Modal choice is a prominent concept within transport studies. However, the term is often used quite loosely, with little known about the factors lying behind the choice, the alternatives available to a person, and whether the person had a ‘choice’ to begin with. This study draws on a travel survey among older people living in Sweden’s large metropolitan regions. The questions posed as part of this survey facilitate a greater insight into the processes at play behind modal choice. An analysis of the differences between: (1) the range of modal options available to respondents and (2) the modes selected from this range  is presented. An analysis of the respondents’ reasoning for choosing the modes they did and not the others they could have chosen is also presented. It was found that more than a quarter of respondents have the option to use and actually use all modes for everyday travel. The car is more inclined to be selected among those who have a range of different modal options. Suitability and comfort are the two main reasons given for modal choice. More positive reasons are given for actively selecting walking and cycling, whereas the motives behind the selection of the car instead tend to be framed as reasons for not selecting other modes. Adaptive preference and adjustment effects are also apparent in the selection processes. This study gives us a deeper understanding of the intricate mechanisms and reasoning at play behind the process of modal choice among this group. In this way, we have a better basis for shaping and implementing measures to promote and encourage sustainable mobility, in such a way that the well-being of older people is also supported. The importance of having the possibility to participate in society while ageing has been studied and documented in several different contexts ,2,3,4, aHaving a range of alternatives from which to choose is widely considered to be beneficial for a person’s well-being , is becoming a more prominent focus . This poevaluation and selection of the best alternative, as deemed ‘best’ by the individual herself.The Capability Approach (CA) has been receiving increasing attention as a conceptual framework in transport research ,20 not lrange of options—and the individual’s selection from this range—is a more appropriate realm of analysis [Choice is a central concept in the field of transport and mobility, with choice—particularly modal choice—a key aspect of many studies e.g., ,25. Reveanalysis .A person’s choice set with respect to mobility is largely considered to reduce in size in tandem with the ageing process ,27, ofteMaintaining the modal choice set in as far as possible could be highly beneficial for older people, in particular older people who have a more limited range of options from which to choose in comparison to what they may have had during earlier stages of life. It has been found that modal options are important in terms of facilitating the fulfilment of travel needs and activity wishes, while a lack thereof can have the opposite effect ,38. A liWhile potential mobility  and realized mobility (encompassing the mode(s) chosen) among older people have been explored in previous studies e.g., ,38,39, iThis study examines the process from potential to realized mobility among older people, with a specific focus on modal choice. The differences between the range of self-reported modal option(s), the modal option(s) selected from this range, and the reasons behind the selection of some modal options over others are explored. Links and exchanges between the experienced so-called ‘real’ options and realized behavior are then analyzed. In this way, we gain a greater insight into the different dynamic elements at play in the process from potential to realized mobility , and the different forms of reasoning associated with the selection of different kinds of options.This study draws on a capability-based travel survey among older people (aged 65–79) and living in Sweden’s large metropolitan regions; Stockholm, Gothenburg and Malmö. This meant that the survey was purposefully designed and shaped to capture elements of the CA. People within this age group (65–79) tend to have a greater scope for action than those aged 80 and above . The queSweden’s three large metropolitan regions (LMRs), Stockholm, Gothenburg and Malmö, comprised the study area. These three LMRs are geographically delimited based on commuter patterns and movement between the central municipality and outer municipalities . For thiA stratified random sample of 3200 people aged 65–79 and living in Sweden’s LMRs was drawn from a population register, with contact details containing a combination of landline and mobile phone numbers. Eleven people opted out before data collection began. A further two people opted out while data collection was underway. Interviewing was conducted by a small group of trained interviewers at a well-established and ISO-certified market research company. The interviews were carried out using CATI, allowing for more interviewer-respondent interaction so as to keep misunderstandings to a minimum and to facilitate a two-way communication process, when compared to other methods of data collection .n = 383 in each LMR). The reasons given for not participating were (1) the person has a principle of not participating in surveys (29%); (2) the person did not answer (the phone) when they were contacted (18%); and (3) the person did not have time to participate when contacted (13%). All interviews were carried out in Swedish and over the phone. This meant that some people who were contacted could not participate. Among those who could not participate, 5.7% had difficulty speaking, spoke unclearly or did not speak Swedish. See [The interviews were conducted over the course of a three-week period during November-December 2015. The progression of the data collection was followed in order to gauge whether respondents understood and answered the questions in a consistent manner. From the remaining sample, attempts were made to contact 2119 people. These people were randomly selected but the probability of being selected changed successively with the completion of each additional interview. From those contacted, 1150 agreed to participate in the study, resulting in a response rate of 54% . In total, 1149 valid interviews were conducted (ish. See  for a moa)A descriptive analysis of the differences between the range of modal options available to respondents and the modes then selected from this range .b)A descriptive and explorative analysis of the reasons behind the selection (and use) of said modes and the links between such reasoning and the different forms of selection processes.For this study, the focus lay on the process from potential to realized mobility among older people, with a specific focus on modal choice, and the reasoning behind the selection (and use) of specific modes over others. This meant that the elements of the material related to ‘capabilities’ and ‘functionings’ were the main focus. The analysis of this material was divided into the following parts:As part of the survey, respondents were asked to report which modal options they have the possibility to use for their everyday travel within the local region, even if they never use them. For this analysis, driving a car, using public transport (PT), walking and cycling were included as modal options. This material formed the basis for the ‘scope for action’, or the range of options from which individuals could choose. It is important to note that whether or not the individual considers it possible to use different modes of transport is highly subjective and is purely based on the perceptions of the individual. As such, two people who, for instance, appear to have all the same pre-conditions to cycle  may report different possibilities to cycle. This could be considered problematic in the sense that comparisons between individuals are not straightforward. However, the individual’s perception is the key aspect of interest here, as it reflects the situation as perceived by the individual and thus, her perceived capability with respect to mobility.The respondents were then asked to state which options from this range they actually use, indicating ‘realized mobility’ and ‘modal choice’. Here, although the respondent may have reported having a range of modal options from which to choose, she might perceive that she does not actually have a choice with respect to the mode she then uses. Similarly, the term choice may be perceived differently by respondents, with some perhaps reporting that they have the freedom to choose, and others reporting that they do not, despite being subject to the exact same circumstances. Respondents were then asked to give some reasons for why they opt to use these specific modal options and not the other modal options they could have selected. These reasons were processed in part as pre-codes. These were reasons, based on literature review, that were predicted to be given by respondents in advance of the survey being conducted. Respondents could also give open-ended reasons or accounts for their selection. It was possible to give several different reasons for selection, both in the form of pre-codes and open-ended answers. This meant that those who stated that in some way one mode was ‘more suitable’ than another often expanded upon their reasoning in the form of an open-ended answer. The actual formulation used in the open-ended answers was analyzed in a qualitative manner, while the information provided was subsequently coded into usually 2–3 different codes. Many respondents gave a combination of several pre-codes as well as an open-ended explanation. The prevalence of the different kinds of codes given by respondents were analyzed and subsequently combined into themes. The unique combinations of different codes provided were also analyzed, as was the actual wording given by respondents (in the open-ended element) for their modal choice. This material formed the basis for the analysis of the motivation and reasoning behind different forms of modal choice.The approach and analysis for this study can be regarded as a combination of quantitative and qualitative methods. The material obtained through the survey allowed for comprehensive, broader, descriptive analyses. However, such analyses are somewhat superficial in nature in terms of the actual meaning for respondents. The subsequent, more qualitative analyses allowed for a deeper and more detailed insight into the nuances behind the respondents’ reasoning, allowing us to a gain a clearer understanding of the processes at play.Below is an account of the results for each part of the analysis.An outline of the various combinations of modal options, choice (use) and the frequency of the different choice processes among respondents is presented in More than one-quarter of respondents (28.8%) belong to the same category; having and using all four modal options. This category can be considered to comprise those at a relative multimodal advantage to the remainder of respondents .By looking at the selection processes taking place among the second and third largest categories, it is clear to see that cycling is the most ‘sensitive’ with respect to selection; it being the first option to drop off among all the modal options. Those cycling, walking and using the car, yet choosing not to use public transport despite it being regarded as a modal option is relatively large (7.1% of respondents).Those who consider that they have walking, cycling, public transport and the car as modal options, yet selecting to use just the car is a larger group than expected, although still relatively small at 2.4%. Those with walking and cycling alongside the car as options yet choosing the car comprise a further 1.4%, with those with public transport as an option yet choosing the car making up a further 1%. Those who have the possibility to cycle yet choose the car comprises a further 0.5%, while those with both cycling and public transport as options yet choosing the car makes up another 0.3%. This means that the group actively selecting the car among a range of modal options amounts to 5.6% of respondents. The proportions of respondents actively selecting the other modal options among their respective ranges are much smaller, with the combinations of their respective categories largely only making up a fraction of the combined category actively selecting the car.Not all respondents have options, with 1.0% of respondents stating that they have no modal options whatsoever (or not answering the question). Several respondents state that they have just one or two options, and yet do not use those one or two options. Others state that they have just one option and use that one option.Some of the socio-demographic differences in modal choice processes are presented in While having cycling as an option, yet choosing to walk and use public transport was linked to an overrepresentation of women, suggesting that the sensitivity of cycling for modal selection may be gendered. Actively selecting to use the car despite having other options is associated with an overrepresentation of men, suggesting that this too may be gendered. Those with lower incomes appear to be overrepresented in the use of cycling, walking and public transport as modes of transport (as a unique combination) while those cohabiting appear to be more inclined to actively select the car. However, this is just intended to give a brief overview of the sample. Further, more detailed statistical analyses are required in order to draw any robust conclusions regarding the differences outlined.An outline of the different kinds of reasons given by respondents for their modal choice is presented in more suitable. Almost one-third (30.6%) of those who answered this question gave this as their one and only reason for choosing the mode(s) they did, while this reason accounted for 36.3% of mentions, either as a sole reason or combined with other reasons. When combined with other reasons or open-ended answers, ‘more suitable’ appears to be related to matters of convenience, and the mode fitting the purpose intended, be that using public transport in order to avoid difficulty getting parking, or opting to walk because activities lie within walking distance. The explanation attached to the pre-code more suitable meant that both positive and negative reasons could be coded in this way i.e., choosing a mode due to it being more efficient in terms of time or not choosing another mode because it does not suit the purpose of the trip. The second most common sole reason given was this/these mode(s) being more comfortable than the one(s) not chosen (at 15.2%). This reason accounted for 23.1% of the total mentions. Several respondents also mentioned a combination of just these two reasons (more suitable and more comfortable) (7.9%).The most common reason given by respondents for choosing the mode(s) they use over the mode(s) they could have used was that they are better for the environment/for environmental reasons. However this was usually combined with at least one other reason, and rarely given as the only or main reason, often combined with enjoyable or exercise/wants to move as much as possible. At the same time, enjoyable, less expensive and better for the environment/environmental reasons also commonly featured as a combined reason for modal selection. These combinations of reasons were often linked to the selection of more active modes  when the open-ended statements were analyzed.A rather small proportion of mentions (1.5%) related to choosing mode(s) because they are Furthermore, people mainly mentioned exercise as a reason when choosing to walk or cycle. However, this was also given as a reason for choosing to use public transport, with public transport trips sometimes described as comprising an element of exercise. Exercise when walking to/from public transport was even given as a reason for choosing one form of public transport over another e.g., opting for a longer walk to the train station instead of walking to a nearby bus stop.Health reasons/poor health/no energy as their sole reason for modal selection . Having poor health was often combined with another reason and was rather rarely given as a reason on its own.Fewer than expected reported There was, in fact, another more positive relationship with health linked to modal selection, with 1.4% of mentions related to exercise, with several stating that they make this specific selection as exercising is healthy.faster/speed (being faster than other modes) as their only reason for choosing the mode(s) they do over the modes they do not, with the proportion of mentions accounting for a more considerable 6.4% when combinations with other reasons were included. Being less expensive/financial reasons was also mentioned by surprisingly few (3.2% of reasons mentioned). Interestingly, these two reasons  were often given in combination with one another. When focusing on the open-ended reasons, being ‘inexpensive’ often meant that a person had, for example, already paid for the car and did not want the extra expense of paying for public transport, or indeed, that a person had already bought a monthly or yearly public transport ticket and wanted to make the most of it. This was also related to choosing to walk and cycle because these modes do not cost anything, in the eyes of some respondents. There was only one mention of free public transport and this was by a person living or travelling in Gothenburg.A rather small proportion (2.2%) mentioned Proximity as a sole reason for modal selection represented a larger proportion of respondents than expected , with 1.0% mentioning a combination of suitability and proximity for their modal selection.flexible, depending on the situation, using all options or the strategic use of modal options accounted for 1.7% of sole mentions , while 2.3% of respondents mentioned having no choice/not much choice or having no alternative(s) as their sole reason for choosing the mode(s) they do. A small proportion (1.9%) explicitly stated that they have no need for alternatives, and gave this as their only reason for modal selection. While 2.4% stated that the sole reason behind their use of these modal options was simply that these were the options available to them. Some of the open-ended answers alluded to this being the life (style) that was chosen; their modal options were limited, but this was a conscious decision. All of the reasons encompassed in these codes allude to having a limited choice set. However, interestingly, the individuals’ perceptions of their limited choice set differ considerably, with some expressing implicit dissatisfaction ) and others expressing implicit satisfaction  despite perhaps having the same limited range of modal options. Again, the highly subjective nature of the survey means that individuals’ perceptions of what is/is not an option could vary to a considerable extent. Some respondents may report not having a choice with respect to which modes they use despite considering that they do have other modal options. Here, there are two aspects to consider, the differences in the scope for action among respondents, as well as the differences in the perceptions of their scope for action.The reason Respondents were inclined to give more positive reasons for selection when selecting more ‘sustainable’ modes. There was a sense of ‘positive’ or ‘active’ selection of the more active modes, with the environment, health, exercise and enjoyment linked to the selection of active or more sustainable modes. Several who chose walking and cycling explicitly mentioned proximity and accessibility to activities, mentioning that they live ‘strategically’ or ‘centrally’ and do not have any need to use a car when everything—or most activities—are within walking or cycling distance of their residential location. This is in contrast to the selection of the car, with this selection mechanism characterized as more passive, as reasons for not selecting other options, as opposed to direct reasons for selecting the car. Several talked about there being no alternatives; this was their option, and being locked into a lifestyle with this modal option. More than expected talked about complications related to the car i.e., having to find and pay for parking being the most prominent (0.3% of mentions). Fewer than expected mentioned freedom or wanting to have flexibility as a reason for choosing the car. Although a few open-ended responses did imply these aspects:“I come and go without a timetable.”Others took the car as a given or had normalized the use of the car as a sort of ‘de facto’ option, stating that they would only look into other options if it was not possible to use the car. Others questioned why one would even think about using other options when then the car is the only necessary option:“I use my own car as the first choice.”Some mentioned that their lifestyle dictates which mode(s) they use, meaning that having to carry heavy loads or living considerable distances from activities in remote locations result in the car being the only viable option. Fewer referred to not choosing public transport but when they did they talked about the non-existence of public transport, or mentioned that the destinations that could be reached using public transport were very limited, or that the public transport itself  was located too far away.Reliability was mentioned by fewer than expected and was usually given in combination with at least one or two other reasons. In the literature, reliability is often mentioned as an important aspect with respect to modal choice ,47 but aThere were rather clear adaptive preference effects see   influencing their modal choice, e.g., having to give the spouse lifts, or not being able to use certain modes because they usually travel with the spouse. On the other hand, others talked about choosing the car because they had to give grandchildren lifts and had installed car seats for this purpose. Others talked about not being able to use the car and instead using public transport when travelling with grandchildren, or using public transport to reach their own children’s homes to visit them and grandchildren.A number of respondents (1.3% of mentions) discussed tailoring their choice according to their needs. This meant having several options but, for instance, opting to use public transport in the city center or cycling or walking for shorter distances, but when it came to longer distances, using the car or public transport On the other hand, others (rather few) mentioned using the car for shorter distances, simply because they were used to using the car.The codes presented in Only the first recorded reason given by the respondent was used to form the codes, and overarching themes. This was because many respondents gave a range of different reasons (some gave up to eight reasons) that could not be placed into just one coding category or theme. This was in order to limit and simplify the categories produced by the cross-tabulation with the selection processes, presented in The cross-tabulation of the different codes  with the selection processes is presented in Those who actively chose the car were also more inclined to give extra benefits as a reason for choosing the car, although in most if not all of these specific cases, respondents were referring to enjoyment and freedom, and not exercise or the environment. As detailed in The aim of this study was to examine and gain a greater insight into the process of selection from potential to realized mobility among older people aged 65–79, with a specific focus on modal choice. The differences between the range of modal option(s), the modal option(s) selected from this range, and the reasons behind the selection of some modal options over others were explored.One of the main findings was that a sizeable proportion (more than a quarter) of respondents have the option to use and actually use all modes for everyday travel. Although this can be considered a rather positive finding, this group can be considered to comprise those at a multimodal advantage relative to the remainder of respondents, a remainder which in turn comprises the majority.If we are to consider that a greater range of modal options can imply a greater freedom with its inherent links to well-being , then thCycling as a modal option appeared to be the most sensitive with respect to selection; it being the first option to drop off among all the modal options. This is in line with previous findings where cycling has been found to be quite difficult for older people, with reports of issues with traffic safety, concerns with cycling environments and the behavior of other road users ,50. Thisno choice/not much choice or having no alternative(s)) and others expressing implicit satisfaction  despite perhaps having the same limited range of modal options. This finding calls into question the conceptualization of modal choice in empirical studies and underscores the importance of exploring the notions of choice and how choice is framed and deciphered as part of empirical studies. Moreover, there may be differences between an observer’s account of the individual’s choice and the individual’s account of her choice, which can also be somewhat problematic, particularly when attempting to objectively assess differences among groups and between people [There were groups who perceived that they had no choice and just used the one or two options available to them. This could either indicate a difference in modal options, or just a difference in perceptions of modal options and choice among respondents, or indeed, both. This is an important aspect and limitation of subjective material and a focus on perceptions. Similarly, there were very different perspectives on having a limited choice set, with some respondents expressing implicit dissatisfaction   was evidThis study facilitated a greater insight into the process behind modal choice; an insight which is often missing from discussions regarding modal choice. This study allows us to better understand the intricate processes at play and the underlying perceptions of individuals when considering their modal options and modal choice.Linking back to the CA, we can see that there are indeed differences in the ways in which people perceive their options (their capabilities), and in how they evaluate their options and select and operationalize their capabilities (functionings). This is an inherent difficulty with the empirical application of the CA as it means that there are discrepancies when attempting to carry out inter-personal analyses, especially with respect to equity concerns. Nonetheless, the empirical material engaged as part of this study simultaneously allowed for both a broader and more in-depth analysis of the perceptions of individuals with respect to modal options and modal choice.This study did, however, have some limitations. Its cross-sectional perspective did not allow for changes to be traced according to the ageing process. A longitudinal perspective would have aided us in understanding the selection process from an ageing standpoint. No ‘objective’ indicator was included, which would have allowed for a calibration of the self-reported responses, and would have given us a more nuanced perspective on some reports. This study did not explicitly include a geographical perspective. A lack of focus on geographical aspects may have resulted in some important considerations being masked as part of this study. Moreover, no comprehensive or clear-cut measure of subjective well-being was included in the study. This would have allowed for an empirical analysis of the links between modal options, modal choice and subjective well-being. This, in turn, would have facilitated a tighter connection to the CA and its conceptualization of the links between the size of the capability set and well-being. Nevertheless, this study did give a greater insight into the selection and reasoning processes behind modal choice, through the lens of those aged 65–79 and living in Sweden’s LMRs.Through engaging the careful treatment of the process of choice and drawing on information about individuals’ reasoning with respect to the choice process, this study gave us a greater insight into the dynamic aspects at play with respect to modal choice for this group. Findings indicated that certain types of reasoning are related to the selection of specific modal options. Drawing on these types of reasoning and tailoring policy accordingly could prove fruitful in encouraging more sustainable travel behavior, and indeed promoting multimodality among this group. This would require a holistic approach by policymakers, encompassing not just transport policy but also community supports where the possibility to use other modes of transport (other than the car) are highlighted and promoted. For instance, training programs for using public transport could be employed. Solutions in the form of promoting multimodal trips , highlighting the benefits in terms of exercise, experience and the environment could also be developed. In this way, individualized mobility suggestions could be developed so that people do not just use the car out of habit, and instead consider other modes. Not only ‘carrot’ but also ‘stick’ measures could prove effective. Several of the respondents remarked that they actively choose not to use the car because it is difficult to find parking. While others mentioned that they use public transport because they have a monthly ticket and want to make the most of it. Reducing parking opportunities alongside more attractive monthly pricing mechanisms could be another effective means of changing perspectives on modal options and intervening in the selection process.Those who had several options were more inclined to choose to use the car. This is despite car use often being framed as a lack of choice. Importantly, ensuring that the car does not reign as the ‘automatic’ option is key to achieving a modal shift. For instance, making public transport and other modes more usable for people with health problems—through infrastructure, but also increasing awareness of other passengers and drivers—could be a crucial way of establishing such a change.This study showed that the size and form of the choice set is important, and that it does matter whether someone chose from a range of options or instead just had to go with their one and only option. These findings have important implications for the consideration and treatment of modal options and modal choice within research going forward, presenting compelling reasons for including more detailed analyses of the processes behind modal choice. This approach could also be taken further by linking it to measures of subjective well-being, in order to draw clearer links between the scope for action (choice set) and well-being. Here, the focus was on those in the earlier stages of later life. These considerations could become even more tangible during the later stages of the ageing process.By studying and plotting out the process of modal choice, the reasons behind the selection of specific modal options and links between these and other aspects of life, we have a better basis for understanding and planning for older people’s mobility. In this way, measures to promote and encourage sustainable mobility can be shaped and implemented, in such a way that the well-being of older people is also supported.


Obtaining high-quality RNA for gene expression analyses from different seed tissues is challenging due to the presence of various contaminants, such as polyphenols, polysaccharides and lipids which interfere with RNA extraction methods. At present, the available protocols for extracting RNA from seeds require high amounts of tissue and are mainly focused on extracting RNA from whole seeds. However, extracting RNA at the tissue level enables more detailed studies regarding tissue specific transcriptomes during seed development.Brassica napus and B. oleracea were sampled for isolation of the embryo, endosperm and seed coat tissues. Ovules and ovary wall tissue were also collected from pre-fertilized buds. Subsequent to testing several RNA extraction methods, modifications applied to E.Z.N.A. Plant RNA and Picopure RNA Isolation kit extraction methods resulted in RNA with high yield and quality. Furthermore, the use of polyvinylpolypyrrolidone for seed coats and endosperm at green stages resulted in high-quality RNA. As a result of the introduced modifications to established RNA extraction methods, the RNA from all the above-mentioned tissues presented clear 28S and 18S bands and high RIN values, ranging from 7.0 to 10.0. The protocols reported in this study are not only suitable for different and challenging seed tissue types, but also enable the extraction of high-quality RNA using only 2 to 3 mg of starting tissue.Seeds from heart stage embryo to mature developmental stages of Brassica spp reproductive tissue types including pre-fertilization and developing seed tissues for diploid and polyploid species. The high-quality RNA obtained is suitable for RNA-Sequencing and subsequent gene expression analysis.Here, we present efficient, reproducible and reliable high-quality RNA extraction methods for diverse oilseed  Arabidopsis thaliana that has investigated seed development at a high level of detail, focusing on different tissue types and subregions during seed development [Brassica spp, where studies have focused only on the whole seed, specific embryo stages or the late phases of seed development, mainly related to oil biosynthesis and storage seed reserves [Seeds are the most important agricultural product, accounting for at least 70% of the world’s food supply . Seeds aelopment . This isreserves –14.Brassica spp seeds is challenging as they are rich in polyphenols, polysaccharides, proteins and lipids which interfere with or degrade the RNA during RNA extraction procedures. Polyphenols limit RNA extraction as they bind to nucleic acids [Oilseed rape is a crucial source of vegetable oil and protein world-wide, being a crop with high economic importance. Extracting high-quality RNA from ic acids , and polic acids . Removinic acids –20; feweic acids , 21–23. Brassica napus and B. oleracea during seed development for subsequent RNA-Sequencing. Protocols for extracting RNA from different seed tissue types are not only essential to identify and study tissue-specific genes, but also to understand how genes or cellular processes interact among different tissue types. Essentially, these protocols enable an integrated and better understanding of seed development. Moreover, the implementation of these methodologies not only reduces the hours of labour-intensive dissections, but also enables more detailed studies characterizing different seed tissues types and the prediction of gene regulatory networks across seed development.Embryos from the Brassicaceae family follow a well-studied developmental timeline, from globular to heart, torpedo, green and mature embryo seed stages . Herein,Brassica plants were grown under controlled environment conditions with a 16 h photoperiod at 18 °C and 15 °C day and night temperatures respectively, under 400 W HQI lighting (225 µmol m¯2s¯1). The relative humidity was maintained at 70% day and night. Perforated bread bags  were used to enclose inflorescences to prevent cross-pollination from neighbouring plants before the plants initiated flowering. Flowers were manually pollinated, and developing seeds were collected at heart, torpedo, green and mature embryo stages. Flowers were tagged when pollinated to determine the developmental stage of the seed, in addition the embryos within the seed were visually checked to ensure they were at the correct stage of development prior to sampling. Buds were collected 24 h before anthesis to obtain ‘pre-fertilization stage’ ovules and ovary wall tissue  for each embryo using a P10 pipette for transfer to a 1.5 ml microfuge tube. During yo stage . For heaAll the samples were dissected and immediately frozen in liquid nitrogen and stored at −80 °C for total RNA isolation. However, a slight modification was applied for ovules, heart and torpedo stage embryos (TSE). These samples were dissected, collected in 150 µl of RB buffer at room temperature and ground with the help of a micro-pestle in the microfuge tube before freezing in liquid nitrogen and stored at −80 °C. Different tubes with RB buffer were used in order to avoid having the dissected tissues in buffer at room temperature for long periods of time. This methodology allowed the collection and grinding of small tissues without degradation of the RNA. Ovary walls, seed coats from all developmental stages, GSEnd, and green and mature stage embryos  were finely powdered by grinding in liquid nitrogen in a mortar and pestle. For HSEnd and torpedo stage endosperm (TSEnd), no grinding was performed as the endosperm is primarily liquid at both these stages of seed development.Based on the tissue type of interest and the developmental stage of the seed, varying amounts of starting seed material may be required. For smaller embryos as in the heart stage, approximately 100 embryos were required for efficient and high-quality RNA extractions. On the other hand, when the embryos were larger, as in torpedo, green and mature stages, only 50 embryos were needed. See Table The RNA from ovules, ovary walls, GSE, MSE, HSEnd and TSEnd were extracted following the standard E.Z.N.A. Plant RNA protocol, including the DNase I  digestion on column protocol. Nevertheless, some modifications in the amount of RB buffer and the final elution volume were required depending on the tissue type for obtaining high-quality RNA. For ovary walls, and for GSEand MSE, a ratio of 1:12.5 and 1:5 of RB buffer was used. Next, the standard E.Z.N.A. Plant RNA protocol was followed, eluting the RNA in 20 µl and 30 µl of diethylpyrocarbonate (DEPC) water, respectively. The protocol for HSEnd or TSEnd and ovules was slightly modified. For HSEnd and TSEnd, 250 µl of RB buffer with 5 µl of β-mercaptoethanol (β-ME) was added to the collected tissue, and the tube was vortexed vigorously for 2 min allowing the sample to thaw in the RB buffer. For ovules, 100 µl of RB buffer with 5 µl of β-ME was added to the sample to obtain a final volume of 250 µl, before vortexing for 2 min. The standard E.Z.N.A. Plant RNA kit protocol was then followed, with the elution of RNA in a final volume of 30 µl of DEPC water.g at 4 °C. After an incubation at room temperature for 5 min, the supernatant was removed and transferred to a new clean microfuge tube. An aliquot of 0.2 ml of chloroform was added per 1 ml of TRIzol reagent. The microfuge tube was vigorously shaken for 15 s and incubated for 2 min at 30 °C, followed by a centrifugation for 15 min at 12,000g at 4 °C. The upper phase of the partitioned sample was transferred to a new microfuge tube. Then, 0.25 ml of isopropanol was added, mixing the solution by tilting the tube up and down. Next, the sample was centrifuged for 8 min at 12,000g at 4 °C. The supernatant was discarded. The pellet was washed with 1 ml of 75% ethanol and the sample was mixed by vortexing, followed by a centrifugation for 5 min of 7,500g to sediment the pellet. Excess ethanol was removed, the pellet was air dried for 10 min and finally resuspended in 25 µl of RNAse free water . For mature stage seed coat (MSSC) RNA, 100 mg of ground tissue was used following the hot borate protocol combined with RNeasy Plant Mini Kit  as described in [TRIzol reagent  was used for the extraction of green stage seed coat (GSSC) RNA. 1 ml of TRIzol reagent was added to 50 mg of ground GSSC tissue. The tube was vortexed vigorously for 2 min to allow the sample to thaw in the TRIzol reagent. The homogenate was then centrifuged for 5 min at 12,000ribed in , elutingg for 2 min to allow the separation of debris and PVPP from the cell extract. This is a critical step, as any impurity can block the column, interfering with the extraction of RNA. Subsequent steps followed the protocol described in the Picopure RNA Isolation kit manual, but for clarity the next steps of the protocol are detailed here: the supernatant was transferred to a new 1.5 ml microfuge tube, and a 50 µl aliquot of 70% ethanol was added and mixed well by pipetting a few times. The cell extract and ethanol mixture was then transferred to a pre-conditioned RNA purification column . The samples were centrifuged for 2 min at 100g, immediately followed by a centrifugation at 16,000g for 30 s. An aliquot of 100 µl of wash buffer (W1) was added to the purification column followed by a centrifugation of 1 min at 8,000g. Another 100 µl aliquot of wash buffer (W2) was added into the purification column followed by a centrifugation of 1 min at 8,000g. A final wash with 100 µl of wash buffer (W2) was performed by centrifugation for 2 min at 16,000g. If there was some residual wash buffer in the column, a re-centrifugation for 1 min at 16,000g was carried out. After the three washes, the purification column was transferred to a new 1.5 ml microfuge tube. An aliquot of 11 µl of elution buffer was pipetted onto the membrane of the purification column, followed by an incubation of 1 min at room temperature. The purification columns were centrifugated for 1 min at 1,000g followed by a final centrifugation for 1 min at 16,000g to elute the RNA. RNA from three technical replicates was extracted for each sample, combined and treated for DNAse treatments using the TURBO DNA-free kit . The same RNA extraction protocol was followed for HSE and TSE. However, no PVPP was added for these tissues. As HSE and TSE were already ground at the time of tissue collection, aliquots of 20–25 µl for HSE and 10–15 µl for TSE were used directly for RNA extraction. Then, the Picopure RNA Isolation kit protocol was followed as described above.For HSE and TSE, seed coats at all developmental stages and GSEnd, the modifications applied to the Picopure RNA Isolation Kit  protocol described here allowed the extraction of high-quality RNA, with the added benefit of starting with small amounts of seed coat and green endosperm tissue. For seed coats and GSEnd, between 2 and 3 mg of ground tissue was used for RNA extraction. A ratio of 1:2 and 1:3 (v/v) polyvinylpolypyrrolidone  to ground tissue was used, respectively. A 50 µl aliquot of extraction buffer from the Picopure RNA Isolation kit was added, and the samples were homogenized by vortexing for 2 min. Next the samples were incubated for 30 min at 42 °C followed by a centrifugation at 2,000The concentration and quality of the RNA was assessed spectrophotometrically at 230, 260 and 280 nm . RNA quality and integrity were evaluated by electrophoresis on 1% agarose gel and the RNA integrity number (RIN) was determined using an Agilent 2100 Bioanalyzer .Different RNA extraction protocols are optimal for different tissue types. By using E.Z.N.A. Plant RNA kit and modifying the amount of ground tissue, the RB buffer ratios as well as the final elution volume, RNA of high-quality and yield was obtained from ovules, ovary walls, GSE or MSE and HSEnd and TSEnd Table . HoweverSeveral methods were tested for different tissues. The RNA extraction for GSSC and GSEnd using E.Z.N.A. Plant RNA kit for 50 mg of ground tissue resulted in low A260/280 and A260/230 ratios for both tissues, indicating the presence of contaminants in the samples and low RNA yield for green endosperm. RNA from 100 HSE and 50 TSE was also extracted using E.Z.N.A. Plant RNA kit. For these tissues, the RNA purity was high, but the A260/230 ratio and the RNA concentration were low for HSE but not for TSE. The use of the TRIzol reagent method for GSSC using 50 mg of ground tissue resulted in low yields and low A260/280 and A260/230 ratios. The hot borate protocol was also tested with 100 mg of MSSC ground tissue. Although proteinase K was added for the removal of proteins, the RNA obtained presented high levels of polyphenols and polysaccharides contaminants as shown by the low A260/230 ratios. The main constraints of these methods are the high amount of tissue needed for obtaining sufficient high-quality RNA.Brassica spp seed tissue types.The use of the Picopure RNA Isolation kit resulted in better quality RNA for all these tissues. By using aliquots of HSE and TSE, the RNA concentration as well as the A260/280 and A260/230 ratios increased significantly, especially for HSE. For GSEnd and MSSC, using between 2 and 3 mg of ground tissue, higher RNA concentrations and A260/280 and A260/230 ratios were obtained. Due to higher concentration and quality of RNA as well as the small amount of starting material required, the Picopure RNA Isolation kit is a better extraction method for these tissues. This method also worked well for heart and torpedo stages seed coat . In spite of the improved quality of the RNA extracted using Picopure RNA Isolation kit, some contaminants were still present in the RNA samples, as indicated by low A260/230 ratios for green endosperm and seed coats. The addition of PVPP reduced the amount of polyphenols significantly in the extracted RNA from these tissues, increasing the A260/230 ratio from 0.2 to 1.2, and for some samples, to 1.8 (Table Brassica spp seeds accumulate polyphenols in the seed coats and polysaccharides in the endosperm. As the 1:2 or 1:3 ratio (v/v) worked for Brassica spp seed tissues, the amount of ground tissue needed as well as the PVPP and ground tissue ratio may vary whilst extracting RNA from other species.Obtaining high-quality RNA from different seed tissues can be challenging, especially for tissue types that do not have high transcriptional activity and may contain secondary metabolites as contaminants that interfere with RNA extraction methods. Several protocols were attempted to extract good quality RNA from these complex tissues. The TRIzol reagent allows the precipitation of RNA, DNA and proteins from a single sample. However, the resulting yield obtained from 50 mg of GSSC was unexpectedly low. Although the use of the chaotropic agent guanidium isothiocyanate for cell lysis and denaturation of ribonucleases, chloroform to separate RNA from DNA and proteins and isopropanol for RNA precipitation was attempted, the RNA obtained from GSSC using this methodology presented high level of contaminants, as shown by the low A260/280 and A260/230 ratios. For other seed species with high levels of polyphenols, RNA obtained using TRIzol reagent was also degraded and presented low A260/230 ratios , 19, sugSesamum indicum, Zea mays, Helianthus annuus, Linum usitatissimum [Arachis hypogaea [Jatropha curcas [Chamaerops humilis [Brassica spp seed development confirmed the high-quality of the extracted RNA using these extraction protocols, being suitable for RNA-Sequencing. The modifications applied to E.Z.N.A. Plant RNA kit and Picopure RNA Isolation kit extraction methods described were successfully used for different Brassica spp genotypes including the diploid and polypoid species, obtaining a minimum of 25 million reads and 7.5 GB of raw data for all the seed tissue types.For other oilseeds, such as tissimum , Arachishypogaea , Jatropha curcas  and Cham humilis , differe humilis , RIN valBrassica spp tissue types. By only using between 2 and 3 mg of tissue, high-quality RNA can be extracted from the most challenging seed tissue types in different stages of seed development. These methodologies can be used for diploid and polyploid species as well as for other oilseed species and for species with procyanidins in their seed coats. The high-quality of the RNA makes it suitable for RNA-Sequencing and subsequent gene expression analysis, allowing more comprehensive studies and the prediction of gene regulatory networks across seed development.Herein, we report efficient, reliable and reproducible high-quality RNA extraction methods for a wide range of oilseed Additional file: VideoS1. Video demonstrating the extraction of liquid HSEnd. The extracted endosperm is shown placed on the glass slide solely for demonstration purpose. This drop would be transferred directly to the 1.5 ml microfuge tube upon confirmation of embryo stage.Additional file 2: Figure S1. Seed coat, cellularized endosperm and embryo tissues separated at green stage.Additional file 3: Figure S2. Gel electrophoresis for different seed tissue types and RNA extraction methods shown in Table


DNA and RNA modifications have important functions, including the regulation of gene expression. Existing methods based on short-read sequencing for the detection of modifications show difficulty in determining the modification patterns of single chromosomes or an entire transcript sequence. Furthermore, the kinds of modifications for which detection methods are available are very limited. The Nanopore sequencer is a single-molecule, long-read sequencer that can directly sequence RNA as well as DNA. Moreover, the Nanopore sequencer detects modifications on long DNA and RNA molecules. In this review, we mainly focus on base modification detection in the DNA and RNA of mammals using the Nanopore sequencer. We summarize current studies of modifications using the Nanopore sequencer, detection tools using statistical tests or machine learning, and applications of this technology, such as analyses of open chromatin, DNA replication, and RNA metabolism. More than 17 and 160 types of modified bases have been found in DNA and RNA, respectively . DNA modRNA modifications are also important for biological processes including development and cancer , 10. NonThe Nanopore sequencer released by Oxford Nanopore Technologies, performs DNA/RNA sequencing directly and in real time. The Nanopore sequencer carries out sequencing by predicting sequences from electric current patterns knows as “squiggle”, which are affected by the bases inside the Nanopore. Oxford Nanopore Technologies has released several Nanopore sequencing instruments, including the MinION and PromethION sequencers , 17. TheExisting short-read sequencing-based methods for modified base detection have difficulty in determining the modification patterns of a chromosome or transcript and cannot detect modifications without an immunoprecipitation-grade antibody, distinguishable base conversion method, or the introduction of reverse transcription errors by modified bases , 24. TheFor the detection of m5C and hm5C on DNA, BS-seq (bisulfite sequencing) approaches such as WGBS (whole genome bisulfite sequencing) and RRBS (reduced representation bisulfite sequencing), based on bisulfite conversion and subsequent short-read sequencing, are the most commonly applied methods  Fig. 1b1b. WhileFor the detection of m6A, methods based on the short-read sequencing of RIP (RNA immunoprecipitation) samples, such as MeRIP-seq (methylated RIP sequencing) and m6A-seq, are the most prevalent , 38 Fig. RIP-basSMRT sequencing with long-read sequencers released by Pacific Bioscience can detect DNA modifications. In the PacBio Sequel sequencer, SMRT cell harboring a million wells is used for SMRT sequencing. The complex of a DNA polymerase and a single DNA molecule is fixed to each well of SMRT cell. SMRT sequencing is performed by continuously observing the incorporation of a fluorescently labeled nucleotide by DNA polymerase in each well. Approximately 365,000 reads with an average length of 10–14 kb are generated by the PacBio Sequel sequencer per an SMRT cell . On the Nanopolish and short-read-based BS-seq. The data showed high correlations, with r = 0.895. Gigante et al. conducted whole-genome sequencing of F1 B6 and Cast strain hybrid mice with ~10X coverage using MinION and PromethION [Nanopolish and compared the data with those obtained from RRBS with an Illumina sequencer. In addition, the authors conducted haplotyping of the Nanopore and Illumina reads utilizing SNPs between the B6 and Cast strains and showed that a larger fraction of the Nanopore reads were assigned compared with the Illumina reads. Known and novel imprinting control regions were detected by Nanopore sequencing.Whole-genome sequencing and methylation calling using Nanopore sequencing has been performed in mammals, including humans and mice , 50, 51.To analyze the modification of target regions without performing whole-genome sequencing, native DNA must be enriched without PCR. Methods involving Cas9-mediated enrichment have been developed to enrich a target region , 53. GilEscherichia coli strain, two mutant strains lacking a guanine methyltransferase or a pseudouridine synthases and a strain expressing another guanine methyltransferase [Nanopolish and showed differences in polyA tail length both between genes and between transcripts of the same gene. Finally, Viehweger et al. performed direct RNA-seq analysis of human coronavirus and detected m5C in the viral RNA using Tombo [Nanopore sequencing enables the direct sequencing of RNA molecules, which is referred to as direct RNA-seq . Direct nsferase . They innsferase  with a fng Tombo , 57.minimap2 [Tombo and eventalign in Nanopolish [Nanopolish, which is based on the HMM (hidden Markov model), can call m5Cs on DNA in the CpG context [Nanopolish is widely used for m5C calling in various species, including humans [SignalAlign is based on the HMM with the hierarchical Dirichlet process [E. coli DNA using SignalAlign. They showed that the detection of m5C was easier than that of m6A, contrary to the situation in SMRT sequencing. mCaller can use the four machine learning classifiers  to detect m6A on DNA [mCaller and Tombo. They showed that which tool is better depends on the sequence motif. DeepSignal and DeepMod detect m5C and m6A in DNA using neural networks [DeepSignal with Nanopolish and SignalAlign for the detection of m5C or m5C and m6A, respectively [DeepSignal showed a higher performance, in terms of both accuracy and sensitivity than the other two tools. DeepSignal can detect m5C at CpGs with ~0.92 accuracy at 1X coverage and m6A at GATC motif with ~0.90 accuracy at 2X coverage. DeepMod showed precision up to 0.99 and 0.9 for m5C and m6A, respectively [DeepMod showed slightly higher performance than mCaller. Statistical test-based tools can detect de novo modifications without training using modified and unmodified samples. Nanoraw employs the Mann–Whitney U test [Tombo is the successor of Nanoraw and supports the detection of m5C on DNA and RNA and m6A on DNA in all sequence contexts without a requirement for unmodified samples in addition to de novo detection. NanoMod uses the Kolmogorov–Smirnov test [NanoMod presented higher performance than Nanoraw for the detection of m5C in E. coli.Tools for the detection of modifications based on several principles have been released Table . In mostminimap2 . For toonopolish , 57 , which is a tool for the detection of RNA modifications [ELIGOS detects RNA modifications by comparing the % error at a specific base between direct RNA-seq analyses of modified and unmodified RNA or direct cDNA-seq analysis, in which the modification information is erased during reverse transcription. They validated ELIGOS through the detection of known modifications in Saccharomyces cerevisiae, E. coli, and human cell line ribosomal RNA. The authors applied ELIGOS for the detection of modification from S. cerevisiae transcriptome data and to the detection of m6A and G-quadruplex (a type of secondary structure) from human cell line transcriptome data. EpiNano is a tool for the detection of the m6A modification of RNA [EpiNano distinguishes modified bases by support vector classification using quality scores, mismatch patterns of base-called sequences, and raw current signals as input data. EpiNano detect the m6A modification in vivo with an accuracy of 0.87. Although ELIGOS and EpiNano can detect sites with modifications from bulk reads, they are not compatible with detection of modified bases in single reads at present.Due to differences in the current signal between modified and unmodified bases, there is a tendency for higher error rates to be observed at modified than at unmodified bases Fig. . Wongsurications . ELIGOS n of RNA . EpiNanoFlappie, which was released by Oxford Nanopore Technologies, is a base caller based on the ‘flip-flop’ algorithm [Flappie. Taiyaki is a model training tool for the Nanopore base caller that can be used in the model training of modified bases [Taiyaki is usable in Guppy, which is a Nanopore base caller. These base callers calling modified bases have not been benchmarked thus far.Although the tools described thus far require base-called sequence data, some base callers can base call modified bases directly. lgorithm . m5C in ed bases . The modAs described above, many detection tools for modified bases have become available recently. In the detection of m5C and m6A on DNA, the tools based on neural networks tend to show higher performance than other tools , 61. ForTombo is used for methylation detection [Nanopolish [DNase-seq, ATAC-seq, and NOMe-seq are short-read sequencing-based methods for the detection of open chromatin regions not protected by nucleosomes. Whereas DNase-seq and ATAC-seq utilize the accessibility of a DNase or transposase to open chromatin and DNA fragmentation by these enzymes, NOMe-seq utilizes the accessibility of a GpC-specific methyltransferase (M.CviPI) to open chromatin and detects open chromatin regions as methylated GpC regions, using BS-seq . nanoNOMetection . Lee et nopolish . FurtherS. cerevisiae and achieved the detection of replication sites in reads 20–160 kb in length. They also developed a BrU detection tool based on the HMM for D-Nascent. Hennion et al. used BrU for the detection of DNA replication in S. cerevisiae [RepNano is not available at present. Georgieva et al. compared the differences in signals between thymidine and 11 species of thymidine analogs containing BrdU [NanoMod for IdU detection.For genome-wide analysis of DNA replication, pulse labeling of replication sites by base analogs, such as BrdU (bromodeoxyuridine), is widely used . Attemptrevisiae . They deing BrdU . To deteRNA abundance measured via typical transcriptome analysis is the result of RNA synthesis and degradation together. Therefore, to understand the regulatory mechanism of gene expression, RNA synthesis and degradation must be measured. Measurements of RNA synthesis and degradation can be conducted through the metabolic labeling with RNA by base analogs, as in the GRO-seq, BRIC-seq, and SLAM-seq approaches –77. BecaIn this review, we summarized the approaches available for modified base detection using Nanopore sequencing. The emergence of Nanopore sequencers has enabled genome-wide and transcriptome-wide analyses of base modifications, including modifications for which detection methods are not available. For the detection of m6A and m5C of DNA, although high-coverage sequencing is required in SMRT sequencing, detection with a high accuracy is possible using low-coverage reads in nanopore sequencing. Many tools for the detection of modifications from Nanopore sequencing data have been developed. The systematic comparison of these tools is not sufficient. The accumulation of data on modified bases from Nanopore sequencing and the further development and maturation of the associated software will allow the detection of various modifications simultaneously. Applications involving labeling with artificial bases or artificial modification are being developed. These methods reveal the relationships of natural modifications and signatures of labeled nucleotides. Nanopore sequencing will reveal detailed regulatory mechanisms via epigenome and epitranscriptome analyses.


The indocyanine green (ICG) clearance test is the main method of evaluating the liver reserve function before hepatectomy. However, some patients may be allergic to ICG or the equipment of ICG clearance test was lack, leading to be difficult to evaluate liver reserve function. We aim to find an alternative tool to assist the clinicians to evaluate the liver reserve function for those who were allergic to the ICG or lack of equipment before hepatectomy.We retrospected 300 patients to investigate the risk factors affecting the liver reserve function and to build an equivalent formula to predict ICG 15 min retention rate (ICG-R15) value.9) − 0.039 × ALT (IU/L) + 0.043 × AST (IU/L) + 23.846. The equivalent formula of serum index was: ICG-R152 = 24.665 + 0.382 × TB (umol/L) − 0.799 × ALB(g/L) − 0.025 × PLT(/109) + 0.048 × AST(IU/L) − 0.045 × ALT(IU/L). And the area under the ROC curve (AUC) of predicting ICG-R15 ≥ 10% was 0.861 and 0.857, respectively.We found that the independent risk factors affecting ICG clearance test were total bilirubin, albumin, and spleen-to-non-neoplastic liver volume ratio (SNLR). The equivalent formula of the serological index combining with SNLR was: ICG-R15 = 0.36 × TB (umol/L) − 0.78 × ALB(g/L) + 7.783 × SNLR + 0.794 × PT (s) − 0.016 × PLT(/10We found that SNLR was an independent risk factor affecting liver reserve function. Combining with SNLR to evaluate the liver reserve function was better than just basing on serology. Hepatectomy is still as the first-line treatment for the patients with hepatic nodules, especially for hepatocellular carcinoma –3. ThougThere are several methods of evaluating the liver function, including Child -Pugh score , model fKawamura et al. has put forward an equivalent formula of ICG-R15 in 2008 , which wWe enrolled 300 patients randomly in our center, liver surgery and liver transplantation center, West China hospital, Sichuan University, from 2012 to 2016 January. The criteria as: (1) Age > 18 years; (2) No history of treatment for other tumors, such as colon cancer, gastric cancer, etc.; (3) No fatal underlying diseases, such as heart disease, respiratory insufficiency, etc.; (4) Abdominal CT or MRI related examination was performed in our hospital; (5) The level of serum total bilirubin was twice as high as the normal level (< 60 μmol/L); (6) All patients were received ICG clearance test in our hospital.IQQA LIVER software , an automatic 3D organ reconstruction of liver/spleen, was used to perform the volumetric analyzes on liver and spleen, and to measure liver and spleen volume. This software can also measure the tumor volume of liver. The non-neoplastic liver volume (NLV) was calculated as: NLV = Total liver volume − lesion volume. The preoperative spleen-to-non-neoplastic liver volume ratio (SNLR) was calculated as: SNLR = [spleen volume/NLV]. The 3D organ reconstruction was performed by Wei Xie, a 5-year experienced radiologist.We copied the images of CT or MRI by blank CD-ROM, and then put the images into the IQQA LIVER software by computer, which has been installed in the computer of our center. By the IQQA LIVER software, we could draw the shape of liver, tumor and spleen, event the hepatic vein, potal vein and hepatic artery. Therefore, we reconstructed the shape and calculated the volume of liver, tumor and spleen, respectively. Further more, we could predict the volume of residual liver before hepatectomy, such as the resection of hepatocellular carcinoma, hepatic adenomas and living donor liver transplantation.2) test or Fisher exact test were used to analyze the categorical variables. The multivariate logistic regression analysis was carried out to identify the independent risk factors affecting the ICG-R15 value, combining with the significant variables in two sample T test, Wilcoxon sign-rank test, Chi-square test or Fisher exact test. The receiver operating characteristic (ROC) was used to predict diagnostic efficacy and to confirm the cut-off values of the independent risk factors. All independent risk factors were taken into propensity score match (PSM), except for SNLR, to identify the SNLR could reflect the liver reserve function, independently.All data were analyzed by SPSS22.0. We divided the ICG-R15 into two groups: ≥ 10% and < 10%. The continuous variables were expressed as mean and standard deviation . The paired T test or paired Wilcoxon sign-rank test was used to analyze the difference between actual ICG-R15 (aICG-R15) value and estimated ICG-R15 (eICG-R15) value in three situations. ROC was used to assess the ability of eICG-R15 predicting aICG-R15 ≥ 10%. All the tests were statistically significant with As showing in the Table We took the significant variables into PSM, except for spleen volume and SNLR. The allowable error of selection was a = 0.1. After PSM, 58 pair patients were obtained. And we found that the difference of age, TB, AST, ALT, HGB, WBC, PLT and ALB were not significant after PSM, just only the spleen volume  and the SNLR  were significant, indicating that the PSM results were credible.Table Table 9) − 0.039 × ALT (IU/L) + 0.043 × AST(IU/L) + 23.846 (R2 = 0.507), and the linear distribution result was shown in Fig. 1 = 15.638 × SNLR + 6.734 (R2 = 0.119), and the linear distribution was shown in Fig. 2 = 24.665 + 0.382 × TB (umol/L) − 0.799 × ALB(g/L) − 0.025 × PLT (/109) − 0.048 × AST(IU/L) − 0.045 × ALT (IU/L), and the linear distribution result was shown in Fig. Table 2) test were performed to compare the difference with actual ICG-R15 (aICG-R15), respectively, as showing in Table p = 0.984). And the W-test suggested that the distribution of aICG-R15 and eICG-R15 was no significant difference (6.05(3.43–12.95) vs 8.64 (5.12–13.69), p = 0.092). The diagnostic efficiency of ICG-R15 ≥ 10% was as following: sensitivity = 84.5%, specificity = 79.7% and AUC = 0.861  vs 11.35 (9.90–13.15), p = 0.189, Table 2, based on serological index, and actual ICG-R15 was no significance  vs 9.26(5.06–13.87), p = 0.060), and the AUC was 0.857, sensitivity was 87.6%, specificity was 76.2% . Paired T-test, paired rank sum (W) test and chi-square (χp = 0.009). Previous studies have shown that patients with HBV would make progress to cirrhosis or even decompensation . The difference of eICG-R15 and aICG-R15 was no significance, indicating that the eICG-R15 value was reliable. Additionally, the coefficient of SNLR was the largest in the formula, indicating that SNLR had the greatest affecting on liver reserve function. The level of AST and ALT can be used as the related variable, mainly because the two enzymes were higher in liver cells and they would be released into the blood when the liver cells were damaged or died, reflecting the liver function situation. On the other hand, from the formula of purely serological index, the difference of eICG-R15 and aICG-R15 was also no significance, indicating that the formula of combining with SNLR and the formula of basing on serological index were comparable.From multiple linear regression analysis, we got the SNLR-related formula for the ICG-R15 value [ICG-R15 = 0.36 × TB (μmol/L) − 0.78 × ALB(g/L) + 7.783 × SNLR + 0.794 × PT(s) − 0.016 × PLT was still the main reason of death in patients after hepatectomy, and its incidence was about 7% , 33. TheCompared with the previous formulas , 20, theThe limitations of this study were as following: (1) The retrospective study has its owe shortcomings, for example, we can not identify when the patients were infected by HBV, so the age was as an independent risk factor in this study. Actually, the time of being infected by HBV should be as the independent risk factor. (2) There needed a large number of clinical samples for further identifying the relationship of SNLR and ICG-R15, however, as far as we know, this study was the first research combining radiology to evaluate the liver function, which met the trend to combine with many ways to evaluate the liver function. (3) We can’t eliminate the effects of intrahepatic vascular  when we reconstructed the 3D model of liver and spleen.SNLR was an independent factor for liver reserve function. The equivalent formula of serological index combined with SNLR and the equivalent formula of purely serological index could be used to predict the aICG-R15 value, but the formula of serological index combined with SNLR was better than the formula based on purely serological index.


Fossils of extinct species allow us to reconstruct the process of Darwinian evolution that led to the species diversity we see on Earth today. The origin of the first functional molecules able to undergo molecular evolution and thus eventually able to create life, are largely unknown. The most prominent idea in the field posits that biology was preceded by an era of molecular evolution, in which RNA molecules encoded information and catalysed their own replication. This RNA world concept stands against other hypotheses, that argue for example that life may have begun with catalytic peptides and primitive metabolic cycles. The question whether RNA or peptides were first is addressed by the RNA‐peptide world concept, which postulates a parallel existence of both molecular species. A plausible experimental model of how such an RNA‐peptide world may have looked like, however, is absent. Here we report the synthesis and physicochemical evaluation of amino acid containing adenosine bases, which are closely related to molecules that are found today in the anticodon stem‐loop of tRNAs from all three kingdoms of life. We show that these adenosines lose their base pairing properties, which allow them to equip RNA with amino acids independent of the sequence context. As such we may consider them to be living molecular fossils of an extinct molecular RNA‐peptide world. Naturally occurring amino acid containing nucleobases could be vestiges of an extinct early Earth RNA‐peptide world. Today they help to translate genetic information, but in the RNA‐peptide world, they might have been the units that equipped RNA with broad catalytic functions. These genotype‐phenotype bridging nucleosides were synthesized, and their properties were investigated. This study shows that these nucleosides do not base pair, which allows them to position amino acids in unpaired parts of complex RNA structures. N6‐amino group of the heterocycle as depicted in Figure 6A)[6A),6A.[6A is found in all three kingdoms of life, it has been suggested that such amino acid modified bases were already present in the last universal common ancestor (LUCA), from which all life forms descended.[6A is for example today found in nearly all ANN decoding tRNAs.6A modified bases . We first prepared the amino acid components for the coupling to the A‐nucleoside .7 and 8  Boc‐deprotection to give the amino acid products 15–17 .24 with the activated carbonate and the corresponding amino acid, provided in all cases the amino acid coupled products 25–31 in good to excellent yields. Subsequent cleavage of the cyclic silylether with HF⋅pyridine complex,[46–52. Standard solid phase RNA chemistry[6A nucleosides stably embedded. The standard RNA synthesis protocol did not require any adjustment. In all cases we observed fair coupling of the aa6A phosphoramidites and no decomposition during deprotection. Deprotection required three steps. First, with DBU in THF at r.t. for 2 h we cleaved the npe‐protecting group. Second, we deprotected the bases and cleaved from the solid support with aqueous NH3/MeNH2. Finally, we removed the 2′‐silyl group with HF in NEt3.The connection of the amino acid with the A‐nucleoside via the urea moiety was next carried out as depicted in Scheme  complex,22, 236A bases would affect the stability of DNA duplexes we also prepared as a representative molecule t6dA as depicted in Scheme 53,22, cleaved the acetyl groups and converted the nucleoside subsequently into the 5′‐DMT protected phosphoramidite 57. The purification of compound 57 was quite difficult due to its high polarity. We needed to use rather polar mixture of EtOAc/Hex (2/1) as the mobile phase for the chromatographic separation. This provided the phosphoramidite 57, however the material had a lower purity in comparison to the RNA phosphoramidites. Nevertheless, solid phase DNA synthesis and deprotection of the DNA strand ODN1 proceeded again smoothly and in high yields.In order to investigate how aaON1 (RNA strand with embedded t6A) and the corresponding chromatogram after purification (inset) together with the obtained MALDI‐TOF mass spectrum , proving again the successful synthesis of t6dA containing oligonucleotide. The aa6(d)A nucleosides can exist in two different conformations.trans, maintains the Watson–Crick hydrogen bonding capabilities with the urea amino acid oriented towards the imidazole ring system , we measured the same melting temperature. This would not be expected if the s‐trans‐conformation and base pairing would be possible. These data suggest that aa6A nucleosides within RNA position a given amino acid outside the A‐form helix in an unpaired situation and hence independent from the counterbase. As such, multiple aa6A containing RNA strands would be structures in which the RNA part is decorated by the amino acid side chains. In order to show that RNA‐structures containing multiple amino acids as representatives of an RNA‐peptide world can stably form, we prepared two RNA duplexes , we placed three t6A bases as extra bases in an otherwise undisturbed RNA duplex. Indeed, now the stability of this duplex was indistinguishable from the same construct containing just canonical bases (D6). Finally, we prepared an RNA duplex D7, in which we placed the amino acids Ser‐Asp‐His directly next to each other to simulate what is known in the peptide world as the catalytic triad present in serine peptidases.6A bases creating a loop. Although we do not show any catalytic activity here, we believe that it is easily imaginable that if these amino acids are properly positioned in a stably folded RNA the structure could gain catalytic properties.This conclusion is also supported by the observation that irrespective of the chirality of the attached amino acid  are today found as key components in the tRNAs of many species. In these tRNAs the aa6A nucleosides reside at the general purine position 37 adjacent to the anticodon loop. They are not involved in base pairing but fine tune the codon‐anticodon interaction to enable faithful translation of information into a peptide sequence.6A nucleosides are stable structures and until today broadly found in today's RNA make them prime candidates to develop idea about the chemical constitution of the vanished RNA‐peptide world.In summary, here we investigated the synthesis and properties of aaThe authors declare no conflict of interest.As a service to our authors and readers, this journal provides supporting information supplied by the authors. Such materials are peer reviewed and may be re‐organized for online delivery, but are not copy‐edited or typeset. Technical support issues arising from supporting information (other than missing files) should be addressed to the authors.SupplementaryClick here for additional data file.


RUNX1-RUNX1T1 and CBFB-MYH11 at diagnosis and their levels of reduction during remission (P < 6.3e−05 and P < 2.2e−13). The level of reduction of RUNX1-RUNX1T1 as measured by RNA-seq and qPCR were highly correlated . A decision tree analysis, based on 3-log reduction of RUNX1-RUNX1T1 and cKIT-D816mut at diagnosis, stratified RUNX1-RUNX1T1 AML patients into three subgroups. These three subgroups had 2-year overall survival rates at 87%, 74%, and 33% (P < 0.08) and 2-year relapse incidence rates at 13%, 42%, and 67% (P < 0.05). On the other hand, although low residual allelic burden was common, it was not associated with long-term outcome, indicating that mutation clearance alone cannot be interpreted as MRD-negative. Overall, our study demonstrates that the clinical utility of RNA sequencing as a potential tool for MRD monitoring in fusion gene-driven AML such as RUNX1-RUNX1T1 AML.DNA sequencing-based measurable residual disease (MRD) detection has shown to be clinically relevant in AML. However, the same methodology cannot be applied to fusion gene-driven subtypes of AML such as core-binding factor AML (CBF-AML). Here in this study, we evaluated the effectiveness of using DNA and RNA sequencing in MRD detection and in tracking clonal dynamics in CBF-AML. Using RNA-seq, we were able to quantify expression levels of  Recent studies showed that targeted DNA sequencing-based measurable residual disease (MRD) detection is feasible in AML7. However, the weakness of targeted DNA sequencing based MRD detection is that it is not efficient to reliably track breakpoints of gene rearrangements using a targeted panel. The prevalence of gene rearrangement-driven AML is about 30%8. For instance, gene rearrangements affecting core binding factor (CBF) complex, RUNX1-RUNX1T1/t or CBFB-MYH11/inv(16) are found in about 15% of AML patients  and inv(16) AML, respectively)9. Compared with other AML subtypes, CBF-AML is considered as a favourable risk subgroup due to favourable long-term outcomes following high-dose cytarabine-based chemotherapy10. However, up to 40% of CBF-AML patients still relapse13. As a post-remission monitoring of CBF-AML, a quantitative PCR (qPCR) assay targeting t and inv(16) at remission can identify patients at high risk of relapse and has become a routine procedure16. Recently, a study by Dillon et al. demonstrated that MRD assessment using targeted RNA sequencing is feasible and correlates well with qPCR assays17.Leveraging on advances in next generation sequencing (NGS) technologies, a wide range of heterogenous genetic baseline characteristics has been discovered in acute myeloid leukemia (AML)While qPCR is highly sensitive and sequence-specific, it can only test a limited set of fusion events, making it neither scalable nor being used as a discovery tool. Thus, multiple tests have to be performed at diagnosis in order to characterize appropriate MRD targets to monitor post-treatment. In addition, standardization procedures make it challenging to compare qPCR results from different institutions and hospitals. On the other hand, RNA sequencing (RNA-seq) is scalable to quantify multiple targets in a single assay. In addition, it can also simultaneously quantify transcript expression, discover novel fusion transcripts, and even detect somatic mutations. Considering these advantages of RNA-seq and reductions in sequencing costs in recent years, we hypothesized that a single assay of RNA-seq can be utilized not only to detect mutations and fusion transcripts at diagnosis, but also quantify their reduction level at complete remission (CR).Here we describe a study to evaluate DNA and RNA sequencing on longitudinal samples taken at diagnosis and at CR, as potential MRD detection tools for post-remission monitoring of CBF-AML. With this approach, we hoped to gain additional insights on how NGS can be applied to monitor MRD in CBF-AML, as well as reveal comprehensive dynamics of somatic mutations, transcripts, and gene rearrangements from diagnosis till CR. To address these questions, we conducted DNA sequencing (DNA-seq) on 223 DNA samples collected from 87 patients including 62 patients with t and 25 patients with inv(16) taken at time points including diagnosis, CR, and relapse. For a subset of patients with available samples, we conducted RNA-seq on 90 samples consisted of 42 pairs of diagnosis-CR samples as well as 6 relapse samples.The main goal of current study was to assess whether residual genetic alterations including primary (i.e. subtype-defining gene rearrangements) and secondary (i.e. single nucleotide variants and short indels) lesions at CR can be quantified using RNA and DNA-seq and are clinically relevant. Most notably, current study demonstrates how RNA-seq can be utilized to track gene rearrangements during remission and serves as a proof-of-concept study for RNA seq-based MRD detection for gene rearrangement-driven hematologic malignancies.Eighty-seven patients with newly diagnosed CBF-AML from January 2003 to September 2015 were included in this study. All patients had an Eastern Cooperative Oncology Group (ECOG) performance status ≤ 2 at the time of diagnosis. Patients treated with investigational drugs before induction therapy were excluded. Treatment and response assessments are summarized in Supplementary Information. Samples for NGS were obtained at the time of initial diagnosis (n = 87 and n = 42 for DNA and RNA-seq), CR (n = 53/87 and n = 42/42 for DNA and RNA-seq), and relapse (n = 15/87 and n = 6/42 for DNA and RNA-seq). CR samples were obtained either at day 28 (n = 45/53 and n = 34/42 for DNA and RNA-seq) or day 56 after re-induction (n = 8/53 and n = 8/42 for DNA and RNA-seq). Among 155 samples subjected for DNA sequencing, 151 samples were taken from bone-marrow (BM) and other 4 samples were taken from peripheral blood (PB). For patients with available serial samples, 90 samples (42 + 42 + 6) from 42 patients were also subjected to RNA-seq. All patient samples were collected after obtaining informed consent. Among 90 samples subjected for RNA sequencing, 86 samples were taken from BM and other 4 samples were taken from PB. All experiments and methods were carried out in accordance with relevant guidelines and regulations. This study was approved by the institutional ethics review boards at Chonnam National University Hwasun Hospital and Severance Hospital, Republic of Korea (CNUHH-2015-118).19. For RNA-seq, the total RNA was extracted from BM/PB mononuclear cells using the RNeasy mini kit . Total RNA concentration was calculated by Quant-IT RiboGreen . To assess the integrity of the total RNA, samples were run on the TapeStation RNA screentape . A total of 100 ng of total RNA was subjected to a sequencing library construction using a TruSight RNA Pan-Cancer Panel (1385 genes)  according to the manufacturer's protocols. Both DNA and RNA samples were multiplexed and sequenced using an Illumina HiSeq 2500 .BM/PB samples used in this study were prospectively procured and banked. After DNA isolation and library preparation, all samples were subjected to targeted sequencing. Targeted gene panel was constructed using a custom Agilent Sureselect design, covering exonic regions of 83 genes Table . The gen19. Detailed methods on variant calling, RNA-seq read processing, transcript quantification, differentially expressed gene analyses and, fusion transcript discovery in diagnostic samples and its tracking in CR samples are provided in the Supplementary Information.Sequencing metrics can be found in Table https://www.r-project.org) and EZR (version 1.33) were used to perform statistical analyses and generate all figures20.Patient characteristics were summarized using descriptive statistics. Categorical variables were analyzed using a Chi-square test and comparison of continuous variables was performed using the Student’s t-test. The overall survival (OS) was defined as the time of diagnosis until death from any cause, which was analyzed using the Kaplan–Meier method. The groups were then compared using the log-rank test. The cumulative incidence of relapse was calculated using the Gray method, considering death without relapse as a competing risk. Non-relapse mortality was defined as any death while in remission with relapse as a competing risk and was assessed with the Gray’s test. The prognostic impact of risk factors on OS was determined using the Cox proportional hazard model. Fine-Gray proportional hazard regression with competing events were used to identify risk factors for relapse. P-values of less than 0.05 were considered significant. R statistical software 3.1.3  AML and 25 were inv(16) AML. The median age of the patients was 44 years (range 16–85 years) and 50 patients (57%) were male. Induction chemotherapy was administered for 76 out of 87 (87%) patients. CR was achieved in 73 out of 76 patients (96%). The CR rate was 96% in both t AML (n = 49/51) and inv(16) AML (n = 24/25). Three patients who failed to achieve CR died of treatment-related mortality (n = 2) or disease progression (n = 1). Post-remission therapy with high dose cytarabine was administered at CR. The patient characteristics are summarized in Table  AML and KIT , NRAS , ASXL2 , and KRAS  were frequently mutated. Frequencies of mutation occurrence within targeted gene panel between two subtypes of CBF-AML, irrespective of genes, were comparable  AML and 88% in inv(16) AML, p < 0.29). When grouped by associated biological pathways, genes associated with signaling pathways , chromatin modifiers  and, the cohesin complex  were mutated in more than 10% of the cohort and frequencies of mutations in RAS (KRAS or NRAS), chromatin modifiers (including ASXL2) and, cohesin complex were significantly different between the two subtypes of CBF-AML  Fig. . The 166KIT-D816mut was identified as an adverse prognostic factor for OS (Hazard ratio (HR) 2.31, 95% confidence interval (CI) 1.04–5.14, P = 0.035). Three-year OS rate was inferior in KIT-D816mut group (47.1 ± 12.9%) to KIT-D816wt group . In terms of CIR (cumulative incidence of relapse), KIT-D816mut was an adverse factor for CIR , while mutations in RAS (NRAS or KRAS) was favorable for CIR . Three-year CIR was higher in KIT-D816mut group (43.8 ± 12.1%) than KIT-D816wt group . CIR was lower in RASmut subgroup than RAStw , while NRM  was higher in RASmut subgroup . Thus the 3-year OS rate was not different between the groups of RASmut and RAStw . Overall, KIT-D816mut was the only adverse prognostic mutation for OS.We performed survival analyses to investigate association of somatic mutation with patient outcome (n = 76/87 after excluding the patients who did not receive chemotherapy) Table . In univKIT (n = 13), NRAS (n = 8), and KRAS (n = 3). Mutations with higher VAFs at diagnosis are more likely to be detected at CR . To estimate mutation-like sequencing errors, we computed the frequency of mutation-like nucleotide changes in CR samples from patients who did not have the mutation in their diagnosis specimens. Overall, we observed that median mutation-like error rate is 0.008% in our data (interquartile range 0.004–0.013%). We then investigated whether the mutation clearance at CR is associated with subsequent occurrence of relapse  and relapse incidence . Out of 99 mutations, 53 mutations were cleared at CR   between t AML and inv(16) AML  AML expresses it nearly 4300 times higher than inv(16) AML  AML  terms related to immune response such as cytokine-cytokine receptor interaction (q < 2.2e−5), allograft rejection (q < 0.02) and, graft − versus − host disease (q < 0.02)  Figs. 23.Figure(SLC45A3-ELK4 and CSNK1G2-JAK2)  AML samples  and 9 t AML) including two recurrent fusion transcripts in t AML K2) Fig. . Six samRUNX1-RUNX1T1 and CBFB-MYH11 showed significant reductions in all CR samples compared to their corresponding diagnostic samples  and RUNX1-RUNX1T1 was detectable in 15/23 (65%) CR samples. On average, 1618 reads were mapped to RUNX1-RUNX1T1(IQR 1284–1764 reads) and 239 reads were mapped to CBFB-MYH11 (IQR 201–416 reads) in diagnostic samples. As the number of aligned reads were comparable between diagnostic and remission samples , limits of detection of RUNX1-RUNX1T1 and CBFB-MYH11 fusion transcripts using RNA-seq in CR samples would be approximately 3-log (1 in 1000) and 2-log (1 in 100) reduction. Reduction level of RUNX1-RUNX1T1 measured by RNA-seq showed positive correlation with the reduction level measured by qPCR . When compared with qPCR-based MRD, RNA-seq-based MRD showed comparable performance in terms of sensitivity, specificity, positive/negative predictive value using 3-log reduction as a cut-off in RUNX1-RUNX1T1 AML   Table .Figure 4KIT-D816mut), we attempted to build a prognostic model using decision tree analysis incorporating genetic features obtained from RNA-seq. Decision tree analysis identified three distinct subgroups of RUNX1-RUNX1T1 AML on the basis of reduction in the level of RUNX1-RUNX1T1 transcript and mutation profile 15. The algorithm further divided the patients who failed to achieve 3-log reduction according to the presence of cKIT-D816 mutation at diagnosis (intermediate and high-risk groups). For three defined groups, 2-year OS rates were 87%, 74%, and 33%  was reliably detected in both DNA and RNA-seq data. Solely using information from RNA-sequencing, we built a prognostic model that can predict long-term outcome for t AML patients. On the other hand, complete clearance of secondary genetic lesions (i.e. single nucleotide variants and short indels) including KIT-D816mut alone was not associated with better long-term outcomes and mutation persistence at low level did not provide prognostic power in addition to reduction level of the fusion transcripts. Altogether, our data and analyses suggest the clinical utility of RNA-seq as a method to detect MRD in CBF-AML and potentially in other fusion-driven hematologic malignancies.Current study demonstrated that RNA-seq can measure reduction levels of 7. To the best of our knowledge, transcriptome along with mutation profile on paired diagnostic-CR samples have not been investigated in CBF-AML. By tracking junctions identified in corresponding diagnostic samples, we were able to quantify the level of RUNX1-RUNX1T1 and CBFB-MYH11 in CR samples. The level of RUNX1-RUNX1T1 reductions measured by RNA-seq was highly correlated with qPCR result . RUNX1-RUNX1T1 was detectable in 15 samples by both techniques and in 6 samples only by qPCR. In 2 samples, RUNX1-RUNX1T1 was undetectable by neither method. qPCR was more sensitive at detecting trace amount of RUNX-RUNX1T1 compared to RNA-seq at the sequencing coverage in our study (avg. = 2.98 M mapped reads per sample). However, we believe that these obstacles can likely to be overcome by achieving deeper sequencing depth as well as more tailored sequencing strategies. CBFB-MYH11 was only detectable in 6/19 CR samples at the sequencing depth used in current study and qPCR results were not available for comparison. Future studies on CBFB-MYH11 cohort with deeper sequencing depth and comparative analyses with qPCR remain to be investigated.Leveraging on paired-diagnosis-CR samples, we assessed the clinical relevance of residual genetic lesions at remission in CBF-AML. Although several studies demonstrated clinical relevance of residual allelic burden in non-CBF-AML, none of the studies thus far focused on CBF-AMLTET2, 1 in JAK3, U2AF1, and ASXL1 each) were observed with a VAF above 1%. This mutation clearance level is higher than what was reported in non-CBF-AML4. This could be due to differences in mutation profile between CBF and non-CBF-AML. Klco et al. only included intermediate and poor risk AML patients and most frequently persistent mutations were in DNA methylation or clonal hematopoiesis-associated genes 3. In CBF-AML, those genes are much less commonly mutated. Incorporation of mutation dynamics with RUNX1-RUNX1T1 fusion transcript reduction level showed that reduction of mutation burden from diagnosis to CR is deeper than or equal to the reduction of RUNX1-RUNX1T1 fusion transcript. This re-confirms that complete clearance of allelic burden (i.e. secondary lesion) during remission does not guarantee eradication of leukemic cells, but deeper reduction of primary lesion (i.e. fusion transcript) is a more appropriate surrogate marker for predicting relapse in CBF-AML. In addition to the assessment of the entire cohort with available CR samples, we also showed that residual allelic burden did not further stratify high risk patients among MRD-positive patients. This result indicates that DNA-based MRD detection, only targeting secondary lesions does not provide additional prognostic power in addition to reduction level of the fusion transcripts in CBF-AML.With respect to single nucleotide variants and short indels, allelic burden of all mutations showed significant reduction from diagnosis to CR (ranges from 86.2 to 100%). At CR, only 5 mutations out of 99 mutations (2 in 26. For example, members of the cohesin complex and chromatin modifiers including ASXL2 were nearly exclusively mutated in RUNX1-RUNX1T1 AML. Genes associated with activated signaling such as KIT, N/KRAS, CBL, and FLT3 were frequently mutated in both subtypes. Survival analyses showed that KIT-D816mut was an adverse prognostic factor. As shown in Lavallée et al.27, the pattern of transcript expressions at diagnosis further distinguished these two related-subtypes of AML. CR samples were clustered regardless of their subtypes. We noticed that there are nine genes that are differentially expressed in CR samples depending on their AML subtypes. However, all nine genes were differentially expressed in at least one of three-way comparisons and this is likely due to the residual leukemia cells in CR samples.Consistent with previous studies, mutation profiles of two CBF-AML subtypes were differentCBFB-MYH11 using RNA-seq in CR samples requires experimental optimizations to be compatible with qPCR. Lastly, as some of fusion calls were only supported by few reads, further validation using alternative techniques such as qPCR should be performed to validate them.There are several limitations in our study. First, the cohort size was relatively small, and the prognostic model developed using the current cohort could not be validated using an independent cohort. Second, the experimental setting of RNA sequencing utilized in this study such as the gene panel, amount of total RNA used, and sequencing depth was not optimized for MRD purposes. For example, detection of RUNX1-RUNX1T1 and CBFB-MYH11 level during remission in CBF-AML. Combining all of these supporting evidences, our data and analyses confirmed that the reduction level of fusion transcripts and baseline characteristics at initial diagnosis are the most important genetic features in CBF-AML when predicting long-term outcome and they provide complementary prognostic information. On the other hand, residual allelic burden of secondary lesion at CR measured by DNA-seq alone does not provide clinically relevant information in addition to those two genetic features. We also showed that a single assay of RNA-seq can be used to stratify RUNX1-RUNX1T1 AML patients into three distinct risk groups according to their long-term prognosis. Our findings warrant prospective studies with larger cohorts on gene rearrangement-driven hematologic malignancies utilizing more tailored sequencing strategies.Altogether, the current study demonstrates that RNA-seq can be utilized to monitor Supplementary Information 1.Supplementary Information 2.Supplementary Information 3.Supplementary Information 4.Supplementary Information 5.Supplementary Information 6.Supplementary Information 7.Supplementary Information 8.Supplementary Information 9.