The pigeon circovirus (PiCV), first described in the literature in the early 1990s, is considered one of the most important infectious agents affecting pigeon health. Thirty years after its discovery, the current review has employed bibliometric strategies to map the entire accessible PiCV-related research corpus with the aim of understanding its present research landscape, particularly in consideration of its historical context. Subsequently, developments, current knowledge, and important updates were provided. Additionally, this review also provides a textual analysis examining the relationship between PiCV and the young pigeon disease syndrome (YPDS), as described and propagated in the literature. Our examination revealed that usages of the term ‘YPDS’ in the literature are characterizations that are diverse in range, and neither standard nor equivalent. Guided by our understanding of the PiCV research corpus, a conceptualization of PiCV diseases was also presented in this review. Proposed definitions and diagnostic criteria for PiCV subclinical infection (PiCV-SI) and PiCV systemic disease (PiCV-SD) were also provided. Lastly, knowledge gaps and open research questions relevant to future PiCV-related studies were identified and discussed. Journal of the Association of Avian Veterinarians is a five-sentence report marking the earliest record on circovirus infections in pigeons. The report describes an infection that is morphologically diagnosable by examination of the presence of inclusion bodies in necropsy tissues, commonly or sometime only found in the bursa of Fabricius of the birds. It was noted that the virus is “apparently distinct from the psittacine virus”  a key role in augmenting [the] disease progression” although further proof certainly is needed  were not specific.” Hence, to our reading of the text, there exist virtually two conceptualizations of YPDS: one that is colloquial, and the other that is more technical in its requirements.We would like to note two key points about the paper  at this We contend that these two points are crucial to the developments in PiCV research that followed the abovementioned report, and to the studies that are still to be conducted in the future. As such, we also investigated how this paper and, in particular, the two key points noted above, were referred to, and propagated in the literature. Our underpinning questions are: (1) How is PiCV described to be related to YPDS?; and (2) What is ‘young pigeon disease syndrome (YPDS)’ as operationalized in the literature?Examination of the retrieved texts showed that the relationship of PiCV infection to YPDS has been described in variable terms of associatedness, importance, and/or cruciality. Specifically, the role of PiCV infection to YPDS has been described in some papers as remaining “uncertain” and that it “has not yet been conclusively clarified”, while also described as “speculated as crucial”, “might be crucial”, “crucial”, “probably associated”, “associated”, “strongly associated”, “important”, “central”, “one of the putative factors”, “a key factor”, “one of the causative agents”, “among the major causative agents”, “the likely aetiological agent”, and “likely causal agent” in other records. Likewise, other texts also described PiCV infection to be “caus[al of] YPDS”, or “responsible for YPDS.” While these descriptors are not mutually exclusive of each other, these are also not equivalents. Additionally, the mechanism by which PiCV infection contributes to the development of YPDS has also been described in various descriptions of certainties, ranging from “assumed to induce immunosuppression”, “possibly by immunosuppression”, to “causes profound immunosuppression”. See ,116,117.Unsurprisingly, we also found that the descriptions, definitions, and/or operationalized usage of the term “young pigeon disease syndrome (YPDS)” also vary, corresponding to the above noted colloquial and technical conceptualizations. Indeed, in most texts, YPDS is referred to as a “multifactorial” or “complex disease” characterized by a combination of symptoms (often described as non-specific), with no to variable level of attributed importance to histopathological markers. Consistent with this is our observation that YPDS is used to describe cases (or loft histories) prior to the detection of PiCV infection in numerous reports. In others, YPDS is a category of viral infections. These are all also while other records explicitly state that YPDS is a consequence of PiCV infection and subsequent immunosuppression, or categorically require specific conditions for YPDS diagnosis, such as the observation of both clinical symptoms and PiCV-specific histological lesions, presence of viral particles, and a demonstration of the presence of a large amount of viral DNA. See ,113,117.Chlamydia, Escherichia coli, Salmonella, and other parasites in varying combinations with varying degrees of support [Problematizing PiCV infection and its pathology, YPDS, and their correlation with each other is not new to this review. These have been, in fact, subjects of several discussions and opinions before. For instance, Dr. Henk de Weerd pointed out, “As you know, about 10 years ago ‘everyone’ (except me) was extremely alarmed about the circovirus. According to many, that was the cause of ‘just about everything’ in terms of pigeon outbreaks, including ‘adeno-coli’,” which, still according to Dr. de Weerd, is internationally known as the “Young Bird disease’’ . Similar support ,115,120. support .Most recently, experimental infection with the newly discovered and isolated pigeon Rotavirus A belonging to a novel clade also produced clinical manifestations that were also described as YPDS-like ,121. SomTherefore, the questions of what YPDS is, what its relation to PiCV infection is, and how this term has been used and propagated in the literature, which reflects how veterinary health practitioners and researchers perceive YPDS, need critical assessment. The current review revealed that usages of the term YPDS in the literature are neither standard nor equivalent. YPDS in the literature is a diverse range of characterizations.In consideration of the complex history of YPDS and PiCV infection, our current opinion is that YPDS, as colloquially conceptualized pointed above—that is, a combination of non-specific clinical symptoms, may still be useful as a putative diagnosis pending identification of the causative and/or confounding agents. We are also under the impression that PiCV must be categorically identified as a causative agent of the diseases that we propose below to be identified as PiCV subclinical infection (PiCV-SI) and PiCV systemic disease (PiCV-SD).From the early days of PiCV research, this virus has been consistently compared and likened to other related pathogens, most remarkably to porcine circovirus (PCV) and the associated disease formerly referred to as post-weaning multisystemic wasting syndrome (PMWS), in terms of basic biology, to genetic diversity, pathobiology, and approaches to treatment and control ,90,93,99Additional parallels between PiCV infection and PCV2 infection can be long, but we would like to highlight several important points of similarities related to pathology, and disease. Like PiCV, early experimental studies with PCV2 sought to demonstrate the capacity of the virus to produce clinical signs compatible to PMWS. In PCV2, successes were achieved mostly after inoculation with another infectious or non-infectious agent. Together with other field observations, these studies lead to the conclusion that PCV2 is immunosuppressive and a “necessary but not sufficient factor to develop the clinical disease” ,123. WhiFrom this perspective of the current understanding of PiCV and YPDS research, and guided by the lessons from PCV2 and PCVD, we draw our proposed PiCV diseases, as shown in Based on epidemiologic and case studies conducted in multiple countries across the world, PiCV infection is quite common among both domestic and feral pigeons, with some reports identifying that most young pigeons in an affected loft would have been infected before reaching 1 year old. Previously performed experimental infections of pigeons with PiCV infected tissue homogenates, as well as documented naturally infected pigeons without clinical symptoms, confirm that while no clinical manifestations were observed, characteristic histopathologic lesions were present in challenged pigeons ,93,105. On other hand, PiCV-SD, like PCV2-SD, is a multifactorial disease in which PiCV is the strictly required factor. Like PCVDs, as PiCV has also been noted for its wide distribution similar to PCV2, cases of PiCV-SD must be considered to be of complex causality. This proposal, therefore, functionally reclassifies Duchatel’s and Szeleszczuk’s conceptualization of YPDS as PiCV-SD. As such, the previously reviewed clinico-pathological picture of PiCV infection with clinical manifestations and characteristic histological lesions would also be descriptive of PiCV-SD ,23. In tFurthermore, the current proposal is not to deny the involvement of other infectious agents in PiCV-SD. For instance, consistent with Schmidt et al. (2020), the discovery of pigeon Rotavirus A does not contradict to its potential importance in the development of what we are currently proposing as PiCV-SD. What this proposal is trying to resolve and formalize is the importance of and necessity for PiCV infection to pigeon health and the development of a particular disease. In extension, the proposal recognizes the pathogenicity of PiCV, perhaps, best evidenced by the demonstration of the virus’ apoptotic ability to B lymphocytes confirming its immunosuppressive function ,92.Guided by our examination of the literature, we are of the view that this proposal is the best step moving forward as an attempt to lodge a more cohesive approach to dealing with matters important to pigeon health management, and in extension, to scientific research.Perhaps, among the most important research gaps in PiCV research is still the absence of a reliable and replicated method for isolating this viral agent. We, however, note two records reporting propagation of PiCV ,112 achiIn Sahindokuyucu et al. (2022), inoculation of tissue homogenates positive for PiAdV-A and PiCV into the chorioallantoic cavity and yolk sack of the SPF embryonated eggs yielded PiCV-positive allantoic fluid at the fifth passage. None of the other tested viruses were positive by PCR. Additionally, cultivation of the virus from the same tissue homogenate using primary chicken embryo fibroblast also yielded PiCV- and PiADV-A-positive supernatant. It was, however, unclear how many cell culture passages were performed . MeanwhiParticularly, the establishment of a reliable isolation protocol for PiCV would enable future research on the more basic understanding of the biology of this virus, including its specific mechanisms for entry, replication, and release, as well as the functions of the other proteins encoded its genome. In particular, the role of ORF C3 protein in PiCV is still unclear. In PCV2 and duck circovirus, ORF C3 has been confirmed to induce lymphocyte depletion by apoptosis ,125,126.On the other hand, with the reported detection of PiCV in other animals, particularly in ticks, it is tempting to speculate whether all or some of these species are hosts (definitive or intermediate) or a vector of PiCV. With most of these reports relying on partial sequences, it remains to be shown if these detected infections are of a different subtype or strain, or perhaps a completely new circovirus species that is a very close relative sharing a common origin with PiCV. Nevertheless, these reports seem to be a prelude to studies needed to uncover what might be a cross-species jumping in play in PiCV evolution, together with its implication to PiCV epidemiology, pathobiology, and genetic diversity. More than thirty years after the discovery of PiCV, there is still a lot to learn about this viral agent.


RNA is a fundamental biomolecule that has many purposes within cells. Due to its single-stranded and flexible nature, RNA naturally folds into complex and dynamic structures. Recent technological and computational advances have produced an explosion of RNA structural data. Many RNA structures have regulatory and functional properties. Studying the structure of nascent RNAs is particularly challenging due to their low abundance and long length, but their structures are important because they can influence RNA processing. Precursor RNA processing is a nexus of pathways that determines mature isoform composition and that controls gene expression. In this review, we examine what is known about human nascent RNA structure and the influence of RNA structure on processing of precursor RNAs. These known structures provide examples of how other nascent RNAs may be structured and show how novel RNA structures may influence RNA processing including splicing and polyadenylation. RNA structures can be targeted therapeutically to treat disease. Arabidopsis, which suggested that RNA structures changed significantly from the nascent RNA to the mature transcript [in vivo versus RNAs extracted from the cell [The sequence of an RNA influences its biological activity. Sequence information still predominates as the most studied aspect of a nucleic acid. However, due to its single-stranded and flexible nature, RNA naturally forms structures as soon as it is synthesized by an RNA polymerase, reviewed in ,2. Thereanscript . Structuanscript . In yeasthe cell . Direct the cell ,12.de novo models of RNA structure, both secondary and tertiary, is questionable [Experimentally based secondary structural models of RNAs can be created with enzyme and chemical probing data, reviewed in . In parttionable .in vivo systems is that most methods depend on a significant number of molecules to quantify reactivity  RNA has a hairpin element at the 5′ splice site of exon 10  is normally spliced at an equal ratio, resulting in mix of mature isoforms with either 3 or 4 microtubule binding domain repeats, which code for Tau proteins with different biological activity [MAPT exon10–intron10 junction directly overlaps with the 5′ splice site and can be disrupted by disease-associated variants [MAPT hairpin blocks normal spliceosomal recognition by the U1 snRNP at the 5′ splice site and causes exon skipping and formation of the shorter 3R MAPT mature transcript [MAPT, including SMN2, VWF, ATM and BCL2L1 [There is no self-evident reason to assume that the structures that RNA molecules form are inherently functional. However, careful research has identified many functional RNA structures and their mechanisms. RNA structures can act to block recognition motifs. Short hairpin motifs that block recognition of the 5′ splice site are common and one of the earliest known structures to functionally impact splicing ,45, revi exon 10 A. MAPT iiewed in . A splicactivity . A hairpvariants ,50. The anscript   precursor RNA has a global fold important for splicing [AdML RNA prevents it from being recognized efficiently by the U1 snRNP in in vitro studies. FRET analysis confirms that the 5′ and 3′ splice sites are in close proximity in the normal structure, but not in poorly spliced mutants with altered structures [AdML precursor RNA the global fold is not dependent on proteins. Similar studies of the human HBB (β globin) precursor RNA support the role of global RNA structure in recruiting U1 and promoting splicing [HBB RNA is influenced by binding of SRSF1 protein as a structural stabilizing factor  precursor RNA [PLEC contributes to a prometastatic cellular environment and is associated with progression and poor prognosis in breast cancer [MBLN1 precursor RNA is autoregulated by MBLN1 protein binding at primarily unpaired YGCY motifs close to the 3′ splice site [MBLN1 RNA restructures a distal branchpoint and results in exon 5 skipping and an isoform of MBLN1 protein with different subcellular localization [MBNL1 exon 5 has also been shown to bring the 3′ and 5′ splice sites into close proximity [RNA structures can be recognized specifically, often in combination with sequence elements. Small nuclear ribonucleoprotein polypeptide A′ (SNRPA1) recognizes intronic sequence-independent stem structures combined with sequence-dependent loops to promote splicing of cassette exons in multiple genes, including rsor RNA . SNRPA1 t cancer . MBLN1 pice site   precursor RNA has been extensively mapped for structural elements [SMN2 splicing studies have focused on exon 7, which can either be skipped or included. Two terminal stem-loops (TSL1 and TSL2) within exon 7 and several structures within intron 7 influence splicing  and long (MCL1-L) RNA and protein isoforms with either pro- or anti-apoptotic functions [SMN2 RNA splicing, very little is known about how RNA structure influences RNA processing in most other nascent RNAs, including MCL1. We use SMN2 and MCL1 nascent RNAs as examples to discuss how to identify functional nascent RNA structures for validation.All RNAs have structural patterns, many of which are functionally important, yet it can be difficult to identify which RNA structures are relevant without prior knowledge. Unlike many nascent RNAs, the survival of motor neuron 2  databases, such as the ENCORE dataset in ENCODE . In the m ratios  , TSL2 and TSL3, all of which have been shown to structurally influence SMN2 splicing . The U1A/SNF/U2A″ family is an example of RRM-containing proteins that can recognize structured RNA . U1A is nylation ,119 (Tabnylation . Althougnylation .Double-stranded RNA-binding domains (dsRBDs) generally have strong structural preferences for their target RNAs. The cleavage factors Drosha and Dicer both contain dsRBDs and interact with precursor microRNAs through structure-dependent mechanisms ,124. MicWhile many RBPs have been extensively studied and their specificity determined, there are more than 2000 predicted RBPs in the human genome, and many ‘moon-lighting’ proteins with unrecognized RNA binding potential, reviewed in ,113. HigWhile the sequence of an RNA is the primary input for many computational structure models, biological regulation affects the structure of RNA in the cell. One mechanism of regulation is the speed of transcription. Slow transcription by RNA pol II can influence the folding of known RNA structures, such as the hairpin structure at the 3′ end of histone transcripts . OverallNucleotide modifications are another cellular mechanism that can impact RNA structure. There are about 180 known types of RNA nucleotide modifications . Two comRNA-binding helicases can change RNA structures in the cell. At least eight helicases, including DEAH-box helicases DHX19, DHX38, DHX8 and DHX15, are essential for splicing in human cells, reviewed in ,146. TheArabidopsis, which suggested that RNA structure changed significantly from the precursor RNA to the mature transcript [in vivo versus purified from cells have been documented in yeast [There is evidence that RNA structure is altered at different stages of an RNA molecule’s life cycle. Liu et al. found major differences between nuclear and cytoplasmic RNA structures in anscript . Structuanscript . Differein yeast . These sMAPT, when exon 10 is skipped the mature transcript produces a Tau protein with three rather than four microtubule binding domains  can be designed to alter RNA structure, reviewed in ,160. In cription . The SARcription . One strcription . A similcription . Toeholdcription ,166 and cription . There icription . Other hcription .SMN2 splicing at exon 7, reviewed in [Although hybridization offers a straightforward mechanism of structure change, ASOs are difficult to deliver to human tissue, whereas small molecules are generally more tractable for medical treatment. Small molecules can target and stabilize or destabilize specific RNA structures, reviewed in . The capiewed in . Althougiewed in ,178. Modin vivo; these methods can be broadly applied to target functional RNA structures, including those that regulate RNA processing.RNA molecules are naturally structured. Due to the low abundance, long-length, and flexible nature of nascent RNAs, precursor RNA structure is understudied. New structural methods are continuing to advance our technological capabilities and document structures within precursor RNAs. In particular, chemical probing and cryo-EM methods have expanded our understanding of secondary and tertiary structures. However, even when structural models are available, it is difficult to identify functional structures and understand their mechanisms. Structures within precursor RNAs determine how nascent transcripts interact with protein and nucleic acid co-factors. By influencing these interactions, RNA structure influences RNA processing pathways. Most studies have focused on the impact of structure on splicing and polyadenylation, but future research may tell us more about how RNA structure affects other processing pathways like RNA editing. Due to their impact on processing, RNA structures impact gene expression and play a role in disease. We are beginning to develop antisense oligonucleotides and small molecule methods to alter RNA structure


Recently, a euthanasia method using an inhalant anaesthetic ‘nitrogen gas in foam’ (ANOXIATM) has gained further interest as it is claimed to be more animal-friendly. However, little is known on its effect on subsequent RNA analysis. There are also no studies on the interaction effect of these euthanasia methods and different tissue storage conditions such as RNAlater™ or snap freezing using liquid nitrogen on RNA measurements. It is very important to investigate if the choice of euthanasia method in research animals as well as the tissue storage condition impact RNA measurements. The current study compared the two euthanasia methods in male piglets and found that the nitrogen anoxia technique (ANOXIATM) could be a suitable alternative to T-61® based on RNA quality parameters because no differences were detected. Storage in RNAlater™ significantly increased the RNA integrity in comparison to snap freezing, but no interaction effect of the euthanasia methods and storage conditions on RNA measurements were detected.Animals have been used as subjects in biomedical research for a long time. In these studies, tissue collection for RNA profiling is often essential, and hence, the animals are euthanised using standard injectable anaesthetics such as sodium pentobarbital or T-61TM), has gained considerable interest as it claimed to be more animal-friendly. However, it is not clear whether the use of this euthanasia method has an influence on RNA measurements. Moreover, there are no studies on the interaction effect of different euthanasia methods on the tissue sample storage conditions. The current study compared RNA measurements from two euthanasia methods (ANOXIATM vs. T-61® injection) and two storage conditions (RNAlater™ vs. snap freezing) in 12 male piglets. The nitrogen anoxia method had a significantly higher RNA yield (p < 0.01) compared to the T-61® method. However, no effect of the euthanasia methods on the A260/230 ratio and RIN value was observed. Tissues stored in RNAlater™ had significantly higher RIN values (p < 0.001) compared to snap frozen samples. The present study could not find a significant difference between the two euthanasia methods in piglets, with regard to RNA quality measurements. Hence, the nitrogen anoxia technique (ANOXIATM) might be considered as a suitable alternative to T-61® for euthanasia of piglets used in research.Animals used in research often have to be euthanised, especially when tissue sampling is essential. Recently, a euthanasia method, utilizing an inhalant anaesthetic ‘nitrogen gas in foam’ in an anoxia box (ANOXIA Exposure to N2 gas in a stable anoxic atmosphere is considered to be a suitable alternative method for euthanasia in pigs .Samples were collected from one-week-old purebred Pietrain male piglets , where nitrogen gas in foam was used as the euthanizing agent. The manufacturer’s guidelines were followed for operating the anoxia box—ANOXIATM [® injection . T-61® is a combination drug, constituting the ingredients embutramide, mebozonium iodide, and tetracaine hydrochloride. Embutramide induces narcosis and respiratory depression, while mebozonium causes nondepolarizing muscular paralysis. Tetracaine is a local anaesthetic. This euthanasia method involved a primary sedation step using xylazine–ketamine injection (dose: 10 mg/kg intramuscular (i.m) injection of 10% ketamine + 2 mg/kg i.m injection of 2% xylazine), followed by T-61® injection (Dose: 0.3 mL/kg intracardiac). Death was confirmed by auscultation of the heart and also by verifying absence of vital reflexes.Six animals from the experimental group were euthanised using an anoxia box- ANOXIAANOXIATM . Six anilaterTM and stored initially at 4 °C for 24 h and subsequently at −20 °C or (ii) initially snap frozen in liquid nitrogen (LN2) and thereafter stored at −80 °C, until RNA extraction. Pituitary samples were collected as soon as possible and were stored exclusively in RNAlaterTM because of the small tissue size. The hypothalamus, DIR and SIR were collected, split into two and stored in both RNAlaterTM and LN2. Heart, lungs, liver, testis, CM and kidneys were sampled on two time points to assess the influence of sampling time on RNA yield and purity. This was to simulate sampling under field conditions and to assess the decay of RNA over time. Care was taken to avoid the puncture site while sampling heart tissues from the T-61® group, but there could be a bias introduced due to the intracardiac administration of the anaesthetic. Samples were collected and stored (i) as soon as possible after collection, within 30 min after euthanasia (early) and (ii) with a delay of 60 min after euthanasia (late). These samples were also split and stored under both conditions. Time elapsed from the start of the euthanasia procedure to the moment of death confirmation and the point of sample storage was recorded (in minutes) for each sample. Time of sampling was defined as the time difference (in minutes) between the point of sample storage and confirmation of death. Samples collected from each tissue type were thus grouped based on the euthanasia method, type of storage condition and the time of sampling. A total of 708 samples, comprising the ten different tissue types, were sampled and stored for RNA extraction. A detailed description of the sampling scheme is given in the Tissue sections from the hypothalamus, pituitary, heart, lungs, liver, kidney, inguinal rings–superficial (SIR) and deep (DIR), cremaster muscle (CM) and testis were collected immediately after euthanasia. SIR, DIR and CM were collected as a part of the pilot experiment conducted prior to a subsequent study on cryptorchidism. Each tissue sample, except from the pituitary, was split into two (size less than 0.5 cm in thickness), and the split samples were stored under two different conditions, (i) in RNA® Evolution tissue homogeniser . RNA was extracted using the Qiagen RNAeasy Mini Kit  and was eluted in a volume of 50 µL RNAse-free water. A measurement of RNA concentration (ng/µL) could be used as an indirect estimate of total RNA extracted, as they are directly correlated, and RNA samples used in this experiment were eluted in a similar volume. The RNA concentration (ng/µL) and purity, measured as A260/230 ratio and A260/280 ratio, were estimated using a SimpliNanoTM Spectrophotometer . The samples were selected randomly for RNA extraction, and RNA measurements from 489 samples were eventually available for further analysis. RIN values were measured for a subset of samples (n = 56) representing the tissue samples of pituitary, hypothalamus, lungs and liver  was included as a covariate in the models for evaluating RNA purity and integrity while time of sampling and tissue weights (in milligrams) were included as the covariates in the model for RNA concentration. However, tissue weights were only available for 271 samples that were used in this analysis. The models were also tested by including the ‘time of sampling’ as a categorical variable (early vs. late) instead of as a covariate. A Box–Cox power transformation was required for the dependent variable ‘A260/230 ratio’ in order to meet the assumption for a linear model—‘normality of the residuals’. First, a full model, including all interaction terms, was fitted and based on the nonsignificant results; a simpler model with only the main effect could be retained. Tukey’s post hoc test  and JamoTM group had a higher mean RNA concentration  and RIN values  compared to the T-61® group . Both the quality parameters, A260/230 and A260/280 ratios, had comparable measurements between the two groups. Similarly, on comparing the two storage conditions, samples stored in RNAlaterTM had a higher average RNA concentration than the ones stored in LN2. Moreover, the A260/230 ratios for the samples stored in RNAlaterTM had substantially higher values compared to the samples stored in LN2. Moreover, the variation among the quality parameters was generally lower with RNAlaterTM storage compared to storage in LN2  was analysed. The ANOXIArefer to .The tissue from kidney, liver, pituitary and testis had higher average RNA concentration compared to that from CM, heart, hypothalamus, lungs, DIR and SIR A. Detailn = 25) that was discarded due to poor RNA measurements, followed by inguinal rings DIR and SIR and heart (N = 9 each). Detailed descriptive data on the retained samples are given in ® group. The filtering step removed 63 samples see . CremastDetails of discarded samples belonging to the two euthanasia groups and storage conditions are plotted in Upon fitting the full model, the interaction effects were nonsignificant, and hence, a simple model with only the main effects was fitted. The final model was as follows:p < 0.001), whereas no significant effect of the storage condition was observed on the response variable (p = 0.49). The results indicated that the sampling time did not have a significant effect on the RNA concentration. Post hoc analysis results arrived at a significantly higher RNA concentration for tissues representing the nitrogen anoxia group  compared against the T-61® group . Similarly, significantly higher values were associated with samples representing the tissue from testis, kidney and liver. The results from the Tukey’s test are plotted in The results from the linear model analysis and ANOVA are given in Since the residuals did not follow a normal distribution, a transformation to the power 4 was made on the A260/230 ratio before carrying out the full model analysis (see p < 0.001). However, no significant effect (p > 0.1) of euthanasia method and storage condition was observed on the response variable. Sampling time did not have an influence on the response variable, and in addition, none of the interaction terms in the model was significant. These results were also cross checked visually by looking at the relationships between the variables using pairs plot . However, euthanasia method and time of sampling did not have any influence on the estimated RIN value. Upon comparison of the mean RIN values, samples stored in RNAlaterTM had a significantly higher value  compared to LN2 . Results from the post hoc analysis comparing the tissue types also confirmed the differences in RIN values among the different tissue types (Results indicated that the storage condition and tissue type had significant effect on the integrity of RNA (ue types . p < 0.001) further confirms the equivalence between ANOXIATM and T-61®. However, the 90% confidence interval for the mean RIN values fell slightly outside the range of upper bound, together with a nonsignificant t-test (p > 0.07) for the upper bound TOST interval. The results from the TOST are given in An equivalence test was used to test whether the proof of no difference (nonsignificant) could be interpreted as a proof of equivalence between the predictor variables. Previously, we showed that euthanasia method did not significantly affect the quality parameter A260/230 ratio and RIN value. Therefore, an equivalence test was performed using the A260/230 ratio and RIN value as the response variables and euthanasia method as the grouping variable. The results from the equivalence test  show thaTM and T-61® is tested using the two one-sided ‘t’ tests on A260/230 ratio and RIN values. The greatest of the two p values is taken as the p value of the equivalence test. Since p value for the upper bound of the TOST interval is nonsignificant, an equivalence between the euthanasia methods cannot be proved based on RIN values. The hypothesis of equivalence between the two euthanasia methods ANOXIATM) for euthanasia of piglets used in research. The anoxia method was compared with the T-61® injection method for euthanizing the animals. The yield and quality of RNA extracted from various tissue types were evaluated for this purpose. Since these samples were stored in two different conditions (RNAlater™ and LN2), their interaction effects with the euthanasia methods were also examined.The present study examined the suitability of a euthanasia method that used an inhalant anaesthetic ‘nitrogen gas’ in foam in an anoxia box  and it was conducted only in young male animals.We observed that different tissue types showed varying levels of RNA yield, which can be attributed to be due to their different metabolic activity levels. Liver, kidney, brain and testis have been reported to have a high metabolic rate compared to skeletal muscle and adipose tissue ,35. In tTime of sampling did not have any influence on the RNA quantity and quality parameters compared in this study. This was in contrast to the previously reported results ,3,4 wherlater™ for tissue storage purposes, especially in field settings, have been already mentioned in previous studies [later™ was significantly better than that for the samples stored in LN2. The variation among the quality parameters was generally lower with samples stored in RNAlater™ compared to storage in LN2. Furthermore, more samples that had to be discarded based on quality thresholds were the ones stored in LN2. This could also explain why a significant effect of the storage condition was not detected in our analysis with the filtered data but with the prefiltered data. Time of sampling was found to have no influence on the estimated RIN value. However, it is to be noted that there were no ‘late’ samples included in this analysis, and the average sampling times was around 15 min.Advantages of using RNA studies ,24. The The importance of RNA purity is widely acknowledged, especially for RT-qPCR analyses . RNA conlater™ for storing tissues for RNA studies gains more support from the above reasoning. However, there are recent publications discussing unfavourable effects of RNAlater™ on gene expression profiles [later™ compared to snap freezing. Similarly, storage of Arabidopsis seedlings in RNAlater™was reported to have resulted in altered transcriptomes as a response to osmotic stress caused by the ammonium salts present in the storage media [The importance of RNAprofiles ,40. Uponge media . This waThe findings from our research could not specify any reasons against the suitability of the nitrogen anoxia method for euthanasia of piglets used in research. This euthanasia method has advantages of operational ease and less animal handling. If future research can confirm the claim of improved animal welfare while using the nitrogen anoxia method, as discussed by Lindahl et al. , this ca® euthanasia methods in male piglets based on the RNA quality parameters. However, the yield of RNA was significantly higher for the anoxia method than that observed in the case of the T-61® method. The nitrogen anoxia-based euthanasia method could be a suitable replacement for other euthanasia techniques, but the claims of improved animal welfare must be further validated before making this conclusion.The present study could not find any difference between the nitrogen anoxia and T-61


Tpref) of individual zebrafish (Danio rerio). AS was greatest 2.5°C above Tpref, which was driven by an increase in maximal metabolic rate. This mismatch between Tpref and the optimal temperature for AS suggests that factor(s) aside from AS maximisation influence the thermal preference of zebrafish.Warming is predicted to have negative consequences for fishes by causing a mismatch between oxygen demand and supply, and a consequent reduction in aerobic scope (AS) and performance. This oxygen and capacity limited thermal tolerance (OCLTT) hypothesis features prominently in the literature but remains controversial. Within the OCLTT framework, we hypothesised that fish would select temperatures that maximise their AS, and thus their performance. We tested this hypothesis using intermittent flow respirometry to measure AS at, above (+2.5°C) and below (–2.5°C) the self-selected, preferred temperature ( Summary: Individual zebrafish select temperatures below their optimum for aerobic scope. Global temperatures are increasing and will continue rising until at least 2050 . Rising TOptAS) pushes an animal to its pejus temperatures, resulting in a reduction in physiological performance and biological fitness , where loss of equilibrium or ‘ecological death’ occurs. Given the central importance of AS within the OCLTT framework, we hypothesised that fish would select the environmental temperature that produces the greatest AS at a given time. This hypothesis has previously been tested in other studies: juvenile barramundi  behaviourally select temperatures ∼6°C lower than those that maximise AS (Petromyzon marinus) Tpref and TOptAS roughly align  select a temperature based on AS maximisation by measuring AS at, above, and below the temperature preference (Tpref) of individuals.One temperature-dependent trait that has received a lot of interest from the scientific community over the past two decades is aerobic scope (AS) . AS is t fitness . The furimise AS , whilst Danio rerio AB strain, mass=0.62±0.019 g (mean±s.e.m.), mixed sex, aged 12–18 months] were raised and maintained at The University of Manchester, UK. Fish were held at 28°C in a 14 h light:10 h dark cycle prior to the experiments. Fish were fasted for 12 h prior to experimentation to achieve a post-absorptive state . Two protocols  of 13 zebrafish was determined individually using a custom-built shuttlebox system  or cold (4°C) reservoirs through titanium plates under the base of the experimental chambers. A stick heater was present in each chamber and, when required, was manually used to maintain the 2°C difference between chambers if any drift occurred. A re-circulating pump in each chamber mitigated thermal stratification and was positioned to limit mixing between the warm and cold chambers. Temperature in each chamber was measured every 30 s using an Elitech RC-4 temperature data logger (±0.05°C)   . The finn period . After tN=12) used in the Tpref trials. Each fish had one trial at its Tpref, one 2.5°C above its Tpref and one 2.5°C below its Tpref [Tpref=28.63±0.53°C (mean±s.e.m.); range, 25.55–31.42°C; N=12; −1 prior to AS measurement, which occurred as soon as the test temperature was reached. Trial order was randomised, and a minimum of 24 h left between trials to allow for recovery. The trial order for individual fish was determined using a random number generator.Respirometry trials were performed to assess maximal metabolic rate (MMR) and standard metabolic rate (SMR) to calculate AS. For protocol A, three respirometry trials were performed on the individual zebrafish  to re-oxygenate the system and prevent the waste products of metabolism accumulating. In the wait period, the chamber was functionally sealed so the oxygen content of the water declined linearly based on the rate at which the fish extracted oxygen. Finally, during the measurement phase, the linear decline in oxygen content over time was measured and used to calculate the fish's aerobic metabolic rate. AquaResp 3.0 was used for the automation of aquatic respirometry .Oxygen uptake rate was measured using a custom-built intermittent flow respirometry system. Four 100 ml (including the volume of associated tubing) Plexiglass chambers were submerged in a 60 l header tank that was supplied with temperature controlled (±0.3°C), aerated, filtered (200 µm filter) water from a 40 l buffer tank. Water was mixed within the chambers through a re-circulating loop where oxygen content was measured every second . Each respirometry loop consisted of a 60 s flush phase, 120 s wait period and 300 s measurement phase. During the flushing phase, water from the buffer tank was pumped into the chambers  prior to being transferred through the air (standardised to 30 s) and placed in the respirometry chamber during the wait phase. Chasing typically occurred in the afternoon, following which the fish were left in the dark until the following morning to reach standard metabolic rate (SMR). Owing to the relatively long wait and measurement phases (420 s combined), MMR is likely to be underestimated in our study; however, this underestimation is probably consistent between our groups and accordingly should not influence our conclusions. Prior to, and immediately following each experiment, background respiration in the system was measured (over 1500 s). Following each experiment, the fish were returned to their holding tank and the respirometry system was bleached, drained, rinsed and re-filled for subsequent trials.https://www.r-project.org/) was used to process the respirometry data. MMR was calculated as the steepest slope (r2 ≥0.95) recorded in the first hour of recordings following the chase. SMR was calculated as the lowest 10% of values (r2≥0.90) recorded throughout the remaining portion of the trial. MMR and SMR were background corrected using the linear relationship between background respiration and time to subtract an appropriately weighted background respiration measurement from each oxygen uptake measurement. Background respiration (mean±s.e.m) accounted for 18.6±0.91% and 40.8±2.19% of uncorrected MMR and SMR, respectively. AS was calculated as MMR–SMR using the background respiration corrected data.FishResp v.1.0.4  in R v.4r2=0.87, MMR; r2=0.93) and Gaussian  functions, respectively. AS, SMR and MMR were compared across temperatures using one-way ANOVAs in Prism.The relationships between mean SMR, MMR and AS against temperature were modelled in GraphPad Prism v.9.1.0 with second order polynomial  and lmerTest (https://CRAN.R-project.org/package=lmerTest) mixed effects models in R v.4.1.2 (Tpref) and trial order were included as fixed effects, and individual included as a random effect. The residuals from each model conformed to normal distributions , and pairwise comparisons between temperature groups were computed using emmeans (https://CRAN.R-project.org/package=emmeans) in R v.4.1.2.SMR, MMR and AS when measured 2.5°C above, 2.5°C below and at each individual's  v.4.1.2 . TemperaTpref =28.63±0.53°C (mean±s.e.m.), range 25.55–31.42°C, N=12; TOptAS) , but there was no change in SMR  are known to be misaligned (TOpt for AS and escape velocity in spiny lobster (Sagmariasus verreauxi) larvae, but not juveniles  hypothesis emphasises a key role for AS in the physiology of aquatic ectotherms . OCLTT psaligned , as are uveniles . Given tTOptAS, implying that traits other than AS dictate the preferred, and thus likely optimal, temperature of zebrafish. Previous research supports this conclusion, with a study on juvenile barramundi  measuring Tpref as 31.7°C, but showing an increase in AS up to 38°C (The multiple performances multiple optima (MPMO) hypothesis, like OCLTT, recognises that AS is a temperature-dependent trait that contributes to an organism's biological fitness . However to 38°C .Tpref in individual zebrafish, at the ‘population level’ (here defined as different groups of fish used at each temperature), there was no significant relationship between AS and temperature  owing to increasing MMR and SMR  with similar Q10 values . Superficially, this lack of relationship between AS and temperature appears to contradict the observation that AS is more than 2.5°C above Tpref rather than at Tpref. However, considering the intraspecific variation in Tpref measured in this study , temperatures that were close to Tpref for some individuals would be below, or above, the Tpref of others. This variation in Tpref and likely TOptAS may preclude any population-level relationship between temperature and AS because, across any given temperature change, the AS of individuals within the population would be changing in different ways. Warming waters may therefore be favouring certain individuals within a population, even when population-level effects of warming are not apparent. Zebrafish have a broad TOptAS (TOptAS (Despite AS increasing at 2.5°C above d TOptAS B, as is  (TOptAS , show thOceans and rivers are warming at an unprecedented rate. A recent IPCC report suggests that under all probable emissions scenarios, global surface temperatures will continue to rise until at least the middle of the 21st century, with temperature increases expected to exceed 1.5°C . As wate10.1242/jexbio.243774_sup1Supplementary informationClick here for additional data file.


RIG-I is an essential innate immune receptor for detecting and responding to infection by RNA viruses. RIG-I specifically recognizes the unique molecular features of viral RNA molecules and selectively distinguishes them from closely-related RNAs abundant in host cells. The physical basis for this exquisite selectivity is revealed through a series of high-resolution cryo-EM structures of RIG-I in complex with host and viral RNA ligands. These studies demonstrate that RIG-I actively samples double-stranded RNAs in the cytoplasm and distinguishes them by adopting two different types of protein folds. Upon binding viral RNA, RIG-I adopts a high-affinity conformation that is conducive to signaling, while host RNA induces an autoinhibited conformation that stimulates RNA release. By coupling protein folding with RNA binding selectivity, RIG-I distinguishes RNA molecules that differ by as little as one phosphate group, thereby explaining the molecular basis for selective antiviral sensing and the induction of autoimmunity upon RIG-I dysregulation.  Wang et al. show that RIG-I uses a domain unfolding event to bind strongly with target viral ligands and an alternative, enzymatically active conformation for sensing and discarding inappropriate host ligands, thereby explaining the molecular basis for selective antiviral sensing and the induction of autoimmunity upon RIG-I dysregulation. One of our most important cellular defenses against RNA virus infection is an innate immune sensor known as RIG-I (Retinoic Acid Inducible Gene-I) . This muRIG-I displays exquisite sensitivity in recognizing minute quantities of viral RNA in the cytoplasm and then forming a tight RNA:protein complex that releases specialized signaling domains , which engage in a cascade of downstream interactions leading to induction of interferons and a rapid antiviral immune response . While eBut just as RIG-I must rapidly and faithfully recognize trace quantities of viral RNA, it must ignore the sea of host RNAs that circulate within the cytoplasmic environment. Abundant dsRNA molecules containing 5’-cap structures, 5’-monophosphate groups, 5’-hydroxyls, and a diversity of blunt, and overhanging dsRNA molecules surround the RIG-I receptor at all times. How does RIG-I become selectively activated by trace amounts of viral dsRNAs while remaining dormant within an abundant pool of host RNAs that differ little from their viral counterparts? The selectivity for viral RNAs rests on an extremely narrow set of molecular determinants. For example, the most potent RIG-I agonists are short, 5’-diphosphorylated RNAs (p2dsRNAs), while the most potent antagonists are 5’-monophosphorylated RNAs (p1dsRNAs). Avoiding p1dsRNAs is particularly critical given that microRNAs are frequently presented as uncapped, 5’-monophosphorylated dsRNA molecules . AbundanUntil now, our existing knowledge of RIG-I conformational states and RNA interactions has stemmed from a small set of crystal structures that were obtained under diverse conditions, using RIG-I truncation constructs employed in different laboratories, making it difficult to compare features that enable RIG-I to differentiate host and viral RNAs . DespiteTo uncover the molecular basis for RIG-I ligand specificity, we created a pipeline for solving single-particle cryo-EM structures of full-length, human RIG-I protein in complex with a diversity of dsRNA molecules that represent different types of viral and host RNA ligands. Using this approach, we have obtained a complete collection of high-resolution RIG-I host and viral complexes, capturing RIG-I in complex with its most biologically significant agonist (p2dsRNA) and antagonist (p1dsRNA) ligands for the first time. By visualizing these and many other complexes, including those obtained in the presence of ATP, we reveal that RIG-I has multiple, layered strategies for differentiating host from viral RNA. We find that RIG-I uses two different conformations to recognize host and viral ligand classes, and that the specificity of RNA ligand binding is amplified by coupling it with distinct protein folding events that guide selectivity and downstream behavior of the protein. The resulting set of structural data, combined with mutational functional analysis, provide a coherent, unifying mechanism that explains the exquisite selectivity of this critical antiviral receptor and the molecular basis for its dysregulation in autoimmune disease.To elucidate the structural basis for RIG-I discrimination between host and viral RNA ligands, and to visualize dsRNA:RIG-I structures in solution, we set out to solve high-resolution structures of full-length RIG-I receptor (RIG-I) bound to four different types of blunt-ended dsRNA duplexes: 5’-triphosphorylated dsRNA (p3dsRNA) and 5’-diphosphorylated dsRNA (p2dsRNA), 5’-monophosphorylated dsRNA (p1dsRNA) and 5’-OH-dsRNA (OHdsRNA). While p3dsRNA and p2dsRNA represent ligands that mimic high affinity viral PAMPs, p1dsRNA and OHdsRNA represent classes of RNA that are abundant in the host cytoplasm  forms specific contacts with backbone residues on the “bottom” strand (3’-end) of the duplex terminus. Together with Hel1, the second RecA fold (Hel2), forms a cleft that contains conserved motifs critical for RNA-dependent ATP binding and hydrolysis  and S8. The alpha-helical bundle inserted into Hel2 (Hel2i) is a unique feature of RLRs and metazoan Dicer proteins. This domain engages with 2’-hydroxyl groups on both strands of the dsRNA  and S8. The C-terminal domain (CTD) is critical for distinguishing host from viral RNA, and in all complexes, it is anchored via hydrophobic interactions with the base-paired RNA terminus  and S7. A comparative analysis of the four dsRNA:RIG-I complexes shows that they fall into two distinct architectural categories. Complexes formed between RIG-I and dsRNAs that are abundant in the host (5’-hydroxyl or 5’-monophosphate RNA (OHdsRNA and p1dsRNA)) adopt one type of conformation, while complexes formed between RIG-I and dsRNAs typical of viral RNAs (5’-di- or triphosphate RNAs (p2dsRNA and p3dsRNA) form a different type of conformation. The grouping of these conformational isoforms is now possible because RIG-I structures with p1dsRNA and p2dsRNA are being reported here for the first time, and all four structures were solved under identical conditions, enabling a head-to-head comparison. As described in the subsequent sections, the primary differences between the host and viral conformations of RIG-I are attributable to changes in the CTD and Hel2.A distinctive feature of the “host RNA” complexes is that the helicase motifs in Hel2 are all well folded (this conformation of RIG-I is therefore designated as HostF (HF); 664-RGKTNQNTG-C672, namely HelTNT) docks within the CTD. Remarkably, as described below, HelTNT interacts with the exact same set of CTD atoms that are also contacted by the 5’-diphosphate of pathogen RNAs, suggesting that HelTNT has evolved as an autoinhibitory motif.In addition to the Hel2i alpha-helical bundle, Hel2 contains a second insertion motif that becomes ordered by intercalating into the central pocket of the CTD, where it forms a network of interactions  and 2D. tnt in p1dsRNA complex , which differ in the distance between Hel1 and Hel2 . Intriguormation   conformation. An overlay of the Vu and HF conformers shows that the position of the β-p within the CTD is incompatible with HelTNT insertion, resulting in a disordered HelTNT  and buries a larger surface with Hel2i (1340.7 Å2 versus 1245.9 Å2) by tilting about 1 Å towards Hel2i. Taken together, these findings suggest that the CTD binds tighter to Hel2i in the p2dsRNA complexes in Vu conformation, which will favor signaling over autoinhibition relative to the p1dsRNA complex in HF conformation.A second feature that distinguishes complexes in the VFeatures of the p2dsRNA:RIG-I complex are being presented here, for the first time. While crystallographic studies have resulted in several p3dsRNA:RIG-I structures, p2dsRNA:RIG-I structures have not been available until now, probably due to the lack of commercially-available 5’-diphosphate RNA ligands. Much like 5’-triphosphorylated ligands, α and β phosphates of the diphosphate linkage bind within the “recognition” pocket of the CTD, forming a network of interactions with highly conserved amino acids. A comparison of the p2dsRNA:RIG-I and p3dsRNA:RIG-I structures establishes that the RIG-I CTD interacts with both α and β phosphates in both cases, but interactions are not formed with the γ-p in the case of p3dsRNA, as observed in our previous crystallographic investigation of this complex  and 3F  is partially unfolded and does not have an intact ATP binding site. To examine whether this lack of a folded ATP binding site helps to freeze RIG-I at dsRNA termini, we needed to obtain a cryo-EM structure of full-length RIG-I in the presence of ATP, in an environment where it was free to move around on the RNA lattice. To this end, we designed a 30 base-pair RNA duplex containing a hairpin loop at one end and a 5’-triphosphate at the opposite, blunt terminus .For samples incubated in the presence of ATP, we obtained two different types of complexes. One class of complexes  contain RIG-I bound at the triphosphorylated terminus , where ieviously . Importaeviously , consisteviously  and S15Aeviously , which pu conformation observed in the context of p3SLR30 does not display clear ADP density within the RIG-I active-site  that remains bound at the duplex terminus, while a majority population (76%) moves to the center of the duplex, supporting the notion that the HF conformer is a dynamically active state that readily binds ATP and assists in proof-reading , we were able to capture and visualize these labile complexes. As described above, these structures are strikingly different from RIG-I end-bound structures previously determined. Regardless of the different RNA duplexes (p3SLR30 and OHSLR30) used here, structures of the internally-bound complex are identical  and reduction in intermolecular contacts (from 187 to 135), although a new set of CTD:RNA interactions are established  between internally-bound and the p2dsRNA:RIG-I complex structures reveals that the CTD is tilted away from the helicase core to avoid clashes with the contiguous RNA duplex. Lacking the typical CTD-terminus interactions, there is disorder within the CTD region that contains the phenylalanine that normally stacks on the duplex terminus  is a partially unfolded state of the protein that forms high affinity interactions with viral RNA PAMPs, exemplified by p2dsRNA and p3dsRNA, resulting in potent induction of interferon signaling. The other isoform  is a tightly folded protein that sensitively detects RNAs that are abundant in the host, exemplified by p1dsRNA and OHdsRNA. It makes few contacts with RNA ligands and contains a dynamic motor domain that facilitates rapid recycling from host RNA binding sites, thereby providing an accurate proof-reading mechanism and minimizing signaling from host dsRNAs.RIG-I selectively differentiates viral RNAs from host RNAs by adopting two different conformations, each of which is specific for a different type of RNA . In this way, RIG-I behaves like two proteins in one: Each of the structural isoforms plays critical role in the function of RIG-I. One isoform  that are stabilized through two different strategies: (A). In the Vu conformation, a massive number of interactions are formed between RIG-I and viral dsRNA termini, essentially fusing the CTD and atoms of the diphosphate terminus, consistent with the high binding affinity  explain the physical mechanism of active proof-reading, revealing why host ligands are not conducive to strong signaling. HF complexes form a reduced network of interactions with dsRNA, which is consistent with their fast off-rates . These processes have their own limiting timescales, which in turn could contribute to the regulation signaling. A second limitation is that this study was designed only to capture structural details of RIG-I:RNA recognition specificity, and it was not designed to facilitate observation of the CARDs, nor their interactions with ubiquitin ligases or the MAVS adapter protein. Understanding of these larger complexes and their dynamic interplay awaits future structure-function studies with an expanded scope. A third limitation is that, while this study provides a first glimpse of actively-translocated RIG-I molecules along the dsRNA duplex, direct kinetic studies of this process, particularly involving disease mutants known to be defective in proof-reading, will be needed to understand whether RIG-I movement along dsRNA serves a specific functional role within the innate immune system.anna.pyle@yale.edu).All requests for resources and reagents should be directed to and will be fulfilled by the Lead Contact, Anna Marie Pyle  and p3SLR30:RIG-I (+AMPPNP) complex .Atomic coordinates and cryo-EM maps are deposited in EMDB and PDB as follows: p3dsRNA:RIG-I complex , p2dsRNA:RIG-I complex , p1dsRNA:RIG-I complex , OHdsRNA:RIG-I complex , p3SLR30 end bound complex , p3SLR30 internally-bound complex , OHSLR30 end bound complex , OHSLR30 internally-bound complex  supplemented with 10% heat inactivated fetal bovine serum (HI-FBS) at 37 °C with 5% CO2.The human RIG-I protein was expressed and purified as described previously  were synthesized in-house using an automated MerMade synthesizer  with phosphoramidites from Glen Research using standard phosphoramidite chemistry. Oligonucleotides were deprotected and gel purified as previously described , and evaTo prepare RNA:RIG-I complexes, purified RIG-I and RNA were mixed and incubated overnight at 4°C. The RNA:RIG-I complexes were separated using a Superdex 200 Increase 10/300 GL column . The complexes were pooled in size-exclusion buffer  and concentrated with a 50 kD Amicon Ultra-0.5 Centrifugal Filter Unit (Millipore Sigma). The absorbance was estimated using a Nanodrop (ThermoFisher) for concentration quantification. RIG-I was mixed with p3dsRNA, p2dsRNA, p1dsRNA, OHdsRNA, p3SLR30 and OHSLR30 in an 8:1, 2:1, 1.5:1, 2:1, 1:1 and 1:1 molar ratio, and peak fractions of RNA:RIG-I complex were collected.As in earlier crystallographic studies, we initially isolated complexes and performed single-particle cryo-EM studies on RIG-I in complex with short dsRNA ligands (14 bp). While these complexes resulted in high quality data, we found that lengthening the RNA stem had several technical advantages and it improved our ability to image the particles due to the inherently high contrast of RNA duplexes. To develop a pipeline for routine analysis of RIG-I complexes on dsRNA we designed a set of 24-bp double blunt-ended RNA duplexes containing potential binding sites for RIG-I binding at each end . This de™ Glow Discharge Cleaning System (Ted Pella) for 35 s at 25 mA. With purified RNA:RIG-I complexes (3 µl) applied onto the grids, the grids were blotted and plunged to the liquid ethane for flash freezing using a Vitrobot Mark IV (ThermoFisher). The blotting conditions for all grids were similar, under conditions of 22 °C and 100% humidity with force -4. Blotting times were 4 s, 5 s, 4 s, 4 s, 3 s and 3 s for p3dsRNA:RIG-I, p2dsRNA:RIG-I, p1dsRNA:RIG-I, OHdsRNA:RIG-I, p3SLR30:RIG-I and OHSLR30:RIG-I complexes, respectively. In the case of p3SLR30:RIG-I complex with ATP in solution, OHSLR30:RIG-I complex with ATP in solution and p3SLR30:RIG-I with AMPPNP in solution, 3, 20, 20 µM  of complex was incubated with 2.5 mM ATP/AMPPNP and 5 mM MgCl2 on ice for 30 min before being applied to the grids, and the blotting conditions are the same as p3SLR30:RIG-I complex in the absence of ATP described above. The frozen grids were all transferred and kept in liquid nitrogen prior to use in data collection.To prepare for freezing cryo-EM grids, the fresh samples were obtained and concentrated. The p3dsRNA:RIG-I, p2dsRNA:RIG-I, p1dsRNA:RIG-I, OHdsRNA:RIG-I, p3SLR30:RIG-I and OHSLR30:RIG-I complexes were concentrated to 0.3, 1.5, 0.4, 1.5, 1.5 and 1.5 mg/ml. The Quantifoil holey carbon R1.2/1.3 300 mesh Cu grids (Ted Pella) were glow discharged using the PELCO easiGlow-/pix/s and total electron exposure of 61, 59, 59 and 59 e-/Å2. For the p3SLR30:RIG-I complex with ATP or AMPPNP addition, 3417 and 2744 micrographs were collected at a nominal magnification of 130,000 × and 81,000 ×, corresponding to calibrated pixel size of 1.05 and 1.068 Å/pix, with a defocus range of -1.2 to -2.7 and -1.2 to -3.0 µm. Each micrograph contains 40, 38 frames and was collected with an exposure rate of 9.4, 18 e-/pix/s and total electron exposure of 55, 60 e-/Å2. For the p3SLR30:RIG-I and OHSLR30:RIG-I complexes without ATP addition, and OHSLR30:RIG-I complex with ATP addition, the data were acquired at Yale Cryo-EM facilities on a Glacios transmission electron microscope (ThermoFisher) operating at 200 keV and equipped with a Gatan K2 Summit direct electron detector using SerialEM software at super-resolution mode. 2773, 2264 and 3663 micrographs were collected at a nominal magnification of 36,000×, corresponding to calibrated pixel size of 1.143, 1.149 and 1.149 Å/pix, with a defocus range of -1.2 to -2.5, -1.0 to -2.0 and -0.8 to -2.0 µm. Each micrograph contains 40, 53 and 53 frames and was collected with an exposure rate of 9.3, 6.3 and 6.3 e-/pix/s and total electron exposure of 57, 50 and 50 e-/Å2. The statistics of data acquisition are summarized in Cryo-EM data were acquired at Yale Cryo-EM facilities on a Titan Krios transmission electron microscope (ThermoFisher) operating at 300 keV and equipped with a Gatan K2/K3 Summit direct electron detector using SerialEM software at super-resolution mode. For the p3dsRNA:RIG-I, p2dsRNA:RIG-I, p1dsRNA:RIG-I and OHdsRNA:RIG-I complexes, 3480, 2586, 2460 and 2838 micrographs were collected at a nominal magnification of 130,000 ×, 81,000 ×, 81,000 × and 81,000 ×, corresponding to calibrated pixel size of 1.05, 1.068, 1.068 and 1.068 Å/pix, with a defocus range of -1.2 to -2.7, -1.2 to -3.0, -1.2 to -3.0 and -1.2 to -3.0 µm. Each micrograph contains 34, 38, 38 and 38 frames and was collected with an exposure rate of 7.9, 17.7, 17.7 and 17.7 eAll datasets were processed through Relion . Microgr2, according to the FSC = 0.143 criterion . The particles were rescaled, and subjected to several rounds of 2D classification, 3D classification as described above. An internally-bound and an end-bound complex were generated. The 3D class of internally-bound complex and end-bound complex contained 366,751 and 253,073 particles, which were subjected to 3D refinement, Ctfrefine and Byaesian polishing, following a final 3D refinement  and Chimera (For the p3dsRNA:RIG-I complex, PDB ID 5F9H was used as the initial search model for building into the cryo-EM map, using molrep in the ccpem software suit (The IFN-β induction assay was implemented to test RIG-I mutants as described previously (Statistical details of cryo-EM analyses can be found in 12u and HF conformations. Related to Movie S1. RIG-I distinguishes viral from host RNA duplexes by staying in equilibrium between the V


Saccharomyces cerevisiae, the main organism used for ethanol production. Understanding the main drivers for xylose assimilation and identify novel and efficient transporters is a key step to make the 2G process economically viable.The need to mitigate and substitute the use of fossil fuels as the main energy matrix has led to the study and development of biofuels as an alternative. Second-generation (2G) ethanol arises as one biofuel with great potential, due to not only maintaining food security, but also as a product from economically interesting crops such as energy-cane. One of the main challenges of 2G ethanol is the inefficient uptake of pentose sugars by industrial yeast S. cerevisiae hxt-null strain, EBY.VW4000, harbouring the xylose consumption pathway. Yeast cells expressing the transporters SpX, SpH and SpG showed a superior uptake performance in xylose compared to traditional literature control Gxf1.By implementing a strategy of searching for present motifs that may be responsible for xylose transport and past adaptations of sugar transporters in xylose fermenting species, we obtained a classifying model which was successfully used to select four different candidate transporters for evaluation in the Modelling xylose transport with the small data available for yeast and bacteria proved a challenge that was overcome through different statistical strategies. Through this strategy, we present four novel xylose transporters which expands the repertoire of candidates targeting yeast genetic engineering for industrial fermentation. The repeated use of the model for characterizing new transporters will be useful both into finding the best candidates for industrial utilization and to increase the model’s predictive capabilities.The online version contains supplementary material available at 10.1186/s13068-022-02153-7. For the last few decades, the scientific community has expended efforts to find cleaner energy alternatives to the fossil-based matrix as a means to mitigate the consequences of climate change from the use of said fossil fuels. One strategy is the use of biofuels produced from lignocellulosic biomass, also called second-generation (2G) biofuels. This strategy is desirable, as lignocellulose is found in the cell walls of all plants and allows many different matrices to be used industrially –4.Plant cell walls comprise mainly cellulose 30–50%), hemicellulose (25–3%) and lignin (15–20%) 0%, hemic–8. 2G biXylose consumption follows two pathways: the first, deemed the oxidoreductive pathway, comprises a conversion of xylose into xylitol through the enzyme xylose reductase, followed by a conversion of xylitol into xylulose by xylitol dehydrogenase. The second, called the isomerase pathway, comprises a one-step conversion of xylose into xylulose by xylose isomerase. Both pathways then have a conversion of xylulose into xylulose-5-phosphate by xylulokinase which then follows the pentose phosphate pathway .Saccharomyces cerevisiae due to its resistance to inhibitors, high product yield, and ease of manipulation [The main organism used industrially for these biotechnological applications is the yeast pulation , 10. Howpulation , 11–14.S. cerevisiae has many hexose transporters that are also capable of transporting xylose, such as the Hxt family of transporters and Gal2 [S. cerevisiae, such as Sut1-3 and Xut1 from Scheffersomyces stipitis [Debaryomyces hansenii [Escherichia coli [Regarding xylose transport, and Gal2 –17. Manystipitis , Gxs1 anstipitis  and XylHhansenii . Detailshansenii , 21–24. hia coli –27.Xylose consumption rates decrease when coupled with glucose due to competition of these sugars by endogenous transporters for access to the transport system, where first the organism depletes all the hexoses in its media, and only then slowly metabolizes xylose , 28, 29.Candida sojae [Many studies have been done to describe new transporters from new species , 30–33, da sojae .E. coli xylE.The use of machine learning models to classify and predict has been previously applied to transporters as a means to separate and differentiate functional classes and families , 49, howIn this work, we believe another step was given on facilitating the search for xylose transporters and understanding what the main drivers for xylose affinity are, while presenting a model that can already help to choose the most likely xylose-transporting candidates to take on for wet-lab work, and that with further use will become even more reliable.Saccharomycotina clade available for download in NCBI to try and understand the evolutionary history and adaptations of different yeasts that conferred an ability or not to ferment or consume xylose (manuscript in preparation). Orthofinder [We selected 182 genomes , and the other 11 for an inability to transport xylose (Xylose-0) , statistical oversampling techniques were implemented to reduce this imbalance. Standard classification metrics were done to evaluate the model, such as receiver operating characteristic (ROC) and precision-recall graphs, in addition to the confusion matrix which allow visualization of the absolute number of samples in each class correctly or incorrectly predicted by the model. The ROC curve showed higher increments of true positive rate than of the false positive rate, which means that the model efficiently classified the positive samples from the testing dataset (AUC = 0.95 for both classes) without losing much precision. Similarly, the precision-recall analysis showed an average precision of 0.73. However, due to the initial imbalance against the positive class, and to reduce overfitting issues arising from oversampling, we sought to remove this bias by analysing the data more attentively, modifying how results were judged and giving more weight to precision. As the default model Model 1) classification threshold is 0.5 to assign a sample to each class , we manually edited (Model 2) so that only samples with prediction probabilities of 0.98 or higher were classified as xylose transporters. At the cost of classification power for true xylose transporters , we were able to almost nullify false positives for this class and thus increase precision and decrease the false positive rate  through BLAST and the MFS HMM from PFAM database . All seq’s clade . SuL washabitans , SpX, Sppora sp.  or HXT5 ubhashii , throughCandida intermedia and Candida sojae, respectively, were also part of fam10, which highlights the potential of homologs in other yeast species that are seldom explored. Caution was taken when the model had not displayed these known xylose transporters in its output, however, on closer inspection of the prediction probabilities, this happened due to the increased restriction on the classification (a 0.95–0.96 threshold would have included them).All 25 sequences were from fam10. Interestingly, known xylose transporters such as Gxf1 (Caninterme1096), Cs4130 (Cansojae5099) and Cs3894 (Cansojae522) from Spathaspora candidates and explained by it probably being an adaptation inherited from their common ancestor during speciation, the Sugiyamaella transporter also contains valine at this site while also having diverged from the clade containing Spathaspora much earlier during these yeasts’ evolutionary history. This pattern might indicate convergent evolution at this site.Additionally, five of these 25 sequences were found to have positive selection evidence on one codon, at protein alignment site 856, which by Interpro analysis and posteriorly by 3D modelling, was observed to be positioned on the extracellular non-cytoplasmic domain of the first helix. This result indicates that these proteins had amino acid substitutions potentially functioning as adaptations related to the xylose fermenting phenotype through their recent evolution and thus emphasizes the importance of these sequences on xylose metabolism. Due to the size of fam10 and the heterogeneity of transporters contained in it, the alignment on this site was mostly indels for most species, however, many interesting patterns were found at the positively selected codon. Firstly, being part of the non-cytoplasmic region, this site was contemplated on the HMM feature, which was also the most impactful for the machine learning model. Secondly, as can be seen in Fig. S. cerevisiae , mannose, fructose, glucose, and galactose. The transformants were grown for 24 h to the exponential phase on Maltose and spotted in tenfold serial dilutions onto solid culture medium. All transporters, except for SuL, were able to confer growth of EBY_Xyl1 on all sugars, indicating a substrate promiscuity commonly seen for sugar transporters. However, SuL was especially surprising as growth in fructose, mannose and glucose was almost non-existent Fig. a.Fig. 3SThe growth of EBY_Xyl1 mutants carrying transporter genes and Gxf1 as positive control were also compared in solid medium with 1, 2, 3 and 5% of xylose as the sole carbon source. All transporters were able to confer growth in all sugar concentrations, and the four transporters showed higher growth than Gxf1 at higher xylose concentrations . Based on the results shown in Fig. Due to the performance results from the spot-assay in different C6 sugars, simultaneous consumption of xylose and glucose by SuL was evaluated by fermentation of a mixture of 10 g/L each of xylose and glucose., Glucose was entirely consumed on the first 4 h of experiment, while xylose was slowly consumed during the same period, only increasing after glucose depletion. After 20 h, cells expressing GXF1 also demonstrated more efficiency in transporting xylose than SuL.xylE crystallographic structure bound to xylose or glucose as a comparison basis [xylE having the highest. These results are partially supported by the comparative fermentation in xylose, in which these affinity patterns can be seen on xylose consumption rate and cell growth. All transporters’ calculated docking affinity to glucose was higher than to xylose, indicating the typical behaviour of substrate promiscuity and preferential uptake of glucose.All four transporters and Gxf1 were modelled through RoseTTAFold in Robetta server  for compon basis . Figure on basis . All seqDescribing novel transporters is an important step to help unravel the underlying causes in which a sugar transporter is able to transport xylose while another does not show this capacity. The use of computational approaches, such as with machine learning or comparative genomics, have become powerful tools in this search effort. Some algorithms have been proposed to predict different transporter classes based on their function to facilitate classification and categorization , 71, howPichia kudriavzevii, which on a first literature screen for the 182 yeast phylogenetics study appears as a species incapable of xylose transport. A second screening showed that they are able to utilize xylose [The use of oversampling techniques coupled with increasing the prediction threshold were able to create a trustful model which identified 25 potential xylose transporters, from which four were experimentally validated. This shows that, even with a restrictive baseline threshold, many transporters from a diverse group of species were returned, highlighting the potential of different microorganisms, many of them rarely studied with an applied biotechnological view, in supplying candidate genes for bio-industrial applications. Regarding other sequences not chosen for further investigation, some transporters were surprising to appear as positive from our model, such as Pickudriav5544 and Pickudriav5977 from e xylose , which iDebaryomyces fabryi Xylhp (Uniprot accession Q64L87). Another interesting feature was GFV tripeptides, which are located on transmembrane portions of the transporters, but their direct relation to xylose transport is unclear. Nonetheless, all predicted transporters had this tripeptide conformation ranging from 1 to 3 groups. PSSM and the custom HMM features highlight and hint that there is a hidden motif associated with xylose affinity, which due to the nature of the boosting algorithm was not yet humanly interpretable, but with future improvements of the model may come to light. These in silico results are also in accordance with previous experimental works that have also shown that xylose affinity is correlated with sequence alterations, key motifs, and amino acid interactions with the sugar ligand [The model also highlighted 13 features as most important for its predictive capability, from which two, PSSM profiling and AAindex, were also used found in previous studies . Interesr ligand , 37.Spathaspora chosen candidates and the Sugiyamaella transporter, as Sugiyamaella diverged much earlier but still has the same adaptations as the Spathaspora transporters. Moreover, the HMM feature was created on the non-cytoplasmic domains of the known xylose transporters and the site under positive selection is also on one of these non-cytoplasmic domains, again highlighting an overlap between evolutionary marks and more recent sequence attributes. As mentioned before, this site is located on the N-terminal region of the first transmembrane helix, which may have some function associated with stabilizing the rocker-switch mechanism when the transporter is active. Mutating this residue in future studies could help to understand more its role for sugar transport, as structural studies of MFS transporters, such as xylE, have focused on mutating amino acids associated with the predicted sugar binding sites [One interesting pattern that we detected posteriorly to choosing the candidates was that SpX, SpG and SpH were on the same clade on the fam10 phylogeny and also form a monophyletic clade on the species’ phylogeny, highlighting an overlap of past adaptations  and recent patterns . This pattern overlap can also be seen on the probable convergent evolution of the site found under positive selection between the 3 ng sites , 73, 74.S. cerevisiae strains adapted to industrial conditions would be required to further validate these candidates. Fermentation assays were also interesting, as SpX, SpG and SpH were all slightly more efficient than the widely used Gxf1 transporter, with SuL lagging. Co-fermentation assays of SuL and Gxf1 showed that the latter has a slightly superior consumption pattern of xylose, using glucose during the first 4 h, and only then using xylose, as expected. Gxf1 also conferred higher growth in the C5 sugar. Again however, an evaluation on an industrial strain with higher xylose and glucose concentrations on the media would reveal the industrial potential of these candidates, as coupled with spot-assay results these patterns suggest that these four xylose transporters can expand the repertoire for industrial use and build a strong case for the model’s use on prospecting novel candidates. Also, even though SuL showed a lower consumption rate both in xylose and during co-fermentation when compared to Gxf1, the lack of growth in glucose and fructose as seen in the spot-assay is a rare phenotype for sugar transporters and could indicate an interesting target for mutagenesis or directed evolution aiming to increase its xylose consumption rate. By using a combination of sugar transporters with different affinities, a future industrial yeast strain could metabolize C6 and C5 sugars more effectively, increasing the viability of the 2G process.Regarding the experimental validation, spot-assay results were surprising for SuL, as there was almost no growth in C6 sugars glucose, fructose, and mannose, while growth in xylose and galactose was restored. Yeasts expressing the four candidates also showed greater growth compared to Gxf1 on concentrations above 10 g/L of xylose indicating that the chosen candidates could be viable for industrial use, as lignocellulosic biomass contains higher xylose concentrations than the condition where Gxf1 is comparable to the other proteins , 76, whiAs an attempt to consolidate all results obtained in this study, the 3D structure of the experimentally evaluated transporters was created, with docking analysis coupled with glucose and xylose. Docking analysis successfully modelled both xylose and glucose poses for the evaluated transporters, which gave us a bigger confidence on the affinity calculations. These affinity results were confirmed during fermentation, where slight growth differences were seen in accordance with the slight differences in predicted affinity.Finally, with future advances on describing novel xylose-transporting proteins and the increase of sequenced genomes, the model can be improved and become an important tool for researchers on helping to prospect industrial candidate transporters.S. cerevisiae, the main organism used for this kind of fermentation. The coupled machine learning and comparative genomics approach presented here yielded several xylose transporter candidates, from which four were experimentally tested against a wide range of sugars. The dimensionality reduction by feature selection highlighted that the most important features were related to HMM and PSSM profiles, indicating that xylose transport can in part be explained by amino acid patterns in the non-cytoplasmic domains of the proteins, especially the pore and binding sites, a result also seen in previous point mutation studies and descriptions of known transporters’ structures, indicating the conformity of the model with previous studies in literature. All transporters tested successfully transported xylose, most of them in rates superior to the traditional Gxf1, one of the best-known heterologous xylose transporters in literature, and all conferred higher cell growth at larger xylose concentrations. Docking analysis showed a similar pattern, with SuL having the lowest affinity to xylose and the other three transporters having a slightly higher affinity than Gxf1.2G ethanol is a promising energy matrix alternative for current and future needs. One challenge for this technology’s viability is an efficient and uninhibited transport of pentose sugars into yeast cells, which drives the search for novel and capable xylose transporters to be engineered into industrial Arabidopsis thaliana. We believe that not only researchers interested in prospecting novel xylose-transporting candidates for industrial application can already make use of the model to aid their selection of best targets for wet-lab evaluation, but also understanding xylose transport on a broader scale then fungi and bacteria, with the help of this model, it will be possible to better understand and reveal the intricacies of xylose transport.For future studies, the model’s predictive capability should be provided with data arising from new xylose transporter characterizations, as well as attempts to create models by adding information of known xylose transporters from other organisms, such as Genomes were retrieved from NCBI, based on if they were the representative genome for that species. 30 genomes had no coding sequences prediction, and so had their genes predicted by using AUGUSTUS 3.3.2  and GeneGenes were clustered into gene families by means of Orthofinder 2.2 . TranspoMachine learning modelling usually undergoes the following steps: data clean-up and division into training and testing datasets, feature extraction and selection, model training, and evaluation. After clean-up, all intermediate steps are done on the training dataset and evaluation is done with the testing dataset. This separation of training and testing allows for a faithful evaluation of a model’s metrics by isolating some of the data in such a way that testing is done on part of the dataset upon which the model has no bias. Also, all these steps can be done using different machine learning algorithms and it is recommended to test several models using different algorithmical approaches and selecting the best performant. Feature extraction in the case of protein modelling represents decomposing the amino acid sequence into different descriptors that either mathematically explain the sequence or highlight some trait of interest, while feature selection is used for dimensionality reduction, computational optimization and highlighting the importance of certain features for classification.Debaryomyces fabryi (Uniprot accession Q64L87) to predict domains and important sites through Interproscan; then, the non-cytoplasmic domains and probable sugar binding sites were isolated from the alignment and the HMM profile was created. We assumed as all sequences are aligned, the binding sites would be roughly in the same position. Other features added were the PS0021 and PS00217 sugar transport signatures from PROSITE database [Sugar transporters from fungi and bacteria were retrieved from Uniprot and TCDB databases. CD-HIT  was donedatabase  using Scdatabase , the protrain_test_split with 70% used for training and 30% for testing.Following feature extraction and clean-up, sequences were divided into training and testing datasets through scikit-learn’s 0.21.2  train_teFeature selection was done by Recursive Feature Elimination with Cross-Validation (RFECV) by using a Gradient Boosting Decision Tree classifier, implemented by XGBoost 0.82 . FeatureSome statistical transformations using oversampling were attempted to mitigate dataset imbalance, at the cost of some overfitting of the data. Additional file Model evaluation was done through usual metrics, such as accuracy, precision-recall, AUC, ROC curve, Balanced Accuracy and MCC, however, we were also attentive to brute numbers, because of the dataset imbalance distorting metric results. False positives were penalized by increasing the classification threshold of the xylose-positive class to 0.98, and this restrictive model was used for choosing candidates. The four transporter families from our phylogenomics dataset had the 13 most important features calculated and the model was ran. Sequences were chosen based on our knowledge if their respective species is a known fermenter or consumer of xylose.https://gitlab.com/Matt_BF/Xylose_Transporter_ML.All code used for model creation and data engineering can be found at XYL1 and XYL2 from S. stipitis and an additional copy of xylulokinase (XKS1) under control of different promoters of the glycolytic pathway of S. cerevisiae as previously described [THD1 gene and further transformed into EBY_Xyl1 through the LiAc/SS-DNA/PEG protocol [Strain EBY_Xyl1 was constructed from EBY.VW4000 by inserting an expression cassette containing the genes escribed . Synthesprotocol . TransfoYeast cells were grown on liquid YP medium (10 g/L yeast extract and 20 g/L peptone) supplemented with 20 g/L D-glucose (YPD) for cell propagation or 20 g/L D-xylose (YPX) for xylose growth analysis. Transformed cells were grown at 30 °C in complete synthetic media YNB  supplemented with 1 g/L drop-out without uracil, 20 g/L glucose and 20 g/L agar . YP was 600 of 10. Fermentation experiments were performed aerobically in 250 mL Erlenmeyer flasks using 70 mL of YNB supplemented with 5 g/L of casamino acids, 1 g/L of tryptophan (Sigma) and 10 g/L of xylose. For simultaneous glucose and xylose co- fermentation, 10 g/L of both sugars were used. The cells were incubated at 30 °C/200 rpm. Experiments were performed in triplicate and samples were collected to measure optical density and for HPLC analysis.Yeast strains were pre-grown on YNB supplemented with 5 g/L of casamino acids (Difco), 1 g/L of tryptophan (Sigma) and 50 g/L of d-maltose for 24 h. Cells were then harvested by centrifugation, washed three times with sterile water and resuspended to an ODMolecular docking analysis was done using Autodock-Vina 1.1.2, ran via UCSF Chimera 1.15. Transporter structures for Gxf1, SuL, SpG, SpH and SpX were modelled through ROSETTAFold via the Robetta server , with thAdditional file 1:Table S1. Information of the species used for comparative genomics, including xylose fermentation or consumption capacity, and associated publication describing.Additional file 2: Table S2. Xylose transporters used as positive samples for machine learning, including Uniprot accession, organism and publication describing xylose utilization.Additional file 3: Table S3. Primers used to validate transformation of EBY_Xyl1.Additional file 4: File S1.S. cerevisiae Codon optimized sequences for the four transporters chosen for characterization in xylose.Additional file 5: File S2. FASTA files of the four transporter families from the comparative genomics analysis.Additional file 6: Figure S1. BUSCO results for gene prediction.Additional file 7: Figure S2. UMAP spatial distribution of data after oversampling.