Response strategies to acute and chronic environmental stress in the arctic breeding Lapland longspur (Calcarius lapponicus)

doi:10.21203/rs.3.rs-4565468/v1

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Response strategies to acute and chronic environmental stress in the arctic breeding Lapland longspur (Calcarius lapponicus)

https://doi.org/10.21203/rs.3.rs-4565468/v1

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The potentially devastating effects of climate change have raised awareness of the need to understand how the biology of wild animals is influenced by extreme-weather events. We investigate how a wild arctic-breeding bird, the Lapland longspur (Calcarius lapponicus), responds to different environmental perturbations and its coping strategies. We explore the transcriptomic response to environmental adversity during the transition from arrival at the breeding grounds to incubation on the Arctic tundra. The effects of an extremely cold spring on arrival and a severe storm during incubation are examined through RNA-seq analysis of pertinent tissues sampled across the breeding cycle. The stress response, circadian rhythms, reproduction and metabolism are all affected. A key protein of the Hypothalamic-Pituitary-Adrenal axis, FKBP5, was significantly up-regulated in hypothalamus. The genome assembly and gene expression profiles provide comprehensive resources for future studies. Our findings on different coping strategies to chronic and acute stressors will contribute to understanding the interplay between changing environments and genomic regulation.

Biological sciences/Ecology/Ecological genetics

Biological sciences/Genetics/Functional genomics/Gene expression profiling

Wild-living species are often exposed to challenging environmental conditions, with many of these situations arising from extreme weather events^1,2. Detrimental weather conditions can have prevailing impact on the physiology and behaviour of wild-living animals, with the response being influenced by the interplay between the varied type of challenge experienced and the life cycle stage (e.g., breeding winter, or moult) when experienced³. For instance, a chronic event constraining food availability prior to the time of arrival can result in an unpredictable adverse situation and aberration in phenology, whereas a storm event during egg-lay and incubation period can force birds to face the dilemma of either allocating energy for thermoregulation or conserving resources for reproduction^4,5.

Inclement weather events impose negative energy challenges, and birds have been observed to prioritize survival over other life history activities (e.g., breeding)^6,7. Such environmental perturbations influence many aspects of the biological process, with the endocrine system acting as a crucial signalling mediator to regulate biological responses like energy metabolism and reproduction ⁴. From an energy metabolism perspective, extreme climate events impose a physiological challenge for survival due to the overall energetic supply failing to meet the overall demand to maintain regular life cycle activities, which is described as Type I allostatic overload^3,8. The concept of allostasis describes how animals ‘maintain stability through change’⁹, which accounts for both predictable and unpredictable changes.

In response to the state of allostatic load, animals may move into an emergency life history stage (ELHS) to reduce energy costs, deviating from the normal life history stages such as breeding³. Essentially, animals redistribute their available resources and internal energy to promote survival. During this process, the Hypothalamic-Pituitary-Adrenal (HPA) axis is responsible for regulating the physiological response by elevating a cascade of hormonal secretions to cope with environmental perturbations³, with cortisol/corticosterone being the most important glucocorticoids¹⁰. Different types of environmental perturbation can cause distinct allostatic load, resulting in various coping strategies dependent on whether the stressor is acute or chronic, and when it occurs according to life history stage. These factors collectively determine how animals pursue the optimal strategy, thereby altering their current physiological and behavioural status, which are often mediated by a change in gene expression. Therefore, transcriptomic studies on how gene expression changes in response to inclement weather events can improve understanding of the genomic basis of these coping strategies.

In order to understand the different survival mechanisms and underlying physiological regulation, we investigated transcriptomic differences in free-living Lapland longspurs (Calcarius lapponicus) in response to two types of inclement weather conditions, which occurred across different life cycle stages. We examined samples collected at the arrival stage during an extremely cold spring, resulting in delayed Lapland longspur arrival and clutch initiation, and from the incubation period, which coincided with a heavy snowstorm, where birds abandoned nesting behaviours and entered an emergency life history stage.

The Lapland longspur is an Artic breeding songbird, known to experience environmental adversity during its life cycle (Fig. 1)^7,11. The spring in Alaska, USA in 2013 was documented as extreme, having higher snow cover compared to all other studied years (i.e., 2010 to 2014)⁴ and resulting in limited food resources^5,6. Previous studies showed that increased snow cover and low temperature in the extremely cold spring of 2013 significantly influenced their breeding behaviour, including delayed arrival and delayed clutch initiation by approximately 3 to 10 days⁴. In comparison, during an acute snowstorm in 2016, birds abandoned their nests, and exhibited elevated corticosterone levels^5,7. We have previously shown that Lapland longspurs experienced elevated concentrations of corticosterone, indicative of a physiological response to environmental challenge, and suggestive of entry into the ELHS^5,7,10.

To study the coping strategies associated with ELHS activation in Lapland longspurs following severe inclement weather, it is important to examine gene expression changes. To ensure accurate quantification of gene expression, we developed a high-quality chromosome-level Lapland longspur genome assembly, and RNA sequencing dataset collected across different tissues. We examined testis, heart, hypothalamus and liver to identify differentially expressed genes in three environmental or life history comparisons, namely (i) birds arriving in their breeding grounds during an extreme spring (May 2013) compared to a normal spring arrival (May 2016); (ii) birds incubating during a snowstorm (5th June 2016) compared with storm-free incubation (17th June 2016), for which we further examined the pituitary gland, adrenal gland, and fat tissue; (iii) birds at different annual life-cycle stages - incubation versus arrival at the breeding ground, in storm free conditions.

Assessment of genome assembly

High quality long read sequence was included for contig construction and Omni-C reads (Fig. S1A, Supplementary material 1) for scaffolding¹². We present an assembly representing a total size of 1,154,813,007 bp, scaffold count of 457, and scaffold N50 of 72.36 Mb (Table 1and Fig. 2A). Results of repetitive element identification and BUSCO assessment indicate that the Lapland longspur genome is comparable to other birds of the Fringillidae family¹³ which includes closely related and well-studied species. Total repeat content of Lapland longspur is 13.45%, comprising 4.28% LINEs and 0.03% SINEs (Supplementary Table S1 and Fig. S1B), which is also close to that of other Fringillidae birds, with these elements ranging from 8.5–15.4%, 3.92–4.02% and 0.06–0.08%, respectively¹³. Similarly, the Lapland longspur genome shows good completeness with BUSCO assessment (aves_odb10: C:96.1% [S:92.5%, D:3.6%], F:0.8%, M:3.1%, n:8338) (Fig. 2B), which is similar to that of previously assembled passerine birds ¹³.

Comparative genomic analysis with zebra finch

We aligned our Lapland longspur assembly to the reference genome of zebra finch (Taeniopygia guttata, bTaeGut1.4.pri). Macro-chromosomes, intermediate chromosomes and known micro-chromosomes are represented by Scaffolds 1 to 23, ordered by chromosome size, as shown in Fig. 2C. The majority of scaffold 4 was assigned as the Z chromosome. Scaffolds 5 and 18 were assigned as chromosome 1A and 4A based on the zebra finch karyotype (Fig. S1C). Acknowledging the alignment uncertainty and potential scaffolding errors for some of the micro-chromosomes, selected micro-chromosomes of T. guttata (chromosome 23–29) were aligned separately and shown to map to Lapland longspur scaffolds 11, 12 and 195 (Fig. S1D). Because of this, the submitted assembly on GenBank (GCA_039654755.1) includes chromosomes with the highest confidence of assignment as well as the MT sequences.

Assembly of the mitochondrial (MT) genome resulted in a 16.827 kb DNA sequence with a circular formation, whose length is comparable to that of other species in the Fringillidae family¹⁴. When the Lapland longspur MT genome was aligned to the MT sequence of chicken, Red Crossbill (Loxia curvirostra)¹⁵ and Japanese Grosbeak (Eophona personata)¹⁴, with the latter two being closely-related species from the Fringillidae family, we found a linear alignment between the Lapland longspur MT sequence and reference sequences, which suggests our MT assembly and the proximal starting position exhibit a typical avian mitochondrial structure (Fig. S2).

Gene expression profile

In order to understand the gene expression changes occurring in response to inclement challenges, we generated data from 48 RNA-seq libraries representing different tissues, different environmental conditions, and different stages of the breeding cycle. Subsequently, we performed a set of focussed RNA sequencing on an additional 18 samples representing three tissues of HPA importance during incubation in both snowstorm and normal conditions.

Overall, the RNA-seq dataset showed good mapping quality to the current genome assembly, with an average unique mapping rate of 75.42% and an average number of uniquely mapped reads of 40.7 million reads per sample. After assigning the reads to individual genetic features, an average of 56.7% reads were confidently assigned, with 4.75% reads unassigned and 33% multi-mappers (Fig. S3A). The overall expression profile displays a tissue-specific pattern separated based on their tissue type and clustered closely within the same tissue, independent of weather conditions and life history stage, which suggested an appropriate sampling process (Fig. S3B).

Our previous study showed that birds arriving during the 2013 extreme spring had significantly lower body mass, fat scores, and haematocrit levels compared to those arriving under benign conditions¹¹. Size and volume of cloacal protuberance (CP) can also indicate the reproductive state of male birds¹⁶.

Genes differentially expressed between life history stages under normal conditions

We evaluated how tissues change their gene expression pattern between life history stages (arrival and incubation) when there is no specific perturbation. This focused on transition from pre-breeding to breeding status, shedding light into tissue-specific expression dynamics in relation to reproductive phase. Differentially expressed genes (DEGs) were identified by comparing birds during incubation with birds arriving at the breeding ground (Condition 3 v 2, as described in Methods) (Fig. 3A).

In testicular tissue, we discovered 383 up- and 160 down-regulated DEGs (Supplementary Table S2), with the most significant DEG being the gene encoding zona pellucida glycoprotein 3 (ZP3), which shows reduced expression during incubation compared to arrival (FDR adjusted P = 1.87×10^− 9 and fold change = 7.46). The expression of ZP3 has recently been reported in the process of spermatogenesis in human and mouse testes¹⁷. Interestingly, transcription factor nuclear factor-κB (NF-κB) is seen to be activated (Fig. S4A), supporting regulated expression of many genes involved in carbohydrate metabolism and immunity¹⁸.

Hypothalamus showed 4 up- and 22 down-regulated DEGs. FKBP5, the gene encoding FK506 Binding Protein 5, showed a reduced expression pattern (FDR adjusted P = 7.02×10^− 24, fold change = 3.02) when birds entered the incubation stage. This was also observed in the liver and heart tissues. This reflects the complex regulation between FKBP5 and glucocorticoid receptor (GR) in multiple tissues. Results also showed that DUSP1, which is important for glucocorticoid-driven anti-inflammatory function, exhibited reduced expression in the hypothalamus of incubating birds. FBXO32, which regulates muscle atrophy, was seen to be up-regulated upon arrival at the breeding ground. Another gene, PER3, which is involved in circadian timing, was differentially expressed in heart and liver. DEGs detected in heart (195 up- and 193 down-regulated) were, unsurprisingly, most significantly enriched for “cardiovascular system development and function” (Fig. S4B).

In liver (231 up- and 118 down-regulated DEGs), the gene encoding proto-oncogene tyrosine-protein kinase (KIT) exhibited a 21.58-fold change in expression level (FDR adjusted P = 8.18×10^− 11). KIT is involved in many important KEGG pathways, for instance the MAPK and Ras signalling pathways¹⁹. Additionally, increased expression of Elongation Of Very Long chain fatty acids protein 5 (ELOVL5) during incubation suggests fatty acid synthesis activity was accordingly regulated. Members of the Mini-Chromosome Maintenance proteins (MCM) gene family, including MCM2 to MCM6, were more highly expressed during incubation compared to arrival. MCM genes form an MCM complex that precedes DNA replication²⁰ which is supported by significantly enriched GO terms, such as “DNA replication” (P = 9.4×10^− 6) and “cell cycle” (P = 1.5×10^− 8).

We discovered the gene encoding Pyruvate Dehydrogenase Kinase 4 (PDK4) to be differentially expressed in all tissues we tested; this gene is shown to reduce its expression level when entering into the incubation stage. PDK4 is located in the mitochondrial matrix and plays a role in regulation of glucose metabolism by inhibiting the pyruvate dehydrogenase which catalyses pyruvate conversion to acetyl-coenzyme A²¹. When comparing the canonical pathways among tissues, (excluding hypothalamus because of the low number of DEGs), we found very few common pathways with similar activation pattern, but pathways associated with cell cycle were prominently overrepresented (Fig. S4C). We also found significant overrepresentation of the E2F transcription factor family (Fig. S4D), including E2F1, E2F1DP1RB and E2F4DP1 which are associated with regulation of genes involved in cell cycle progression.

Differentially expressed genes associated with extreme spring

Focussing on DEGs detected in response to a chronic stressor, we investigated samples from birds that had experienced an extremely cold spring upon their arrival in the breeding grounds and compared them to birds arriving under benign conditions (Condition 1 v 2, as described in Methods) (Fig. 3B). In line with the curtailed reproductive activity that was observed in the field, we found interesting differential expression of genes in the testes. Although the number of DEGs was not shown to be large (5 up- and 5 down-regulated), their functional association with reproduction was apparent. In particular, genes encoding ZP3 and Formin 2 (FMN2) showed significant down-regulation. Recent studies identified testicular ZP3 expression during spermatogenesis^17,22, which further supports our observation of its reduced expression during normal transition between breeding stages. This suggests the molecular mechanism underlying decreased reproductive activity may have its roots in gametogenesis^23,24. Enriched biological functions associated with DEGs included “cell morphology”, “organ morphology” and “reproductive system development and function” (Fig. S5A).

In keeping with our observation of reduced body mass and fat stores¹¹, we found genes involved in glycolysis/gluconeogenesis, tricarboxylic acid cycle and fatty acid biosynthetic process to be highly expressed in the liver. We found 95 up- and 67 down- regulated genes in the liver. Among those, a significant number of DEGs are involved in “lipid metabolism”, “molecular transport”, and “carbohydrate metabolism”. This includes genes such as Malic Enzyme 1 (ME1), Triokinase and FMN Cyclase (TKFC), Pyruvate Dehydrogenase E1 Subunit Alpha 2 (PDHA2), and Citrate Synthase (CS). Pathways associated with the tricarboxylic acid (TCA) cycle and respiratory electron transport show the most interesting changes in liver, with significantly positive Z-scores (Fig. S5B). The most significant pathway is the extrinsic prothrombin activation pathway which is responsible for blood coagulation.

Although very few DEGs were detected in the hypothalamus (5 down-regulated), genes encoding Matrix Gla Protein (MGP) and Sodium Voltage-Gated Channel Beta Subunit 3 (SCN3B) were found to be downregulated during the extreme spring relative to normal conditions. Finally, transcriptomic comparison of heart (12 up-regulated DEGs and 10 down-regulated) revealed genes that are important for the cardiovascular system (e.g., blood coagulation) to be differentially expressed, such as coagulation factor gene (F3) and the epidermal surface antigen FLOT2. We also found the gene encoding collagen type XIII alpha 1 chain (COL13A1) to be up-regulated.

The snowstorm-induced stress response during incubation

In contrast to the extreme spring, the snowstorm in 2016 was extremely acute (Condition 4 v 3, as described in Methods) causing a series of severe energy challenges and stress responses, which could be explained by birds moving into the emergency life history stage (ELHS) as a coping strategy. In total, we detected 49 DEGs in the hypothalamus (29 up- and 20 down-regulated), 99 in the gonad (35 up- and 64 down-regulated), 1,631 in the liver (807 up- and 824 down-regulated), and 315 in heart (140 up- and 175 down- regulated) (Fig. 3C). Genes involved in the circadian rhythm and biological periodicity (e.g., PER2 and PER3) were perturbed across tissues, with CRY1 additionally down-regulated in the hypothalamus. One of the most consistently up-regulated genes across the hypothalamus, heart and liver (but not testis) was FKBP5. Additional stress-related genes were differentially expressed in heart and liver, e.g., genes encoding Angiotensinogen (AGT), Galectin 2 (LGALS2), Adrenoceptor Beta 2 (ADRB2), Catechol-O-Methyltransferase (COMT), along with Haem-Binding Protein 2 (HEBP2 or SOUL) in the hypothalamus. The corticotropin releasing factor, UCN3, was also down-regulated in the hypothalamus. The gene encoding AGT, involved in the renin-angiotensin-aldosterone system, was shown to be down-regulated in liver^25,26. The increased expression of ADRB2 in the liver, is functionally important in the adrenergic system and in metabolism²⁵ and the reduced expression of COMT in heart suggests the moderation of catecholamine degradation such as that of epinephrine^27,28.

Liver DEGs were found to be involved in various metabolic processes, e.g., the regulation of gluconeogenesis and included genes such as Phosphoenolpyruvate Carboxykinase 1 (PCK1) and Malic Enzyme 1 (ME1) that both produce key enzymes in cytosolic pyruvate metabolism. These differentially expressed genes were found to be enriched for terms including many metabolic functions, such as ‘lipid metabolism’ and ‘carbohydrate metabolism’. Interestingly, significant pathways, such as ‘activation of gene expression by SREBF (SREBFP)’ and ‘TR/RXR activation’, are shown to exhibit a positive activation pattern. Taken together, this suggests the overall activation of metabolic gene expression regulation is of endocrinological relevance (Fig. S5C). A significant enrichment of genes with binding sites for transcription factors including HNF1/4, E2F, HIF1, AP4 and MEF2 was also noted (Fig. S5D). Functions of these regulators include determination of cell fate, cell cycle, cell development, energy homeostasis and mediation of the stress response.

Hypothalamic-Pituitary-Adrenal axis tissues

In the snowstorm scenario, we additionally investigated 3 tissues to achieve an overview of HPA axis expression patterns in response to the acute snowstorm (Fig. 4A). Similar to DEG discovery in hypothalamus, we did not find an extensive number of DEGs in the pituitary (n = 5) or adrenal glands (n = 31). However, a few genes of importance in stress regulation showed prominent changes, e.g., PTGER3 and LSS. Regarding subcutaneous fat, we found 327 up- and 254 down-regulated DEGs, among which, many are important for energy metabolism. During the snowstorm, 106 DEGs were shared between liver and fat, among which 93 exhibited the same direction of change (Fig. 4B). This interesting pattern further suggests a cohesive response in the metabolic system to cope with the energy needs in different tissues. Enriched KEGG pathways included MAPK signalling pathway, steroid biosynthesis and metabolic pathways (Fig. 4C).

Comparison of differential gene expression

The Venn diagrams in Fig. 5 show the unique and overlapping genes between comparisons and tissues. In general, there are not many genes shared between tissues in the same comparison, yet it is to be noted that genes such as FKBP5 (stress) and PCK1 (energy demands) suggest a consistent molecular response occurring in corresponding tissues. We also note that differential expression of periodicity-related genes (e.g., PER2 and PER3) is shared across tissues in the storm comparison (Fig. S6). Gene Ontology results using DEGs related to incubation in the storm condition show significantly enriched terms for ‘steroid hormones’, ‘glycolysis/gluconeogenesis’ and ‘ketone body biosynthetic process’. This sheds light into the switch of physiological preference for energy usage under extreme snowstorm, whereas DEGs associated with extreme spring show enriched GO terms for acrosome reaction and response to testosterone.

Although the two stress events showed unique gene expression responses, the overlap between the two inclement conditions highlights 90 out of 92 DEGs in the liver with a similar up- or down-regulated pattern (Fig.S7). The two inclement comparisons shared the most significant up-regulated gene, ME1. This gene encodes the malic enzyme that catalyses the conversion of malate to pyruvate while generating NADPH in the cytosol, which links the glycolytic and citric acid cycles. ME1 has been reported to rescue human mitochondrial disease complex I mutant cells under nutrient stress conditions²⁹. These DEGs are enriched for KEGG terms including ‘Pyruvate metabolism’ (P = 8.4×10^− 6), ‘Glycolysis/Gluconeogenesis’, and ‘TCA cycle’ (Fig. S8), which highlights similar gene expression changes in metabolic pathways.

Lapland Longspur FKBP5

One of the highlighted genes from our study, and a gene known to be an important stress-regulator, is FKBP5. FKBP5 is an evolutionary-conserved gene across many vertebrate species and so we decided to investigate the gene in Lapland longspur in a bit more detail. Multiple sequence alignment with various other species shows that the predicted Lapland longspur protein sequence is highly homologous to that of chicken (G. gallus) with 93.32% identity (Fig. 6A). The 3-dimensional structure of Lapland longspur FKBP5 protein was predicted by the AlphaFold3 modelling server³⁰ and is shown in Fig. 6B. A gene interaction network of the human ortholog was also reconstructed and is shown in Fig. 6C. The overall expression dynamics of FKBP5 across tissues and conditions suggest that the FKBP5 is responsive in different stress scenarios and detectable in various tissue types (Fig. 6D).

In order to understand the effect of extreme weather conditions and how wild, free-living animals respond to them, we generated a genome assembly and performed RNA-sequencing on key tissues in the Arctic specialist, the Lapland longspur, collected during two distinct types of extreme weather conditions (i.e., extreme spring and snowstorm). The high-quality genome assembly enabled an accurate annotation of genomic features and reliable quantification of gene expression changes.

We first investigated gene expression changes occurring between arrival and incubation under normal seasonal conditions, using this as a baseline to understand the transcriptomic dynamics in response to environmental adversity. The major transition between the two life history stages is the physiological adjustment to breeding status, which includes reproductive activity and cardiovascular function. Among other DEGs, ZP3 shows the most significant down-regulation pattern in testes. ZP3 encodes a protein essential for the sperm acrosome reaction, especially for binding of sperm to the zona pellucida. ZP3 knockout mice and human genetic mutation in this gene (OMIM:617712) have been shown to exhibit female infertility due to zona pellucida defect^31–34. Recently, significant ZP3 expression was found in normal human and mouse testes (spermatogonia and spermatocytes) ¹⁷. A study in a non-mammalian vertebrate, the gilthead seabream (Sparus aurata) also demonstrated that the orthologous ZP3 gene is up-regulated in ejaculated sperm compared to haploid germ cells²². Evidence is accumulating for its important role in the male reproductive system, however, the molecular function of a down-regulated ZP3 in testis is unknown. Another interesting finding was in the cardiovascular system. DEGs in heart exhibit strong enrichment in cardiovascular development, circulatory system development, and positive regulation of vascular associated smooth muscle cell migration (e.g., IGFBP5). In addition, the glucocorticoid responsive gene FKBP5 also expresses at a lower level during incubation relative to arrival in hypothalamus and heart. This supports the hypothesis that during parental stages, the HPA axis activity is attenuated in wild breeding birds to prevent nest abandonment and favour parental care³⁵. These DEGs and functional pathways identified under normal conditions provide a foundation to understand the interplay between life history stages and inclement weather events.

The two distinct inclement weather conditions triggered significant negative energy balance referred to as Type I allostatic overload ⁹, which makes an interesting proxy study for understanding the similarity and differences in their effects. The two extreme weather conditions act similarly as a source of allostatic load with limited food availability and higher thermoregulation demand, which limits the amount of external energy availability in the environment (EG) and increases the cost of maintaining minimum day-to-day homeostasis (EE) as well as energy required to make predictable changes under ideal conditions (EI). Yet, the influence of adversity and corresponding responses of animals largely depend on interplay of varied inclement weather types and the breeding cycle phenology.

The two extreme weather conditions in this study enabled us to compare transcriptomic changes between the two stress scenarios. In liver, genes with similar up- or down-regulated expression patterns reveal an energy expenditure that is consistent in the two scenarios. For instance, shared up-regulated genes (e.g., DLAT, PDHA2 and ELOVL5) are associated with terms such as ‘coenzyme biosynthetic process’ and ‘thioester biosynthetic process’. In liver, one of the most important thioesters is acetyl-CoA, which participates in the tricarboxylic acid cycle to produce energy in the form of ATP or GTP upon oxidization. Usually, acetyl-CoA is produced via glycolysis of glucose and degradation of fatty acid or amino acid. Genes that positively regulate cellular response to insulin such as SRC and NR1H4 were down-regulated in both inclement conditions, despite the fact that we did not identify statistically significant GO terms using shared down-regulated liver DEGs. Importantly, the large number of differentially expressed genes detected in liver, many of which are important and rate-limiting enzymes in processes of glycolysis and gluconeogenesis (e.g., PCK1), were found to associate with related GO terms, which indicates that the dynamic and metabolic activity in liver tissues is providing fuel to cope with the stress-imposed negative energy imbalance.

We have highlighted genes in hypothalamus whose expression is crucially regulated under the inclement conditions studied. FKBP5 was the most up-regulated gene in hypothalamus during the snowstorm incubation period. This gene is known to have significant relevance as a stress regulator ^36,37. The acute snowstorm immediately affected the function of the HPA axis of Lapland longspur, with an increase in expression of FKBP5 in the brain. FKBP5 is a co-chaperone associated with Hsp90, negatively regulating glucocorticoid signalling, which is recognized as a mediator of stress-response. Both nucleotide and protein sequence of FKBP5 are also highly conserved across species, and a homozygous knockout of FKBP5 in mice was shown to cause abnormal depression or anxiety-related behaviours. Decreased circulating corticosterone levels are also seen in stressed mice ³¹. Normally, in response to stress, FKBP5 expression is increased by elevated glucocorticoids. This correlation is especially interesting in hypothalamus, as it subsequently modulates GR activity by reducing ligand-binding sensitivity and delays the translocation of GR. In turn, GR regulates the transcription of many nuclear expressed genes, including FKBP5 which is also shown to be determined by the epigenetic context and polymorphism of the locus ³⁷. High FKBP5 expression additionally leads to GR resistance and subsequently influences many biological functions and gene transcription, for instance reduced expression of PCK1, as we observed ³⁷. Induced GR resistance can result in an attenuated HPA negative feedback mechanism, whilst a failure to restore the baseline GC level has maladaptive and pathological consequences ^38,39. Collectively, we demonstrate that during the snowstorm, the acute stress was extremely intense, reflected by the activity of HPA axis genes and the significantly induced FKBP5 expression level in the hypothalamus, eventually leading to a negative balance that had destructive and fatal outcomes.

Lapland longspurs had to fight against the chronic and prolonged inclemency of weather in the extreme spring scenario by triggering the emergency life history stage (ELHS), and actively trading off their breeding windows for the chance of survival. The reduced expression level of ZP3 and FMN2 in testes sheds light on the gene expression dynamics occurring during reproductive curtailment. In our analysis between benign life history stages, ZP3 was the most significantly down-regulated gene in testes of incubating birds. Recent evidence of up-regulated ZP3 expression was found in testes of some vertebrates ^17,22. Similar high expression was found for FMN2 in early spermatids in a recent human single-cell study ³². FMN2 encodes a protein crucial for actin binding and cell polarity (mostly associated with female infertility) ^33,34, as well as being an important component involved in stress-induced cell-cycle arrest in human cells ⁴⁰. In addition, expression of TBC1D8 is increased in testis, with recent studies suggesting its function in cell death as a new cell apoptosis inducer ⁴¹, which may mark it as a potential key protein during curtailment of reproduction. Our discovery of low expression of ZP3 (and FMN2) in reproductively curtailed passerine birds further demonstrates that reduced gene expression can cause arrested spermatogenic maturation which underlies the impaired male reproduction in extreme spring. We therefore hypothesize that the testicular expression of ZP3 is crucial for the initiation of spermatogenesis, with function rapidly reducing upon entering the incubation period. To our knowledge, this study shows, for the first time in wild living aves, expression of ZP3 has fundamental function during spermatogenesis, and reduced expression may indicate reproductive inhibition. Compared to previous studies on the hypothalamic-pituitary-gonadal response to stress ^42,43, novel genes identified in our study complement the few discovered genes in the male transcriptomic stress response and expand our understanding of the influence of a prolonged stressor on body systems.

We noted that the expression of FKBP5 was not significantly regulated in any tissue during the extreme spring, which may suggest that the HPA axis had likely acquired a temporal balance with improved plasticity and had adapted to perturbations at the sampling time point. These temporal dynamics could be achieved by many possible mechanisms (e.g., corticosterone binding globulin) ^11,44. The fitness of the birds during the extreme spring may also be explained by the previously-studied association between HPA flexibility and FKBP5 expression ⁴⁵. This pattern agrees with the “leave-it” strategy as previously proposed ^5,8. Although FKBP5 levels can be a proxy for a chronic stressor ⁴⁴, our study highlights the importance of considering the interplay of life history stage, extreme environmental conditions, coping strategy, and the temporal gene expression fluctuations.

When comparing the two scenarios from an allostatic load perspective, spring is thought to provide higher energy supplies in food compared to winter, however, the chronic effect of an extremely cold period imposes constant challenge on the energy balance, on top of the expenditure of life history stage transition (e.g., migration and pre-breeding). The ELHS was thus adopted by Lapland longspurs, which in turn suppressed the regular LHS (i.e., breeding). Through these series of physiological and behavioural stress challenges, the birds successfully managed to reduce their negative energy balance and reach a favourable outcome of survival. However, this was at the cost of a failed breeding attempt. In comparison, the extreme snowstorm is expected to have caused immediate overloads by dramatic energy expenditure via the environmental perturbation (EO) that is often expected during adversity along with glucocorticoid secretion. This negative energy balance resulted in an acute effect of EE + EI + EO > EG, with individuals failing to reduce the allostatic load eventually suffering pathological effects and, to a greater degree, death. This additionally supports the double-edged sword theory of glucocorticoid function and other allostasis mediators, which play protective and adaptive roles in the stress response, yet can also have damaging effects.

Beyond the above-mentioned genes, further genes are of interest, including those which contribute to the comprehensive stress response that induces physiological, behavioural and phenological changes. For instance, genes (e.g., FKBP5) that have been reported to be stress-related in human studies are associated with the adrenergic system, bipolar condition and the renin-angiotensin-aldosterone system (RAAS). These genes were detected mostly in heart (COMT and LGALS2) and liver (AGT, ADRB2, and LGALS2) in our comparisons ²⁵. In addition, we also found ZBTB16 to be upregulated in liver and heart in response to the snowstorm. Gene ZBTB16 encodes a zinc finger transcription factor that responds to corticosteroid regulation, and shows a robust induction by GR activation or stress exposure in human, mouse, and bird studies ^43,46,47.

In summary, here we present a high-quality genome assembly of the Lapland longspur (Calcarius lapponicus), which has enabled a comprehensive transcriptomic study to investigate gene expression changes in key tissues, occurring in response to environment stress caused by extreme weather events at different stages of the Lapland longspur breeding cycle. Biological processes such as activation of HPA axis, reproductive function and energy regulation are shown to be central to the adaptive changes required in the host, with genes including FKBP5, ZP3 and ME1 being key players in these responses. This study provides data fundamental to our understanding of how species will have to be able to adapt to the rapidly changing climate we all now face.

Lapland longspur genome assembly

A pectoralis muscle sample from a female Lapland longspur (Calcarius lapponicus) was selected for generating the genome assembly. In order to generate a high-quality genome, high molecular weight DNA (50 to100 kb) was extracted which was subsequently used for long-read PacBio library preparation and sequencing. Sequencing of the genome was performed with PacBio Sequel II SMRT Cell, and we subsequently assembled the sequences into scaffolds by using Wtdbg2 ⁴⁸. In addition, Omni-C scaffolding was carried out by Dovetail Genomics (CA, USA). The Omni-C protocol provides a sequence-independent approach: chromatin was fixed in place and then optimized DNase I was employed to digest the chromatin. The DNA was sequenced on an Illumina HiSeqX platform to produce 2 x 150 bp paired-end libraries, yielding a coverage of around 30X. The HiRise pipeline was then used to identify sequence joins and produce scaffolds ⁴⁹. The species in this study is not a model species nor from an inbred line, therefore the heterozygous nature of the genome could potentially influence the analyses, thus we employed Purge_Dups to detect and remove false duplications such as heterotype sequence with default features ⁵⁰. We performed gene annotation using the information from the RNA-seq data to refine splice junctions and Iso-seq data from a closely-related species (Z. leucophrys) to provide cross-species evidence by using GMAP (> 90% identity) ⁵¹. The clean sequence was mapped to the genome, with the cross-species evidence used as a guide reference. Predicted splice junctions were then quality-controlled by Portcullis (v1.2.0) (https://github.com/EI-CoreBioinformatics/portcullis). Multi-sample transcripts were combined to robustly predict transcripts by majority voting with PsiCLASS (v1.0.2) ⁵². Coding probability was estimated by CPC2 (version 0.1) ⁵³ using the longest transcript with ‘-r’ function to check the reverse strand. In total, we estimated 17,096 coding genes. The gene features were subsequently used for mapping and quantifying the RNA-seq reads.

Evaluation of genome assembly

In order to assess the quality of the Lapland longspur genome, basic quality statistics of the assembly were computed by measuring the total size, count of scaffolds/contigs, contiguity (such as N50/N90, or contig N50/N90), and GC content. Repeat elements like transposable elements were trained and identified by RepeatMasker and Repeatmodeler ^54,55. We quantified the completeness of expected conserved genes in the genome using Benchmarking Universal Single-Copy Orthologs (BUSCO), with databases of vertebrata_odb10, aves_odb10, and passeriformes_odb10 ⁵⁶. Mitochondrial genome (MT) sequence was detected as MT scaffold. MitoZ software (version 2.2) ⁵⁷ was used to detect the circular MT sequence and annotate the genes. We reordered the sequence to anchor the proximal starting position of the circular sequence by using the MT genome of a closely-related species, the Red Crossbill (Loxia curvirostra, NC_025623.1) ¹⁵, as well as using the chicken MT sequence (GRCg6a, NC_040902.1) and Japanese Grosbeak (Eophona personata, KX812499) ¹⁴ MT genome for verification.

Comparative genomic analyses

As for comparative analyses between the genome of Lapland longspur and that of other avian species, the Mummer aligner (v3.2.3) ⁵⁸ was used to align our genome to the reference genome of chicken (Gallus gallus, GRCg6a) and zebra finch (Taeniopygia guttata, bTaeGut1.4.pri, GCF_003957565.2). Due to lack of karyotype information for the Lapland longspur genome and the mapping uncertainty for micro-chromosomes in birds, we performed the comparative analyses and visualized the macro-, micro- and sex chromosome results separately, shown by scaffolds 1 to 23 in Fig. 2A and the other major scaffolds in Fig. S1C-D. The chromosome assignment of our assembly (Supplementary Table S3) was based on zebra finch and chicken chromosomes. Among others, scaffold 4 was assigned as the Z chromosome and scaffolds 5 and 18 were assigned as chromosomes 1A and 4A based on the zebra finch genome. The circular visualization of the scaffolds was conducted using the R package circlize (0.4.15) ⁵⁹, GC content and repeats were summarized in 200 kb windows, and Ns or gaps were summarized in 50 kb bins. A DOT plot was used to visualize the cross-species alignment by adapting R code from dotPlotly (https://github.com/tpoorten/dotPlotly).

Sample collection

Tissues from 12 male Lapland longspurs were collected across the breeding season in 2013 and 2016. Samples were collected from four breeding sites on the north slope of Alaska, USA. Birds were captured with seed-baited potter traps or mist nets. Blood samples were collected from the alar vein with a 26-gauge needle and surface blood collected by heparinized microcapillary tube within 3 minutes of capture. The birds were sedated with isoflurane and euthanized by rapid decapitation (3 min 20 s ± 52 s post capture). After euthanasia, tissues were dissected, wrapped in aluminium foil, frozen on dry ice, placed into labelled plastic bags, and kept frozen on dry ice until they were stored in a -80⁰C freezer upon returning the laboratory. Samples were later shipped on dry ice to the Roslin Institute, University of Edinburgh, UK where they were stored at -80⁰C until use. Blood samples were centrifuged at 13,000 rpm for 5 minutes, the plasma was aspirated using a Hamilton syringe, transferred to a microcentrifuge tube, and frozen at -30⁰C till assay.

RNA preparation and sequencing

Four environmental conditions at two life-history stages (arrival and incubation) were investigated: (1) 2013 arrival - extreme spring, (2) 2016 arrival - spring and storm free, (3) 2016 incubation - storm free, (4) 2016 incubation - snowstorm. For each condition and tissue, we sequenced three replicates to ensure statistical power. RNA samples from the testis, heart, hypothalamus, and liver were prepared by homogenization in TRIzol reagent (Invitrogen, Paisley, UK) and extracted following the protocol based on the Direct-zol RNA Miniprep kit (Zymo Research USA), with a final elution of RNA in RNAse-free water. Following RNA extraction, total concentration of RNA was determined via nanodrop spectrophotometer (Thermo Fisher, USA). After library construction and PolyA selection, RNA was sequenced on an Illumina NovaSeq platform, producing 2 × 150bp paired-end reads. Forty-eight samples were sequenced in total.

Additionally, in order to validate the effect of snowstorm along the HPA axis and in HPA target tissues, we conducted additional RNA-seq on pituitary gland, adrenal gland, as well as subcutaneous fat tissues. Here we focused on the comparison between environmental conditions (3) and (4); birds captured during the incubation period during a snowstorm relative to normal conditions. The RNA was prepared and sequenced using the same methods mentioned above. An additional 18 samples were thus sequenced.

Detection of differentially expressed genes

The raw RNA-seq reads were quality checked using FastQC with default parameters⁶⁰, and adaptors were trimmed using Trimmomatic⁶¹. Clean reads were mapped to the Lapland longspur genome using STAR (v2.7.8a_2021-03-08)⁶² with the following parameters: --outFilterType BySJout --outSAMunmapped None --outSAMtype BAM SortedByCoordinate --runThreadN. Quantification of gene expression level was performed by assigning the reads to genomic features using featureCounts⁶³, which is part of the Subread package. We counted reads meeting the following criteria, both ends of paired reads aligned (-B), paired-end distance thresholds of 5000 (-P -D 5000), count fragments of reads (-p), and feature type to count is ‘exon’. Expression count matrix was then processed by the R packages DESeq2⁶⁴ and edgeR⁶⁵. We pre-processed the data for sample normalization and to remove the poorly aligned genes, where genes with an average read count of at least 1 per sample were included. Differentially expressed genes (DEGs) with considered with P-value < 0.05 and fold change > 1.5. DEGs related to stress response were identified by comparing the samples from inclement to normal weather conditions for each tissue. Arrival during extreme and normal spring (Condition 1 v 2) was compared along with samples from birds incubating during storm and normal conditions (Condition 4 v 3). DEGs associated with the annual life-cycle were detected by comparing birds during the incubation period with those collected during arrival at the breeding ground - both collected during storm-free, normal spring (Condition 3 v 2).

Functional Annotation

The differentially expressed genes were compared between tissues and their function further investigated. Venn diagrams were generated for each type of environmental stress to find unique and overlapping DEGs across the four tissues using the “ggvenn”, “nVennR” and “euler” packages in R. To understand the biological importance of genes of interest, we used either chicken or human homologous genes for functional analyses due to availability of better gene functional annotation. The differentially expressed gene sets were analysed for Gene Ontology (GO) enrichment and KEGG pathways annotation based on chicken orthologous genes using shinygo 0.77 (http://bioinformatics.sdstate.edu/go/). The FDR P- value and Fold Enrichment were calculated to measure the overrepresentation of genes. Homologous human genes were analysed by Ingenuity Pathway Analysis (IPA) software (QIAGEN Inc., https://digitalinsights.qiagen.com/IPA)⁶⁶ to reveal biological pathways and functions pertaining to the DEGs identified in our analyses. The P-value was calculated using the right-tailed Fisher Exact Test with Benjamini-Hochberg correction (threshold P < 0.05). Enriched transcription factor binding sites were identified using WebGestalt (WEB-based GEne SeT AnaLysis Toolkit) algorithms (http://www.webgestalt.org/) ⁶⁷. Over-representation analysis (ORA) was carried out using the network functional database to identify transcription factor targets. The ‘chicken genome’ was used as the background reference. Results showing an FDR < 0.05 were considered significant.

FKBP5 gene network and protein structure prediction

As a specific differentially expressed gene of interest, a gene network was generated for FKBP5 using the association network prediction server GeneMANIA⁶⁸. Based on the known function of human FKBP5, the program generated a functional association gene network by a label propagation algorithm, which shows how the genes interact with each other. A multi-species alignment was performed using 8 other vertebrate FKBP5 protein sequences retrieved from NCBI including Homo sapiens (NP_004108.1), Pan troglodytes (XP_001172420.1), Canis lupus familiaris (XP_538880.2), Bos Taurus (NP_001179791.1), Mus musculus (NP_034350.1), Rattus norvegicus (NP_001012174.1), Gallus gallus (NP_001005431.1) and Danio rerio (NP_998314.1), multiple sequence alignment was performed using the Clustal Omega aligner⁶⁹ with default settings. The results were visualized by Jalview (version: 2.11.2.4)⁷⁰. A neighbour-joining (NJ) tree was also generated by the build-in function for the purpose of comparing pair-wise sequence identity and detecting the most closely related protein sequence. The 3D modelling of FKBP5 protein was conducted on the AlphaFold3 online server (https://www.alphafoldserver.com, accessed May 10th, 2024), using the default protein modelling³⁰.

Data availability

Sequence data presented in this paper have been submitted to the NCBI public database under the following accession numbers: Calcarius lapponicus genome assembly - JBBFKN01 (GenBank accession: GCA_039654755.1); RNA-seq data - PRJNA1023066; Iso-seq data - SRR21856897, SRR21856898, SRR21856899.

Contributions:

Conceptualization: SLM, JSK, JCW, JS, ZW

Sample collection: SLM, JK, JHP, JCW

Laboratory work: VRB, AMAR, JSK, KM, ZW

Genome assembly and RNA-seq analyses: ZW, MMH, JS

Visualization: ZW, JS

Supervision: JS, SLM

Competing interests:

Authors declare that they have no competing interests.

Funding

acquisition: JS, MMH, SLM

Acknowledgments

This work was partly funded by the Biotechnology and Biological Sciences Research Council (BBSRC) under grant number BB/V001647/1 to Jacqueline Smith and Simone L. Meddle. This work was also supported by the National Science Foundation Office of Polar Programs ARC 0909133 and Integrative Organismal Systems IOS 1558049 to John C. Wingfield and Roslin Institute Strategic Grant funding from the UK Biotechnology and Biological Sciences Research Council (BB/P013759/1) to Simone L. Meddle. John C. Wingfield would like to acknowledge the University of California, Davis Endowed Chair in Physiology. Additional RNA-sequencing was supported by the Roslin Institute Early Career Grants scheme to Zhou Wu to fund the project entitled “Discovering genes for resilience: Uncovering the transcriptomic stress response to inclement weather conditions” through the ISPs (Institute Strategic Programmes). The authors would like to acknowledge the services of Edinburgh Genomics (Edinburgh, UK) for carrying out the RNA-Sequencing and Dovetail Genomics (CA, USA) for genome assembly. For the purpose of open access, the author has applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising from this submission. We would like to thank Helen E. Chmura and Jeffrey Cheah for their help in the field and logistic support from the staff at Toolik Field Station, The University of Alaska Fairbanks, USA. We thank Brian Barnes and Jeannette Moore for logistical support while in Fairbanks, Alaska, USA.

Root, T.L., et al.: Fingerprints of global warming on wild animals and plants. Nature 2003 421:6918 421, 57–60 (2003)
Aguirre-Liguori, J.A., Ramírez-Barahona, S., Gaut, B.S.: The evolutionary genomics of species’ responses to climate change. Nat. Ecol. Evol. 5, 1350–1360 (2021)
Wingfield, J.C., et al.: How birds cope physiologically and behaviourally with extreme climatic events. Philosophical Trans. Royal Soc. B: Biol. Sci. 372, 20160140 (2017)
Boelman, N.T., et al.: Extreme spring conditions in the Arctic delay spring phenology of long-distance migratory songbirds. Oecologia. 185, 69–80 (2017)
Krause, J.S., et al.: The stress response is attenuated during inclement weather in parental, but not in pre-parental, Lapland longspurs (Calcarius lapponicus) breeding in the Low Arctic. Horm. Behav. 83, 68–74 (2016)
Wingfield, J.C., et al.: Ecological bases of hormone-behavior interactions: The emergency life history stage. Am. Zool. 38, 191–206 (1998)
Krause, J.S., et al.: Weathering the storm: Do arctic blizzards cause repeatable changes in stress physiology and body condition in breeding songbirds? Gen. Comp. Endocrinol. 267, 183–192 (2018)
Wingfield, J.C.: Endocrine Responses to Unpredictable Environmental Events: Stress or Anti-Stress Hormones? Integr. Comp. Biol. 42, 600–609 (2002)
McEwen, B.S., Wingfield, J.C.: The concept of allostasis in biology and biomedicine. Horm. Behav. 43, 2–15 (2003)
Smith, S.M., Vale, W.W.: The role of the hypothalamic-pituitary-adrenal axis in neuroendocrine responses to stress. Dialogues Clin. Neurosci. 8, 383–395 (2006)
Krause, J.S., et al.: The effect of extreme spring weather on body condition and stress physiology in Lapland longspurs and white-crowned sparrows breeding in the Arctic. Gen. Comp. Endocrinol. 237, 10–18 (2016)
Lieberman-Aiden, E., et al.: Comprehensive Mapping of Long-Range Interactions Reveals Folding Principles of the Human Genome. Sci. (1979). 326, 289–293 (2009)
Feng, S., et al.: Dense sampling of bird diversity increases power of comparative genomics. Nature. 587, 252–257 (2020)
Sun, G., et al.: Sequence and organisation of the mitochondrial genome of Japanese Grosbeak (Eophona personata), and the phylogenetic relationships of Fringillidae. Zookeys. 995, 67–80 (2020)
Lerner, H.R.L., Meyer, M., James, H.F., Hofreiter, M., Fleischer, R.C.: Multilocus Resolution of Phylogeny and Timescale in the Extant Adaptive Radiation of Hawaiian Honeycreepers. Curr. Biol. 21, 1838–1844 (2011)
Schut, E., Magrath, M.J.L., Van Oers, K., Komdeur, J.: Volume of the cloacal protuberance as an indication of reproductive state in male Blue Tits Cyanistes caeruleus. Ardea. 100, 202–205 (2012)
Pulawska, K., et al.: Novel expression of zona pellucida 3 protein in normal testis; potential functional implications. Mol. Cell. Endocrinol. 539, 111502 (2022)
Capece, D., et al.: NF-κB: blending metabolism, immunity, and inflammation. Trends Immunol. 43, 757–775 (2022)
Huang, Z., et al.: The stem cell factor/Kit signalling pathway regulates mitochondrial function and energy expenditure. Nat. Commun. 5, (2014)
Tye, B.K.: MCM Proteins in DNA Replication. Annu. Rev. Biochem. 68, 649–686 (1999)
Dou, X., et al.: PDK4-dependent hypercatabolism and lactate production of senescent cells promotes cancer malignancy. Nat. Metab. (2023). 10.1038/s42255-023-00912-w
Castro-Arnau, J., et al.: Developmental RNA-Seq transcriptomics of haploid germ cells and spermatozoa uncovers novel pathways associated with teleost spermiogenesis. Sci. Rep. 12, 14162 (2022)
Bansal, P., Chakrabarti, K., Gupta, S.K.: Functional activity of human ZP3 primary sperm receptor resides toward its C-terminus. Biol. Reprod. 81, 7–15 (2009)
Li, N., et al.: Formins: Actin nucleators that regulate cytoskeletal dynamics during spermatogenesis. Spermatogenesis. 5, 1–9 (2015)
Ising, M., Holsboer, F.: Genetics of stress response and stress-related disorders. Dialogues Clin. Neurosci. 8, 433–444 (2006)
Tiret, L., et al.: Gene polymorphisms of the renin-angiotensin system in relation to hypertension and parental history of myocardial infarction and stroke. J. Hypertens. 16, 37–44 (1998)
Hall, K.T., et al.: Catechol-O-methyltransferase and cardiovascular disease: MESA. J. Am. Heart Assoc. 8, (2019)
Montag, C., Jurkiewicz, M., Reuter, M.: The Role of the Catechol-O-Methyltransferase (COMT) Gene in Personality and Related Psychopathological Disorders. CNS Neurol. Disord Drug Targets. 11, 236–250 (2012)
Balsa, E., et al.: Defective NADPH production in mitochondrial disease complex I causes inflammation and cell death. Nature Communications 2020 11:1 11, 1–12 (2020)
Abramson, J., et al.: Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature. (2024). 10.1038/s41586-024-07487-w
Smith, C.L., Eppig, J.T.: The mammalian phenotype ontology: enabling robust annotation and comparative analysis. Wiley Interdiscip Rev. Syst. Biol. Med. 1, 390–399 (2009)
Karlsson, M., et al.: A single–cell type transcriptomics map of human tissues. Sci. Adv. 7, (2021)
Li, J., et al.: Heterozygous FMN2 missense variant found in a family case of premature ovarian insufficiency. J. Ovarian Res. 15, 31 (2022)
Kim, K.H., Lee, K.A.: Maternal effect genes: Findings and effects on mouse embryo development. Clin. Exp. Reprod. Med. 41, 47 (2014)
Lattin, C.R., Breuner, C.W.: Michael Romero, L. Does corticosterone regulate the onset of breeding in free-living birds? The CORT-Flexibility Hypothesis and six potential mechanisms for priming corticosteroid function. Horm. Behav. 78, 107–120 (2016)
Zimmer, C., Hanson, H.E., Wildman, D.E., Uddin, M., Martin, L.B.: FKBP5: A Key Mediator of How Vertebrates Flexibly Cope with Adversity. Bioscience. 70, 1127–1138 (2020)
Zannas, A.S., Wiechmann, T., Gassen, N.C., Binder, E.B.: Gene–Stress–Epigenetic Regulation of FKBP5: Clinical and Translational Implications. Neuropsychopharmacology. 41, 261–274 (2016)
Hartmann, J., et al.: Mineralocorticoid receptors dampen glucocorticoid receptor sensitivity to stress via regulation of FKBP5. Cell. Rep. 35, 109185 (2021)
Lee, R.S.: Glucocorticoid-Dependent Epigenetic Regulation of Fkbp5. in Epigenetics and Neuroendocrinology vol. 1 97–114 (2016)
Yamada, K., Ono, M., Perkins, N.D., Rocha, S., Lamond, A.I.: Identification and functional characterization of FMN2, a regulator of the cyclin-dependent kinase inhibitor p21. Mol. Cell. 49, 922–933 (2013)
Okada, J., et al.: TBC1D8B, a GTPase-activating protein, is a novel apoptosis inducer. Biomed. Res. 42, 95–102 (2021)
Calisi, R.M., Austin, S.H., Lang, A.S., MacManes, M.: D. Sex-biased transcriptomic response of the reproductive axis to stress. Horm. Behav. 100, 56–68 (2018)
Austin, S.H., et al.: Isolating the Role of Corticosterone in the Hypothalamic-Pituitary-Gonadal Transcriptomic Stress Response. Front Endocrinol (Lausanne) 12, 120 (2021). (2021)
Yurtsever, T., et al.: Temporal dynamics of cortisol-associated changes in mRNA expression of glucocorticoid responsive genes FKBP5, GILZ, SDPR, PER1, PER2 and PER3 in healthy humans. Psychoneuroendocrinology. 102, 63–67 (2019)
Zimmer, C., Hanson, H.E., Martin, L.B.: FKBP5 expression is related to HPA flexibility and the capacity to cope with stressors in female and male house sparrows. Horm. Behav. 135, 105038 (2021)
Peppi, M., Kujawa, S.G., Sewell, W.F.: A corticosteroid-responsive transcription factor, promyelocytic leukemia zinc finger protein, mediates protection of the cochlea from acoustic trauma. J. Neurosci. 31, 735–741 (2011)
Menke, A., et al.: Dexamethasone Stimulated Gene Expression in Peripheral Blood is a Sensitive Marker for Glucocorticoid Receptor Resistance in Depressed Patients. Neuropsychopharmacology 2012 37:6 37, 1455–1464 (2012)
Ruan, J., Li, H.: Fast and accurate long-read assembly with wtdbg2. Nat. Methods. 17, 155–158 (2020)
Putnam, N.H., et al.: Chromosome-scale shotgun assembly using an in vitro method for long-range linkage. Genome Res. 26, 342–350 (2016)
Guan, D., et al.: Identifying and removing haplotypic duplication in primary genome assemblies. Bioinformatics. 36, 2896–2898 (2020)
Wu, T.D., Watanabe, C.K.: GMAP: A genomic mapping and alignment program for mRNA and EST sequences. Bioinformatics. 21, 1859–1875 (2005)
Song, L., Sabunciyan, S., Yang, G., Florea, L.: A multi-sample approach increases the accuracy of transcript assembly. Nat. Commun. 10, 1–7 (2019)
Kang, Y.J., et al.: CPC2: A fast and accurate coding potential calculator based on sequence intrinsic features. Nucleic Acids Res. 45, W12–W16 (2017)
Tarailo-Graovac, M., Chen, N.: Using RepeatMasker to Identify Repetitive Elements in Genomic Sequences. Curr Protoc Bioinformatics 25, 4.10.1–4.10.14 (2009)
Flynn, J.M., et al.: RepeatModeler2 for automated genomic discovery of transposable element families. Proc. Natl. Acad. Sci. U S A. 117, 9451–9457 (2020)
Waterhouse, R.M., et al.: BUSCO Applications from Quality Assessments to Gene Prediction and Phylogenomics. Mol. Biol. Evol. 35, 543–548 (2018)
Meng, G., Li, Y., Yang, C., Liu, S.: MitoZ: A toolkit for animal mitochondrial genome assembly, annotation and visualization. Nucleic Acids Res. 47, e63–e63 (2019)
Kurtz, S., et al.: Versatile and open software for comparing large genomes. 5, 12 (2004)
Gu, Z., Gu, L., Eils, R., Schlesner, M., Brors, B.: Circlize implements and enhances circular visualization in R. Bioinformatics. 30, 2811–2812 (2014)
Andrews, S.: FastQC A quality control tool for high throughput sequence data. (2010). http://www.bioinformatics.babraham.ac.uk/projects/fastqc/
Bolger, A.M., Lohse, M., Usadel, B.: Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 30, 2114–2120 (2014)
Dobin, A., et al.: STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 29, 15–21 (2013)
Liao, Y., Smyth, G.K., Shi, W., FeatureCounts: An efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics. 30, 923–930 (2014)
Langfelder, P., Horvath, S.W.G.C.N.A.: An R package for weighted correlation network analysis. BMC Bioinform. 9, 1–13 (2008)
Robinson, M.D., McCarthy, D.J., Smyth, G.K.: edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 26, 139–140 (2010)
Krämer, A., Green, J., Pollard, J., Tugendreich, S.: Causal analysis approaches in Ingenuity Pathway Analysis. Bioinformatics. 30, 523–530 (2014)
Zhang, B., Kirov, S., Snoddy, J.: WebGestalt: an integrated system for exploring gene sets in various biological contexts. Nucleic Acids Res. 33, W741–W748 (2005)
Zuberi, K., et al.: GeneMANIA Prediction Server 2013 Update. Nucleic Acids Res. 41, W115–W122 (2013)
Sievers, F., et al.: Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. Mol. Syst. Biol. 7, 539–539 (2014)
Waterhouse, A.M., Procter, J.B., Martin, D.M.A., Clamp, M., Barton, G.J.: Jalview Version 2-A multiple sequence alignment editor and analysis workbench. Bioinformatics. 25, 1189–1191 (2009)

Table 1

Assessment of the Lapland longspur (Calcarius lapponicus) genome assembly.
Lapland longspur genome	Summary
Total Length (bp)	1,154,813,007
Counts of scaffold sequences	457
Largest scaffold length	157,814,153
Scaffold N50	72,367,164
Counts of N50	6
Scaffold N90	14,454,378
Counts of N90	22
GC content (%)	42.72%
N Length	106,522
N content (%)	0.009%
Counts of contigs	1,492
Maximum length of contigs	10,135,631
Contig N50	1,995,792
Counts of contig N50	174
Contig N90	427,527
Counts of contig N90	625

There is NO Competing Interest.

SuppTableS1repeats.xlsx
Supplementary Table S1
TableS2.xlsx
Supplementary Table S2
SuppTableS3chrassign.csv
Supplementary Table S3
ZWuNsupplementarymaterialstemplateLALO.pdf
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Response strategies to acute and chronic environmental stress in the arctic breeding Lapland longspur (Calcarius lapponicus)

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Abstract

Figures

Main

Results

Assessment of genome assembly

Comparative genomic analysis with zebra finch

Gene expression profile

Genes differentially expressed between life history stages under normal conditions

Differentially expressed genes associated with extreme spring

The snowstorm-induced stress response during incubation

Hypothalamic-Pituitary-Adrenal axis tissues

Comparison of differential gene expression

Lapland Longspur FKBP5

Discussion

Methods

Lapland longspur genome assembly

Evaluation of genome assembly

Comparative genomic analyses

Sample collection

RNA preparation and sequencing

Detection of differentially expressed genes

Functional Annotation

FKBP5 gene network and protein structure prediction

Declarations

Data availability

Contributions:

Competing interests:

Funding

Acknowledgments

References

Tables

Additional Declarations

Supplementary Files

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