mcomp = glm.nb(value ~ origin, data = my_data)

summary(mcomp)
Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-0.9625  -0.9047  -0.9047   0.1212   3.5232  

Coefficients:
              Estimate Std. Error z value Pr(>|z|)   
(Intercept)   -0.01657    0.06571  -0.252  0.80097   
originisolate -0.21911    0.08180  -2.679  0.00739 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for Negative Binomial(0.3418) family taken to be 1)

    Null deviance: 2053.5  on 2679  degrees of freedom
Residual deviance: 2046.3  on 2678  degrees of freedom
AIC: 6517.5

Number of Fisher Scoring iterations: 1


              Theta:  0.3418 
          Std. Err.:  0.0186 

 2 x log-likelihood:  -6511.4590 

mcomp = glm.nb(value ~ origin + Substrate, data = comb_data) 
summary(aov(mcomp))
              Df Sum Sq Mean Sq F value Pr(>F)    
origin         1     23   22.55   6.612 0.0102 *  
Substrate     44   1445   32.84   9.631 <2e-16 ***
Residuals   2634   8981    3.41                   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 

mcomp = glm.nb(value ~ origin, data = my_data)

summary(mcomp)
Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-0.9625  -0.9047  -0.9047   0.1212   3.5232  

Coefficients:
              Estimate Std. Error z value Pr(>|z|)   
(Intercept)   -0.01657    0.06571  -0.252  0.80097   
originisolate -0.21911    0.08180  -2.679  0.00739 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for Negative Binomial(0.3418) family taken to be 1)

    Null deviance: 2053.5  on 2679  degrees of freedom
Residual deviance: 2046.3  on 2678  degrees of freedom
AIC: 6517.5

Number of Fisher Scoring iterations: 1


              Theta:  0.3418 
          Std. Err.:  0.0186 

 2 x log-likelihood:  -6511.4590 

mcomp = glm.nb(value ~ origin + Substrate, data = comb_data) 
summary(aov(mcomp))
              Df Sum Sq Mean Sq F value Pr(>F)    
origin         1     23   22.55   6.612 0.0102 *  
Substrate     44   1445   32.84   9.631 <2e-16 ***
Residuals   2634   8981    3.41                   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 

If I, for example, have a dataframe like seen below, how would I determine if the origin of the sample has a significant effect on the value? (this is the number of enzymes capable of degrading the substrate f that matters)

If I, for example, have a dataframe like the one seen below, how would I determine if the origin of the sample has a significant effect on the value? (this is the number of enzymes capable of degrading the substrate of that matter)