The Bioinformatics CRO Podcast

Episode 50 with Alfredo Andere

Alfredo Andere, co-founder and CEO of Latch Bio, discusses the unique challenges facing young entrepreneurs and the future of cloud computing in biology. 

On The Bioinformatics CRO Podcast, we sit down with scientists to discuss interesting topics across biomedical research and to explore what made them who they are today.

You can listen on Spotify, Apple Podcasts, Google Podcasts, Amazon, and Pandora.

Alfredo is co-founder of and CEO of Latch Bio, a cloud bioinformatics platform that enables collaboration between computational biologists and wet lab researchers.

Transcript of Episode 50: Alfredo Andere

Coming soon…

States Ranked by Age adjusted COVID Deaths - Updated April 20 - See table for more details

States Ranked by Age-Adjusted COVID Deaths

Data updated on September 21, 2022

We’re inundated with statistics on how US states have fared relative to one another throughout the pandemic. Sometimes these can appear contradictory because the data can be cut to support a variety of narratives. We wanted an updated source of cumulative age-adjusted COVID-19 deaths by state, as death is an important measure of the impact of a pandemic, and states have adopted widely divergent policies.

While COVID-19 deaths are usually adjusted for state population (deaths per 100,000), they are usually not adjusted for the age distribution of a state. It’s important to adjust for age when considering state-to-state differences in outcomes as age is the dominant risk factor for death provided someone is infected with COVID, and state age distributions vary considerably. In the following analysis, we present age-adjusted cumulative COVID-19 deaths and rank each state plus Puerto Rico and the District of Columbia accordingly. We generated the plot and table using the CDC’s Provisional COVID-19 Death Counts by Sex, Age, and State database, which sourced its data from death certificates. These numbers are more consistently processed across states, though they may differ slightly from other sources. 

 

Bubble areas are proportional to state populations and the horizontal arrangement is arbitrary to reduce overlap.

Here we see dramatic state-to-state differences in cumulative age-adjusted COVID deaths per capita to date, spanning a range of over five fold. In the end, some states that adopted dramatically divergent policies had comparable outcomes (Florida and California, for example).

Mississippi is exceptionally high. A few regional clusters have fared markedly better than the rest: Vermont, New Hampshire & Maine, Oregon & Washington, and Hawaii & Puerto Rico.

Why do COVID deaths vary by state? 

Explore the relationships between age-adjusted COVID deaths and several state-level metrics including: vaccination coverage, obesity rate, the strictness of COVID policy and more. 

Explore the Data

 

Age-Adjusted COVID Deaths Ranking

State

COVID-19 Deaths per 100,000

Age-Adjusted COVID-19 Deaths per 100,000

1 Mississippi 465 476
2 Oklahoma 426 435
3 Tennessee 404 409
4 Texas 340 406
5 Kentucky 400 404
6 Alabama 410 403
7 Nevada 374 396
8 Arkansas 387 376
8 Indiana 368 376
8 New Mexico 390 376
11 Louisiana 356 371
12 North Dakota 380 370
13 Arizona 388 369
13 Ohio 388 369
15 Georgia 319 364
15 West Virginia 415 364
17 South Carolina 371 362
18 New York 381 361
19 New Jersey 373 360
20 District of Columbia 295 349
21 South Dakota 361 341
22 Missouri 350 335
23 Pennsylvania 378 332
24 Michigan 337 322
25 Rhode Island 355 317
26 Montana 344 316
27 Kansas 319 313
28 Idaho 292 309
29 North Carolina 294 299
30 Wyoming 293 294
31 Florida 349 292
32 Iowa 319 291
33 Delaware 317 289
34 Connecticut 321 287
35 Illinois 283 283
36 Colorado 243 277
36 Maryland 270 277
38 California 254 270
39 Nebraska 268 264
40 Massachusetts 278 262
41 Virginia 244 255
42 Wisconsin 265 252
43 Alaska 187 244
44 Minnesota 240 236
45 Utah 169 225
46 Washington 174 184
47 Oregon 189 183
48 New Hampshire 194 179
49 Maine 201 168
50 Puerto Rico 156 135
51 Hawaii 116 103
51 Vermont 118 103

Calculation 

We determined the age adjusted mortality per 100,000 people (maa) for each state using the formula:

m_aa = SUM (D_x * P_x / (N_x * 100,000))

Where Dx is the total deaths in age group x in the state, Nx is the total population in age group x in the state, and Px is the percent of the population in age group x in the United States.

Citations

COVID deaths from CDC: “Provisional COVID-19 Death Counts by Sex, Age, and State” (Updated on September 21, 2022)

Population data from U.S. Census Bureau: “State Population by Characteristics: 2010-2019”

The Bioinformatics CRO Podcast

Episode 49 with Joshua Hare

Joshua Hare, Professor of Medicine at the University of Miami and co-founder of Longeveron, discusses the regenerative and reparative potential of MSCs and how cell therapies will revolutionize medicine.

On The Bioinformatics CRO Podcast, we sit down with scientists to discuss interesting topics across biomedical research and to explore what made them who they are today.

You can listen on Spotify, Apple Podcasts, Google Podcasts, Amazon, and Pandora.

Joshua is professor of Medicine at the University of Miami and co-founder of Longeveron, a biotech company using MSCs to treat chronic diseases. He is also founding director of the Interdisciplinary Stem Cell Institute at the University of Miami’s Miller School of Medicine. 

Transcript of Episode 49: Joshua Hare

Coming soon…

Why do COVID Deaths Vary by State?

Cumulative COVID deaths are an endpoint to compare the effectiveness of states’ pandemic-related health policies. As the major risk factor for death from COVID is age, and some states have younger populations than others, it’s important to adjust for age when trying to understand factors that contribute to variable outcomes among states. Here we examine the relationships among age-adjusted COVID deaths and several variables of interest. Note that such comparisons cannot, on their own, be used to identify causal contributors to cumulative age-adjusted COVID deaths. Many features are highly co-linear and may serve as an imperfect proxy for underlying causes. However, these plots can be useful for hypothesis generation, and can support (but not prove) the lack of a strong causal relationship when there appears to be no association at all. We recommend bearing in mind the ecological fallacy.

Unadjusted COVID deaths versus Age-adjusted deaths | y = 0.934 * x + 15.3 | R^2 = 0.912

The chart above shows the relationship between age-adjusted COVID deaths and unadjusted deaths. States with younger populations such as Texas perform worse after age adjustment, while states with older populations such as Florida perform better.

Summary 

  • Vaccination rate is the single biggest predictor of age-adjusted deaths by state.
  • COVID deaths do not correlate with state stringency after adjusting for age and obesity rate.
  • State stringency correlates with unemployment rate but not with non-COVID excess deaths, depression or suicide vs baseline.
  • Partisanship is a strong predictor of vaccination rate, but not of deaths after accounting for age, vaccination rate and obesity.

This is incomplete and there are many factors as yet unexamined. We will add more visualizations over time.

 

Vaccination

Vaccination is highly effective in preventing severe outcomes and death from COVID infection. Indeed state vaccination rate has a strong negative correlation with age-adjusted COVID deaths (P<0.01).  In the 65+ age group, there is a similarly high correlation (P<0.01) with age adjusted COVID deaths. Note that the CDC caps vaccination coverage metrics at 95%, due to issues tracking first and second doses. Because vaccination over 65 is the strongest single predictor of age-adjusted deaths, we also present cumulative COVID deaths adjusting for both age and vaccination rate over 65.

Vaccination Rate and Age Adjusted COVID Deaths

Fully Vaccinated versus Age-adjusted deaths | y = -555 * x + 645 | R^2 = 0.4

Vaccination Rate Over 65 Years of Age

Fully vaccinated over 65 versus Age-adjusted deaths | y = -1113 * x + 1282 | R^2 = 0.39

Vaccination Rate 65+ and Age & Obesity Adjusted COVID Deaths

Vaccination over 65 versus Obesity and Age-adjusted deaths | y = -527 * x + 770 | R^2 = 0.144

There is a moderate negative correlation (P<0.01) between vaccination and COVID deaths when adjusting for age and obesity. The reduction in correlation is due to collinearity between state obesity rates and vaccination rates. That is, states with higher adult obesity rates tend to have lower vaccination rates (P<0.01). In a multivariate linear model of age-adjusted deaths, vaccination over 65, and obesity, both showed significant correlation with deaths and had a multiple R² of 0.48. 

Stringency Index

Stringency Index is a useful tool for comparing government response to the pandemic. It is the average of nine policy metrics, including: 

  • School closures
  • Workplace closures
  • Cancellation of public events
  • Restrictions on public gatherings
  • Closures of public transport
  • Stay-at-home requirements
  • Public information campaigns
  • Restrictions on internal movements
  • International travel controls

The higher the stringency index, which has a maximum value of 100, the stricter a government’s response to the pandemic. Stringency index does not factor in compliance with government policy. For more information on Stringency Index and its calculation visit ourworldindata.org/ 

The charts above show a moderate negative correlation between the average strictness of pandemic-related policy and age adjusted COVID deaths (P<0.01) and a weak negative correlation between vaccination and age adjusted COVID deaths (Vaccination 65+ adjusted P=0.045). However, when deaths are additionally adjusted for obesity, there is no longer a significant correlation (Obesity adjusted P=0.16, Obesity and Vaccination 65+ adjusted P=0.22). Although some studies have suggested policies are effective in reducing COVID deaths and reducing strain on hospitals (1, 2, 3), effectiveness may decline over time with reduced compliance as more people are experiencing “lockdown fatigue“. Additionally, a recent meta-analysis found effectively no impact of lockdowns on mortality. As we are visualizing cumulative deaths across the pandemic, we will not see short-term impacts.

Mean Stringency Index and Adult Depression & Anxiety 

Mean Stringency Index versus % Change in Anxiety/Depression | y = -0.0058 * x + 0.639 | R^2 = 0.04

Stringency Index and Suicide

Mean Stringency Index versus % Change in Suicide Mortality | y = 0.0017 * x + 0.013 | R^2 = 0.024

All states in the US observed an increase in depression and anxiety symptoms over the course of the pandemic. However, these increases were not significantly correlated with mean stringency index (P=0.16).  Further, mean stringency index was not correlated with the % change in suicide mortality between 2018 and 2021 (P=0.28). 

Stringency Index and Unemployment 

Mean Stringency Index versus Change in Seasonally adjusted % Unemployment | y = 8.66E-4 * x + 0.0185 | R^2 = 0.28

Stringency index was moderately correlated with the % change in unemployment from the start of the pandemic to Sept 2021 (P<0.01) For further reading about the economic impacts of the COVID pandemic and government interventions see the following articles: The COVID-19 crisis: what explains cross-country differences in the pandemic’s short-term economic impact?, Epidemiological and economic impact of COVID-19 in the US, and
Pandemic Impact on Mortality and Economy Varies Across Age Groups and Geographies.

Obesity

Our data show that adult obesity rate has a moderate positive correlation with age adjusted COVID deaths both before and after adjusting for vaccination status (P<0.01 and P<0.05 respectively). This is consistent with prior studies which identify obesity as a significant risk factor for death from COVID infection (OR = 1.61). However, the slope of these plots is a few fold higher than one would expect given the individual-level risks of obesity alone even after adjusting for age & vaccination, which suggests that state obesity rate may serve as a proxy for other population-level risk factors for COVID death.

Obesity and Age Adjusted COVID Deaths

Adult Obesity Rate versus Age-adjusted deaths | y = 1198 * x -97.8 | R^2 = 0.371

Obesity and Age & Vaccination 65+ Adjusted COVID Deaths

Adult Obesity in 2021 versus Age and Vaccination adjusted COVID deaths | y = 523 * x + 122 | R^2 = 0.117

Poverty & Income Inequality

The Gini Index represents the degree of inequality in the distribution of income in a particular location. It’s value ranges from 0 to 1 with higher values indicating greater income inequality. You can find a more detailed explanation of its calculation here. The charts below show a significant (P<0.01) positive correlation between income inequality and age-adjusted COVID deaths, even after adjusting for obesity and vaccination. 

We also examined the poverty rate, using the supplementary poverty measure (SPM). The SPM takes into account differences in regional cost of living as well as taxes and the value of government assistance programs. Click here for more information about its calculation and comparison to the official poverty measure. Although Gini Index and the SPM are highly correlated with one another, it appears that Gini Index demonstrates a higher correlation with COVID mortality, especially after adjusting for vaccination and obesity. 

Partisanship

We used the percent of the population who voted for Trump in the 2020 presidential election to examine associations with partisanship. Although this will not be directly causal of COVID deaths, there has been a partisan aspect to vaccination in the United States, which does influence risk of death from COVID infection. Our analysis shows a very strong negative correlation between vaccination status and percent Trump vote.  Additionally, although there was a strong correlation between obesity and Trump vote, in a multivariate linear model with obesity, and vaccination over 65, percent Trump vote was not significantly associated with age-adjusted COVID deaths (P=0.76).

% Trump Vote (2020) and Vaccination Rate

% Trump Vote versus Fully Vaccinated | y = -0.756 * x + 1.02 | R^2 = 0.775

% Trump Vote and Vaccination Rate 65+

% Trump Vote versus % Fully Vaccinated Over 65 | y = -0.282 * x + 1.03 | R^2 = 0.442

Most states showed high vaccination rates in people aged 65 and older, and significantly reduced vaccination rates in younger populations. Although states with high Trump vote had significantly lower vaccination rates in the 65+ age group (P<0.01), the difference was exaggerated in younger populations (P<0.01). This is also shown in the chart below (P<0.01). 

Difference Between % Fully Vaccinated Over 65 & Under 65 and Trump Vote

% Trump Vote versus % Vaccinated Over 65 Minus Under 65 | y = 0.519 * x - 0.057 | R^2 = 0.674

Stringency Index and Age Adjusted COVID Deaths (Colored by Governor Partisanship)

Summary Statistics for States with Democrat Governorss

Mean Age-adjusted COVID Deaths/100K = 272±77 (std dev)

Mean Stringency Index = 40.4±6.3 (std dev)

Summary Statistics for States with Republican Governors

Mean Age-adjusted COVID Deaths/100K = 305±79 (std dev)

Mean Stringency Index = 31.4±6.0 (std dev)

Race & Ethnicity

Members of some racial and ethnic minority groups are more likely to experience severe outcomes from COVID-19 infections. The prevalence of underlying comorbidities that increase risk of severe outcomes from COVID infection (i.e. cardiovascular disease, asthma, obesity) vary by race and ethnicity and they may face differences in access to adequate healthcare resources.  As the racial composition of Hawaii is significantly different from the rest of the United States (i.e. an outlier), it was excluded from this analysis. 

Percent Population White versus Age-adjusted deaths | y = -555 * x + 645 | R^2 = 0.4 It should be noted that the race and ethnicity data are not normally distributed (Shapiro Test P<0.01 for all). White P<0.01 | Black P<0.01 | Asian P=0.22 | Indigenous P=0.66 | Hispanic P=0.30
Age-adjusted deaths versus % Population Black | y = -2.7E-4 * x + | R^2 = 0.214

Race & Ethnicity and COVID Deaths Adjusted for Age, Vaccination Over 65 & Obesity

Percent Population White versus Age-adjusted deaths | y = -171 * x + 426 | R^2 = 0.114 It should be noted that the race and ethnicity data are not normally distributed (Shapiro Test P<0.01 for all). White P<0.05 | Black P=0.19 | Asian P=0.26 | Indigenous P=0.70 | Hispanic P<0.01
Age-adjusted deaths versus % Population Black | y = 105 * x + 282 | R^2 = 0.036
% Population Asian versus Age-adjusted deaths | y = 316 * x + 282 | R^2 = 0.027
% Population Indigenous versus Age-adjusted deaths | y = 94.3 * x + 291 | R^2 = 0.003
% Population Hispanic versus Age-adjusted deaths | y = 238 * x + 263 | R^2 = 0.212

In a multiple regression model with obesity and vaccination over 65, % Hispanic population was significantly positively correlated with age adjusted deaths (P<0.05).

Mental Health

There are many new stresses associated with the pandemic that could lead to increases in mental illness in the population: worry about health, death of a loved one, chronic symptoms from long COVID, increased unemployment, and social isolation. All states observed an increase in depression and anxiety symptoms over the course of the pandemic and many had increases in suicides. However, based on our data these increases were not correlated with COVID deaths (depression & anxiety P=0.76, suicide P=0.12) or stringency index (depression & anxiety P=0.16, suicide P=0.28).

Age Adjusted COVID Deaths and % Change in Anxiety & Depressions Symptoms

Age-adjusted deaths versus % Change in Anxiety/Depression | y = -7.64E-5 * x + 0.454 | R^2 = 0.001

No Correlation P=0.76

Age Adjusted COVID Deaths and % Change in Suicide Mortality 

Age-adjusted deaths versus % Change in Suicide Mortality | y = -2.7E-4 * x + 0.126 | R^2 = 0.067

No Correlation P=0.12

% Change in Unemployment and % Change in Anxiety & Depressions Symptoms

% Change in Unemployment versus % Change in Anxiety/Depression | y = 0.935 * x + 12.9 | R^2 = 0.915

The chart to the left shows no significant correlation between anxiety and depression symptoms and the difference in seasonally adjusted unemployment from Jan 2020 to Sept 2021 (P=0.053)

Excess Deaths

Excess death is the difference between observed deaths in a specific time period and expected deaths in the same time period. The data below show the excess deaths in each state from Jan 2020 to Dec 2021. Expected deaths are based on historical trends from 6 years prior to the initial outbreak. The data below show a negative correlation between excess deaths and vaccination over 65 (P<0.01), which may be indicative that some deaths coded as non-COVID may in fact be related. For example, several studies have shown that even mild COVID infection significantly increases a person’s risk of cardiovascular disease. One found that the risk of stroke was 52% higher and heart failure was 72% higher in people who had been infected with COVID than those who had not. 

Excess Deaths and Vaccination Rate

Including COVID Deaths y = -0.252 * x + 0.291 R^2 = 0.218 | Excluding COVID Deaths y = -0.10 * x + 0.072 R^2 = 0.073

Including COVID Deaths:  P<0.01 | Excluding COVID Deaths: P=0.057

Excess Deaths and Vaccination Rate 65+

Including COVID Deaths y = -0.627 * x + 0.688 R^2 = 0.376 | Excluding COVID Deaths y = -0.278 * x + 0.255 R^2 = 0.156

Including COVID Deaths:  P<0.01 | Excluding COVID Deaths: P<0.01

Excess Deaths and Mean Stringency Index

Including COVID Deaths y = -0.0016 * x + 0.195 R^2 = 0.071 | Excluding COVID Deaths y = -2.2E-4 * x + 0.019 R^2 = 0.003

Including COVID Deaths:  P=0.061 | Excluding COVID Deaths: P=0.71

Excess Deaths and Obesity

Including COVID Deaths y = -0.296 * x + 0.042 R^2 = 0.066 | Excluding COVID Deaths y = 0.0681 * x - 0.0112 R^2 = 0.007

Including COVID Deaths:  P=0.071 Excluding COVID Deaths: P=0.56

Notes on Methods

  • P-values for plots were calculated using the Fisher R to Z transformation
  • P-values for multivariate models were calculated by multiple regression
  • Significance level was set at 0.05

Citations

The Bioinformatics CRO Podcast

Episode 48 with Alex Shalek

Alex Shalek, Associate Professor of Chemistry at MIT, discusses new methods and technologies in systems biology that have enabled advances in the diagnosis and treatment of autoimmune diseases and cancer. 

On The Bioinformatics CRO Podcast, we sit down with scientists to discuss interesting topics across biomedical research and to explore what made them who they are today.

You can listen on Spotify, Apple Podcasts, Google Podcasts, Amazon, and Pandora.

Alex is Associate Professor of Chemistry at MIT, where his multi-disciplinary research aims to create and implement broadly-applicable methods to improve prognostics, diagnostics, and therapeutics for autoimmune diseases and cancer. 

Transcript of Episode 48: Alex Shalek

Coming soon…

The Bioinformatics CRO Podcast

Episode 47 with Jamie Smyth

Jamie Smyth, Associate Professor at Virginia Tech, discusses intercellular communication in the heart and how viral infection of cardiac cells can result in heart disease. 

On The Bioinformatics CRO Podcast, we sit down with scientists to discuss interesting topics across biomedical research and to explore what made them who they are today.

You can listen on Spotify, Apple Podcasts, Google Podcasts, Amazon, and Pandora.

Jamie is Associate Professor at Virginia Tech, where his research aims to define cardiomyopathy at a subcellular level, searching for potential targets for therapeutic interventions to help restore normal cardiac function to diseased hearts.

Transcript of Episode 47: Jamie Smyth

Coming soon…

The Bioinformatics CRO Podcast

Episode 46 with Amar Gajjar

Amar Gajjar, world-renowned neuro-oncologist and chair of the Department of Pediatric Medicine at St. Jude Children’s Research Hospital, discusses emerging treatments for pediatric brain tumors such as medulloblastoma.

On The Bioinformatics CRO Podcast, we sit down with scientists to discuss interesting topics across biomedical research and to explore what made them who they are today.

You can listen on Spotify, Apple Podcasts, Google Podcasts, Amazon, and Pandora.

Amar is Chair of Department of Pediatric Medicine at St. Jude Children’s Research Hospital. He is principle investigator on several clinical trials aiming to treat brain cancers like medulloblastoma and improve the quality of life for patients in recovery. 

Transcript of Episode 46: Amar Gajjar

Coming soon…

The Bioinformatics CRO Podcast

Episode 45 with Jill Reckless & Jon Heal

Jill Reckless, CEO and co-founder of RxCelerate, and Jon Heal, Head of In Silico Designs at RxCelerate, discuss the virtual biotech industry, outsourcing drug development, and using bioinformatics for target optimization.

On The Bioinformatics CRO Podcast, we sit down with scientists to discuss interesting topics across biomedical research and to explore what made them who they are today.

You can listen on Spotify, Apple Podcasts, Google Podcasts, Amazon, and Pandora.

Jill is co-founder and CEO of RxCelerate, an outsourced drug development platform. Having completed her PhD at the National Heart and Lung Institute in London, Jill was an academic at the University of Cambridge until December 2011 before founding RxCelerate.

Jon is Head of In Silico design at RxCelerate. After completing a PhD at Imperial College London, he founded a computational biology-based drug development company Prosarix, which was acquired by RxCelerate in 2019. 

Transcript of Episode 45: Jill Reckless & Jon Heal

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The Bioinformatics CRO Podcast

Episode 44 with Adam Siepel

Adam Siepel, Professor of quantitative biology at Cold Spring Harbor Laboratory, discusses the applications of evolutionary genomics in anthropology, infectious disease, and cancer.

On The Bioinformatics CRO Podcast, we sit down with scientists to discuss interesting topics across biomedical research and to explore what made them who they are today.

You can listen on Spotify, Apple Podcasts, Google Podcasts, Amazon, and Pandora.

Adam is Professor of quantitative biology at Cold Spring Harbor Laboratory, where his lab studies molecular evolution and transcriptional regulation in cancer, infectious disease, anthropology, and agriculture.

Transcript of Episode 44: Adam Siepel

Grace: [00:00:00] Welcome to The Bioinformatics CRO Podcast. My name is Grace Ratley. I’ll be your host for today’s show, and today I’m joined by Adam Siepel. Adam is chair of the Simon Center for Quantitative Biology and Professor of Biology at Cold Spring Harbor Laboratory. Welcome, Adam.

Adam: [00:00:15] Thank you. It’s great to be here.

Grace: [00:00:17] Yeah, it’s great to have you. So can you tell us a little bit about the research that you’re doing?

Adam: [00:00:21] Yeah. So we do work in a variety of areas in genomics. My laboratory is completely a dry lab. We do only computational work, but we collaborate closely with a number of experimentalists and really try to stay as close as possible to data generation and biological questions. But we do have strong backgrounds in probabilistic modeling, algorithms, machine learning and related areas, and we try to bring those skills to bear on our work. We are interested broadly in questions involving evolutionary genomics, in particular evolution of gene expression. We are interested in demographic reconstruction of human populations involving both humans themselves and human interactions with archaic hominins such as Neanderthals and Denisovans.

[00:01:11] And we’re also interested in natural selection making inferences about the strength of natural selection, which parts of the genome are affected by natural selection, and natural selection on different time scales: ancient natural selection affecting primates and more recent natural selection affecting human populations, for example. And then, I should say also, we’re interested in applications not only in human population genetics, but also in cancer and agriculture and other areas. We’ve also done some recent work on COVID modeling, for example.

Grace: [00:01:43] Yeah. I saw a really interesting paper from your lab on COVID, looking at the influence of daylight savings time on immune response.

Adam: [00:01:53] That’s right. Yeah, it sounds like sort of a crazy connection. But one of my colleagues here, a senior scientist, Rob Martienssen, HHMI investigator, had a hypothesis that there could be an interaction between daylight savings time and seasonal patterns of COVID infection relating to the fact that at certain times of the year, people are most likely to be interacting with other people during their daily commute, exactly at the time of a nadir in immunity, which occurs around sunrise. And he had the observation that daylight savings time changing the clocks prolongs the period of time when the daily commute coincides with sunrise. And so we did some modeling to show that indeed, there seems to be a signal in the public data indicating that there is an effect and that infections could be reduced by eliminating daylight savings time. So that’s one of many reasons to eliminate daylight savings time.

Grace: [00:02:53] Yeah. I’m all for that research as long as I don’t have to wake up an hour earlier than normal.

Adam: [00:02:58] That’s right.

Grace: [00:02:59] Yeah. So can you tell me a little bit about how you get those data sets to predict how human genomes have evolved over time?

Adam: [00:03:09] Well, all of our research is based on data that has been collected by other groups. Much of it collected by Svante Pääbo group at the Max Planck Institute in Leipzig, Germany. And they have over many years now develop techniques for extracting DNA from fossilized bones. The techniques are quite sophisticated because if you’re not careful, it’s very easy to contaminate the ancient hominin DNA with modern human DNA. And so they’ve developed clean rooms and special DNA extraction techniques and special purification techniques. And then post-processing bioinformatics techniques to ensure that the DNA they’re sequencing really represents the ancient remains and not the modern humans who are handling the fossils. But we can’t claim responsibility or credit for any of those works. We’re consumers of the data that they have produced and made publicly available.

Grace: [00:04:10] Right. Of course, I didn’t know if it was some sort of reverse modeling like taking current human DNA to predict what the DNA looks like previously.

Adam: [00:04:19] Well, there’s some of that because what you get is kind of a noisy readout of the the DNA for the ancient remains. And then we have higher quality representations of modern human DNA. And then we try to model the processes that could have given rise to both of those samples. And that does involve sophisticated statistical methods for reconstructing ancestral DNA, as well as explaining the observed samples.

Grace: [00:04:48] Yeah. And so can you tell us a little bit about what you found in that research?

Adam: [00:04:53] Sure. It’s now fairly well known and well accepted based on findings that were developed over the last decade that there has been a genetic interaction between modern humans and Neanderthals. In particular human populations outside of Africa, including Europeans and East Asians show a signal of Neanderthal DNA at something like three percent of their genomes that traces back to Neanderthals. And the best explanation we have for that signal is that there was some sort of interbreeding between modern humans and Neanderthals, probably outside of Africa, after humans had migrated from Africa, something like seventy or eighty thousand years ago. And that signal persisted as these out-of-Africa populations spread across the globe.

[00:05:55] We came in already knowing these findings and already familiar with these findings. And we tried to develop a model that would jointly explain a number of ancient samples and a number of modern samples from around the world. And our goal was to see whether we could both explain this known pattern of Neanderthal-human interaction, but also possibly detect other signals of interest. And what we found interestingly, and this was published in 2016 in a paper in Nature that I jointly co-led with Sergi Castellano, who was then at the Max Planck Institute in Leipzig and has now moved to London. What we found was that there was a signal, surprisingly in the opposite direction of modern human DNA in Neanderthals. And this was something that hadn’t been reported previously. And the signal was quite subtle. And it was quite difficult to convince the community that it even existed.

[00:06:49] But we were able to convince reviewers of our paper, and it has since been supported by a variety of other analyses. And interestingly, this signal is not specific to out-of-Africa populations. It’s shared by Africans as well, and it appears to be much older. We’ve since in more recent work, dated it to somewhere around two hundred fifty thousand years ago. And so that suggests that there was an earlier integration event that left a signature in the opposite direction from modern humans to Neanderthals, and it affected all human populations. So it probably occurred in the ancestor to all modern humans. Furthermore, that’s interesting because it must have predated the migration of humans out of Africa. So it seems like there was a group of early modern humans that migrated out of Africa interacted with Neanderthals, leaving this signature in Neanderthal DNA that we’ve detected.

[00:07:52] And then that group of modern humans either just went extinct or ended up being absorbed back into human populations in Africa before a second migration out of Africa seventy or eighty thousand years ago. So anyway, it suggests not only another interaction between modern humans and Neanderthals, but one that’s much earlier, and it paints a picture of multiple migrations of modern humans out of Africa. And only the more recent cases led to the current populations that we know of today outside of Africa.

Grace: [00:08:28] Yeah, that’s so fascinating. I feel like bioinformatics is already such an interdisciplinary subject. I mean, taking together biology and computer science, and then you take it a whole step further and add anthropology in there. Do you work closely with people in anthropology or studying human history?

Adam: [00:08:48] Yeah, I have not worked directly with anthropologists myself. Although we did have an anthropologist collaborator on the paper in Nature, although we worked more closely with Sergi than with me. But there is a lot of interest across the field at this intersection between genetics and anthropology and Svante Pääbo and David Reich and others have been quite proactive about interacting across fields. I attended a meeting here at Cold Spring Harbor a few years ago that was organized by co-organized by David Reich. That was a group of geneticists and a group of anthropologists together discussing these issues, and it was fascinating. But it’s not something that’s really in the center of my own research.

Grace: [00:09:29] You seems to have a very broad reach with your research. It’s great. Yeah. So you started out doing computational modeling and phylogenetic modeling in HIV. Can you tell us a little bit about that work?

Adam: [00:09:41] Yeah. So this was my first job actually straight out of college. I hadn’t gone to graduate school yet. Through a friend of mine who had been an undergraduate with at Cornell, found out about this opportunity to work at Los Alamos, doing HIV sequence analysis. And it was interesting to me for a number of reasons. I had done an undergraduate degree in agricultural and biological engineering. And so I had been interested for a long time in sort of the intersection between mathematical modeling and questions in biology. But I had never dealt with DNA sequence data before, and I had never dealt with phylogenetics or evolutionary reconstruction. And when I was exposed to those fields, I just found them fascinating. I mean, they resonated with me in a whole variety of different ways. I’ve always been interested in reconstructing the past.

[00:10:28] I’m interested in random processes. I’m interested in computer algorithms. I’m interested in evolution. And so all of these interests sort of came together in this fascinating area of using phylogenetics. And then that work also had an epidemiological component. We were building phylogenetic trees to describe HIV sequences, but then we were making use of them to understand the spread of HIV across the globe because we were seeing different strains emerge in different regions of the world. And then we began to see interactions between these strains and the production of recombinant strains of HIV. So my first scientific paper was actually on an algorithm that I developed, a very simple algorithm, to detect recombinant strains of HIV, which at the time was a kind of a new idea and something that was of great interest in the field.

Adam: [00:11:23] My first experience was very exciting publishing a scientific paper. I think I was 23 years old and was able to publish a paper that established researchers in the HIV field were excited about, and I got to present at meetings and so on. And after that I was hooked. I was hooked on science, I was hooked on computational biology and I was hooked on evolutionary genomics.

Grace: [00:11:45] Do you still keep up with emerging HIV research and things like that?

Adam: [00:11:49] I haven’t participated in HIV research since that time. I moved on to other questions. Although I have to say I got interested again recently in this question of recombination and viruses with the emergence of COVID-19. And reread some of those old papers and including my own old work on detecting recombination in viruses because there was some discussion about the role that recombination might have played in the emergence of SARS‑CoV‑2 in human populations. But that’s my only experience in that area in the last 25 years or so.

Grace: [00:12:30] Yeah. So I guess speaking of the pandemic, from an evolutionary standpoint, do you think we need to worry about new variants and things like that?

Adam: [00:12:41] The basic evolutionary fact is the probability of emergence of a new variant should be proportional to the number of viral replication events, which is going to be proportional to the overall number of cases. And so we need to get the number of cases down. And the best way to do that is through vaccinations. It’s been extremely discouraging to me to have these effective vaccines, more effective than anyone could have hoped, and see people reluctant to use them. So I think we just have to keep hammering on the vaccination efforts. They need to be available across the entire world, not just in rich, first-world countries. We need to push really hard on getting access to them, convincing and incentivizing people to use them.

[00:13:36] Ultimately, I think new variants will emerge. We will develop over time an increasing sort of baseline resistance for most people in the world who will eventually be exposed. And I think the pandemic will eventually reduce itself to a baseline level. But I think the virus will be endemic and we’ll have to adjust to it being part of life. I’m optimistic that with increased baseline resistance, increased vaccination, increased ability to provide new vaccines quickly and efficiently, that we won’t be brought back to our knees by emerging variants. But it’s difficult to say for sure what could happen as new variants emerge.

Grace: [00:14:29] I always like to hear the different perspectives of people in different fields of science on the pandemic. I think the evolutionary take on it is very interesting. You also did a study in bats, on the evolution of bat immunity and things like that.

Adam: [00:14:46] That’s right. Yeah, we’ve gotten very interested in comparative genomics of bats, in part because of their connection with SARS-CoV-2. But for other reasons as well. In fact, our initial work on bats has been funded by our cancer center here at Cold Spring Harbor. Because bats are remarkably resistant to cancer and we’ve been trying through DNA sequencing and comparative analysis to shed light on the genetic underpinnings of both bat specific immune responses, bat specific cancer resistance and longevity of bats. Bats are extremely long-lived mammals for their body size. If you plot body size versus lifespan in mammals, you see a general proportionality. But bats are an outlier. They live much longer than other mammals of similar size, such as mice. Bats can live 35-40 years or more.

[00:15:42] We’ve been doing DNA sequencing and analysis. We have an initial preprint out on our findings. We’ve found some interesting things in both immunity and cancer, in particular a massive contraction of the IFN1 locus. And a strong enrichment among apparently positively selected genes for tumor suppressors and DNA repair genes. And we’re in the process of working closely with experimentalists to begin to test the actual molecular basis of some of these differences between bats and other mammals. And we’re also in the process of applying for grants from the NIH on this topic.

Grace: [00:16:22] That’s so cool. Because I guess I wanted to ask a little bit about the importance of evolutionary biology in the study of cancer, because I wasn’t necessarily sure how those two topics connected. So that’s a really interesting take on comparative genomics and looking at how that immune system has influenced their susceptibility to different cancers. And bats have a reduced immune response. They don’t have a very active immune system, is that correct?

Adam: [00:16:51] Yeah. They seem to be able to tolerate viral infections without having a very powerful immune response. And it’s interesting because when you look at what makes humans sick when they become infected by SARS-CoV-2 or other viruses, it’s often an overly powerful immune response that makes them very sick. And so in some cases, it seems that viruses are killing us, not because our immune response is inadequate, but because it’s too powerful. And one hope is that we can learn something from bats in the way that they’re able to keep from getting sick from these viruses and yet not have an overly powerful immune response that ends up harming them more than the virus itself. Yeah. So that’s one of our interests in this area.

[00:17:44] Of course, it’s also interesting to just understand the dynamics of zoonotic transmission and the way in which bats are harboring viruses and then transmitting them to people. The fact that bats seem to be able to tolerate such high viral loads does seem to be essential to their role as a reservoir for viruses that get transmitted to humans. And so understanding their viral tolerance is also important and interesting in that regard.

Grace: [00:18:16] Yeah. So I mean, evolutionary biology is kind of a pretty popular science topic. So what do you think are some misconceptions that people in the general public have about evolutionary biology?

Adam: [00:18:30] One misconception is understanding the diversity of selective forces that have influenced humans. People tend to think in conventional terms about the strongest humans being the ones that propagate, you know, the ones that are least likely to be killed by predators and that sort of thing. And undoubtedly avoiding predators was a source of selective pressure on humans. But there are many others that I think tend to be underappreciated. One of them is infectious disease. I mean, humans have been enormously shaped by infectious disease. And one of the strongest selection pressures on us is the resistance to infectious disease. The pandemic is helping make this issue more clear. But I think in general, we tend to have forgotten a lot about infectious diseases because they play much less of a role overall in modern life than they have in the past.

[00:19:28] Another really important selective pressure is sexual selection. The choices people make about who they mate with for various reasons. And then there are very strong selective pressures that influence reproduction in a way that humans have no choice over. So, for example, sperm competition individual sperm cells competing with one another to fertilize an egg. So there are many, many levels at which selection acts. And I think when people just think about a caveman dodging a mountain lion or a bear, they’re only getting at a very small sliver of the diversity of selective forces that have influenced human evolution.

Grace: [00:20:14] Yeah, that’s true. There are some really interesting selection events. So after you worked in Los Alamos, where did you head after that?

Adam: [00:20:22] Well, I was working in Los Alamos in the mid 90s. And I had an engineering background and I had a lot of interest in developing computer software. And at the time, I felt that my interests lay more in the software development area than in the scientific research area. And it coincided with a time where there was a lot of opportunity for software development in bioinformatics. A lot of companies were creating bioinformatics groups. A lot of people were developing and either selling or making publicly available bioinformatics software. And so I took a job at a group in Santa Fe, New Mexico, called NCGR, National Center for Genome Resources that was doing a lot of software development. I went there and I worked for about 5 years as a software developer and learned a lot about software development and then kind of came to the conclusion that I wanted to get closer to the science.

[00:21:20] And after many years of putting off going back to graduate school, I decided I really needed to bite the bullet and get my PhD. I was sort of a reluctant academic, I have to say. At the time, I was of the mindset that I could teach myself anything I needed to know. But I finally decided that there was value in getting my PhD and diving back into scientific research. So I left software development, became a full time PhD student and went to Santa Cruz, California, to join David Haussler Laboratory. And from that point on, I have plunged myself into the world of comparative genomics, population genetics, evolutionary modeling and so on.

Grace: [00:22:01] Yeah. And despite that reluctance to going into academic science, you stuck with it after your PhD because you went and became a professor at Cornell and now Cold Spring Harbor. Can you talk a little bit about that decision? How did you change your mind?

Adam: [00:22:16] I actually had not planned to go into academia. I wasn’t sure what I was going to do. But it was an exciting time, the early 2000s for academic computational biology. There were a lot of opportunities emerging, a lot of new departments, new research centers. And in my third year as a PhD student, I had been working with Rasmus Nielsen, who’s now at UC Berkeley, on a book chapter project. He was editing a book and I was writing a chapter with my advisor. And he sent me a job ad at Cornell and I read this job ad and it just sounded like it was written for me. I mean, they were looking for someone who had exactly the sort of expertise I had. And, you know, I had been an undergraduate at Cornell, so I had a lot of affection for the place.

[00:23:07] Coincidentally, I also was considering moving closer to family. My family’s from upstate New York and Cornell is in upstate New York, and I had two small children and we were getting tired of putting them on planes every time we wanted to see family. So I said, what the heck? I’ll apply to this job. I applied and I got the job. So I said, well, you know, I never really planned to be an academic, but this sounds like fun. It sounds like a great opportunity. I love what I’m doing. This is an opportunity to keep doing what I’m doing. And I took the job and never looked back. I’ve really enjoyed academic work since then and have been able to make it work, been able to keep the lab funded and keep publishing papers and keep recruiting students.

[00:23:49] And I think I’ll just keep doing that as long as I can. But it was a different time. I mean, I mentor a lot of my own graduate students and postdocs in their job searches. And I think the job market is much more competitive now than it was then. There was a lot of opportunity in computational biology in the early 2000s, and I benefited from being in the right place at the right time. Sometimes I see the job searches we carry out now, and I wonder if I would have even gotten an interview for some of these jobs.

Grace: [00:24:18] Yeah. Academic science is a very competitive space these days. But there is such a strong need for bioinformaticians and computational biologists. So I mean, there’s a lot of job security in that, but maybe academia is a lot harder.

Adam: [00:24:34] Yeah. I think there are more industry opportunities now than there were at that time. And, you know, the combination of the competitive academic job market and the opportunities in industry means that a lot of young trainees are going into industry, which I think is great. I have a number of recent postdocs from my lab who’ve taken industry jobs and are very happy in them. But, you know, the pendulum tends to swing from one side to another on these things. And I wouldn’t be surprised if in a few years the supply and demand dynamics have changed and things open up in academia again.

Grace: [00:25:08] Certainly. And how have you seen bioinformatics and computational biology as a field evolve in the last few years?

Adam: [00:25:18] One change is just, as I mentioned, a swing toward more activity in research and industry. Another change that I’ve seen in my time in computational biology is just a general shift toward embracing the biology side of the field. They need to ask good biological questions, they need to engage with the data and people not being satisfied with just taking whatever the latest algorithmic or machine learning advances and applying it to a biological data set. I think when I started in the field in the early 2000s, there was a lot of that. There were a lot of people doing computational biology who weren’t that interested in biology and didn’t know that much biology. They were just taking off-the-shelf computational methods and applying them to biological questions in a not very imaginative way.

[00:26:10] And I think over time, people have really realized that in order to do computational biology well, you have to engage with the biology. It’s not enough to just have a computational hammer and look for nails. You have to really think imaginatively about biological questions and how computational methods can be used to address them. And about the interaction between computational methods and experimental methods. About how experimental methods can lead to hypotheses that can be tested computationally and vice versa. Computational methods can generate hypotheses that can be tested experimentally. That feedback between computation and experiment, I think is extremely important and has become more pervasive in the field.

[00:26:54] I think the field is also just bigger and more competitive. Early on, there were really just a handful of people who had this joint background in computer science and biology. And if you were one of those people, then you could sort of write your own job description. It was relatively easy to find a job in the field. Now there are many, many people who have those backgrounds. There are people emerging from PhD programs in bioinformatics and computational biology. There’s a lot more awareness of these questions in biostatistics departments or biophysics departments. It’s just a much more established and competitive academic field.

Grace: [00:27:39] Do you think you would have chosen the same path if you had graduated in bioinformatics today?

Adam: [00:27:46] I really don’t know. I mean, I think I was attracted to the field being so new. And maybe I would feel today that it was too established and I would look for something newer and more niche. But it’s hard for me to say. I also think it’s possible that if I were finishing my PhD now that I would end up in an industry job rather than in an academic job just because of the dynamics of the field at the moment. But it’s always hard to ask these counterfactual questions.

Grace: [00:28:19] True, true. So given the hyper competitive job market for positions and bioinformatics, can you maybe give advice to people who want to enter that field? Like what sorts of skills are most important today?

Adam: [00:28:37] Yeah. I guess, I think it’s true that a graduate from a bioinformatics program who’s interested in this field needs to be fluent in data science and machine learning, basic statistics. But I think that those things are necessary but not sufficient for success in the field. And I think what really will push a person over the edge is also really thinking like a scientist, not just like an engineer. So developing a good taste in problems, developing a nose for questions that can be effectively addressed using computational methods, developing a fluency in the biological technologies and biological questions of interest, the ability to interact closely with experimentalists. I think these are the things that push a person over the edge from being just a data scientist to being a computational biologist who can lead the way in the scientific side of the field.

Grace: [00:29:41] It’s very good advice. So tell me a little bit about you. Like, who is Adam the non-scientists? What do you do outside of research?

Adam: [00:29:49] Well, I have two kids. My daughter just started at the University of Rochester. So I’m adjusting to going from two kids at home to one kid. I live on Long Island in Huntington, New York and live in an old Victorian house and spend a lot of my time fixing that up. And I like to do a lot of cycling and some hiking and spend as much time as I can outdoors. That’s probably a pretty good summary.

Grace: [00:30:16] Yeah. I actually saw in your Twitter that you were planning on heading to Iceland. Did you make it out there?

Adam: Yeah, we did.

Grace: Nice. Yeah, I was just there a couple of weeks ago.

Adam: [00:30:27] Ok. Yeah, we really enjoyed it. We had a fantastic trip. It’s a beautiful place and it felt like the right sort of first trip out of the country after COVID. Relatively safe and controlled.

Grace: [00:30:39] Yeah, that’s excellent. Yeah. Actually, Iceland is a really, probably a very interesting area to study because it’s so isolated and they have a huge dataset. Haven’t they sequenced everybody in Iceland?

Adam: [00:30:52] Yeah. The studies by deCODE have been extremely influential in a variety of different ways, both for association studies and also for studies of rates and patterns of human mutation, which they’re able to trace in great detail, taking advantage of their genealogical databases and pedigrees. So, yeah, it’s been very important in human population genetics. It’s also interesting to look at Iceland from an ecological perspective. I think the largest land mammal was the Arctic Fox in Iceland when Scandinavians arrived and began bringing agricultural animals. So there’s a very short history of large land mammals there. And then there have been interesting events like the introduction of the Icelandic horses and then subsequent genetic isolation, of those horses. And it’s interesting to see the way they have been shaped by the Icelandic landscape and climate, as well as by human selection. But yeah, it’s a fascinating place for questions in evolutionary biology. Certainly.

Grace: [00:31:59] Certainly. And yeah, Iceland horses are really interesting. They had such strict laws that if an Icelandic horse was taken out of Iceland that it couldn’t be brought back into the country. It was just really interesting. And with humans, they have an app. It’s like a dating app where you can check and see if it’s okay to date somebody based on your familial relationship to them.

Adam: [00:32:24] Ah, to see whether you might be related, yeah.

Grace: [00:32:26] Yeah. You put their name in and I think the generally accepted okay line is like fourth cousin or something like that.

Adam: [00:32:33] I see. Well, amazing.

Grace: [00:32:35] Yeah, it’s a really interesting country. Yeah. So as we wrap up the episode, do you have any other any final thoughts on the future of bioinformatics?

Adam: [00:32:46] Well, I guess the future of bioinformatics, I think it’s an open question whether bioinformatics will remain a distinct field. I think that to some degree, the tools of bioinformatics are being absorbed by broader biological sciences. They’re just becoming part of the toolkit of doing biology. And I think in the future, biologists will need to be much more fluent in computational methods and the use of machine learning and the use of powerful computers. And we may not think of it as a distinct field. It may just become part of being trained to do biology. And I think that’s okay. I think often new fields emerge at the interfaces of other fields, and they may or may not remain distinct. They may be absorbed over time, and I think that’s okay. I’m personally very excited to see quantitative methods and computational methods become so central in biology.

[00:33:50] You know, our center at Cold Spring Harbor, it’s called the Science Center for Quantitative Biology. It has begun as kind of a distinct group of investigators doing developing quantitative methods. But increasingly we’re being absorbed by the broader scientific community at Cold Spring Harbor. And the talks when we gather at our annual symposium or some other event to talk about our research. The talks from the quantitative biologists are beginning to involve more experimental biology and more collaboration with experimentalists. And then conversely, the talks by the experimentalists are beginning to incorporate more data analysis and quantitative methods. And I think the logical conclusion of this process is that we probably won’t be a distinct group anymore. We’ll all just be biologists using whatever tools and techniques are available, a combination of experimental and computational tools and techniques. So I guess that’s what I think about the future of the field. It’s dying, and that’s okay.

Grace: [00:34:56] It’s dying, and that’s a good thing. Fantastic. Well, thank you so much for joining me today, Adam. I had a great time listening to your thoughts on evolutionary genetics.

Adam: Yeah, thanks, Grace.

The Bioinformatics CRO Podcast

Episode 43 with Erich Jarvis

Erich Jarvis, Professor at Rockefeller University and investigator at HHMI, explains how studying the neural and genetic mechanisms of vocal learning in songbirds gives insights into the development of spoken language in humans.

On The Bioinformatics CRO Podcast, we sit down with scientists to discuss interesting topics across biomedical research and to explore what made them who they are today.

You can listen on Spotify, Apple Podcasts, Google Podcasts, Amazon, and Pandora.

Erich is professor at The Rockefeller University and investigator at HHMI, where he studies the mechanisms of vocal learning in songbirds and other animals. He has earned numerous honors for this research, including the Alan T. Waterman Award and National Institutes of Health Director’s Pioneer Award.

Transcript of Episode 43: Erich Jarvis

Grant: [00:00:00] Welcome to The Bioinformatics CRO Podcast. I’m Grant Belgard, and joining me today is Erich Jarvis. Eric is Professor at Rockefeller University and investigator at HHMI. Thanks for joining us, Erich.

Erich: [00:00:11] You’re welcome.

Grant: [00:00:12] So can you tell us about what your lab does?

Erich: [00:00:15] My lab studies the brain basically, but we study more specifically brain regions that control our ability to imitate sounds like that I’m doing now, producing the learned speech. And we do so in non-human animals that have that ability, like parrots and songbirds. These are rare few species in the world that can do this that are shared with us, even though they’re not closely related to us. So this is why parrots can imitate us where a dog can’t. We also have a genome lab that is generating lots of high quality genome data, not only for our neuroscience projects, but for the scientific community broadly, ranging anything from neuroscience to conservation.

Grant: [00:00:53] How do you think the genome effort can inform the neuroscience effort?

Erich: [00:00:57] Most traits are controlled by what’s in the genetic code of your genome and the genes in your genome. And so if we find this specialized trait like spoken language in humans that we don’t find in our closest relatives or like in parrots, we can compare the genome of humans in chimpanzees or parrots and falcons and ask the question, what differs there in their genetic code? And the more species you have with these traits, the higher the N number, which helps you statistically find the specific genetic markers that will develop a brain pathway that allows you to develop a neural circuit for, let’s say, speech or learning how to fly.

Grant: [00:01:38] And how similar are these circuits between humans and birds with vocalization?

Erich: [00:01:43] What’s surprising is that some of the similarities outweigh the differences–when I say we, it’s me and other people in the scientific community that study this not just my lab–but overall, we find that humans and parrots and songbirds have a specialized forebrain circuit in the parts of the brain you would call the cortex and the basal ganglia that you don’t find in any species that cannot imitate sounds or find it to a very rudimentary degree. So the fact that they are there is similar, right. And then they have some connections that are similar. Like the cortical regions in us humans and the equivalent in birds project down to the neurons in the midbrain that control the voice.

[00:02:30] And this connection is direct from the cortex where you don’t find those direct connections in species that can’t imitate. What’s remarkable is that we’re separated from birds by 300 million years of evolution. We have so many species in between that don’t have these circuits. Further, what’s remarkable is that the mammalian brain, including us humans, the cortex is a set of layered neurons like six layers, one on top of another. Whereas in birds, the neurons are clustered in the cortex. But yet, from the layered structured or clustered structure, you get a similar type of neural network that controls speech.

Grant: [00:03:08] So is this a case of convergent evolution?

Erich: [00:03:11] Yes, convergent evolution. It happened more than once in evolution, and it happened in similar way.

Grant: [00:03:16] Do you have any idea how many times this has happened?

Erich: [00:03:20] Yes. So far we think at least five times in mammals, us humans, whales and dolphins that together are called cetaceans, bats, elephants and then another marine mammal, pinnipeds. Those are seals and walruses and so forth. So it’s five out of like thirty two major orders of mammals. So you don’t find it in horses, for example. And amongst birds, we have parrots, hummingbirds and songbirds. So far no reptiles, no fish, no amphibians are known to have this trait.

Grant: [00:03:52] And is the kind of neural solution to the vocalization problem, for example, in mammals always the same?

Erich: [00:04:00] Yeah, it’s similar. So the similarity is that in all the species we’ve looked at so far, which is basically the three bird groups in humans compared to their closest relatives. I would call this spoken language pathway or the vocal learning pathway, I think they’re nearly equivalent that it is embedded inside of a motor pathway that controls learning how to move, like learning how to move the hands, learning how to move other parts of the body. And this motor learning pathway we can find in all vertebrate species that move right. So what we think happened is that the brain pathway for spoken language evolved out of the brain pathways controlling learning how to move and evolved by a brain pathway duplication. And so that’s why we think it’s similar in birds and humans because the ancestral brain regions out of which the speech pathways evolved were already there.

Grant: [00:04:54] How generalizable do you think the circuitry patterns are? Do you think that everything is built on this motor substrate and there are many, many paths to get to vocal learning and this is just the most common based on what was pre-existing?

Erich: [00:05:10] I actually think there are limited paths in which you can get to vocal learning. It’s like the evolution of wings, especially amongst vertebrates. Each time they evolved in bats, birds and ancient flying dinosaurs, the pterosaurs, they evolved on the upper arms, not one on the back or one around the tail and one by the feet and so forth. And the reason why is that there’s an environmental constraint. And that environmental constraint is the center of gravity. So if you’re going to fly in the sky, you need your wings to be near the center of gravity to fly more energetically. I think the same thing is happening to the brain. There’s limited ways you can evolve a circuit that’s going to control, learned sound production, especially through our vocal organs like the larynx.

[00:05:57] And I like to think of the surrounding motor pathway as the arms of the wings, right. And the vocal learning circuit as the wing structures themselves. But does that theme happen multiple times? I think my prediction is yes, no one really knows. But my prediction is that the motor learning circuits for different traits can emerge out of sort of canonical motor learning circuit and then become specialized. The specializations for speech in us humans and songbirds are of two forms. One is this is an advanced sensorimotor integrative behavior. We need to take sound coming through our ears and integrate it with movement of the jaw muscles and the laryngeal muscles and other things that control the sound production.

[00:06:46] That kind of sensory motor integration of auditory input motor output, I think requires its own special formulas–or algorithms in computer science terms–to work with each other. In fact, after vocal learning evolved it looks like only vocal learning species are the ones that can learn how to dance, synchronizing your body movements to rhythmic beats of sounds. And the reason why I think that is the case is because you need to synchronize rapid auditory input with motor output. So you need something special to have the auditory information talk to the motor information. And once that happened, it contaminated the surrounding motor circuits that control not just the larynx, but the hands, the feet.

[00:07:28] And so to have auditory input synchronize our body to rhythmic sounds was a side effect of spoken language. The second specialization I think that happened is the larynx has the fastest firing muscles in the body to produce sound. You need to move those muscles really, really quick to vibrate the air and produce sound or modulate the sounds. And we find that the neurons in the speech circuit for humans and in the vocal learning circuits of these birds are over enriched with molecules that control rapid-fire neurons that control neuroprotection so you don’t kill the neurons just by speaking. That’s another specialization you don’t find in the surrounding motor circuits.

Grant: [00:08:15] You brought up some pretty interesting things there. So you mentioned dancing is maybe almost a side effect. But when I hear dancing in the context of evolution, I think sexual selection, right. Do we know which came first?

Erich: [00:08:27] Yeah, we don’t. But there are a lot of theories out there of what cause vocal learning to evolve, and that includes spoken language and sexual selection is one of them. I can say that I tend to believe that because all the vocal learning species use their learned sounds for mate attraction or in case of non-humans for territorial defense. What we can do it for territorial defense as well, very few of them use it for more abstract semantic communication like we’re doing now to communicate ideas, to communicate concepts. Instead, they sing to attract mates. The more varied the songs or the more you steal sounds from the environment incorporated in songs like in mockingbirds, then more likely you’ll attract the opposite sex.

[00:09:16] Now, you would think semantic abstract communication, referential communication should be the first thing we use it for. What most people don’t realize—and even a lot of scientists–is that referential communication like using a sound to mean a bear, using a sound to mean this object over here, that’s already happening before even spoken language evolved. Like vervet monkeys have an innate repertoire of sounds that through cultural experience, they will learn to use for different predators or food and so forth. But the first thing vocal learners do with their learned sounds is to attract a mate. So that’s why I think it came first.

Grant: [00:09:56] In what species that have vocal learning, do you see rudimentary elements of culture? You’d certainly expect groups of dolphins and so on to learn from one another.

Erich: [00:10:07] Yeah. I’m going to answer the question you’re asking, but I think you might be asking a different question, right. Because all vocal learners culturally transmit their repertoires, whether they’re using it for semantic information or what we call effective information, like to attract mates. So it’s cultural once you have vocal learning. And by being cultural, you get different dialects, like we get different language and so forth. They further geographically you are separated. Now I think the question you’re asking on top of that is what species will culturally transmit information about their vocal repertoire that’s more informative like this sound means predator and so forth. And there are very few species that do that besides humans. Dolphins are thought to be one, corvid songbirds, which are basically crows, and Blue Jays and so forth, thought to be another. And the parrots, like African grey parrots.

Grant: [00:11:05] I guess another really interesting question that this raises is obviously comparative neuroanatomy and comparative genomics are enormously powerful tools to study this. But of course, a complementary approach is classic genetics or human genetics, right. I mean, looking at broken genes, seeing what they do, studying diseases and verbal defects and so on. How complementary have those been and have they pointed in the same direction, since work on FOXP2 and so on?

Erich: [00:11:35] Those two questions basically describe the approach we take in my research broadly. So yes, we use comparative genomics like a natural experiment. The more genomes you sequence out there with species with different traits, the higher probability you will find whatever genetic difference is responsible for those traits. So we sequence genomes and we compare genomes to find out if there’s convergence in all these different species with and without vocal learning. And if we find convergence, then we take those genes that have these convergent genetic changes. And what we’re doing now is taking the genes of a species that learns how to imitate sounds and putting it into the genome of a species that don’t like a mouse. And see if we can induce a change in the brain circuit to get us further along the path to becoming a vocal learner.

[00:12:34] And if we do, that proves that this gene is responsible for contributing to the trait and we can study what it does, its function and so forth. And if it doesn’t, we falsified our hypothesis. Another way of doing it is within one species like humans is you compare different people, and you find a family out there that has a speech disorder, speech deficit. They can do everything else fine, but they have difficulty in learning speech. And then you sequence their genome and you find something that’s different from them compared to all the people who can produce speech normally. And this is exactly what happened, more than a decade ago in the discovery of the FOXP2 gene. This is a gene, it’s a transcription factor, meaning that it regulates how much a gene product is made for other genes in the brain, that controls neural connectivity.

[00:13:30] And when this gene is mutated, it causes people to have difficulty learning how to produce speech. And we put that same mutation in mouse in collaboration with Simon Fischer in Germany. We found that even though these mice are producing mostly innate sounds, like humans they had difficulties switching to the more complex innate sounds that females like to hear in their courtship, right. We find this gene in songbirds and if you block its function in songbirds, just like in humans, it also prevents vocal learning from happening normally.

Grant: [00:14:15] So how do you think about translatability?

Erich: [00:14:18] For a long time, I was hoping that the work we were doing in songbirds–because I started out with songbirds–people would take those discoveries that we found and then try to translate it not only into understanding human knowledge, but to for human health. And I found that people were not doing that. So the first thing we did was find out if we could find convergent parallel changes in human genomes that we see in songbirds for vocal learning. And the answer is yes. Not everything is convergent for the genes but there is a lot of overlap in the genetics. And now what we’re trying to do is to see if we can induce a mouse to become more of a vocal learner species and study things like stuttering or autistic types of speech deficits and these FOXP2 mutations in mice. So that one day those can be translated to helping humans not only understand the disorder better, but to repair it.

Grant: [00:15:24] And how do you think about that in the context of developmental windows, right? Because by the time someone is diagnosed with a speech disorder usually that ship has sailed. But do you think that can be reopened in some way?

Erich: [00:15:37] Because you can have multiple people out there with the similar speech disorder all affecting the same genes. You don’t have to wait for somebody to grow up, to become an adult, to discover it. So by doing a population analysis as opposed to longitudinal analysis, you can get at discoveries quicker. Although waiting 13-14 years for a person to go through puberty and then find out whether they can produce normal speech and so forth is also necessary. That leads to that other part of the question the critical period of years. In all vocal learning species, there is a critical period or what we now call sensitive period, where it is easier to learn how to acquire speech early in life and later in life. And then once you pass puberty, it’s harder, like it’s harder to pick up a new language.

[00:16:24] There are certain set of genes that are turned up or turned down in the brain during those critical periods that close off the ability to learn as much as you can when you’re a child. And there are people who are now trying to switch those genes back on. So that allows you to learn, in this case, spoken language as well as you did when you were a child or at least getting closer to that. And I think that’s going to one day be possible, but it’s going to cause some problems also. And the problem that people don’t appreciate is that. Why can’t I learn as well as when I was younger? Why is it taking me so long to learn how to ride a bike as an adult then when I was a child?

[00:17:04] And the reason is that if your brain is in a very plastic stage where you can mold it and learn a lot quickly, you’re also going to forget quickly. And this is why, sometimes it’s hard to hold on to early childhood memories because you forgot a lot of it because there’s only so much capacity in the brain. If you’re going to learn, sometimes you’re going to erase. And yes, you can erase memories. So if you’re going to reopen the critical period, you better do it for a brief period of time. Learn what you can so you don’t erase a lot.

Grant: [00:17:35] This sounds like a premise for a good novel.

Erich: [00:17:38] That’s right.

Grant: [00:17:39] So what excites you the most about this field? What do you think is most promising, and what do you think are some of the biggest as yet unanswered questions?

Erich: [00:17:51] Yeah. What excites me most? Well, maybe I went into neuroscience because I was interested in something that was mysterious. The brain is one of the organs that we have the least knowledge about, but the biggest investment in or one of the biggest investments. I guess the biggest investment is in cancer broadly, but cancer affects the whole body. And so I’m talking about organ systems. Maybe the heart gets bigger investment. But I’m just fascinated by the fact that we have this kind of behavior or spoken language that allows us to culturally transmit knowledge from one generation to the next. It makes us humans the advanced species that we are. That’s what really fascinates me.

[00:18:32] Jumping many years later from me, starting this lab over 20 years ago. The biggest problem now is I would say mental health. It’s one of those things that’s a real mystery and is hard for us humans to figure out how to repair. It’s not as simple as stitching a wound and fixing it. And I think mental health is a bigger problem in humans than in other species. Not a lot of people think about mental health in other species, but think about your dogs who are home lonely and so forth, right. That can cause mental health disorders. Now people will listen to this, and they won’t want their dogs to be home alone. Get another dog to play with it, right. I think the problem is goes to another gene that’s actually involved or interacts with genes involved in language and spoken language circuits.

[00:19:22] In us humans, we have extra duplication of a gene called SRGAP2, spelled out as SLIT-ROBO GTPase. SLIT is a molecule that binds the receptor called ROBO. When they interact, they influence connections in the brain. Those two genes, SLIT and ROBO are turned up or down in certain brain regions that control speech that we think control the connection to the muscles that control speech. I’m sorry, I’m giving you all these molecular terms that the general audience might know, but I’m going to do it anyway. This GTPase modifies this SLIT-ROBO interaction to influence connections in the brain. It either dampens down its function or enhances its function. And so we humans have an extra copy of this GTPase molecule.

[00:20:16] And what that extra copy does is it inhibits the function of the normal gene. And by inhibiting the function of the normal gene, we slow down brain development in humans compared to other species. So our brain is developing at a slower pace and staying in a more juvenile state throughout adulthood compared to all other mammalian species or vertebrate species. And I think it’s leaving our brains in a more immature state, which then leads to more mental health disorders compared to other species. So some of these mental health disorders, are a consequence of having a more advanced civilization and our brains staying in a more juvenile-state so we can continue to learn throughout life.

Grant: [00:21:04] That’s a fascinating hypothesis. So I guess in that case, you might expect that there could be SRGAP knockouts, assuming its survivable out there walking around. Do you know if anyone’s kind of looked into what those phenotypes look like? Are there people whose brains basically mature much, much faster?

Erich: [00:21:22] That’s an interesting question. Because I have never thought of that, but it’s actually a doable question. And it makes me think, in humans there are either one or two extra copies of this SRGAP2 with people walking around. And it’s making me think, why have two extra copies in some people? And I’m thinking, maybe because one extra copy is not enough, you knock it out and then we become primitive human beings. The additional extra copy is like a safeguard in case one of them goes awry. I don’t know. There’s lots of genome sequencing being done on many people out there nowadays, and this question can be answered, theoretically. It might be difficult because what a lot of people don’t know in the genome world is a lot of the sequencing that’s being done on humans out there is being generated with what we call short reads.

[00:22:15] These are nucleotide base-pair sequences that are like 100 to 150 nucleotides long, whereas the genome is three billion, right. Whenever you have repetitive sequences like the SRGAP2 duplications, with short reads, it’s hard to figure out which copy is which. So you need long reads, long reads are more expensive, like from PacBio, Pacific Biosciences and Oxford Nanopore. And in the genome world when we produce high quality data, we’re using long reads. So they’re really figuring out this question. To answer your question, we’re going to need to sequence the genomes of a lot of people with long reads and then look to see if somebody is missing these extra copies of SRGAP2.

Grant: [00:22:57] That’s interesting. Yeah. I just had a quick look on gnomAD as well. And there are six observed putative loss of function SNV’s when there thirty-eight expected. And there are exactly zero homozygotes out there in all the genome sequencing databases that Nomad aggregates. So even the heterozygote knockouts are pretty rare.

Erich: [00:23:18] Interesting. And to think that this gene is in extra copies in humans. So you would think if we lose it, we will be like all mammals and would be OK, but probably and not.

Grant: [00:23:31] Maybe it’s especially important. So all this kind of leads us almost into our next segment. We’re talking a lot about dancing and its relevance for vocalization, and you were on the verge of going down the path of being a professional dancer, right. Can you tell us more about that?

Erich: [00:23:51] Yeah, that’s right. Actually a lot of my family were into the performing arts, and that’s the direction I was headed in. And a lot of them sang. I was an okay singer, but not as good as the rest of them. So what could I do? I started dancing in dance clubs and so forth as a teenager, back when they allowed teenagers to go to dance clubs. And I started winning dance contests, and I thought, oh, so I can dance. And I auditioned for a high school of performing arts here in New York City and got in. And was on my way to becoming a professional ballet dancer and jazz dancer. But at the end of high school, you know, I was really trying to make that career decision that many teenagers are trying to make. What are you going to do when you graduate high school?

[00:24:32] And I liked science as well. And my mother always said, do something that has an important impact on society. And that stuck in my head and I was choosing dance or science. And I thought as a scientist, I could have a bigger impact on society than I could as a dancer. And I think as a dancer, if you become a well-known dancer, you can have popular influence like anybody in the performing arts like actors and so forth. But as a scientist, I can make a direct impact. So that’s why I chose science. I went to Hunter College here in New York City. I got into a laboratory they were doing bacterial molecular genetics, studying genes that are involved in synthesizing proteins.

[00:25:18] And I found out there that being trained as a dancer trained me to become a scientist. They both require a lot of discipline. They both require creativity. Lots of failure before success. They’re not nine to five jobs. And so, so many things. Basically, I consider being a scientist is also being an artist.

Grant: [00:25:40] It’s fascinating. I never really thought of that before. What do you think doesn’t translate as well beyond the obvious?

Erich: [00:25:47] Being an academic scientist is like running like a small business. You have a lab, you have a people in your lab, you have the staff scientists, and you have the students and so forth. If you’ve got to raise money, your publications or your product. The more you publish, the more likely you’ll get money. So let me backtrack a little bit on that, right. It’s not as ruthless as the business world maybe, but politics and science, they seem to be two different worlds to me, especially at this time. You know, especially in the past four years before the current era where politics seemed to be anti-science.

Grant: [00:26:22] It’s interesting, right. You had a postdoc, who went on to be a pretty prominent politician in Puerto Rico.

Erich: [00:26:28] Yeah, that’s right. Ricky Rossello, my former postdoc, the governor of Puerto Rico. And I guess he’s a scientist, but I guess his politics didn’t mix with science that well, because he stepped down. I very much appreciated his time in the lab. He and I got along very well. I mean, he was a very creative, forward-thinking person. But I do encourage my students and postdocs and others not just to go into academia, but to go into other walks of life and including in politics. And even though I said they don’t mix that well, I do think we need more scientists in politics to help the world.

Grant: [00:27:08] And outside of academia, which is obvious. What paths do you think the PhD route is especially good training for?

Erich: [00:27:16] I think the PhD route is training you for the kind of jobs that require lots of problem solving. You know, let’s say city planning in the business world as well. I think it helps with problem solving because in science, you’re always challenged with a mystery, an unknown that you’ve got to figure out and solve. So maybe being a detective, you know, maybe being a forensic scientist, something like this.

Grant: [00:27:44] And are you glad you did it? I mean, if you had it to do over again, would you be a scientist? Would you be a life scientist? Would you study vocal circuits?

Erich: [00:27:52] Well, I guess if I had to do over again. There are two questions that for me are like: would I come back to the same field? And outside of science, what would I have chosen? And within science, I was always fascinated with the origins of the universe. So I was trying to choose between the origins of the universe and of the brain. So I might choose that or how life began or something like that. Outside of science, I was fascinated with history. So maybe origins of human civilization, origins of culture. So I might have chosen that or maybe I would’ve just stuck with dance. Those are the ones that come to mind. You know, I also considered becoming an astronaut.

[00:28:32] Well, you can’t just say that. You have to competitively apply for that. To me, it’s kind of connected to science, but you know what I mean. I could have been flying planes or something like that. I have no idea. But that’s something I was considering.

Grant: [00:28:46] So it sounds like pretty much big questions like fundamental questions as opposed to kind of applied questions.

Erich: [00:28:54] That’s right. So fundamental questions and that’s what excites me. I’m as excited about being a scientist as I was being a dancer. Because I feel like I’m getting to fundamental principles, I’m doing something that is fun, even though, you know, it’s not fun to try to raise money and get rejected from grants or get your papers rejected and so forth. But hey, you know what, in the interim, I’m having fun.

Grant: [00:29:18] So walk us through your kind of key decisions in your career. You know, in college, you decided to go the life sciences route and then you stayed in New York for your PhD, right?

Erich: [00:29:29] That’s right.

Grant: [00:29:30] And what made you decide neuroscience over, you know, cosmology or something?

Erich: [00:29:35] Yeah. Well, let me give you a little bit more context because actually, I started out wanting to be a magician when I was a young child. And I was emulating Houdini as a teenager, going down different parts in the New York City with my cousin Sean to be tied up and escape from chains and ropes and so forth. And we would figure out how to trick people into believing that something magic happened when it really didn’t happen. And so that kind of actually got me into science. And plus my father was interested in science. Because I started getting tired of trying to trick people. I really wanted to know how things really work. And then, jumping years later into my transition into neuroscience, it was really connected to dance. I felt with dance, the brain controls dancing. It’s something I can hold in my hands. It’s here on Earth. I don’t have to look up to the sky to try to figure it out.

[00:30:30] And I don’t know, it was something I felt more biologically grounded. It was a simple holistic way of thinking, and that’s why I chose neuroscience. But during my undergraduate years at Hunter, I was still kind of undecided in that trajectory. You know, is it going to be something in the biological sciences like neuroscience or is it going to be or is it the universe? So I double majored in biology and in math because if I went into physics and astrophysics, I knew I needed a strong mathematical background so I would have the choice by the time I graduated, which one I was going to do. The mathematics gave me a decent bioinformatics foundation for biology.

[00:31:06] And nowadays, you know, biology is so heavy with big data that that mathematical background is helpful. And then I was going to add one more thing onto that, which is I was toying around with the idea of should I go into activism, into politics? My mother said, do something has an important impact on society. And I did think about politics and so forth. And even within the sciences, me as a person who’s an underrepresented minority of African-American descent and mixed up with lots of other things, I thought about becoming more active in trying to change the culture. And I got asked to do this a lot, but I find that it’s actually like having two jobs.

[00:31:50] And so I figure I want to make a change in society by being a role model, by being an example that as opposed to putting a lot of energy in trying to change policy, which means politics, right. So that’s something else I had to consider that I had to toy around with and make a decision. Including now I was asked to be a director of this or X, Y and Z, because now I’ve made a name for myself in science. Maybe I could or help change. You know, you can’t do it alone. But I’m really still, even at this time in my career I want to make those discoveries about how the world works, how the brain works.

Grant: [00:32:31] What are your thoughts on how people should think about changes that need to happen in the culture of science and so on, right? Are there things out there that you think are especially productive? Things you think are counterproductive? How do you think about it?

Erich: [00:32:45] Yeah. I think what’s counterproductive is for the scientific community as a whole and individual institutions or departments to expect that the folks who are–I don’t want to call them victims, but who are being negatively affected by discrimination and so forth–are the ones who should be given the keys to try to find the solution. You know, I think the solution to what I call society’s racial disease is everybody needs to be get involved, whether or not they are perpetrators or racism or benefit from racial discrimination and so forth, we all need to be part of the solution for it to work.

[00:33:40] The other is that the scientific community needs to do more scientific research on not just social research, right, where you’re looking at behavior only.

But what is it in our human behavior that leads to tribalism in the form of racism? Why does that happen? And is there something genetic about that? Or is there some type of nature versus nurture influence of your social upbringing that leads some people to become white supremacists and others to become activists against those white supremacists? I think there needs to be more hard science that goes into this to find solutions.

Grant: [00:34:07] What do you think are the most important questions to answer along those lines?

Erich: [00:34:12] I think some of the most important ones are where is the overlap or the interaction between tribalism, economics and health? Ok, so I’ll say it again tribalism, economics and health. Because I think those three together are the problems that are contributing to this racial disease where the economics and the health become a problem, right, then the tribalism breaks out. And so where is that tipping point? Then we can find ways to prevent that from happening.

Grant: [00:34:44] What are the ways that you kind of approach this problem that you think differ from maybe how others may?

Erich: [00:34:51] What I think I do that’s differing from many others–I won’t say many others, but enough others–is I think I learned how to have more resilience than I realized. It’s a level of resilience that I don’t think should be necessary, but it was necessary. And that resilience is not only resilience to folks who say or do things that would be purposely discriminatory. Some people really thought I had an unfair advantage with affirmative action programs, for example, or that I am less than or because of the color of my skin, my ethnicity, that I’m not as smart. I’ve met people who think that way in the sciences, right. So you’ve got to have resilience to that. And you’re going to need resilience to implicit bias where someone is saying or doing something, but they don’t realize what they’re saying or doing is discriminatory and they have all good intentions.

[00:35:50] And I say that because I have had enough people in my office, people of color who come to my office crying about something or feeling less than. And I’ll say resilience also to the impostor syndrome. Do I belong here? Do I belong at Rockefeller? I’ve had that question both as a student and interviewing as a faculty member. You’ve got to have resilience to your own imposter syndrome as well.

Grant: [00:36:15] And that’s interesting because at least most recently, right, when you returned to Rockefeller, you were already very, very well established at Duke, right. And even at that time, was that still kind of a something you were dealing with?

Erich: [00:36:27] Yes, it was something that I was dealing with. And it even surprised me when I realized that. And it was a few other famous scientists basically saying that, Erich, you’re lowering yourself too much. And I was surprised because I would rather underestimate what I am, than overestimate. But, some people were saying that I was doing too much of an underestimation and I realized it was because of my minority status that I was doing that to myself.

Grant: [00:36:57] And so how would you encourage earlier career scientists of color?

Erich: [00:37:02] Yeah. I found a way to evaluate myself because like I said, you don’t want to overestimate either. If you get too confident, you might do something stupid in the field and send a grant proposal in that’s really horrible. And then get a reputation that you’re sending in horrible grant proposals for your work. I try to balance my evaluation with my own self-evaluation and what others think. In the beginning phase it’s going to be hard because you’re just starting. So I would say to the younger scientists, and that includes the students, you know, go get some opinions of different faculty members. Don’t depend on one because especially as a person of color, you might find one or two that are going to undervalue you anyway.

Erich: [00:37:46] And if they start saying to you that it’s okay that you’re not going to achieve as much as somebody who’s white, be careful because they might undervalue you. That’s why I say get multiple opinions. Don’t accept everything they say, but listen, understand everything they’re saying and try to improve what you’re doing based upon that knowledge. Later in life as you start to publish papers and so forth, the way I do a self-evaluation is at the end of every year, I see what the citation rate of my papers are, what impact my papers are having on the scientific field. And now I have out of the hundred and sixty or so papers we’ve published over my career even since I was an undergraduate student.

[00:38:33] Something like 20-25 of them, maybe more than that are cited in the top one percent of papers in their field, according to the metrics. So that can’t be because of the color of my skin, you know. So I use that as a metric to answer that question.

Grant: [00:38:53] That’s fascinating. Yeah, we actually, on our blog have compiled, to my knowledge, the only database of H-index distributions at different institutions for biological scientists who involve computational biology in their work. And so you can go and say I’m an assistant professor, associate full professor at this kind of institution, like what the H Index distribution looks like.

Erich: [00:39:21] Wow, that’s good to know. I’ll check it out.

Grant: [00:39:24] Cool. So what do you think has changed about how science is done today from when you were, just entering the field?

Erich: [00:39:34] I was born in 1965, right. And so I entered science when I was eighteen years old, right. So we’re talking early 80s. So when I started out in the early 80s, science was a lone ranger approach. Especially when I got to graduate school, I was taught, you have to figure out everything yourself. And you have to be first or last author on the paper. You know, I mean, this kind of thing is still the same now, but it’s less so than it was before. And I found that that was a Western European model of thinking because I grew up in an African American family, with some Native American culture mixed in there. And of course we were surrounded by European culture around this right. I was thinking of like a Martin Luther King approach, you know, bring everybody together be very collaborative.

[00:40:21] And the advice that I was getting is, I’m doing too much collaboration. That I’m not distinguishing myself enough and so forth. Even from when I was getting tenure and so forth and some other people pulled me aside and whispered, don’t pay attention, you’re doing just fine. So I took this more collaborative approach to science, and now I’m finding that I’m good at it and I’m leading large international consortia for genomics or neuroscience and so forth. And I’m getting credit for it. We produce more papers and we switch around authors on different papers and so forth. And they’re coming out as some of the most highly cited papers in the field.

[00:40:58] And also for me, what really counts is not so much your credit, but the discoveries that are made. And so that was my Malcolm X training, right, which is by any means necessary. So if you need to collaborate, do it. If you need to take the lone ranger Western European approach, do it. What is the necessary approach to make the scientific discoveries? And what I’m finding is that as science diversifies more and as big data in the biological science grows, collaboration is necessary. This lone ranger approach is becoming less and less viable and prevalent.

Grant: [00:41:37] And there seem to be a lot of people who are unhappy with that. You see a lot of complaints about money that goes to the consortia, even though their data are indispensable, right. At the CRO, we rely on GTex, we rely on TCGA, we rely on ENCODE and so on, right. We rely on these big consortia data all the time, every single day.

Erich: [00:42:00] Yeah. I’ve heard people who aren’t into big consortia projects. They just want to have six people in their lab and that’s it. They complain about our consortium projects, about how competitive they are.

Grant: [00:42:14] What do you think is done poorly right now in science? What do you think most needs to be improved?

Erich: [00:42:24] I think two things right. One of the biggest things we poorly do in science is communicate to the public about what we’re doing. I think the public is undereducated in the sciences and sometimes miseducated and purposely so for political reasons. This is something that is changing. When I jump back to the 80s and 90s, we were taught don’t talk to the media that much. Don’t do a podcast, right. Because you’re selling yourself or you could say something wrong. Or somebody could misinterpret it because they’re not a scientist. And therefore, you get a bad reputation in the scientific community. Or you are trying to make a big name for yourself, like Carl Sagan.

[00:43:08] But I think that attitude does us a disservice. I think when it comes to the public good and the scientific community throw the humility out the door, educate the world as to what we’re doing and learn from them as well. So that’s one. And the other is, I think we don’t have a big enough financial investment in the sciences. There is a lot of money going into, you know, I guess the one that get criticize all the time is the military, right. But I think the business world and political world could invest more money into science education, even if those students don’t become scientists. I think it’s going to be better for whatever they go into and for the scientists themselves. Of course, that’s self-serving since I’m a scientist. But I think we can we can do a lot more for the climate turning around what’s going on, climate change for our own health and so forth. And just for basic understanding of the world if we invest it more.

Grant: [00:44:05] Totally agree. And to your first point, the reason I went into the life sciences in the end was actually reading some pop science books that Richard Dawkins had written. And then when I went to grad school and was classmates with the number of people in his department and so on. I learned that within the department, people would complain about him a lot and really it sounded more or less like they were jealous that this guy who gets all this attention. But you know, they’re publishing better and all this stuff. And my thought all along was well, but yeah, OK, maybe his publications aren’t as impactful internally. But big picture, he’s probably having a lot more impact because he’s drawing a lot of people into the field who otherwise may not have gone.

Erich: [00:44:46] Right, exactly. So we need we need people like him.

Grant: [00:44:51] Cool. Do you have any final words for our audience, words of encouragement?

Erich: [00:44:57] You know, pigging backing off this last topic. I feel that what scientists need to learn how to do, including myself, is to translate. Not only to translate from bench to bedside type of translation of discoveries, but translate knowledge from the scientific establishment to the general public. And we don’t have enough good translators. And so it’s good to have you as a translator. But we scientists, we need to learn how to make that vocabulary and grammar and so forth understandable.

Grant: [00:45:34] Totally agree. Well, thank you so much for joining us. It was a lot of fun.

Erich: [00:45:39] You’re welcome.