Here we have used next-generation sequencing and bioinformatics to profile circulating miRNAs in 10 COVID patients that were sampled longitudinally over time. An artificial intelligence methodology was used to identify a miRNA signature, consisting of miRp, miRa-3p, miRp, which could independently classify COVID patients from healthy controls with In summary this study profiles the host miRNA response to COVID and suggests that the measurement of select host molecules may have potential to independently detect disease cases.
Several studies have established that host responses to infection play a critical role in determining disease outcomes in infected patients. As there are currently no approved curative treatments for COVID, the characterisation of host factors associated with SARS-CoV-2 pathogenesis is critically important for the design of novel therapies.
The scientific rationale for investigating miRNAs during viral infections is two-fold. Firstly, miRNA profiles offer unique insight into cellular pathways associated with virus replication and pathogenesis.
There is also evidence that coronaviruses co-opt the host miRNAs response to subvert antiviral immune responses. Secondly, the characterisation of host miRNAs responses to virus infection informs the development of biomarkers for improved disease detection and forecasting of disease outcome [ 12 ].
In this study we have investigated the circulating miRNA profiles in the plasma of ten COVID patients and ten age and gender matched healthy donors. We observed that among patient samples collected during early-stage disease, COVID induced differential expression of 55 host-encoded microRNAs, with miR, and strongly up-regulated and miR, and b down-regulated.
As patients recovered from disease, the three-miRNA plasma signature returned to that of the healthy controls. Longitudinal samples were available for some COVID patients, categorized by visit V , with V1 representing the plasma sample first taken following hospital admission Table 2.
Plasma samples were first obtained from COVID patients 2—15 days average 8 days post symptomatic disease onset. A total of different mature miRNA transcripts were detected, corresponding to different precursors 5p and 3p miRNAs were counted separately.
We did not observe a significant difference in the total number of miRNAs identified in infected versus uninfected patients. This dataset consisted of 50 miRNAs, of which 20 were up-regulated elevated in infected patients and 30 were down-regulated Fig 1A and S2 Table.
Other patient cytokine data is shown in S3 Table. The most up-regulated, down-regulated, and statistically significant miRNAs have been labelled. Boxes are the 25 th - 75 th percentile, line is the median, and whiskers are 1.
Technologies most commonly utilized for COVID diagnosis are virus-targeting molecular assays or serology, both of which can be associated with relatively high false-positive rates [ 22 , 23 ]. A supervised machine learning method was implemented for the identification of the most predictive miRNAs and refined to identify the minimum targets necessary for accurate prediction and classification between healthy control and COVID V1 samples.
A logistic regression model was implemented that randomly split the data into discovery and validation sets, trained and tested the model, which was repeated 1, times to determine reproducibility. Increasing candidates within the biomarker signature to more than three miRNAs did not improve test performance.
A, Feature miRNA selection lineplot showing the impact of increasing numbers of miRNAs on the performance of a logistic regression model. Each combination of miRNAs was randomly assessed 1, times. C, Decision boundary graph showing the logistic regression decision point solid black line and the probability a person is infected with SARS-CoV-2 blue to red shading.
A decision boundary graph showed clear distinctions between healthy and infected patients based on these three miRNAs Fig 2C. The absence of points close to the boundary supports the high predictive accuracy of this miRNA signature. Interestingly, samples taken at successive timepoints V2, V3 and V4 cluster with the healthy controls, indicating a return to normal baseline and suggesting that the three-miRNA signature is associated with the early stages of COVID Fig 2D.
To further support the hypothesis that this signature detects early symptomatic COVID, we tested the model on the later time points V2,3 and 4 saw the accuracy reduced to In this analysis the need for infected patients to receive supplemental oxygen O 2 or intubation was used as a proxy marker for severe disease, a metric previously used to categorise COVID severity [ 4 , 5 ].
Interestingly, four miRNAs let-7e-5p, miRp, miRp, and miRb-5p were differentially expressed in both groups, suggesting that these molecules might be potential candidates for stratifying patients based on severity. It is important to note the small number of samples involved in this analysis and that this finding requires validation in larger patient cohorts. Finally, we investigated whether the biomarker of early-stage COVID was robust in an animal model and could distinguish between different viral respiratory infections.
To address this, infection studies were performed in domestic ferrets Mustla putorius furo , a well-established model for human respiratory viruses, including SARS-CoV-2 and influenza virus[ 24 ]. Twenty adult ferrets were exposed to SARS-CoV-2 via the intranasal route and monitored for clinical signs, with four ferrets euthanized at 3, 5, 7, 9, and 14 days post-exposure d. High viral load was detected in nasal wash samples from day 3, which declined over time and was negative in all ferrets by 14 d.
Eleven ferrets were infected with influenza A H1N1 virus via the intranasal route, with animals euthanized at days 1, 2, 3, 5, 6 and 7 d. Influenza virology data in tissue and swab samples is shown in S4 Fig.
Viral load was detected in nasal wash samples from 1 to 7 d. Data is presented as log10 copies per g of tissue or ml of sample. C, Decision boundary graph showing the logistic regression decision point solid black line and the probability a sample is infected with SARS-CoV-2 blue to red shading. Sera from 12 uninfected ferrets were included as controls. In the ferret model, the previously identified biomarker signature miRp, miRa-3p and miRp could independently distinguish uninfected ferrets from COVID infected ferrets with As with the human plasma samples, the decision boundary graph displayed high confidence in the predicted groupings Fig 3C.
The decision boundary graph comparing predicted grouping and true grouping is shown in Fig 3E. Ferret infection trials showed that this signature response was robust across species and was still valid during timepoints where SARS-CoV-2 replication was observed in internal organs but not in nasal wash samples.
We hypothesize that a biomarker consisting of multiple miRNAs is more robust than one based on absolute or relative levels of a single miRNA. This multivariate approach, coupled with advanced machine learning analysis, can highlight a biomarker pattern that may not be identified via traditional DE analysis.
While miRp, miRa-3p and miRp measured in combination have not been defined previously as a biomarker for a specific disease, increased expression of circulating miRp is observed during heart failure [ 25 ] and pulmonary tuberculosis [ 26 ].
Increases in circulating miRp are associated with osteosarcoma [ 27 ], autism [ 28 ] and gestational diabetes mellitus [ 29 ]. Interestingly, increased plasma expression of miRp is also observed during HIV-1 infection, with miRp forming part of a four-miRNA signature that can identify HIV-1 infection with high confidence [ 30 ].
These findings raise the possibility that miRp is induced in response to acute stressors such as SARS-CoV-2 in order to curtail an excessive inflammatory response. Unfortunately, even the most advanced current molecular diagnostic tests i. Thus, their sensitivity during the early pre-symptomatic phase of disease incubation period , when the viral load is still low, is poor.
Further studies involving larger patient groups, including pre-symptomatic, asymptomatic and mild non-hospitalised patients, in addition to different infections, are planned to assess whether this miRNA biomarker can improve COVID detection rates.
These findings, in addition to data from ferret infection trials in the present study, indicate that miRNA profiling may be able to classify different infection types. It also presents novel opportunities for treatment and diagnosis of viral diseases. Formal consent was not obtained from patients due to anonymity. All animals were acclimatized for at least 7 days prior to entering the study, given food and water ad libitum, and monitored daily. Environmental enrichment was also provided in the cages during the study.
Plasma samples were collected from patients admitted to the Alfred Hospital Melbourne, Australia from February to April Plasma samples were also collected from patients defined as healthy controls i.
Patient metadata is shown in S1 Table. The ethnicity of the cohort was not reported. Twenty outbred ferrets 10 male and 10 female, approximately four months of age were exposed to 4. After virus exposure, animals were monitored for clinical signs of disease, and fever. They were randomly assigned to euthanasia on post-exposure days 3, 5, 7, 9 or 14, when clinical samples including nasal washes, serum and urine were collected together with multiple tissue specimens. After confirmation of death, a necropsy was performed and a panel of swab and tissues including oral swab, nasal swab, turbinate and lymph node collected without fixation for virological assessment.
All libraries were analysed on the Bioanalyser using the High Sensitivity DNA Kit Agilent to ensure correct insert size and minimal adapter or primer carryover. Adapters were trimmed using [ 39 ] with a read length parameter 18—26 nucleotides. The remaining reads were examined using FastQC www. Raw read counts were normalized and differential expression analysis was completed using the DESeq2 [ 42 ] package in R. All machine learning analysis was conducted using the scikit-learn [ 43 ] module in python.
Highly correlated features miRNAs can impact the performance of machine learning algorithms. Multicollinearity can cause skewed or misleading results, especially in models such as logistic regression.
The remaining normalized miRNA counts were scaled using either a standard z-score transformation or a robust scaler where the median is removed and the data is scaled according to the interquartile range. Feature selection was performed using recursive feature elimination RFE to identify the miRNAs that contributed the most to the classification model.
For binary classification, a logistic regression model was used. For multiclass classification, a linear support vector classifier was used. Each model underwent hyperparameter tuning using GridSearchCV. This process was repeated 1, times to ensure confidence in the classification performance. The machine learning models were assessed on their accuracy how many of the predictions were correct , precision how many of the predicted positives were true positives , and recall how many of the true positives were found by the model.
The logistic regression model was also assessed using the receiver operating characteristic area under the curve ROC AUC , which is a succinct metric to describe a binary classification model [ 12 ]. Cycle threshold for all assays was set to 0. Statistical analyses were completed using the SciPy v1.
All measurements were taken from distinct samples. Differences in qRT-PCR results were examined using a one-sided Mann-Whitney U test due to the non-parametric nature of the fold-over-detectable transformation. Normality was tested using a combination skew and kurtosis test scipy. Differences in IL-6 expression was assessed using a one-sided t-test. All statistics and p-values can be found in S6 Table. After adaptor trimming, reads that fell outside the expected size range for miRNAs 18—26 nt were filtered out, as were reads that failed to map to a miRNA precursor.
Decision boundary graph showing the logistic regression decision point solid black line and the probability a person is infected with SARS-CoV-2 blue to red shading.
Detection of influenza H1N1 genomic RNA in lung tissue blue , nasal swab orange , nasal wash green and serum red of infected ferrets 2—4 ferrets per time point. Our readers who had never heard um with the Robin Hood Legend. So consider the answer choices which are each suggestions.
So it would make sense to include a summary of the Robin Hood Legend if you are unfamiliar. So this would be answer choice D. Because the passage currently lacks this. And C. Would go off on tangent about other issues so they are irrelevant. Thank you choices and be is incorrect because he doesn't fit with the stance of the writer.
In mathematics, the absolute value or modulus x of a real number x is its numerical value without regard to its sign. The absolute value of a number may be thought of as its distance from zero along a number line; this interpretation is analogous to the distance function assigned to a real number in the real number system.
For example, the absolute value of? Click 'Join' if it's correct. Lisa G. Algebra 1 month, 3 weeks ago. View Full Video Already have an account?
Liuxi S. Discussion You must be signed in to discuss. Video Transcript this is an A. Upgrade today to get a personal Numerade Expert Educator answer! Ask unlimited questions. Test yourself. Join Study Groups. Create your own study plan. Join live cram sessions. Live student success coach.
Top Algebra Educators Catherine R. Missouri State University. Heather Z. Oregon State University.
0コメント