVID and other conditions with similar pathology, such as myalgic encephalomyelitis.

Acute COVID-19 infection is followed by prolonged symptoms in approximately one in ten cases: known as Long COVID. The disease affects ~65 million individuals worldwide. Many pathophysiological processes appear to underlie Long COVID, including viral factors (persistence, reactivation, and bacteriophagic action of SARS CoV-2); host factors (chronic inflammation, metabolic and endocrine dysregulation, immune dysregulation, and autoimmunity); and downstream impacts (tissue damage from the initial infection, tissue hypoxia, host dysbiosis, and autonomic nervous system dysfunction). These mechanisms culminate in the long-term persistence of the disorder characterized by a thrombotic endothelialitis, endothelial inflammation, hyperactivated platelets, and fibrinaloid microclots. These abnormalities of blood vessels and coagulation affect every organ system and represent a unifying pathway for the various symptoms of Long COVID.

Multidrug transporters (MDTs) are major contributors to microbial drug resistance and are further utilized for improving host phenotypes in biotechnological applications. Therefore, the identification of these MDTs and the understanding of their mechanisms of action in vivo are of great importance. However, their promiscuity and functional redundancy represent a major challenge towards their identification. Here, a multistep tolerance adaptive laboratory evolution (TALE) approach was leveraged to achieve this goal. Specifically, a wild-type E. coli K-12-MG1655 and its cognate knockout individual mutants ΔemrE, ΔtolC, and ΔacrB were evolved separately under increasing concentrations of two lipophilic cations, tetraphenylphosphonium (TPP+), and methyltriphenylphosphonium (MTPP+). The evolved strains showed a significant increase in MIC values of both cations and an apparent cross-cation resistance. Sequencing of all evolved mutants highlighted diverse mutational mechanisms that affect the activity of nine MDTs including acrB, mdtK, mdfA, acrE, emrD, tolC, acrA, mdtL, and mdtP. Besides regulatory mutations, several structural mutations were recognized in the proximal binding domain of acrB and the permeation pathways of both mdtK and mdfA. These details can aid in the rational design of MDT inhibitors to efficiently combat efflux-based drug resistance. Additionally, the TALE approach can be scaled to different microbes and molecules of medical and biotechnological relevance.

Earlier variants of SARS-CoV-2 have been associated with plasma hypercoagulability (as judged by thromboelastography) and an extensive formation of fibrin amyloid microclots, which are considered to contribute to the pathology of the coronavirus 2019 disease (COVID-19). The newer Omicron variants appear to be far more transmissible, but less virulent, even when taking immunity acquired from previous infections or vaccination into account. We here show that while the clotting parameters associated with Omicron variants are significantly raised over those of healthy, matched controls, they are only raised to levels significantly lower than those seen with more severe variants such as Beta and Delta. We also observed that individuals infected with Omicron variants manifested less extensive microclot formation in platelet poor plasma compared to those harbouring the more virulent variants. The measurement of clotting effects between the different variants acts as a kind of ‘internal control’ that demonstrates the relationship between the extent of coagulopathies and the virulence of the variant of interest. This adds to the evidence that microclots play an important role in determining the severity of symptoms observed in COVID-19.

BackgroundPost-acute sequelae of COVID-19 (PASC), also now known as long COVID, has become a major global health and economic burden. Previously, we provided evidence that there is a significant insoluble fibrin amyloid microclot load in the circulation of individuals with long COVID, and that these microclots entrap a substantial number of inflammatory molecules, including those that might prevent clot breakdown. Scientifically, the most challenging aspect of this debilitating condition is that traditional pathology tests such as a serum CRP (C-reactive protein) may not show any significant abnormal inflammatory markers, albeit these tests measure only the soluble inflammatory molecules. Elevated, or abnormal soluble biomarkers such as IL-6, D-Dimer or fibrinogen indicate an increased risk for thrombosis or a host immune response in COVID-19. The absence of biomarkers in standard pathology tests, result in a significant amount of confusion for patients and clinicians, as patients are extremely sick or even bed-ridden but with no regular identifiable reason for their disease. Biomarkers that are currently available cannot detect the molecules present in the microclots we identified and are therefore unable to confirm their presence or the mechanisms that drive their formation.MethodsHere we analysed the protein content of double-digested microclots of 99 long COVID patients and 29 healthy controls. The patients suffering from long COVID reported their symptoms through a questionnaire completed by themselves or their attending physician.ResultsOur long COVID cohort’s symptoms were found to be in line with global findings, where the most prevalent symptoms were constant fatigue (74%,) cognitive impairment (71%) and depression and anxiety (30%). Our most noteworthy findings were a reduced level of plasma Kallikrein compared to our controls, an increased level of platelet factor 4 (PF4) von Willebrand factor (VWF), and a marginally increased level of α-2 antiplasmin (α-2-AP). We also found a significant presence of antibodies entrapped inside these microclots.ConclusionOur results confirm the presence of pro-inflammatory molecules that may also contribute to a failed fibrinolysis phenomenon, which could possibly explain why individuals with long COVID suffer from chronic fatigue, dyspnoea, or cognitive impairment. In addition, significant platelet hyperactivation was noted. Hyperactivation will result in the granular content of platelets being shed into the circulation, including PF4. Overall, our results provide further evidence of both a failed fibrinolytic system in long COVID/PASC and the entrapment of many proteins whose presence might otherwise go unrecorded. These findings might have significant implications for individuals with pre-existing comorbidities, including cardiovascular disease and type 2 diabetes.

Antibiotic resistance is a major global healthcare issue. Antibiotic compounds cross the bacterial cell membrane via membrane transporters, and a major mechanism of antibiotic resistance is through modification of the membrane transporters to increase the efflux or reduce the influx of antibiotics. Targeting these transporters is a potential avenue to combat antibiotic resistance. In this study, we used an automated screening pipeline to evaluate the growth of a library of 447

The number of ‘small’ molecules that may be of interest to chemical biologists — chemical space — is enormous, but the fraction that have ever been made is tiny. Most strategies are discriminative, i.e. have involved ‘forward’ problems (have molecule, establish properties). However, we normally wish to solve the much harder generative or inverse problem (describe desired properties, find molecule). ‘Deep’ (machine) learning based on large-scale neural networks underpins technologies such as computer vision, natural language processing, driverless cars, and world-leading performance in games such as Go; it can also be applied to the solution of inverse problems in chemical biology. In particular, recent developments in deep learning admit the in silico generation of candidate molecular structures and the prediction of their properties, thereby allowing one to navigate (bio)chemical space intelligently. These methods are revolutionary but require an understanding of both (bio)chemistry and computer science to be exploited to best advantage. We give a high-level (non-mathematical) background to the deep learning revolution, and set out the crucial issue for chemical biology and informatics as a two-way mapping from the discrete nature of individual molecules to the continuous but high-dimensional latent representation that may best reflect chemical space. A variety of architectures can do this; we focus on a particular type known as variational autoencoders. We then provide some examples of recent successes of these kinds of approach, and a look towards the future.

Tuberculosis (TB) claims nearly 1.5 million lives annually. Current TB treatment requires a combination of several drugs administered for at least 6 months.

Early in the coronavirus disease 2019 (COVID-19) pandemic, global governing bodies prioritized transmissibility-based precautions and hospital capacity as the foundation for delay of elective procedures. As elective surgical volumes increased, convalescent COVID-19 patients faced increased postoperative morbidity and mortality and clinicians had limited evidence for stratifying individual risk in this population. Clear evidence now demonstrates that those recovering from COVID-19 have increased postoperative morbidity and mortality. These data—in conjunction with the recent American Society of Anesthesiologists guidelines—offer the evidence necessary to expand the early pandemic guidelines and guide the surgeon’s preoperative risk assessment. Here, we argue elective surgeries should still be delayed on a personalized basis to maximize postoperative outcomes. We outline a framework for stratifying the individual COVID-19 patient’s fitness for surgery based on the symptoms and severity of acute or convalescent COVID-19 illness, coagulopathy assessment, and acuity of the surgical procedure. Although the most common manifestation of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is COVID-19 pneumonitis, every system in the body is potentially afflicted by an endotheliitis. This endothelial derangement most often manifests as a hypercoagulable state on admission with associated occult and symptomatic venous and arterial thromboembolisms. The delicate balance between hyper and hypocoagulable states is defined by the local immune-thrombotic crosstalk that results commonly in a hemostatic derangement known as fibrinolytic shutdown. In tandem, the hemostatic derangements that occur during acute COVID-19 infection affect not only the timing of surgical procedures, but also the incidence of postoperative hemostatic complications related to COVID-19-associated coagulopathy (CAC). Traditional methods of thromboprophylaxis and treatment of thromboses after surgery require a tailored approach guided by an understanding of the pathophysiologic underpinnings of the COVID-19 patient. Likewise, a prolonged period of risk for developing hemostatic complications following hospitalization due to COVID-19 has resulted in guidelines from differing societies that recommend varying periods of delay following SARS-CoV-2 infection. In conclusion, we propose the perioperative, personalized assessment of COVID-19 patients’ CAC using viscoelastic hemostatic assays and fluorescent microclot analysis.

Membrane transport proteins are potential targets for medical and biotechnological applications. However, more than 30% of reported membrane transporter families are either poorly characterized or lack adequate functional annotation. Here, adaptive laboratory evolution was leveraged to identify membrane transporters for a set of four amino acids as well as specific mutations that modulate the activities of these transporters. Specifically, Escherichia coli was adaptively evolved under increasing concentrations of L-histidine, L-phenylalanine, L-threonine, and L-methionine separately with multiple replicate evolutions. Evolved populations and isolated clones displayed growth rates comparable to the unstressed ancestral strain at elevated concentrations (four-to six-fold increases) of the targeted amino acids. Whole genome sequencing of the evolved strains revealed a diverse number of key mutations, including SNPs, small deletions, and copy number variants targeting the transporters leuE for histidine, yddG for phenylalanine, yedA for methionine, and brnQ and rhtC for threonine. Reverse engineering of the mutations in the ancestral strain established mutation causality of the specific mutations for the tolerant phenotypes. The functional roles of yedA and brnQ in the transport of methionine and threonine, respectively, are novel assignments and their functional roles were validated using a flow cytometry cellular accumulation assay. To demonstrate how the identified transporters can be leveraged for production, an L-phenylalanine overproduction strain was shown to be a superior producer when the identified yddG exporter was overexpressed. Overall, the results revealed the striking efficiency of laboratory evolution to identify transporters and specific mutational mechanisms to modulate their activities, thereby demonstrating promising applicability in transporter discovery efforts and strain engineering.

Transporter discovery and engineering play an important role in cell factory development. Decreasing the intracellular concentration of the product reduces product inhibition and/or toxicity. Lowering intracellular concentrations is especially beneficial for achieving a robust strain at high titers. However, the identification of transporters for xenobiotic chemicals in the host strain is challenging. Here we present a high-throughput workflow to discover

Lipopolysaccharide (LPS) is the main structural component of the outer membrane of most Gram-negative bacteria and has diverse immunostimulatory and procoagulant effects. Even though LPS is well described for its role in the pathology of sepsis, considerable evidence demonstrates that LPS-induced signalling and immune dysregulation are also relevant in the pathophysiology of many diseases, characteristically where endotoxaemia is less severe. These diseases are typically chronic and progressive in nature and span broad classifications, including neurodegenerative, metabolic, and cardiovascular diseases. This Review reappraises the mechanisms of LPS-induced signalling and emphasises the crucial contribution of LPS to the pathology of multiple chronic diseases, beyond conventional sepsis. This perspective asserts that new ways of approaching chronic diseases by targeting LPS-driven pathways may be of therapeutic benefit in a wide range of chronic inflammatory conditions.

Post-acute sequelae of COVID (PASC), usually referred to as ‘Long COVID’ (a phenotype of COVID-19), is a relatively frequent consequence of SARS-CoV-2 infection, in which symptoms such as breathlessness, fatigue, ‘brain fog’, tissue damage, inflammation, and coagulopathies (dysfunctions of the blood coagulation system) persist long after the initial infection. It bears similarities to other post-viral syndromes, and to myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). Many regulatory health bodies still do not recognize this syndrome as a separate disease entity, and refer to it under the broad terminology of ‘COVID’, although its demographics are quite different from those of acute COVID-19. A few years ago, we discovered that fibrinogen in blood can clot into an anomalous ‘amyloid’ form of fibrin that (like other β-rich amyloids and prions) is relatively resistant to proteolysis (fibrinolysis). The result, as is strongly manifested in platelet-poor plasma (PPP) of individuals with Long COVID, is extensive fibrin amyloid microclots that can persist, can entrap other proteins, and that may lead to the production of various autoantibodies. These microclots are more-or-less easily measured in PPP with the stain thioflavin T and a simple fluorescence microscope. Although the symptoms of Long COVID are multifarious, we here argue that the ability of these fibrin amyloid microclots (fibrinaloids) to block up capillaries, and thus to limit the passage of red blood cells and hence O2 exchange, can actually underpin the majority of these symptoms. Consistent with this, in a preliminary report, it has been shown that suitable and closely monitored ‘triple’ anticoagulant therapy that leads to the removal of the microclots also removes the other symptoms. Fibrin amyloid microclots represent a novel and potentially important target for both the understanding and treatment of Long COVID and related disorders.

Antibiotic resistance is a major global healthcare issue. Antibiotic compounds cross the bacterial cell membrane via membrane transporters, and a major mechanism of antibiotic resistance is through modification of the membrane transporters to increase the efflux or reduce the influx of antibiotics. Targeting these transporters is a potential avenue to combat antibiotic resistance. In this study, we used an automated screening pipeline to evaluate the growth of a library of 447 Escherichia coli transporter knockout strains exposed to sub-inhibitory concentrations of 18 diverse antimicrobials. We found numerous knockout strains that showed more resistant or sensitive phenotypes to specific antimicrobials, suggestive of transport pathways. We highlight several specific drug-transporter interactions that we identified and provide the full dataset, which will be a useful resource in further research on antimicrobial transport pathways. Overall, we determined that transporters are involved in modulating the efficacy of almost all the antimicrobial compounds tested and can, thus, play a major role in the development of antimicrobial resistance.

Background Fibrin(ogen) amyloid microclots and platelet hyperactivation previously reported as a novel finding in South African patients with the coronavirus 2019 disease (COVID-19) and Long COVID/Post-Acute Sequelae of COVID-19 (PASC), might form a suitable set of foci for the clinical treatment of the symptoms of Long COVID/PASC. A Long COVID/PASC Registry was subsequently established as an online platform where patients can report Long COVID/PASC symptoms and previous comorbidities. Methods In this study, we report on the comorbidities and persistent symptoms, using data obtained from 845 South African Long COVID/PASC patients. By using a previously published scoring system for fibrin amyloid microclots and platelet pathology, we also analysed blood samples from 80 patients, and report the presence of significant fibrin amyloid microclots and platelet pathology in all cases. Results Hypertension, high cholesterol levels (dyslipidaemia), cardiovascular disease and type 2 diabetes mellitus (T2DM) were found to be the most important comorbidities. The gender balance (70% female) and the most commonly reported Long COVID/PASC symptoms (fatigue, brain fog, loss of concentration and forgetfulness, shortness of breath, as well as joint and muscle pains) were comparable to those reported elsewhere. These findings confirmed that our sample was not atypical. Microclot and platelet pathologies were associated with Long COVID/PASC symptoms that persisted after the recovery from acute COVID-19. Conclusions Fibrin amyloid microclots that block capillaries and inhibit the transport of O2 to tissues, accompanied by platelet hyperactivation, provide a ready explanation for the symptoms of Long COVID/PASC. Removal and reversal of these underlying endotheliopathies provide an important treatment option that urgently warrants controlled clinical studies to determine efficacy in patients with a diversity of comorbidities impacting on SARS-CoV-2 infection and COVID-19 severity. We suggest that our platelet and clotting grading system provides a simple and cost-effective diagnostic method for early detection of Long COVID/PASC as a major determinant of effective treatment, including those focusing on reducing clot burden and platelet hyperactivation.

Transporter discovery and engineering play an important role in cell factory development. Decreasing the intracellular concentration of the product reduces product inhibition and/or toxicity. Lowering intracellular concentrations is especially beneficial for achieving a robust strain at high titers. However, the identification of transporters for xenobiotic chemicals in the host strain is challenging. Here we present a high-throughput workflow to discover Escherichia coli transporters responsible for the efflux of the inhibitory xenobiotic compound melatonin. We took advantage of the Keio collection and screened about 400 transporter knockouts in the presence of a high concentration of melatonin. We found five transporters that when knocked out showed decreased tolerance to melatonin, indicating they are exporters of melatonin. We overexpressed these five genes individually in the production strain and found that one of them, yhjV, encoding a transporter with unknown substrates, resulted in a 27% titer increase in cultivation mimicking fed-batch fermentation. This study demonstrates how microbial cell factories can be improved through transporter identification and engineering. Further, these results lay the foundation for the scale-up of melatonin production in E. coli.

We recognise that fibrin(ogen) amyloid microclots and platelet hyperactivation, that we have previously observed in COVID-19 and Long COVID/Post-Acute Sequelae of COVID-19 (PASC) patients, might form a suitable set of foci for the clinical treatment of the symptoms of long COVID/PASC. We first report on the comorbidities and symptoms found in a cohort of 845 South African Long COVID/PASC patients who filled in the South African Long COVID/PASC registry, of which hypertension and high cholesterol levels (dyslipidaemia) were the most important comorbidities. The gender balance (70% female) and the most commonly reported Long COVID/PASC symptoms (fatigue, brain fog, loss of concentration and forgetfulness, shortness of breath, as well as joint and muscle pains) were comparable to those reported elsewhere. This suggests that our sample was not at all atypical. Using a previously published scoring system for fibrin amyloid microclots and platelet pathology, we analysed blood samples from 70 patients, and report the presence of significant fibrin amyloid microclots and platelet pathology in all cases; these were associated with Long COVID/PASC symptoms that persisted after the recovery from acute COVID-19. A subset of 24 patients was treated with one month of dual antiplatelet therapy (DAPT) (Clopidogrel 75mg/Aspirin 75mg) once a day, as well as a direct oral anticoagulant (DOAC) (Apixiban) 5 mg twice a day. A proton pump inhibitor (PPI) pantoprazole 40 mg/day was also prescribed for gastric protection. Such a regime must only be followed under strict and qualified medical guidance to obviate any dangers, especially haemorrhagic bleeding, and of the therapy as a whole. Thromboelastography (TEG®) was used to assist in determining their clotting status. Each of the 24 treated cases reported that their main symptoms were resolved and fatigue as the main symptom was relieved, and this was also reflected in a decrease of both the fibrin amyloid microclots and platelet pathology scores. Nine patients were genotyped for genetic variation in homocysteine metabolism implicated in hypertension, a common COVID-19 co-morbidity reported in both patients found to be homozygous for the risk-associated MTHFR 677 T-allele. Fibrin amyloid microclots that block capillaries and inhibit the transport of O2 to tissues, accompanied by platelet hyperactivation, provide a ready explanation for the symptoms of Long COVID/PASC. The removal and reversal of these underlying endotheliopathies provide an important treatment option that seems to be highly efficacious, and warrants controlled clinical studies.

Introduction The diagnosis of COVID-19 is normally based on the qualitative detection of viral nucleic acid sequences. Properties of the host response are not measured but are key in determining outcome. Although metabolic profiles are well suited to capture host state, most metabolomics studies are either underpowered, measure only a restricted subset of metabolites, compare infected individuals against uninfected control cohorts that are not suitably matched, or do not provide a compact predictive model. Objectives Here we provide a well-powered, untargeted metabolomics assessment of 120 COVID-19 patient samples acquired at hospital admission. The study aims to predict the patient’s infection severity (i.e., mild or severe) and potential outcome (i.e., discharged or deceased). Methods High resolution untargeted UHPLC-MS/MS analysis was performed on patient serum using both positive and negative ionization modes. A subset of 20 intermediary metabolites predictive of severity or outcome were selected based on univariate statistical significance and a multiple predictor Bayesian logistic regression model was created. Results The predictors were selected for their relevant biological function and include deoxycytidine and ureidopropionate (indirectly reflecting viral load), kynurenine (reflecting host inflammatory response), and multiple short chain acylcarnitines (energy metabolism) among others. Currently, this approach predicts outcome and severity with a Monte Carlo cross validated area under the ROC curve of 0.792 (SD 0.09) and 0.793 (SD 0.08), respectively. A blind validation study on an additional 90 patients predicted outcome and severity at ROC AUC of 0.83 (CI 0.74–0.91) and 0.76 (CI 0.67–0.86). Conclusion Prognostic tests based on the markers discussed in this paper could allow improvement in the planning of COVID-19 patient treatment.

In this study, we used stimulated Raman spectroscopy (SRS) microscopy to collect Raman signatures from live Saccharomyces cerevisiae cells in the spectral range 2804−3060 cm−1 and 830−2000 cm−1 with a spectral resolution of 8 cm−1. To effect this, we tuned the pump beam to several distinct wavelengths and thus acquired a series of chemical maps in order to reconstruct SRS spectra based on the intensity of the pixels, an approach also referred as hyperspectral SRS (hsSRS). One of the advantages of hsSRS over spontaneous Raman is that it is not overtly plagued by fluorescence and so fluorescent samples like yeast can be analysed. We show however that Raman signatures acquired by this approach may be subject to spectral artefacts that manifest as drops in intensity of Raman signal due to the movement of lipid droplets (LDs) within the yeast cells. To overcome this issue, yeast cells were chemically fixed with 4% formaldehyde and no artefacts were observed in the Raman signatures acquired from ‘stationary’ samples. Our findings indicate that caution must be applied when analysing SRS signatures obtained through hsSRS from mobile LDs and/or any other moving target within a system, whether biological or not.

The ‘inverse problem’ of mass spectrometric molecular identification (‘given a mass spectrum, calculate/predict the 2D structure of the molecule whence it came’) is largely unsolved, and is especially acute in metabolomics where many small molecules remain unidentified. This is largely because the number of experimentally available electrospray mass spectra of small molecules is quite limited. However, the forward problem (‘calculate a small molecule’s likely fragmentation and hence at least some of its mass spectrum from its structure alone’) is much more tractable, because the strengths of different chemical bonds are roughly known. This kind of molecular identification problem may be cast as a language translation problem in which the source language is a list of high-resolution mass spectral peaks and the ‘translation’ a representation (for instance in SMILES) of the molecule. It is thus suitable for attack using the deep neural networks known as transformers. We here present MassGenie, a method that uses a transformer-based deep neural network, trained on ~6 million chemical structures with augmented SMILES encoding and their paired molecular fragments as generated in silico, explicitly including the protonated molecular ion. This architecture (containing some 400 million elements) is used to predict the structure of a molecule from the various fragments that may be expected to be observed when some of its bonds are broken. Despite being given essentially no detailed nor explicit rules about molecular fragmentation methods, isotope patterns, rearrangements, neutral losses, and the like, MassGenie learns the effective properties of the mass spectral fragment and valency space, and can generate candidate molecular structures that are very close or identical to those of the ‘true’ molecules. We also use VAE-Sim, a previously published variational autoencoder, to generate candidate molecules that are ‘similar’ to the top hit. In addition to using the ‘top hits’ directly, we can produce a rank order of these by ‘round-tripping’ candidate molecules and comparing them with the true molecules, where known. As a proof of principle, we confine ourselves to positive electrospray mass spectra from molecules with a molecular mass of 500Da or lower, including those in the last CASMI challenge (for which the results are known), getting 49/93 (53%) precisely correct. The transformer method, applied here for the first time to mass spectral interpretation, works extremely effectively both for mass spectra generated in silico and on experimentally obtained mass spectra from pure compounds. It seems to act as a Las Vegas algorithm, in that it either gives the correct answer or simply states that it cannot find one. The ability to create and to ‘learn’ millions of fragmentation patterns in silico, and therefrom generate candidate structures (that do not have to be in existing libraries) directly, thus opens up entirely the field of de novo small molecule structure prediction from experimental mass spectra.

An important component of severe COVID-19 disease is virus-induced endothelilitis. This leads to disruption of normal endothelial function, initiating a state of failing normal clotting physiology. Massively increased levels of von Willebrand Factor (VWF) lead to overwhelming platelet activation, as well as activation of the enzymatic (intrinsic) clotting pathway. In addition, there is an impaired fibrinolysis, caused by, amongst others, increased levels of alpha-(2) antiplasmin. The end result is hypercoagulation (proven by thromboelastography® (TEG®)) and reduced fibrinolysis, inevitably leading to a difficult-to-overcome hypercoagulated physiological state. Platelets in circulation also plays a significant role in clot formation, but they themselves may also drive hypercoagulation when they are overactivated due to the interactions of their receptors with the endothelium, immune cells or circulating inflammatory molecules. From the literature it is clear that the role of platelets in severely ill COVID-19 patients has been markedly underestimated or even ignored. We here highlight the value of early management of severe COVID-19 coagulopathy as guided by TEG®, microclot and platelet mapping. We also argue that the failure of clinical trials, where the efficacy of prophylactic versus therapeutic clexane (low molecular weight heparin (LMWH)) were not always successful, which may be because the significant role of platelet activation was not taken into account during the planning of the trial. We conclude that, because of the overwhelming alteration of clotting, the outcome of any trial evaluating an any single anticoagulant, including thrombolytic, would be negative. Here we suggest the use of the degree of platelet dysfunction and presence of microclots in circulation, together with TEG®, might be used as a guideline for disease severity. A multi-pronged approach, guided by TEG® and platelet mapping, would be required to maintain normal clotting physiology in severe COVID-19 disease.

Over the years, my colleagues and I have come to realise that the likelihood of pharmaceutical drugs being able to diffuse through whatever unhindered phospholipid bilayer may exist in intact biological membranes in vivo is vanishingly low. This is because (i) most real biomembranes are mostly protein, not lipid, (ii) unlike purely lipid bilayers that can form transient aqueous channels, the high concentrations of proteins serve to stop such activity, (iii) natural evolution long ago selected against transport methods that just let any undesirable products enter a cell, (iv) transporters have now been identified for all kinds of molecules (even water) that were once thought not to require them, (v) many experiments show a massive variation in the uptake of drugs between different cells, tissues, and organisms, that cannot be explained if lipid bilayer transport is significant or if efflux were the only differentiator, and (vi) many experiments that manipulate the expression level of individual transporters as an independent variable demonstrate their role in drug and nutrient uptake (including in cytotoxicity or adverse drug reactions). This makes such transporters valuable both as a means of targeting drugs (not least anti-infectives) to selected cells or tissues and also as drug targets. The same considerations apply to the exploitation of substrate uptake and product efflux transporters in biotechnology. We are also beginning to recognise that transporters are more promiscuous, and antiporter activity is much more widespread, than had been realised, and that such processes are adaptive (i.e., were selected by natural evolution). The purpose of the present review is to summarise the above, and to rehearse and update readers on recent developments. These developments lead us to retain and indeed to strengthen our contention that for transmembrane pharmaceutical drug transport “phospholipid bilayer transport is negligible”.

Dysregulation of the kynurenine pathway (KP) leads to imbalances in neuroactive metabolites associated with the pathogenesis of several neurodegenerative disorders, including Huntington’s disease (HD). Inhibition of the enzyme kynurenine 3-monooxygenase (KMO) in the KP normalises these metabolic imbalances and ameliorates neurodegeneration and related phenotypes in several neurodegenerative disease models. KMO is thus a promising candidate drug target for these disorders, but known inhibitors are not brain permeable. Here, 19 new KMO inhibitors have been identified. One of these (1) is neuroprotective in a Drosophila HD model but is minimally brain penetrant in mice. The prodrug variant (1b) crosses the blood–brain barrier, releases 1 in the brain, thereby lowering levels of 3-hydroxykynurenine, a toxic KP metabolite linked to neurodegeneration. Prodrug 1b will advance development of targeted therapies against multiple neurodegenerative and neuroinflammatory diseases in which KP likely plays a role, including HD, Alzheimer’s disease, and Parkinson’s disease., Kynurenine 3-monooxygenase (KMO) regulates levels of neuroactive kynurenine pathway metabolites associated with neurodegeneration. Its inhibition ameliorates disease phenotypes in animal models but current inhibitors are not brain permeable. Through structure-based virtual screening and compound synthesis, Zhang et al. now develop KMO inhibitors that can enter the rodent brain to modulate metabolism in this pathway.

Directed evolution requires the creation of genetic diversity and subsequent screening or selection for improved variants. For DNA mutagenesis, conventional site-directed methods implicitly utilize the Boolean AND operator (creating all mutations simultaneously), producing a combinatorial explosion in the number of genetic variants as the number of mutations increases. We introduce GeneORator, a novel strategy for creating DNA libraries based on the Boolean logical OR operator. Here, a single library is divided into many subsets, each containing different combinations of the desired mutations. Consequently, the effect of adding more mutations on the number of genetic combinations is additive (Boolean OR logic) and not exponential (AND logic). We demonstrate this strategy with large-scale mutagenesis studies, using monoamine oxidase-N ( Aspergillus niger) as the exemplar target. First, we mutated every residue in the secondary structure-containing regions (276 out of a total 495 amino acids) to screen for improvements in kcat. Second, combinatorial OR-type libraries permitted screening of diverse mutation combinations in the enzyme active site to detect activity toward novel substrates. In both examples, OR-type libraries effectively reduced the number of variants searched up to 1010-fold, dramatically reducing the screening effort required to discover variants with improved and/or novel activity. Importantly, this approach enables the screening of a greater diversity of mutation combinations, accessing a larger area of a protein's sequence space. OR-type libraries can be applied to any biological engineering objective requiring DNA mutagenesis, and the approach has wide ranging applications in, for example, enzyme engineering, antibody engineering, and synthetic biology.

Severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2)-induced infection, the cause of coronavirus disease 2019 (COVID-19), is characterized by unprecedented clinical pathologies. One of the most important pathologies, is hypercoagulation and microclots in the lungs of patients. Here we study the effect of isolated SARS-CoV-2 spike protein S1 subunit as potential inflammagen sui generis. Using scanning electron and fluorescence microscopy as well as mass spectrometry, we investigate the potential of this inflammagen to interact with platelets and fibrin(ogen) directly to cause blood hypercoagulation. Using platelet-poor plasma (PPP), we show that spike protein may interfere with blood flow. Mass spectrometry also showed that when spike protein S1 is added to healthy PPP, it results in structural changes to β and γ fibrin(ogen), complement 3, and prothrombin. These proteins were substantially resistant to trypsinization, in the presence of spike protein S1. Here we suggest that, in part, the presence of spike protein in circulation may contribute to the hypercoagulation in COVID-19 positive patients and may cause substantial impairment of fibrinolysis. Such lytic impairment may result in the persistent large microclots we have noted here and previously in plasma samples of COVID-19 patients. This observation may have important clinical relevance in the treatment of hypercoagulability in COVID-19 patients.

Background Severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2)-induced infection, the cause of coronavirus disease 2019 (COVID-19), is characterized by acute clinical pathologies, including various coagulopathies that may be accompanied by hypercoagulation and platelet hyperactivation. Recently, a new COVID-19 phenotype has been noted in patients after they have ostensibly recovered from acute COVID-19 symptoms. This new syndrome is commonly termed Long COVID/Post-Acute Sequelae of COVID-19 (PASC). Here we refer to it as Long COVID/PASC. Lingering symptoms persist for as much as 6 months (or longer) after acute infection, where COVID-19 survivors complain of recurring fatigue or muscle weakness, being out of breath, sleep difficulties, and anxiety or depression. Given that blood clots can block microcapillaries and thereby inhibit oxygen exchange, we here investigate if the lingering symptoms that individuals with Long COVID/PASC manifest might be due to the presence of persistent circulating plasma microclots that are resistant to fibrinolysis. Methods We use techniques including proteomics and fluorescence microscopy to study plasma samples from healthy individuals, individuals with Type 2 Diabetes Mellitus (T2DM), with acute COVID-19, and those with Long COVID/PASC symptoms. Results We show that plasma samples from Long COVID/PASC still contain large anomalous (amyloid) deposits (microclots). We also show that these microclots in both acute COVID-19 and Long COVID/PASC plasma samples are resistant to fibrinolysis (compared to plasma from controls and T2DM), even after trypsinisation. After a second trypsinization, the persistent pellet deposits (microclots) were solubilized. We detected various inflammatory molecules that are substantially increased in both the supernatant and trapped in the solubilized pellet deposits of acute COVID-19 and Long COVID/PASC, versus the equivalent volume of fully digested fluid of the control samples and T2DM. Of particular interest was a substantial increase in α(2)-antiplasmin (α2AP), various fibrinogen chains, as well as Serum Amyloid A (SAA) that were trapped in the solubilized fibrinolytic-resistant pellet deposits. Conclusions Clotting pathologies in both acute COVID-19 infection and in Long COVID/PASC might benefit from following a regime of continued anticlotting therapy to support the fibrinolytic system function.

Ergothioneine (ERG) is an unusual sulfur-containing amino acid. It is a potent antioxidant, which shows great potential for ameliorating neurodegenerative and cardiovascular diseases. L-ergothioneine is rare in nature, with mushrooms being the primary dietary source. The chemical synthesis process is complex and expensive. Alternatively, ERG can be produced by fermentation of recombinant microorganisms engineered for ERG overproduction. Here, we describe the engineering of S. cerevisiae for high-level ergothioneine production on minimal medium with glucose as the only carbon source. To this end, metabolic engineering targets in different layers of the amino acid metabolism were selected based on literature and tested. Out of 28 targets, nine were found to improve ERG production significantly by 10%–51%. These targets were then sequentially implemented to generate an ergothioneine-overproducing yeast strain capable of producing 106.2 ± 2.6 mg/L ERG in small-scale cultivations. Transporter engineering identified that the native Aqr1 transporter was capable of increasing the ERG production in a yeast strain with two copies of the ERG biosynthesis pathway, but not in the strain that was further engineered for improved precursor supply. Medium optimization indicated that additional supplementation of pantothenate improved the strain's productivity further and that no supplementation of amino acid precursors was necessary. Finally, the engineered strain produced 2.39 ± 0.08 g/L ERG in 160 h (productivity of 14.95 ± 0.49 mg/L/h) in a controlled fed-batch fermentation without supplementation of amino acids. This study paves the way for the low-cost fermentation-based production of ergothioneine.

Pyrithione (2-mercaptopyridine-N-oxide) is a metal binding modified pyridine, the antibacterial activity of which was described over 60 years ago. The formulation of zinc-pyrithione is commonly used in the topical treatment of certain dermatological conditions. However, the characterisation of the cellular uptake of pyrithione has not been elucidated, although an unsubstantiated assumption has persisted that pyrithione and/or its metal complexes undergo a passive diffusion through cell membranes. Here, we have profiled specific membrane transporters from an unbiased interrogation of 532 E. coli strains of knockouts of genes encoding membrane proteins from the Keio collection. Two membrane transporters, FepC and MetQ, seemed involved in the uptake of pyrithione and its cognate metal complexes with copper, iron, and zinc. Additionally, the phenotypes displayed by CopA and ZntA knockouts suggested that these two metal effluxers drive the extrusion from the bacterial cell of potentially toxic levels of copper, and perhaps zinc, which hyperaccumulate as a function of pyrithione. The involvement of these distinct membrane transporters contributes to the understanding of the mechanisms of action of pyrithione specifically and highlights, more generally, the important role that membrane transporters play in facilitating the uptake of drugs, including metal–drug compounds.

BackgroundSevere acute respiratory syndrome coronavirus 2 (SARS-Cov-2)-induced infection, the cause of coronavirus disease 2019 (COVID-19), is characterized by acute clinical pathologies, including various coagulopathies that may be accompanied by hypercoagulation and platelet hyperactivation. Recently, a new COVID-19 phenotype has been noted in patients after they have ostensibly recovered from acute COVID-19 symptoms. This new syndrome is commonly termed Long COVID/Post-Acute Sequelae of COVID-19 (PASC). Here we refer to it as Long COVID/PASC. Lingering symptoms persist for as much as 6 months (or longer) after acute infection, where COVID-19 survivors complain of recurring fatigue or muscle weakness, being out of breath, sleep difficulties, and anxiety or depression. MethodsGiven that blood clots can block microcapillaries and thereby inhibit oxygen exchange, we here investigate if the lingering symptoms that individuals with Long COVID/PASC manifest might be due to the presence of persistent circulating plasma clots that are resistant to fibrinolysis. We use techniques including proteomics and fluorescence microscopy to study plasma samples from healthy individuals, individuals with Type 2 Diabetes Mellitus (T2DM), with acute COVID-19, and those with Long COVID/PASC symptoms. ResultsWe show that plasma samples from Long COVID/PASC still contain large anomalous (amyloid) deposits (microclots). We also show that these microclots in both acute COVID-19 and Long COVID/PASC plasma samples are resistant to fibrinolysis (compared to plasma from controls and T2DM), even after trypsinisation. After a second trypsinization, the persistent pellet deposits were solubilized. We detected various inflammatory molecules that are substantially increased in both the supernatant and trapped in the solubilized pellet deposits of acute COVID-19 and Long COVID/PASC, versus the equivalent volume of fully digested fluid of the control samples and T2DM. Of particular interest was a substantial increase in α(2)-antiplasmin (α2AP), various fibrinogen chains, as well as Serum Amyloid A (SAA) that were trapped in the solubilized fibrinolytic-resistant pellet deposits. ConclusionsClotting pathologies in both acute COVID-19 infection and in Long COVID/PASC might therefore benefit from following a regime of continued anticlotting therapy to support the fibrinolytic system function.

The ‘inverse problem’ of mass spectrometric molecular identification (‘given a mass spectrum, calculate the molecule whence it came’) is largely unsolved, and is especially acute in metabolomics where many small molecules remain unidentified. This is largely because the number of experimentally available electrospray mass spectra of small molecules is quite limited. However, the forward problem (‘calculate a small molecule’s likely fragmentation and hence at least some of its mass spectrum from its structure alone’) is much more tractable, because the strengths of different chemical bonds are roughly known. This kind of molecular identification problem may be cast as a language translation problem in which the source language is a list of high-resolution mass spectral peaks and the ‘translation’ a representation (for instance in SMILES) of the molecule. It is thus suitable for attack using the deep neural networks known as transformers. We here present MassGenie, a method that uses a transformer-based deep neural network, trained on ~6 million chemical structures with augmented SMILES encoding and their paired molecular fragments as generatedin silico, explicitly including the protonated molecular ion. This architecture (containing some 400 million elements) is used to predict the structure of a molecule from the various fragments that may be expected to be observed when some of its bonds are broken. Despite being given essentially no detailed nor explicit rules about molecular fragmentation methods, isotope patterns, rearrangements, neutral losses, and the like, MassGenie learns the effective properties of the mass spectral fragment and valency space, and can generate candidate molecular structures that are very close or identical to those of the ‘true’ molecules. We also use VAE-Sim, a previously published variational autoencoder, to generate candidate molecules that are ‘similar’ to the top hit. In addition to using the ‘top hits’ directly, we can produce a rank order of these by ‘round-tripping’ candidate molecules and comparing them with the true molecules, where known. As a proof of principle, we confine ourselves to positive electrospray mass spectra from molecules with a molecular mass of 500Da or lower. The transformer method, applied here for the first time to mass spectral interpretation, works extremely effectively both for mass spectra generatedin silicoand on experimentally obtained mass spectra from pure compounds. The ability to create and to ‘learn’ millions of fragmentation patternsin silico, and therefrom generate candidate structures (that do not have to be in existing libraries)directly, thus opens up entirely the field ofde novosmall molecule structure prediction from experimental mass spectra.

Severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2)-induced infection, the cause of coronavirus disease 2019 (COVID-19), is characterized by acute clinical pathologies, including various coagulopathies that may be accompanied by hypercoagulation and platelet hyperactivation. Recently, a new COVID-19 phenotype has been noted in patients after they have ostensibly recovered from acute COVID-19 symptoms. This new syndrome is commonly termed Long COVID/Post-Acute Sequelae of COVID-19 (PASC). Here we refer to it as Long COVID/PASC. Lingering symptoms persist for as much as 6 months (or longer) after acute infection, where COVID-19 survivors complain of recurring fatigue or muscle weakness, being out of breath, sleep difficulties, and anxiety or depression. Given that blood clots can block microcapillaries and thereby inhibit oxygen exchange, we here investigate if the lingering symptoms that individuals with Long COVID/PASC manifest might be due to the presence of persistent circulating plasma clots that are resistant to fibrinolysis. We use techniques including proteomics and fluorescence microscopy to study plasma samples from healthy individuals, individuals with Type 2 Diabetes Mellitus (T2DM), with acute COVID-19, and those with Long COVID/PASC symptoms. We show that plasma samples from Long COVID/PASC still contain large anomalous (amyloid) deposits. We also show that these anomalous deposits in both acute COVID-19 and Long COVID/PASC plasma samples are resistant to fibrinolysis (compared to plasma from controls and T2DM), even after trypsinisation. After a second trypsinization, the persistent pellet deposits were solubilized. We detected various inflammatory molecules that are substantially increased in both the supernatant and trapped in the solubilized pellet deposits of acute COVID-19 and Long COVID/PASC, versus the equivalent volume of fully digested fluid of the control samples. Of particular interest was a substantial increase in α(2)-antiplasmin (α2AP), various fibrinogen chains, as well as Serum Amyloid A (SAA) that were trapped in the solubilized fibrinolytic-resistant pellet deposits. Clotting pathologies in both acute COVID-19 infection and in Long COVID/PASC might therefore benefit from following a regime of continued anticlotting therapy to support the fibrinolytic system function.

The question of molecular similarity is core in cheminformatics and is usually assessed via a pairwise comparison based on vectors of properties or molecular fingerprints. We recently exploited variational autoencoders to embed 6M molecules in a chemical space, such that their (Euclidean) distance within the latent space so formed could be assessed within the framework of the entire molecular set. However, the standard objective function used did not seek to manipulate the latent space so as to cluster the molecules based on any perceived similarity. Using a set of some 160,000 molecules of biological relevance, we here bring together three modern elements of deep learning to create a novel and disentangled latent space, viz transformers, contrastive learning, and an embedded autoencoder. The effective dimensionality of the latent space was varied such that clear separation of individual types of molecules could be observed within individual dimensions of the latent space. The capacity of the network was such that many dimensions were not populated at all. As before, we assessed the utility of the representation by comparing clozapine with its near neighbors, and we also did the same for various antibiotics related to flucloxacillin. Transformers, especially when as here coupled with contrastive learning, effectively provide one-shot learning and lead to a successful and disentangled representation of molecular latent spaces that at once uses the entire training set in their construction while allowing “similar” molecules to cluster together in an effective and interpretable way.

Severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2)-induced infection, the cause of coronavirus disease 2019 (COVID-19), is characterized by unprecedented clinical pathologies. One of the most important pathologies, is hypercoagulation and microclots in the lungs of patients. Here we study the effect of isolated SARS-CoV-2 spike protein S1 subunit as potential inflammagensui generis. Using scanning electron and fluorescence microscopy as well as mass spectrometry, we investigate the potential of this inflammagen to interact with platelets and fibrin(ogen) directly to cause blood hypercoagulation. Using platelet poor plasma (PPP), we show that spike protein may interfere with blood flow. Mass spectrometry also showed that when spike protein S1 is added to healthy PPP, it results in structural changes to β and γ fibrin(ogen), complement 3, and prothrombin. These proteins were substantially resistant to trypsinization, in the presence of spike protein S1. Here we suggest that, in part, the presence of spike protein in circulation may contribute to the hypercoagulation in COVID-19 positive patients and may cause substantial impairment of fibrinolysis. Such lytic impairment may result in the persistent large microclots we have noted here and previously in plasma samples of COVID-19 patients. This observation may have important clinical relevance in the treatment of hypercoagulability in COVID-19 patients.

Our previous work demonstrated that two commonly used fluorescent dyes that were accumulated by wild-type Escherichia coli MG1655 were differentially transported in single-gene knockout strains, and also that they might be used as surrogates in flow cytometric transporter assays. We summarize the desirable properties of such stains, and here survey 143 candidate dyes. We eventually triage them (on the basis of signal, accumulation levels and cost) to a palette of 39 commercially available and affordable fluorophores that are accumulated significantly by wild-type cells of the ‘Keio’ strain BW25113, as measured flow cytometrically. Cheminformatic analyses indicate both their similarities and their (much more considerable) structural differences. We describe the effects of pH and of the efflux pump inhibitor chlorpromazine on the accumulation of the dyes. Even the ‘wild-type’ MG1655 and BW25113 strains can differ significantly in their ability to take up such dyes. We illustrate the highly differential uptake of our dyes into strains with particular lesions in, or overexpressed levels of, three particular transporters or transporter components (yhjV, yihN and tolC). The relatively small collection of dyes described offers a rapid, inexpensive, convenient and informative approach to the assessment of microbial physiology and phenotyping of membrane transporter function.