Teaching assistants (TAs) often lead courses using curricula they did not design. Therefore, examining how curriculum and professional development (PD) interact to influence TAs’ teaching practices is critical. This study describes the effects of a curriculum and PD intervention in two contexts: when TAs are teaching curriculum that is explicitly linked to PD, and when teaching curriculum that is not linked to PD. The Intervention curriculum featured structured opportunities for reform-oriented teaching practices. The Intervention PD was situated in the context of these specific curriculum activities and modelled the desired teaching practices. TAs that participated in the intervention implemented more student-centered teaching practices than TAs that did not participate in the intervention, even when teaching curriculum that was not designed to be student-centered and was not linked to PD. A linear model of TAs’ teaching practices that included PD type, task cognitive demand and curriculum type indicates that cognitive demand has the largest relationship with teaching practices, followed by PD type. These results have implications for policy. They suggest that investment in curriculum-linked TA PD can be effective even when teaching curricula that is not linked to PD. Additionally, investment in development of higher-cognitive-demand tasks may be an effective strategy to support implementation of student-centered practices. Undergraduate biology instruction is shifting from an instructor-centered to a student-centered pedagogical style that engages students in more cognitively demanding exploration of core concepts and practices in biology –4. TheseThe conceptual framework guiding this study draws from work on professional development, teacher professional knowledge, and cognitive demand. Many factors converge to influence instructor teaching practices. Teaching practices influence students’ practices within the classroom, affecting student outcomes  9–11]. –11. 9–11Instructors’ teaching practices consist of the actions implemented to facilitate instruction, including lesson planning, in-person instruction, and assessment . InstrucPD has been well-studied in the context of K-12 teachers, and elements of effective PD for K-12 teachers have been identified, including: PD should be linked to classroom lessons and focus on subject matter content, PD should model teaching strategies that teachers are asked to use with their students, and PD should engage teachers in reflection –22. The In contrast to the large body of literature on K-12 teacher PD, fewer studies have investigated PD for TAs –32. Few Many graduate students report that they do receive PD for teaching, but state that these PD programs do not provide adequate support for developing pedagogical skills , 37. OneMultiple reports of sustained, multi-session PD for TAs describe programs that are directly linked to the curriculum for a specific course –36, 43. Student-centered curricula push students to engage with core concepts and practices of a discipline through scientific inquiry , 2, 45. Doing Science by Tekkumru-Kisa et al. [An example of a high-cognitive-demand task (level 5 of 5) is one where students tackle an open scientific question with very little prescribed structure or directives from the instructor . This ta et al. , 47, 48.a et al. , 45. Howa et al. . These eThere may be an association between cognitive demand on students and teaching practices , 50. CogIn this study, we implement a multi-session curriculum-linked PD program that is relatively short in duration (4 hours). Elements shown to be effective for K-12 teachers and TAs  were included in this PD intervention , 31, 36.To what extent do the intervention curricular activities and PD prepare TAs to implement student-centered teaching practices in the classroom?To what extent do TAs that participated in the curriculum/PD intervention implement student-centered teaching practices when teaching curriculum that was not linked to PD?To what extent do PD and the cognitive demand of the curricular tasks impact teaching practices of TAs when controlling for curriculum type?This study was conducted within a larger study on the impact of a curriculum intervention within an undergraduate introductory biology laboratory course across three semesters at a large, American, Ph.D.-granting university. This study was approved by the University of Minnesota Institutional Review Board ID#: STUDY00003137. Consent followed the process approved by the IRB. TAs were provided with information about the study on the BioVEDA survey and an introductory email. They were asked on the BioVEDA survey whether they agreed to have their observation and survey data included in the study. TAs were also informed that they could opt out of the study at any time by contacting one of the researchers.The course was divided into laboratory sections each containing approximately 20 students. Half the sections were randomly designated as ‘Traditional’ instruction sections, and the remaining sections were designated as ‘Intervention’ sections. TAs in Intervention sections  implemented five short (~30 minute) Intervention curricular tasks in addition to the typical laboratory exercises. TAs in Traditional sections  only implemented the typical laboratory exercises. All TAs participated in course preparation meetings designed to support procedural implementation of the typical laboratory exercises. Intervention TAs also participated in four additional hours of PD that was designed to support implementation of Intervention tasks, while Traditional TAs participated in time-matched PD that focused on inclusive teaching . This study was a collaboration between the course coordinators and the research team. The research team designed the Intervention curriculum and PD. The course coordinators designed the Traditional curriculum and PD. The research team had no day-to-day involvement in hiring TAs, the Traditional course curriculum or facilitation of laboratory preparation meetings.Study participants included one teaching specialist and 41 graduate students who were paid TAs for an introductory biology laboratory course. TAs were randomly assigned to Traditional or Intervention sections by a course coordinator who was not directly involved in this study. There were no statistically significant differences between Traditional and Intervention TAs in the number of years in graduate school, TA experience, or gender . InterveThe Traditional core curriculum consisted of largely scripted laboratory exercises based on a lab manual detailing procedures that students completed in class . Content covered by the Traditional curriculum included laboratory techniques, experimental design, and data analysis. All TAs  taught the Traditional curriculum. Instructional style was not prescribed for TAs.The Intervention curriculum consisted of five short tasks that supplemented the Traditional curriculum during the first third of the semester in Intervention laboratory sections. The supplemental Intervention tasks comprised approximately 3% of the total instructional contact hours across the semester, and the remaining 97% of instructional time was spent on Traditional tasks. The content covered by the Intervention curriculum complemented the laboratory exercises specified in the Traditional curriculum. A detailed explanation of the Intervention curriculum is published elsewhere . Table 3For Intervention curricular tasks, Intervention TAs were provided with instructor supports modelled after educative curricular materials (ECM) used for K-12 teachers . The ECMAll TAs attended weekly preparation meetings (~25 hours per semester) where course coordinators lectured on logistics, procedures, and content. All TAs also participated in 4 hours of Traditional or Intervention PD that occurred over 4 sessions during the first third of the semester.Traditional and Intervention PD differed in topic and structure. Traditional PD focused on shifting the attitudes and interest of TAs and their students about course content to make laboratory exercises more inclusive and accessible. Targeted course content varied by semester and was chosen by the course coordinator. In these sessions, TAs learned about teaching techniques and then designed ways to implement them in the classroom.Intervention PD focused on preparing TAs to teach the Intervention curricular tasks and aimed to elevate TAs’ knowledge of subject matter within the tasks, and knowledge of teaching practices that should be used during task enactment. Facilitators presented each task’s learning goals. Trainings were structured as modelled versions of curricular tasks, with facilitators acting as TAs, and TAs acting as students. Facilitators modelled teaching strategies that TAs could use to enact the tasks. TAs were asked to reflect on their thought process as they participated in the tasks, note any difficulties they had, anticipate areas of difficulty for students, and reflect on teaching practices that facilitators implemented.The inclusion of modelled teaching practices linked to specific tasks and opportunities for reflection are evidence-supported practices in K-12 PD and represent key differences between Intervention and Traditional PD , 21. WhiIntervention PD facilitators were members of the research team and part of the curriculum development team. These facilitators consisted of a Ph.D. student in a STEM Education program and a postdoctoral associate with a biology Ph.D. and one year of biology education research experience. Traditional PD facilitators consisted of a course coordinator (teaching faculty) and a member of the research team .To examine how PD and curriculum impacted classroom practices, TAs were audio recorded teaching Traditional and Intervention curricular tasks (a maximum of two times per semester). TAs were assured that the research team would not share any identifiable information with persons involved in managing or hiring for TA appointments. Traditional TAs were recorded teaching Traditional tasks and Intervention TAs were recorded teaching both a Traditional and an Intervention task.The Reformed Teaching Observation Protocol (RTOP) was used to measure teaching practices that TAs enacted in the classroom . The RTOA research assistant who was not involved in the design or implementation of the curriculum or PD was the primary coder of the audio recordings of classroom lessons. A second coder independently rated 45% of the audio recordings of classroom lessons. The second coder was involved in the design and implementation of the curriculum and PD. Both coders were blinded to the identity of the TAs, their Traditional/Intervention group status, and Traditional/Intervention designation of the recorded curricular task. The intra-class correlation coefficient for the two coders is 0.75 , indicating excellent inter-rater agreement .This study assessed the cognitive demand of the tasks that were taught by TAs during the recorded lessons. Cognitive demand was determined by using the Task Analysis Guide in Science (TAGS) framework  to analyThe Biological Variation in Experimental Design and Analysis (BioVEDA) assessment was used to measure TA content knowledge because it can be used to evaluate respondents’ understanding of variation in experimental design and data analysis, which is a learning goal for this course . All TAsd).Differences in RTOP scores across PD and curriculum type were examined via one-way ANOVA. Assumptions of ANOVA were met . Post-hoc analyses were conducted via Tukey’s HSD. Effect sizes have been provided for comparisons between groups  and cognitive demand of the task (measured by TAGS) on classroom teaching practices (measured by RTOP) while controlling for curriculum type , linear models were fitted to the data using forward and backward stepwise regression. Assumptions of linear regression  were considered satisfied after graphical examination. The assumption of multicollinearity was met (Variance Inflation Factor (VIF) is 3.6) .An alpha level of .05 was used to determine statistical significance for all analyses. Data used in these analyses has been provided in a CSV file in .F = 101.9, p < .001, Cohen’s d = 2.9; M = 32.4) represents lecture-style instruction with minor student participation [M = 61.6) represents active student participation in critiquing or carrying out investigations [To investigate the effect of Intervention curriculum and PD on TAs teaching practices, total RTOP scores were compared between Traditional TAs teaching Traditional tasks and Intervention TAs teaching Intervention tasks. The difference in total RTOP scores between these groups is significant ..Figations  .F = 27.1, p < .001, Cohen’s d = 1.5; M = 32.4) represents lecture-style instruction with minor student participation, whereas the mean RTOP score for Intervention TAs (M = 48.8) represents ‘significant student engagement with some minds-on as well as hands-on involvement’ [Intervention PD was focused solely on preparing TAs to teach Intervention curricular tasks. Curriculum developers and PD facilitators neither asked nor specifically encouraged Intervention TAs to continue to implement student-centered teaching practices when teaching Traditional tasks. Interestingly, Intervention TAs still have 1.5-fold higher RTOP scores than Traditional TAs, even when teaching Traditional tasks ..FPlanned cognitive demand  varied within both the Intervention and Traditional curricula. Traditional and Intervention curricular tasks differed in multiple ways, including instructional style, expected role of the TA, expectations of students, and the type of instructor supports provided . TherefoF = 59, p < .001, R2 = .72), accounted for approximately 70% of the variation in RTOP scores. Content knowledge (BioVEDA score) was not a significant contributor to teaching practices. When controlling for curriculum type, planned cognitive demand of the task  had a two-fold larger contribution to TA teaching practices than PD type   .Multiple factors can affect instructors’ teaching practices , 46, 63.Intervention TAs had the highest RTOP scores when they were teaching Intervention curriculum. The RTOP scores were lower when Intervention TAs were teaching Traditional curriculum. These findings are consistent with those of Addy & Blanchard . They foSignificantly, Intervention TAs still used more student-centered teaching practices than Traditional TAs on Traditional tasks not covered in either PD. Intervention TAs were not asked to implement student-centered teaching practices when teaching the observed Traditional tasks. This result suggests that Intervention TAs are independently transferring their knowledge about teaching learned during the Intervention PD to a novel curricular task.TAs’ knowledge about the subject matter covered by the curriculum was not a direct predictor of RTOP score. Other studies have shown that increased subject matter knowledge does not necessarily correspond to more effective instruction , 65. TheThe effect of instructors’ teaching practices on the cognitive demand of an activity is well-studied , 11, 69.The strong relationship between the planned cognitive demand of a task (measured by TAGS) and TAs’ teaching practices (measured by the RTOP) is not simply a reflection of alignment between these instruments. Both the TAGS framework and the RTOP instrument award higher measures to lessons that are student-directed and inquiry-oriented , 58. HowThe observed tasks comprised only part of the range of the levels of cognitive demand developed by Tekkumru-Kisa et al. . None ofThese results suggest ways to prioritize resource investment when designing TA PD. While pre-semester orientations are a popular, low resource-investment form of PD , studiesStudent-centered teaching practices facilitate the implementation of a higher-cognitive demand curriculum  and thisThis study presents evidence indicating that Intervention PD was a significant predictor of TAs’ implementation of more student-centered teaching practices. However, several features of the Intervention PD differed from features of Traditional PD. Therefore, teasing apart the effects of individual features of the Intervention PD awaits further research. Moreover, the study reported here was for one curriculum context (statistics in a laboratory setting) at one institution. Additional studies are needed to see if the findings generalize across contexts and institutions.Individual TAs have differing experiences, motivations, and abilities regarding teaching . Any of This study suggests that TAs are transferring knowledge learned during Intervention PD to activities that are not related to the PD sessions. This inference is made based on observations of teaching analyzed using the RTOP, which limits the ability to draw stronger conclusions about how TAs may be applying knowledge about teaching gained from PD to novel activity contexts. Future studies should include qualitative analysis of interviews with TA participants to gain greater insight into TAs’ choices about whether and how to apply student-centered teaching practices to activities that were not part of PD.The demand for student-centered, inquiry-driven science instruction is increasing. This kind of instruction is cognitively demanding for students and requires instructors to implement student-centered pedagogical practices. This study indicates that reform-oriented curriculum implemented alongside curriculum-linked PD supports TAs’ enactment of student-centered teaching practices, with a relatively short time investment. The structure of PD appears important: TAs that participated in curriculum-linked PD that incorporated modelling of teaching practices and instructor-driven reflection about those practices (Intervention PD) taught curricular tasks in a more student-centered manner than TAs that participated in PD in which instructors learned about teaching practices and designed small changes to the traditional curriculum to incorporate those strategies . Curriculum-linked (Intervention) PD was not only effective in supporting TAs’ in using more reform-oriented teaching practices for the tasks covered during PD; TAs also transferred student-centered teaching practices learned during PD to novel lesson contexts. This study suggests that curricular tasks that are planned to be cognitively demanding are associated with implementation of student-centered teaching practices. These data indicate that student-centered teaching practices can be supported by PD situated in content that instructors will be teaching and by cognitively demanding curricula.S1 FileThis csv file contains information on each TA’s BioVEDA score, RTOP score for each observation. The file also contains the average cognitive demand TAGS level for each observed activity.(CSV)Click here for additional data file.


The accelerated SARS-CoV-2 evolution under selective pressure by massive deployment of neutralizing antibody-based therapeutics is a concern with potentially severe implications for public health. We review here reports of documented immune escape after treatment with monoclonal antibodies and COVID-19-convalescent plasma (CCP). While the former is mainly associated with specific single amino acid mutations at residues within the receptor-binding domain , a few cases of immune evasion after CCP were associated with recurrent deletions within the N-terminal domain of the spike protein . The continuous genomic monitoring of non-responders is needed to better understand immune escape frequencies and the fitness of emerging variants. The SARS-CoV-2 spike protein is the target of neutralizing antibody (nAb)-based therapeutics. Control of the COVID-19 pandemic is being hampered by continued evolution of SARS-CoV-2, which includes mutations in the spike protein that can affect immunogenicity and antibody-mediated neutralization. Evolutionary modeling suggests that SARS-CoV-2 strains harboring 1–2 deleterious mutations naturally exist, and their frequency increases steeply under positive selection by monoclonal antibodies (mAb) and vaccines  or molnuhttps://jbloomlab.github.io/SARS-CoV-2-RBD_MAP_clinical_Abs/) [Mutations that confer in vitro resistance to therapeutic anti-spike mAbs have been characterized with various methods and are informative about treatment-emergent immune escape. Deep mutational scanning (DMS) predicts protein expression, ACE2 binding, and mAb binding . The metal_Abs/)  and finaal_Abs/) . nAb binal_Abs/) ,10.4 led to ΔF140 spike mutation at day 45, followed by E484K at day 73, and an insertion in the N-terminal domain (NTD): these accumulating mutations led to complete immune escape [Although DMS was also applied to polyclonal antibodies in COVID-19-convalescent plasma (CCP) , the proe escape . Similare escape . AlthougIn vivo, while intrahost-SARS-CoV-2 mutation development is typically very low , faster We mined PubMed  for keywords related to COVID-19 , immune escape  and nAb-based therapeutics  both in vitro and in vivo. Clinical cases were annotated for eventual underlying immune deficiency, concurrent treatments and outcome. The 3D structural coordinates of the full spike protein   and the Our literature search revealed 32 papers that were then manually inspected to determine whether they included relevant information that was then retrieved, evaluated and organized into Tables.Escape from nAb-based therapeutics provides a crucial demonstration that these immune therapies target protective antigens, which the pathogen actively evades. Hence, the emergence of neutralizing-resistant variants in individuals receiving mAb and CCP provides powerful evidence for their antiviral activity. This evidence is independent of reduction in viral load, which has been reported with mAbs given early in disease but have been an inconsistent finding in randomized controlled trials (RCT) of CCP for COVID-19 .Obtaining the frequencies for this phenomenon from case series is not possible due to the high risk of selection biases, which would yield unrealistically high frequencies. In contrast, RCTs with their control groups are the suggested reference. With bamlanivimab, resistance was reported in 7% of patients, regardless of dosage (700/2800/7000 mg) versus <1% in patients treated with placebo ,29. AparIn contrast to mAb therapeutics, immune escape under CCP has not been investigated in RCTs. Hence, evidence exclusively stems from case series and case reports  and is fDeletions of amino acids from a protein structure generally result in greater structural changes than single amino acid changes, since these reduce the size of the protein and can trigger changes that propagate through the whole structure. Furthermore, the mechanism for the emergence of deletion variants appears to be very different from the single amino acid changes that are frequent from error-prone RNA replication and could involve deletions from RNA editing. Since CCP targets a large number of epitopes in the spike protein while mAbs target a single epitope, these molecular differences parallel what is expected from their respective selection pressures in the sense that escape from polyclonal preparations requires larger antigenic structural changes than escape from mAbs. In contrast to escape mutations selected for by mAb therapy, CCP selection yields point mutations throughout the spike protein. This reflects the vast antigenic surface area covered by the polyclonal antibodies within CCP. Escape mutations would be theoretically selected for on the basis of the most potent antibodies present in a particular CCP unit, which may vary markedly from donor to donor, which could explain the generally divergent evolution of SARS-CoV-2 in the presence of CCP. However, residues 141–144 and 243–244 are the sites of mutations or deletions in several cases, indicating these sites may offer effective escape from CCP derived from many donors, possibly by triggering a large-scale conformational rearrangement, as discussed above. As RBD binding antibodies are often neutralizing via ACE2 receptor occlusion, it is interesting that only 23% of CCP case studies identified the escape mutations within the RBD . This suNothing can be inferred about the fitness of an emerging mutant in the absence of selective pressure, but it is of interest that one variant with the E484K mutant that emerged after bamlanivimab therapy was able to infect multiple household contacts . In vitrWithin-host variation  is a natural phenomenon which has been reported for SARS-CoV-2 in immunocompetent patients and ultimately facilitates the persistence of infection. Among 33 patients having positive NPS PCR for an average of 18 days, Voloch et al., observed a distinguishing pattern of mutations over the course of the infection mainly driven by increasing A→U and decreasing G→A signatures, including spike mutations . G→A mutations are driven by the RNA-editing enzyme activities typical of innate immunity . NeverthImmunosuppression has been postulated to be an accelerator for viral evolution. Actually, On the other hand, co-administered small chemical antivirals can be mutagenic per se. Remdesivir has both amino and imino tautomers when pairing with RNA bases . Both amIn summary, our survey of the available mutational data show that escape variants associated with mAb and CCP therapy manifest different type of mutations. For mAbs, most mutations are single amino acid replacements in the RBD domain, while most variants eliciited in patients treated with CCP exhibited amino acid deletions. In fact, it is noteworthy that RBD mutations were relatively rare in CCP escape variants. Although the numbers are relatively small, which suggests caution in making generalizations, this dichotomy in geography of mAb and CCP mutations could reflect the fact that mAbs target a single epitope where the mAb–antigen interaction can be significantly altered by single amino acid changes while CCP targets many epitopes and has several mechanisms of action, such that evading polyclonal antibody immunity is likely to require much larger spike protein structural changes. Despite the relatively small set of variants for which there is molecular data available, the large variation of molecular solutions that allow SARS-CoV-2 to escape antibody-mediated protection is striking and suggest the need for continued vigilance in genomic surveillance, especially in cases refractory to therapy.


Alternative splicing (AS) of RNA is a key mechanism that results in the expression of multiple transcript isoforms from single genes and leads to an increase in the complexity of both the transcriptome and proteome. Regulation of AS is critical for the correct functioning of many biological pathways, while disruption of AS can be directly pathogenic in diseases such as cancer or cause risk for complex disorders. Current short-read sequencing technologies achieve high read depth but are limited in their ability to resolve complex isoforms. In this review we examine how long-read sequencing (LRS) technologies can address this challenge by covering the entire RNA sequence in a single read and thereby distinguish isoform changes that could impact RNA regulation or protein function. Coupling LRS with technologies such as single cell sequencing, targeted sequencing and spatial transcriptomics is producing a rapidly expanding suite of technological approaches to profile alternative splicing at the isoform level with unprecedented detail. In addition, integrating LRS with genotype now allows the impact of genetic variation on isoform expression to be determined. Recent results demonstrate the potential of these techniques to elucidate the landscape of splicing, including in tissues such as the brain where AS is particularly prevalent. Finally, we also discuss how AS can impact protein function, potentially leading to novel therapeutic targets for a range of diseases. Alternative splicing (AS) enables the production of multiple RNA isoforms from single genes, greatly increasing both transcriptomic and proteomic diversity . AS is tThe expression of different RNA isoforms can drive cellular differentiation, control cell functions and allow cells to respond to their environment . AS is hIn this review, we discuss current and emerging long-read sequencing methodologies for full-length RNA isoform detection and quantification including target enrichment, single cell and spatial transcriptomics approaches and ask how these can help us uncover the roles of alternative isoforms in health and disease.Early methods for transcriptome-wide identification of expressed genes and isoforms involved cloning cDNA libraries into vectors followed by Sanger sequencing to ascertain isoform sequences. While laborious, such methods, combined with innovations such as cap-trapping and normalisation, identified hundreds of thousands of full-length isoforms in various cells and tissues, helping to reveal the complexity of the transcriptome . ConversShort-read sequencing (SRS) with Illumina is currently the most popular sequencing technology. SRS is a well-supported method for transcriptomics and is both high-throughput and affordable . SequencSingle molecule, real-time (SMRT) sequencing developed by PacBio  detects differently labelled dNTPs as they are incorporated into a DNA strand . The reaNanopore sequencing, commercialised by ONT , uses an array of biological nanopores within a membrane that translocate nucleic acid under an electric current . As nuclLarge numbers of reads are required to deeply profile the transcriptome and short-read methods will commonly generate 30–50 million reads per bulk sample. This is higher than the throughput currently obtainable with long-read platforms for the same experimental cost. However, PacBio can now generate up to 4 million HiFi reads on a Sequel II and ONT >50 million reads on a PromethION flow cell respectively. These continual increases in throughput have made comprehensive expression profiling feasible with long read platforms .Third generation, LRS technologies have been widely applied to discover and quantify gene isoforms across a range of species , cell types and disease states. They have proved effective in characterising genes and isoforms in organisms with poorly annotated transcriptomes , novel iLong-read sequencing methods  can coveIn a recent benchmarking study, four sequencing methods  cDNA and PCR cDNA) were compared using five human cell lines . While SVarious computational programs have been developed to produce a set of high-confidence isoforms from LRS data such as FLAIR, FLAMES, SQANTI and TALON . These pAim2 , which causes chickenpox and shingles, within infected human neuronal cells . VZV estSF3B1 are known to impact alternative splicing in several cancers  has now been successfully implemented in several studies to enable sensitive profiling of full-length isoforms. Many genomic regions have been implicated in neurological disorders by genome-wide association studies (GWAS). However, many of these genomic regions are annotated as intergenic . Short-rA key limitation of CaptureSeq is the high cost of the oligonucleotide capture probes. Recently, Generation of full-length isoforms using LCS takes a substantial step further towards a complete map of the human transcriptome, however there are some limitations that remain including the lower capture efficiency observed with long-reads and potential artifacts from incomplete reverse transcription, RNA degradation, or incorrectly identified splice junctions due to sequencing errors .Bos taurus) resulted in approximately 50,000 full-length immunoglobulin G (IgG) cDNA reads and the characterisation of a similar number of variable antigen binding regions, which was previously not possible with SRS . This unbiased approach identified between 9 and 258 isoforms per gene, the absence of any dominant isoform and the alternative splicing of an exon previously thought to be constitutive (NRXN1α isoforms in human PFC and in neurons derived from induced pluripotent stem cells (iPSCs)  . Treutletitutive . A more  (iPSCs) . A catalin mouse . iPSC-neactivity .These studies on brain genes demonstrate that existing isoform annotations are far from complete and that novel transcripts may be playing a larger role in gene expression changes and in disease than previously understood. These findings can inform our understanding of the pathophysiology of complex neuropsychiatric disorders such as schizophrenia, where identifying the full repertoire of genes isoforms is a critical step towards a more genetically informed pathway to precision medicine . The ideNDUFB10. This previously unannotated exon contained an early in-frame stop codon leading to nonsense mediated decay (NMD) of the altered transcript  profiles gene expression in individual cells. Short-read (SR) scRNA-Seq is now well established and has been very successful in identifying cell types and trajectories; cellular and tumour heterogeneity; expression differences between cell types and for performing high-throughput perturbation assays . SR scRNSR scRNA-Seq studies using “whole transcript” methods have identified significant cell-to-cell differences in isoform expression . HoweverInitial studies combining long reads with single cell technology were all performed on less than ten cells using either ONT  or PacBiThe first demonstration of LR scRNA-Seq in a significant number of cells utilised the PacBio LRS ScISOr-Seq method, complemented by SR scRNA-Seq, to study neurons, astrocytes and microglia from mouse cerebellum . While iThe higher error rate of nanopore sequencing provides additional challenges for LR scRNA-Seq due to the critical importance of identifying the cell barcode and UMIs present in each read. To address this, As demonstrated by LMNA expressing a unique isoform in most cells  are incredibly diverse and unique to each T- or B- cell lineage due to DNA rearrangement, AS and somatic hypermutation, however typing and tracing lineages at the single cell level has proven difficult  and generation of chimeric cDNA . Both isSample quality is critical for long-read data as degraded RNA or DNA can lead to fragmented sequences negating the primary advantage of long-read approaches. The quality of RNA, measured by the RNA integrity number (RIN), can vary significantly between samples and sample types. A minimum RIN of 6 , was recommended for long-read amplicon sequencing , howeverThe accuracy of ONT and PacBio long reads is generally lower than that of SRS and this can impact determination of splice sites and isoform identity as well as barcode and UMI assignment . In addiAs long-read sequencing continues to build a foundation of high-confidence isoforms, particularly for complex genes, the question of how these isoform changes functionally impact the proteome can begin to be answered. There is considerable debate over functional roles for alternative transcripts, with some studies reporting a single major isoform for most genes and little evidence for protein variants due to alternative splicing . Other sCACNA1C, 2+ channel. An additional splice-site-level analysis identified almost half of the isoforms had splice junction changes encoding microdeletions of 3–5 amino acids within regions previously implicated in channel conductance  and higher throughput . While lower accuracy can necessitate sophisticated error correction tools and/or paired short-read data, the advantages of long-over short-reads for isoform detection and quantification already outweigh many of the drawbacks in error rate . FurtherLong-read transcriptomics is still a nascent field of research, however it has already had a major impact on our understanding of spliced isoform diversity and expression . As tran


Desmodus rotundus, a highly social species in which adults feed only on blood, and engage in both mouth-to-body allogrooming and mouth-to-mouth regurgitated food sharing. Shotgun sequencing of samples from six zoos in the USA, 15 wild-caught bats from a colony in Belize and 31 bats from three colonies in Panama showed that faecal microbiomes were more similar within colonies than between colonies. To assess microbial transmission, we created an experimentally merged group of the Panama bats from the three distant sites by housing these bats together for four months. In this merged colony, we found evidence that dyadic gut microbiome similarity increased with both clustering and oral contact, leading to microbiome convergence among introduced bats. Our findings demonstrate that social interactions shape microbiome similarity even when controlling for past social history, kinship, environment and diet.The ‘social microbiome’ can fundamentally shape the costs and benefits of group-living, but understanding social transmission of microbes in free-living animals is challenging due to confounding effects of kinship and shared environments . Here, we report evidence for convergence towards a social microbiome among introduced common vampire bats,  A sociaUnderstanding the effects of social transmission on microbiome similarity in free-living animals is challenging, however, due to confounding effects of kinship and shared environments : offspriDesmodus rotundus, a highly social species and obligate blood feeder. Due to this specialized diet, dietary variation plays a smaller role in explaining microbial diversity among vampire bats [Here, we introduce individuals from distant sites and track their social interaction rates to assess the role of social interactions in shaping gut microbiome composition. We used the common vampire bat, ire bats –12 compaire bats . On the ire bats . In addiire bats –17. This. In the experimentally merged colony, social networks of rates of clustering and oral contact predicted gut microbiome similarity, and faecal samples showed evidence for microbiome convergence over time.To assess evidence for group-level convergence in the vampire-bat social microbiome, we sampled the faecal and gut microbiome of captive-born vampire bats from six zoos, 15 female vampire bats from a wild colony in Belize and an ‘experimentally merged’ colony composed of 26 wild-caught vampire bats and five captive-born offspring sourced from three distant sites in Panama, then housed together in captivity for four months. 2 (a)To assess colony-level variation in faecal microbiomes, we obtained three to six faecal samples from vampire bats housed at six zoos: (i) North Carolina Zoological Park, Asheboro, North Carolina; (ii) Cincinnati Zoo and Botanical Garden, Cincinnati, Ohio; (iii) Dallas World Aquarium, Dallas, Texas; (iv) Memphis Zoological Garden and Aquarium, Memphis, Tennessee; (v) Aquarium and Rainforest at Moody Garden, Galveston, Texas; and (vi) Sedgwick County Zoo, Wichita, Kansas. In Belize, we collected faecal samples from 15 female bats (capture and fieldwork described previously ). In PanStaphylococcus infection during the study, which required administration of enrofloxacin and isolation from the rest of the colony from 21 July to 5 August. However, no faecal samples were collected after 13 July, and we also failed to detect any clear effect of this antibiotic on the diversity of final gut microbiome sampled on 15 October . For the final gut sample, bats from the experimentally merged colony were sacrificed using isoflurane to anaesthetize them  prior to rapid decapitation. Gut samples were collected from the distal colon and smeared on FTA cards.In the experimentally merged colony, bats were fed with cattle or pig blood from a meat processing plant that was chemically defibrinated with 44 g of sodium citrate and 16 g of citric acid per 19 l container. The blood was either refrigerated for up to 6 days or stored frozen, then thawed immediately before being provided to the bats. Nine bats developed a  (b)In the merged colony, we recorded clustering and oral contact from video recorded by three infrared surveillance cameras (Foscam NVR Security System) for 6 h each day from 23 June 2019 to 4 August 2019, and from 11 August 2019 to 14 October 2019 , as described previously [We calculated undirected clustering networks using the simple ratio index in the R package asnipe , and und (c)DNA was extracted from FTA cards in a designated pre-PCR BSL-2 laboratory using the Qiagen PowerSoil kit . Every batch of 8–24 sample extractions included a negative control. Samples with discoloration after the wash buffer step in the protocol were eluted and re-bound to filter columns with binding buffer for one to three additional washes.Genomic libraries were constructed using the Illumina DNA Prep kit . Duplicate samples were randomly selected or combined for separate library builds for the Belize colony, the aggregated zoo populations and the experimentally merged colony . Extraction negatives had negligible quantities of DNA as quantified by Qubit readings , and were combined to make per-population negative libraries. A separate library negative was also constructed. Overall, 145 samples, including samples, duplicates and listed negative controls, were sequenced on a NovaSeq 6000 S4 lane (Illumina), resulting in approximately 1.75 billion 150 bp paired-end reads. Because we used DNA extraction without reverse transcription, our microbiome analyses do not include RNA viruses. (d). We found high consistency in the duplicates from these initial profiles, varying less than 5% in read counts attributed to each taxa. Once initial analyses were complete, duplicates (both paired sets of reads) were combined with their partner for further analyses.Sequences from negative extraction and sequencing libraries were used to create population-specific filtering databases in addition to the human genome database provided by KneadData to filter all sample reads . Paired-MetaPhlAn 3 identified 102 species-level taxa across all three populations . Alignme (e)To test the simultaneous effects of clustering and oral contact networks on gut microbial similarity in the experimentally merged colony, we used multiple regression quadratic assignment procedure with double semi-partialing (MRQAP-DSP) from the asnipe R package ,27. To cp-value, we fit the model with the same fixed effects (no random effects), then simulated the null hypothesis by randomizing the time differences within each dyad 1000 times to generate a distribution of expected values to compare with the observed.To test for evidence of convergence in similarity of faecal samples, we compared the similarity of initial opportunistic pre-merge faecal samples from five Lake Bayano bats on 19 May 2019 to later samples from other Lake Bayano bats and to later samples from three Tolé bats that were also sampled before and after the merge on 14 June 2019. We then calculated the change in similarity for the 25 pairs where both bats were sampled before and after the merge (10 pairs captured at the same wild roost and 15 pairs introduced in captivity). To test the prediction that faecal microbiomes converged for introduced pairs and diverged for same-roost pairs, we fit a general linear mixed effects model (MCMCglmm function and package using default priors) where fixed effects were time , dyad type (same-roost versus introduced) and the interaction between time and dyad type. Both bats were entered as a multi-membership random effect. After an interaction was detected, we fit the same mixed model for both dyad types separately with time as the fixed effect. As an alternative approach, we also tested the interaction term using a permutation test; to get a permutation . 3β = 0.55, n = 57 bats, p < 0.001; Bats from the same colony had more similar faecal microbiomes than bats from different colonies . Each effect also remained when controlling for whether the bats shared their capture site . After four months together, bats sourced from the same wild roost did not have more similar gut microbiomes than introduced bats , and we did not detect that the five mothers had more similar gut microbiomes to their own pups  compared to other pups .Over four months together, all of the introduced Panama bats from distant sites engaged in clustering, and most engaged in oral contact . The final gut microbiome similarity was predicted by rates of both oral contact and clustering, each when controlling for the other ; they increased in the 15 introduced pairs , and decreased in the 10 same-roost pairs  among both familiar individuals and individuals with no previous contact. Our findings confirm that direct horizontal social transmission of microbes is an important component of microbiome similarity [These findings corroborate previous studies of primates and rodents showing that social networks predict microbiome similarity after statistically controlling for environment and diet ,29–41. Fmilarity , these mmilarity –39,45. EA common challenge in microbiome studies, especially in novel host animals, is the inability to identify significant portions of the microbial community. In studies of social transmission of microbes, strain-level identification appears to present the most robust evidence for social behaviours transmitting microbes, yet this method is rare ,47. Incr


The Inclusion of ethnically diverse populations in genetic research can provide deep insights into specific pathogenic variants that differ across and within populations. Such insights may be useful to guide diagnosis and provide relevant clinical interventions . UnfortuWe accepted only high-quality research work in this research topic, including review manuscripts describing the genetics of diseases in underrepresented populations such as African, Asian, Latin American, and other indigenous populations. Topics could include Genome-wide association studies, Gene-environment interactions, Population genetics, epigenetics, pharmacogenomics, Polygenic/Genetic Risk Scores. The final topic issue has 10 published articles out of 18 submitted manuscripts, covering various genetic studies in African, Asian, Arab, and other previously under-studied populations.Font-Porterias et al. analysed genome-wide array data and exome sequences of 89 healthy Spanish Roma individuals to investigate chronic diseases and traits. The Roma represents the largest trans-national minority ethnic group in Europe, originating from South Asia and receiving extensive gene flow from West Eurasia. The authors found frequency differences between the Roma and other non-Roma populations for disease-causing variants and variants linked to drug response. The work demonstrated the value of merging population genetics and genetic epidemiology methodology.Most of the studies demonstrated that better representation of ethnically and ancestrally diverse populations is crucial for our ability to comprehensively characterise the genetic architecture of complex traits and human disease. For example, Chande et al. analysed the effect of genetic ancestry and ethnicity on observed disease prevalence. They predicted disease risk in Colombia populations by mainly comparing the disease prevalence and socioeconomic indicators of Colombia’s three major ethnic groups; from Mestizo, Afro-Colombian, and the Indigenous group. The study demonstrated a strong link between ethnicity, ancestry, and health outcomes. In their mini review Nagaraj and Toombs discussed “the history of pharmacogenomics and highlight the inequalities that must be addressed to ensure equal access to pharmacogenomic-based healthcare and reviewed the efforts to conduct the pharmacogenomic profiling of chronic diseases among Australian Indigenous populations including the impact of the lack of drug safety-related information on potential ADRs among individuals in these Australian Indigenous communities”.Other papers demonstrated how such differences in clinically relevant genetic variation can impact health inequalities, if populations are not sufficiently represented in genetic research. Soremekun et al. meta-analysed summary statistics from genome-wide association studies of multiple African data, carried out a Bayesian fine-mapping, and assessed the causal relationship between White Blood Cell (WBC) subtypes and Asthma including a two-sample Mendelian Randomization (MR) analysis. This study identified five novel genes not previously reported associated with any WBC subtype. Their MR analysis showed that monocytes count and neutrophils counts are associated with an increased risk of asthma. Singh et al. conducted a systematic review to summarise studies on genetic association studies for blood pressure-related traits in populations with African ancestry. The review highlighted the limited number of available studies, especially in Africa. Nevertheless, across the five blood pressure-related traits, they found that 26 genome-wide significantly associated SNPs have been identified, including 12 associations that have not previously been described in non-African studies.Papers in this special issue actively addressed underrepresentation by investigating the genetics of complex traits in diverse populations. One approach used to address the imbalance is synthesising existing studies to empower more generalizable conclusions. Al-Homedi et al. leveraged several small-scale genome-wide association studies of metabolic syndrome (MetS) performed in Arab populations to investigate the genetic basis of MetS in the population. Their systematic review and meta-analysis identified 36 studies and shows that the most frequently studied genes were FTO and VDR. The study failed to find any unique genetic association between MetS and Arabian populations.Similarly, Dlamini et al. investigated the associations between genetic variation in CYP17A1 and SERPINA6/A1 and circulating glucocorticoid concentrations in black South African adults. The authors found that a variant rs17090691 with G effect allele at SERPINA6/A1 was associated with increased diastolic blood pressure in all adults at p-value of 9.47 × 10–6. However, no association was seen between these genetic variants and glucocorticoids or between any variants in CYP17A1 and metabolic outcomes after adjusting for multiple testing.It may be unsurprising that other studies in this Special Issue focussed on cardiometabolic outcomes, given the immense public health burden of cardiovascular disease. Wang et al., the authors recruited over 1,400 Han Chinese patients with coronary artery disease. They conducted linear regression analyses on the genetic variants of mitochondrial DNA (mtDNA) and lipidomic traits in two independent groups where they found three associations. They also explored the associations between genetic variants in mitochondrial and lipids traits and reported two significant associations. The study provided “insights into the lipidomic context of mtDNA variations and highlighted the importance of studying mitochondrial genetic variants related to lipid species”.In Owalla et al. investigated the effects of stress and the genetic variation within the FK506 binding protein 5 (rs1360780) and glucocorticoid receptor (rs10482605) genes on internalizing disorders (ID) status in a CA-HIV cohort in Uganda. Using logistic regression, the authors assessed the relationships between IDs and recent stress, chronic stress, and the investigated genotypes. They reported no significant association between IDs and rs1360780 and rs10482605 polymorphisms within the FKBP5 and glucocorticoid receptor genes nor observed any gene–environment effect on vulnerability IDs in the Ugandan population, but shows that severe recent stress increases the vulnerability to IDs among CA-HIV. The findings further supported that polymorphisms at genetic loci only contributed minimal to the genetic vulnerability to IDs.Two of the studies focused on mental health genetics in African populations. Genetic research could play an important role to decrease stigma and better understand the causes of this highly prevalent, yet poorly understood global health problem. Kalungi et al. assessed the relationships between acute stress and Internalizing mental disorders (IMDs), and moderation by chronic stress and SNPs in SLC6A4 and TPH2 in Ugandan populations. The authors observed a “statistically significant association between severe acute stress and IMDs. Acute stress was found to interact with 5-HTTLPR-rs25531S-A-S-A haplotype to increase the risk for IMDs among Ugandan HIV+ children and adolescents but no evidence for a combined interaction of acute stress, chronic stress, and 5-HTTLPR-rs25531 on IMDs”.Similarly, Overall, the studies in this Special Issue highlighted the importance of genetic research in diverse global populations. They synthesized existing data to enable conclusions for diverse populations, demonstrate population variation linked to clinically relevant genes, discover novel genetic associations, and investigate the implications for health inequalities.


Cardiac diseases such as myocardial infarction (MI) can lead to adverse remodeling and impaired contractility of the heart due to widespread cardiomyocyte death in the damaged area. Current therapies focus on improving heart contractility and minimizing fibrosis with modest cardiac regeneration, but MI patients can still progress to heart failure (HF). There is a dire need for clinical therapies that can replace the lost myocardium, specifically by the induction of new myocyte formation from pre-existing cardiomyocytes. Many studies have shown terminally differentiated myocytes can re-enter the cell cycle and divide through manipulations of the cardiomyocyte cell cycle, signaling pathways, endogenous genes, and environmental factors. However, these approaches result in minimal myocyte renewal or cardiomegaly due to hyperactivation of cardiomyocyte proliferation. Finding the optimal treatment that will replenish cardiomyocyte numbers without causing tumorigenesis is a major challenge in the field. Another controversy is the inability to clearly define cardiomyocyte division versus myocyte DNA synthesis due to limited methods. In this review, we discuss several studies that induced cardiomyocyte cell cycle re-entry after cardiac injury, highlight whether cardiomyocytes completed cytokinesis, and address both limitations and methodological advances made to identify new myocyte formation. Cardiovascular diseases such as myocardial infarction (MI) are a major cause of human mortality . MI is cStudies in lower vertebrates such as zebrafish and newts have demonstrated that their hearts can regenerate after injury by proliferation of pre-existing cardiomyocytes . The aduCardiomyocytes exit the cell cycle shortly after birth and are thought to be terminally differentiated . HoweverInhibition of cyclin-dependent kinase inhibitors (CKIs) has also been shown to reactivate the cell cycle in cardiac myocytes. Triple knockdown of CKIs including p21, p27, and p57 induced myocyte proliferation with evidence of karyokinesis and cytokinesis in neonatal and adult cardiomyocytes . LikewisEmbryonic heart development and postnatal myocyte maturation are regulated by multiple signaling pathways that have been modulated to stimulate cardiomyocyte proliferation. One of the conserved signaling cascades involved in myocyte growth is the hippo pathway. This pathway functions to restrict cardiomyocyte proliferation through phosphorylation-mediated inactivation of its downstream transcription co-activators YAP/TAZ, to cause cardiomyocytes to withdraw from the cell cycle. Therefore, inactivation of hippo signaling in embryos results in enlarged hearts from overactivation of the cardiomyocyte cell cycle ; conversOther hippo-related genes can also induce adult cardiomyocytes to re-enter the cell cycle. Inactivation of the hippo pathway by conditional knockout of its regulatory Salv gene in adult mice has been shown to reduce scar size, induce new myocyte formation, and attenuate cardiac dysfunction in MI and HF disease models ,41. RelaThe neuregulin/ERBB2/ERBB4 pathway is also involved in cardiomyocyte proliferation. Neuregulin (NRG1) is an extracellular mitogen that binds to its tyrosine receptor subunits ERBB2 and ERBB4 to activate downstream signaling pathways including the PI3K/AKT pathway to induce cardiomyogenesis during fetal heart development. In adult mammals, the neuregulin pathway can activate the cell cycle in mature cardiomyocytes to induce proliferation. Treatment of adult rat ventricular myocytes with NRG1 promoted mostly mononucleated cardiomyocytes to re-enter the cell cycle and complete cytokinesis . In vivoOther signaling pathways, including PI3K/AKT/CDK7, Wnt/β-catenin, PDGFR-β, and Notch signaling pathways can also stimulate cardiomyocytes to proliferate and induce cardiac regeneration during heart disease or injury ,63,64,65miR-17-92 cluster, which is required for cardiomyocyte proliferation in embryonic and neonatal hearts; overexpression of this cluster in the heart can induce cardiomyocyte DNA synthesis at all ages in mice and following cardiac injury, thus, protecting the heart from MI-induced remodeling and dysfunction [Chek1) to inhibit myocyte proliferation after birth. Thus, knockdown of the miR-15 family increased mitosis in neonatal cardiac myocytes [MicroRNAs (miRNAs) regulate post-transcriptional gene expression and several miRNAs have been linked to cardiomyocyte proliferation. High throughput functional screening has identified 40 human miRNAs that promote cardiomyocyte growth and division, with miR-590 and miR-199a as the major regulators of this process in neonatal and adult rodents . Followifunction . Of thisfunction . Alternafunction ; miR-195myocytes  and in amyocytes .Several miRNAs have also been shown to regulate genes in signaling pathways that influence cardiomyocyte mitosis. The miR-302-367 cluster represses signal transduction in the hippo pathway to induce cardiomyocyte proliferation and this cluster is required for embryonic development, as cardiac knockdown of miR-302-367 results in hypoplasia . TherapeDuring embryonic development, the mammalian heart primarily utilizes anaerobic metabolism to produce energy and fetal cardiomyocytes have a high proliferative capacity; immediately after birth, the heart is exposed to blood with a much greater oxygen content and there is a switch to aerobic metabolism to fuel the increased contractile and functional requirements. This switch to higher oxygen levels is associated with the exit of cardiomyocytes from the cell cycle and reduces their mitotic potential . NeonataPrevious studies have shown hypoxic preconditioning of cardiac myocytes limited ROS production, decreased cardiomyocyte death, and reduced infarct size following prolonged ischemia in rodents and large mammals ,84,85. IFetal mitotic cardiomyocytes utilize pyruvates in anaerobic glycolysis to generate energy, but by postnatal day 7, they switch to utilize fatty acids in mitochondrial oxidative phosphorylation for energy and most cardiomyocytes have exited the cell cycle. Reversal of this process can prolong cardiomyocyte proliferation, as neonatal mice fed a fatty acid deficient diet extended the proliferative window of cardiomyocytes up to 3 weeks postnatally . In adulSeveral methods have been employed to study cardiomyocyte proliferation . Most coA number of mouse models have been generated to provide more direct evidence of cardiomyocyte renewal. Mosaic analysis with double markers (MADM) mouse models fluorescently label cardiomyocytes that have completed cytokinesis. Using Cre-loxP recombination, daughter cells are single-labeled green or red after cell division . This moMore recently, an Aurora Kinase B (Aurkb) mouse system was generated to label cardiomyocytes red when Aurora B was expressed during mitosis and cytokinesis . This apThe human heart is believed to be a post mitotic organ, but numerous studies in animals and humans have provided evidence suggesting there can be a low rate of myocyte renewal. The mammalian heart has a high myocyte proliferative capacity following birth, but after several days, these cells exit the cell cycle. There are only a few cardiomyocytes that maintain their proliferative potential throughout life which is likely responsible for the yearly 1% cardiomyocyte renewal in humans. Following cardiac injury, embryonic and postnatal animals can completely or partially regenerate their myocardium by proliferation of pre-existing cardiomyocytes. In adults, few cardiomyocytes re-enter the cell cycle and divide after injury, which leads to adverse cardiac remodeling and function. Studies of embryonic cardiac development and postnatal cardiomyocytes have identified regulators of the cardiomyocyte cell cycle that can be manipulated in adults to induce cardiomyocyte mitosis.Overexpression of cell cycle promoters such as cyclins and CDKs enhance cardiomyocyte proliferation and prolonged expression can result in cardiac hyperplasia. These cell cycle stimulators are reparative in MI and TAC injury models by promoting new myocyte formation, reducing fibrosis, and attenuating cardiac pump malfunction. Likewise, repression of CKIs or cell cycle inhibitors induce DNA synthesis in cardiac myocytes which can be synergistically enhanced when combined with cell cycle activators. Signaling pathways involved in cell cycle progression such as the neuregulin/ERBB2/ERBB4 pathway or in cardiomyocyte cell cycle arrest including the hippo-YAP pathway have been targeted to promote proliferation of pre-existing myocytes. Upregulation of YAP or inhibition of hippo genes in neonatal and adult hearts increase cardiac mass by inducing proliferation of mature cardiac myocytes. In disease models, modulation of this pathway improves cardiac repair and animal survival, but persistent activation of YAP can leave many cardiomyocytes in a dedifferentiated state which can lead to heart failure. However, regulation of YAP by transient expression of miR-302-367 has been reported to prevent heart failure and increased cardiac regeneration following MI. Other miRNAs have also been reported to work independently or in conjunction with several signaling pathways to reactivate the cardiomyocyte cell cycle and promote division, thus, improving MI outcomes. Importantly, miRNA modulation of mitotic gene expression and manipulation of cell cycle signaling pathways need to be tightly controlled to restore cardiomyocyte numbers after injury without provoking tumorigenesis or accumulation of immature cardiac myocytes. Recent studies have suggested that hypoxemia in adult mice could induce proliferation of cardiomyocytes in healthy and post MI hearts by causing a metabolic switch in cardiomyocyte mitochondrial oxidation, from oxidative phosphorylation to glycolysis. These studies only showed modest improvement in cardiac function and cardiomyocyte proliferation, and while myocytes may have entered the cell cycle and increased DNA synthesis, it was not clear that they completed cytokinesis and generated new myocytes. More research is required to discover new pathways and mechanisms that can influence cardiomyocyte division and avoid endoreplication or endomitosis. Implementation of these practices can increase the therapeutic potential of these treatments for enhancing cardiac regeneration and repair following injury.An obstacle in determination of new myocyte formation is the accurate interpretation of studies using proliferative markers such as EdU, Ki67, pHH3, and Aurora B. Most of these markers indicate cell cycle re-entry and DNA synthesis but only Aurora B is present during cytokinesis . HoweverThese new approaches should be implemented in future studies to develop reliable strategies for generating new cardiac myocytes and clarifying if/when new myocytes are generated. For example, it was originally thought that cardiomyocytes retain their proliferative capacity up to 7 days after birth  but a st


A rare form of X-linked Charcot-Marie-Tooth neuropathy, CMTX3, is caused by an interchromosomal insertion occurring at chromosome Xq27.1. Interestingly, eight other disease phenotypes have been associated with insertions (or insertion-deletions) occurring at the same genetic locus. To date, the pathogenic mechanism underlying most of these diseases remains unsolved, although local gene dysregulation has clearly been implicated in at least two phenotypes. The challenges of accessing disease-relevant tissue and modelling these complex genomic rearrangements has led to this research impasse. We argue that recent technological advancements can overcome many of these challenges, particularly induced pluripotent stem cells (iPSC) and their capacity to provide access to patient-derived disease-relevant tissue. However, to date these valuable tools have not been utilized to investigate the disease-associated insertions at chromosome Xq27.1. Therefore, using CMTX3 as a reference disease, we propose an experimental approach that can be used to explore these complex mutations, as well as similar structural variants located elsewhere in the genome. The mutational hotspot at Xq27.1 is a valuable disease paradigm with the potential to improve our understanding of the pathogenic consequences of complex structural variation, and more broadly, refine our knowledge of the multifaceted process of long-range gene regulation. Intergenic structural variation is a critically understudied class of mutation, although it is likely to contribute significantly to unsolved genetic disease. Charcot-Marie-Tooth (CMT) neuropathy refers to a group of inherited conditions characterized by the length-dependent degeneration of both motor and sensory neurons within the peripheral nervous system. In the absence of a cure for this debilitating condition, individuals with CMT present with varying degrees of distal muscle wasting and sensory impairment, leading to chronic disability . This diInterestingly, the CMTX3 insertion occurs within a quasi-palindromic sequence on chromosome Xq27.1 which is a recurring hotspot for genomic rearrangements. To date, eight additional disease phenotypes have been associated with complex insertions, or insertion-deletions, occurring at this specific locus . For simThis challenge has largely arisen from our incomplete understanding of the non-coding genome, which has historically been overlooked and considered “junk DNA”. Efforts of The ENCODE Project have provided fundamental resources to highlight the role of non-coding DNA in transcriptional regulation , howeverSince the discovery of the first disease phenotype caused by a complex insertion at Xq27.1 , variousWith this in mind, the purpose of this review is to summarize the various phenotypes associated with complex rearrangements at Xq27.1 and highlight where recent technological advancements can be applied to gain insight into their underlying pathomechanisms. Using CMTX3 as the reference disease model, we propose an experimental approach that incorporates permutations of next-generation sequencing and induced pluripotent stem cell (iPSC) technologies to investigate how intergenic genomic rearrangements can produce disease. Understanding the contribution of intergenic structural variation to genetic disease is a growing area of interest, and the mutational hotspot at Xq27.1 provides a unique paradigm for increasing our knowledge of the complex, multi-faceted process of gene regulation.de novo insertion from chromosome 9q21 as part of a complex disease phenotype that also included bilateral anophthalmia, orbital teratoma, and neurodevelopmental anomalies  which is predicted to form an unstable hairpin loop structure . Accordiectively  (Zhu et ectively . WithoutPMP22) gene  mutations. SV is a broad term used to describe mutations that affect a minimum of 50 bp of DNA at a time . SV can 22) gene . However22) gene .SOX9, which identified two novel enhancer clusters regulating SOX9 expression within a narrow developmental window  are sequences of non-coding DNA controlling the expression of nearby genes . Enhancel window . Strong ecursors . Furtherpression . Accordipression . Therefopression , inferripression .Drosophila model recently identified a surprisingly large number of ‘bifunctional elements’, i.e., regulatory sequences that can function as either enhancers or silencers, depending upon the spatiotemporal context, further attesting to the complexity of gene regulation   and reprgulation .Another important class of distal CREs are insulator elements. These are often binding sites for ‘insulator proteins’ such as CCCTC-binding factor (CTCF) . InsulatTranscriptional regulation heavily relies on the spatial organization of chromatin within the three-dimensional nucleus. Addressing the many complexities of 3D chromatin architecture is beyond the scope of this review, however, excellent articles provide in-depth discussion of this topic .The functionality of CREs is intimately dependent upon chromatin structure . For exaHere, we have provided a simplified overview of long-range gene regulation, which is a highly complex, multi-faceted process and remains an active area of research. Despite significant progress in recent years, our current understanding of long-range gene regulation remains incomplete . In someThe genomic landscape of the Xq27.1 locus has shown the disease-associated insertions occur within a gene desert. Therefore, the pathomechanism of these complex mutations does not involve disrupting the sequence of known coding genes located within this genomic region. However, the insertions introduced at Xq27.1 often contain regions of DNA that encode either partial  and/or fin silico splice site prediction for the rearrangement associated with congenital ptosis suggests that it cannot be excluded as a possibility , altering spatiotemporal regulation by physically distancing the gene from its typical CREs . InitialTranscriptional dysregulation can also arise from mutations that disrupt TAD boundaries, given that long-range regulatory interactions typically occur within the constraints of these defined genomic domains . AccordiEnhancer (or silencer) deletions should also be considered as potential pathomechanisms for the diseases caused by complex genomic rearrangements, such as those observed in XL-HPT, where the insertions occur in combination with deletions at Xq27.1. Interchromosomal insertions from 2p25.3 accompanied by deletions at Xq27.1 were reported in two unrelated XL-HPT kindreds . The insShh) gene which contains enhancer elements that drive ectopic Shh expression during limb development  and alter spatiotemporal expression profiles . Multiplelopment .SOX3  proposed a systematic pipeline for solving SV pathomechanisms that combines bioinformatics with experimental methods, whilst also considering the patient’s unique phenotype. This approach was used to investigate a range of complex SV found in patients with genetically unsolved (neuro)developmental disorders. Briefly, potential candidate genes (within or 2 Mb adjacent to the SV), were ranked based on phenomatch scores calculated by comparing the patient’s phenotype to known phenotypes already associated with each gene . TranscrSHH. Coding mutations in SHH cause holoprosencephaly  and its important role in developmental regulation  generated knockout animal models with targeted deletions of either SOX3 or a nearby ultraconserved element (uc482) presumed to regulate SOX3 expression. For both transgenic lines, null hemizygotes displayed normal parathyroid gland development and function, suggesting that XL-HPT involves spatiotemporal disturbances in SOX3 expression that cannot be recapitulated by a knockout model  it is important, although often difficult, to perform studies in the appropriate cellular context, which can depend on both cell-type and developmental stage, and 2) it is critical to utilize the patient’s genetic background to retain the pathogenic rearrangement, as generic animal knockout models are not reflecting the regulatory disruptions causing these specific diseases. Fortunately, recent developments in cellular reprogramming technologies can now provide access to patient-derived disease-relevant tissue, thereby circumventing the major challenges that have previously led to a research impasse for these disorders.Induced pluripotent stem cells (iPSC) theoretically have the capacity to differentiate into any somatic cell population and can be generated from readily accessible sources such as fibroblasts , periphein vivo developmental processes to produce the desired cell type  can be specifically assessed through targeted transcriptome profiling. By utilizing tools such as the NanoString Technologies nCounterGlobal transcriptome profiling using RNA-seq should also be performed on CMTX3 neuronal tissue. This will provide an additional dataset to validate dysregulation of candidate genes, and through gene ontology analysis, can identify specific biological pathways impacted by the SV. Importantly, RNA-seq analysis will also detect fusion transcripts and/or novel transcript variants arising from the inserted fragment, which are alternative pathomechanisms that may cause CMTX3.SOX9 expression within iPSC-derived cranial neural crest cells were recently predicted (in vitro and/or in vivo reporter assays (Once a strong candidate gene (or genes) has been identified, experimental tools can help uncover the molecular mechanisms driving the dysregulation. Using the candidate gene(s) promoter as an anchor, Capture Hi-C , can ideredicted . Any potr assays . A globar assays , allowinr assays .Gene dysregulation may also arise through position effect variegation, whereby a gene can be silenced if it is placed in the vicinity of heterochromatin . If the in vitro migration assays were performed, revealing an impaired migratory capacity in patient-derived cranial neural crest cells (in vivo. The nematode Caenorhabditis elegans (C. elegans) is particularly useful for modelling neuronal diseases, including CMT, due to their simple and well-characterized nervous system. Recently, another form of X-linked CMT, CMTX6, was modelled using this approach, which was able to recapitulate the locomotion deficits and neurodegeneration presenting in patients  at Xq27.1. This intriguing group of diseases may act as a unique paradigm for providing valuable insight into the pathogenic mechanisms underlying complex intergenic SV and more broadly, enhance our understanding of long-range gene regulation. Overall, SV involving the non-coding genome represents an understudied class of mutation contributing to unsolved genetic diseases that have been excluded for gene coding mutations. Understanding the molecular mechanisms will be essential for improving genetic diagnosis and guiding the development of future therapies for patients suffering from these debilitating conditions.


Combining machine learning-based modeling and whole transcriptome data with prior variable selection through protein-protein interaction network analysis by a diffusion kernel successfully predicted KRAS dependency in the KRASwt subgroup and in all investigated cancer cell lines. In contrast, modeling by RAS activating events (RAE) or previously published RAS RNA-signatures did not provide reliable results, highlighting the heterogeneous distribution of RAE in KRASwt cell lines and the importance of methodological references for expression signature modeling. Furthermore, we show that predictors of KRASwt models contain non-substitutable information signals, indicating a KRAS dependency phenotype in the KRASwt subgroup. Our data suggest that KRAS dependent cancers harboring KRAS wild type status could be targeted by directed therapeutic approaches. RNA-based machine learning models could help in identifying responsive and non-responsive tumors.Recent progress in clinical development of KRAS inhibitors has raised interest in predicting the tumor dependency on frequently mutated RAS-pathway oncogenes. However, even without such activating mutations, RAS proteins represent core components in signal integration of several membrane-bound kinases. This raises the question of applications of specific inhibitors independent from the mutational status. Here, we examined CRISPR/RNAi data from over 700 cancer cell lines and identified a subset of cell lines without KRAS gain-of-function mutations (KRAS Frequently, gain-of-function mutations or copy number alterations (CNA) at different levels of the signaling cascade lead to overactivity and thereby increased cell growth, migration, and invasion5. Due to its high relevance in cancer per se, intensive research has been conducted aiming to develop targeted therapies. In recent years, several drugs entered clinical application focusing on inhibitors that are directed against membrane receptors preventing constitutively activated signal transduction8. EGFR represents one of the most well-known examples9. However, therapy is limited by various resistance mechanisms in the receptor itself or other RAS/RAF pathway elements12. Common mechanisms with high clinical relevance include mutations in downstream RAS GTPases10, which represent important nodes in signal integration from cell membrane to nucleus13. Here, somatic point mutations lead to constitutive activation downstream of membrane receptors, thereby hindering therapeutic success10. Despite intensive research, drug binding pockets could not be identified in these proteins for a long time, making the development of direct inhibitory pharmacotherapy difficult. MEK inhibitors have become a first option to overcome this mechanism of resistance by inhibiting downstream mitogen-activated protein kinase kinases. However, until now, clinical efficacy has only been demonstrated for specific applications such as NRAS/BRAF mutated melanoma16. More recently, mutation-specific and panKRAS inhibitors have been developed that inhibit KRAS activity directly or indirectly20. The mutation-specific inhibitors exploit structural changes in the KRAS protein that result from oncogenic point mutations so that cells expressing wild-type protein are less affected, which is expected to reduce toxicity of the therapy. However, so far specific inhibitors could only be designed for a few KRAS mutations such as G12C. This led to the development of the panKRAS inhibitors, which downregulate KRAS activity by binding to SOS1 and thus enable an application independent of the mutation status. PanKRAS inhibitors such as BI1701963 are currently being tested in clinical trials including combinations with MEK inhibitors21. Furthermore, following the results of a phase II trial, Sotorasib became the first drug of mutation-specific inhibitors to receive preliminary approval for patients with therapy-refractory NSCLC22.The RAS signaling pathway is a key driver of carcinogenesis in many different tumor entities23. The selection of predictors depends on the respective tumor entity and its characteristics. For example, activating EGFR and ERBB2 mutations are considered positive predictive markers for therapy with tyrosine kinase inhibitors against receptors of the EGFR-family in non-small cell lung cancer (EGFR) and colorectal cancer (ERBB2)24. Response to ERBB2-antibody Trastuzumab correlates with ERBB2 expression and copy number status in breast cancer25. Furthermore, EGFR resistance mutations such as T790M are important in anti-EGFR therapy in non-small-cell lung cancer as well as activating KRAS mutations in colorectal cancer26. Besides activating mutations, expression-based RAS signatures may also improve therapy response prediction including treatment with KRAS inhibitors28. This could be particularly important in tumors with more complex activation mechanisms by yet unknown RAS-activating events (RAE).The emergence of new therapies with small molecule RAS inhibitors also increases the relevance of identifying responsive and resistant tumors as accurately as possible. Depending on the drug and tumor entity, different markers are currently used as predictors. Those include mutations, CNA and gene expression32. We identified a subgroup of KRAS dependent cell lines harboring wild type status in KRAS  and the Score-Project (CRISPR). Again, cell lines classified as KRAS dependent in the initial Achilles CRISPR screen exhibited a significantly higher dependency in both data , the overall composition of the dependent cell lines demonstrated a heterogeneous distribution . We chose AUC (GDSC) or Active Area (CCLE) for drug sensitivity quantification because validity of extrapolated IC50 values is limited for cell lines that were only partially responsive or unresponsive within the experimentally tested inhibitor concentrations, as stated by GDSC35. To this end, the AUC has been successfully used in a variety of other publications38. As expected, we found consistent associations to MEK inhibitors across multiple compounds and datasets . These include mutations, overexpression and CNA in upstream genes as well as amplification of KRAS itself41. Using differential expression analysis in KRASwt cancer cell lines between KRAS dependent and independent subsets we were able to detect several of already known pathway activating regulators. These included ERBB2 and KRAS  to various receptor tyrosine kinase (RTK) signaling pathways such as EGFR, FGFR, MET, NTRK, and IGFR . This simple approach allowed us to assign at least one RAE to 67% of the KRAS dependent cell lines either by co-dependency or amplification , which underlines the need for improved variable selection and integrative modeling.Therefore, we used the genome wide CRISPR data from the Achilles Project for the detection of RAE, as they reflect direct information about the survival potential of the gene. Consistent with the expression data, genes of the pathways mentioned above were associated with higher dependency in the KRAS dependent subgroup. In addition, we found other RTK pathway associations such as those to KIT and PI3K Fig. . In the ion Fig. . For theion Fig. . Howeverion Fig. . To furtwt cancers by known RAE. We therefore searched for suitable alternative modeling strategies. Regarding the identification of RAS- and KRAS-dependent cancers, several approaches have been taken including RNA-based expression signatures28. To characterize whether these known signatures represent an improvement in prediction, we created machine learning models and examined their predictive performance (see methods). Overall, the resulting predictive performance was not satisfactory. Models based on the signature published by Loboda et al. achieved a correlation of 0.15 , those based on the signature by Singh et al. 0.18 . However, it should be noted that we were not able to assign all gene identifiers in the given signatures, leading to a loss of four genes (2.7%) in the signature by Loboda et al. and 14 (2.7%) in the signatures from Singh et al. , Diseases of signal transduction by growth factor receptors and second messengers (p = 2.4 * 10−15) as well as several pathways which involve tyrosine kinase signaling. Again, this highlights the heterogeneity of RAE and further potential approaches for combination therapies in the KRASwt subgroup.Next, we characterized the predictors of KRAS dependency in KRAS wild type models. Among the 12,000 models of CRISPR/RNAi data using gene sets selected by the diffusion kernel and hyperparameter optimization, the frequency of non-zero model coefficients was quantified for each predictor. In total, 1964 genes were used at least once in a model. Most frequently used predictors were NRAS and KRAS expression, followed by GNG11, HRAS, and RBX1 Fig. . KRAS exwt group exhibit a distinctive phenotype or activation state, which might be represented by expression data. However, RNA expression data are highly structured and intercorrelated48, so that genes selected by our literature-based approach might be replaced without losing predictive power of the models. Consequently, a specific phenotype or gene regulation in the context of KRAS dependency would be less likely. On the other hand, a decrease in prediction power would mean a loss of information that could not be compensated by other predictors and context-specific regulation would be more likely. To investigate this, we selected the models with best performing hyperparameters and extracted all predictors with non-zero coefficients, resulting in lists of 293 (CRISPR) and 184 (RNAi) genes. Then, separately for each gene list and dependency dataset we excluded the lists from the superset of all proteins in the interaction network and repeated modeling with these selections. In fact, we found a lower correlation for both datasets compared to the models using the total number of network genes. Performance in models of Achilles CRISPR dependency data dropped about 20%  and for DRIVE RNAi data about 29% . The loss of predictive power suggests that a non-compensable loss of information has taken place. This speaks for a regulation of genes selected by our interaction network-based in context of KRAS dependency, indicating a KRAS dependency phenotype in the KRASwt subgroup.The results indicate that KRAS dependent cancer cell lines in the KRASwt subgroup we also applied our selective modeling strategy to the entire set of cancer cell lines. Initial reference models using Lasso regression and all predictors available in the RNA expression dataset achieved correlations (Pearson’s r) of 0.55 (CRISPR) and 0.52 (RNAi) between observed and predicted KRAS dependency (CRISPR) , but not for the RNAi data, where models performed weaker (Pearson’s r: 0.51). Despite the good results, consistent with recently published literature models based on KRAS mutation status outperformed RNA-based models 47. Again a closer analysis of the models revealed specific challenges. For both types of predictors, we observed that absolute errors increase significantly toward the outsides of the distribution  Fig. . Prior vion Fig. . As expeThe RAS signaling pathway possesses a central position in the oncogenesis of several tumor entities via numerous mechanisms. Recent progress in clinical development of KRAS inhibitors has raised interest in predicting the tumor dependency on frequently mutated RAS-pathway oncogenes. However, even without such activating mutations, RAS proteins represent core components in signal integration of several membrane-bound kinases. This raises the question of applications of the inhibitors independent from the mutational status.17, these pan-KRAS inhibitors do not require a specific mutation and could be used in the therapy of KRAS wild type malignancies20. In addition, since the subgroup exhibits a significantly increased sensitivity to MEK inhibitors, a combined therapy, as currently being tested in clinical trials for KRAS mutated tumors, may also offer further advantages for some patients21.In this work we identified a KRAS dependent subset of KRAS wild type cell lines. Consistently, across six different datasets from CRISPR, RNAi, and chemosensitivity experiments, we found evidence for increased RAS activity in the elaborated subgroup. Our results suggest that patients without activating KRAS mutations may also benefit from targeted therapies against KRAS. Here, compounds such as the SOS1-KRAS interaction inhibitors BI-3406 or BI-1701963 could be of particular interest. In contrast to mutation-specific inhibitorswt tumors. The high number of KRAS-dependent cell lines within the KRASwt subgroup raises expectations for an equally high proportion of patients with increased response to therapy. In the future, further clinical research efforts are needed. In this regard our study should serve for deeper understanding of mechanisms in panKRAS inhibition and associated current clinical trials.Our results provide evidence for the presence of a KRAS-dependent subgroup in KRASwt subgroup. Moreover, RAEs were heterogeneously distributed, and presence of RAE did not necessarily follow a KRAS dependency and vice versa. This observation is corroborated by results from clinical trial data, as the presence of an activating KRAS G12C mutation predicted response to Sotorasib in only 32% of patients17. We interpreted this as further evidence for the complexity of signal transduction as previously described49. To address this complexity, we investigated modeling solutions using whole transcriptome RNA sequencing data as predictors. Recently, difficulties in RNA-based modeling of gene dependencies of the tumor drivers KRAS, NRAS, and HRAS have been described47.Identification of responsive and resistant tumors will be an essential task for an optimal therapy of the identified subgroup. We demonstrated that RAEs such as KRAS amplifications or EGFR co-dependencies are not reliable predictors of KRAS dependency in the KRASwt cell lines when compared to models using all available RNA predictors, RAE-based models or those of previously published expression signatures. Although not the main focus of the depicted study, we also achieved improvements in performance by our approach in the complete cell line set but could not outperform KRAS mutation status.In our analyses we tested modeling using Lasso regression with varying sets of predictors with a focus on variables selected by a literature-based protein-protein interaction network and subsequent gene centrality quantification through a diffusion kernel. This systematic approach with an initial variable reduction to 100–1000 genes significantly improved predictions of KRAS dependency in KRAS28. However, in our study the reported performance dropped significantly. This suggests that predictions of expression-based models are only valid for applications with high similarity to the training data and tend to overfitting. Generalizations of specific gene signatures without methodological and algorithmic reference seem to be limited, which underlines the need for standardized procedures for the prediction of therapy response to KRAS inhibitors in tumor patients.The importance of variable selection for modeling is emphasized by inferior predictions of models based on genes of previous RAS expression signatures. In the respective initial publication these signatures could provide robust predictions for training and test datawt subgroup, using predictors selected by the diffusion kernel, indicate the advantages of this strategy. Future efforts with the objective to establish diagnostically applicable models may possibly benefit from similar approaches as well.Even with high agreement between the training set data and those of the planned application, steps to reduce overfitting caused by artifacts in the training data are highly relevant to ensure external validity. In this regard, besides the use of statistical methods suitable for high-dimensional datasets collecting additional information about the predictors to select variables with context specific biological relevance can be important before modeling. Literature-based protein-protein interaction networks as used in this work represent one option for this purpose since they enable the integration of preexisting scientific knowledge to identify key regulatory genes in the context of KRAS signaling. These genes possibly represent more robust predictors, which reduce overfitting caused by dataset-specific artifacts. Due to the lack of additional data, we were not able to validate this hypothesis. Nevertheless, the significantly improved internal performance of KRAS dependency models in the KRASIn summary, our results suggest that a subset of patients without oncogenic KRAS mutations may benefit from targeted therapy with KRAS inhibitors. In the long run, we assume that machine learning models based on high-dimensional RNA expression data could help with therapeutic decisions. As already mentioned before, a crucial factor for clinical applicability of the proposed models will be a highly standardized test methodology. This includes all steps of the analysis including sample preparation, sequencing and bioinformatic analysis as well as the choice of a suitable parameter for therapy response quantification. The realization of such solutions appears to be possible and reasonable as costs for quantifying gene expression continue to fall.50. GDSC drug data (Release 8.2) were downloaded from the project’s website51. For all analyses, only cell lines originating from solid tumors were used.Cancer cell line data were obtained from the website of Dependency Map Consortium including Genomic, RNA-expression, CRISPR, RNAi and CCLE drug sensitivity data (Release 21Q1)wt/HRASmut, KRASwt/KRASmut, NRASwt/NRASmut). Initial analyses were performed in cell lines with wild type status and for each gene individually  were used.Overrepresentation analysis was performed using R package ReactomePA54, resulting in 43 different markers for RAE-based predictions  were used as dependent variables in gene dependency modeling. Different predictor types  were processed as follows. RAE were represented by Achilles CRISPR gene effect format and KRAS gene copy number. We restricted gene dependency RAE to oncogenes annotated in oncoKB database and receptor kinases/non-RTKs from HGNC56. Gene identifiers were assigned to each protein and duplicated interaction scores between the same genes were averaged. Interactions with a score lower than the 90th percentile were discarded. To focus the network on genes which may be involved in the context of KRAS signaling we restricted genes to those with a direct KRAS interaction (1st shell) and their respective interaction partners (2nd shell). The final network consisted of 7070 genes. Centrality (closeness) of each gene was determined by a diffusion kernel which captures the information flow within the network as previously described58. [Eq. β to a parameter which controls the degree of information flow. Similar to Lee et al.58 we tested several values of the β parameter  for variable selection. This resulted in seven different closeness estimates of the genes in our network.For our workflow based on whole transcriptome RNA-expression data (TPM) we either used the total number of available RNA predictors or subsets selected by our variable selection approach. For this purpose, we constructed a literature-based protein-protein network to identify significant predictors by gene centrality Fig. . The hyperaction st shell β parameter was tuned for the values mentioned above. Subsequently, the number of selected genes by centrality was tested for 100, 500, and 1000 genes. This approach resulted in a total of 60 different predictor sets for modeling. Independent of the hyperparameter optimization, we also tested modeling either with all unfiltered genes of the network (6967 genes) or with the total number of RNA predictors available . For all predictor sets gene expressions with missing values were discarded before the final selection step.Hyperparameter optimization of the variable selection by the diffusion kernel was performed for the following variables. The minimum level of gene expression was tested for the percentiles P25, P50, P75, and P95. wt subgroup after variable selection through the diffusion kernel. Lasso and Elastic Net regression minimize the following quantities [Eqs. n is the number of observations, p the number of different predictors, β0 the y-intercept, βj the coefficient of the respective predictor, yi the values of the independent variable, and xji the values of the predictors.Despite our variable selection workflow, the number of predictors for RNA-based models was still high. To prevent overfitting, we consistently used Lasso regression for all models and predictor types. In addition, we also tested Elastic Net and Random Forest regression in KRAS dependency models of the KRASies Eqs. –3:2\docuFor every set of independent variables 100 models were trained, each using 80% of the cell lines randomly selected by R base sample function. The remaining 20% served as an independent test set. Test set predictions were averaged for each predictor set and correlated with the experimental data Fig. .59. The package glmnet was used for Lasso regression and Elastic Net60. Random Forest regression was performed with randomForest package61. Differential expression analyses were carried out with DESeq262 and network analysis was generated with igraph63. RAWGraphs 2.0 was used for additional graphics64. Standard statistical methods are mentioned in figures and legends.All statistical analyses were performed in R programming languageFurther information on research design is available in the Supplementary InformationReporting SummarySupplementary Data 1-6