In Pursuit of Real Coronavirus Numbers

This [below] is a lengthy article from Medscape.com yesterday — providing some further perspective about those several preprints that these GEITP pages shared during the past several days. Everyone is in agreement that the most burning question right now is: “When it will be safe to get back to some version of normal life?” ☹
As an aside, there is also an intriguing discussion going on in sciencemediacentre.org (based in London), concerning those with fairly serious medical conditions and those in need of fairly serious elective surgeries — but are regarded as “not quite in the category of an emergency status” (i.e. are some patients’ medical and surgical needs suffering — because of this extreme level of focus on the pandemic?).
DwN

In Pursuit of Real Coronavirus Numbers

Jillian Mock

April 19, 2020

As the COVID-19 pandemic stretches on, there’s one big question everyone wants to answer: When it will be safe to get back to some version of normal life? Opening the country back up safely to limit the economic devastation of the shutdown depends on aggressive testing and tracing, which many say the US public health system is not equipped to do. But part of the puzzle is figuring out how many people are — or have been — infected with the virus.

On Friday, researchers posted a preprint declaring that the number of people infected with SARS-CoV-2 in one California (Santa Clara) county is far, far higher than previously thought, making the presumed fatality rate much lower. Another recent preprint suggests millions of people across the United States had already contracted COVID-19 in March.

These findings come with appealing implications. In the words of one article in the Economist : “If millions of people were infected weeks ago without dying, the virus must be less deadly than official data suggest.” Data from these studies supports the current push to end lockdowns that have strained the world’s social and economic fabric, as one Wall Street Journal opinion columnist wrote.

But multiple scientists who took a close look at the California study and shared their thoughts on Twitter said such hopeful findings are misleading, as the analysis does not stand up to close scientific scrutiny. And the researchers behind the second study have already revised their numbers downward to account for data discrepancies.

“We need these kinds of studies and data badly. Unfortunately, this paper is badly misleading (bordering on purposeful?)” wrote A. Marm Kilpatrick, PhD, a zoologist who studies infectious diseases at University of California, Santa Cruz, on Twitter of the preprint testing the seroprevalence of COVID-19 antibodies in Santa Clara County, California.

“People want to be able to say that the disease is less severe than it is,” Natalie Dean, PhD, a biostatistician who studies infectious disease surveillance, surveys, and vaccines at the University of Florida in Gainesville, told Medscape Medical News. But Dean, like at least five other scientists who took to Twitter to conduct their own peer review of the California study, is very skeptical of the attention-grabbing headlines.

In Santa Clara County, researchers from Stanford University conducted what they say is the first large-scale community-based COVID-19 prevalence study in a large US county. The scientists recruited patients via Facebook ads targeted by geography and demographics. Participants came to one of three drive-through testing stations, where researchers took a small blood sample and tested it for SARS-CoV-2 antibodies using Premier Biotech’s serology test. After testing, the researchers adjusted their findings to account for under-represented zip codes, sexes, and races/ethnicities, and then adjusted those results again to account for the limitations of the test.

Ultimately, out of 3,330 people tested, 50 came back positive for COVID-19 via either IgG or IgM antibodies in the sample. Unadjusted, the seroprevalence in Santa Clara County was 1.5% (exact binomial 95% confidence interval [CI], 1.11 – 1.97%). After weighting the data for demographics and the test characteristics, the researchers determined the population prevalence ranged from 2.49% to 4.16% (95% CI, 1.80 – 3.17% and 95% CI, 2.58 – 5.70%, respectively). This suggests between 48,000 and 81,000 people were infected in Santa Clara County in early April, a 50- to 85-fold increase over the confirmed 956 cases. As the authors write in the study, that corresponds to an infection fatality rate of 0.12% to 0.2%, well below most estimated fatality rates, which range from 4.3% in the US to 13% in Italy.

Among scientists dissecting the study on Twitter, the main criticisms of the paper revolved around a failure to fully account for the imperfections with the antibody test and bias in the population sample, says Dean.

“The only thing they did well was to try to answer a question, but they miserably failed in every aspect of it,” says cardiologist Eric Topol, MD, founder and director of the Scripps Research Translational Institute in La Jolla, California, and editor-in-chief of Medscape. (On Twitter, Topol compared the results to those in Seattle, saying those were much lower.)

When a disease is rare in a population, even a really accurate test with a high specificity will turn up a lot of false positives, says Dean. And an accurate antibody assay for COVID-19 is proving extremely challenging to produce, says Topol. Companies and organizations around the world are currently struggling to deliver speedy and dependable COVID-19 antibody tests.

The assay used in the Santa Clara study has not been validated in an extensive number of people, says Topol, suggesting it could be far less accurate than the researchers presumed. The Food and Drug Administration (FDA) has waived its usual approval process for COVID-19 antibody tests, so assays like this one can be deployed without waiting on FDA validation.

Before testing patients, the researchers ran 67 samples through the test to estimate its sensitivity and specificity; the manufacturer assessed the test using 531 total samples. Pooling the results, the researchers concluded that this particular antibody test had a combined sensitivity of 80.3% (95% CI, 71.1 – 87.0%) and a specificity of 99.5% (95% CI, 98.3 – 99.9%). But as John Cherian, a statistician and biomolecular simulation expert at DE Shaw Research, pointed out on Twitter, if the true specificity of the test was in fact closer to 98.3%, this would mean that almost all of the positive results in the unadjusted 1.5% prevalence rate could potentially be dismissed as false negatives.

Attempts to adjust bias in the sampled population also could have skewed the data, Dean explains. White women, ages 19 to 64, were overrepresented, while Hispanic and Asian populations were underrepresented. As the authors, who were unavailable for comment before press time, acknowledge in the paper, this could be because of their Facebook recruitment method. Not everyone can take off work or even has a car to go to a drive-through testing facility, says Dean.

When the number of positive cases is so small (just 50 total), influential observations can easily muddle the results. For example, if only two people showed up from a particular zip code, and one of them tested positive, that area appears to have an estimated sero-prevalence of 50%. “What they did I think was in good faith and it’s a reasonable approach, it can just have undesirable characteristics in small surveys,” says Dean.

In areas that have high hospitalizations and death, seroprevalence is at about the 10% mark, says Dean. Given that Santa Clara has not been particularly hard-hit by the virus so far, Dean is skeptical of the 4% sero-prevalence finding, and would guess the actual seroprevalence could be around 1% or 2%. This would, in turn, imply a higher infection fatality rate than the one cited in the paper.

Tens of Millions?

“In the last weeks, we’ve seen a flood of studies that are really supporting this view that there are lots of people who have this and we’re vastly undercounting,” says Justin Silverman, MD, PhD, assistant professor of information science and technology at Penn State University in State College, Pennsylvania.

Silverman and his colleagues recently published their own preprint that used influenza surveillance data to estimate at least 8.7 million people in the United States were infected with COVID-19 during a three week period in March. In the original manuscript, the researchers published that an estimated 28 million people in the US had COVID-19 during this time period, but Silverman says they had to revise the number after taking a closer look at some of the underlying data collected by the Centers for Disease Control and Prevention (CDC).

“What we found starting on March 8 on was there was a strong correlation between excess influenza-like illnesses and the path of coronavirus spread across the US,” says Silverman.

To produce these results, the researchers extracted data from the CDC’s ILINet database, which tracks the number of people presenting with influenza-like symptoms to more than 2,600 enrolled providers across the country each week. From these data, Silverman excluded the number of patients with positive flu tests and then subtracted expected seasonal variation in influenza-like illness (ILI) prevalence, estimated using a model trained on 10 years of ILINet data.

For a 3-week period in March, Silverman and his colleagues assumed nearly all of the remaining, unexplained ILI cases were caused by COVID-19. Finally, the researchers scaled up their results, using the estimated total number of providers per 100,000 residents in each state to estimate the number of unexplained ILI cases nationwide. After publication, the researchers revised the method they used to scale up the findings when they realized the CDC data sometimes counted groups of clinicians as just one provider in the database.

The researchers also estimated the number of COVID-19 cases doubled every 3.5 days, which Silverman says seems to match with the death rate doubling every 3 days so far.

Silverman notes all these statistical findings need to be verified by expanded seroprevalence testing on the ground via studies like the one conducted in Santa Clara.

This study hasn’t been discussed as widely on Twitter as the Santa Clara manuscript, but Topol is skeptical. While the US has vastly undercounted the actual number of COVID-19 cases, numbers this high don’t make any sense, says Topol. Right now, the total number of confirmed cases worldwide is around 2.3 million, and in the United States confirmed cases are currently over 700,000, according to tracking data by Johns Hopkins University. Unfortunately, we likely will never know how many people were truly infected with COVID-19 because of the slow and flawed rollout of testing, Topol says.

While neither study went through peer review, Topol doesn’t think the preprint process, which some are concerned will allow results to reach the public before they’re ready for prime time, is necessarily the problem. After all, the same thing can happen with papers that are peer-reviewed, and preprints have allowed the rapid dissemination of new knowledge about COVID-19, which we desperately need right now, he says.

Still, Topol cautions that studies like these can give the wrong impression about the seriousness of the pandemic and fuel arguments and protests against social distancing, the main tool for slowing the spread of SARS-CoV-2 right now.

And survey studies like the one in Santa Clara can serve an important purpose, says Dean. “I think these quick-and-dirty surveys are very valuable because they provide rapid information, but they have important limitations,” she says. “You can’t overinterpret a single number.”

For more news, follow Medscape on Facebook, Twitter, Instagram, and YouTube.

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High-throughput discovery of genetic determinants of “circadian misalignment”

This topic is “made for” these GEITP pages, i.e. the gene-environment interactions theme leaps out. The environmental signal is “light” or “absence of light” (i.e. day vs night, light-dark cycles called circadian rhythm), and virtually all animals “sense” these signals; their response then comprises multiple genetic networks (encoded by their genomes), which regulate physiological and critical life functions. This “circadian clock” is one of the best-characterized mechanisms” studied — that can mediate effects of “environmental cues” on molecular, physiological and behavioral activities.

The suprachiasmatic nucleus (SCN) is the central circadian pacemaker in mammals; the SCN receives photic information via the retina, integrates time-related information of tissues and organs, and then transmits timing information to cells and tissues, which in turn regulate physiology and behavior to cause animals to respond to daily changes of environmental cues. Chronic misalignment between the circadian clock and the environment — has been implicated in many pathological processes (e.g. sleep disorders, cardiovascular diseases, metabolic disorders, and cancer).

In humans, dysfunction or misalignment of the circadian clock with environmental cues — can alter timing of the sleep-wake cycle. Mice with mutations orthologous to human mutations (PER2*Ser662Gly, CK1*δT44A) recapitulate human phase-advanced behavioral rhythms. Transgenic mice carrying the Per1*Ser714Gly mutation affects feeding behavior, indicating that mice are a good model for human circadian functions. In addition, activity, feeding, temperature, and glucocorticoid signals — can also affect circadian rhythmicity. These are all zeitgebers (rhythmically occurring natural phenomena that act as cues to regulate the body’s circadian rhythms) and will impart phase information on their target tissues; circadian misalignment is therefore a consequence of conflicting signals of these zeitgebers.

Authors collected and analyzed indirect calorimetry data from >2000 wild-type mice available from the International Mouse Phenotyping Consortium (IMPC); authors demonstrated that onset time & peak phase of activity, and food intake rhythms — are reliable parameters for screening defects of circadian misalignment. Authors then developed a machine-learning algorithm to quantify these parameters in their screen of 750 mutant mouse lines from five IMPC phenotyping centers. Mutations in five genes [Slc7a11 (solute carrier 7A11), Rhbdl1 (rhomboid-like-1), Spop (speckle-type BTB/POZ protein), Ctc1 (CST telomere replication complex component-1) and Oxtr (oxytocin receptor)] were shown to be associated with altered patterns of activity or food intake. By further studying the Slc7a11 transgenic mouse, authors comfirmed its advanced-activity-phase phenotype — in response to simulated jetlag and skeleton photoperiod stimuli. Disruption of the Slc7a11 gene affected the intercellular communication within the SCN, suggesting a defect in synchronization of clock neurons. This herculean study has established a systematic phenotype analysis approach that should be useful for uncovering mechanisms of circadian entrainment in mice. 😊

DwN

PLoS Genet Jan 2020; 16: e1008577

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Scientists have uncovered social behavior in response to intra-mouse squeaking/speaking !!!

Throughout the animal kingdom, social interaction is fundamental for survival and reproduction. Such behavior consists of dynamic, complex interactions between animals and can promote cooperation or competition within the group. Vocalizations, in particular, play a substantial role in group dynamics — by warning group members of specific predators, or indicating that reproductive opportunities exist. From past experience of these GEITP pages, while working in the mouse facilities late into the night, we were often convinced that the various squeaks emitted by those creatures were actually their little voices saying nasty things about us, and likely proposing a massive uprising. ☹

Substantial advances in understanding the neurobiology of social behavior, as well as complex social network dynamics, have been made — using mice as a model system — because mice are social animals that engage in diverse behaviors accompanied by ultrasonic vocalizations (USVs). [USVs are auditory signals spanning a range of 35 to 110 kHz.] Mice readily approach and investigate the source of USVs, and female mice prefer to spend time near a vocalizing male, rather than a mute male [the same is true in humans]. Together, these measures used to quantify mouse behavior imply that USVs are relevant to the dynamics underlying social interactions.

USVs show a diverse acoustic profile, suggesting that multiple categories or types of vocalizations exist. The types of USVs that mice produce vary with recording conditions or context, genetic differences, and an animal’s developmental stage (i.e. age). Although several variables appear to influence the types of vocalizations produced, the extent to which mouse vocalizations influence social dynamics is unknown. Therefore, by combining a sound-source localization system to track vocal activity of individual adult mice — with a machine-learning algorithm to automatically detect specific social behaviors, and with a vocalization-clustering program to group similarly shaped vocal signals — authors [see attached article] were able to associate different types of USVs with distinct behaviors of the vocalizing mouse.

They demonstrated that specific patterns of vocalization influence the behavior of only the socially engaged partner. Authors showed that distinct patterns of vocalization emerge as male mice perform specific social actions (mice dominating other mice were more likely to emit different vocal signals than mice avoiding social interactions). Furthermore, it was shown that patterns of vocal expression influence behavior of the socially engaged partner, but do not influence the behavior of other animals in the cage. These findings clarify the function of mouse communication by revealing a communicative ultrasonic-signaling repertoire. This approach could help elucidate the role that communication plays in social dynamics — thus paving the way to a mechanistic understanding of the neural basis of social behavior. 😊

DwN

Nature Neurosci http://doi.org/dm6g (2020)

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More than 48,000 Santa Clara County residents likely been infected by coronavirus

Sorry to keep harping on the COVID-19 pandemic, but it seems our waking hours are consumed with lots of hypotheses (of origin, rate of infectivity, and projections of outcome over the next few months), conspiracy theories, and proposed treatments. This study from Stanford (just out; see below) might be important for several reasons:

[a] If the rate of infection is far greater than that reported, then in human populations many more people carrying the virus are asymptomatic than previously thought.

[b] These data suggest a greater likelihood of “herd immunity” might be able to occur.

[c] These findings also suggest the currently reported fatality rates of ~2-3% are too high, i.e. the actual COVID-19 infection fatality rate is between 0.12% and 0.20%.

[d] Therefore, statistics of “COVID-19 death rate” divided by “number of COVID-19 confirmed cases” — makes little sense, if the total number of infected cases is 50-100 times greater than the “number of confirmed cases.”

[e] Lastly, this article has been uploaded on the preprint server medRxiv, which as we all know, is one of Fred’s favorite web sites. 😉 Thus, the study need not be in final form; it may likely be revised before submission to a journal.

DwN

More than 48,000 Santa Clara County residents likely been infected by coronavirus
Survey of blood samples suggests between 2.5% and 4.2% of county residents may have coronavirus antibodies

by Gennady Sheyner / Palo Alto Weekly

Updated: Fri, Apr 17, 2020, 7:47 pm

The number of coronavirus infections in Santa Clara County could be between 50 and 80 times higher than the officially confirmed count, preliminary results from a community-based study by a team of Stanford University researchers indicates.

The prevalence study, led by Stanford Assistant Professor Eran Bendavid, has not been formally published and is still undergoing peer reviews. It has, however, been published on the preprint server medRxiv. As such, it is effectively a first draft, subject to change, based on input before formal publication.

That said, the early findings indicate that between 48,000 and 81,000 residents in Santa Clara County were infected as of April 1, back when the official count was 956. The estimate is based on 3,330 blood samples that were taken from volunteers in Mountain View, Los Gatos and San Jose on April 3 and April 4 and tested for antibodies to SARS-CoV-2 .

When adjusted for Santa Clara County’s population and demographics, the number of positive results suggests that between 2.49% and 4.16% of the county’s 1.93 million residents have had COVID-19.

The study’s results “represent the first large-scale community-based prevalence study in a major U.S. county completed during a rapidly changing pandemic, and with newly available test kits,” the authors wrote.

The most important implication, the preprint notes, is that “the number of infections is much greater than the reported number of cases.”

“The population prevalence of SARS-CoV-2 antibodies in Santa Clara County implies that the infection is much more widespread than indicated by the number of confirmed cases,” the researchers concluded. “Population prevalence estimates can now be used to calibrate epidemic and mortality projections.”

Jay Bhattacharya, a professor of medicine at Stanford University and one of the study’s authors, said the goal of the study is to understand how widespread the disease is.

“To do that, we need to understand how many people are infected,” Bhattacharya told this new organization on April 4, as the second day of tests was kicking off. “The current test people use to check whether they have the condition – the PCR (polymerase chain reaction) test – it just checks whether you currently have the virus in you. It doesn’t check whether you had it and recovered. An antibody test does both.”

Participants in the prevalence study were targeted through Facebook ads, with the goal of getting a representative sample of the county by demographic and geographic characteristics, the study states. Because the sampling strategy relied on people who have access to Facebook and a car, there was an overrepresentation of white women between 19 and 64, as well as an under-representation of Hispanic and Asian populations, relative to the community, according to the study. The study attempted to compensate for that by weighting the results for race, sex and ZIP code so that they better reflect the countywide population.

The group’s analysis indicated 50 blood samples from the study, or 1.5% of the total, tested positive for either immunoglobulin M (IgM), the antibody that the body produces when the infection occurs and that disappears after several weeks, or immunoglobulin G (IgG), the antibody that appears later, stays longer and provides the basis for immunity.

After weighting to match the county population by race, sex and ZIP code, the prevalence rate was adjusted to 2.81%, according to the study. Other factors, including uncertainties relating to the sensitivity of the tests that were used, contributed to the range of up to 4.16%.

County, state and federal health experts have consistently acknowledged that the number of COVID-19 cases is far higher than the official statistics show, a problem they attribute largely to the lack of widespread testing. Even though California is looking to greatly ramp up serological (blood) testing and to establish new community-testing sites, the state continues to experience both a shortage of tests and a backlog in processing tests.

As of April 15, more than 246,400 tests had been conducted in California. In Santa Clara County, there were 17,774 tests completed as of April 17, with 10.52% testing positive for the coronavirus.

The new study suggests that the undercounting of COVID-19 infections — the extent to which they vary from official case numbers — is far greater than has been assumed.

“The under-ascertainment of infections is central for better estimation of the fatality rate from COVID-19,” the study states. “Many estimates of fatality rate use a ratio of deaths to lagged cases (because of duration from case confirmation to death), with an infections-to-cases ratio in the 1-to-5-fold range as an estimate of under-ascertainment. Our study suggests that adjustments for under-ascertainment may need to be much higher.”

The Stanford study suggests that the undercounting of cases can also be attributed to a lack of widespread testing and reliance on PCR for case identification, which misses “convalescent” cases (those who have already recovered from the infection). The official count also doesn’t capture asymptomatic or lightly symptomatic infections that go undetected, the study states.

The range of results also reflects uncertainty in both test sensitivity (how good it is at correctly identifying COVID-19 antibodies) and test specificity (how likely it is to produce a false positive). Researchers relied on tests manufactured by the Minnesota-based company Premier Biotech, rather than the newly developed serological test by Stanford, which has been used to test health care workers.

Bendavid told this news organization earlier this week that the tests were chosen because they are very easy to use (they produce a line reading similar to a pregnancy test) and produce results within 15 minutes. They are, however, less precise than laboratory-based tests and give you an underestimate of how many people have coronavirus – a shortcoming that was factored in the study.

To determine their accuracy, the research team used the kits it received from Premier Biotech to test blood samples from Stanford Hospital patients that were shown to be positive through a DNA test, as well as samples that were known to be negative because they were taken before the pandemic. These results led researchers to conclude that the sensitivity is about 91.8%, a rate that was factored in to produce the final range.

The authors acknowledge the study’s other limitations. While they factored in sex, race and ZIP code, the survey does not account for age imbalances or a potential bias, favoring individuals who were in good health and, therefore, able to volunteer. The effect of such biases, the study notes, is hard to ascertain.

Bendavid and Bhattacharya had both argued in the past that the COVID-19 fatality rate is far lower than many experts had assumed. That’s because the number of actual infections far exceeds the official case counts.

“If the number of actual infections is much larger than the number of cases – orders of magnitude larger – then the true fatality rate is much lower as well. That’s not only plausible but likely based on what we know so far,” Bendavid and Bhattacharya wrote in a Wall Street Journal opinion piece on March 24.

As of April 10, the study notes, 50 people in Santa Clara County had died of COVID-19 in the county, with an average increase of 6% daily in the number of deaths. Given the trajectory, the study estimates that the county will see about 100 deaths by April 22.

Given the study’s estimate of 48,000 to 81,000 infections in early April – and a three-week lag from infection to death – the 100 deaths suggest that the actual infection fatality rate is between 0.12% and 0.2%.

That’s a far cryt from the county’s mortality rate based on official cases and deaths as of April 17 — 3.9%.

The study states that the new data “should allow for better modeling of this pandemic and its progression under various scenarios of non-pharmaceutical interventions.”

“While our study was limited to Santa Clara County, it demonstrates the feasibility of seroprevalence surveys of population samples now, and in the future, to inform our understanding of this pandemic’s progression, project estimates of community vulnerability, and monitor infection fatality rates in different populations over time,” the study states.

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Epigenetic therapy can inhibit metastases by disrupting premetastatic niches ??

Many cancers are successfully treated with surgery, but there’s always the possibility of recurrence — at the surgical site or in a distant organ site. Consequently, surgery is often followed-up by adjuvant therapy (i.e. chemotherapy and/or radiotherapy to help decrease risk of the cancer recurring); these procedures help improve survival of the patient (preventing ‘relapse’) by killing cancer cells that remain at the surgical site or those that have already migrated elsewhere. However, adjuvant therapy is not always effective; it might not prevent certain processes that aid cancer resurgence (e.g. recruitment of white blood cells — called myeloid cells — to distant organs, where they can help malignant cells to ‘settle down’ and ‘thrive’).

Authors [see attached article & editorial] show how the chemical structure of DNA in the myeloid-cell nucleus is an “Achilles heel” that might “able to be controlled” — by targeting these tumor-promoting cells and limiting cancer spread. When tumor cells spread from its primary site to distant organs (called ‘metastasis’), this requires complex interactions between malignant cells and surrounding healthy tissues. Evidence is growing that primary cancers can produce signals that modify normal cells to generate a “favorable medium” in distant organs — termed a pre-metastatic niche — that “allows” subsequent “seeding”’ and establishment of cancer cells at a secondary site. These metastatic tumors are the ones that are often lethal to the patient.

Authors [see article] reveal that — after surgical removal of primary lung and other types of cancer, low-dose adjuvant epigenetic therapy disrupts the premetastatic microenvironment and inhibits both formation and growth of lung metastases through the selective effects on myeloid-derived suppressor cells (MDSCs). In mouse models of lung metastases, MDSCs are key factors in formation of the premetastatic microenvironment after resection of primary tumors. Adjuvant epigenetic therapy — which uses low-dose DNA methyltransferase and histone deacetylase inhibitors, 5-azacytidine and entinostat — disrupts the premetastatic niche by inhibiting the trafficking of MDSCs [via down-regulating the CCR2 (C-C motif chemokine receptor-2) and CXCR2 (C-X-C motif chemokine receptor-2) genes], and by promoting MDSC differentiation into a more mature macrophage-like phenotype.

Decreased accumulation of MDSCs in the premetastatic lung therefore produces longer periods of disease-free survival and increased overall patient survival — compared with chemotherapy. These data demonstrate that, even after removal of the primary tumor, these nasty MDSCs contribute to the development of premetastatic niches and settlement of residual tumor cells. This combination of low-dose adjuvant epigenetic modifiers that disrupts this premetastatic

microenvironment and inhibits metastases [detailed in this article] may permit a “favorable adjuvant approach to cancer therapy” in the near future. 😊

DwN

Nature 12 Mar 2020; 579: 284-290 + Editorial pp 196-197

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Can We Beat SARS-CoV-2? Lessons from Other Coronaviruses

Because these GEITP pages have discussed the possibility of a vaccine, this article might be of interest to some of you. It has been established that the optimized binding site and polybasic cleavage site of the SARS-CoV-2 positive-strand single-stranded RNA [(+)-ssRNA] carry multiple mutations — most likely reflecting evolutionary natural selection for the human or human-like angiotensin-converting enzyme-2 (ACE2), important during silent zoonotic transfer (e.g. bat-to-human). These high-frequency mutations have resulted in at least five differentiated SARS-CoV-2 strains to date, in which these mutations are predicted to enhance viral entry into host cells, virulence, and viral transmission. Given sufficient time and numbers of patient samples, scientists must first identify relatively unchanging viral protein(s) — as suitable targets for possible development of a vaccine.

Efforts to develop a vaccine for SARS-CoV-1 have been unsuccessful due to antibody-dependent enhancement (ADE)-mediated vaccine-induced infection aggravation. In ferrets, a vaccine was successful in inducing a rapid memory immune response — but when these ferrets were challenged with SARS-CoV-1, they developed liver damage. A vaccine in mice also induced antibody formation and protection against SARS-CoV-1; however, challenged mice exhibited T cell-2 (Th2)-type immunopathology, suggesting hypersensitivity to SARS-CoV-1 components. These data underscore the importance of identifying in SARS-CoV-2 different viral proteins, or anti-Spike sera concentrations, which would not induce ADE.

Although a vaccine does not sound promising, the literature regarding historical coronavirus outbreaks appears to provide ample guidance as to how to design an effective SARS-CoV-2 vaccine. Aside from developing prophylactics (to prevent disease), there must also be a focus on screening for antiviral compounds from synthetic and/or natural chemical libraries to improve patient-recovery rates. Finding such compounds is especially important — if SARS-CoV-2 continues to mutate rapidly and/or infected individuals develop only short-term immunity; otherwise, (at least some) individuals would be susceptible to getting infected a second time. ☹ This (frank, sobering) article [below] is posted on ContagionLive.com

DwN

Can We Beat SARS-CoV-2? Lessons from Other Coronaviruses

MAR 27, 2020 | JENNIFER S. SUN, PhD

When news broke that a novel coronavirus SARS-CoV-2 had emerged, scientists like myself were grimly aware of how difficult this novel coronavirus would be to control. For context, my research focuses on engineering bacteriophages for use as therapeutics, whereby bacterial viruses (phages) are used to infect and lyse bacteria as a replacement for traditional antibiotics.1 We recently discovered a bacteriophage that hijacks a bacterial quorum sensing (QS) autoinducer (AI) and uses the information encoded in it to drive transitions between lysis and lysogeny.2,3 This eavesdropping mechanism occurs in pandemic Vibrio cholerae, and it allows the phage to execute its lytic cycle exclusively at high host cell density, as well as drive the host biofilm dispersal program. So here we have evidence that viruses can naturally mutate to mimic host biology so as to ensure successful viral propagation. This knowledge of viral evolution and divergence rates parallels that of coronaviruses which precede pandemic SARS-CoV-2, and could provide guidance when predicting the virulence of SARS-CoV-2 variants in eukaryotic hosts, as well as the potential of prophylactics or treatments.

Could an effective vaccine for SARS-CoV-2 be in the horizon? Since the emergence of SARS-CoV-1 in 2003, scientists had been warning of the possibility for long-term affliction by coronaviruses, whereby the advice was to design broad-spectrum antiviral drugs and vaccines against this viral cluster. While such a therapeutic has not yet been discovered, due to the similarity in structure and cellular entry receptor, some drugs and pre-clinical vaccines against SARS-CoV-1 could theoretically be used to treat SARS-CoV-2.4 However, there are 2 caveats to this approach: SARS-CoV-2 can mutate into a strain that the vaccine would not protect against, and SARS-CoV-1 vaccine candidates exhibited adverse side effects and even exacerbated symptoms upon viral challenge.

Knowing that viral genomes are notoriously pliable, I am wary in regard to the efficacy of prophylactics against SARS-CoV-2. Just by comparing phage and bacterial genomes among mammalian hosts, one can already appreciate how rapidly phages and bacteria co-evolve and shape one another’s biology.5,6 Coronaviruses have certainly been shown to exhibit high frequency recombination events,7,8 and favored high frequency recombination sites have been documented.7 For instance, sustained human MERS-CoV infections are the result of several seasonal viral challenges from camels into humans, resulting in at least 3 different MERS-CoV genomes.9

Recent studies indeed demonstrate that the optimized binding site and polybasic cleavage site of SARS-CoV-2 is the product of accumulated mutations in the receptor-binding domain, most likely from natural selection on a human or human-like angiotensin converting enzyme II (ACE2),10,11 during passage via repeated silent zoonotic transfer.12 A host of high frequency mutations have resulted in at least 5 differentiated SARS-CoV-2 strains to date,13 whereby the mutations are predicted to enhance viral entry into host cells,14 virulence and viral transmission.15,16 Thus, with enough time and patient samples, lineage tracing of SARS-CoV-2 must first identify relatively unchanging viral proteins as suitable targets for prophylactic development.

Efforts to develop a SARS-CoV-1 vaccine have been thwarted in the past by antibody-dependent enhancement (ADE)-mediated vaccine-induced infection aggravation.17,18 In ferrets, rMVA-S vaccines were successful in inducing a rapid memory immune response, which is an essential feature of an effective prophylactic; but, when these ferrets were challenged with SARS-CoV-1, they developed enhanced liver damage.19,20 Likewise, in mice, SARS-CoV-1 vaccines utilizing either live SARS-CoV-1 or DNA-based S-protein were able to induce antibody formation and protection against SARS-CoV-1;21,22 however, challenged mice exhibited Th2-type immunopathology suggesting hypersensitivity to SARS-CoV-1 components.23 These results suggest that comprehensive evaluation of target SARS-CoV-2 signatures is required before vaccine trials ensue in humans, so as to prevent organ damage upon viral challenge. Specifically, scientists must identify different viral proteins or anti-Spike sera concentrations which would not induce ADE.

While bleak, the literature regarding historical coronavirus outbreaks appear to provide ample guidance as to how to design an effective SARS-CoV-2 vaccine. Aside from developing prophylactics, there must also be a focus on screening for antiviral compounds from synthetic and/or natural chemical libraries to improve patient recovery rates. Finding such compounds is especially important if SARS-CoV-2 continues to mutate rapidly and/or infected individuals only develop short-term immunity, in which case individuals would be susceptible to reacquiring the infection.

Overall, as a scientist engaged in the SARS-CoV-2 volunteer task force, I am optimistic and firmly believe that our collective prowess in mathematical modeling and artificial intelligence will help to arrive at solutions for medical prevention and/or intervention.

Sun is a postdoctoral research associate in molecular biology at Princeton University. She received her PhD in molecular, cellular, and developmental biology from Yale University.

References:
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2. Silpe, J. E.; Bassler, B. L. A Host-Produced Quorum-Sensing Autoinducer Controls a Phage Lysis-Lysogeny Decision. Cell 2019, 176 (1–2), 268-280.e13. https://doi.org/10.1016/j.cell.2018.10.059.
3. Silpe, J. E.; Bassler, B. L. Phage-Encoded LuxR-Type Receptors Responsive to Host-Produced Bacterial Quorum-Sensing Autoinducers. mBio 2019, 10 (2). https://doi.org/10.1128/mBio.00638-19.
4. Zhou, P.; Yang, X.-L.; Wang, X.-G.; Hu, B.; Zhang, L.; Zhang, W.; Si, H.-R.; Zhu, Y.; Li, B.; Huang, C.-L.; Chen, H.-D.; Chen, J.; Luo, Y.; Guo, H.; Jiang, R.-D.; Liu, M.-Q.; Chen, Y.; Shen, X.-R.; Wang, X.; Zheng, X.-S.; Zhao, K.; Chen, Q.-J.; Deng, F.; Liu, L.-L.; Yan, B.; Zhan, F.-X.; Wang, Y.-Y.; Xiao, G.-F.; Shi, Z.-L. A Pneumonia Outbreak Associated with a New Coronavirus of Probable Bat Origin. Nature 2020, 579 (7798), 270–273. https://doi.org/10.1038/s41586-020-2012-7.
5. Scanlan, P. D. Bacteria–Bacteriophage Coevolution in the Human Gut: Implications for Microbial Diversity and Functionality. Trends Microbiol. 2017, 25 (8), 614–623. https://doi.org/10.1016/j.tim.2017.02.012.
6. Koskella, B.; Brockhurst, M. A. Bacteria–Phage Coevolution as a Driver of Ecological and Evolutionary Processes in Microbial Communities. Fems Microbiol. Rev. 2014, 38 (5), 916–931. https://doi.org/10.1111/1574-6976.12072.
7. Makino, S.; Keck, J. G.; Stohlman, S. A.; Lai, M. M. High-Frequency RNA Recombination of Murine Coronaviruses. J. Virol. 1986, 57 (3), 729–737.
8. Kottier, S. A.; Cavanagh, D.; Britton, P. Experimental Evidence of Recombination in Coronavirus Infectious Bronchitis Virus. Virology 1995, 213 (2), 569–580. https://doi.org/10.1006/viro.1995.0029.
9. Dudas, G.; Carvalho, L. M.; Rambaut, A.; Bedford, T. MERS-CoV Spillover at the Camel-Human Interface. eLife 2018, 7, e31257. https://doi.org/10.7554/eLife.31257.
10. Andersen, K. G.; Rambaut, A.; Lipkin, W. I.; Holmes, E. C.; Garry, R. F. The Proximal Origin of SARS-CoV-2. Nat. Med. 2020, 1–3. https://doi.org/10.1038/s41591-020-0820-9.
11. Wan, Y.; Shang, J.; Graham, R.; Baric, R. S.; Li, F. Receptor Recognition by the Novel Coronavirus from Wuhan: An Analysis Based on Decade-Long Structural Studies of SARS Coronavirus. J. Virol. 2020, 94 (7). https://doi.org/10.1128/JVI.00127-20.
12. Sheahan, T.; Rockx, B.; Donaldson, E.; Sims, A.; Pickles, R.; Corti, D.; Baric, R. Mechanisms of Zoonotic Severe Acute Respiratory Syndrome Coronavirus Host Range Expansion in Human Airway Epithelium. J. Virol. 2008, 82 (5), 2274–2285. https://doi.org/10.1128/JVI.02041-07.
13. Wang, M.; Li, M.; Ren, R.; Brave, A.; Werf, S. van der; Chen, E.-Q.; Zong, Z.; Li, W.; Ying, B. International Expansion of a Novel SARS-CoV-2 Mutant; preprint; Infectious Diseases (except HIV/AIDS), 2020. https://doi.org/10.1101/2020.03.15.20035204.
14. Letko, M.; Marzi, A.; Munster, V. Functional Assessment of Cell Entry and Receptor Usage for SARS-CoV-2 and Other Lineage B Betacoronaviruses. Nat. Microbiol. 2020, 1–8. https://doi.org/10.1038/s41564-020-0688-y.
15. Hamming, I.; Timens, W.; Bulthuis, M. L. C.; Lely, A. T.; Navis, G. J.; Goor, H. van. Tissue Distribution of ACE2 Protein, the Functional Receptor for SARS Coronavirus. A First Step in Understanding SARS Pathogenesis. J. Pathol. 2004, 203 (2), 631–637. https://doi.org/10.1002/path.1570.
16. Lu, G.; Wang, Q.; Gao, G. F. Bat-to-Human: Spike Features Determining ‘Host Jump’ of Coronaviruses SARS-CoV, MERS-CoV, and Beyond. Trends Microbiol. 2015, 23 (8), 468–478. https://doi.org/10.1016/j.tim.2015.06.003.
17. Yip, M. S.; Leung, N. H. L.; Cheung, C. Y.; Li, P. H.; Lee, H. H. Y.; Daëron, M.; Peiris, J. S. M.; Bruzzone, R.; Jaume, M. Antibody-Dependent Infection of Human Macrophages by Severe Acute Respiratory Syndrome Coronavirus. Virol. J. 2014, 11, 82. https://doi.org/10.1186/1743-422X-11-82.
18. Luo, F.; Liao, F.-L.; Wang, H.; Tang, H.-B.; Yang, Z.-Q.; Hou, W. Evaluation of Antibody-Dependent Enhancement of SARS-CoV Infection in Rhesus Macaques Immunized with an Inactivated SARS-CoV Vaccine. Virol. Sin. 2018, 33 (2), 201–204. https://doi.org/10.1007/s12250-018-0009-2.
19. Weingartl, H.; Czub, M.; Czub, S.; Neufeld, J.; Marszal, P.; Gren, J.; Smith, G.; Jones, S.; Proulx, R.; Deschambault, Y.; Grudeski, E.; Andonov, A.; He, R.; Li, Y.; Copps, J.; Grolla, A.; Dick, D.; Berry, J.; Ganske, S.; Manning, L.; Cao, J. Immunization with Modified Vaccinia Virus Ankara-Based Recombinant Vaccine against Severe Acute Respiratory Syndrome Is Associated with Enhanced Hepatitis in Ferrets. J. Virol. 2004, 78 (22), 12672–12676. https://doi.org/10.1128/JVI.78.22.12672-12676.2004.
20. Marshall, E.; Enserink, M. Caution Urged on SARS Vaccines. Science 2004, 303 (5660), 944–946. https://doi.org/10.1126/science.303.5660.944.
21. Subbarao, K.; McAuliffe, J.; Vogel, L.; Fahle, G.; Fischer, S.; Tatti, K.; Packard, M.; Shieh, W.-J.; Zaki, S.; Murphy, B. Prior Infection and Passive Transfer of Neutralizing Antibody Prevent Replication of Severe Acute Respiratory Syndrome Coronavirus in the Respiratory Tract of Mice. J. Virol. 2004, 78 (7), 3572–3577. https://doi.org/10.1128/jvi.78.7.3572-3577.2004.
22. Yang, Z.; Kong, W.; Huang, Y.; Roberts, A.; Murphy, B. R.; Subbarao, K.; Nabel, G. J. A DNA Vaccine Induces SARS Coronavirus Neutralization and Protective Immunity in Mice. Nature 2004, 428 (6982), 561–564. https://doi.org/10.1038/nature02463.
23. Tseng, C.-T.; Sbrana, E.; Iwata-Yoshikawa, N.; Newman, P. C.; Garron, T.; Atmar, R. L.; Peters, C. J.; Couch, R. B. Immunization with SARS Coronavirus Vaccines Leads to Pulmonary Immunopathology on Challenge with the SARS Virus. PLOS ONE 2012, 7 (4), e35421. https://doi.org/10.1371/journal.pone.0035421.

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Medical Schools Need an Overhaul

Here are two dissenting opinions by physicians who did not like the Goldfarb editorial. —DwN

This is a sobering topic that needs to be addressed by all practicing physicians, physician-scientists, teachers in academia, and hospital administrators. Compared with what courses and rotations were available for students in medical schools 50+ years ago, some of the courses and rotations that have replaced the old ones — are a joke. How many of you, in the last decade, have gone to a new primary care physician (who is under age ~40) for the first time, and that physician looks at his/her computer screen, without laying a hand on you during the entire visit? And the same can be said for many of the specialty physicians. ☹

This article was online in Wall Street Journal evening of April 13, and out in print morning of April 14.

DwN

Medical Schools Need an Overhaul
Doctors should learn to fight pandemics, not injustice.

By Stanley Goldfarb

April 13, 2020 6:53 pm ET

Emergency-medicine residents load boxes of face shields in Las Vegas, April 10.

As the number of COVID-19 infections rises and the death toll mounts, the media is doing a good job of focusing on the safety of the health-care workforce and the capacity of hospitals to deal with a surge of desperately ill patients. What has received less attention is that many doctors haven’t been adequately trained in medical school to deal with a situation like this.

Most medical schools don’t require students to do coursework on pandemic response or practical preparation for a widespread and sustained emergency. American medical training as a whole doesn’t include a strong grounding in public-health issues or disaster preparedness. Instead, two of the nine specific curricular requirements decreed by the body that accredits medical schools are focused on social issues in medicine, including “the diagnosis of common societal problems and the impacts of disparities in health care on medically underserved populations,” particularly “in a multidimensional and diverse society.” None mention public health or epidemics.

Physicians are highly educated, but that doesn’t mean they know everything—even things broadly related to the practice of medicine. When doctors speak on topics they don’t understand, they can confuse the public and other physicians. While medical schools require students to study statistics, these courses are generally superficial. They wouldn’t equip most physicians to grapple with epidemic models like the ones on which Deborah Birx has been briefing the White House press.

It has been discouraging to see doctors on news programs struggle to explain the principles of drug testing, the nature of the scientific method, and the meaning (both positive and negative) of uncontrolled drug trials. Television audiences love a good story, but clinical anecdotes can’t prove a drug is useful. That job belongs to randomized controlled trials or other complex experimental approaches, the design of which is a complicated topic that many, if not most, doctors would struggle to explain.

A critical examination of undergraduate medical education will be among the many reassessments this country has to make in the wake of this crisis. Many schools don’t require students to do formal training in emergency medicine. While physicians receive valuable practical experience during their residencies in internal medicine and surgery, they should all have the benefit of rigorous classroom study in ventilator management and other aspects of critical-care medicine, preferably in the fourth year of medical school.

Above all, the medical profession should abandon the fantasy that physicians can be trained to solve the problems of poverty, food insecurity and racism. They have no clinical tools with which to address these issues. The public may not realize that well-funded organizations like the Beyond Flexner Alliance advocate for devoting a substantial part of medical-school teaching to social and organizational topics.

If curricular reform is to come, it should take into account the essential role physicians must play in a public-health crisis. It should aim to produce physicians who are prepared to help battle deadly pandemic diseases like Covid-19.

Students should enter the field of medicine with a clear understanding that they will one day face a public-health catastrophe like the one New York’s doctors and nurses are currently staring down with great courage. Health-care workers are the tip of the spear during an outbreak of disease. We need to be sure they have the tools and training to succeed in fighting the next pandemic when it comes. And it will.

Dr. Goldfarb is a former associate dean of curriculum at the University of Pennsylvania’s Perelman School of Medicine.

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Why sucralose diet drinks and sugar drinks differ

This topic might be considered by some as slightly obtuse to the theme of gene-environment interactions; but the environmental signal is “diet drinks, with vs without nonsweetened carbohydrate,” and there is variability in alleles of genes in each of our genomes that respond to this signal. There is substantial controversy about effects of drinking excessive amounts of zero- or low-calorie sweeteners (LCSs). Clinical studies have noted that LCS consumption is associated with increased risk of weight gain and/or diabetes, or with lower body mass index (BMI) and weight loss, or unrelated to metabolic and body weight measures. Similar inconsistencies exist in animal studies.

Central to resolving this debate is defining and testing biologically plausible mechanisms by which LCSs could lead to metabolic perturbation. Among suggested mechanisms: [a] LCS binding to extra-oral taste receptors in pancreas and intestine might influence glucose absorption by affecting glucose transporters SGLT1 and GLUT2 or by altering glucose metabolism by promoting incretin release that stimulate decreases in blood glucose levels [there are three intestinal glucose transporters — sodium/glucose co-transporters-1 (SGLT1) and -2 (SGLT2), and facilitative glucose transporter-2 (GLUT2) — in intestinal mucosal cells]; [b] central nervous system (CNS)-sensing of sweetness-elicited conditioned responses, leading to the subsequent development of glucose intolerance.

Authors [see attached article] randomly assigned 45 healthy volunteers to consume [a] beverages sweetened with sucralose

(sweet uncoupled from calories – LCS), [b] beverages sweetened with sucrose (sweet coupled with calories – Sugar), or [c] beverages sweetened with sucralose combined with maltodextrin (Combo). Oral glucose tolerance tests, sensory tests, and neuroimaging were conducted before and after participants consumed seven of their assigned beverages over 2 weeks in the laboratory.

Sucralose over 10 days is known to decrease insulin sensitivity in healthy human participants — an effect that correlates

with reductions in midbrain, insular, and cingulate responses to sweet (but not sour, salty, or savory) taste, as assessed by functional magnetic resonance imaging (fMRI). Authors found that taste perception was unchanged; i.e. consuming the carbohydrate (sucrose or sucralose) alone had no effect. However, consumption of sucralose in the presence of a “non-sweet carbohydrate” (maltodextrin) rapidly impairs glucose metabolism, resulting in longer-term decreases in brain, but not “perceptual sensitivity to sweet taste.” These findings (not surprisingly?) suggest “dysregulation of gut-brain control of glucose metabolism,” i.e. the brain receives these mixed signals and then responds by perturbing glucose metabolism. ☹

DwN

Cell Metab 2020; 31: 493-502

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SARS-CoV-2 directly affects heme synthesis ??

Well — these GEITP pages would like to “move on” from the mechanisms of SARS-CoV-2 infection — but the intriguing hypotheses continue to appear in our current literature. THIS article [attached; from Sichuan and Yibin Universities], pushed on me last evening by a fellow GEITP-er, is at least worth examining more closely. ☹

SARS-CoV-2 is a positive-strand single-stranded RNA [(+)-ssRNA] virus that exhibits high homology to bat coronavirus and is the cause of our current COVID-19 pandemic. Authors [see attached article] used “conserved domain analysis, homology modeling, and molecular docking” (structural chemistry analysis) to “compare biological roles of certain proteins of the novel virus.” Their results showed the open-reading frame-8 (ORF8) protein, and surface glycoprotein, “could both bind to porphyrin.” Concomitantly, the orf1ab, ORF10, and ORF3a proteins “could coordinate an attack on the heme of the 1-beta chain of hemoglobin — to dissociate iron, which is needed to form the porphyrin.”

“This attack would then cause increasingly less less functional hemoglobin [required to carry oxygen (O2) to the body and carbon dioxide (CO2) back through the venous system to the lungs for exhalation).” Clinically, lung epithelial cells do display severe inflammatory changes — which present like high-altitude pulmonary edema (HAPE), i.e. oxygen deprivation. Authors state that “dissociation of iron from hemoglobin would also interfere with other pathways in which heme is needed for various functions of other molecules in the body.”

According to this analysis, authors suggest that chloroquine “could prevent the orf1ab, ORF3a, and ORF10 proteins from attacking the heme” — which might reflect the clinical response to the drug of effectively relieving symptoms of dyspnea (respiratory distress). Authors also suggest that favipiravir “could inhibit the envelope protein and ORF7a protein from binding to porphyrin.” Authors do warn that their “paper is only for academic discussion,” and proof of their hypotheses “would require confirmation by further studies.” In the humble opinion of these GEITP pages (having had first-hand experience during a MS degree in biophysics in which many artifacts in vitro were never relevant to clinical medicine) — this (structural biology in silico) paper represents a lot of hand-waving and smoke-and-mirrors and has little to do with reality. ☹

DwN

ChemRxiv. Preprint. https://doi.org/10.26434/chemrxiv.11938173.v6

COMMENT:

Thank you, Andy, for your input from a heme biosynthesis expert. 😊
As far as elevated hemoglobin levels in these ARDS patients, Alvaro, the first thing — that comes to mind as a clinician — is dehydration (loss of fluids from the intravascular space, which leads to increased concentration of red cells and other components), which of course is seen with fever, and occurs more commonly among young children and geriatric patients with underlying medical conditions. ☹

DwN

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The COVID-19 vaccine development landscape

For those interested (and/or able to understand the details) — this article is a letter published just today in Nature Rev Drug Discov. I have no expertise in “likelihood of making a successful vaccine or not”, so I have no comment. ☹

DwN

https://www.nature.com/articles/d41573-020-00073-5?utm_source=Nature+Briefing&utm_campaign=98c3a376d1-briefing-dy-20200409&utm_medium=email&utm_term=0_c9dfd39373-98c3a376d1-43900977

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