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Aging is associated with a systemic length-associated transcriptome
imbalance
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  * Open Access
  * Published: 09 December 2022

Aging is associated with a systemic length-associated transcriptome
imbalance

  * Thomas Stoeger  ORCID: orcid.org/0000-0002-5540-4278^1,2,3,
  * Rogan A. Grant^4,5,
  * Alexandra C. McQuattie-Pimentel^5,
  * Kishore R. Anekalla^5,
  * Sophia S. Liu^1,
  * Heliodoro Tejedor-Navarro^2,
  * Benjamin D. Singer  ORCID: orcid.org/0000-0001-5775-8427^5,6,7,
  * Hiam Abdala-Valencia^5,
  * Michael Schwake^8,9,
  * Marie-Pier Tetreault^10,
  * Harris Perlman^11,
  * William E. Balch  ORCID: orcid.org/0000-0003-0899-8381^12,
  * Navdeep S. Chandel  ORCID: orcid.org/0000-0001-7208-3886^5,6,
  * Karen M. Ridge^5,6,
  * Jacob I. Sznajder^5,6,
  * Richard I. Morimoto^4,13^ na1,
  * Alexander V. Misharin  ORCID: orcid.org/0000-0003-2879-3789^5,6^ 
    na1,
  * G. R. Scott Budinger  ORCID: orcid.org/0000-0002-3114-5208^5,6^ 
    na1 &
  * ...
  * Luis A. Nunes Amaral  ORCID: orcid.org/0000-0002-3762-789X^1,2,14
    ^ na1 

Show authors

Nature Aging (2022)Cite this article

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Subjects

  * Ageing
  * Data integration
  * Systems biology

Abstract

Aging is among the most important risk factors for morbidity and
mortality. To contribute toward a molecular understanding of aging,
we analyzed age-resolved transcriptomic data from multiple studies.
Here, we show that transcript length alone explains most
transcriptional changes observed with aging in mice and humans. We
present three lines of evidence supporting the biological importance
of the uncovered transcriptome imbalance. First, in vertebrates the
length association primarily displays a lower relative abundance of
long transcripts in aging. Second, eight antiaging interventions of
the Interventions Testing Program of the National Institute on Aging
can counter this length association. Third, we find that in humans
and mice the genes with the longest transcripts enrich for genes
reported to extend lifespan, whereas those with the shortest
transcripts enrich for genes reported to shorten lifespan. Our study
opens fundamental questions on aging and the organization of
transcriptomes.

Main

The transcriptome responds rapidly, selectively, reproducibly and
strongly to a wide variety of molecular and physiological insults
experienced by an organism^1. While the transcripts of thousands of
genes have been reported to change with age^2, the magnitude by which
most transcript levels change is small in comparison with classical
examples of gene regulation^2,3. We hence hypothesize that aging is
associated with a phenomenon that affects the transcriptome in a
subtle but global manner that goes unnoticed when focusing on the
changes in expression of individual genes. Specifically, the small
magnitudes of change for individual genes open the possibility that
analyses requiring the transcript levels of individual genes to reach
specific statistical significance thresholds might not be able to
discern statistically significant global changes.

Supporting the perspective that changes may occur at a global level,
several studies on animals have reported that RNA formation decreases
during aging^4,5,6,7,8,9. Moreover, Vermeij et al.^10 suggested for
mice and humans that age-dependent DNA damage leads to a reduction in
the expression of long genes by inferring with transcriptional
elongation. In contrast to this global perspective, many studies
report that transcription factors and microRNAs (miRNAs) can also
mediate age-dependent change of transcripts^11,12. While both global
and gene-centric processes contribute to age-dependent changes, it
remains unclear which one dominates.

Results

Gradient-boosting regression of transcriptomic changes

To avoid potential ambiguity about experimental or analytical choices
within datasets from published studies, we performed RNA sequencing
(RNA-seq) to measure and survey the transcriptome of 17 tissues from
C57BL/6J mice raised under standardized conditions and provided from
the colonies of the National Institute on Aging (NIA). We collected
data on male mice of 4, 9, 12, 18 and 24 months of age. For every
age, we considered six mice, except for rare occasions where a mouse
died before data collection, or sample preparation failed for
experimental reasons (see Supplementary Table 1 for the number of
mice for each tissue and age).

We defined age-dependent transcriptional changes of an individual
gene as the fold change of its transcript abundance, which in turn we
measured as the log[2]-transformed ratio of the signal attributed to
transcripts of one gene at a given age relative to the signal
attributed to transcripts of that gene in the same tissue of
4-month-old mice. As total RNA abundance changes for several tissues
and cell types during the lifespan of animals^4,5,6,7,8,9, it is
important to point out that most transcriptomic studies--including
ours--included an implicit normalization of the abundance of one
transcript relative to all other transcripts. Hence, an observed fold
increase for a transcript could still correspond to a lower number of
transcript molecules if the molarity of transcripts was reduced for
most genes. To explicitly acknowledge this normalization, we used the
terms 'relative fold increase/decrease' and 'relative fold change'
instead of the more commonly used terms 'fold increase/decrease' and
'fold change'. To avoid introducing assumptions about the dynamics of
temporal changes of transcripts with aging, throughout the paper we
considered pairwise comparisons between a given age and 4-month-old
mice.

We used a machine learning approach to identify molecular features
associated with the relative fold change with age of every
protein-coding gene of mice (Supplementary Fig. 1). To be
comprehensive, we considered 2,236 broadly cataloged^13,14 features
of individual genes and transcripts. Of these, 310 corresponded to
transcription factor binding sites that have been validated in at
least one genome-wide assay^14, whereas 1,912 corresponded to
predicted miRNA binding sites^15. Lastly, 14 features corresponded to
architectural properties of genes or transcripts such as the number
of exons, guanine-cytosine (GC) content, chromosome number, the
number of alternate transcripts and the length of the gene and mature
transcripts (Supplementary Table 2).

We used gradient-boosting regression^16 because it is widely regarded
to avoid over-fitting and does not require the amounts of data needed
for deep learning approaches. Briefly, gradient-boosting regression
creates ensembles of decision trees where optimal criteria for the
branching of the tree are determined by the features considered.
Gradient boosting iteratively adds decision trees to the ensemble so
that the difference between observed changes and changes inferred by
the ensemble decreases. We quantified the difference between observed
and inferred changes using a Huber loss function^17, which for a
given total absolute distance will favor those ways of branching the
trees where the distance will arise from many genes having a small
distance while disfavoring those ways of branching the trees where
the distance would arise from a few genes having a large distance.
This is called the model training step.

For every unique tissue-age pair, we created ten gradient-boosting
regression models with each model randomly sampling 90% of all genes.
We then evaluated the prediction accuracy for every tissue-age pair
by applying the trained model to the 10% of genes excluded from the
training steps. We evaluated the prediction accuracy as the Spearman
correlation between observed and median predicted relative fold
changes. Note that the evaluation of the prediction accuracy of the
gradient-boosting regression (Extended Data Fig. 1a-c) remained
unchanged when following an alternative procedure to sample genes
(Extended Data Fig. 1d). Further, the prediction accuracy of
gradient-boosting regression matched the experimental accuracy
between two cohorts of mice, which each consist of three mice
euthanized on different days (Methods and Supplementary Fig. 2).

Lastly, we compared gradient-boosting regression against differential
gene expression analyses^2 and found both approaches to be in good
agreement, but gradient-boosting regression was more sensitive (
Methods and Extended Data Fig. 2).

Transcript lengths explain transcriptomic changes

Gradient-boosting regression not only yields predictions about the
relative fold change of transcripts but also informs on the
importance of individual features to the accuracy of those
predictions. We are thus able to return to our initial motivation to
identify molecular features associated with the relative fold change
of every protein-coding gene.

Among the 2,236 considered features, we found that the most
consistently informative feature is the median length of mature
transcript molecules (Supplementary Table 2), followed by the number
of transcription factors, the length of the gene and the median
length of the coding sequence (Fig. 1a and Supplementary Figs. 3 and
4). Note that we started assembling the features before the first
publication reporting on the association between aging and
transcriptional elongation^10.

Fig. 1: Observation of length-associated transcriptome imbalances.
figure 1

a, Importance of individual features or of the best-ranked feature
within one group of related features in the gradient-boosting
regression across n = 68 tissue-age pairs examined over one
independent experimental series. b, Association between log[10]
transcript length and the relative fold change in transcript
abundance between esophagus samples from 4-month-old and 24-month-old
mice. We quantified the observed length correlation using the
Spearman correlation between transcript length and relative fold
changes (r[TI]). Kernel density estimates use a linear gray scale.
The thin and thicker black lines indicate outermost boundaries of 80%
and 90% of kernel density estimates, respectively. Black circles
highlight those genes with a relative fold decrease (58) and relative
fold increase (46) identified by gene-specific differential
expression at a Benjamini-Hochberg-adjusted P value below 0.05 with
the latter P value obtained by DESeq2, which uses a two-sided Wald
test. c, Box plot of the 68 r[TI] obtained from age comparisons
across the n = 17 tissues and four ages examined over one independent
experimental series. To provide a baseline for individual sample
variability, we used permutation testing (beige shading) by assigning
mice of the same age to two groups and then obtaining r[TI] between
them. 'All' marks all n = 330 such permutations from data across all
17 tissues and median per tissue. n = 17 tissues were examined over
one independent experimental series. d, NanoString analysis with a
metabolism panel to analyze relative fold changes in large-intestine
samples gathered from six 24-month-old and six 4-month-old mice.
'Decr.' and 'incr.' refer to genes with a relative fold decrease and
relative fold increase of their transcripts, respectively. We found n
 = 343 genes with a relative fold decrease and n = 420 genes with a
relative fold increase of their transcripts over one independent
experiment. In the box plots (a, c and d), the center is the median,
notches indicate the bootstrapped 95% confidence intervals of the
median, bounds of the box represent the 25% and 75% percentiles,
whiskers extend up to 1.5 times the height of the box, and minima and
maxima are the observed minima and maxima. In c and d, we estimated P
values using two-sided Mann-Whitney U tests (Extended Data Figs. 1-5,
Supplementary Figs. 1-8 and Supplementary Tables 1 and 2).

Full size image

Features relating to length--namely, the length of the mature
transcript, the length of the gene or the length of the coding
sequence--were correlated (Extended Data Fig. 3a). These 2,236
features do not include the length of primary transcripts because
typically it is not well defined within the GenBank sequence
database. Among the 68 tissue-age pairs (17 tissues multiplied by 4
age comparisons to 4-month-old animals), 63 pairs had at least one of
the features relating to length among the top 5 explanatory features
(Fig. 1a). Demonstrating redundancy across distinct features
describing length, omission of any of these features did not affect
model performance (Extended Data Fig. 3b).

Note that length-related features are more informative than features
describing the binding of any specific transcription factor or miRNA
(Fig. 1a). Transcript length also was the most informative feature
for the earliest age comparison possible with our experimental
survey, 9-month-old versus 4-month-old animals (Extended Data Fig. 3c
), hinting at a process with an early onset.

To determine whether transcript length could directly associate with
age-dependent changes of the transcriptome, or whether transcript
length informs on age-dependent transcriptional change through
combinatorial interactions with transcription factors, miRNAs or
other architectural features of the genome, we directly compared
observed relative fold changes against transcript length.

We used the Spearman correlation to create a global, genome-wide
metric that considers all detected transcripts instead of focusing on
a subset of individual genes. We defined the length correlation (r
[TI]) as the Spearman correlation between transcript lengths and
relative fold changes. We found that this global age-dependent
transcriptional change extends beyond individual genes identified to
be differentially expressed (at adjusted P < 0.05) when considered in
isolation (Fig. 1b). When comparing transcriptomes of 9-month-old
tissues against those of 4-month-old tissues, 10 of 17 tissues
already demonstrated a statistically significant anticorrelation (P 
< 0.01; Fig. 1c and Extended Data Fig. 4). The number of affected
tissues then remained statistically indistinguishable in 12-month-old
and 18-month-old tissues (see 95% confidence intervals of medians as
indicated by notches in Fig. 1c). For 24-month-old animals, 14 of 17
different tissues displayed a statistically significant
anticorrelation between transcript lengths and relative fold changes
(P < 0.01; Fig. 1c and Extended Data Fig. 4) and the remaining 3 of
17 different tissues displayed a statistically significant positive
correlation between transcript lengths and relative fold changes (P 
< 0.01; Fig. 1c and Extended Data Fig. 4). Thus, the relative
abundance of transcripts from long genes can change compared to those
from short genes for old mice. To emphasize the systemic nature of
the association between transcript length and relative fold changes,
we termed this phenomenon 'length-associated transcriptome
imbalance'.

Before attempting a more detailed biological investigation of the
length-associated transcriptome imbalance in mice and other
vertebrates, we first performed a detailed interrogation of its
robustness. This interrogation showed that the imbalance can
alternatively be quantified by the difference in length in the median
transcript length of genes with a statistically significant relative
fold increase of their transcripts (adjusted P < 0.05) and the median
transcript length of genes with a statistically significant fold
decrease of their transcripts (adjusted P < 0.05; Supplementary Fig.
5). We could not find evidence for potential technical artifacts
related to the quality of sequencing, the choice of data analysis
pipeline, the induction of stress-response genes, inter-animal
variability, or whether we consider the length of the transcripts or
the genes or the coding sequence (Methods, Fig. 1c and Supplementary
Figs. 6-8). Finally, and most importantly, we directly confirmed our
main finding of an age-related anticorrelation between transcript
length and relative fold changes by means of two experimental
approaches other than RNA-seq: NanoString (Fig. 1d) and proteomics
(Extended Data Fig. 5)^18.

Length association is robust across organisms

To refine the scope of our observation of a length-associated
transcriptome imbalance in old mice, we extended our analyses to
datasets from transcriptomic studies in mice and other vertebrates
from a variety of laboratories^19,20,21,22. To ensure that our
findings are representative of earlier reports on aging--and do not
reflect upon some idiosyncrasy of our experiments--in our
calculations, we used the relative fold changes reported by the
authors of the published studies. For studies where relative fold
changes for all genes are reported, we quantified the transcriptome
imbalance through the length correlation r[TI], defined as the
Spearman correlation between transcript lengths and relative fold
changes. If only the genes found to have a significant relative fold
change of their transcripts were reported, we quantified the
transcriptome imbalance by difference in median transcript length of
relative fold-increased genes versus relative fold-decreased genes
(Supplementary Fig. 5).

We reanalyzed mouse data from two studies: Benayoun et al.^20 and
Schaum et al.^22. For the latter, we found that 10 of 17 mouse
tissues showed a length-associated imbalance against long transcripts
(P < 0.01; Fig. 2a and Supplementary Fig. 9a-c). For the former, we
found that 2 of 4 mouse tissues showed a length-associated imbalance
against long transcripts between 3-month-old and 29-month-old mice (P
 < 0.01; Fig. 2a and Supplementary Fig. 9d). Further, transcripts
reported by Benayoun et al.^20 to change in relative abundance with
age independent of any specific tissue showed a length-associated
imbalance against long transcripts at the significance cutoff of P 
< 0.05 (Supplementary Fig. 9d).

Fig. 2: Age-dependent length-associated changes persist across
organisms and laboratories.
figure 2

a, Presence of a significant length-associated transcriptome
imbalance (Methods) in work by Schaum et al.^22, Benayoun et al.^20,
Shavlakadze et al.^21 and Reichwald et al.^19. NR, not registered;
NS, not significant. b, Fraction of n = 17 tissues of this study and
n = 28 tissues of other studies listed in a that showed a significant
relative fold decrease of long transcripts or a significant relative
fold increase. Tissues were counted for each occurrence in one of the
other studies. c, Fraction of detected n = 28 cell types in Kimmel et
al.^23, and n = 121 cell types processed by FACS and n = 46 cell
types processed using a droplet-based approach (Tabula Muris Senis)^
24 with a fold decrease of long transcripts or a significant relative
fold increase. Cell types present in different tissues were counted
for each occurrence in one tissue. Comparisons are for each
occurrence of a cell type, and relative fold changes observed between
an old and a young age group. For Kimmel et al.^23, the comparison is
between 7-month-old and 22- to 23-month-old mice and yielded 28
detected cell types, and for Tabula Muris Senis^24, between
3-month-old and 24-month-old mice and yielded 121 cell types detected
by FACS and 46 cell types detected by droplet-based sequencing. d,
Box plot of calculated Spearman correlation (r[TI]) between log[10]
transcript length and relative fold changes for the different cell
types (n) reported for individual tissues in Tabula Muris Senis FACS
data. In the box plots, the center is the median, notches indicate
bootstrapped 95% confidence intervals of the median, bounds of boxes
are the 25% and 75% percentiles, whiskers extend up to 1.5 times the
height of the box, minima and maxima are the observed minima and
maxima. Black dots indicate individual cell types. In a-c, the
presence of an association with length was assessed by a two-sided t
-distribution significance test of Spearman correlation^66 at P 
< 0.01. In b and c, individual data points are not shown as bars
represent the fraction of tissues or cell types; thus, error bars are
95% confidence intervals of bootstrapped estimates of these fractions
(Extended Data Fig. 6, Supplementary Figs. 9-13 and Supplementary
Table 3).

Full size image

We also reanalyzed the transcriptome data from rat samples reported
by Shavlakadze et al.^21 for four tissues and compared transcriptome
changes between 6-month-old and 24-month-old animals. We found a
statistically significant anticorrelation between relative fold
changes and transcript length for samples from gastrocnemius and
hippocampus tissues (P < 0.01; Fig. 2a and Supplementary Fig. 10),
and a significant positive correlation for samples of kidney (P 
< 0.01; Fig. 2a and Supplementary Fig. 10a), but no significant
correlation for samples of liver (although the latter also showed a
significant anticorrelation when considering gene length instead of
transcript length; Supplementary Fig. 10b).

Finally, we reanalyzed the transcriptome data from killifish samples
reported by Reichwald et al.^19 and compared transcriptome changes
between 5-week-old and 39-week-old animals. We quantified length here
as gene lengths, as this was the feature reported by the authors. We
found a length-dependent and age-dependent relative fold change in
two of three killifish tissues (P < 0.01; Fig. 2a and Supplementary
Fig. 11).

In their totality, these results support the hypothesis that the
tissues of several vertebrate model organisms display an
age-dependent transcriptome imbalance and that this imbalance
primarily displays a relative fold decrease of long transcripts or
genes. The fraction of such tissues that we found in our study (~80%)
is comparable (Fisher's exact P = 0.11) to the fraction in other
studies (~60%; Fig. 2b).

Length association is robust across cell types

To resolve whether the length-associated imbalance observed among the
bulk transcriptomes of entire tissues reflects a change in cellular
composition or in the molecular processes occurring in a subset of
cell types, we reanalyzed three datasets reporting single-cell
transcriptomic measurements during mouse aging. Data reported by
Kimmel et al.^23 enabled us to compare samples from 7-month-old and
22- and 23-month-old mice. Fluorescence-activated cell sorting (FACS)
-sorted cells subjected to full-length transcriptome sequencing and,
less-sensitive^24, droplet-based cell isolation paired with
sequencing of the 3' end of transcripts data reported by Tabula Muris
Senis enabled to compare samples from 3-month-old and 24-month-old
mice. Our reanalysis revealed that most cell types demonstrated a
statistically significant anticorrelation between transcript lengths
and age-dependent relative fold changes (21 of 28, 102 of 121 and 31
of 46, respectively; Fig. 2c, Extended Data Fig. 6 and Supplementary
Table 3). We thus conclude that a length-associated imbalance of the
transcriptome occurs in most cell types of mice, and--consistent with
our findings for bulk transcriptomes of entire tissues--mainly
disfavors long transcripts in aged individuals.

We next explore whether the extent of the length-associated imbalance
differed among all detected cell types when comparing individual
tissues. We primarily considered FACS-sorted data of Tabula Muris
Senis as it provides insight into the largest number of tissues, and
then ranked tissues according to the median length-associated
transcriptome imbalance of their cell types (Fig. 2d). Of all 136
pairwise comparisons across the different tissues, only 5 comparisons
show significant (P < 0.01) differences (Supplementary Fig. 12a).
This overall absence of statistically detectable tissue-specific
differences persisted when considering the other single-cell datasets
(Supplementary Fig. 12b-e). Further, it resembled a similar ranking
of tissues based on the bulk RNA-seq data generated by us. While it
is possible to rank tissues according to the correlation between
transcript lengths and age-dependent relative fold changes of
transcripts in this bulk RNA-seq data (Extended Data Fig. 4 and
Supplementary Fig. 13), biological variability between two cohorts of
mice often exceeds observed differences across tissues (Supplementary
Fig. 13). We thus conclude that either there are few differences
across tissues or current mouse studies do not have sufficient
analytical power to reliably detect these differences (Discussion).

Length-associated transcriptome imbalance in human aging

To determine whether a length-associated transcriptome imbalance also
occurs in human aging, we next reanalyzed data collected by the
Genotype-Tissue Expression (GTEx) consortium^25 from human tissues at
the time of death (Supplementary Fig. 14a). We again identified
length-related features were predictive of relative fold change of
transcripts (Fig. 3a), with gene length here being the most
informative individual feature in the trained models (Fig. 3a and
Supplementary Table 4). For consistency with our preceding analyses,
however, we focused on transcript length (Fig. 3b,c). Our analysis of
human tissues again showed an anticorrelation between transcript
length and relative fold changes (Fig. 3b,c and Supplementary Tables
5 and 6). This finding was also recovered when considering either the
length of the gene or the length of the coding sequence instead of
transcript length (Supplementary Fig. 14b,c).

Fig. 3: Length-associated transcriptome imbalance in humans.
figure 3

a, Importance of features or best-ranked feature of one group of
features in the gradient-boosting regression across the n = 591
combinations of individual decades, sexes and tissues. b, Box plots
of the length correalations r[TI] obtained from age comparisons
across all n = 43 tissues of female donors + n = 43 tissues of male
donors (dark gray) and across n = 12 brain tissues of female
donors + n = 12 brain tissues of male donors (brown). To provide a
baseline for sample variability, shaded in beige, we show the box
plot of values of r[TI] obtained from calculating relative fold
changes across all permutations of assigning up to six randomly
chosen same-age samples to two groups. 'All' marks all 2,198
permutations obtained from tissues of male and female donors in the
20- to 29-year and 30- to 39-year age brackets, and 'median'
indicates the median length-associated changes for n = 38 unique
tissue in female or male donors. We estimated P values with two-sided
Mann-Whitney U tests. c, Relative fold change in transcript abundance
in human frontal cortex samples. Kernel-density estimates use a
linear gray scale with the thin and thicker black lines being the
outermost 80% and 90%, and black circles denote the 349 genes with a
relative fold decrease and 266 genes with a relative fold increase by
gene-specific differential expression at an adjusted P value below
0.05 according to the DESeq2 Wald test. d, Extending upon Fig. 2b,
the bar plots show the fraction of all (86) and brain-specific (24)
tissue-gender pairs of old donors that showed a significant (P 
< 0.01, using t-distribution for two-sided significance test for
Spearman correlation^66) relative fold change. P values were not
corrected for multiple hypotheses. As individual data points cannot
be shown as representing the fraction of tissues, error bars are the
95% confidence intervals of bootstrapped estimates of these
fractions. Tissues were counted for each occurrence in another
studies. In the box plots (a and b), the center is the median,
notches indicate bootstrapped 95% confidence intervals of the median,
bounds of boxes are the 25% and 75% percentiles, whiskers extend up
to 1.5 times the height of the box, minima and maxima are the
observed minima and maxima (Extended Data Fig. 7, Supplementary Figs.
14 and 15 and Supplementary Tables 4-6).

Full size image

An anticorrelation between transcript lengths and relative fold
changes was already apparent for most tissues from middle-aged donors
(40-59 years) in comparison with those from young donors (20-39
years; Fig. 3b). The share of tissues affected (~60%; Fig. 3d) in old
donors (60-79 years) was similar to that reported above for the
vertebrate model (Fig. 2). We conclude that a length-associated
transcriptome imbalance also occurs in humans and that it too
preferentially leads to a relative fold decrease of long transcripts.

Given the large number of donors within the GTEx (Supplementary Fig.
14a), we revisited the question of whether the transcriptome
imbalance affects all tissues equally. We ranked tissues by the
magnitude of their transcriptome imbalance and found that we could
reject this null hypothesis (Supplementary Fig. 15) as we observed
that the imbalance was significantly (P < 0.001) stronger in brain
tissues (Mann-Whitney U test values of 561 and 486 for tissues from
middle-aged and old donors; Fig. 3b and Supplementary Fig. 15); this
finding persisted even when excluding genes relating to inflammatory
and neuronal processes (Extended Data Fig. 7a) or controlling for
gender (Extended Data Fig. 7b,c). This pattern is also consistent
with the findings of an independent study on human hippocampus that
explicitly demonstrated a relative fold decrease with age of the
transcripts from long genes^10.

Length association responds to antiaging interventions

To systematically investigate whether the length-associated
transcriptome imbalance is related to aging or merely to the passage
of time, we considered 11 antiaging interventions with strong support
for extending lifespan within the Interventions Testing Program of
the NIA^26,27 and where differential expression results of
transcripts were published and directly provided by the authors of
the corresponding studies. Within these published differential
expression results, we found statistically significant (P < 0.01)
support for a preferential fold increase of long transcripts for 7
interventions (Fig. 5a-f and Extended Data Fig. 8): fibroblast growth
factor 21 (FGF21) excess, Myc heterozygosity, rapamycin, resveratrol,
S6 kinase 1 (S6K1) deletion, senolytics and Snell mice. We did not
find statistically significant support (P > 0.01) for the 4 remaining
interventions: Ames mice, eating every other day, Little mice and
metformin.

We cannot say whether the absence of a significant length-associated
relative fold change for 4 interventions reflects upon the true
potential of these interventions or upon experimental parameters,
sample size or the number of genes reported by the authors of the
original studies. Curiously, among all tested combinations of
antiaging interventions and tissues and ages, the incidence rate of a
significant (P < 0.05) effect seemed somewhat smaller for in vivo
than for cell culture settings (7 of 16 and 3 of 4, respectively;
two-sided Fisher's exact test, P = 0.58). Given the relative fold
increase of long transcripts following 7 of the 11 interventions
analyzed by us and preceding results by Vermeij et al.^10 on caloric
restriction opposing the length-associated transcriptome imbalance in
an ERCC excision repair 1 (ERCC1) progeroid mouse model, we can
conclude that the length-associated transcriptome imbalance is
responsive to antiaging interventions and that several interventions
promoting longevity oppose the direction of the transcriptome
imbalance that we are reporting here to be the primary direction of
the relative fold change encountered within vertebrates (Figs. 2b,c
and 3d).

In addition to the above interventions, we probed rejuvenation by
partial reprogramming through ectopic expression of Pou5f1, Sox2 and
Klf4. We turned to a recent study by Lu et al.^28, which reported
transcriptomic data on retinal ganglion cells in middle-aged
12-month-old mice relative to 5-month-old mice. Surprisingly, but
consistent with our observations in a minority of tissues (Fig. 2b,c
and Fig. 3d), we found a length-associated transcriptome imbalance
with a relative fold increase (rather than fold decrease) of long
transcripts during aging (Fig. 4g). However, following rejuvenation,
the relative fold increase of long transcripts in 12-month-old mice
relative to 5-month-old mice reverted (Fig. 4g,h). We thus conclude
that the length-associated transcriptome imbalance is responsive to
antiaging interventions.

Fig. 4: The age-dependent length-associated transcriptome imbalance
is responsive to several antiaging interventions.
figure 4

a, Share of the 11 antiaging interventions of the Intervention
Testing Program^26 tested that reduced the relative fold decrease of
long transcripts with aging. NS, not statistically significant at the
P < 0.01 level following two-sided Mann-Whitney U test. b, Transcript
length for genes reported to be differentially expressed in Ames mice
^90 for liver in an analysis including 3-, 6-, 12- and 24-month-old
animals. c, FGF21 excess for adipose tissue and skeletal muscle
following exposure of rhesus macaques to FGF21 (ref. ^86). d,
Rapamycin findings reported by Martinez-Nunez et al.^91 for HEK293
cells following exposure, reported by Flynn et al.^87 for hearts of
24-month-old mice following 3 months of exposure, and reported by
Mattson et al.^92 for cytotoxic T cells following 3 months of
exposure. e, Transcript length for genes reported to be
differentially expressed in Snell mice^93 for brown adipose tissue. f
, Deletion of ribosomal S6 protein kinase 1 (S6K1)^94 for 600-day-old
female mice. g, Relative fold changes of transcripts in retinal
ganglion cells of 12-month-old mice compared to 5-month-old mice^28.
h, Retinal ganglion cells of 12-month-old mice following ectopic
expression of Pou5f1, Sox2 and Klf4 (ref. ^28). In b-h, the box plots
of the lengths of transcripts show a relative fold decrease (decr.)
or relative fold increase (incr.) upon exposure (for details of ref.
^87, see the Methods). In the box plots, the center is the median,
notches indicate bootstrapped 95% confidence intervals of the median,
bounds of boxes are the 25% and 75% percentiles, whiskers extend up
to 1.5 times the height of the box, minima and maxima are the
observed minima and maxima. Black dots indicate observations if the
number of genes <= 10. We estimated P values with two-sided
Mann-Whitney U tests. n indicates the number of genes with a relative
fold decrease and relative fold increase. The notches display
bootstrapped 95% confidence intervals of transcript length (Extended
Data Fig. 8).

Full size image

Shortest and longest transcripts impact longevity opposingly

To further probe the relation between the observed length-associated
transcriptome imbalance and aging, we next examined a previously
established catalog of 'pro-longevity' and 'anti-longevity' genes for
which a loss-of-function mutation decreases or increases the lifespan
of model organisms, respectively^29. We mapped the genes listed in
the catalog onto their human and mouse orthologs and performed an
enrichment analysis considering all protein-coding genes that
occurred at least once in the database used for annotation. The
identified 665 genes accounted for a small subset of the genome (3%)
and, notably, in human tissues showed a nearly indistinguishable
length-associated transcriptome imbalance as other genes (Spearman,
0.93; Supplementary Fig. 16a).

We focused on the genes with the 5% shortest median transcript
lengths and the genes with the 5% longest median transcript lengths
as those genes are most poised to show a length-associated relative
fold change during aging (Supplementary Fig. 16b,c). The enrichment
analysis showed that genes encoding the shortest transcripts were
significantly depleted from pro-longevity genes and significantly
enriched for anti-longevity genes, whereas the genes encoding the
longest transcripts were significantly enriched for pro-longevity
genes and significantly depleted from anti-longevity genes (Fig. 5a,b
). This observation was robust against the specific threshold chosen
for transcripts to be classified as short or long (Extended Data Fig.
9a,b) and held when considering differences in median transcript
length between pro-longevity and anti-longevity genes (Mann-Whitney U
test P = 0.008 and P = 0.004 for human and mouse, respectively;
Extended Data Fig. 9c-f). Further, the enrichments and depletions
reached significance (P < 0.01) for one of four human cases and three
of four murine cases when considering all human protein-coding genes
instead of only those annotated for longevity phenotypes (Methods and
Extended Data Fig. 9g,h).

Fig. 5: Enrichment analysis suggests that the shortest and longest
transcripts have opposing associations with longevity.
figure 5

a,b, Fold enrichment for 'pro-longevity' (P, green) and
'anti-longevity' (A, orange) of protein-coding human genes (a) or
mouse genes (b) among the genes with transcript lengths in the bottom
5% or top 5% of lengths. Negative enrichment indicates depletion; n
indicates the observed number of genes with a pro-longevity or
anti-longevity phenotype among these genes with extreme transcript
lengths; e indicates expected number of genes with a pro-longevity or
anti-longevity phenotype if there was no association between
transcript lengths and longevity phenotypes. We estimated P values
using two-sided Fisher's exact test. The data suggest that
pro-longevity genes may be depleted among the shortest genes and may
be enriched among the longest genes. c, Human Gene Ontology analysis
for annotation enrichment among genes with transcripts in the bottom
5% of transcript lengths and annotation depletion among genes with
transcripts in the top 5% of transcript lengths. Area of circle is
proportional to number of genes. Edges represent highest embedding of
a lower-level hierarchical annotation (smaller circle) within a
higher-level one (larger circle). Red (blue) indicates genes enriched
in genes with shortest (longest) transcripts (P < 0.05;
Benjamini-Hochberg-corrected Fisher's exact test; Extended Data Figs.
9 and 10, Supplementary Fig. 16, Supplementary Data 1 and
Supplementary Tables 7-10).

Full size image

As cellular pathways can be encoded by genes of different gene
lengths^30, we next investigated whether the shortest and longest
transcripts in humans encode cellular processes and physiology that
has been associated with aging. To identify candidate processes, we
performed a Gene Ontology analysis for annotations enriched among
transcripts of one length extreme and simultaneously depleted among
transcripts of the other length extreme. We determined the
false-positive rate of this approach to identify such annotations.
Briefly, we determined the false-positive rate was negligible as no
more than one single annotation appeared to be enriched within 200
different randomizations (Methods).

Upon analysis of the annotations observed within the human genome, we
found 138 annotations of mouse genes and 140 annotations of human
genes to be enriched among transcripts of one length extreme and
simultaneously depleted among transcripts of the other length
extreme. Reassuringly, our approach independently recovered
well-established observations concerning the molecular, cellular and
physiological processes associated with aging (Supplementary Tables 7
-10) and partially recapitulated a recent study that only considered
the enrichment of individual annotations^31. We found that the
anabolic branches of proteostasis, mitochondrial function, telomere
maintenance, chromatin organization and immune function^32,33 were
enriched among the shortest transcripts and depleted from the longest
transcripts. Transcriptional regulation^34, developmental processes^
35, ATP binding^36, cytoskeletal structure and synaptic activity^37
were enriched among the longest transcripts and depleted from the
shortest transcripts. To help navigation across related annotations,
we constructed a network representation that presents individual
annotations according to the shared overlap of annotated genes rather
than the hierarchical structure provided by the Gene Ontology
analysis (Methods, ref. ^38 and Fig. 5c). See Extended Data Fig. 10
and Supplementary Data 1 for labels of individual Gene Ontology terms
within this network. Collectively, these findings demonstrate a
remarkably high overlap between the functions encoded by the shortest
and longest transcripts and the biological hallmarks of aging^33,39.

The results presented herein thus strongly support the idea that
genes encoding opposing roles toward longevity could be distinctively
and systemically affected by the length-associated transcriptome
imbalance. In most tissues, the length-associated transcriptome
imbalance should thus promote the expression of genes that appear to
contribute to aging (Figs. 2b,c and 3d), whereas in some tissues
(Figs. 2b,c and 3d), the length-associated transcriptome imbalance
should promote the expression of genes that appear to contribute to
staving off aging.

Discussion

We recognize that our study holds a far-reaching implication on how
RNA-seq studies are analyzed and interpreted. Technical biases in
RNA-seq studies, which affect gene-specific readouts according to
their length, have been reported widely, and several tools have been
subsequently deployed to computationally counter the effects of this
length association^40,41,42,43. As demonstrated by our NanoString
experiment, and by our reanalysis of published proteomics data, this
data processing step may hide biologically driven associations
between transcript length and relative fold change.

Our observational study invites further work on the molecular and
temporal onset of the length-associated transcriptome imbalance, the
role of gene regulatory networks, population variability in larger
cohorts, the normalization of gene expression data during aging and
whether an initial length-associated transcriptome imbalance could be
causative to aging. Moreover, whether our current findings on aging
would extend to further genetic backgrounds and genes whose role in
shortening or lengthening lifespan have not yet been discovered^44.

However, perhaps the most pressing remaining question relates to the
origin of the length-associated transcriptome imbalance during aging.
Our findings about the genes with the shortest and longest
transcripts enriching for genes with different roles toward longevity
could be viewed as support for longevity-related roles of genes
driving the evolution of their transcript length. However, this
explanation would presently only appear to account for a fraction of
the genes that show a transcript length-associated change during
aging (Figs. 2b,c and 3d). Further, gene lengths appear largely
invariant in a phylogenetic kingdom^45,46 and differences in gene and
protein lengths have already been attributed to multiple factors,
including cellular energy constraints, expression levels and gene
duplications^47,48.

Turning to earlier literature, a length-associated transcriptome
imbalance does not appear specific to aging itself. Moreover, there
seem to be multiple potential molecular origins for a
length-associated transcriptome imbalance. Most prominent among the
specific molecular mechanisms, DNA damage has been explicitly
demonstrated to yield a length-associated transcriptome imbalance
with a relative fold decrease of the longest transcripts in a
progeroid model of aging^10. Heat shock, which challenges
proteostasis, a hallmark of aging^33, leads to a length-associated
transcriptome imbalance by causing premature transcriptional
termination through cryptic intronic polyadenylation^49. Similarly,
loss of splicing factor proline/glutamine (Sfpq), encoded by the gene
that displays the strongest differential splicing during human aging^
50, has been shown to yield a length-associated transcriptome
imbalance by interfering with transcriptional elongation of long
genes^51. Methyl CpG binding protein 2 (MeCP2) opposes a
length-associated transcriptome imbalance by dysregulating
transcriptional initiation according to the length of the gene body^
52,53. Further, patients with Alzheimer's disease show a
length-associated transcriptome imbalance whose onset has been
suspected to stem from somatic mutations that affected transcript
stability^54.

Jointly, these observations invite the unsupported hypotheses that
during aging there may not be a single origin for the
length-associated transcriptome imbalance and that the
length-associated transcriptome imbalance in aging instead represents
an intermediate step within a 'bowtie structure' through which
multiple environmental and internal conditions simultaneously affect
multiple downstream outputs^55,56,57. The length-associated
transcriptome imbalance thus may offer itself as an explanation for
the recent observation of inter-tissue convergence of gene expression
during aging^58. Further arguing in favor of an integrative role of
the length-associated transcriptome imbalance, we find evidence that
several distinct antiaging interventions counter the
length-associated transcriptome imbalance against long transcripts
despite these different antiaging interventions partially affecting
different aspects of cellular and organismal physiology^59,60.

One superficially conflicting observation, which may further help to
narrow down the origin of the length-associated transcriptome
imbalance, is the realization that in a small subset of tissues and
cell types the longest transcripts display a relative fold increase
rather than a relative fold decrease (Figs. 2b,c, 3d and 4g), with
the former also appearing to be malleable to antiaging interventions
(Fig. 4h). One possible explanation is that there exist two
independent types of length-associated transcriptome imbalances of
which the first one displays a relative fold increase of the longest
transcripts, and the second one a relative fold decrease of the
longest transcripts. Alternatively, both types of length-associated
transcriptome imbalances could be vis-a-vis manifestations of a
single phenomenon such as changes in the net processivity of the
formation of mature transcripts. Specifically, we suspect that the
age-dependent length-associated transcriptome imbalance in
vertebrates primarily displaying a relative fold decrease (rather
than relative fold increase) of long transcripts may arise from the
hypothesis that there are more ways for perturbations to break than
to improve cellular function^61.

As the shortest and longest transcripts enrich for opposing roles
toward longevity, opposing types of the length-associated
transcriptome imbalance in different tissues also open the questions
on whether these tissues are poised differentially toward aging and
there could be trade-offs between tissues during aging beyond the
recognized trade-offs between somatic and germline tissue^62.
Independent of the specific direction of relative fold changes,
altered transcript levels of thousands of genes or of specific
subsets of genes may also promote aging by challenging epigenetic,
transcriptional and proteomic homeostasis^63,64 and reducing the
capacity of cells to properly respond to internal or external factors
(such as protein aggregates or pathogens). Further independent of any
transcript length, imbalances in gene expression have the potential
to alter subcellular stoichiometries^65.

Spurred by our findings on antiaging interventions, we believe that
understanding the direction of causality between other age-dependent
cellular and transcriptomic changes and length-associated
transcriptome imbalance could open novel research directions for
antiaging interventions.

Methods

Statistics and reproducibility

No statistical methods were used to predetermine sample sizes, but
our sample sizes are similar to those reported in previous
publications^21,22,24. We performed a two-cohort design to estimate
reproducibility after our experiments and focused our analytical
approach on the identification of patterns that are detectable within
our given sample number. For testing the generalizability of our
findings, we considered external datasets in mice and other
organisms.

In an initial pilot analysis, we used a Lilliefors test to assess
assumptions on normality. For comparing different ages, we used a
two-sided Mann-Whitney U test to account for non-normality and
visually double-checked that compared groups would have a similar
skewness. For quantifying the significance of genome-wide
correlations (and thus bypassing the need for gene-specific P
values), we used a t-distribution test as the latter appears to be an
accurate approximation when working with thousands of data points^66.

Mann-Whitney U test, Spearman correlations and Fisher's exact test
were computed through scipy.stats (version 1.2.1)^67. Bootstrapped
estimates of the 95% confidence intervals of the medians were
obtained through Seaborn^68. For comparison purposes, we also
performed differential gene expression analyses using DESeq2 (ref. ^
69), which provide significance values that follow a set of frequent
assumptions on gene expression distributions^70. Significance of the
difference between two correlations was tested with Daniel Soper's
Free Statistics Calculators 4.0 (https://www.danielsoper.com/statcalc
/), which implemented a corresponding test developed by Fisher^71.

For adjusted P values, we followed a Benjamini-Hochberg correction^72
.

No randomization of samples was performed by us as we had ordered
mice of different ages and allocated mice of different ages to
different groups for analysis.

Investigators were not blinded to group allocation during data
collection and outcome assessment and further data analysis. Blinding
during data collection was not possible as old mice look different
from younger mice; blinding was not relevant during data analysis, as
the latter used a machine learning strategy to find the properties
informing on age-dependent change.

No data were excluded from the analyses, except for additional
control analysis, which tested the robustness of the conclusion
against different exclusion criteria (Supplementary Fig. 10c-f).

After completion of the manuscript, however, we noted that the
original experiment contained muscle tissues for which no sequence
data were obtained, and that the original preparation of sequence
data included sorted alveolar macrophages, alveolar type 2 cells and
monocyte-derived dendritic cells. Preceding the analysis started in
this paper, sequence data of the latter had not been carried forward
toward analysis as quality-control (QC) metrics appeared different
and indicative of lower quality than the other experimental
preparations. Retrospectively, analyzing these three cell populations
toward a length-associated transcriptome imbalance after the
(otherwise) completion of this paper, we find, consistent with our
comprehensive reanalysis of cell types through public single-cell
transcriptomic data^23,24, a length-associated transcriptome with a
relative fold reduction of long transcripts in alveolar macrophages
and alveolar type 2 cells (Supplementary Fig. 17).

The experiments were not and could not have been randomized.

Estimation of confidence intervals for bar plots

Bar plots represent empirically observed fractional data; for
example, phenomenon present in 14 of 20 tissues would be 70%.
Confidence intervals were estimated by bootstrapping 1,000 times.
Each bootstrap corresponds to drawing with replacement. In the above
example, this would mean doing 20 independent randomizations where
for each randomization there was a 70% chance that the phenomenon
would be present (and 30% chance that not).

Animal keeping

All mouse procedures were approved by the Institutional Animal Care
and Use Committee at Northwestern University. All strains including
wild-type mice were bred and housed at a specific-pathogen-free
barrier facility at the Center for Comparative Medicine at
Northwestern University. Male C57BL/6J mice were provided by the NIA,
one of the National Institutes of Health (NIH), and were housed at
Northwestern University Feinberg School of Medicine Center for
Comparative Medicine for 4 weeks before euthanasia. Our rationale was
to focus on a standardized murine model that is commonly used across
different laboratories so that our findings could be investigated or
continued by others.

Mice were euthanized by pentobarbital sodium overdose. Immediately
the chest cavity was opened, and animals were perfused via the left
ventricle with 10 ml of HBSS (Ca/Mn^+). The following tissues were
collected: lung, heart, liver, kidney, adrenal gland, white
(perigonadal) and brown adipose tissue, skin, muscle satellite cells,
frontal cortex, cerebellum, esophagus, stomach, and small and large
intestines. Gut epithelial cells were isolated after flushing large
intestine with EDTA/EGTA solution. Lungs were subjected to enzymatic
digestion to release stromal and immune cells and sorted by FACS as
described elsewhere^73. All tissues and cells were immediately frozen
on dry ice and stored at -80 degC for processing. Muscle satellite
cells were prepared as described in work by Runyan et al.^74.

RNA isolation and RNA sequencing

RNA was isolated using an RNeasy DNA/RNA kit after homogenization and
lysis in guanidine thiocyanate buffer supplemented with
b-mercaptoethanol. RNA concentration and quality were assessed using
an Agilent TapeStation. RNA-seq libraries were prepared using an NEB
Next RNA Ultra kit with a polyA enrichment module using an Agilent
Bravo NGS Automated fluidics handling platform as described elsewhere
^73. Libraries were multiplexed and sequenced on an Illumina NextSeq
500 platform using 75 cycles of high-output flow cells and a dual
indexing strategy. Our rationale was to use a protocol that had been
standardized and applied by our sequencing facility.

While targeting 6 mice per age and organ, we ultimately only obtained
sequenced samples for an average of 5.76 mice per age and organ
(because of errors in sample isolation and/or liquid handling).

Bioinformatics

Sequencing reads were analyzed using an implementation of Ceto (
https://github.com/ebartom/NGSbartom/) in Nextflow^75. Briefly, BCL
files were demultiplexed and converted to fastq files using bcl2fastq
(version 2.17.1.14), with default parameters. The raw reads were
trimmed using trimmomatic^76 (version 0.36), with the following
parameters: trailing = 30 and minlen = 20. Trimmed reads were aligned
to the mouse reference genome (GRCm38.p3) with annotations from
Ensembl v78 using tophat (version 2.1.0)^77, with the following
parameters: no novel junctions, read-mismatches = 2,
read-edit-distance = 2 and max-multihits = 5. Aligned reads were
counted using Htseq-count from htseq^78, with the following
parameters: intersection-nonempty, reverse strand, feature-type =
 exons, and id-attribute = gene_id. Our rationale was to use a
bioinformatic setup that had been standardized and applied by our
facilities.

Differential expression of bulk RNA sequencing

For measurements derived from multiple individuals, differential gene
expression analysis was performed with DESeq2 (ref. ^69), version
1.20 (mouse) and 1.22 (human) using an a value of 0.05 for the
adjusted P-value cutoff. We subsequently only kept genes that mapped
unambiguously between Ensembl gene identifiers and NCBI (Entrez) gene
identifiers^13.

To estimate the differential gene expression between individuals, we
directly computed the log[2] ratio of raw counts for transcripts
detected in both individuals.

Characteristics of genes

For transcription factors, we mapped the Gene Transcription
Regulatory Database (v18_06)^14 to +-200 nucleotides to
transcriptional start sites supplied by BioMart for the human
reference genome build GRCh38.p12 and the mouse reference genome
build GrCm38.p6. For miRNAs, we used miRDB (v5.0)^15. For mature
transcripts, length parameters and GC content were identified from
GenBank and mapped to genes as described elsewhere using the median
across different transcripts^13. Number of exons, and their minimal,
median and maximal lengths, were extracted from BioMart. For genes
and chromosomes, characteristics were extracted as described
elsewhere^13. Our rationale was to consider a broad set of
information that might inform on the formation or turnover of
transcripts.

Modeling

Gradient-boosting regression models were built in scikit-learn
(version 0.20.3)^16. Of the transcripts, 90% were included as the
training set and 10% were used as a test set. The 10% of the test set
transcripts that had been withheld during training were used to
evaluate the performance of the models. Our rationale for the
gradient boosting was to account for possible non-linearities. We
only considered protein-coding genes with at least one research
publication and an official gene symbol, and which unambiguously
mapped in a 1:1 relation between NCBI (Entrez) gene identifiers and
Ensembl gene identifiers.

Kernel-density visualizations

Kernel-density visualizations were created with Seaborn^68 using
default parameters.

Comparison to two cohorts of mice

To quantitatively evaluate the performance of our machine learning
approach, we first estimated the maximal performance that should be
achievable by our experimental data. The latter will depend on
biological, experimental and technical variability, the true number
of genes that change expression during aging and their true
magnitudes of change, and the sensitivity of RNA-seq to detect
transcript molecules and their change. We built upon the two-cohort
design of our experimental survey and compared transcriptional fold
changes between the two cohorts. Specifically, the six mice of each
age and tissue pair had drawn from two cohorts with three mice per
age each that were euthanized on different days (or two and three
mice per age if we only obtained samples from five mice, or two mice
per age if we only obtained samples from four mice). As anticipated^2
, Spearman correlations for the relative fold changes between
measurements obtained by two cohorts of mice appeared small
(interquartile range (0, 0.250)) and, in some cases, even slightly
negative (Supplementary Fig. 2b). Of direct relevance to our efforts
to evaluate the performance of our machine learning approach, the
Spearman correlations between observed relative fold changes and
predicted relative fold changes (Extended Data Fig. 1a), however,
resembled or exceeded those observed between both cohorts
(Supplementary Fig. 2b).

Comparison to differential gene expression analysis

We found good agreement between the prediction accuracy of our
gradient-boosting regression models and the number of expressed genes
detected to be differentially expressed (Extended Data Fig. 2). We
also found that gradient-boosting regression reached statistical
significance (P < 0.01) for tissue-age pairs where the transcript of
no single gene was statistically significantly differentially
expressed at a Benjamini-Hochberg-adjusted P-value cutoff of 0.05
(Extended Data Fig. 2c), suggesting that differential gene expression
analyses might yield false-negative findings.

Alternate bioinformatics

To ensure robustness of results beyond individual bioinformatic
pipelines, we reanalyzed in-house bulk RNA-seq datasets using the
publicly available nf-core/RNA-seq pipeline version 1.4.2 implemented
in Nextflow 19.10.0 using Singularity 3.2.1-1 with the minimal
command: nextflow run nf-core/rnaseq -r 1.4.2 -singleEnd -profile
singularity -unStranded -fc_group_features_type 'gene_id'^75,79,80.
Briefly, lane-level reads were trimmed using trimGalore! 0.6.4 and
aligned to the GRCm38 genome using STAR 2.6.1d^81. Gene-level
assignment was then performed using featureCounts (1.6.4)^82.
Included in the nf-core/RNA-seq QC output is a matrix of Pearson
correlations of log[2](CPM) values generated using edgeR (3.26.5)^83.
Lanes with Pearson R < 0.7 compared to all other lanes constituting a
given sample were excluded from further analysis. Extant lanes were
then merged by sample with SAMtools 1.6 using the minimal command
'samtools merge --r'^84. Merged BAM files were then reassigned using
Rsubread 1.32.4 in R 3.5.1 using the minimal command 'featureCounts
(files, annot.inbuilt = 'mm10', minFragLength = 25)' and merged into
separate datasets by tissue in DESeq2 (1.22.2)^69,82. Using a
combined factor of age, and influenza dose (plaque-forming units),
differential expression analysis (DEA) was performed with the formula
'~combined'. A local estimate of gene dispersion best fit observed
dispersions in all cases. DEA was therefore performed using the
minimal command 'DESeq(dataset, fitType = 'local', parallel = T)'.
DEA tables were output for all permutations of age and influenza dose
for a given tissue and analyzed as above.

Length correlations

To avoid assumptions on linearity, we used the Spearman correlation
between transcript length and relative fold change of transcripts in
older individuals over younger ones. Significance was obtained
through the scipy.stats (version 1.2.1) implementation of the
Spearman correlation^67.

For human GTEx data, we restricted our analysis to tissues present in
samples from young and middle-aged and old donors.

Quantification through difference in median transcript length

We alternatively quantified the length-associated transcriptome
imbalance through the difference in median transcript length among
those genes that, in a differential gene expression analysis, showed
a significant relative fold increase and the transcript length of
those genes that showed significant relative fold decrease.

Technical robustness checks

As technical artifacts could affect transcripts according to length^
40,42,43, we further tested the robustness of the recovered
length-associated transcriptome imbalance.

First, we repeated our initial analysis (Fig. 1c) and filtered the
datasets for those organs where the relative fold changes correlated
across both cohorts exceed a Spearman correlation of 0.2 and found
that the correlation between transcript lengths and relative fold
changes persisted (Supplementary Fig. 6a). Further, and based on
counting the tissues exceeding a Spearman correlation of 0.2, we
noted, as expected^21, that the age-dependent changes were most
reproducible when comparing 24-month-old organs against 4-month-old
organs (Supplementary Fig. 6a). Second, we did not observe changes in
RNA integrity with age (Supplementary Fig. 6b). Third, we observed
the same correlations between transcript length and relative fold
change if we removed the requirement for the annotation of genes to
be supported by several lines of evidence such as gene symbols or
literature published in MEDLINE, lowering the likelihood that our
findings could be an artifact of the stringency of applied gene
annotations (Supplementary Fig. 6c).

For our fourth robustness check, we excluded samples where the
sequencing data yielded lower-quality metrics. Reassuringly, this
yielded practically identical measurements of the length
correlations--with two conditions (12-month-old lung, and 18-month-old
large intestine) even turning a positive correlation (favoring long
genes) toward the more negative correlation (disfavoring long genes)
as seen for the majority of conditions (Supplementary Fig. 6d).
Solely, the condition with the strongest imbalance (18-month-old
large intestine), now showed a weaker imbalance (Spearman correlation
with length, from -0.67 to -0.24; Supplementary Fig. 6d). As a fifth
robustness check, and after our initial discovery of the
age-dependent length correlation, we asked one team member, who was
not involved in the design or execution of the initial bioinformatic
preprocessing, to prepare an independent bioinformatic pipeline, QC
and sample filtering, and differential gene expression analysis. To
mitigate the potential risk associated with custom pipelines, the
team member used nf-core/RNA-seq, which provides community-curated
bioinformatics pipelines^80. Again, we observed practically identical
measurements of the length correlations--with 18-month-old hearts
again turning a positive correlation toward the more representative
negative correlation (Supplementary Fig. 6e). Sixth, we excluded,
after the bioinformatic processing and differential gene expression
analysis, genes with a known annotation relating to the inflammatory
response (genes that tend to be short) and neuronal genes (genes that
tend to be long). Again, we observed practically identical
measurements of the length correlations (Supplementary Fig. 10f).

Seventh, after a non-parametric LOWESS regression between transcript
length and relative fold changes^42,85, we no longer observed any
length-associated transcriptome imbalance when considering the
correlation between transcript lengths and residual fold changes
after LOWESS regression. This suggests that the length-associated
transcriptome imbalance is a transcriptome-wide phenomenon that could
be accounted for by transcript length alone (Supplementary Fig. 10g).

Eighth, reanalyzing published transcript degradation rates^43, we
found that longer transcripts were slightly more stable when compared
to other transcripts across the genome (Spearman, -0.03;
Supplementary Fig. 10h).

Ninth, we tested if a length-associated transcriptome imbalance
persists if, instead of transcript length, we considered other,
correlated (Extended Data Fig. 3a) readouts of length. We therefore
measured the correlation between the observed relative fold changes
and the median gene length (Supplementary Fig. 7a,b) and the median
length of the coding sequence (Supplementary Fig. 7c,d). Indeed, we
continued to detect a length-associated transcriptome imbalance
(Supplementary Fig. 7).

Biological variation

We determined whether our sample size of six mice would be sufficient
to conclude whether the length-associated transcriptome imbalance
measured between mice of distinct chronological ages would exceed the
length-associated transcriptome imbalance seen among mice of the same
chronological age. We performed retrospective subsampling of all mice
of a given chronological age and separated them into two equally
sized groups (or three versus two mice if one sample had not been
processed; Supplementary Table 1). For each possible permutation of
separating mice into two different groups, we measured the
correlation between transcript lengths and the relative fold changes
of transcripts between those two groups. Comparing these permutations
against the length-associated imbalance that we observed across
distinct chronological ages, we inferred that--given the number of 17
organs and target sample size of 6 mice--we can presently only
conclude for the comparison between 4-month-old and 24-month-old mice
that the majority of organs demonstrate an imbalance with age that
exceeds interindividual variability (Mann-Whitney U < 0.001; Fig. 1c
and Supplementary Fig. 8a,b).

Next, we performed all pairwise comparisons between all mice of a
given age relative to all 4-month-old mice. Reminiscent of our
preceding analysis, we observed the imbalance was most pronounced
when comparing 24-month-old mice against 4-month-old mice
(Supplementary Fig. 8b). For all but two organs (lung and skin;
Supplementary Fig. 8c), we found a relative fold decrease of long
transcripts for more than half of all pairwise comparisons. Notably,
the occurrence of a general trend of the length correlation by age
does not indicate that all possible combinations of mice show a
fitting reduction of long transcripts by age. Across all
combinations, and organs, we found this fraction to be 67%
(interquartile range (53%, 81%)), indicating that the
length-dependent imbalance against long transcripts is not fully
penetrant (Extended Data Fig. 8c).

NanoString

NanoString analysis was performed by NUSeq Core using the metabolism
panel (v1).

Samples were hybridized overnight at 65 degC for 16 h, according to
NanoString's recommended protocol. As all samples were DV300 >90%
(that is, 90% of the RNA species should be of 300 nucleotides or
longer), 75 ng input RNA was used for each hybridization reaction.
Samples were then immediately loaded into the NanoString nCounter
cartridges and processed. Differential expression was performed using
nSolver version 4.0.70 (NanoString) between the 4-month group and the
24-month group using default parameters.

Reanalysis of previous studies

We considered genes reported to show a relative fold decrease or
relative fold increase in earlier studies. For mice and rats, we used
protein-coding genes with at least one research publication and an
official gene symbol, and the median transcript lengths derived from
GenBank. For killifish, we used genes and gene lengths as reported by
Reichwald et al.^19.

For studies reporting transcriptome measurements (Schaum et al.^22
and Shavlakadze et al.^21), we used a significance threshold of P 
< 0.01 for the correlation between transcript length and relative
fold changes. For studies that only provided lists of differentially
expressed genes and their relative fold increase or relative fold
decrease (Benayoun et al.^20 and Reichwald et al.^19), we applied a
two-sided Mann-Whitney U test, to determine whether the median
transcript length of transcripts with a relative fold increase was
different at the P < 0.01 level from the median length of transcripts
with a relative fold decrease. In case that multiple ages were tested
separately by individual studies, we selected the ages closest to 4
and 24 months of age for young and old, respectively.

Single-cell transcriptome imbalance

As cell types, we considered the cell_type and cell_ontology_class
columns within the respective meta-data tables contained in the h5ad
files of Kimmel et al.^23 and Tabula Muris Senis^24. We only
considered protein-coding genes that were detected in at least one
cell of a given cell type in an individual organ in a given study. We
determined the transcriptome imbalance for each cell type by
correlating transcript length against the log[2]-fold ratio formed by
the summed raw counts of the older animals divided by the summed raw
counts of the younger animals.

Exclusion of genes with inflammation and neuronal function

To determine robustness beyond short stress-induced genes and long
neuronal genes, we removed them from our analysis after bioinformatic
processing. We excluded genes if the lowercase spelling of the Gene
Ontology term contained 'immune', 'stress', 'inflamm' or 'infect',
'brain', 'neuro', 'nerv', 'cerebral', 'cortex' or 'memory'.

Mapping of rhesus macaque genes

For the analysis of Murray et al.^86, we mapped genes to human
transcript length through gene symbols shared with humans.

Analysis of Flynn et al.

Contrasting other anti-interventions, we reanalyzed the raw data of
Flynn et al.^87 as, despite the statement in a corresponding figure
legend, their study did not include the corresponding supplementary
table with differential gene expression results (Supplementary Table
3).

Functional enrichments

We considered the genes with the 5% shortest and 5% longest median
transcript length. We used the annotation of pro-longevity and
anti-longevity genes from HAGR^29,88,89. If genes were simultaneously
annotated as pro-longevity and anti-longevity genes (21 of 665 in
human, 19 of 665 in mice), we kept both annotations. After
intersecting with protein-coding genes with a reported transcript
length, this yielded 417 anti-longevity and 267 pro-longevity genes
in humans, and 307 anti-longevity and 200 pro-longevity genes in
mice. Our rationale was to adhere to the most comprehensive curation
of individual longevity genes.

For differential enrichment, we considered genes enriched among the
genes with transcripts of one length extreme (5% shortest and 5%
longest) at a Benjamini-Hochberg P value < 0.05 and depleted among
the genes with the other length extreme. Unless indicated otherwise,
we restricted the background gene lists for the enrichment to those
genes carrying at least one annotation.

False-positive rate of opposing enrichment and depletion

To understand whether an opposing enrichment and depletion of Gene
Ontology terms is common, we asked explicitly whether the number of
categories that were opposingly enriched among short and long
transcripts (Supplementary Tables 7-10) was higher than would be
expected by chance when comparing two random samples drawn from the
length distributions observed in the mouse and human genomes. We
performed 100 randomizations for mouse and 100 randomizations for
human genes. For mice, no single randomization identified any
annotation. For humans, 1 of 100 randomizations identified a single
annotation (of 14,223 possible annotations for mouse genes, and
15,371 possible annotations for human genes), while the remaining 99
randomizations identified no annotations. These values were
significantly lower than the number of annotations that we observed
when using the shortest and longest transcripts of mouse and humans,
that is, 138 and 140, respectively (Supplementary Tables 7-10).

Alternative enrichment analysis for longevity genes

As an additional analysis, which we expected to have lower
statistical power due to the increased number of uninformative genes,
we repeated our enrichment analysis while considering all
protein-coding genes, rather than solely those occurring in the
database used for annotation (Extended Data Fig. 9g,h). We observed
that the directionality in these trends with aging always persisted
in human and persisted in three of four cases in mice--with the
exception being anti-longevity genes among the longest transcripts.
Notably, for humans, one case--the depletion of anti-longevity genes
from the longest transcripts did not reach statistical significance
(two-sided Fisher's exact P = 0.31). Further, in mice, the enrichment
of anti-longevity genes among the shortest transcripts did not reach
statistical significance (two-sided Fisher's exact P = 0.31).

Annotation network construction

To organize annotations according to their similarity in the shared
genes rather than the human-imposed hierarchical organization, we
represented the annotations found to be enriched as nodes, drew edges
between two nodes if at least one gene carried both annotations and
simply the network as follows: Starting with node with the fewest
attached genes, we kept the edge from that node to the node with the
largest intersection set of attached genes. In case of a tie, that
is, in case there were several nodes with intersection sets of
attached genes of the same size, we kept the edge to the node with
the fewest number of attached genes. In case a tie remained, we kept
the edge to the annotation node with the fewest genes attached but
now including genes that were not included in the enrichment
analysis. We repeated this procedure for the other nodes in order of
increasing number of genes attached.

Reporting summary

Further information on research design is available in the Nature
Portfolio Reporting Summary linked to this article.

Data availability

RNA-seq data created during this study, and used for Fig. 1 have been
deposited under GSE141252. Data underlying other figures have been
generated by other research groups and are available from them and/or
their respective publications.

Externally generated data can be obtained from the following
resources listed according to their order of usage:

Gallego Romero et al.^43 (Additional File 11), Schaum et al.^22 (
https://figshare.com/articles/Differential_Gene_Expression/12227531),
Benayoun et al.^20 (Supplementary Table 4), Shavlakadze et al.^21
(Supplementary Table 1), Reichwald et al.^19 (Supplementary Data 4),
Kimmel et al.^23 (http://mca.research.calicolabs.com), Tabula Muris
Senis (https://figshare.com/articles/
Processed_files_to_use_with_scanpy_/8273102 and https://figshare.com/
articles/Processed_files_to_use_with_scanpy_/8273102), GTEx (https://
gtexportal.org/home/datasets version 7; dbGaP accession phs000424.v7
); Martinez-Nunez et al.^91 (Supplementary Data file);

Flynn et al.^87 (GSE48043); Mattson et al.^92 (Table S2);

Amador-Noguez et al.^90 (Supplementary Table 1 for Ames Dwarf mice
and Supplementary Table 2 for Little mice), Ng et al.^95 (Table S8),
Murray et al.^86 (Supplementary Tables 1A and 1B); Luizon et al.^96
(Supplementary Table 1); Hofmann et al.^97 (Supplementary Fig. 11);
Dembic et al.^98 (Appendix); Selman et al.^94 (Supplementary Tables
2-4);

Hoffman et al.^93 (Supplementary Table 5);

Jochems et al.^99 (Supplementary Table 2); Lu et al.^28
(Supplementary Fig. 4).

Additional data used in this study were:

Gene Transcription Regulation Database version 18.06 (http://
gtrd.biouml.org:8888/downloads/18.06/); miRDB version 5.0 (http://
mirdb.org/download/miRDB_v5.0_prediction_result.txt.gz);

Genes and transcript sequences from GenBank (GRCh38.p10 for human,
and GRCm38.p5 for mice; ftp://ftp.ncbi.nlm.nih.gov/genomes);

GTEx Portal version 7 (https://www.gtexportal.org/home/datasets/);

Exons from Biomart, using human genome GRCh38.p12 and mouse genome
GRCm38.p6 (https://www.ensembl.org/biomart/);

HAGR^29,88,89, specifically Longevity Map Build 3 and GenAge Build 19
(https://genomics.senescence.info/);

Homologene, version 68 (https://ftp.ncbi.nlm.nih.gov/pub/HomoloGene/
).

Gene Ontologies using the mapping to NCBI were provided by the
National Library of Medicine (https://ftp.ncbi.nlm.nih.gov/gene/DATA/
gene2go.gz)

Code availability

Code used during data analysis can be accessed through https://
github.com/NUPulmonary/stoeger_et_2022_transcriptome_imbalance/.

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Acknowledgements

We thank E. Ceco, C. -I. Chen, C. Chen, Y. Cheng, M. Chi, S. Chiu, C.
Cuda, A. Flozak, F. Gonzales, K. Helmin, E. Hogan, P. Homan, D.
Kimelman, E. Lecuona, N. Mangani, V. Morgan, T. Nicholson, R.
Ratsimandresy, C. Runyan, R. Saber and S. Soberanes for sample
preparation; E. Bartom for the template of bioinformatic processing
of the RNA sequences toward counts; P. A. Reyman and R. Verma for
creating the template for differential gene expression; M. Sun, J.
Davis, C. Wang and S. Loguerico for comments on the paper; and
Northwestern University IT and Genomics Nodes for infrastructural
support. We thank N. Schaum and T. Wyss-Coray for sharing the results
of their differential gene expression analysis before publication,
and for creating a FigShare entry to make this analysis publicly
available.

This work was supported by NIH AG068544 (to T.S.), NSF 1956338 (to
T.S. and N.A.N.A.), AG071225 (to R.A.G.), NIH HL128867 (to B.D.S.),
Parker B. Francis Research Opportunity Award (to B.D.S.), NIH
AI135964 (to B.D.S., A.V.M., G.R.S.B. and N.A.N.A.), Heisenberg
Fellowship from the Deutsche Forschungsgemeinschaft (to M.S.), NIH
DK116988 (to M.P.T.), NIH DK117824 (to M.P.T.), NIH AR064546 (to
H.P.), NIH HL134375 (to H.P.), NIH AG049665 (to H.P., W.E.B., N.C.,
J.I.S., A.V.M. and G.R.S.B.), NIH UH2AR067687 (to H.P.), Rheumatology
Research Foundation Agmt 05/06/14 (to H.P.), United States-Israel
Binational Science Foundation no. 2013247 (to H.P.), Mabel Greene
Myers Professorship of Medicine, generous donations to the
Rheumatology Precision Medicine Fund (to H.P.), NIH DK051870 (to
W.E.B.), NIH HL141810 (to W.E.B.), NIH HL079190 (to K.R.), NIH
HL124664 (to K.R.), NIH HL048129 (to J.I.S.), NIH HL071643 (to J.I.S.
and G.R.S.B.), NIH HL085534 (to J.I.S.), NIH AG054407 (to R.I.M.),
NIH AG026647 (to R.I.M.), NIH AG057296 (to R.I.M.), NIH AG059579 (to
R.I.M.), a gift of the Daniel F. and Ada L. Rice Foundation (to
R.I.M.), NIH HL135124 (to A.V.M.), Department of Defense grant
PR141319 (to A.V.M.), Office of the Assistant Secretary of Defense
for Health Affairs W81XWH-15-1-0215 (to G.R.S.B. and A.V.M.), NIH
ES013995 (to G.R.S.B.), The Veterans Administration BX000201 (to
G.R.S.B.), Department of Defense grant PR141319 (to G.R.S.B.), NSF
1764421-01 (to N.A.N.A.) and a gift of John and Leslie McQuown (to
N.A.N.A.). Additional support is from NCI Cancer Center Support Grant
P30 CA060553 awarded to the Robert H. Lurie Comprehensive Cancer
Center (Northwestern University Flow Cytometry Facility),
computational resources and staff contributions provided by the
Genomics Computing Cluster (Genomic Nodes on Quest), which is jointly
supported by the Feinberg School of Medicine, the Center for Genetic
Medicine and Feinberg's Department of Biochemistry and Molecular
Genetics, the Office of the Provost, the Office for Research and
Northwestern Information Technology.

The funders had no role in study design, data collection and
analysis, decision to publish or preparation of the manuscript.

Opinions, interpretations, conclusions and recommendations are those
of the authors and are not necessarily endorsed by the Department of
Defense.

Author information

Author notes

 1. These authors contributed equally: Richard I. Morimoto, Alexander
    V. Misharin, G. R. Scott Budinger, Luis A. Nunes Amaral.

Authors and Affiliations

 1. Department of Chemical and Biological Engineering, Northwestern
    University, Evanston, IL, USA

    Thomas Stoeger, Sophia S. Liu & Luis A. Nunes Amaral

 2. Northwestern Institute on Complex Systems, Northwestern
    University, Evanston, IL, USA

    Thomas Stoeger, Heliodoro Tejedor-Navarro & Luis A. Nunes Amaral

 3. Center for Genetic Medicine, Northwestern University, Evanston,
    IL, USA

    Thomas Stoeger

 4. Department of Molecular Biosciences, Northwestern University,
    Evanston, IL, USA

    Rogan A. Grant & Richard I. Morimoto

 5. Division of Pulmonary and Critical Care Medicine, Northwestern
    University, Evanston, IL, USA

    Rogan A. Grant, Alexandra C. McQuattie-Pimentel, Kishore R.
    Anekalla, Benjamin D. Singer, Hiam Abdala-Valencia, Navdeep S.
    Chandel, Karen M. Ridge, Jacob I. Sznajder, Alexander V.
    Misharin & G. R. Scott Budinger

 6. Simpson Querrey Lung Institute for Translational Science at
    Northwestern University (SQLIFTSNU), Evanston, IL, USA

    Benjamin D. Singer, Navdeep S. Chandel, Karen M. Ridge, Jacob I.
    Sznajder, Alexander V. Misharin & G. R. Scott Budinger

 7. Department of Biochemistry and Molecular Genetics, Northwestern
    University, Evanston, IL, USA

    Benjamin D. Singer

 8. Department of Neurology, Northwestern University, Evanston, IL,
    USA

    Michael Schwake

 9. Faculty of Chemistry, University of Bielefeld, Bielefeld, Germany

    Michael Schwake

10. Division of Gastroenterology and Hepatology, Northwestern
    University, Evanston, IL, USA

    Marie-Pier Tetreault

11. Division of Rheumatology, Northwestern University, Evanston, IL,
    USA

    Harris Perlman

12. The Scripps Research Institute, La Jolla, CA, USA

    William E. Balch

13. Rice Institute for Biomedical Research, Northwestern University,
    Evanston, IL, USA

    Richard I. Morimoto

14. Department of Physics and Astronomy, Northwestern University,
    Evanston, IL, USA

    Luis A. Nunes Amaral

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Contributions

T.S. conceptualized the data analysis, discovered length correlations
in empirical transcriptomic data, performed data curation, developed
software, performed the data analysis, visualized results, cowrote
the original draft and supervised the upscaling of bioinformatic
preprocessing; R.A.G performed an early draft of the meta-analysis,
assisted with bioinformatics and pipeline development and performed
QC on all in-house RNA-seq data; A.C.M.-P. performed experiments;
K.A., S.S.L. and H.T.-N. upscaled bioinformatic preprocessing; B.S.
performed experiments; H.A.-V. performed RNA-seq; M.S. and M.-P.T.
performed experiments; H.P. supervised parts of the mouse
experiments; W.E.B., N.C. and K.R. supervised parts of the
experiments and reviewed the manuscript; J.I.S. reviewed and edited
the manuscript and acquired funding; R.I.M. supervised the study,
reviewed and edited the manuscript and acquired funding; A.V.M.
conceptualized, supervised and performed mouse experiments and
supervised development of bioinformatic templates and reviewed and
edited the manuscript; G.R.S.B. conceptualized the study, provided
project administration, acquired funding and reviewed and edited the
manuscript; and L.A.N.A. conceptualized and supervised data analysis
and cowrote the original draft.

Corresponding authors

Correspondence to Thomas Stoeger, Richard I. Morimoto, Alexander V.
Misharin, G. R. Scott Budinger or Luis A. Nunes Amaral.

Ethics declarations

Competing interests

B.D.S. holds US patent 10,905,706: 'Compositions and methods to
accelerate resolution of acute lung inflammation'. B.D.S. serves on
the Scientific Advisory Board of Zoe Biosciences and holds stock
options in the company. All other authors declare no competing
interests.

Peer review

Peer review information

Marc Vermulst and the other, anonymous, reviewer(s) for their
contribution to the peer review of this work.

Additional information

Publisher's note Springer Nature remains neutral with regard to
jurisdictional claims in published maps and institutional
affiliations.

Extended data

Extended Data Fig. 1 Performance of Gradient Boosting Regression
(GBR) models, related to Fig. 1.

(a) We defined prediction accuracy (r[GBR]) as the Spearman
correlation between observed and predicted relative fold-changes. (b)
Significance of the prediction accuracy using t-distribution for
two-sided significance test for Spearman correlation^66. (c) Density
map of scatter plot of observed and predicted relative fold-changes
obtained from the cerebella of 9-months old mice. There is clear
correlation between the predicted and observed relative fold-changes,
which we quantify through the GBR prediction accuracy (r[GBR]). (d)
Comparison of prediction accuracy following Monte Carlo
cross-validation, the default cross-validation scheme of this
manuscript, against predication accuracy following four-fold
cross-validation for individual combinations of tissues and ages
(black dots). In Monte Carlo cross-validation a given gene will on
average be used nine times for developing the gradient boosting
regression, and on average once for quantifying its performance. In
four-fold cross-validation each gene will be considered exactly three
times for developing the gradient bossing regression, and exactly
once for quantifying its performance.

Extended Data Fig. 2 Gradient boosting regression prediction provides
good accuracy even when no genes are detected as significantly
differentially expressed, related to Fig. 1.

(a) Number of differentially expressed genes relative to 4-month-old
tissues detected at a false discovery rate of 0.05. Values shown are
log[10] (detected differentially expressed genes + 1). For example, a
value of 1 in the figure indicates that 9 genes were detected to be
differentially expressed. (b) Relationship between performance of the
prediction accuracy (r[GBR]) and the number of individual genes
detected as differentially expressed at a false discovery rate of
0.05 across all tissue-age pairs. Reassuringly, we find a positive
correlation between the two measures indicating that the ability to
predict relative fold-changes is related to there being more genes
differentially expressed. Shaded grey area indicates tissue-age pairs
without any gene that is being detected as differentially expressed.
(c) As panel b, but showing the significance of correlation between
number of differentially expressed genes and the prediction
accuracies instead of the prediction accuracy (r[GBR]). Significance
is determined using t-distribution for two-sided significance test
for Spearman correlation^66.

Extended Data Fig. 3 Length-related features contain highly similar
information toward age-dependent changes of the mouse transcriptome,
related to Fig. 1.

(a) Pairwise Spearman correlation between selected features. (b)
Prediction accuracy of relative fold-change for samples from tissues
of 24-month-old animals after excluding, in turn, gene length,
transcript length, coding sequence (CDS) length, or no feature. The
similarity of results confirms that all three length-related features
provide the same level of information. (c) Importance of individual
features or of the best-ranked feature within one group of related
features in the gradient boosting regression models across n=17
tissues from 9-months-old mice. In boxplots center is median, notches
bootstrapped 95% confidence interval of median, bounds of box 25% and
75% percentiles, whiskers extend height of box 1.5 times, minima and
maxima observed minima and maxima.

Extended Data Fig. 4 Length-associated transcriptome imbalance for
individual tissue-age pairs, related to Fig. 1.

(a) Length correlations (r[TI]) are defined as Spearman correlation
between median transcript length and relative fold-changes observed
during aging. Note how most length correlations are negative. (b)
Significance of correlation of panel a. Displayed significance values
are cropped at 10^-50. Note how most P values are lower than 10^-20.
Significance is determined using t-distribution for two-sided
significance test for Spearman correlation^66. (c) Number of detected
genes underlying the computed length correlations (r[TI]). Note how
in nearly all cases, the correlation was obtained using more than
5,000 protein-coding genes.

Extended Data Fig. 5 Age-dependent length correlation persists in
transcriptomic and proteomic data of Takemon et al.^18, related to
Fig. 1.

(a) Relative fold-change of age-dependent transcript change as
reported by Takemon et al.^18 who integrate information of
transcriptomic change in kidneys of 12- and 18-month-old mice versus
6-month-old mice. Length correlations (r[TI]) describes Spearman
correlation between median transcript length, and relative
fold-change of transcripts. We highlight with black circles those 10
genes with a relative fold-decrease and 186 genes with a relative
fold-increase identified by an adjusted P value below 0.01 provided
by the authors, who obtained P values from DESeq2 using a likelihood
ratio test. (b) Relative fold-change of age-dependent protein change
as reported by Takemon et al.^18 who integrate information of
proteomic change in kidneys of 12- and 18-month-old mice versus
6-month-old mice. Proteome length correlations (r[PI]) describes
Spearman correlation between median transcript length, and relative
fold-change of proteins. The thin and thicker black lines indicate
outermost boundaries of 80% and 90% of kernel density estimate,
respectively. We highlight with black circles those 292 genes with a
relative fold-decrease and 186 genes with a relative fold-increase
identified by an adjusted P value below 0.01 provided by the authors,
who obtained P values from DESeq2 using a likelihood ratio test.

Extended Data Fig. 6 Length correlation with relative fold-changes
obtained for individual cell types inferred from single-cell RNA
sequencing data, related to Fig. 2.

Brown lines are visual guides.

Extended Data Fig. 7 Robustness of observation of length-associated
transcriptome imbalance in humans, related to Fig. 3.

(a) Length correlation after excluding genes annotated for
inflammatory and neuronal processes across all 43 tissues of female
donors and all 43 tissues of male donors (dark grey) and across 12
brain tissues of female donors and 12 brain tissues of male donors
(brown) considered. P values represent two-sided Mann Whitney U tests
with U values of 571 and 459 tissues from middle-aged and old donors,
respectively. (b) We find a length-associated transcriptome imbalance
for tissues from female (top) and male donors (bottom). n=median
across all 43 tissues of female donors and all 43 tissues of male
donors (dark grey) and across 12 brain tissues of female donors and
12 brain tissues of male donors (brown), respectively. In boxplots
center is median, notches bootstrapped 95% confidence interval of
median, bounds of box 25% and 75% percentiles, whiskers extend height
of box 1.5 times, minima and maxima observed minima and maxima. P
values represent two-sided Mann Whitney U tests with U values of 136
and 122 for tissues from middle-aged and old female donors and U
values of 148 and 121 from middle-aged and old male donors,
respectively. (c) As Fig. 2a but additionally including human
tissues.

Extended Data Fig. 8 Anti-aging interventions oppose the relative
fold-decrease of long transcripts, related to Fig. 4.

(a) We use color-coding in all panels to indicate genes transcripts
showing a relative fold-decreased (white) or relative fold-increase
(grey) according to the authors of the respective studies. (b)
Transcript length for genes reported by Ng et al.^95 to be
differentially expressed in the liver of mice eating every other day
for three months after reaching 3 months of age. (c) Transcript
length for genes reported to be differentially expressed in Little
mice by Amador-Noguez^90 for the liver in an analysis including 3, 6,
12, and 24-month-old animals. (d) Transcript length for genes
reported to be differentially expressed by Luizon et al.^96 following
exposure of primary human hepatocytes to metformin. (e) Transcript
length for genes reported to be differentially expressed in Myc
haploinsufficient mice by Hofmann et al.^97 for indicated tissues and
in 5-month-old (young) and 24-month-old (old) mice. Note that for
5-month-old muscle no comparison is given as no gene was reported to
show a relative fold-decrease. (f) Transcript length for genes
reported to be differentially expressed by Dembic et al.^98 following
exposure of human primary fibroblasts to resveratrol. Given the
genome-wide readout provided by the authors we could additionally
compute P values using t-distribution for two-sided significance test
for Spearman correlation^66, which will yield 0.0002. (g) Transcript
length for genes reported to be differentially expressed by Jochems
et al.^99 following exposure of cultured cells toward at least one
senolytic compound. P values were estimated by two-sided Mann-Whitney
U test. n indicates number of genes with a relative fold-decrease and
relative fold-decrease of their transcripts. Given the genome-wide
readout provided by the authors we could additionally compute P
values using t-distribution for two-sided significance test for
Spearman correlation^66, which will yield 7.0e-9. (b-g) In boxplots
center is median, notches bootstrapped 95% confidence interval of
median, bounds of box 25% and 75% percentiles, whiskers extend height
of box 1.5 times, minima and maxima observed minima and maxima.

Extended Data Fig. 9 Transcript length and reported association to
the biology of aging, related to Fig. 5.

(a) Fold enrichment for 'pro-longevity' (P, green) and
'anti-longevity' (A, orange) genes among the genes with the 10%
shortest and 10% longest median transcript lengths in humans.
Negative enrichment indicates depletion. (b) As panel a, but for
mice. (c) Letter value plot of all 294 pro-longevity and 455
anti-longevity genes across the human genome, with all protein-coding
genes ranked according to length and rank transformed to percentile.
(d) Letter value plot but for all 219 pro-longevity and 335
anti-longevity genes across the mouse genome, with all protein-coding
genes ranked according to length and rank transformed to percentile.
(e) Letter value plot of transcript length of all 294 pro-longevity
and 455 anti-longevity genes across the human genome. (f) Letter
value plot of transcript length all 219 pro-longevity and 335
anti-longevity genes across the mouse genome. (g) As Fig. 5a, but
using all human protein-coding genes as the set being analyzed
instead of restricting enrichment analysis toward genes with at least
one annotation and focusing on the 5% shortest and 5% longest median
transcript lengths. (h) As Fig. 5b, but using all protein-coding
genes as the set being analyzed instead of restricting enrichment
analysis toward genes with at least one annotation and focusing on
the 5% shortest and 5% longest median transcript lengths in mice. (
a,b, g, h) n indicates the absolute number of genes with a
pro-longevity and anti-longevity phenotype among the genes with 5% or
10% (as indicated as focus in pales) shortest and longest median
transcript lengths and e indicates expected number of genes with a
pro-longevity and anti-longevity phenotype if there was no
association between transcript lengths and longevity phenotypes. P
values were estimated by two-sided Fisher's exact test. (c-f) Letter
value plots shows median as black lines, boxes of decreasing width
percentiles of data according to letter ratios (+/- 25%, +/-37.5, +/
-43.75, etc.), and observed minima and maxima as diamonds. P values
are from two-sided Mann-Whitney U test.

Extended Data Fig. 10 Network of Gene Ontologies enriched for
transcripts of one length-extreme and depleted from the other,
related to Fig. 5.

Human Gene Ontology analysis for annotation enrichment among genes
with transcripts in the bottom 5% of gene lengths and annotation
depletion among genes with transcripts in the top 5% of gene lengths.
Area of circle is proportional to number of genes in set. Edges
represent highest embedding of a lower-level hierarchical annotation
within a higher level one. Red (blue) indicates genes enriched in
genes with shortest (longest) transcripts (P < 0.05;
Benjamini-Hochberg corrected two-sided Fisher's exact test).

Supplementary information

Supplementary Information

Supplementary Figs. 1-17

Reporting Summary

Supplementary Tables 1-10

Supplementary Table 1. Number of mice analyzed for each combination
of tissue and age, related to Fig. 1. Supplementary Table 2. Median
rank of distinct features for different tissues at distinct ages
compared to 4-month-old animals. Columns named by tissue_age, related
to Fig. 1. Supplementary Table 3. Correlations between transcript
lengths and relative fold changes in cell types identified in
single-cell studies, related to Fig. 2. Supplementary Table 4. Median
rank of distinct features for different tissues at distinct ages
compared to young human donors (20-39 years). Columns named by
gender_tissue_old decade_young_decade, related to Fig. 3.
Supplementary Table 5. Transcriptome imbalance in female samples of
the GTEx. Column 'comparison' shows compared decades. 'rho'
represents the correlation between transcript lengths and relative
fold changes, and 'pval' indicates the corresponding significance,
related to Fig. 3. Supplementary Table 6. Transcriptome imbalance in
male samples of the GTEx. Column comparison shows compared decades.
rho is the correlation between transcript lengths and relative fold
changes, and pval is the corresponding significance, related to Fig.
3. Supplementary Table 7. Gene Ontologies enriched among mouse genes
with 5% shortest median transcript length, related to Fig. 5.
Supplementary Table 8. Gene Ontologies enriched among mouse genes
with 5% longest median transcript length, related to Fig. 5.
Supplementary Table 9. Gene Ontologies enriched among human genes
with 5% shortest median transcript length, related to Fig. 5.
Supplementary Table 10. Gene Ontologies enriched among human genes
with 5% longest median transcript length, related to Fig. 5.

Supplementary Data 1

Supplementary Network 1. Gene Ontology network of genes with opposing
functional enrichments among shortest and longest transcripts,
related to Fig. 5.

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Stoeger, T., Grant, R.A., McQuattie-Pimentel, A.C. et al. Aging is
associated with a systemic length-associated transcriptome imbalance.
Nat Aging (2022). https://doi.org/10.1038/s43587-022-00317-6

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  * Received: 26 March 2021

  * Accepted: 21 October 2022

  * Published: 09 December 2022

  * DOI: https://doi.org/10.1038/s43587-022-00317-6

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