Nutritional compensation of the circadian clock is a conserved process influenced by gene expression regulation and mRNA stability

Christina M Kelliher; Elizabeth-Lauren Stevenson; Jennifer J Loros; Jay C Dunlap

doi:10.1371/journal.pbio.3001961

. 2023 Jan 5;21(1):e3001961. doi: 10.1371/journal.pbio.3001961

Nutritional compensation of the circadian clock is a conserved process influenced by gene expression regulation and mRNA stability

Christina M Kelliher ^1,^2,^*, Elizabeth-Lauren Stevenson ¹, Jennifer J Loros ³, Jay C Dunlap ^1,^*

Editor: Gad Asher⁴

PMCID: PMC9848017 PMID: 36603054

Abstract

Compensation is a defining principle of a true circadian clock, where its approximately 24-hour period length is relatively unchanged across environmental conditions. Known compensation effectors directly regulate core clock factors to buffer the oscillator’s period length from variables in the environment. Temperature Compensation mechanisms have been experimentally addressed across circadian model systems, but much less is known about the related process of Nutritional Compensation, where circadian period length is maintained across physiologically relevant nutrient levels. Using the filamentous fungus Neurospora crassa, we performed a genetic screen under glucose and amino acid starvation conditions to identify new regulators of Nutritional Compensation. Our screen uncovered 16 novel mutants, and together with 4 mutants characterized in prior work, a model emerges where Nutritional Compensation of the fungal clock is achieved at the levels of transcription, chromatin regulation, and mRNA stability. However, eukaryotic circadian Nutritional Compensation is completely unstudied outside of Neurospora. To test for conservation in cultured human cells, we selected top hits from our fungal genetic screen, performed siRNA knockdown experiments of the mammalian orthologs, and characterized the cell lines with respect to compensation. We find that the wild-type mammalian clock is also compensated across a large range of external glucose concentrations, as observed in Neurospora, and that knocking down the mammalian orthologs of the Neurospora compensation-associated genes CPSF6 or SETD2 in human cells also results in nutrient-dependent period length changes. We conclude that, like Temperature Compensation, Nutritional Compensation is a conserved circadian process in fungal and mammalian clocks and that it may share common molecular determinants.

Introduction

Circadian clocks exist at the cellular level to allow cell types, tissues, and organisms to properly align physiology with time of day. True circadian clocks are sensitive to the external environment in two distinct ways. Discrete pulses of bright light, temperature, nutrients, hormones, or other chemicals reset circadian oscillators and reorient the clock’s phase to the new environment (reviewed in [1]). This resetting feature of circadian clocks is most commonly experienced when jetlagged humans travel across multiple time zones and entrain to the destination’s light/dark cycles. On the other hand, circadian clocks are also shielded or buffered from changes in the ambient environment within the physiological range of an organism [2,3]. In the filamentous fungus Neurospora crassa, circadian period length is maintained at approximately 21.5 hours when grown at constant temperatures ranging from 16°C to 32°C [4] or under different nutrient conditions [5]. This circadian property is known as compensation, and the molecular mechanisms underlying period length compensation remain elusive.

At the molecular level, the circadian oscillator is a transcription-translation feedback loop (TTFL) that is functionally conserved from fungi to animals. The positive arm of the clock is composed of transcription factor activators (WC-1/WC-2 in fungi; BMAL1/CLOCK in mammals), which form a heterodimeric complex, drive transcription of direct target genes, and recruit chromatin modifiers [6–8]. The positive arm directly regulates the negative arm of the clock (FRQ in fungi; PERs/CRYs in mammals) [9–11]. Negative arm clock components form a stable complex with Casein Kinase I (CKI) and other factors, leading to feedback via posttranslational inhibition of the positive arm to close the circadian feedback loop [12,13]. The positive and negative arms are sufficient for rhythmicity, although accessory feedback loops confer additional clock robustness (reviewed in [14]). Such individual cellular clocks are coordinated in a coupled network to align organismal physiology in mammals (reviewed in [15]).

Temperature Compensation was first proposed to be a circadian property in the dinoflagellate Gonyaulax polyedra when increasing temperatures led first to period lengthening (so-called “over-compensation”) and then to period shortening at even higher temperatures, a result that plainly conflicted with models based on biochemical reaction rates strictly increasing with temperature [2]. In genetic model systems like Neurospora and Drosophila where organisms operate at ambient temperatures, and even in homeothermic animals, cellular circadian clocks are temperature compensated [4,16–20]. The forward and reverse genetics that have driven current models for Temperature Compensation have led to casein kinases as central regulators across multiple circadian model systems. In Neurospora and in plants, Casein Kinase II (CKII) is required for Temperature Compensation [21,22]. Neurospora CKII activity increases linearly with temperature and directly phosphorylates the negative arm of the clock [21]. The tau mutant hamster (CKIε^R178C) was the first characterized mammalian Temperature Compensation defect [23–25]. Recent structural work has demonstrated that CKI tau alters both priming and progressive phosphorylation events on the negative arm of the clock [26], and a Neurospora tau-like allele CKI^R181Q reduces in vitro kinase activity on phospho-primed substrates [27]. Taken together, the tau mutant phenotype of period shortening with temperature (so-called “under-compensation”) is likely due to altered progressive phosphorylation, and perhaps to altered protein stability, of negative arm components. Indeed, temperature-sensitive target binding has been shown for mammalian CKI [28], and the interaction strength between CKI and FRQ in Neurospora has been implicated in Temperature Compensation as well [29]. Taken together, CKI and CKII each have temperature-sensitive aspects of enzyme activity, both can directly phosphorylate core clock components, and casein kinases appear to play a conserved role in Temperature Compensation of fungal, plant, and animal clocks.

In contrast to Temperature Compensation, mechanisms underlying Nutritional Compensation (also known as Metabolic or Glucose Compensation) are poorly understood in Neurospora and completely unstudied in other eukaryotic circadian systems. Period compensation to a variety of ATP:ADP ratios has been well described in the prokaryotic cyanobacteria Synechococcus elongatus (reviewed in [30]). A handful of Nutritional Compensation defects have arisen sporadically in Neurospora, the most developed involving the transcription factor repressor CSP-1 [31]. CSP-1 directly regulates and is regulated by the clock’s positive arm White Collar Complex (WCC), forming an accessory negative feedback loop. In a Δcsp-1 mutant, period significantly shortens as a function of glucose concentration [32]. In fact, direct overexpression of wc-1 also causes nutritional under-compensation [33]. Nutritional Compensation is defective in the absence of the general transcription repressor RCO-1 [34], likely due its normal role in preventing WCC-independent frq transcription [35] and/or its dual repressor function in complex with CSP-1 [36]. Over-compensation was also found in loss-of-function mutants of an RNA helicase, PRD-1, which normally localizes to the nucleus only under high-glucose conditions [37]. Nutrient sensing and signaling pathways should presumably also play a role in Nutritional Compensation of the clock, and RAS2 and cAMP signaling have been implicated [38]. Taken together, the current incomplete model for Nutritional Compensation in Neurospora assembled from random hits implicates transcriptional and posttranscriptional regulation of core clock factors by transcription factors, an RNA helicase, and cAMP signaling.

Since the realization of circadian compensation, the field has speculated that Temperature and Nutritional Compensation pathways may share common regulators [30,39,40]. We directly test this model and find in Neurospora that previously reported compensation mutants are specific to either Temperature or Nutritional Compensation. Given this separation of function and relatively little mechanistic knowledge about Nutritional Compensation, we designed a genetic screen to identify new compensation mutants in Neurospora crassa. We identify 16 gene knockouts with Nutritional Compensation phenotypes, greatly expanding the list of 4 previously characterized mutants. We also provide the first evidence that, like Temperature Compensation, Nutritional Compensation is relevant in the mammalian circadian system, and confirm that knockdowns of the human genes homologous to two hits from our genetic screen, CPSF6 and SETD2, impair the clock’s ability to maintain its period length at different glucose levels. These data establish a potentially conserved genetic basis for the phenomenon of circadian Nutritional Compensation and anchor the phenomenon for further genetic and molecular dissection.

Results

Nutritional compensation is distinct from temperature compensation in Neurospora

We first set out to characterize the properties of Nutritional Compensation in wild-type Neurospora. Traditional circadian experiments have utilized the ras-1^bd mutant, which promotes the formation of circadianly regulated distinct bands of conidial spores in a race tube assay [41,42]. However, the ras-1 gene (NCU08823) is implicated in growth and regulation of reactive oxygen species. Thus, to accurately profile normal Nutritional Compensation, a wild-type ras-1⁺ strain containing a frq clock box transcriptional reporter was used to measure period length across glucose concentrations ranging from 0 to 111 mM (0 to 2% w/v). The Neurospora circadian period is slightly under-compensated to nutrients (Fig 1A); under-compensation has also been observed in wild-type Neurospora with respect to temperature [21,29]. Fungal biomass increases by orders of magnitude when grown in the range of 0% to 0.75% w/v glucose (S1 Fig), and this increased biomass accounts for the increased magnitude of luciferase rhythms observed at higher glucose levels (Fig 1B) (S1 Movie).

Fig 1 — Circadian period length was determined by bioluminescent recordings of a *ras-1*^WT strain cultured on race tubes, where the fungal growth front encounters constant glucose concentrations (N = 6 race tubes per concentration). No arginine was added to the medium. Period shortens slightly as a function of glucose levels, which is indicated by the negative slope of the linear fit (glm in R, Gaussian family defaults, slope = −0.48 ± 0.12) (A). Averaged biological and technical replicates are shown for 0 mM (0% w/v), 5.6 mM (0.1% w/v), 27.8 mM (0.5% w/v), and 55.5 mM (1% w/v) glucose levels (standard deviation error bars). Period lengths are 21.9 ± 0.5, 20.8 ± 0.6, 20.8 ± 0.2, and 20.7 ± 0.6 hours, respectively (average ± SD) (B). Surprisingly, the *ras-1*^WT strain can form distinct conidial bands when grown on 0% glucose starvation medium, contrasted with constitutive conidiation seen at high glucose levels (C). The raw data underlying parts (A) and (B) can be found in S1 Data.

Having established that the Neurospora clock displays compensation for period length across glucose concentrations, we asked whether Temperature Compensation mutants also have Nutritional Compensation phenotypes, and vice versa, together in the same assay. CKII is required for normal Temperature Compensation, and its catalytic subunit reduced function mutant cka^prd-3 (Y43H) has an over-compensation phenotype by race tube assay [21]. The frq⁷ (G459D) point mutant is temperature under-compensated [4]. The frq clock box transcriptional reporter was integrated into the cka^prd-3 and frq⁷ mutant backgrounds, and temperature over- (Q₁₀ = 0.88) and under-compensation (Q₁₀ = 1.11) phenotypes were confirmed by luciferase assay (Fig 2A). Known Nutritional Compensation mutants prd-1 and Δcsp-1 were also tested, and each had normal Temperature Compensation profiles (Q₁₀ = 1.04, 1.06). The same 4 compensation mutant reporter strains were then grown on race tubes containing zero or high glucose medium, and bioluminescence was recorded to track period length and Nutritional Compensation phenotypes. Controls were slightly under-compensated (Figs 1A and 2B). Nutritional Compensation mutants prd-1 and Δcsp-1 showed the over- and under-compensation phenotypes reported by previous studies [32,37]. Temperature Compensation mutants cka^prd-3 and frq⁷ have normal Nutritional Compensation (Fig 2B). We conclude that Temperature and Nutritional Compensation are controlled by distinct pathways in Neurospora. These data suggest that further examination of available mutants defective in Temperature Compensation will not inform our understanding of Nutritional Compensation and that a separate genetic screen is warranted.

To achieve this, we leveraged the whole genome knockout collection in Neurospora crassa [43] to initiate a screen for new Nutritional Compensation regulators. Two major classes of gene knockouts were selected to search for compensation phenotypes. Kinases are central to many aspects of cellular processes and regulation including responses to the environment (pheromones, osmotic conditions, carbon/nitrogen regulation, etc.), and a collection of approximately 100 different kinase knockout circadian reporter strains was available from previous work [44]. In addition to posttranslational modifications by kinases, posttranscriptional regulation is emerging as critically important for circadian output [45]. Together with the dramatic nutritional over-compensation defect seen in prd-1 RNA helicase mutants [37] (Fig 2B), we selected 350 putative RNA regulatory protein knockouts to screen for compensation regulators. Our list of putative RNA-binding and RNA regulatory proteins was derived from bioinformatic databases and from the literature [46–49] to include proteins with nucleotide-binding functional annotation but exclude known transcriptional regulators. Multiple circadian period alterations were identified in a recent characterization of transcription factor knockouts [50], and Nutritional Compensation defects among transcription factors, in addition to CSP-1 and RCO-1, will be the subject of future study. Finally, a handful of manually selected candidate genes were included in the compensation screen. Eight classical alleles with long or short circadian period (the series of prd mutants and frq point mutants) were included, and 12 knockouts were manually selected based on reported roles in nutrient sensing and signaling.

The genetic screen for Nutritional Compensation defects was divided into three phases (Fig 2C). The 350 previously uncharacterized RNA regulatory protein knockout strains were grown on glucose starvation race tubes (Fig 1C) to identify those with period length differences greater than 1 hour as compared to internal wild-type controls or those “output” mutants clearly lacking a circadian banding pattern (N = 2 to 8 replicate race tubes per KO strain). Approximately 71% of the putative RNA regulatory protein knockout strains showed normal period length on starvation medium and were eliminated in the primary screen (Fig 2C and S1 Table). During this phase of the screen, 7 knockout strains were found with enhanced conidial banding patterns (and variable growth rates) relative to wild-type controls, reminiscent of the ras-1^bd mutant phenotype (S2 Fig). In the secondary screen, the frq clock box transcriptional reporter was integrated into all knockouts of interest, along with the existing kinase deletion collection [44] and manually selected candidate strains. This collection of more than 200 strains was screened in a high-throughput 96-well plate format using glucose and amino acid starvation medium to identify period length differences greater than 1 hour from internal wild-type controls (N = 6 to 12 replicate wells per KO strain). About 77% of the candidate strains were eliminated during the secondary screen due to normal period length on starvation medium (Fig 2C and S1 Table). The approximately 50 remaining knockout strains and wild-type controls were then advanced to the lowest throughput tertiary screen: bioluminescence race tube assays directly comparing zero versus high glucose and amino acid medium (0.5% w/v glucose, 0.17% w/v arginine). It should be noted here that the race tube assay is particularly well suited for a Nutritional Compensation mutant screen because the growth front, which produces most of the bioluminescence, always encounters fresh medium; thus, there is no complicating effect of nutrient depletion over the course of a six-day assay (S1 Movie). Circadian period lengths were quantified to assay Nutritional Compensation phenotypes. Sixteen new mutants emerged from the genetic screen showing large period changes between zero and high nutrient conditions (Fig 2C and S1 Table).

mRNA stability and polyadenylation factors emerge as key regulators of circadian period length across nutrients

We first examined the group of 12 new under-compensation mutants, where period length shortens as a function of nutrient levels (Fig 3A and S3 Fig and S1 Table). To prioritize screen hits for further analysis, a Gene Ontology (GO) Enrichment of the 12 hits was performed using FungiDB [47]. Unsurprisingly, the top-scoring GO Term was RNA binding (GO:0003723, BH-adjusted p = 2.18 × 10⁻⁷). poly(A) binding was also a top GO Term (GO:0008143, BH-adjusted p = 1.28 × 10⁻⁴), and upon further manual inspection of the new under-compensated hits, 4 of the 12 mutants share a common function in polyadenylation of nascent RNAs. NCU02736 (FGSC12857) is designated as an uncharacterized gene in Neurospora but is broadly conserved in fungi. Its Saccharomyces cerevisiae homolog is a component of the mRNA cleavage and polyadenylation factor I complex (YGL044C, RNA15). PABP-2 (NCU03946, FGSC19900) binds in poly(A) tail regions and can broadly regulate mRNA stability. NAB2 (NCU16397, FGSC22799^Het) shares a common domain with the yeast gene NAB2 (YGL122C), which plays a role in mRNA export and stability. NCU09014 (FGSC12033) is a putative component of a Neurospora cleavage and polyadenylation complex and is described in further detail below. The two knockout mutants with the most significant period change between high and zero nutrients are both subunits of the nonsense-mediated decay (NMD) machinery in Neurospora (Δupf1^prd-6 and Δupf2) and showed more dramatic nutrient-responsive period shortening than Δcsp-1 [32]. Taken together, regulation of polyadenylation and/or mRNA stability is a common axis of period maintenance with increasing nutrients. The other 6 new under-compensation mutants did not share an obvious functional pathway by GO Term analysis, although some of these genes do show circadian rhythms at the mRNA or protein level and/or are regulated in response to light (S2 Table). However, given the finding that wild-type Neurospora is slightly under-compensated (Figs 1A, 2B and 3), the remaining under-compensation mutants (S3 Fig) were not pursued further in favor of functionally related under-compensation mutants and the group of new over-compensation mutants.

The NMD pathway is required for a normal circadian period length due to its regulation of casein kinase I mRNA levels [52]. We hypothesized that NMD regulation of ck-1a also underlies its nutritional under-compensation defect. NMD in Neurospora can be triggered by long 3′ UTR sequences, and the ck-1a 3′ UTR is among the longest 1% in Neurospora [52]. We generated a ck-1a mutant strain lacking its entire 3′ UTR, hypothesizing that this would decouple ck-1a transcripts from NMD targeting (Fig 3B). The ck-1aΔ3′UTR mutant does indeed have a short period length and shows nutritional under-compensation, phenocopying NMD mutants (Fig 3C). Further, we confirmed that CKIa protein levels are highly up-regulated in both Δupf1^prd-6 and ck-1aΔ3′UTR mutant backgrounds (Fig 3D), consistent with our previous work [52]. However, the ck-1aΔ3′UTR under-compensation phenotype may not be as severe as observed in NMD mutants, as it displays average period shortening of only 3.5 hours (versus 4.5 hours) in the high nutrient condition (Fig 3C, ANOVA Tukey HSD p-values 4 to 5× lower for Δupf1^prd-6 versus ck-1aΔ3′UTR). Although the difference between ck-1aΔ3′UTR and Δupf1^prd-6 is not statistically significant (ANOVA Tukey HSD p-value = 0.057), the additional 1-hour period shortening in Δupf1^prd-6 suggested that multiple NMD targets may be required for normal Nutritional Compensation. Interestingly, NMD mutants also show an approximately 1.4-fold increase in wc-2 and an approximately 1.4-fold decrease in frh mRNA expression levels (Figure 5B from [52,53]). wc-2 is up-regulated in NMD mutants, which could easily explain the under-compensation phenotype because wc-1 overexpression alone is sufficient to drive nutritional under-compensation [33]. Curiously, we find that frh, the obligate binding partner of the disordered protein FRQ [54], is 18-fold down-regulated in response to carbon starvation in wild-type Neurospora [55] (S4 Fig). In fact, both prd-1 and frh are among the top 220 genes in the entire Neurospora transcriptome that decrease in expression after glucose starvation. WC-2 levels are on average approximately 1.7-fold up-regulated in the Δupf1^prd-6 mutant background, while FRH levels are unchanged (Fig 3D). Thus, period shortening with increasing nutrient levels in NMD mutants (Fig 3A) can be explained by an increase in the mRNA and protein expression of CKIa (Fig 3B–3D) [52] and by higher levels of WC-2 protein (Fig 3D).

Mutations in two genes, NCU02152 (FGSC16956) and NCU09014 (FGSC12033), revealed unique nutritional phenotypes compared to the remaining set of 12 new under-compensation mutants (Fig 3A and S3 Fig). ΔNCU02152 (FGSC16956) had the shortest period phenotype observed in the entire primary and secondary screens (S1 Table). NCU02152 was undescribed in the Neurospora literature but contains protein domain homology to the mammalian Cleavage and Polyadenylation Specificity Factor subunit 6 (CPSF6). CPSF6 is a member of the CFIm complex, which binds in the 3′ end of nascent mRNAs and facilitates cleavage and poly(A) tail placement. The CFIm complex also contains a second essential component CPSF5. Using the human CPSF5 protein sequence, NCU09014 was confidently identified as its Neurospora homolog (reciprocal BLAST e-values = 1e⁻⁷²/3e⁻⁷⁴). The single mutant ΔNCU09014/Δcpsf5 (FGSC12033) had the same short period length as Δcpsf6 (S1 Table), which indicates an obligate multimeric complex as with the mammalian CFIm complex [56]. In the tertiary screen, Δcpsf5 and Δcpsf6 mutants showed progressive period shortening after approximately 60 hours into the circadian free run, specifically when grown on high nutrient medium. Recalling the prd-1 region-specific Nutritional Compensation phenotype where period defects were seen at the growth front encountering fresh medium, but not at the point of inoculation where nutrients were depleted [37], we checked period length from old/aging tissue surrounding the point of fungal inoculation in Δcpsf5 and Δcpsf6 mutants. An approximately 18-hour short period length was observed in the old tissue region of Δcpsf5 and Δcpsf6 mutants grown on high arginine (Fig 4A), compared to an approximately 20-hour period length on zero nutrient medium (S1 Table). This result indicates that Δcpsf5 and Δcpsf6 mutants must undergo a transition from high-to-low amino acid levels in old tissue to reveal the shorter period defect (S2 Movie). The Δcpsf5 and Δcpsf6 mutants differ from other under-compensation mutants (Fig 3 and S3 Fig) because the short period defect was induced by amino acids, not by glucose, and because the Nutritional Compensation phenotype is specific to old tissue (Fig 4A).

The Alternative Polyadenylation (APA) landscape is altered in Nutritional Compensation mutants

Given the large period defect observed in Δcpsf5 and Δcpsf6 mutants (Fig 4A) and other under-compensation mutants related to polyadenylation (Fig 3A), we hypothesized that poly(A) tail maintenance and concurrently the stability of a core clock mRNA(s) is required for Nutritional Compensation. To assay the biochemistry of Δcpsf mutants, we required a solid medium growth regime to carefully control nutrient levels and to confidently compare results to our genetic screen for Nutritional Compensation phenotypes. Neurospora biochemistry is traditionally accomplished using extracts from liquid-grown cultures [57,58], where nutrient consumption rates are less well defined and less relevant to the ecological niche of the organism. A cellophane petri plate assay was developed to harvest biomolecules from solid medium cultures of Neurospora (Materials and methods) [34]. We confirmed that the circadian clock is equally functional on cellophane plates and in liquid cultures (S5 Fig). We next generated a Δcpsf5 Δcpsf6 double mutant strain (hereafter referred to as ΔCFIm) and confirmed that its period length and Nutritional Compensation phenotype matched results from race tube assays (S5 Fig). Biological duplicate wild-type control and ΔCFIm mutant cellophane plates were grown at 25°C under constant light for 3 days on high nutrient medium (containing 0.25% w/v glucose 0.17% w/v arginine; high-to-low nutrient transition will occur in aged tissue before 72 hours growth), and total RNA was extracted for 3′ End Sequencing (Materials and methods).

Nascent mRNA transcripts can contain multiple sites for polyadenylation to occur, which is known as Alternative Polyadenylation (APA). APA events generate mRNA isoforms with variable 3′ UTR lengths and nucleotide sequences (reviewed in [59]). Along with other factors involved in mRNA 3′ end cleavage and polyadenylation, CFIm mutants have been shown to alter APA patterns genome wide [60–62]. Changes in APA have been directly linked to RNA stability in yeast and in mammals [63,64]. We first sought to identify normal instances of APA in wild-type Neurospora 3′ UTRs by comparing our 3′ End Sequencing dataset to an existing 2P-Seq dataset [65] (Materials and methods). A total of 843 consensus genes (>10% of Neurospora 3′ UTRs) contain multiple poly(A) sites after applying a strict intersection cutoff for identification in all 4 wild-type datasets (S3 Table), which is a lower genome-wide estimate than >60% APA in mammals. Next comparing ΔCFIm and controls, we found 193 examples of APA events in controls collapsing to a single poly(A) peak in mutants (21%), 123 examples of a single poly(A) peak in controls expanding to multiple APA events in mutants (13%), and 155 examples of APA events in both control and mutant where the location of the predominant poly(A) peak was significantly changed in mutants (16%) (S4 Table and S6 Fig). Taken together, approximately 50% of the APA landscape is altered in Neurospora ΔCFIm mutants. Knockdown of the mammalian CFIm complex causes global 3′ UTR shortening, as proximal poly(A) sites become preferred over distal poly(A) sites [60–62]. In Neurospora, we find that a distal-to-proximal shift occurs in a majority (63%) of the 155 altered APA events in the ΔCFIm mutant.

Nine core clock and compensation genes are among the consensus list of 3′ UTRs with APA events: ck-1a (NCU00685), frq (NCU02265), upf1^prd-6 (NCU04242), ckb-1 (NCU05485), rco-1 (NCU06205), pkac-1 (NCU06240), NCU06565, prd-1 (NCU07839), and nab2 (NCU16397) (S3 Table). Given the strict cutoff for consensus APA, we added 7 additional genes after visual inspection: set-2 (NCU00269), wc-2 (NCU00902), set-1 (NCU01206), csp-1 (NCU02713), cka^prd-3 (NCU03124), frh (NCU03363), and pabp-2 (NCU03946). 3′ UTR regions containing APA events were visualized in a heatmap (Fig 4B). Two out of 16 genes of interest, ckb-1 and pabp-2, are among the list of genes with altered APA patterns in the ΔCFIm mutant (S4 Table and Fig 4B, asterisks *). poly(A) read pileups were visualized for 3′ UTRs of ckb-1 and pabp-2 to confirm significant reorganization of poly(A) tail locations in the ΔCFIm mutant (Fig 4C). Following our original hypothesis, altered 3′ UTR length in ΔCFIm should lead to altered mRNA stability and changes in gene expression compared to control samples. An extremely slight and statistically nonsignificant increase (t test, p = 0.5) was observed for both ckb-1 and pabp-2 gene expression levels (Fig 4D). ckb-1 encodes the regulatory subunit of Neurospora CKII, and interestingly, the catalytic alpha subunit of CKII (cka^prd-3) increased significantly in the ΔCFIm mutant, despite no visible changes in 3′ UTR poly(A) sequence coverage for cka^prd-3 (Fig 4B). In addition, ck-1a gene expression levels are modestly (1.2-fold) but significantly decreased in ΔCFIm (Fig 4D), a result that is counterintuitive given the mutant’s short period length phenotype [52]. Future work will determine whether overexpression of CKII underlies the ΔCFIm short period length and Nutritional Compensation phenotypes.

Chromatin modifiers emerge as key regulators of circadian period length across nutrients

We next examined the 4 new over-compensation mutants identified in our genetic screen, where period lengthens as a function of nutrient levels (Fig 5A and S1 Table). The wild-type Neurospora clock is slightly under-compensated (Figs 1–3), and, therefore, this group of over-compensation mutants, together with prd-1 (Fig 2B) [37], represent clear and bona fide Nutritional Compensation defects. Protein Kinase A (pkac-1) shows extended compensation, or approximately the same period length at zero and high nutrients (Fig 5A). The effect of loss of PKA on Nutritional Compensation is subtle and likely due to its phosphorylation and inhibition of RCM-1, activating WCC-independent frq transcription—RCM-1 normally acts as a general transcription corepressor with RCO-1 and prevents frq transcription in a Δwc-1 or Δwc-2 background [35,66]; however, a Δrcm-1 mutant did have normal Nutritional Compensation in our screen (S1 Table). PKA also directly regulates WCC and FRQ by phosphorylation [67]; thus, an alternative explanation for its extended compensation phenotype (and the lack of phenotype in Δrcm-1) could be nutrient-dependent posttranslational regulation of core clock proteins.

Fig 5 — Circadian bioluminescence was recorded from race tube cultures of the indicated deletion mutants. High nutrient medium (yellow lines) contained 0.5% w/v glucose 0.17% w/v arginine, and zero nutrient medium (blue lines) contained 0% glucose 0% arginine (standard deviation error bars). Period lengths are indicated as insets (N = 2–4 biological replicates per nutrient concentration) (A). RNA-Sequencing data were mined from a previous study [71] (Materials and methods), where 2% (high) glucose liquid cultures were harvested at circadian time point DD24. Log₂-transformed FPKM values are shown for core clock gene expression levels (N = 2 biological replicates per genotype). The asterisk indicates p = 0.05 (*) by Student t test comparing mutant to control levels. The *frq* transcript is 1.78-fold more abundant in Δ*set-1* (B). RNA-Sequencing data were mined from a previous study [73] (Materials and methods), where 1.5% (high) sucrose liquid cultures were sampled. Log₂-transformed FPKM values are shown for core clock gene expression levels (N = 2 biological replicates for Δ*set-2*, and N = 6 biological replicates for controls). The asterisk indicates p < 0.05 (*) by Student t test comparing mutant to control levels. The *frh* transcript is 1.61-fold less abundant in Δ*set-2* (C). RNA expression levels were measured from the indicated genotypes grown on 0.25% w/v glucose + 0.17% w/v arginine high nutrients versus zero nutrient (0% glucose + 0% arginine) solid medium for 72 hours in constant light at 25°C (N = 2 biological replicate samples; N = 3 technical replicates for each RT-qPCR reaction; standard deviation error bars). *frq* mRNA expression levels are detectable in the WCC-independent transcription mutant Δ*wc-2* Δ*set-2* at zero nutrients but are reduced compared to the high nutrient condition, suggesting that nutrients contribute to the extent of WCC-independent *frq* transcription (D). The entire *frh* locus (plus 1 kb of its upstream promoter sequence, a C-terminal V5 epitope tag, and 500 bp of its 3′ UTR) was integrated at the *csr-1* locus (strain # 1192–1). In a second mutant with full-length *frh* integrated at *csr-1*, the endogenous *frh* locus was replaced with the FRH^R806H mutant and marked with ignite resistance (strain # 1163–1). The *frh* + *frh* overexpression mutant, the *frh* + *frh*^R806H mutant, and control strain were compared by race tube (N = 10–19 biological replicates). The *frh* overexpression condition led to nutritional over-compensation (zero nutrients: 21.0 ± 1.3 hour [SEM: 0.29; N = 19]; high nutrients: 22.8 ± 0.8 hour [SEM: 0.19; N = 19]). The *frh* + *frh*^R806H mutant (zero nutrients: 22.6 ± 1.3 hour [SEM: 0.42; N = 10]; high nutrients: 21.2 ± 1.1 hour [SEM: 0.35; N = 10]) and control (zero nutrients: 23.4 ± 0.5 hour [SEM: 0.13; N = 13]; high nutrients: 21.5 ± 0.4 hour [SEM: 0.13; N = 12]) showed slight under-compensation to nutrients (E). The raw data underlying parts (A–E) can be found in S5 Data.

RBG-28 (NCU02961, FGSC16412) is a broadly conserved ribosome biogenesis factor in fungi, currently uncharacterized in the Neurospora literature but homologous to RNH70 (YGR276C) in S. cerevisiae. Rnh70p (Rex1p) is a 3′- to 5′ exonuclease involved in 5S and 5.8S rRNA processing [68]. The completely arrhythmic phenotype of Δrbg-28 on high nutrients (Fig 5A) is intriguing and implicates translational machinery in Nutritional Compensation for the first time (see Discussion).

Half of the novel over-compensation mutants are involved in chromatin regulation and both contain SET domains (reviewed in [69]). SET-1 (NCU01206) is a histone H3 lysine 4 (K4) methyltransferase and the catalytic subunit of the COMPASS complex in Neurospora. Δset-1 was previously reported to be arrhythmic [70], but here we find an increasing period length as a function of glucose levels (Fig 5A). SET-1 was convincingly shown to regulate methylation levels and transcriptional repression at the frq locus [70,71], and upon reanalysis of the Δset-1 transcriptome, frq is the only core clock gene significantly altered in the Δset-1 mutant due to loss of repression (Fig 5B). The Δset-1 nutritional over-compensation phenotype is consistent with loss of chromatin repression on frq—as glucose levels increase, more cellular energy is available for transcription/translation, and circadian period is lengthened due to high frq levels and prolonged negative feedback. This SET-1 nutritional mechanism is analogous to CKII’s role in regulating the larger pool of FRQ protein at higher temperatures in Temperature Compensation [21].

Because the Δset-1 mutant was reported to be arrhythmic in previous work [70], yet we identified period lengthening as a function of nutrients, we investigated whether the role of SET-1 in Nutritional Compensation involved its reported role in the COMPASS complex. The frq clock box transcriptional reporter was integrated into individual knockouts of COMPASS complex subunits, which were previously assayed only by race tube [70]. Consistent with previous findings, swd2 (NCU07885) and sdc1 (NCU03037) are not required for circadian function and have a wild-type period length by luciferase assay (S7 Fig) and by race tube [70]. Curiously, Δswd1 (NCU02104) had an intact clock by luciferase (S7 Fig) despite its arrhythmic phenotype by race tube [70]. set-1 and swd3 (NCU03244) mutants each had approximately 24-hour long period phenotypes by luciferase assay (S7 Fig). To investigate whether the long period phenotype of Δset-1 was exclusively due to loss of function of the COMPASS complex, we generated a double mutant Δset-1 Δswd3 and assayed period length and Nutritional Compensation. The single and double mutants each showed equivalent period lengthening and extended or over-compensation to nutrients (S7 Fig). Thus, SET-1 and the COMPASS complex are required for functional Nutritional Compensation through direct regulation of frq mRNA abundance.

SET-2 (NCU00269) is a histone H3 K36 methyltransferase in Neurospora, which deposits inhibitory chromatin marks in actively transcribed regions via a physical association with RNA Polymerase II and prevents improper transcription initiation inside coding regions [72,73]. Δset-2 was also previously reported to be arrhythmic [35,74], but here we find low amplitude Δset-2 rhythms that are completely intact with a short period length on nutrient starvation medium (Fig 5A) and an arrhythmic phenotype observed at high nutrients (S3 Movie). SET-2 is required to maintain H3K36me2 and H3K36me3 marks across the frq locus, and the Δset-2 mutant results in hyper-acetylation of frq, improper activation of WCC-independent frq transcription, constitutively high frq expression levels, and, presumably, the arrhythmic clock phenotype observed under high nutrients [35,74]. Our result indicates for the first time that WCC-independent frq transcription is nutrient dependent and only reaches levels sufficient for arrhythmicity at high nutrient levels. To test this hypothesis, we assayed frq mRNA expression level by quantitative reverse transcription PCR (RT-qPCR) on high versus zero nutrients in control compared to a Δwc-2 Δset-2 double mutant. We observed less WCC-independent frq transcription in the zero nutrient condition compared to high nutrients under constant light conditions (Fig 5D).

However, low WCC-independent frq transcription does not explain the short period length of the Δset-2 mutant in zero nutrient medium (Fig 5A), as low WCC expression results in lengthened periods [75]. Curiously, upon mining existing data for the Δset-2 transcriptome, frq levels were not significantly increased, and instead frh levels were down-regulated (Fig 5C). frh is among the top down-regulated genes during glucose starvation (S4 Fig), and perhaps Δset-2 further affects frh transcription, leading to a circadian period change. Notably, two RNA helicases physically associated with the mammalian negative arm complex, DDX5 and DHX9, show a short period length upon siRNA knockdown [76], and so decreased levels of the frh helicase in Δset-2 could potentially explain its short period phenotype. To test whether frh gene expression regulation is important for Nutritional Compensation, we used a mutant background with a second copy of full-length frh expressed at the csr-1 locus (strain # 1192–1 from [54]). Increased expression of frh directly caused a nutritional over-compensation phenotype, demonstrating that down-regulation of frh mRNA under starvation conditions is physiologically relevant to the circadian clock (Fig 5E and S4 Fig). Furthermore, the extra copy of FRH requires circadian functionality to produce the over-compensation phenotype. The FRH^R806H mutant, which preserves its helicase activity but cannot bind FRQ [77], expressed as the second copy of FRH (strain # 1163–1 from [54]) matches the wild-type nutritional under-compensation profile (Fig 5E).

Taken together, our genetic screen revealed mRNA stability, polyadenylation, and chromatin modifier pathways converging on gene expression regulation to enact circadian Nutritional Compensation in fungi. Nutritional Compensation effectors then directly regulate core clock factors, such as wc-1, wc-2, frh, and ck-1a gene expression levels, to maintain circadian period length across nutrient environments.

Nutritional Compensation also functions in a mammalian circadian system

Compensation is a defining principle of circadian oscillators. Temperature Compensation mechanisms are active in mammalian tissue culture [17,18], despite long evolutionary timescales in homeothermic organisms. Thus, we hypothesized that Nutritional Compensation is also a conserved feature between the fungal and mammalian circadian clocks. Four previous studies hinted that Nutritional Compensation mechanisms may be actively maintaining circadian period length across physiologically relevant nutrient environments.

Inhibiting transcription using α-amanitin or actinomycin D against RNA Polymerase II activity led to dose-dependent shortening of the NIH3T3 period length [78]. In other words, the mammalian circadian oscillator is over-compensated with respect to transcription rates, reminiscent of temperature over-compensation observed in Gonyaulax [2]. Similarly, induction of autophagy and amino acid starvation shortens period length in mouse embryonic fibroblast (MEFs) [79], once again indicating an over-compensation phenotype to amino acid levels in mammals. Varying levels of FBS do not alter the circadian period length in NIH3T3 cells [80] despite substantial changes in cell growth rate. Rhythms at the single-cell level of MEFs grown in microfluidic devices show slight period shortening when cells were given fresh medium every hour compared to a single medium supply at the beginning of the experiment, suggesting instead nutritional under-compensation [81]. Thus, preliminary evidence is consistent with functional Nutritional Compensation in the mammalian circadian system.

We used a U2OS Bmal1-dLuc reporter cell line to compare rhythms in high versus low glucose medium and found a slight under-compensation phenotype (S8 Fig), similar to the single-cell rhythms report [81]. Period length was 0.5 hours shorter in 25 mM (high) glucose compared to 5.56 mM (low) glucose, trending in the same direction as the approximately 1.5-hour period shortening seen for the wild-type Neurospora clock across glucose concentrations (Figs 1–3). In this circadian assay, Bmal1-dLuc cells have reached confluency and are no longer actively dividing, but metabolism and drug responses of cultured cells are reported to be significantly different in high versus low glucose tissue culture medium (reviewed in [82]).

Maintenance of circadian period length between high and low glucose medium and a small amount of literature precedent does not, however, prove the relevance of Nutritional Compensation to the mammalian clock. A mutant phenotype showing nutrient-dependent period changes (i.e., defective Nutritional Compensation) would provide much stronger evidence for the biological relevance of compensation. Therefore, we selected two compensation mutants from our fungal genetic screen—Δcpsf6 (NCU02152) and Δset-2 (NCU00269)—and identified the homologous human genes CPSF6 and SETD2 (reciprocal BLAST e-values = 7e⁻⁶/7e⁻⁸ for Cpsf6 and 4.4e⁻⁶¹/5.7e⁻⁶² for Set-2). If the circadian functions of CPSF6 or SETD2 are indeed conserved with Neurospora, we expected to observe a period length change as well as a Nutritional Compensation defect. CPSF6 and SETD2 were not among the hits from a genome-wide screen for period length defects using U2OS cells [83] or tested in a kinase/phosphatase siRNA screen [84]; however, visual inspection of the genome-wide screen data confirmed that a subset of the pooled siRNAs did show period effects after CPSF6 or SETD2 knockdown (source: BioGPS database, “Circadian Genomics Screen” plugin). Thus, we used the U2OS siRNA genome-wide screen database [83] as preliminary data to select CPSF6 and SETD2 for further study.

CPSF6 and SETD2 were knocked down using siRNAs in Bmal1-dLuc cells, and period length was measured from high and low glucose medium (Fig 6). AllStars Negative Control siRNA, which does not target any known mammalian transcript, was used as an internal control for each biological replicate assay, and CRY2 siRNA knockdown was used as a positive control for a known long period phenotype [85,86]. Control Bmal1-dLuc cells had a slight under-compensation phenotype (Fig 6A), matching preliminary results (S8 Fig). CRY2 knockdown lengthened period by approximately 4 hours in both high and low glucose conditions, indicating no effect on Nutritional Compensation (Fig 6A). CPSF6 knockdown lengthened period by approximately 1.5 hours in high glucose and further lengthened period by approximately 3 hours in low glucose, indicating an under-compensation phenotype compared to control cells (Fig 6A). The long period observed in CPSF6 knockdowns was the opposite of the short period phenotype in Neurospora (Fig 4A). Most interestingly, SETD2 knockdown drastically increased the amplitude and magnitude of the Bmal1-dLuc transcriptional reporter compared to controls. As with Neurospora, SETD2 knockdown rhythms were less robust (Figs 5A and 6A). SETD2 knockdown lengthened period by approximately 2.5 hours in high glucose but only lengthened period by approximately 1 hour in low glucose (Fig 6A). To further validate nutritional over-compensation in SETD2 knockdowns, Bmal1-dLuc cells were compared across higher concentrations of glucose with more robust rhythms (Fig 6B). Just like Neurospora, SETD2 mutants show a nutritional over-compensation phenotype. This genetic evidence strongly suggests that Nutritional Compensation mechanisms also regulate the mammalian circadian clock in physiologically relevant environments.

Discussion

We present the largest genetic screen to date for mutants displaying altered circadian Nutritional Compensation (S1 and S2 Tables and Fig 2C). Together with a recent survey of 177 transcription factor knockouts [50], circadian functional genomics is well underway utilizing the Neurospora deletion collection [43]. In this study, 16 new Nutritional Compensation mutants were identified and characterized along with the 4 previously characterized mutants (Δcsp-1, prd-1, Δrco-1, and Δras2)—fungal Nutritional Compensation occurs at the level of gene regulation and involves transcription factors, RNA helicases, chromatin modifiers, and polyadenylation machinery. Nutritional Compensation effectors are responsible for directly regulating the core oscillator to maintain the circadian period length across different nutrient levels. In high nutrient environments, CSP-1 forms a negative feedback loop on WCC expression and activity by inhibiting wc-1 overexpression and preventing nutritional under-compensation [32,33]. RCO-1, SET-2, and possibly PKA are required to block WCC-independent frq transcription in high nutrient conditions (Fig 5) [34,35,74]. More broadly, repressive chromatin marks at the frq locus, including those regulated by SET-2, SET-1, and the COMPASS complex, are required to maintain Nutritional Compensation, especially in high nutrients (Fig 5 and S7 Fig). The NMD machinery is required for circadian function through its regulation of ck-1a expression [52], and ck-1a transcript stability appears to undergo additional, nutrient-dependent regulation because deletion of the ck-1a 3′ UTR (its NMD-targeting sequence) causes nutritional under-compensation (Fig 3C). The APA landscape in Neurospora is dynamic across nutrient conditions and requires CFIm complex activity (Fig 4).

This screen and associated candidates have opened up several avenues for further work on Nutritional Compensation mechanisms in Neurospora. For instance, PRD-1 RNA helicase target genes under high glucose conditions are completely unknown. In this study, we provide some evidence that CKII activity is altered in ΔCFIm mutants (Fig 4), but the effect of CKII overexpression on both circadian period length and Nutritional Compensation remains to be tested. Genetic screens extending from this work can examine both transcription factor and chromatin modifier knockouts for Nutritional Compensation phenotypes. Curiously, the subset of chromatin readers and writers that have been screened to date (using standard medium conditions) have an unusually high approximately 15% hit rate for circadian period defects [87,88]. In this study, chromatin modifiers not only regulate chromatin state at the frq locus, but dynamic expression of frh across nutrient levels may also be involved in compensation, potentially mediated by SET-2 (Fig 5C and 5E). Intriguingly, quantitative modeling has suggested that nutrient-dependent FRH sequestration away from the negative arm complex is a plausible mechanism for Nutritional Compensation [89]. Mechanistic work in prokaryotes has indicated that Nutritional Compensation can be derived from the same core clock enzyme, KaiC, through its protein domains with equal and opposite balancing reaction rates [90,91]. In fungi and perhaps other eukaryotic clocks, Nutritional Compensation appears to be maintained through regulation of multiple different core clock factors, including wc-1, wc-2, frh, and ck-1a. Nutritional Compensation mechanisms very likely extend to other physiological environmental variables such as nitrogen sources and levels [92], vitamins, and soil pH [93].

This work establishes Nutritional Compensation in the mammalian circadian clock for the first time, and its clinical relevance may be high. There is a direct link between pancreatic clock function and risk for type 2 diabetes [94]. Fasting glucose concentration in human serum is approximately 5 mM in healthy controls, but glucose levels can increase by 3+ orders of magnitude in type 2 diabetic hyperglycemia [95,96]. Thus, deleterious SNPs in Nutritional Compensation-relevant genes could exacerbate disease outcomes when cellular clocks encounter nutrients outside of the physiological range. In addition to Nutritional Compensation, there is a large body of quality literature describing the set of metabolites and metabolic enzymes that can directly feed back and affect circadian function, including adenosine monophosphate (AMP) and AMP kinase, nicotinamide adenine dinucleotide (NAD+) and SIRT1 activity, acetylglucosamine (O-GlcNAc) [97], and mTOR activity [98] (reviewed in [99–102]). In Neurospora, extensive metabolic rhythms are also present [45,103], and rhythmic metabolic reaction fluxes likely function similarly to the mammalian clock [104–106]. For the mammalian clock, it will be critical to define “physiologically relevant” nutrient levels, which could vary by organ or even cell type. A high-fat diet has been shown to lengthen circadian behavioral rhythms by approximately 0.5 hours [107], suggesting an over-compensation phenotype. In this study, we observed nutritional under-compensation in U2OS osteosarcoma cells (Fig 6). Does a high-fat diet push cells out of the physiological range of compensation for the circadian oscillator, or are different mammalian cell types differentially over- or under-compensated to nutrients? Notably, time-restricted feeding has been shown to alleviate circadian disruption and some metabolic consequences of a high-fat diet [108,109]. Future work will implement more metabolically relevant cell types, such as hepatocytes and adipocytes [110], to answer these questions about mammalian clock compensation.

Circadian control of polyadenylation is an emerging topic. Modern transcriptomic approaches have identified hundreds of rhythmic APA events in mice and in plants [111–114]. We and others have begun to define the set of APA events in Neurospora [65] (S3 Table), which can be extended to circadian APA events. In Neurospora, CPSF5 may physically interact with the negative arm complex [56]. Two cleavage and polyadenylation factors (CPSF1 and 7) were found to physically interact with the negative arm complex in mice [115], and CPSF6 oscillates at the transcriptional level in mouse kidney and brain [116]. During preparation of this manuscript, circadian colleagues reported the long period length of CPSF6 knockdown [117], which our results further support (Fig 6A). Interestingly, CPSF6 knockdown cells showed a temperature under-compensation defect, and multiomics identified EIF2S1 as the key effector gene upon CPSF6 knockdown [117]. The Neurospora homolog of EIF2S1 is eIF2α/NCU08277 (reciprocal BLAST e-values = 6e⁻⁹¹/1e⁻⁸¹), which is a central hub of rhythmic translation initiation peaking in the subjective evening [118]. We identified a ribosome biogenesis exonuclease, RBG-28, which is required for rhythmicity under high nutrient conditions (Fig 5A). Transcription, RNA processing reactions (such as NMD, splicing, and polyadenylation) and translation are tightly coupled processes. In fact, rhythmic polyadenylation of rRNAs has been linked to translational rhythms in mouse liver [119]. Nutritional Compensation pathways likely occur at multiple steps in the gene expression of multiple core clock components.

By suggesting CPSF6 and SETD2 as targets, Neurospora functional genetics has again informed mammalian-relevant circadian mechanisms (reviewed in [120]), and the over-compensation defect of SETD2 provides affirming evidence of eukaryotic Nutritional Compensation outside of Neurospora (Fig 6). A handful of previous studies have implicated histone methyltransferases in mammalian circadian function, including both activating [121,122] and repressive chromatin marks [123]. SETD2 joins a growing list of circadianly relevant histone methyltransferases and chromatin modifiers. In fact, recent work has demonstrated a novel function for a key histone methyltransferase in the circadian TTFL (TRITHORAX in insect, MLL1 in mammals), further highlighting the utility of circadian model systems for understanding the mammalian clock [124].

Materials and methods

Neurospora strains, growth conditions, and genetic screen

Strains used in this study were derived from the wild-type background (FGSC2489 mat A), ras-1^bd background (87–3 mat a or 328–4 mat A), or the Fungal Genetics Stock Center (FGSC) knockout collection as indicated (S1 Table). Strains were constructed by transformation or by sexual crosses using standard Neurospora methods (http://www.fgsc.net/Neurospora/NeurosporaProtocolGuide.htm). The frq clock box transcriptional reporter was transformed and used as previously described [52]. The fungal biomass reporter gene (S1 Fig) is composed of 430 bp of the gpd promoter from Cochliobolus heterostrophus driving constitutive levels of codon optimized luciferase and integrated at the csr-1 (NCU00726) locus [125].

All race tubes contain a base medium of 1X Vogel’s Salts, 1.5% w/v Noble agar (Thermo Fisher # J10907), and 50 ng/ml biotin. Noble agar was used instead of standard bacteriological agar (Thermo Fisher # J10906) because impurities in bacteriological agar can be metabolized by Neurospora and interfere with accurate quantification of Nutritional Compensation phenotypes [37]. Glucose and arginine were supplemented into race tube medium as indicated. High glucose was defined as 0.5% w/v (27.8 mM) based on literature precedent [32,34] and based on growth rate and period length similarity to higher glucose concentrations (Fig 1A and S1G Fig). High arginine was defined as 0.17% w/v because concentrations higher than 0.5% w/v interfere with circadian banding in race tube assays [5]. To optimally visualize and quantify the circadian banding pattern in the primary genetic screen (Fig 2C), screen medium contained 1X Vogel’s Salts, 1.5% Noble agar, 50 ng/ml biotin, and 0.17% arginine. To quantify period lengths in carbon and nitrogen starvation conditions, the secondary screen medium contained 1X Vogel’s Salts, 1.5% Noble agar, 50 ng/ml biotin, and 25 μM luciferin (GoldBio # 115144-35-9). The tertiary screen medium contained 1X Vogel’s Salts, 1.5% Noble agar, 50 ng/ml biotin, 25 μM luciferin, and glucose/arginine levels as indicated (Figs 3–5). For Temperature Compensation 96-well plate experiments (Fig 2A) and COMPASS complex subunit knockout assays (S7A and S7B Fig), the standard medium recipe contained 1X Vogel’s Salts, 1.5% bacteriological agar, 50 ng/ml biotin, 25 μM luciferin, 0.03% w/v glucose, and 0.05% w/v arginine.

Liquid medium cultures were grown from fungal plugs in Bird Medium + 1.8% w/v glucose (S5 Fig) as previously described [58]. Solid medium cultures were implemented to determine mRNA (or protein) levels from Nutritional Compensation mutants of interest with modifications to previous work [34]. Medium was poured into 100-mm petri plates and cooled to solidify (approximately 20 ml per plate; 1X Vogel’s Salts, 1.5% Noble agar, 50 ng/ml biotin, 25 μM luciferin, and glucose/arginine levels as indicated). Cellophane (Idea Scientific # 1080) paper discs were cut to the size of 100-mm plates and autoclaved to sterilize. A sterile cellophane disc was then placed on top of the solidified medium. Conidia from strains of interest were resuspended in 100 μl of sterile water, vortexed to mix, pipetted on top of the cellophane disc, and spread with a sterile plate spreader (in order to maintain approximately the same conidial age across the cellophane plate). Inoculated plates were then covered with a Breathe-Easy strip for gas exchange (USA Scientific # 9123–6100). After growing tissue on cellophane plates for the indicated amount of time, mycelia and conidia were harvested from atop the cellophane layer by scraping with a 1,000-μl pipette tip. Harvested fungal tissue (approximately the size of 1 US quarter per each 100-mm cellophane plate) was rapidly hand dried using paper towels and an Eppendorf tube rack and flash frozen in liquid nitrogen for storage before biochemical extraction.

Most strains were genotyped by growth on selective medium (5 μg/ml cyclosporine A and/or 200 to 300 μg/ml Hygromycin). Key strains were genotyped by PCR as previously described [52] using genotyping primers:

ck-1a^LONG-VHFΔ3′UTR::hyg^R (NCU00685 Δ3′UTR): 5′ GCTGCTGCTCGTAAGGAC 3′ and 5′ CATCAGCTCATCGAGAGCCTG 3′
Δcpsf5::hyg^R (FGSC KO mutant): 5′ CTCTGGTCGAGAACACTGCG 3′ and 5′ CAGGCTCTCGATGAGCTGATG 3′
Δcpsf6::hyg^R (FGSC KO mutant): 5′ CACCAACCCTAACCCGTGAT 3′ and 5′ CAGGCTCTCGATGAGCTGATG 3′
Δset-2:hyg^R (FGSC KO mutant): 5′ GACGTCATCGGTGTTGAGAC 3′ and 5′ CAGGCTCTCGATGAGCTGATG 3′
Δwc-2:hyg^R (FGSC11124 KO mutant): 5′ GGTGCTATGTACTCTGGGAT 3′ and 5′ CAGGCTCTCGATGAGCTGATG 3′
Δset-1:hyg^R (FGSC KO mutant): 5′ AGTGGAAAGTCGAGCATTTGC 3′ and 5′ CAGGCTCTCGATGAGCTGATG 3′
Δswd1:hyg^R (FGSC KO mutant): 5′ TGTAGTCGCGCAGGTTCTGG 3′ and 5′ CAGGCTCTCGATGAGCTGATG 3′
Δswd2:hyg^R (FGSC KO mutant): 5′ TCAGGGAGAACTCAAGTCGAGT 3′ and 5′ CAGGCTCTCGATGAGCTGATG 3′
Δswd3:hyg^R (FGSC KO mutant): 5′ AGTTTGGAGATCTCCCAGGACTG 3′ and 5′ CAGGCTCTCGATGAGCTGATG 3′
Δsdc1:hyg^R (FGSC KO mutant): 5′ AGAGCAAATGTGCAGCTTGCT 3′ and 5′ CAGGCTCTCGATGAGCTGATG 3′

Neurospora luciferase reporter detection and data analysis

The 96-well plates were inoculated with conidial suspensions and entrained in 12-hour light:dark cycles for 2 days in a Percival incubator at 25°C. Temperature inside the Percival incubator was monitored using a HOBO logger device (Onset # MX2202) during entrainment and free run. Race tubes were entrained in constant light at 25°C for 3 to 24 hours (mean entrainment time for all experiments was 14 hours in LL/overnight). Entrained 96-well plates or race tubes were then transferred into constant darkness to initiate the circadian free run. Individual race tubes were separated by approximately 3 cm tall strips of 6-ply black railroad board paper to prevent contamination of light signal between cultures. Luminescence was recorded using a Pixis 1024B CCD camera (Princeton Instruments). Bioluminescent signal was acquired for 10 to 15 minutes every hour using LightField software (Princeton Instruments, 64-bit version 6.10.1).

The average bioluminescent intensity of each 96-well or race tube was determined using a custom ImageJ Macro with background correction for each image [51,126]. Most race tube period lengths reported in this study were derived from luciferase signal measurements across the entire race tube (Figs 1, 3 and 5). However, the long period defect in the prd-1 mutant only occurs at the growth front (i.e., high nutrients) region of fungal tissue [37]. For the prd-1 strain, an ImageJ macro was modified to quantify only the fungal growth front (Fig 2B). On the other hand, the Δcpsf5 and Δcpsf6 mutants showed additional period shortening in aged tissue (Fig 4A) (S2 Movie). For the cpsf mutants, an ImageJ macro was modified to quantify only the old tissue region. Custom ImageJ Macros to quantify the growth front or old tissue regions of race tubes from Princeton *.spe image files are available at: https://github.com/cmk35.

To calculate the circadian period length, background-corrected luminescence traces were run through two different algorithms and averaged as previously described [52]. Race tubes period lengths were measured using ChronOSX 2.1 software. For Temperature Compensation experiments, the Q₁₀ temperature coefficient was calculated using the formula: [(frequency of clock at 30°C) / (frequency of clock at 20°C)] ^[10°^{C / (30}°^{C– 20}°^C)], where frequency = period length⁻¹.

Neurospora RNA isolation and 3′ End Sequencing analyses

Frozen Neurospora tissue was ground in liquid nitrogen with a mortar and pestle. Total RNA was extracted with TRIzol (Invitrogen # 15596026) and the Direct-zol RNA MicroPrep kit (Zymo Research # R2060) according to the manufacturer’s instructions and including the on-column DNAse I treatment step (Roche # 04 716 728 001, 10 U/μl stock, 30 U used per sample). Total RNA samples were prepared for Northern Blotting, RT-qPCR, 3′ End Sequencing, or stored at −80°C.

Northern blotting was performed as previously described [52] with slight modifications. Equal amounts of total RNA (7 μg) were loaded per lane of a 0.8% w/v agarose gel (S5 Fig). For blot visualization, anti-Digoxigenin-AP Fab fragments was purchased from Sigma (Roche # 11 093 274 910) and used at 1:10,000 (75 mU/ml).

Neurospora RT-qPCR was performed as previously described [52]. cDNA was synthesized using the SuperScript IV First-Strand synthesis kit (Invitrogen # 18091050). RT-qPCR was performed using SYBR green master mix (Qiagen # 204054) and a StepOne Plus Real-Time PCR System (Applied Biosystems). C_t values were determined using StepOne software (Life Technologies) and normalized to the actin gene (ΔC_t). The ΔΔC_t method was used to determine mRNA levels relative to the zero nutrient control samples. Relevant RT-qPCR primer sequences are as follows: frq (NCU02265): 5′ TGGCTCGGATAAGAATGGTC 3′ and 5′ ATGAAAGGTGTCCGAAGGTG 3′. actin (NCU04173): 5′ GTCCCCGTCATCATGGTATC 3′ and 5′ CTTCTCCATGTCGTCCCAGT 3′.

For 3′ End Sequencing (Fig 4 and S6 Fig), RNA was extracted from solid medium high nutrient cultures (0.25% w/v glucose + 0.17% w/v arginine) grown for 72 hours at 25°C. Total RNA was submitted to the Dartmouth Genomics Shared Resource (GSR) for 3′ end library preparation and sequencing. Using the Lexogen QuantSeq 3′ REV kit, 75 bp single-end (SE) strand-specific libraries were prepared, multiplexed, and sequenced on an Illumina Mini-Seq. For each sample, 6.92 ± 0.30 million reads were obtained, and read quality was confirmed using FastQC. Raw FASTQ files were aligned to the Neurospora crassa OR74A NC12 genome (FungiDB version 45; accessed October 25, 2019) using STAR [127]. Around 91.5% to 93.5% of the reads mapped uniquely to the NC12 genome. Because 3′ end libraries generate only 1 sequencing read at the extreme 3′ end of a given mRNA transcript (directly before its poly(A) tail), gene expression was quantified by counting reads assigned to each genetic locus using HTSeq-count [128]. Gene count normalization by library size between samples was performed using a custom R script. 3′ End Sequencing data have been submitted to the NCBI Gene Expression Omnibus (GEO; https://www.ncbi.nlm.nih.gov/geo/) under accession number GSE201901.

RNA-Sequencing datasets from 4 other studies were mined in the analyses presented. To examine wild-type Neurospora gene expression under carbon starvation (S4 Fig and S2 Table), RNA-seq data were taken from a study where liquid cultures (25°C, LL) were grown for 16 hours in 1X Vogel’s 2% sucrose minimal medium and shifted to either 0% or 2% glucose 1X Vogel’s medium for 60 minutes [55] (GSE78952). To examine gene expression in the Δset-1 mutant background (Fig 5B), RNA-seq data were taken from a study where liquid cultures (25°C, DD24) were grown in 2% glucose Liquid Culture Medium for 48 hours of total growth [71] (GSE121356). To examine gene expression in the Δset-2 mutant (Fig 5C), RNA-seq data were taken from a study where liquid cultures (32°C) were grown in 1X Vogel’s 1.5% sucrose medium [73] (GSE82222 and GSE118495). These three RNA-Seq datasets were reprocessed exactly as previously described [52], and FPKM gene expression values were used in the analyses presented. To examine and compare wild-type poly(A) tail locations (S3 Table), 2P-Seq data were taken from a previous study where nuclear fractions were isolated from 1X Vogel’s 2% glucose liquid medium [65] (SRA PRJNA419320). Raw 2P-Seq data were filtered according to custom a Perl script from the original study (“Step 1”: https://github.com/elifesciences-publications/poly-A-seq). After read filtering, duplicate 2P-Seq FASTQ files were processed in exactly the same manner as the new 3′ End Sequencing dataset generated in this study.

NC12 mapped reads from 3′ End Sequencing (this study) and 2P-Seq [65] data were sorted and indexed using Samtools (BAM file outputs) and then visualized using IGV. Read pileups denoted the location of poly(A) tails in both datasets. To map locations of poly(A) tails genome wide, the ChIP-Seq peak calling algorithm MACS2 was repurposed [129]. The relevant MACS2 parameters used to identify poly(A) peaks were as follows: effective genome size (-g 4.014e7), retention of duplicate reads in pileups (—keep-dup all), summit and subpeak identification (—call-summits), fragment size estimation and shifting turned off (—nomodel—extsize 75), and a false discovery rate cutoff for significant peaks (-q 0.01). MACS2 peaks were assigned to the corresponding gene 3′ UTR region using a custom R script. The Neurospora crassa NC12 transcriptome annotation remains partially incomplete with only 7,793 out of 10,591 unique NCU IDs having 3′ UTRs annotated. As a result, approximately 14% to 18% of all MACS2 peaks were pruned from consideration due to missing annotations. Importantly, there are also examples of under-annotated 3′ UTR regions, where the poly(A) read pileup signal is clearly located outside of the 3′ UTR annotation. One such critical example occurs at the frequency locus (positive/Watson strand gene), where the predominant poly(A) peak is centered at LG VII coordinate 3,136,633, and its longest 3′ UTR annotation ends at coordinate 3,136,464. The Neurospora NC12 transcriptome annotation was last updated in March 2015, before migration from the Broad Institute to the FungiDB database [47]. The 3′ End Sequencing analyses presented here can be updated upon release of an improved transcriptome annotation. Furthermore, there are 621 instances of overlapping coordinates within the 3′ UTRs of tail-to-tail oriented genes, and any poly(A) peaks falling in these gene assignment ambiguous regions were also removed from consideration (approximately 9% of MACS2 peaks). The remaining MACS2 peaks (approximately 6,600 unique poly(A) peaks per sample) were assigned to the corresponding gene 3′ UTR region and analyzed using custom R scripts. APA events were defined as instances of more than one distinct MACS2 peak assigned to a single 3′ UTR region. poly(A) tail read pileups from genes of interest were extracted using the igvtools count function, and genome coordinate plots were generated using the R Bioconductor package Gviz (Fig 4C). 3′ UTR heatmaps were generated using a custom R script (Fig 4B). All custom R scripts for gene expression analyses and APA analyses are available at: https://github.com/cmk35.

Protein isolation and detection

Frozen Neurospora tissue was ground in liquid nitrogen with a mortar and pestle. Total protein was extracted in buffer (50 mM HEPES (pH 7.4), 137 mM NaCl, 10% glycerol v/v, 0.4% NP-40 v/v, and cOmplete Protease Inhibitor Tablet according to instructions for Roche # 11 836 170 001) and processed as described [130]. Protein concentrations were determined by Bradford Assay (Bio-Rad # 500–0006). For western blots, equal amounts of total protein (30 μg) were loaded per lane into 4% to 12% Bis-Tris Bolt gels (Invitrogen # NW04125/NW04127) or 8% Bis-Tris Bolt gels (Invitrogen # NW00085/NW00087). Western transfer was performed using the Mini Blot Module (Invitrogen # B1000) and BOLT Transfer Buffer (Invitrogen # BT0006) onto an Immobilon-P PVDF membrane (Millipore # IPVH00010). Primary antibodies used for western blotting were anti-CK1a (1:2,000; rabbit raised) [52], anti-Tubulin alpha (1:5,000; Sigma # T6199), anti-WC-2 (1:5,000; rabbit raised) [131], and anti-FRH (1:10,000; rabbit raised) [77]. The secondary antibodies, goat anti-mouse or goat anti-rabbit HRP, were used at 1:5,000 (Bio-Rad # 170–6516, # 170–6515). SuperSignal West Pico PLUS Chemiluminescent Substrate (ThermoFisher # 34578) or Femto Maximum Sensitivity Substrate (Thermo # 34095) was used for detection. Immunoblot quantification and normalization were performed in ImageJ.

Mammalian cell culture, synchronization, and siRNA knockdown reagents

U2OS cells were stably transfected under puromycin selection using a construct containing the mouse BMAL1 promoter [132,133] driving destabilized luciferase [134]. U2OS-mBMAL1-dLuc-Puro (referred to as: Bmal1-dLuc) cells were maintained at 37°C and 6% CO₂ in 25 mM (high) glucose DMEM (Thermo Fisher # 11995–065 with 1 mM sodium pyruvate; or Thermo Fisher # 11965–092 without pyruvate) supplemented with 10% v/v FBS (Thermo Fisher # 10437–036, LOT # 2199672RP) and 1.5 μg/ml of puromycin (Sigma # P9620, 10 mg/ml stock).

For control Nutritional Compensation assays (S8 Fig), Bmal1-dLuc cells were subcultured from the same 100-mm dish and grown to 95% to 100% confluence in 35-mm dishes (Corning # 430165) containing 2 ml of DMEM 25 mM glucose, 10% FBS, and puromycin. Confluent cells were washed once in warm 1X PBS (pH 7.4) (Corning # 21-040-CV). The medium was changed to either 2 ml of DMEM 25 mM high glucose (Thermo Fisher # 11995–065 with 1 mM sodium pyruvate) or 2 ml of DMEM 5.56 mM low glucose (Thermo Fisher # 11885–084 with 1 mM sodium pyruvate). Both synchronization-release medium formulations were prewarmed and each contained 10% v/v FBS, 1.5 μg/ml puromycin, 0.1 mM luciferin (GoldBio, 0.1 M stock), and 0.1 μM dexamethasone (Sigma # D2915, 1 mM stock). Dexamethasone is used to reset cells to the same circadian phase and initiate the circadian free run for recording.

For siRNA knockdown assays (Fig 6), Bmal1-dLuc cells were subcultured from the same 100-mm dish and grown to 60% to 80% confluence in 35-mm dishes containing DMEM 25 mM glucose, 10% FBS, and puromycin. Cells were washed once in 1X PBS, and the medium was changed to 2 ml of Opti-MEM (Thermo Fisher # 31985–070) with 5% v/v FBS. Cells were transfected with the indicated siRNAs (15 pmol of total siRNA per 35-mm dish) [85] using the Lipofectamine 3000 transfection reagent (Thermo Fisher # L3000) and according to the manufacturer’s instructions for 6-well plates. Although the Opti-MEM medium formulation is not publicly available, one study reported the Opti-MEM glucose concentration as 2.5 g/L or 13.88 mM [135]. siRNAs were obtained from Qiagen: AllStars Negative Control siRNA (Qiagen # 1027280); human SETD2 siRNA (Qiagen # 1027416, FlexiTube GeneSolution GS29072, 4× siRNAs used at 3.75 pmol each); human CPSF6 siRNA (Qiagen # 1027416, FlexiTube GeneSolution GS11052, 4× siRNAs used at 3.75 pmol each); human CRY2 siRNA (Qiagen # 1027416, FlexiTube GeneSolution GS1408, 4× siRNAs used at 3.75 pmol each) [86]. Cells were incubated for 2 days before removing the siRNA transfection medium and proceeding with RNA extraction or with circadian recordings.

RT-qPCR was used to validate siRNA knockdown efficiencies. Two-day transfected cells were washed once in 1 ml of ice-cold 1X PBS. Cells were harvested by scraping in 1 ml of TRIzol (Invitrogen), and total RNA extraction was performed according to the manufacturer’s instructions. Approximately 500 ng of mRNA was converted into cDNA using the oligo(dT) method from the SuperScript IV First-Strand synthesis kit (Invitrogen # 18091–050). RT-qPCR was performed using SYBR green master mix (Qiagen # 204054) and a StepOne Plus Real-Time PCR System (Applied Biosystems). C_t values were determined using StepOne software (Life Technologies) and normalized to the GAPDH gene (ΔC_t). The ΔΔC_t method was used to determine mRNA levels relative to nontransfected negative control samples. Relevant RT-qPCR primer sequences are as follows: hGAPDH: 5′ TGCACCACCAACTGCTTAGC 3′ and 5′ ACAGTCTTCTGGGTGGCAGTG 3′. hCPSF6: 5′ GATGTGGGTAAAGGAGCAG 3′ and 5′ CTTCATCTGTTGTCCACCA 3′. hSETD2: 5′ CTTTCTGTCCCACCCCTGTC 3′ and 5′ CCTTGCACCTCTGATGGCTT 3′.

Two-day transfected cells were washed in warm 1X PBS and prepared for circadian synchronization. Synchronization-release medium was prewarmed and contained 1% v/v FBS, 1.5 μg/ml puromycin, 0.1 mM luciferin, and 0.1 μM dexamethasone. siRNA assays in DMEM were conducted using 2 ml of DMEM (Thermo Fisher # 11966–025) supplemented with 10 mM (low glucose) or 30 mM (high glucose) from a D-glucose stock solution (Sigma # G8644, 100 g/L stock). DMEM base medium contains more total nutrients than MEM—approximately 2-fold higher levels of the 13 essential amino acids, about 4-fold higher levels of the 8 vitamins, and includes the nonessential amino acids (Gly and Ser) in its formulation. siRNA assays in MEM were conducted using 2 ml of MEM (Thermo Fisher # 11095–080) containing 5.56 mM (low glucose) or supplemented up to 25 mM (high glucose) from a D-glucose stock solution (Sigma). Unlike Neurospora, complete glucose starvation medium did not support cell viability in preliminary experiments using DMEM medium containing 10% v/v FBS but zero additional glucose. “Low” 5 to 10 mM glucose was defined by manufacturer formulations as well as physiological levels of fasting serum glucose in humans.

Mammalian luciferase reporter detection and period length calculations

Immediately prior to bioluminescent recording, Bmal1-dLuc cells in 35-mm dishes were covered with 40-mm circular microscope cover glass (Fisher Scientific # 22038999 40CIR-1) and sealed using high-vacuum silicone grease (Dow Corning # Z273554). Luciferase data were collected in a LumiCycle 32 (ActiMetrics) luminometer every 10 minutes for 5 to 6 days. Raw luciferase traces in bioluminescence counts/second units were exported using LumiCycle analysis software (ActiMetrics, version 2.56). Data from individual plates were manually combined and converted to hours postsynchronization using Microsoft Excel. Period lengths for each luciferase trace were calculated using 3 different methods and averaging the period results with equal weights. For the first method, signal peaks and troughs were extracted from days 1 to 3 of raw data, and period was estimated by subtracting consecutive peaks or troughs as described [136]. Second, the WaveClock algorithm was implemented in R [137]. Finally, the suite of Neurospora period length tools was used as previously described [52]. For Bmal1-dLuc luciferase trace data visualization purposes (Fig 6 and S8 Fig), raw counts per second values sampled within the same hour were averaged together (i.e., data were down-sampled from 10-minute measurement intervals to 1-hour measurement intervals).

Data visualization

All figures were plotted in R, output as scalable vector graphics, formatted using Inkscape, and archived in R markdown format. Data represent the mean of at least three biological replicates with ±1 standard deviation error bars, unless otherwise indicated. All statistical tests were performed in R.

Supporting information

S1 Fig. Additional properties of Nutritional Compensation in Neurospora crassa.

A fungal biomass control was implemented to ask whether the amplitude of the core clock transcriptional reporter changes with glucose levels (e.g., Fig 1B). The gpd promoter from Cochliobolus heterostrophus driving constitutive luciferase was used as a reporter for fungal biomass. Not surprisingly, biomass increases as a function of glucose (N = 3 race tubes per glucose concentration). Many traces showed a decrease in bioluminescence at approximately 100 hours, and this correlates with the fungal growth front reaching and surpassing the end of the device’s recording area (A) (see S1 Movie). Averaged replicates are shown for the frq clock box transcriptional reporter across all glucose levels (N = 6; expanded Fig 1B) (B). Detrended clock reporter traces were plotted on a circadian time (CT) scale to normalize for the slight period differences (Fig 1A). Circadian phase is consistent across glucose levels (C). Amplitude was computed for each individual biomass reporter trace using data from hours 25–108 (amplitude calculation: [maximum value − minimum value] / 2) (D). Average amplitude was computed for each individual core clock reporter trace using data from days 2–5 (hours 25–112) to extract 4 peak and 4 trough values. The first day (hours 0–24) was omitted due to low fungal biomass and consequently low luciferase signal during the first recording day (E). Core clock amplitudes were normalized to biomass by computing the amplitude ratio at the respective glucose concentrations. There is no clear increasing or decreasing trend of normalized core clock amplitude as a function of glucose, and, therefore, the higher magnitude oscillations observed at high glucose concentrations are most likely a function of increased biomass only (F). Growth rates were computed from biomass reporter experiments by estimating the linear growth rate at 5 consecutive 12-hour intervals from 36–84 hours in constant darkness (G). No arginine was added to the race tube medium for any experiment shown. The raw data underlying parts (A–G) can be found in S7 Data.

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S2 Fig. A subset of FGSC knockout strains were identified with strong conidial banding phenotypes on glucose starvation medium.

A representative race tube from the primary genetic screen is shown. Six out of 7 knockout strains with strong banding phenotypes have a wild-type circadian period length and normal compensation, except for ΔNCU06565 (FGSC15333; S3 Fig). Like the ras-1^bd (NCU08823) point mutant, all knockout strains have a reduced linear growth rate compared to the wild-type control FGSC2489. FGSC = Fungal Genetics Stock Center, curator of the Neurospora whole genome knockout collection.

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S3 Fig. Six out of 12 new nutritional under-compensation mutants, which lack clear GO Term relationships.

Circadian bioluminescence was recorded from race tube cultures of the indicated deletion mutants. High nutrient medium (yellow lines) contained 0.5% w/v glucose 0.17% w/v arginine, and zero nutrient medium (blue lines) contained 0% glucose 0% arginine (standard deviation error bars). Period lengths were computed (N ≥ 2 biological replicate period estimates per nutrient concentration) and summarized in a bar graph compared to controls. “Het” indicates heterokaryon strains derived from the Neurospora whole genome deletion collection, which were maintained on hygromycin selection medium prior to the bioluminescence race tube assays. FGSC = Fungal Genetics Stock Center, curator of the Neurospora whole genome knockout collection. The raw data underlying this Figure can be found in S8 Data.

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S4 Fig. Core clock and compensation gene expression upon glucose starvation.

RNA-Sequencing data were mined from a previous study [55] (Materials and methods), where liquid cultures were either maintained in 2% glucose or shifted to glucose starvation for 60 minutes. Duplicate transcriptomes from the 2% glucose condition were compared to 0% glucose starvation replicates, Z-scores were computed for the 8,796 expressed genes in the dataset, and prd-1 (NCU07839) and frh (NCU03363) were found among the top 220 genes (top 2.5%) in the entire transcriptome down-regulated after glucose starvation. The raw data underlying this Figure can be found in S9 Data.

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S5 Fig. Solid medium cellophane plate cultures maintain circadian function and recapitulate Nutritional Compensation phenotypes of interest.

Liquid cultures containing wild-type fungal plugs (1.8% glucose) and cellophane plate cultures inoculated with wild-type conidia (0.5% w/v glucose, 0.17% w/v arginine) were set up concurrently. Liquid and solid cultures were entrained in constant light at 25°C for at least 16 hours, and serial light-to-dark transfers were performed to sample 1.5 cycles of circadian time points from DD4 to DD28 (4-hour sampling density, 44–48 hour total culture ages). Total RNA was isolated from each time course sample, and frq mRNA rhythms were examined by northern blot (N = 1 time course replicate). RNA levels were quantified using ImageJ densitometry, normalized, and plotted as line graphs. The circadian clock is clearly functional in both growth regimes (A). Circadian bioluminescence was recorded from cellophane plate cultures of the indicated genotypes grown on high nutrient medium (0.25% w/v glucose, 0.17% w/v arginine). One representative luciferase trace is shown from N = 2 biological replicates per strain. Period lengths were calculated, and results agree with Nutritional Compensation phenotypes derived from the race tube screen (S1 Table): control: 20.6 ± 0.3 hours; Δcpsf5 Δcpsf6 double mutant: 17.5 ± 0.4 hours (B). Circadian bioluminescence was recorded from cellophane plate cultures of the indicated genotypes grown on zero nutrient medium (0% glucose, 0% arginine). One representative luciferase trace is shown from N = 2 biological replicates per strain. Period lengths were calculated, and results agree with Nutritional Compensation phenotypes derived from the race tube screen (S1 Table): control: 22.0 ± 0.6 hours; Δset-2: 20.5 ± 0.1 hours (C). The raw data underlying parts (A–C) can be found in S10 Data.

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S6 Fig. Representative changes in the APA landscape (50% of genes) in Δcpsf5 Δcpsf6 double mutants compared to controls.

Integrative Genomics Viewer (IGV) screenshots are shown for 4 example 3′ UTR regions. NCU01154 (sub-1) is 1 out of the 193 APA events in controls collapsing to a single poly(A) peak in mutants (A). NCU02435 (histone H2B) is 1 out of the 123 APA events in mutants collapsing to a single poly(A) peak in controls (B). NCU01418 (ccg-6) and NCU16757 are 2 examples out of the 155 APA events in control and in mutant where the location of the predominant poly(A) peak was significantly changed (C). The majority of the 155 predominant poly(A) peak changes involve a distal-to-proximal peak shift in mutant (e.g., NCU01418) and a minority involve a proximal-to-distal peak shift (e.g., NCU16757).

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S7 Fig. Removal of COMPASS complex subunit swd3 phenocopies the long period length and Nutritional Compensation defects of Δset-1, indicating that SET-1 and the COMPASS complex are required for normal compensation.

The 96-well plate luciferase assays were used to measure the circadian period length of the indicated COMPASS complex subunit knockout mutants in constant darkness at 25°C on standard medium. Averaged replicates are shown for Δsdc1 (N = 3 biological replicates), Δswd1 (N = 2 biological replicates), and Δswd2 (N = 3 biological replicates) (standard deviation error bars). The table (right) summarizes circadian phenotypes of COMPASS complex subunit knockouts in this study compared to previous work [70] (A). Averaged replicates are shown for 96-well plate luciferase assays of Δset-1 (N = 3 biological replicates), Δswd3 (N = 4 biological replicates), and Δset-1 Δswd3 (N = 2 biological replicates) (standard deviation error bars). Period lengths were computed and summarized in a bar graph. All 96-well plate biological replicates contained N = 3 technical replicates per genotype (B). Circadian bioluminescence was recorded from race tube cultures of the indicated knockout mutants. High nutrient medium (yellow lines) contained 0.5% w/v glucose 0.17% w/v arginine, and zero nutrient medium (blue lines) contained 0% glucose 0% arginine (standard deviation error bars). Period lengths were computed for Δset-1 (N = 4 biological replicates per nutrient condition), Δswd3 (N = 2 biological replicates), and Δset-1 Δswd3 (N = 2 biological replicates) and summarized in a bar graph compared to the control (standard deviation error bars) (C). The raw data underlying parts (A–C) can be found in S11 Data.

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S8 Fig. Bmal1-dLuc cells are slightly under-compensated from 5.56 mM low glucose to 25 mM high glucose.

U2OS cells were synchronized using dexamethasone and released into DMEM low or high glucose medium. Representative luciferase traces are shown from one biological replicate with N = 4 technical replicates per glucose concentration (standard deviation error bars) (A). Period lengths were calculated for 25–26 total replicates per glucose concentration and plotted as a boxplot. As observed in Neurospora, the human circadian period length is under-compensated and shortens slightly with increasing glucose (p < 0.01, Student t test). Average period lengths were 24.5 ± 0.7 hours (low glucose) and 24.0 ± 0.4 hours (high glucose) (B). The raw data underlying parts (A) and (B) can be found in S12 Data.

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S1 Table. Genetic screen for Nutritional Compensation defects.

Results are presented from the 3-phase genetic screen for Nutritional Compensation defects among Neurospora knockout strains. Period lengths are shown for each knockout strain. Knockout strains were retained through each phase of the screen if circadian period changes were observed relative to the wild-type control or if a clear lack of circadian banding pattern was observed in the primary screen (“output” defective mutants). Nutritional under-compensation mutants were defined by ratios of high-to-zero glucose period lengths ≤ 0.90 (pink font). Nutritional over-compensation mutants had period length ratios ≥ 1.02 (green font).

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S2 Table. Informatic description of Neurospora genes screened for Nutritional Compensation defects.

The first tab of the Table is filtered for gene knockouts that progressed to the tertiary genetic screen, and the second tab of the Table contains all novel genes screened for compensation defects. Circadian rhythmicity at the gene and protein level among knockouts screened was determined from previous studies [45,138]. Promoter binding by the WCC positive arm transcription factors was determined from previous studies [138–140]. Light-regulated gene activation or repression was determined from previous studies [139,141,142]. Gene expression Z-scores from carbon starvation conditions were also reported [55] (S4 Fig).

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S3 Table. Consensus list of Neurospora genes with Alternative Polyadenylation (APA) in 3′ UTRs.

A total of 843 genes contain multiple poly(A) sites within 3′ UTR regions from the intersection of this study and previous work [65]. Nine out of 843 genes with 3′ UTR APA are highlighted as core clock genes or compensation screen hits. Annotated MACS2 poly(A) peak results are shown for the 843 genes with APA from each dataset as individual tabs.

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S4 Table. Alternative Polyadenylation (APA) events altered in the ΔCFIm knockout mutant compared to controls.

A total of 1,447 total genes contain multiple poly(A) sites within 3′ UTR regions in wild-type control and/or ΔCFIm mutant data from this study. In all 4 datasets, 940/1,447 genes display APA. Out of 1,447 instances, 123 are recorded where a single poly(A) peak in control expands to multiple APA events in mutant (dark green highlight). Out of 1,447 instances, 193 are reported of a single poly(A) peak in mutant expanding to multiple APA events in wild-type controls (light green highlight). Out of 1,447 APA events, 155 occur where the location of the predominant poly(A) peak was significantly changed in the mutant background (orange highlight). Annotated MACS2 poly(A) peak results are shown for the APA genes from each dataset as individual tabs.

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S1 Movie. Nutritional Compensation properties of wild-type Neurospora crassa.

Circadian bioluminescence of exemplar wild-type race tubes (Fig 1) is shown for zero nutrient medium (blue line; 0% glucose 0% arginine) (A) compared to high nutrient medium (yellow line; 0.5% w/v glucose 0% arginine) (B). Average period lengths are 21.9 ± 0.5 hours for 0% glucose and 20.8 ± 0.2 hours for 0.5% w/v glucose (average ± 1 SD).

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S2 Movie. Nutritional Compensation defect in the Δcpsf6 mutant.

Circadian bioluminescence of one exemplar Δcpsf6 race tube (Fig 4A) is shown for whole-tube quantification of high amino acid medium growth (yellow line; 0% glucose 0.17% w/v arginine; average period = 19.4 ± 0.3 hours) (A) compared to old tissue quantification (blue line; 0% glucose 0.17% w/v arginine; period = 17.3 ± 0.3 hours) (B). This approximately 2-hour period difference indicates that Δcpsf mutants must undergo a transition from high-to-low amino acid levels to reveal the Nutritional Compensation defect.

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S3 Movie. Nutritional Compensation defect in the Δset-2 mutant.

Circadian bioluminescence of one exemplar Δset-2 race tube (Fig 5A) out of 4 biological replicates. Rhythms on zero nutrient medium (blue line; 0% glucose 0% arginine) (A) are compared to high nutrient medium (yellow line; 0.5% w/v glucose 0% w/v arginine) (B). Average period lengths are 20.3 ± 0.7 hours for 0% glucose and arrhythmic for high nutrients.

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S1 Data. Raw data accompanying main text Fig 1.

The first tab of the Data File corresponds to period length measurements for Fig 1A. The second and third tabs of the Data File correspond to raw and processed traces for Fig 1B.

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S2 Data. Raw data accompanying main text Fig 2.

The first tab of the Data File corresponds to temperature compensation period length measurements for Fig 2A. The second and third tabs of the Data File correspond to raw and processed traces for Fig 2B.

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S3 Data. Raw data accompanying main text Fig 3.

The first and second tabs of the Data File correspond to raw and processed traces for Fig 3A. The third, fourth, and fifth tabs of the Data File correspond to raw and processed traces for Fig 3C as well as circadian period lengths. The sixth tab of the Data File corresponds to immunoblot densitometry measurements derived from ImageJ for Fig 3D.

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S4 Data. Raw data accompanying main text Fig 4.

The first, second, and third tabs of the Data File correspond to raw and processed traces for Fig 4A as well as circadian period lengths. The fourth tab of the Data File corresponds to averaged and log₂-normalized read counts of core clock and compensation relevant genes for Fig 4D.

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S5 Data. Raw data accompanying main text Fig 5.

The first and second tabs of the Data File correspond to raw and processed traces for Fig 5A. The third tab of the Data File corresponds to averaged and log₂-normalized fpkm values of core clock genes for Fig 5B. The fourth tab of the Data File corresponds to averaged and log₂-normalized fpkm values of core clock genes for Fig 5C. The fifth tab of the Data File corresponds to RT-qPCR values for Fig 5D. The sixth tab of the Data File corresponds to race tube period lengths for Fig 5E.

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S6 Data. Raw data accompanying main text Fig 6.

The first, second, and third tabs of the Data File correspond to raw and processed traces for Fig 6A as well as circadian period lengths. The fourth tab of the Data File corresponds to RT-qPCR values for Fig 6A. The fifth, sixth, and seventh tabs of the Data File correspond to raw and processed traces for Fig 6B as well as circadian period lengths.

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S7 Data. Raw data accompanying S1 Fig.

The first and second tabs of the Data File correspond to raw and processed traces for S1A Fig. The third and fourth tabs of the Data File correspond to raw and processed traces for S1B Fig. The fifth tab of the Data File corresponds to detrended traces on a circadian time (CT) scale for S1C Fig. The sixth tab of the Data File corresponds to average amplitude measurements and ratios for S1D–S1F Fig. The seventh tab of the Data File corresponds to growth rate measurements for S1G Fig.

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S8 Data. Raw data accompanying S3 Fig.

The first and second tabs of the Data File correspond to raw and processed traces for S3 Fig.

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S9 Data. Raw data accompanying S4 Fig.

The first tab of the Data File corresponds to normalized fpkm values of core clock and compensation relevant genes with Z-score comparing starvation to glucose replete conditions for S4 Fig.

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S10 Data. Raw data accompanying S5 Fig.

The first tab of the Data File corresponds to immunoblot densitometry measurements derived from ImageJ for S5A Fig. The second tab of the Data File corresponds to raw traces and circadian period lengths for S5B Fig. The third tab of the Data File corresponds to raw traces and circadian period lengths for S5C Fig.

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S11 Data. Raw data accompanying S7 Fig.

The first, second, and third tabs of the Data File correspond to raw and processed traces as well as circadian period lengths for S7A Fig. The fourth, fifth, and sixth tabs of the Data File correspond to raw and processed traces as well as circadian period lengths for S7B Fig. The seventh and eighth tabs of the Data File correspond to raw and processed traces as well as circadian period lengths for S7C Fig.

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S12 Data. Raw data accompanying S8 Fig.

The first and second tabs of the Data File correspond to raw and processed traces for S8A Fig. The third tab of the Data File corresponds to circadian period lengths for S8B Fig.

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S1 Raw Images. Raw immunoblot data accompanying Fig 3D and S5 Fig.

All immunoblots are included with 1 or 2 TIF images each showing raw blots uncropped and labeled with antibody used, method, sample ID, and molecular weight markers (when applicable). “X”s mark lanes and samples that were not shown in Fig 3D.

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Acknowledgments

We thank the Fungal Genetics Stock Center (Kansas City, Missouri, USA) for curating N. crassa strains and the whole genome deletion collection (P01 GM068087). We thank Adrienne Mehalow and Arko Dasgupta for generously sharing the collection of Neurospora kinase knockout circadian reporter strains, Bradley Bartholomai for the constitutive promoter driving luciferase construct, Consuelo Olivares-Yañez and Alejandra Goity from the Luis Larrondo laboratory at the Pontificia Universidad Católica de Chile for their virtual assistance with the cellophane plate growth method, Lauren Francey from the John Hogenesch laboratory at Cincinnati Children’s Hospital Medical Center for virtual assistance with the U2OS siRNA knockdown experiments, Chris Baker at the Jackson Laboratory for discussions on the Neurospora CFIm complex, Josh Gamsby at the University of South Florida for the Bmal1-dLuc stable cell line, and Christine Mayr at Memorial Sloan Kettering Cancer Center for discussions and mentorship on the Alternative Polyadenylation (APA) work. 3′ End Sequencing was carried out at the Geisel School of Medicine at Dartmouth in the Genomics Shared Resource (GSR) in collaboration with Fred Kolling IV. Dartmouth GSR is supported by NIH and NSF equipment grants as well as an NCI Cancer Center Core Grant (P30 CA023108) and an NIGMS COBRE Grant (P20 GM130454).

Abbreviations

AMP: adenosine monophosphate
APA: Alternative Polyadenylation
CKI: Casein Kinase I
CPSF6: Cleavage and Polyadenylation Specificity Factor subunit 6
FGSC: Fungal Genetics Stock Center
GEO: Gene Expression Omnibus
GO: Gene Ontology
GSR: Genomics Shared Resource
MEF: mouse embryonic fibroblast
NAD: nicotinamide adenine dinucleotide
NMD: nonsense-mediated decay
RT-qPCR: quantitative reverse transcription PCR
SE: single-end
TTFL: transcription-translation feedback loop
WCC: White Collar Complex

Data Availability

RNA-Sequencing gene expression data from this manuscript have been submitted to the NCBI Gene Expression Omnibus (GEO; http://www.ncbi.nlm.nih.gov/geo/) under accession number GSE201901. All other relevant data are within the paper and its Supporting information files.

Funding Statement

This work is supported by the National Institutes of Health (NIH) grants # F32 GM128252 to CMK (https://reporter.nih.gov/project-details/10513105), R35 GM118021 to JCD (https://reporter.nih.gov/project-details/10330086), and R35 GM118022 to JJL (https://reporter.nih.gov/project-details/9902458). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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PLoS Biol. doi: 10.1371/journal.pbio.3001961.r001

Decision Letter 0

Lucas Smith

23 May 2022

Dear Dr Kelliher,

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PLoS Biol. doi: 10.1371/journal.pbio.3001961.r002

Decision Letter 1

Lucas Smith

23 Jun 2022

Dear Tina,

Thank you again for your patience while your manuscript "A role for gene expression and mRNA stability in nutritional compensation of the circadian clock" was peer-reviewed at PLOS Biology. It has now been evaluated by the PLOS Biology editors, an Academic Editor with relevant expertise, and by several independent reviewers.

In light of the reviews, which you will find at the end of this email, we would like to invite you to revise the work to thoroughly address the reviewers' reports.

As you will see from their comments, all of the reviewers appreciated the importance of the topic examined here, and Reviewer 1 has suggested that we accept the manuscript for publication. However, Reviewers 2 and 3 raise a number of important concerns which would need to be carefully and thoroughly addressed, with new data where needed, before we can consider your manuscript for publication. In particular, the reviewers have highlighted conclusions that are not yet adequately supported by the data, noting that additional work is needed to support the role of the identified candidate genes in nutritional compensation and your conclusion that this is a conserved process.

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Lucas Smith, Ph.D.

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------------------------------------

REVIEWS:

Reviewer #1: This is a very solid and comprehensive study of the roles of new regulators of Nutritional Compensation in filamentous fungus Neurospora crassa and in human cells. By performing a genetic screen under glucose and amino acid starvation conditions, the authors showed that 16 new mutants play different roles in regulating gene expression and mRNA stability in nutritional compensation of circadian clock in Neurospora crassa. Surprisingly, they found that knockdown of CPSF6 or SETD2 in human cells also results in nutrient-dependent period length changes. Together, these results prove that Nutritional Compensation is a conserve circadian process in fungal and mammalian clock and suggest that it may share similar molecular mechanism. Despite the large amount of work, the quality of the results is high and the conclusions are well support. I think it is suitable for publication in PLOS BIOLOGY though I have a minor question.

Are these 16 factors also involved in the nutritional compensation besides glucose?

Reviewer #2: A fundamental property of circadian clocks is their ability to keep time over abroad range of physiological conditions. The underlying mechanisms are purely understood. Kelliher et al performed an elaborate three-step screen in Neurospora to identify genes involved in nutritional compensation of the circadian clock. They then further analyze two candidate genes, set2 and cpsf6, respectively. Downregulation of the mammalian homologs of these genes impacts NC of the clock of cultured mammalian cells, implying that mechanisms underlying NC of the circadian clock are similar in fungi and mammals. I find that the work needs more data supporting the role of the identified candidate genes in NC.

Particularly, the Neurospora ∆set2 strain looks rhythmic to me (Fig. 5A) and period length is potentially not altered between low and high glc/arg levels. This has to be thoroughly examined. Neither did ∆cpsf6, by the author's definition, have a large enough phenotype to be considered under-compensated. If the NC phenotype of ∆cpsf6 is real but subtle, more N's are needed to show that the difference is statistically significant. Furthermore, period length of ∆cpsf6, which is short in general, is affected by arg concentration but not by glc concentration, while knockdown of its mammalian homolog in cell culture induces a long period phenotype, which is glc sensitive. It begs the question if the role in nutritional compensation of these to genes and the underlying mechanisms are indeed similar in fungi and mammals.

Substantial work went into the data collection for this manuscript and the data gives direction as to where mechanisms for NC could be found. It is a bit disappointing that rather than conducting follow up experiments that would help to pinpoint how the identified genes affect the clock machinery, such experiments are mostly announced to be done in "future work". Data from published transcriptomes, as well as data from other publications, provide evidence that is partly circumstantial and partly contradictory. Potential connections are discussed but experiments showing or confirming a direct impact of the identified genes on NC of the clock are missing.

There are a number of issues that should be addressed in a revised version which I list by order of appearance (and not priority) in the manuscript:

Lines 103-105: There is a recent paper by Marzoll et al. (2022) showing that Neurospora FRQ as well as mammalian PER2 are phosphorylated by their respective CK1 in vitro on a circadian scale in a temperature compensated fashion. This should be cited here.

120-122: While general transcription repressor RCO-1 has been shown to downregulate frq transcription as cited (Zhou et al. 2013), importantly, together with RCM-1 it is also part of the CSP-1 repressor complex (Sancar et al 2011). ∆rco-1 has a more severe effect on rhythmic conidiation as well as period and amplitude of the clock than ∆csp-1, yet the metabolic compensation effect is not very different if one looks at the data presented in Olivares-Yanez et al, 2016. This should be considered and discussed also in the later sections where RCO-1 is mentioned (e.g. in the pkac-1 mutant context).

From Figure 2 ff: what are the error bars of the bioluminescence traces if N=2 in almost all cases? It might be better showing both traces and the average.

Figure 2C: Please expand the abbreviation "FGSC" in the figure legend or in the text describing the figure.

It would also improve clarity if the authors additionally mention here what kind of experiments were performed in the individual screens, e.g. 1st screen: conidiation rhythms on glucose starvation racetubes, 2nd screen: luciferase reporter assays on 96-well plates -Glc -Arg…

263ff and Supplementary Table 1. Here it gets a little bit confusing with the number of the under-compensated mutants if you look at table S1. There, 14 genes are marked as under-compensated when mutated. In line 263 the authors examine "the group of 12 under-compensated mutants". I know the other two are looked at more closely from 311ff but this is still confusing. Please rewrite for clarity.

Please mention that ∆upf1 and ∆upf2 are the two most significant hits in 265. Otherwise they would not be mentioned at all in the main text.

Supplementary Table 1:

mek-1 is listed in the table as RNA binding and not as a (MAPK-)kinase.

Please write "Arrhythmic" instead of ARR.

Define "output" in primary screen.

Supplementary Table 2: The table is used to show potential clock or light regulation of the screen results. Therefore, for easier reading, only the potential genes involved in temperature compensation should be shown or at most the 52 strains plus controls from the tertiary screen.

277-279: I cannot follow the reasoning why the other under-compensation mutants were not pursued further as they do meet the period ratio criteria.

Figure 3: The prd-4 (S493L) mutant does not really belong here and the authors should consider taking it out completely as, in contrast to the other strains tested, it is a gain of function mutant. The Dunlap/Loros labs published quite some time ago that the mutation enhances interaction of PRD-4 with FRQ leading to phosphorylation of FRQ by this checkpoint kinase thereby accelerating the clock (Pregueiro et al, 2006). The prd-4 KO strain did not show altered nutritional compensation (see Supplementary Table 1). With the space gained the bar graph could be enlarged a little bit.

299: It is very interesting that the CK1a 3'UTR mutant has a similar NC phenotype as ∆upf-1prd-6. To state that the phenotype of the ck1a gene mutant is "not as severe" as the phenotype of ∆upf-1prd-6, though, would require statistical analysis of a sufficiently large set of independent experiments, as this would be a rather important point the authors make here.

301: Figure 5B doesn't show this!

Are wc-2 and frh both up-regulated in the NMD mutants?

301-310: Here, western blots and qRT-PCR data should be shown, confirming data from transcriptome analyses cited that in the mutants wc-2 and frh transcript as well as the respective protein levels are altered.

312: "compared to the 11 remaining under-compensation mutants". 11 in this case because the authors probably did not include the csp-1 "positive control" strain?! Please be consistent with the numbers. Moreover, cspf6 is marked in the table but doesn't meet the authors' criteria for under-compensation (yet is described later by the authors as "one of the most significant compensation phenotypes" line 541. This turns out to be a major issue; see below).

365-372: For comparison to Figure 4B it would be good to show representative examples for the 3 groups of changes in the APA landscape in the supplementary section.

378-429, Figure 4:

In this screen only two (of the handpicked) genes had altered APA in the double mutant. Apparently, however, this did not change gene expression levels of these genes. On the other hand, genes with altered expression levels in the double mutant did not exhibit an altered APA landscape in the assay. So, there is no correlation and thus no indication that control of APA has a direct effect on NC. Is there an effect on protein translation levels maybe?

Additionally, altered gene expression levels should have been confirmed via qRT-PCR.

438-439: briefly mention how PKA regulates RCO-1 (inactivation of the repressor by phosphorylation). Also see above regarding RCO-1. Observation is also compatible with inhibition of CSP-1/RCO-1/RCM-1 complex.

446-448: Is it completely arrhythmic? In supp Table 1 you write: 31.7+-0 // ARR. Does that mean it was rhythmic in one experiment? If it's not clear the experiment should be repeated with sufficient N's and data should be detrended to potentially uncover a masked rhythm (see also below).

463-505, Figure 5: In Figure 5A, to my eye, ∆set-2 oscillates at high nutrition levels with a similar period length as at low nutrition, albeit at low amplitude and potentially masked by the steeply increasing average bioluminescence. As above this experiment should be repeated in order to get data from more independent experiments and data should also be detrended to reveal potential rhythms. This is important as the authors use the mammalian homolog for their cell culture experiments in the assumption that this is good candidate involved in NC.

471: "Δset-2 mutant results in … high frq expression levels … (Zhou et al., 2013; Sun et al., 2016)." This observation is not reproduced in the ∆set-2 transcriptome dataset from Bicocca et al 2016 shown in Figure 5C.

Curiously FRQ protein levels in ∆set-2 were not shown to be elevated in Sun et al., 2016. They were actually slightly lower. The observed decrease in frh found by Bicocca could account for this but these data would need to be confirmed via qRT-PCR or western blots with the strains used in this work.

472-474: The authors should back their claim and show that WCC-independent frq transcription is indeed nutrient dependent using the appropriate strains.

541-551: now here is where the narrative does not fit: As mentioned above ∆cpsf6 did not meet the threshold criteria the authors defined for mutants to be under-compensated. And the phenotype is only seen in old mycelia and in response to changes in arginine levels not glucose. So, it is hardly one of the most significant compensation phenotypes. For ∆set-2 the data did not convince me. See above.

The most convincing, interesting and obvious candidates to investigate NC in cell culture would have been upf-1 and upf-2 as well as set-1 or prd-1. Why not analyze homologs of these?

So, while the authors did find an NC phenotype in the siRNA knockdown of the homolog genes, the reasoning behind picking these genes leaves me puzzled.

553: In Neurospora, ∆cpsf6 exhibited the NC phenotype in response to amino acid starvation and not glucose. Yet, in the cell culture experiments the knockdown of its mammalian homolog has a NC phenotype in response to changes in glucose levels. This is not discussed by the authors.

It is also strange that knockout / knockdown of cpsf6 both exhibit an under-compensated clock phenotype, yet has the opposite effect on period length in cell culture.

Figure 6: Are the differences in period length statistically significant for CPSF1 and SETD2 in 6A?

What are the error bars in the Bioluminescence traces in 6A?

563-564: looks more like the luciferase expression levels are higher than the amplitude of oscillation. See/compare also Fig 6B.

781-782: format reference

Reviewer #3: In this manuscript, Kelliher et al reported a genetic screen for genes involved in nutritional compensation of the circadian clock. They successfully identified a number of novel regulators of nutritional compensation, including genes with known functions in RNA stability, mRNA cleavage and polyadenylation, and histone modifying enzymes. Beyond the screen, they performed additional experiments to demonstrate that: 1) the Neurospora CFIm complex, similar to its mammalian homologues, is involved in mRNA alternative polyadenylation; 2) CPSF6 and SETD2 also regulate nutritional compensation in mammalian cells.

Overall this study described an interesting screen that identified a number of novel regulators of nutritional compensation of the circadian clock. The results are of broad interest. However, characterization of these regulatory factors are lacking or very limited. For example, the data quality for CFIm-mediated regulation of mRNA alternative polyadenylation seemed poor and no data is provided to suggest that CFIm-mediated regulation of mRNA 3' UTRs is involved in nutritional compensation. Overall this study seems quite promising, but is a bit premature in its current form for publication in Plos Biology.

PLoS Biol. 2023 Jan 5;21(1):e3001961. doi: 10.1371/journal.pbio.3001961.r003

Author response to Decision Letter 1

27 Oct 2022

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PLoS Biol. doi: 10.1371/journal.pbio.3001961.r004

Decision Letter 2

Lucas Smith

28 Nov 2022

Dear Dr Kelliher,

Thank you for your patience while we considered your revised manuscript "A role for gene expression and mRNA stability in nutritional compensation of the circadian clock" for publication as a Research Article at PLOS Biology. This revised version of your manuscript has been evaluated by the PLOS Biology editors, the Academic Editor and the original reviewers.

The reviews are appended below. As you will see, the reviewers are largely satisfied with the revision, although Reviewer 2 has raised two minor concerns. Therefore, we are likely to accept this manuscript for publication, provided you satisfactorily address the remaining points raised by Reviewer 2.

**IMPORTANT: Additionally, as you address Reviewer 2's comments in a revised manuscript, we also ask that you attend to the following editorial requests:

1) TITLE: After some discussion within the team, we are wondering if the title should be edited slightly to more strongly present the main findings of the paper. If you agree (and if you feel it is supported), we suggest that you change the title to something like ""Nutritional compensation of the circadian clock is a conserved process controlled by gene expression regulation and mRNA stability"

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------------------------------------------------------------------------

Reviewer remarks:

Reviewer #1, Qun He (note, reviewer 1 has signed this review): This manuscript is acceptable for publication.

Reviewer #2: The authors have addressed most of my comments in the revised version.

I have two final points that should be addressed:

Figure 3: The figure legend needs to be adjusted to the new figure. Please check.

regarding PKA and RCO-1/RCM-1: In my original comment I asked to briefly mention how PKA regulates RCO-1. That was an error on my part. I meant to ask the authors to mention how PKA regulates RCO-1/RCM-1. However, the authors are mistaken that "PKA activates RCO-1/RCM-1...". The opposite is the case: Liu et al. 2015 found that "PKA regulates frq transcription by inhibiting RCM-1 activity through RCM-1 phosphorylation", which is good because this is compatible with their other results. Please check and revise.

Reviewer #3: The authors have addressed my concerns satisfactorily.

PLoS Biol. 2023 Jan 5;21(1):e3001961. doi: 10.1371/journal.pbio.3001961.r005

Author response to Decision Letter 2

12 Dec 2022

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PLoS Biol. doi: 10.1371/journal.pbio.3001961.r006

Decision Letter 3

Lucas Smith

15 Dec 2022

Dear Dr Kelliher,

Thank you for the submission of your revised Research Article "Nutritional compensation of the circadian clock is a conserved process influenced by gene expression regulation and mRNA stability" for publication in PLOS Biology. Your revised manuscript has now been evaluated by the PLOS Biology editorial team and we are satisfied by the changes made. Therefore, on behalf of my colleagues and the Academic Editor, Gad Asher, I am pleased to say that we can in principle accept your manuscript for publication, provided you address any remaining formatting and reporting issues. These will be detailed in an email you should receive within 2-3 business days from our colleagues in the journal operations team; no action is required from you until then. Please note that we will not be able to formally accept your manuscript and schedule it for publication until you have completed any requested changes.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Fig. Additional properties of Nutritional Compensation in Neurospora crassa.

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S2 Fig. A subset of FGSC knockout strains were identified with strong conidial banding phenotypes on glucose starvation medium.

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S3 Fig. Six out of 12 new nutritional under-compensation mutants, which lack clear GO Term relationships.

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S4 Fig. Core clock and compensation gene expression upon glucose starvation.

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S5 Fig. Solid medium cellophane plate cultures maintain circadian function and recapitulate Nutritional Compensation phenotypes of interest.

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S6 Fig. Representative changes in the APA landscape (50% of genes) in Δcpsf5 Δcpsf6 double mutants compared to controls.

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S8 Fig. Bmal1-dLuc cells are slightly under-compensated from 5.56 mM low glucose to 25 mM high glucose.

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S1 Table. Genetic screen for Nutritional Compensation defects.

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S2 Table. Informatic description of Neurospora genes screened for Nutritional Compensation defects.

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S3 Table. Consensus list of Neurospora genes with Alternative Polyadenylation (APA) in 3′ UTRs.

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S4 Table. Alternative Polyadenylation (APA) events altered in the ΔCFIm knockout mutant compared to controls.

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S1 Movie. Nutritional Compensation properties of wild-type Neurospora crassa.

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S2 Movie. Nutritional Compensation defect in the Δcpsf6 mutant.

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S3 Movie. Nutritional Compensation defect in the Δset-2 mutant.

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S1 Data. Raw data accompanying main text Fig 1.

The first tab of the Data File corresponds to period length measurements for Fig 1A. The second and third tabs of the Data File correspond to raw and processed traces for Fig 1B.

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S2 Data. Raw data accompanying main text Fig 2.

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S3 Data. Raw data accompanying main text Fig 3.

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S4 Data. Raw data accompanying main text Fig 4.

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S5 Data. Raw data accompanying main text Fig 5.

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S6 Data. Raw data accompanying main text Fig 6.

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S7 Data. Raw data accompanying S1 Fig.

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S8 Data. Raw data accompanying S3 Fig.

The first and second tabs of the Data File correspond to raw and processed traces for S3 Fig.

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S9 Data. Raw data accompanying S4 Fig.

The first tab of the Data File corresponds to normalized fpkm values of core clock and compensation relevant genes with Z-score comparing starvation to glucose replete conditions for S4 Fig.

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S10 Data. Raw data accompanying S5 Fig.

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S11 Data. Raw data accompanying S7 Fig.

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S12 Data. Raw data accompanying S8 Fig.

The first and second tabs of the Data File correspond to raw and processed traces for S8A Fig. The third tab of the Data File corresponds to circadian period lengths for S8B Fig.

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S1 Raw Images. Raw immunoblot data accompanying Fig 3D and S5 Fig.

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Data Availability Statement

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PERMALINK

Nutritional compensation of the circadian clock is a conserved process influenced by gene expression regulation and mRNA stability

Christina M Kelliher

Elizabeth-Lauren Stevenson

Jennifer J Loros

Jay C Dunlap

Roles

Abstract

Introduction

Results

Nutritional compensation is distinct from temperature compensation in Neurospora

Fig 1. Nutritional Compensation properties of Neurospora crassa.

Fig 2. Nutritional Compensation mutants have normal Temperature Compensation in Neurospora, catalyzing a genetic screen for new mutants.

mRNA stability and polyadenylation factors emerge as key regulators of circadian period length across nutrients

Fig 3. Nutritional under-compensation mutants are enriched for regulators of mRNA stability, and nonsense-mediated decay (NMD) regulation of the casein kinase I 3′ UTR is required for functional Nutritional Compensation and normal period length.

Fig 4. The Neurospora CFIm complex is involved in Alternative Polyadenylation (APA), and a subset of core clock and compensation-relevant genes are altered in ΔCFIm mutants.

The Alternative Polyadenylation (APA) landscape is altered in Nutritional Compensation mutants

Chromatin modifiers emerge as key regulators of circadian period length across nutrients

Fig 5. Nutritional over-compensation mutants are enriched for chromatin regulators, implicating frh regulation in Nutritional Compensation mechanism.

Nutritional Compensation also functions in a mammalian circadian system

Fig 6. CPSF6 and SETD2 are required for normal Nutritional Compensation of U2OS Bmal1-dLuc cells cultured in high versus low glucose.

Discussion

Materials and methods

Neurospora strains, growth conditions, and genetic screen

Neurospora luciferase reporter detection and data analysis

Neurospora RNA isolation and 3′ End Sequencing analyses

Protein isolation and detection

Mammalian cell culture, synchronization, and siRNA knockdown reagents

Mammalian luciferase reporter detection and period length calculations

Data visualization

Supporting information

Acknowledgments

Abbreviations

Data Availability

Funding Statement

References

Decision Letter 0

Lucas Smith

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Decision Letter 1

Lucas Smith

Roles

Author response to Decision Letter 1

Decision Letter 2

Lucas Smith

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Author response to Decision Letter 2

Decision Letter 3

Lucas Smith

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Associated Data

Supplementary Materials

Data Availability Statement

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