Mycobacterium tuberculosis complex (MTBC) includes several human- and animal-adapted pathogens. It is thought to have originated in East Africa from a recombinogenic Mycobacterium canettii-like ancestral pool. Here, we describe the discovery of a clinical tuberculosis strain isolated in Ethiopia that shares archetypal phenotypic and genomic features of M. canettii strains, but represents a phylogenetic branch much closer to the MTBC clade than to the M. canettii strains. Analysis of genomic traces of horizontal gene transfer in this isolate and previously identified M. canettii strains indicates a persistent albeit decreased recombinogenic lifestyle near the emergence of the MTBC. Our findings support that the MTBC emergence from its putative free-living M. canettii-like progenitor is evolutionarily very recent, and suggest the existence of a continuum of further extant derivatives from ancestral stages, close to the root of the MTBC, along the Great Rift Valley.The  Mycobacterium tuberculosis complex (MTBC) includes several pathogens thought to have originated in East Africa from an ancestor closely related to Mycobacterium canettii. Here, the authors describe a clinical tuberculosis strain isolated in Ethiopia that has typical M. canettii features but is phylogenetically much closer to the MTBC clade, supporting that the emergence of MTBC pathogens is a recent evolutionary event.The  Mycobacterium tuberculosis and other members of the M. tuberculosis complex (MTBC), has been a major killer throughout human history2. Based on the current knowledge, the MTBC is comprised of nine human-adapted lineages (L), representing M. tuberculosis sensu stricto (L1-4 and L7-8) and M. africanum (L5-6 and L9), plus at least nine animal-adapted lineages5. Strains belonging to these lineages share highly conserved genomes, differing by no more than ~2000 single nucleotide polymorphisms (SNPs), and with no significant trace of genetic exchange among them, suggesting that this entire population expanded clonally from a single bacterial progenitor8. As seen in host adaptation of other bacterial pathogens9, this single-cell bottleneck might reflect a drastic change in ecology and selective pressures that putatively coincided with the jump to an obligate pathogen lifestyle in a mammalian host10.Tuberculosis (TB), a disease caused by M. canettii, which is the phylogenetically closest relative of the MTBC13. In contrast to the worldwide distribution of the MTBC, clinical isolates of M. canettii are geographically restricted to the Horn of Africa, and fewer than 150 isolates have been described, mainly obtained from Djibouti residents. There is no evidence of inter-human transmission of M. canettii14, and M. canettii strains are less persistent and virulent than MTBC strains in infection models15, indicating a lower degree of pathoadaptation. Moreover, in contrast with MTBC members, M. canettii strains show marked genome mosaicism indicating multiple intraspecies horizontal gene transfer (HGT) events, explained by a distributive conjugal transfer (DCT) mechanism and consistent with frequent inter-strain contacts in an environmental reservoir16. Furthermore and consistently with the hypothesized M. canettii-like progenitor of the MTBC, recent phylogenomic analyses suggested that HGT events have also happened with ancestors of extant M. canettii strains in the ancestral evolutionary branch leading to the MTBC genesis. This finding provides additional support for a shared gene pool and ecological niche between both parental populations17.In accordance with this scenario, the current view is that the MTBC progenitor clone emerged from a diversified and recombinogenic pool of free-living mycobacteria resembling extant strains of pks5 genes in M. canettii, resulting in abrogation of lipooligosaccharides (LOS) production and a change in colony morphotype from smooth to rough. This recombination results in a single pks5 configuration similar to that conserved among all MTBC members, which lack LOS and show a rough morphotype. This conservation suggests a unique recombination event in the MTBC progenitor after separation from its M. canettii-like ancestor18. Importantly, such M. canettii morphotype variants show increased virulence in cellular- and animal-infection models, suggesting that pks5 recombination generated a beneficial genetic background in the MTBC ancestor for colonization and spread in mammalian hosts18.This hypothesis is further supported by the observation of homologous recombination spontaneously occurring in vitro between two adjacent M. canettii pool and these key ecological and adaptive changes happened in the ancestral branch of the MTBC remains unknown. Indeed, previous analysis showed that M. canettii strains can differ by up to 65,000 SNPs from each other, and at least 14,000 SNPs separate the hitherto known closest lineages from the currently acknowledged most recent common ancestor (MRCA) of the MTBC12.However, when this emergence from the recombinogenic pks5 configuration of M. canettii strains, but is phylogenetically much closer to the MTBC clade. We used short and long read sequencing to reconstruct the complete circular genome of the ET1291 strain and performed in-depth comparative genomics with all previously available and other newly sequenced M. canettii genomes and representatives of all known MTBC lineages. The obtained findings provide proximal insights into the genomic and biological changes involved in the emergence of the MTBC, and on its original lifestyle.Here, we report the discovery of a TB clinical isolate (hereafter referred to as ET1291) from Ethiopia, which shares the characteristic smooth colony morphology and twin-19, enrolling for 16 months newly registered and previously treated bacteriologically confirmed pulmonary TB cases. The primary culture of ET1291 became positive after 6 days using BD BACTEC MGIT liquid culture and colonies were observed after 5 weeks on the Löwenstein–Jensen medium. Such colonies showed the archetypal smooth morphotype that distinguishes M. canettii strains from MTBC strains. Extended drug susceptibility testing showed monoresistance to pyrazinamide, consistent with the natural resistance of M. canettii to this drug20. Moreover, ET1291 showed a faster growth rate in liquid medium (Middlebrook 7H9 supplemented with OADC and glycerol) than M. tuberculosis H37Rv, similarly to a reference M. canettii strain STB-A (CIPT 140010059)  data obtained from ET1291  and/or M. canettii  using Deeplex Myc-TB testing22 resulted in M. canettii identification with a predicted pyrazinamide mono-resistant profile, based on hsp65 sequencing data and detection of specific phylogenetic SNPs including pncA A46A . This M. canettii classification was supported by multiple other archetypal characteristics shared with previously described M. canettii strains, as described below.Analysis with the MTBseq pipelineM. canettii strains. This reference dataset of M. canettii strains represents the closest outgroup to ET1291 strain and the MTBC, as shown by in-depth analysis of the ET1291 genome (see below) and recent genomic analysis of phylogenetically closest non-tuberculous mycobacterial species, comprising M. decipiens, M. shinjukuense, M. lacus, and M. riyadhense (forming a clade defined as the “MTB-associated phylotype”)24 , thus indicating reduced lateral gene flow closer to the emergence of the MTBC progenitor , similarly to M. canettii genomes (currently culminating at 4.53 Mb for STB-K) previously identified12. As reductive evolution is a hallmark of mycobacterial pathogens27, this larger genome size relative to the MTBC further supports an ancestral status shared with M. canettii strains.To comprehensively identify the genomic features associated with the proximal phylogenetic position of ET1291 relatively to the MTBC, the ET1291 strain was subjected to Oxford Nanopore Technologies (ONT) MinIon long read sequencing, followed by polishing and hybrid assembly using Illumina short reads. This hybrid approach resulted in a single complete circular genome of 4,515,631 bp containing 4086 predicted genes  in the MTBC  synthesis, only shared by strains of the MTBC L8, representing a sister clade compared to the rest of the MTBC4.Additional ancestral characters were found in common with known ers Fig. 18. MoreoM. tuberculosis H37Rv of the ET1291 genome is 99.55%, incrementally above the 97.97–99.37% range observed with all the previously described genomes of M. canettii strains12 , a member of the PE_PGRS protein family , a probable fatty-acid-CoA synthetase , as well as proteins of unknown functions. The corresponding genes were thus probably acquired already by lateral transfer in a common ancestor of ET1291 and the MTBC, before the emergence of the MTBC MRCA.Nevertheless, other genome features are consistent with an evolutionarily closer branching point relatively to the MTBC. The average nucleotide identity versus M. canettii strains, the ET1291 genome also contains prophage regions of ~9–10 kb found in the same genomic locations as the PhiRv1 and PhiRv2 prophage regions of MTBC strains   locus encoding a system of type I-E, also present with 98.7-100% identical M. canettii and the MTB-associated phylotype were further denoted by the finding that genomic regions of the MTBC members such as Rv0402c-Rv0406, Rv1041c-Rv1050, Rv1499–1510 and Rv2307c-Rv2309c were replaced in the genomes of ET1291 and/or M. canettii STB-J and -K by only distantly related or unrelated gene segments, with coding sequences sharing ~90–99% identity at nucleotide level with their equivalents in M. shinjukuense (or >80% identity with homologs in M. riyadhense)  than the MRCA of known extant lineages, is an open and largely debated question35. The discovery in East Africa of a TB clinical isolate that shares typical features of M. canettii strains but is phylogenetically much closer to the MTBC reveals that this was likely not the case, indicating that the emergence of the ancestral clone of the MTBC from its putative free-living M. canettii-like progenitor pool is evolutionarily very recent.Tracing the origins and conditions of emergence of the MTBC provides fundamental clues to understand the causes of its outrageous evolutionary successM. canettii, including its smooth colony morphotype associated with the twin-pks5 gene configuration, genome-wide imprints of HGT and a larger genome size compared to the MTBC, it distinctively harbors some specific genes likely acquired in a recent common ancestor shared with the MTBC MRCA. By hypothesizing similar molecular clocks across the respective evolutionary branches, this inferred relative phylogenetic distance would thus imply that the original MTBC progenitor that emerged from a M. canettii-like ancestor in East Africa is perhaps only twice as old evolutionarily than the currently reconstructed MRCA of the MTBC. More data would be required for estimating a dating for this emergence, also in the view of the debated dating of the MTBC MRCA35.After correction for recombination, the clonally inherited SNP distance separating ET1291 from the currently known MTBC MRCA is only about two-fold the maximal distance separating any two known extant MTBC strains. Consistent with this proximal phylogenetic position, while ET1291 shares archetypal phenotypic and genomic characteristics of 17, these results imply at least partially shared habitats between ancestors of M. canettii strains, including those part of the ancestral branch of the MTBC, but also other environmental (myco)bacterial (including members of the MTB-associated phylotype) and/or phage populations. However, our data suggest an increased barrier to gene flow during evolution in the branches phylogenetically closest or leading to the MTBC, before becoming extreme within the MTBC17, post-emergence. This raises intriguing questions of whether such increase already reflected partial adaptation to a new, potentially host-associated ecological niche separate from the rest of the ancestral free-living M. canettii strain pool, and/or from relative reduction of natural abilities of some ancestral strains to receive extrinsic DNA. Both hypotheses are consistent with certain experimental findings on previously known M. canettii strains. Indeed, some correlation was observed between phylogenomic distance of these strains from the MTBC and degrees of virulence/persistence in animal infection models12. Moreover, some, but not all, extant M. canettii strains can act in vitro as recipients of chromosomal segments transferred from MTBC or other M. canettii donor strains, while MTBC strains appear incapable to do so36. Further experiments would be needed to determine the degree of virulence/persistence of ET1291 in infection models and to which extent this strain has retained a recombinogenic property.Regardless of temporal estimation, the multiple traces of HGT in the ET1291 genome and those inferred, after the separation of the ET1291 lineage, in the evolutionary branch leading to the MTBC represent evidence for continued exposure to lateral gene flow until the emergence of the MTBC progenitor clone. Consistent with previous presumptionM. canettii-like strain identified in the national drug resistance survey conducted in Ethiopia. Likewise, only two M. canettii strains were identified in another national survey recently conducted in the same geographic region37, including one representing an additional intermediate lineage between the MTBC and the formerly identified M. canettii strains, albeit with a branching point more distant from the MTBC (and thus closer to the previously known M. canettii strain pool) compared to ET1291 (in preparation). M. canettii strains represented a more notable, but still small, proportion  of the TB isolates only in a national survey previously done in Djibouti38. These population-based proportions represent evidence of the infrequency of human TB caused by M. canettii even in the Horn of Africa, further arguing for spillover infection from a local environmental reservoir. It is thus all the more fascinating that extant representatives of M. canettii lineages reflecting distinct ancestral stages towards the obligate MTBC pathogens can still be sporadically isolated from tuberculosis patients living in different places of this region. When also combined with the recent identification of a new sister clade of the MTBC in the African Great Lakes region4, these findings suggest that a continuum of extant derivatives from further ancestral stages near the root of the MTBC may well exist along the Great Rift Valley. With wider use of genome sequencing in the region, more strains might thus be uncovered, which will help further dissect and resolve the different molecular and evolutionary events leading to the emergence of M. tuberculosis. Last but not least in this respect, we showed that M. canettii strains even genetically very distant from the MTBC have the potential to rapidly evolve towards enhanced, M. tuberculosis-like virulence and persistence phenotypes, via spontaneous pks5 recombination in vitro18 or by different mutational pathways during in vivo experimental evolution33, respectively. Taken altogether, these data thus suggest a possibility of (re-)emergence of a (novel) prototype of professional TB pathogen.ET1291 was the sole The ET1291 strain was isolated from a patient with TB in the framework of a national drug resistance survey in Ethiopia according to WHO recommendations.19. Phenotypic drug susceptibility testing (DST) for first-line drugs was performed at the Ethiopian Public Health Institute (EPHI) and a subculture of all survey strains including ET1291 strain were shipped to the TB Supranational Reference laboratory (SRL) at the San Raffaele Scientific Institute in Milan for further phenotypic tests and whole-genome sequencing. Morphological and growth properties were assessed on Löwenstein–Jensen , Middlebrook 7H10 , and MGIT media . Growth rate in the Middlebrook 7H9 medium  supplemented with OADC  and 0,5% glycerol  was monitored using optical density measurements39. Extended DST to first- and second-line anti-TB drugs was performed using the BACTEC MGIT 960 System according to the manufacturer’s instructions and WHO’s recommendations40. Minimum inhibitory concentrations (MICs) for bedaquiline, delamanid, and pretomanid were performed with the BACTEC MGIT 960 system41.The ET1291 strain was isolated from a patient with TB in the framework of a national drug resistance survey in Ethiopia (manuscript in preparation) according to WHO recommendations22.Targeted next-generation sequencing analysis was performed on DNA from cultured isolates using the Deeplex Myc-TB kit and the associated web application as per the manufacturer’s instructions42 and reads shorter than 20 bp were discarded. The resulting reads were aligned to M. tuberculosis H37Rv ATCC 27294 (NC_000962.3) as reference genomes using the MTBseq pipeline21 by application of the mem algorithm of the Burrows-Wheeler alignment tool v0.7.1743. Duplicated reads were marked using the Picard tool v2.23.4-0 (https://github.com/broadinstitute/picard) and local realignment of reads around InDels was performed using the Genome Analysis Toolkit v3.8. SNPs were called with Samtools mpileup v1.644 using the following thresholds: minimum mapping quality of 20, minimum base quality at a position of 20, minimum read depth at a position of 8X, maximum strand bias for a position of 90%.Genomic DNA was extracted and purified using the Maxwell® 16 Cell DNA Purification kit . Paired-end DNA libraries were prepared using the Nextera XT DNA Library Prep Kit  and sequenced on an Illumina NextSeq 500 platform with 150-bp read lengths. The corresponding sequence reads, as well as the Nanopore reads, the associated sequence assembly and annotation (see below), were submitted to the NCBI Sequence Read Archive with Project number PRJNA823537. In order to detect drug resistance-associated mutations and identify strain (sub)lineages, the sequenced raw reads underwent adapter trimming with Trimmomatic5), as well as 39 available and one newly sequenced genomes of previously known M. canettii strains , and with other parameters set to default. The false positive variant calling rate with this analysis pipeline was verified by mapping Illumina sequence reads from M. tuberculosis H37Rv genomic DNA against a previously assembled reference genome obtained after PacBio sequencing of the same genomic DNA preparation, resulting in absence of any detectable SNP. Positions of single nucleotide variants (SNV) identified in the whole dataset were defined using the “Merge Variant Tracks and Identify Known Mutations” tool. A custom script was used to extract 82,637 positions commonly covered in all genomes, to avoid biases due to variable read coverages across the genome dataset, and to analyze recombination and compare inter-strain SNP distances based on a same, normalized number of sequence positions. The utilization of a reconstructed ancestral genome sequence of the MTBC as a common reference for SNP analysis was further justified by previous findings that M. canettii and MTBC strains share >89% of their genomes, excluding repetitive regions such as PE_PGRS- and PPE_MPTR-encoding regions (accounting for ~8% of the coding capacity of M. tuberculosis), and that individual accessory genomes of M. canettii strains show very few genes in common12.Genome-wide SNP calling of ET1291 and 120 other genomes including 80 representatives of all known human- and animal-adapted lineages of the MTBC , and 2096 to 2263 SNPs separated M. bovis from M. tuberculosis  genomes, in line with previous results8.These SNPs, along with their positions on the reference genome, were used to generate a whole-genome alignment of the same length as the reference genome . This alignment was used as input to build the phylogeny using PhyML v3.347 Fig. . No Lewi 25 Fig. . For eachttps://www.bv-brc.org/app/PhylogeneticTree. The pipeline uses concatenated nucleotide and encoded amino acid sequences from up to 1000 single-copy core genes, identified via detection of cross-genus BV-BRC global Protein Families (PGFams) homology groups48, for constructing a RAxML tree. Out of 1160 single-copy core genes identified in all 59 genomes analyzed, 1000 were picked randomly. Corresponding protein and nucleotide coding sequences were aligned using MUSCLE49 and the Codon_align function of BioPython, respectively. The concatenated alignment of all protein and nucleotide sequences written in a PHYLIP file was partitioned for describing the alignment in terms of protein sequences and first, second, and third codon positions of nucleotide sequences. The resulting file was used for constructing a maximum-likelihood tree using RAxML-NG v1.0.2 with ‘–model GTR + G + ASC_LEWIS50. We used the general time reversible model of nucleotide substitution under the gamma model of rate heterogeneity and performed 1000 alternative runs on distinct starting trees. Support values were generated using 100 rounds of the “Rapid” bootstrapping and the best-scoring maximum-likelihood topology was “midpoint rooted” using FigTree v1.4.4 (https://github.com/cdeanj/figtree). The topology was annotated and colored using the Evolview v3 online tool51.A phylogeny additionally including more distantly related genomes of the MTB-associated phylotype was inferred by using the Codon Tree pipeline of the Bacterial and Viral Bioinformatics Resource Center , available at https://files.zymoresearch.com/protocols/. Input genomic DNA prepared with the Rapid Barcoding Sequencing (SQK-RBK004) kit  was sequenced on a MinION device using a flow cell R9.4.1.Genomic DNA of ET1291 was purified from culture with the Genomic DNA Clean & Concentrator kit  to recover large-size DNA, according to 52 using Illumina paired-end short reads. Corrected long reads were assembled using Flye de novo assembler v2.9.1 (https://github.com/fenderglass/Flye). Initial genome annotation was done using the BV-BRC RASTtk-enabled genome annotation service53. A circular map of the chromosome was visualized with BRIG54. Comparative alignments and final genome annotation was performed similarly to the strategy described in4, against an ensemble of genome sequences including representatives of MTBC L1-4 and L7-8 , L5-6 &9 (M. africanum), M. bovis, M. canettii strains STB-A, -D, -E, -G, -H, -I, -J and -K, M. kansasii, M. marinum, M. lacus, M. shinjukuense M. decipiens, and M. riyadhense. These steps were performed based on BLAST searches and analysis of gene synteny, using Artemis and Artemis comparison tool55 as well as a custom Multiple Annotation of Genomes and Differential Analysis (MAGDA) software previously used for annotation of MTBC L8, M. canettii and Helicobacter pylori genomes56. Where applicable, annotations were transferred from those of M. tuberculosis or M. canettii orthologues in the TubercuList/Mycobrowser database or from data from Supply et al.12, using BLAST matches of >90% protein sequence identity, an alignable region of >80% of the shortest protein length in pairwise comparisons and visual inspection of the gene synteny. Finally, in order to estimate the potential false indel rate in the complete genome assembly, we determined the number of frameshift indels among coding sequences of the orthologues of the 461 genes identified as being essential for in vitro growth of M. tuberculosis H37Rv, by comprehensive essentiality analysis30. Only three (0.7%) occurrences were detected, indicating a low false indel rate.In order to reconstruct a high-quality assembled genome based on the Illumina short reads and ONT long reads, a hybrid genome assembly was constructed as follows. Nanopore long reads were corrected using the Ratatosk de novo error correction tool v0.7.6.3M. tuberculosis H37Rv ATCC 27294 was done using the ANI calculator online tool with default parameters (http://enve-omics.ce.gatech.edu/).Analysis of average nucleotide identity (ANI) of the ET1291 genome versus the genome of 57. To detect clustered regularly interspaced short palindromic repeats (CRISPR) -Cas loci in assembled genomes, the CRISPRCasFinder58 pipeline (https://github.com/dcouvin/CRISPRCasFinder) was used with default parameters. CRISPR-Cas type and subtype were assigned by using CRISPRmap59.In silico spoligotyping was performed with Illumina sequence reads using the kvarQ software v 0.12.2Further information on research design is available in the Supplementary InformationPeer review fileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Reporting SummarySource Data


Inspired by recent observations on active self-organized critical (SOC) systems, we designed an active pile (or ant pile) model with two ingredients: beyond-threshold toppling and under-threshold active motions. By including the latter component, we were able to replace the typical power-law distribution for geometric observables with a stretched exponential fat-tailed distribution, where the exponent and decay rate are dependent on the activity’s strength ( Self-organized criticality is a widespread phenomenon referring to how individual constituents organize their interactions to create critical behaviors. This concept can be identified in various natural phenomena, such as neural dynamics in the brain ,3,4,5,6,The interaction among the active constituents of the system leads to critical states in active Brownian motion ,46,47 antoppling takes place  wander around on top of each other within the system. If the population of ants in a region exceeds a threshold, e models ), and thheight of the column) of ants at each lattice site. We performed simulations for different lattice sizes, defined as i exceeds the threshold of four, it is called unstable, and toppling takes place: the site loses four ants, which are either distributed to neighboring sites or wasted at the lattice boundaries. More precisely, during the toppling of site i, i and j are neighbors, and 0 otherwise , which is the total number of toppled sites, and “avalanche size” (s), which is the total number of toppling events that occur during the avalanche. We also focus on geometrically observable properties that will be defined later.During an “active movement”, an ant is randomly selected to move to a neighboring site within the lattice. This can cause the site to become unstable and topple. The strength of the activity can result in multiple toppling events throughout the system between successive additions of ants. The series of toppling events that occur between two successive ant injections is referred to as an T, with a proportionality constant set to one. L. The ant pile dynamically self-organizes into the same recurrent states, indicating that this recurrent state is a fixed point of the ant pile dynamics. We refer to this regime as the anomalous self-organized state, where in analogy with glassy dynamics, the term “anomalous” reflects the presence of stretched exponential distributions (SEDs), which will be further discussed.The average height, L. Notably, since f in the hyper-active pile limit, and L.In the stationary regime, Tζ=0*∝L2  , we can writex in the anomalous self-organized state and Active motions destroy the criticality of a system, leading to distinct  systems ,55. Unli systems , the disinset in b. Both qed point .l , and i belongs to G and zero otherwise, and i, stretching exponent, which gives the function a slower decay than a pure exponential function , as demSEDs are well-known for slow relaxation in complex condensed systems , as demo problem ); fracti problem ; and asyelations .The stretching exponent quantifies the level of temporal correlation within a system. A stretching exponent smaller than one indicates a high degree of correlation, while a stretching exponent greater than one implies a low level of correlation. The behavior of d theory . This reecreases c, an abrexponentially decaying components with well-defined attenuation factors. The Laplace transformation provides an effective way of achieving this representation of SEDs, as shown below.s showing a specified decay rate. s under which Although Equation  and the which is (7)p˜α(yfunction . It is nwn to be (8)p˜12p˜αloglDfBTW=54 . In ordeEquation , the folnd DfBTW :(13)DfBTquation  state. Parameter


Previous research on factors affecting video game player aggression has mainly reflected on the violent content of video games; in recent years, some researchers have focused on competitive factors in video games. However, little research has examined the sole impacts of competitive factors in video games without violent content on aggression, and the neurological processes of these effects are still unknown. The present study was the first to examine the electrophysiological characteristics of short-term competitive video game exposure and aggression. Thirty-five participants played a video game in either competitive or solo mode for 15 min, followed by an ERP experiment based on the oddball paradigm and the hot sauce paradigm to measure aggressive behavior. Results showed that playing competitive game mode was associated with faster judgment of aggressive words, larger P300 amplitudes, and selection of more chili powder than in solo mode. Mediation analysis further revealed that the P300 amplitude evoked by the aggressive words partially mediated the relationship between competitive game exposure and aggressive behavior. These findings support the general aggression model. However, this study has limitations, such as a single form of competitive game examined and single blindness, which need further improvement in future studies. Video games have become one of the most well-liked kinds of entertainment worldwide, with a projected 3.2 billion players and a market value of USD 196.8 billion in 2022, expected to rise to USD 225.7 billion by 2025 ,11 and hCompetition involves a negative correlation between individual goals, where achieving one person’s goal implies that others do not achieve theirs . The genIn terms of behavioral research, the first study to examine how competitive factors in video games affect aggressive behavior was conducted by Anderson and Morrow in 1995 . HoweverFor why players engage in aggressive behavior after a short exposure to video games, aggression cognition, emotion, and arousal are all explained in the general aggression model . PreviouStudies on the effect of competitive video games on aggression were at the behavioral level, and no studies have been conducted to examine the relationship more directly and effectively at the neural correlates level. Relevant EEG and ERP studies have been conducted in the video game domain, where the P300 component has been commonly involved. The P300 is a positive amplitude that appears approximately 300 ms after the stimulus and is involved in cognitive processing, is used to examine cognitive processes, and is considered an indicator of neural resource allocation, and its amplitude is positively correlated with the mental resources invested ,40. The The present study aimed to investigate the effect of competitive factors on aggression in a nonviolent video game using ERP. Two game modes of a nonviolent racing game were selected: a competitive and a solo game play mode. Aggressive cognition was assessed through participants’ reaction time and P300 amplitude to aggressive words using the oddball paradigm, while aggressive behavior was evaluated using the hot sauce paradigm. This study had three research objectives. Firstly, we aimed to examine the effects of competitive mode in nonviolent video games on aggressive cognition and behavior. Secondly, we sought to explore the neural correlates of the effects of competitive video games. Lastly, we aimed to explore the mediating role of aggressive cognition in the effect of competitive game mode on aggressive behavior. According to the GAM and empirical studies, our first hypothesis (H1) was that players would exhibit higher levels of aggressive cognition and behavior in the competitive mode compared to the solo mode. The second hypothesis (H2) stated that participants with short-term exposure to the competitive mode of a racing game would produce larger P300 amplitudes for the aggressive words compared to the solo mode. Given that aggressive cognition is believed to mediate the relationship between video game exposure and aggressive behavior in GAM, our third hypothesis (H3) proposed that P300 amplitude for aggressive words would mediate the effect of the competitive game mode on aggressive behavior. f = 0.25 , d = 0.04, 95% CI , between the aggressive  and neutral words . There was also no difference in understandability, t(88) = 0.07, p = 0.95, 95% CI , d = 0.02, 95% CI , between the aggressive  and neutral words . A significant difference was found only in aggression, t(88) = 40.74, p < 0.001, 95% CI , d = 9.11, 95% CI , with aggressive words  being significantly more aggressive than neutral words .The words used for this task were determined by a preliminary experiment. A total of 30 aggressive words and 60 neutral words were selected from the Modern Chinese Dictionary. Thirty participants who did not participate in the formal experiment rated these words on a scale of 1−5 on three dimensions, including universality, understandability, and aggression. There was no difference in universality, Aggressive behavior was measured using the hot sauce paradigm ,51. Partt(33) = −0.43, p = 0.68, 95% CI , d = −0.14, 95% CI , between the competitive  and solo mode groups , indicating a match between two groups on the trait aggression.Trait aggression was measured using the Aggression Questionnaire , a validt(33) = −0.60, p = 0.55, 95%CI , d = −0.21, 95%CI , between the competitive  and solo mode groups . There was also no difference in competitive video game experience, t(33) = −1.20, p = 0.24, 95%CI , d = −0.41, 95%CI , between the competitive  and solo mode groups . The results showed that both groups of participants were matched on both violent and competitive video game experiences.Participants’ exposure to both violent and competitive video games was measured. Violent video game exposure was measured using the Video Game Experience Questionnaire . ParticiParticipants were screened to ensure that they had no or minimal experience with the experimental game QQ Speed. Upon arrival at the laboratory, participants first signed an informed consent form. They were informed that they would be evaluating a video game and participating in an ERP experiment examining attention. Participants then filled out the Aggression Questionnaire and the Video Game Experience Questionnaire. After this, participants had an EEG electrode cap placed on their heads and electrode impedances were reduced. Next, participants were randomly assigned to play either the competitive or solo mode of QQ Speed for 15 min after a 5-min practice session. Participants completed the Game Evaluation Questionnaire following the game. After a two-minute rest, EEG recording started and participants completed an oddball paradigm and the hot sauce paradigm. After completing all tasks, participants were asked to report their perceived purpose of the experiment they had just participated in. A 5-min prosocial video was shown to participants at the end of the experiment to avoid initiating aggression.EEG data were recorded using 64-channel 10–20 system EEG caps with active Ag/AgCl electrodes . A vertical electrooculography (VEOG) was recorded below the midpoint of the right eye, and a horizontal electrooculography (HEOG) was recorded from the outer canthi of the right eye. The electrode impedances were maintained below 5 kΩ prior to the start of recording. EEG data were analyzed using MATLAB R2016a  and the EEGLAB toolbox . EEG sigt-tests to examine the differences between the two game play modes. The average P300 amplitude was analyzed using a 2 (game play mode: competitive vs. solo) × 2  repeated-measures factorial ANOVA. The mediation analysis was performed using the PROCESS macro for SPSS. Model 4 , d = −1.26, 95% CI , with a significantly higher degree of competition in the competitive mode than in the solo mode. There were no significant differences between the two modes in the other dimensions . These results suggest that participants could differentiate between the competitive and solo modes and that the two modes were comparable in the other dimensions.An independent samples tion see . The rest(33) = 2.87, p = 0.007, 95% CI , d = 0.98, 95% CI . Specifically, participants in the competitive mode  responded significantly faster to aggressive words than those in the solo mode . However, there was no significant difference in the correctness of responses to aggressive words between the two modes, t(33) = −1.14, p = 0.26, 95% CI , d = −0.39, 95% CI . The mean correctness was 0.993 (SE = 0.003) for the competitive mode group and 0.988 (SE = 0.004) for the solo mode group.The reaction time (ms) for aggressive words differed significantly between the competitive and solo modes, t(33) = −3.28, p = 0.002, 95% CI , d = −1.11, 95% CI . The hot sauce score was higher for the competitive mode  than for the solo mode . A sensitivity analysis was conducted by G*Power 3.1 ). The game play mode significantly predicted larger P300 amplitude evoked by aggressive words , and larger P300 amplitude significantly predicted increased hot sauce scores . Furthermore, P300 amplitude evoked by aggressive words partially mediated the relationship between game play mode and hot sauce scores . The effect ratio was 0.32, indicating that aggressive cognition mediated approximately 32% of the relationship between competitive game mode and aggressive behavior.A mediation model was conducted with video game play mode  as an independent variable (X), aggressive cognition  as mediator (M), and aggressive behavior (hot sauce scores) as a dependent variable (Y) . The resThe present study aimed to investigate the effects of competitive video games on aggression at the behavioral and neural levels. The results showed that players in the competitive game mode reacted to aggressive words significantly more quickly and chose more chili powder than those in the solo mode, supporting H1. Consistent with previous findings, competitive factors in video games can increase aggressive cognition ,26 and aThe current study was the first to examine the relationship between competitive video games and aggression at the level of neural correlates. Our findings revealed that P300 amplitudes were larger for aggressive words than neutral words, consistent with previous research suggesting that target stimuli elicit larger P300 amplitudes than nontarget stimuli ,62. The The mediation analysis revealed that the P300 amplitude in response to aggressive words partially mediated the relationship between game play mode and hot sauce scores, supporting H3. According to the GAM, the situational input variable (exposure to competitive video games) contributes to aggressive behavior through aggressive cognition, negative affect, and arousal . Of the The practical implications of this study are significant, as they shed light on the importance of considering the effects of competitive game modes on players’ aggression. Regarding social policy, the findings urge researchers to exercise caution when making causal inferences about the relationship between video game violence and aggression, thereby enhancing public policy . Within In addition, this study had some limitations. First, the competitive mode was created through game ranking, and other forms of competition may have different effects on aggression , so the The present study utilized behavioral and neural indicators to examine the effects of competitive factors in video games without violent content on player aggression. This study found that short-term exposure to the competitive play mode in nonviolent video games can directly promote players’ aggressive cognitions (shorter reaction times and larger P300 amplitudes for aggressive words) and aggressive behaviors and also indirectly increase aggressive behaviors by activating aggressive cognitions (P300 amplitudes for aggressive words). The results support the general aggression model and contribute to understanding the relationship between video games and player aggression. Amidst the ongoing controversy surrounding the effects of violent video games on aggression, this study provides empirical evidence supporting the notion that competitive factors in video games can elicit aggression, even in the absence of violent content.


Anti-HLA donor-specific antibodies (DSA) of the IgG isotype are mainly responsible for ABMR. Recently DSA of the IgE isotype were demonstrated in murine models as well as in a small cohort of sensitized transplant recipients. In the present study, we aimed to determine the frequency of pre-existing and in vitro mediator release assay. Antibodies of the IgG1-4 subclasses and Th1 and Th2 cytokines were measured in anti-HLA IgE positive patients.We prospectively measured anti-HLA IgE antibodies in a cohort of kidney (n=60), liver, heart and lung (n=15 each) transplant recipients before and within one-year after transplantation, employing a single-antigen bead assay for HLA class I and class II antigens. Functional activity of anti-HLA IgE antibodies was assessed by an de novo anti-HLA IgE antibodies , whereas no de novo IgE-DSA was detected. IgG subclass antibodies showed a distinct pattern in patients who were positive for anti-HLA IgE. Moreover, patients with anti-HLA IgE showed elevated Th2 and also Th1 cytokine levels. Serum from IgE positive recipients led to degranulation of basophils in vitro, demonstrating functionality of anti-HLA IgE.Pre-existing anti-HLA IgE antibodies were detected in 10% of renal recipients (including 3.3% IgE-DSA) and in 4.4% of non-renal solid organ transplant recipients . Anti-HLA IgE occurred only in patients that were positive for anti-HLA IgG, and most IgE positive patients had had a previous transplant. Only a small fraction of patients developed These data demonstrate that anti-HLA IgE antibodies occur at low frequency in kidney, liver, heart and lung transplant recipients. Anti-HLA IgE development is associated with sensitization at the IgG level, in particular through previous transplants and distinct IgG subclasses. Taken together, HLA specific IgE sensitization is a new phenomenon in solid organ transplant recipients whose potential relevance for allograft injury requires further investigation. The oriThe first evidence for the existence of IgE in kidney transplantation was published in 1979. The authors found increased amounts of IgE bound to basophils of kidney transplant patients and observed basophil degranulation upon stimulation with donor lymphocytes, but without directly identifying the responsible donor antigens . More rede novo anti-HLA IgE antibodies, including IgE-DSA, in a cohort of kidney, liver, heart and lung transplant recipients. Additionally, we assessed whether the existence of IgE antibodies is associated with the development of specific IgG1-4 subclass antibodies. Functionality of IgE antibodies was determined by means of an in vitro basophil degranulation assay and Th1 and Th2 signature cytokines were measured.The prospective pilot study presented herein is exploratory in nature. We aimed to determine the incidence of pre-existing and 22.1This study is an investigator-initiated, prospective, single-center study. Participants were recruited at the Medical University of Vienna/Vienna General Hospital (AKH). The study was approved by the ethics review board of the Medical University of Vienna (EK no. 267/2011 and EK no. 1535/2016). 105 patients were enrolled upon written informed consent including, 60 kidney and 45 lung, heart and liver transplant recipients (15 for each organ cohort) and 20 healthy male volunteers. Blood was collected at baseline (pre-transplant), three months and twelve months post-transplant.2.2Blood samples were collected in a native blood vacutainer containing clot activator. After approx. 30 min of storage at room temperature (RT), the tube was centrifuged for 10 min at 1,500x g, at 10°C. The serum was aliquoted and stored at -80°C. Biomaterial was processed and stored according to standard operating procedures by the MedUni Wien Biobank in an ISO 9001:2015-certified environment .2.3Luminex Flow Analyzer 200. Threshold definition was informed by several factors and calculated by using the mean of the median fluorescence intensity (MFI) values of six healthy volunteers plus 2* standard deviation (SD). The minimum cut-off value for a positive IgE signal was set at an MFI > 25 [based on our previous observations  of six healthy donors (HD) plus two times the standard deviation (SD). As an additional condition, the following must also apply: HLA-specific IgE MFI > 25.2.4Luminex Flow Analyzer 200. Threshold for positive signals was calculated by using Equation 1.Human serum sample preparation was performed as described for HLA-specific IgE measurement. After incubation of serum and single-antigen beads (LABScreen Single Antigen Bead HLA class I and class II), samples were washed twice and incubated with PE-labeled monoclonal anti-human IgG subclass antibodies at concentrations of 12.5 µg/mL for IgG1, 10 µg/mL for IgG2, 16 µg/mL for IgG3 and IgG4  for 30 min on a shaker at RT in the dark. After two washing steps, samples were measured on the 2.5LEGENDplex multi-analyte flow assay kit (Biolegend) and subsequently measured with a BD LSRFortessa flow cytometer. Raw data were analyzed using the Qognit software .Cytokine levels of IL-2, IL-4, IL-5, IL-6, IL-10, IL-13, INFγ and TNFα were investigated in serum samples from kidney transplant recipients. Samples were stained in technical duplicates according to the manufacturer’s protocol using the 2.65 huRBL cells were incubated together with sera from kidney transplant recipients or with HD serum overnight, to a final serum dilution of 1:10 and stimulated with HLA class II tetramers . We used 5 µg/mL of DQA1*05:01, DQB1*02:01 and 0.12 µg/mL of DQA1*01:01, DQB1*05:01 tetramer. Mediator release of β-hexosaminidase was measured with a TECAN microplate reader at λ extinction: 360 nm and at λ emission: 465 nm. Mediator release was calculated by the β-hexosaminidase release after complete cell lysis (100%) by addition of 10% triton X-100. Samples were measured in triplicates, the background in six replicates.RS-ATL8 cell line, a rat basophilic leukemia cell line expressing the human α, β, γ high affinity IgE receptor chain (huRBL) was kindly provided by Prof. Ryosuke Nakamura. The serum samples were pre-incubated overnight (o/n) in a 1:5 ratio in MEM medium, supplemented with 5% FCS, 1% L-Glutamin, 1% PenStrep, 0.25% Geneticin and 0.25% Hygromycin B. In a next step, 2x102.7Data were analyzed with GraphPad Prism8  and IBM SPSS statistics version 25. Th1/Th2 cytokine values were analyzed as median with 95% confidence interval (CI). Values from huRBL assays were expressed as mean ± standard deviation (SD). P values were calculated using Fishers exact test and Mann-Whitney test, respectively, and p< 0.05 was considered as statistically significant.33.1Sixty kidney (KTX), 15 lung (LuTX), 15 heart (HTX) and 15 liver (LTX) recipients were prospectively enrolled into the study before transplantation Figure 1Twenty percent of recipients had pre-existing anti-HLA IgG antibodies  and 11.4% showed pre-existing IgG-DSA  as detected in routine clinical diagnostic bead-based analysis Table 2.Pre-existing IgE-DSA were detectable in two kidney recipients .de novo HLA-specific IgE antibodies three and twelve-months post-transplantation. 1.7% of all kidney (1/60) and 4.4% of the non-renal transplant recipients (2/45) developed de novo anti-HLA IgE  versus 30% of anti-HLA IgE negative patients (29 of 97) (p= n.s.), showing a trend for a correlation of atopy with the occurrence of anti-HLA IgE. However, the low sample size of HLA-IgE positive patients was a limiting factor for our analysis.2 ; at one-year post-transplant, creatinine was 1.07 ± 0.30 mg/dL (mean ± SD) and GFR 53.0 ± 18.4 mL/min/1.73m2 . Among the liver and heart recipients who were positive for HLA-specific IgE, graft function also remained stable.Among the kidney transplant recipients positive for anti-HLA IgE antibodies, graft function was stable during the 12 month follow-up. At 3 months post-transplant, serum creatinine was 1.23 ± 0.46 mg/dL (mean ± SD) and GFR 50.6 ± 20.5 mL/min/1.73mOne lung and two kidney recipients (both negative for anti-HLA IgE) had a biopsy proven antibody-mediated rejection (ABMR) in the first-year post-transplant. Whereas, none of the anti-HLA IgE positive patients developed ABMR during the follow-up period. Nine kidney recipients experienced Banff borderline rejections, including one patient positive for anti-HLA IgE antibodies, and one had a suspected chronic rejection. 16.7% of anti-HLA IgE positive kidney recipients (1 of 6) experienced a borderline episode within the first-year post-transplant versus 14.8% of IgE negative patients (8 of 54) (p= n.s.). The sample size and the number of IgE positive recipients are limiting further conclusions about the potential impact of IgE antibodies on clinical graft outcomes.The majority of anti-HLA IgE positive patients received induction therapy with anti-thymocyte globulin (ATG) (n= 6 out of 8), with three from those six patients receiving ATG receiving a re-transplant. Two IgE positive patients received ATG, three recipients a combination of ATG + immunoadsorption (IAS) therapy (kidney recipients), one patient received a combined ATG + ECP treatment (heart recipient) and two received the IL-2 receptor antagonist Basiliximab.These data demonstrate that anti-HLA IgE antibodies and IgE-DSA occur in low frequency in renal and non-renal transplant recipients. The occurrence of anti-HLA IgE antibodies was accompanied in all patients by the presence of anti-HLA IgG antibodies, although the pattern of HLA-specificities was only overlapping but not identical in the majority of patients. Anti-HLA IgE is associated with sensitization through previous transplants or pregnancies.3.2in vitro basophil degranulation assay. Basophil mediator release assays are commonly performed in the field of IgE-mediated allergy  in all kidney recipients positive for anti-HLA IgE antibodies  and in selected controls positive for anti-HLA IgG but negative for IgE  and patients without any HLA-specific sensitization (n=4). Th2 cytokines were elevated in patients with anti-HLA IgE antibodies compared to controls negative for anti-HLA IgE and negative for anti-HLA IgG  and compared to controls negative for anti-HLA IgE but positive for anti-HLA IgG  Figure 43.4The IgG subclasses IgG1 and IgG3 have a high capability to bind complement and activating Fc receptors whereas antibodies of the IgG4 subclass do not. IgG2 binds weakly to complement. We measured IgG1-4 subclasses in IgE positive kidney recipients and in IgG positive but IgE negative patients and determined if a specific subclass is associated with the existence of anti-HLA IgE antibodies. In anti-HLA IgE positive patients , for each HLA antigen we assessed whether IgE occurred as a single isotype or together with IgG1, IgG2, IgG3 or IgG4 using a cross table. We found that IgE and IgG2 against the same HLA antigen were significantly more likely to occur together than individually . Associations between the development of anti-HLA IgE and other IgG subclasses  for the same specificity were also statistically significant, but to a lesser extent  Table 3.Therefore, these data indicate that IgG subclass distribution is different in anti-HLA IgE positive patients.4de novo HLA-specific IgE antibodies in recipients of renal and non-renal transplants.Late graft loss caused by antibody-mediated rejection (ABMR) is a major factor limiting long-term outcome after renal and non-renal transplantations , 34, 35.IgE has unique properties that sets it apart from all other antibody isotypes. The main site of action for IgE lies within tissues, where it persists bound to its main effector cells, mast cells and basophils. Upon binding of a multivalent antigen and cross-linking, IgE triggers effector cell activation through its FcεRI high affinity receptor. As a result, numerous pro-inflammatory events are initiated, including histamine and lipid mediator release and upregulation of cytokine production, including TNFα and IL-6. A second major effector pathway of IgE is mediated through its low affinity receptor FcεRII (CD23). CD23 positive B cells can bind soluble IgE-antigen complexes and were shown to thereby promote antigen presentation and the antigen-specific T cell response in allergy . Thus, Iin vitro in a basophil degranulation assay. Our experimental set up was limited by the availability of the required HLA-tetramers in combination with the low number of anti-HLA IgE positive patients. Analyses of larger sample sizes in the future would provide additional insight into the functional activity of anti-HLA IgE. Anti-HLA IgE was found less frequently than anti-HLA IgG , but was nevertheless present in a considerable number of patients. Anti-HLA IgE occurred only in patients that also had anti-HLA IgG and most of them even had IgG-DSA. These data could indicate that anti-HLA IgE develops predominantly through sequential class switch from anti-HLA IgG antibodies. IgE can derive from two pathways of development, the direct route (IgM → IgE) and the indirect/sequential route (IgM → IgG → IgE)  transplant recipients, we found pre-existing anti-HLA IgE antibodies in 10% of kidney recipients and 4% of non-renal transplant recipients. Anti-HLA IgE was functional as it could trigger mediator release G → IgE) . Howeverde novo IgE-DSA became detectable within one-year post-transplantation. The sample size of the present study – even though being the largest cohort analyzed for anti-HLA IgE to date – limits a detailed analysis of IgE-DSA. Likewise, the potential effect of anti-HLA IgE on clinical endpoints cannot be assessed due to the size of the present cohort. 16.7% of anti-HLA IgE positive patients had a Banff borderline rejection episode, a similar frequency as IgE negative patients. ABMR was detected in three solid organ recipients, who were IgE negative. Long term data (beyond 1 year) of anti-HLA IgE positive patients could provide insight about the impact of IgE antibodies on the long-term allograft survival and if specific immunosuppressive regimens are required. However, larger sample sizes are required to definitively answer the question of the clinical impact of anti-HLA IgE. The current study is important in this respect as it demonstrates that anti-HLA IgE occurs only in IgG positive patients. Therefore, anti-HLA IgE studies in the future can focus only on pre-screened IgG positive patients, dramatically increasing the feasibility of large scale studies investigating IgE. While great care was taken in the establishment and execution of the assays measuring anti-HLA IgE in order to avoid false negative and false positive results (Pre-existing IgE-DSA were observed rarely (only in two patients) and these IgE-DSA became undetectable post-transplant. No  results , the minde novo (IgG) DSA  and identified in the graft deposits of IgE and the presence of mast cells and activated basophils . MoreoveIgG) DSA . While IIn summary, IgE antibodies targeting HLA occur in renal and non-renal transplant recipients. Given the unique effector mechanisms of IgE the possible role and relevance of IgE-DSA in immune injury of allografts should be further investigated.The original contributions presented in the study are included in the article/The studies involving human participants were reviewed and approved by the ethics review board of the Medical University of Vienna. The patients/participants provided their written informed consent to participate in this study.JM and TW designed the research, interpreted data and wrote the manuscript. JM performed research. AC, AW, MM, AGH, GB, AZ, PJ and RO contributed to patient recruitment and sample collection and processing. JM, MW and TW analyzed the data. AH and MM contributed to statistical analysis. MW, BL, PG and RV contributed with analytical tools and reagents. All authors contributed to the article, reviewed the manuscript and approved the submitted version.


Schizophrenia is a highly heterogeneous disorder and salient functional connectivity (FC) features have been observed to vary across study sites, warranting the need for methods that can differentiate between site-invariant FC biomarkers and site-specific salient FC features. We propose a technique named Semi-supervised learning with data HaRmonisation via Encoder-Decoder-classifier (SHRED) to examine these features from resting state functional magnetic resonance imaging scans gathered from four sites. Our approach involves an encoder-decoder-classifier architecture that simultaneously performs data harmonisation and semi-supervised learning (SSL) to deal with site differences and labelling inconsistencies across sites respectively. The minimisation of reconstruction loss from SSL was shown to improve model performance even within small datasets whilst data harmonisation often led to lower model generalisability, which was unaffected using the SHRED technique. We show that our proposed model produces site-invariant biomarkers, most notably the connection between transverse temporal gyrus and paracentral lobule. Site-specific salient FC features were also elucidated, especially implicating the paracentral lobule for our local dataset. Our examination of these salient FC features demonstrates how site-specific features and site-invariant biomarkers can be differentiated, which can deepen our understanding of the neurobiology of schizophrenia. The diagnosis and management of this condition has to take into account the significant heterogeneity in neurobiology2 and clinical presentations3 which are actively being investigated across sites and clinical settings5. The examination and discovery of biological factors including neuroimaging biomarkers that are generalisable across sites would promote a better understanding of this disorder and potentially allow for better predictive models, novel treatment targets and prognostication of this condition8.Schizophrenia is a chronic and debilitating mental illness affecting more than 27 million individuals worldwide11. This suggests that non-linear relationships do exist and warrants the use of more complex models for better elucidation. However, actual implementation of such algorithms is complicated by the high dimensionality of fMRI datasets and coupled with the often smaller datasets available, there is concern about the potential issue of model overfitting on such datasets12. To address overfitting, principal component analysis or recursive feature elimination can be used to reduce data dimensionality when training disease classification models13. Recently, Gupta et al.14 had suggested to address overfitting in deep learning models by removing accessory nodes (that are irrelevant to the prediction) from hidden layers in the neural network. While feasible when the dataset size is larger , such feature selection or pruning approaches eventually reach a limit when being applied on even smaller datasets from a single site.One way to extract neuroimaging biomarkers  is to fit a machine learning model on the datasets and identify the most important features that contributed to the model’s decision. Notably, deep neural networks have been used to model functional connectivity (FC) datasets and have shown better performance than conventional machine learning algorithms such as support vector machine models15, ADHD-20016, SchizConnect17), (ii) aggregation with protocol standardisation, i.e. carefully designed and enforced data collection protocols that have to be observed at all sites to minimise site differences . The latter is ideal since it minimises site differences and labelling inconsistencies, but the process is laborious. We need solutions to better utilise datasets that fall under the former category, consisting of datasets collected over time from different sites with different protocols.Another way to address overfitting is to find other open-source datasets and use it along with the smaller locally collected dataset or through pooling data into collaborative consortiums across multiple sites. In this regard, there are various approaches of pooling datasets together: (i) aggregation without protocol standardisation, i.e. gathering multi-site data into a uniform file format and folder structure  and data harmonisation in its architecture. This allows as much data as possible to be used for model training. Furthermore, its harmonisation approach—based on a generalisation of ordinary least squares—makes it possible to sieve out site-invariant biomarkers easily, as opposed to extant domain adaptation methods21 which cannot produce site-invariant biomarkers as they only map a source domain to a target domain, while existing domain generalisation methods22 do not consider the effects of labelling inconsistencies. Thus, SHRED makes it possible to disambiguate biomarkers by separating site-invariant biomarkers from site-specific features. The site-specific salient FC features may indicate neuroimaging features associated with certain subpopulations or subtypes while the site-invariant salient FC features could point to neuroimaging biomarkers generalisable across a wider population.In this regard, SHRED (short for Semi-supervised learning with data HaRmonisation via Encoder-Decoder-classifier)17 and the UCLA Consortium for Neuropsychiatric Phenomics LA5c Study23. In view of the need for better data harmonisation across four sites with differing samples, we used a deep neural network approach based on SHRED20 to determine site-specific  salient FC features and site-invariant FC biomarkers  in schizophrenia.In this study, we sought to better identify neuroimaging biomarkers in schizophrenia from resting state functional MRI assessments by combining our own dataset with other openly available datasets from SchizConnectWe recruited local subjects  from the Institute of Mental Health, Singapore and community respectively after a full explanation of the study procedures and provision of informed consent by all participants. The inclusion criteria were: (1) DSM-IV diagnosis of schizophrenia; (2) Age 21–65 years old; (3) English speaking subjects, and exclusion criteria consisted of: (1) history of significant head injury; (2) neurological diseases such as epilepsy, cerebrovascular accident; (3) DSM alcohol or substance use or dependence; (4) contraindications to MRI ; (5) pregnancy. This study was approved by the Institution Review Boards of Institute of Mental Health and National Healthcare Group . All methods were performed in accordance with the relevant guidelines and regulations.2) with axial orientation , covering the whole brain for structural-anatomic detail were acquired.For the local dataset, single session MRI scans were acquired on a state-of-the-art, clinical 3-Tesla system . Subjects were instructed to remain still and keep their eyes open and fixed on the centre cross of the screen. Whole brain, high resolution, 3D MP-RAGE (magnetisation-prepared rapid acquisition with a gradient echo) volumetric scans , COllaborative Informatics and Neuroimaging Suite Data Exchange tool  and Neuromorphometry by Computer Algorithm Chicago (NMorphCH) dataset (http://nunda.northwestern.edu/nunda/data/projects/NMorphCH). Further summarised details of the 4 datasets can be found in the Supplementary Note.In addition, data sets from 3 separate sources were used i.e. UCLA Consortium for Neuropsychiatric Phenomics LA5c Study (24 to derive 264 regions of interest (ROI), (ii) computing the mean time series by taking the average of all voxels within a 2.5 mm radius from the ROI, (iii) calculating the Pearson’s correlation score between every ROI pair. This produces a 264 × 264 correlation matrix for every subject. Since the matrix is symmetrical, only the bottom triangular is used as input features to SHRED-III by flattening them into a vector as vanilla deep neural networks can only take in vectors as input.All resting state fMRI scans were pre-processed via fMRIPrep 21.0.1. Details about the pre-processing steps can be found in the Supplementary Note. In brief, pre-processing steps included motion correction, slice timing correction and band-pass filtering. After pre-processing, FC matrices were generated by (i) applying Power’s atlas20 along with additional improvements which outperformed previous versions of the SHRED model. We named this updated version SHRED-III. Source code for the architecture is available at https://github.com/SCSE-Biomedical-Computing-Group/SHRED.Here in Sections “As illustrated in Fig. SHRED-III introduces two key changes from SHRED: (i) covariates of interests in the design matrix are not added back (i.e. both site effects and confounders are removed), (ii) the loss function, specifically the formulation of the reconstruction loss, is improved to better approximate the site effects. Additionally, SHRED-III is trained in an end-to-end manner, unlike SHRED-II where ComBat was first applied before the harmonised data is passed to EDC in a two-stage approach.20, let Following the notations in Chan et al.For a subject 25, but we note that our optimisation process differs  with a vanilla deep neural network (DNN) classifier attached at the bottleneck layer of the VAE to perform disease classification. The VAE takes in the output of the harmonisation module The VAE component of the EDC module is made up of two encoders, The DNN component of the EDC module takes in the latent representation The VAE in the EDC module has produced SHRED-III is trained end-to-end via a single multi-objective loss function where weights are learnt to simultaneously remove site effects and maximise classification performance. Just like how the EDC module was presented, the components of the loss function can be grouped under two categories: the DNN component and the VAE component.The DNN component of the EDC module makes use of the cross-entropy loss The VAE component of the EDC module, on the other hand, makes use of both labelled and unlabelled data. It consists of three parts: First, a likelihood loss 26.Second, VAE also minimizes the Kullback–Leibler divergence 20 . Denoting Third, for accurate reconstruction of input data All 4 loss terms are then combined, forming a joint loss 27 to generate the scores. Many of these feature attribution methods are equivalent, especially when the model is simple  but amongst them, Integrated Gradients remains usable for a wider variety of situations28. Along with the clear choice of baseline to use , these reasons explain the rationale behind our choice of using Integrated Gradients.Once a model is trained, feature attribution scores are computed from the output layer to input layer. In this work, we use Integrated GradientsLet These scores provide a quantitative measure of each feature’s contribution to the task. Notably, these are computed for individual samples. This means that feature attribution scores computed for each sample can be used to generate individualised biomarker heatmaps, allowing for personalised treatments to be designed.Group-level studies (case–control) are performed too by aggregating the feature attribution scores:Whole datasetIn this study, we are interested to elucidate (i) site-invariant FC biomarkers and (ii) site-specific salient FC features. Thus, two sets of experiments were conducted.To arrive at site-invariant FC biomarkers, data from multiple sites are used simultaneously. We aim to demonstrate that SHRED-III is capable of producing site-invariant biomarkers even when the whole dataset is used. This is in contrast to existing approaches that use supervised learning models on the whole dataset, which we hypothesise will produce biomarkers that are biased towards the larger sites.2.Individual sitesSHRED-III involves both data harmonisation and SSL being used simultaneously. To study how each component of SHRED-III contributes to the model performance, an ablation study was conducted. This involves DNN , DNN (DH) (harmonisation but no SSL). EDC (SSL but no harmonisation), SHRED-III (harmonisation and SSL). Since the whole dataset is used, SSL does not incorporate the idea of using unlabelled data in this setting—rather, it is the use of the reconstruction loss that is being analysed. We also conducted experiments with SHRED and SHRED-II to compare with approaches proposed in previous studies.To arrive at site-specific salient FC features, models were fitted to individual sites separately. This involved four experiment settings: (i) Supervised learning (SL), (ii) SL + data harmonisation, (iii) SSL, (iv) SSL + data harmonisation. For SL, two architectures were used: vanilla DNN and EDC (without unlabelled data). For SSL, EDC (with unlabelled data) and 3 SHRED variants  were used.20. In brief, tuning was performed on a subset of the ABIDE I dataset which was not used for the main set of experiments. There are several reasons supporting this decision: (i) given the relatively small dataset size in this work , we do not want to tune and overfit to this dataset, (ii) all 3 datasets are based on FC data and the obtained hyperparameters were already shown to generalise well to the ADHD-200 dataset in the previous work, thus it is reasonable to expect that they will work for these schizophrenia datasets as well, (iii) tuning specifically to the Schizophrenia dataset will leave too little data for model testing and we want to run our models over multiple seeds and folds in a cross-validation approach so as to demonstrate the robustness of our obtained biomarkers.Hyperparameters used in these architectures were tuned following the approach mentioned in Chan et al.The following parameters were tuned for EDC: number of hidden layers in the DNN branch The final architecture for EDC includes: a single Dense layer for the classifier (with two output neurons for binary disease classification), 32 hidden neurons in the encoders For SHRED-III In this section, we provide details about the datasets used and present results in terms of model performance and biomarkers obtained via SHRED-III.Four relevant resting state fMRI datasets were included in this study, including a local dataset (IMH). Table Overall, 417 resting state fMRI scans comprising of 249 normal controls (NC) and 168 schizophrenia patients (SZ) were included from 4 sites. PANSS scores are only available for our local dataset and we note that these patients have a relatively low Positive and Negative Syndrome Scale (PANSS) score, indicating that most have mild symptoms (PANSS scores ranging from 30 to 53).Site differences in the 4 datasets are shown in Fig. p-values reported below were computed based on Welch t-test .All experiments were performed with fivefold cross-validation repeated across 10 seeds. Tables When data harmonisation was introduced to the vanilla DNN Table , the chang Table , a statiThe introduction of SSL brought about a statistically significant increase in model performance  when DNN was used, but got much higher when SSL was introduced. Sensitivity generally reduced except for UCLA—this could be explained by how UCLA was relatively imbalanced as compared to other sites. Finally, we note that SHRED-III generally had the highest specificity for both individual sites  and whole dataset. It also experienced a smaller decrease in specificity  and sensitivity (for whole dataset) due to data harmonisation, as compared to other SHRED models.Overall, most models were able to perform disease classification rather well. This gives greater confidence to the biomarker heatmaps illustrated in the next section.24 does not provide anatomical labels, the ROIs were mapped to the labels in Crossley et al.29 by matching the closest ROIs in the Crossley atlas to those in the Power atlas, with closeness computed based on Euclidean distance.To ensure that the salient FC features generated via our approach are robust, saliency scores were averaged across all 50 trained models (from 10 seeds × 5 folds) and standardised. Since the atlas from Power et al.Features were generated for two scenarios: on individual sites and on the whole dataset. Features generated from individual sites using models trained via SL will represent site-specific salient FC features, while the features generated from the whole dataset require further examination. It is hypothesised that in the whole dataset setting, (i) when SL models are used, they will produce salient features that are biased towards the larger datasets, (ii) when SHRED-III is used, site-invariant biomarkers will be produced, since site effects and labelling inconsistencies were removed.Figure From Fig. The model from EDC (SL) were used for this as they gave high model performance across all sites, as seen in Supplementary Table S1. Figure Figure Salient FC features from NMorphCH bears some similarities with a subset of salient features that are found in COBRE and IMH. There does not seem to be any significant salient features that are specific to NMorphCH. Only 2 salient features from EDC (SL) (whole dataset) remained: transverse temporal gyrus—paracentral lobule and transverse temporal gyrus—posterior cingulate . Notably, transverse temporal gyrus—posterior cingulate was present in COBRE but not IMH. NMorphCH also shares a common salient feature with IMH .Finally, salient FC features from UCLA are shown in Fig. Overall, it can be observed from Fig. 30, showing the brain modules they belong to.The heatmaps presented in Figs. FC values of salient features can also be correlated with measures of symptom severity of the disorder, such as PANSS for Schizophrenia. To study such associations, FC values of the top 10 salient ROI pairs are correlated with the total PANSS scores, as well as the scores for each subdomain . After corrections for multiple comparisons via the Benjaminini-Yekutieli false discovery rate control procedure, we found that the connection between inferior parietal lobule and parahippocampal gyrus has a significantly strong negative correlation with both total PANSS score  is clearly the main contributor to improvements in model performance. This suggests that it is possible to train deep learning models on individual sites and small datasets (< 100 samples) to produce site-specific salient FC features. However, an EDC-based architecture should be used instead of a vanilla DNN (i.e. reconstruction loss should be used) since the specificity of vanilla DNN on small sites was relatively low. It is also evident that data harmonisation caused a slight decrease in model accuracies. Notably, one trend in recent works is to pool datasets from multiple sites together (i.e. whole dataset setting) followed by analysis of the harmonised data. Our results suggested that models trained on such datasets could have poorer generalisability and this was attributed to a large decrease in model sensitivity that outweighed the increase in specificity . Thus, data harmonisation should be used with care and our results suggested that SHRED-III was less susceptible to this problem (much less than SHRED) but there is still some room for improvements to reduce the extent that sensitivity is lowered by data harmonisation.31. Another possible explanation could be derived from observing the heatmap of the IMH site in Fig. Another example of a scenario where data harmonisation should be used with care can be seen from the local dataset results. At the level of individual sites, the results from Table 20 on the ABIDE and ADHD-200 datasets. Thus, our results on this smaller dataset suggested that it might be necessary to collate adequate data from multiple sites  before the benefits (in terms of model performance) of combining data harmonisation with SSL could be fully observed. One notable phenomenon when training VAE models in small but high dimensional datasets is the possibility of the loss exploding to infinity. In our study, this was resolved by introducing gradient clipping, but we noted that this was not performed in study by Chan et al.20 whereby the datasets were larger. Despite these issues, the proposed SHRED-III model still retained its utility in terms of accurately discovering site-invariant biomarkers. Figure Overall, combining data harmonisation and SSL via the SHRED variants generally led to better performance than baseline DNN models. However, the combination did not provide as much improvements seen in an early study by Chan et al.33. Furthermore, we highlighted an issue with the current approach of biomarker discovery (represented by DNN (SL) in Fig. 34 and without14 data harmonisation.In terms of biomarker discovery, our study revealed both site-invariant biomarkers  as well as several site-specific salient FC features. These analyses provide more granular insights than existing evaluationsFor site-invariant biomarkers, Fig. 35. For resting state fMRI, it has been found to be implicated in SZ patients too, with studies showing that SZ patients with auditory hallucinations had higher left Heschl's gyrus functional connectivity with left frontoparietal regions36. Among the ROIs that have salient connections with the Heschl’s gyrus, those unique to COBRE included cuneus, which was also found to have higher FC in SZ patients37.For site-specific salient FC features, from the COBRE dataset, transverse temporal gyrus was noted as a prominent feature, with salient connections with six other ROIs. The transverse temporal gyrus, also known as Heschl’s gyrus, is responsible for processing incoming auditory information and it has been demonstrated that an increase in blood oxygen level–dependent occurs at the Heschl’s gyrus during episodes of auditory hallucinations38. Furthermore, in a meta-analysis of resting state fMRI studies on schizophrenia39, it was noted that SZ patients have significantly lower local FC density in the paracentral lobule.From the IMH dataset, paracentral lobule has salient connections with five other ROIs. The paracentral lobule influences motor functions in the lower limbs and it was noted that components of motor abnormalities that could be caused by paracentral deficits were useful for predicting 1-year outcome in first-episode schizophrenia40. Past studies using traditional machine learning methods have also implicated the fusiform gyrus in schizophrenia41. On the other hand, the uncus is involved in olfaction, emotions and memory formation42 while the postcentral gyrus is involved in receiving sensory inputs and their roles in the neural basis of schizophrenia needs further evaluation.From the UCLA dataset, there are two notable site-specific salient FC features: the connection between the uncus and fusiform gyrus and between the transverse temporal gyrus and postcentral gyrus. Fusiform gyrus has long been associated with the ability to recognise faces and interpret facial expressions43. In this study, the most salient connections involved ROIs in the auditory, visual and sensorimotor networks. In particular, a strong site-invariant biomarker in transverse temporal gyrus—paracentral lobule involves the auditory and sensorimotor networks. Each site-specific salient FC feature were shown to involve unique pairs of brain networks and the site-specific feature from COBRE  have been reported in a previous study44, albeit not having an exact match of ROIs.Overall, in the existing literature, it has been demonstrated that schizophrenia patients have significant impairments in connections between various brain networks, including auditory, salience, default mode, executive control and sensorimotor networksOne key limitation of this study was the uncertainty with regards to salient FC features that were neither unique enough to be site-specific, nor widespread enough to be considered site-invariant. For example, the connection between the transverse temporal gyrus and posterior cingulate was highlighted as a salient feature in COBRE but also in NMorphCH (with a relatively lower score). While SHRED-III assigned a score that is in between that of transverse temporal gyrus—paracentral lobule (clearly site-invariant) and uncus—fusiform gyrus (site-specific to UCLA), it was less clear how to interpret the other salient FC features  that laid between these extremes. Future work could involve the design of an algorithm to identify a threshold that separates site-invariant biomarkers and site-specific salient FC features based on the saliency scores in heatmaps seen in Figs. 45 setting. Although numerous data consortiums have been formed in recent years, it is often still the case that datasets are not allowed to be shared outside of the site due to data privacy concerns. Current versions of SHRED still require pooling datasets from multiple sites together for it to produce site-invariant biomarkers. Future work could explore how site-invariant biomarkers could be arrived at without the need to pool datasets together.One potential area of future work is to extend SHRED to work in a federated learning46. To allow dynamic FC data to be modelled under our framework, the architecture can be enhanced by adding recurrent neural networks (or transformers) to capture both spatial and temporal relationships47. To allow subtypes to be captured, a clustering module could be introduced into the auto-encoder48.Another possible future work would be to extend SHRED in terms of both data modalities and tasks. While this study was limited to static FC, dynamic FC have been shown to be effective in distinguishing SZ patients from healthy subjects and revealing SZ subtypesIn summary, the neural network architecture proposed in this work (SHRED-III) could perform data harmonisation and semi-supervised learning simultaneously via an encoder-decoder-classifier architecture to deal with site differences and labelling inconsistencies even in smaller datasets. Thus, it potentially offers a scalable approach of discovering site-invariant disease biomarkers and it helps to isolate site-specific salient FC features without the need to scour each site manually (which will be increasingly unfeasible as pooled datasets and data consortia get larger). Our examination of 4 schizophrenia datasets revealed transverse temporal gyrus-paracentral lobule as a site-invariant biomarker and an example of a site-specific salient FC feature observed in our local site include multiple ROIs linked to the paracentral lobule which were not seen in other sites.Supplementary Information.


The extracellular-signal-regulated kinase 2 (ERK2) plays a key role in the Ras-Raf-MEK-ERK signal transduction cascade and is involved in the regulation of many cellular processes. The ERK2 signaling converts extracellular stimuli into cell proliferation and, when deregulated, can promote oncogenic transformation. The ERK2 missense variants, carrying a single amino acid substitution in the protein sequence, have been identified in cancer tissues. This study reports a comprehensive biochemical and biophysical study of the ERK2 wild-type and variants in the common docking site present in cancer. A detailed analysis, from a molecular point of view, of the variants clarifies the impact of a single nucleotide variation on protein structure, stability and function, is essential to design alternative therapeutic strategies, and is a preliminary step to personalized medicine.The extracellular-signal-regulated kinase 2 (ERK2), a mitogen-activated protein kinase (MAPK) located downstream of the Ras-Raf-MEK-ERK signal transduction cascade, is involved in the regulation of a large variety of cellular processes. The ERK2, activated by phosphorylation, is the principal effector of a central signaling cascade that converts extracellular stimuli into cells. Deregulation of the ERK2 signaling pathway is related to many human diseases, including cancer. This study reports a comprehensive biophysical analysis of structural, function, and stability data of pure, recombinant human non-phosphorylated (NP-) and phosphorylated (P-) ERK2 wild-type and missense variants in the common docking site (CD-site) found in cancer tissues. Because the CD-site is involved in interaction with protein substrates and regulators, a biophysical characterization of missense variants adds information about the impact of point mutations on the ERK2 structure–function relationship. Most of the P-ERK2 variants in the CD-site display a reduced catalytic efficiency, and for the P-ERK2 D321E, D321N, D321V and E322K, changes in thermodynamic stability are observed. The thermal stability of NP-ERK2 and P-ERK2 D321E, D321G, and E322K is decreased with respect to the wild-type. In general, a single residue mutation in the CD-site may lead to structural local changes that reflects in alterations in the global ERK2 stability and catalysis. Most biological processes are regulated by a complex network of protein–protein interactions (PPI) constituting critical regulatory nodes in many cell-signaling pathways . The disProtein kinases and phosphatases are involved in many signal transduction pathways mediated by specific PPIs, and a balanced state between protein kinases and phosphatases is essential for the regulation of cellular functions. Phosphorylation, on one hand, is essential for normal cellular processes, while, on the other hand, abnormal phosphorylation is one of the prime causes for alteration of structural, functional, and regulatory proteins in disease conditions, such as cancer ,6.The extracellular signal-regulated kinase 2 (ERK2) is a Ser/Thr kinase that belongs to the MAP kinase (MAPK) family and, with the isoform ERK1, participates to the signal transduction cascade Ras-Raf-MEK-ERK, or the ERK1/2 cascade. This cascade regulates many cellular processes, such as cell differentiation and proliferation. The ERK1/2 are activated by phosphorylation on 1 tyrosine (Tyr204/Tyr187) and 1 threonine (Thr202/Thr185) residue within the activation loop by mitogen-activated protein kinase 1 and 2 (MEK1 and MEK2)  7,8,9,1,11,12. TRemoval of phosphate by specific phosphatases from either the tyrosine, the threonine, or from both residues inactivates the ERK2. Three groups of protein phosphatases—protein Ser/Thr phosphatase , proteinThe ERK2 mediates key events throughout the cell by phosphorylation of transcription factors, cytoskeletal proteins, and other protein kinases and enzymes ,19, thusThe ERK2 is a key regulator of the ERK pathway by recruiting interaction partners and substrates that can be either substrates or regulators through two docking sites: a conserved common docking site (CD-site), and an F-recruitment site (F-site). The CD-site is located at the opposite site of the catalytic cleft and interacts with the D-docking domain of the substrate (D-site). The F-site is in the proximity of the catalytic cleft and of the activation-loop; it is fully formed upon ERK2 activation and interacts with the F-docking domain of substrates ,22.The ERK2 CD-site is composed of a negatively charged component and of a hydrophobic component . The negCOSMIC database , the mean residue ellipticity, assuming a mean residue molecular mass of 110.The ERK2 wild-type and variants  were incubated at increasing GdmCl concentrations (0−8 M), at 4 °C, in 20 mM Tris/HCl, pH 7.5, 0.1 M NaCl, and 0.2 mM DTT. Equilibrium was reached after 30 min, then intrinsic fluorescence emission and far-UV CD spectra (0.2-cm cuvette) were recorded at 10 °C. The reversibility of the unfolding was tested by unfolding the ERK2 wild-type and variants  in 8.0 M GdmCl, at 10 °C, in 20 mM Tris/HCl, pH 7.5, 2 mM DTT, and 0.1 M NaCl. After 5 min, refolding was started by a 10-fold dilution of the unfolding mixture at 4 °C into solutions containing decreasing GdmCl concentrations. Equilibrium unfolding experiments were carried out in triplicate.−1. The dichroic activity at 222 nm and the photomultiplier were continuously monitored as described in i is the GdmCl concentration after the ith addition, ΔGH2O is the extrapolated unfolding free energy change in the absence of denaturant, and m is the slope of a ΔGunfolding versus [X] plot. Data were globally fitted with the m values shared between the data sets; all other parameters were not constrained. The denaturant concentration at the midpoint of the transition, [GdmCl]0.5, according to Equation (3), is calculated as:The GdmCl-induced changes in intrinsic fluorescence emission spectra were quantified as the intensity-averaged emission wavelength, λ¯, Ref.  calculaton model , accordiGraphPad Prism 7.03 was used to fit all unfolding transition data.Far-UV CD spectra recorded at an increasing GdmCl concentration were analyzed by a singular value decomposition algorithm (SVD) using the software MATLAB  to remove the high frequency noise and the low frequency random errors, and to determine the number of independent components in any given set of spectra, as described in .222 or in the intrinsic fluorescence induced by increasing GdmCl concentrations were fitted to the following equation, assuming a three-state model:F is λ (Equation (1)), or [Θ]222, m is a constant that corresponds to the increase in solvent-accessible surface area between the two states of the transition; D50I-N and mI-N are the midpoint and m-value for the transition between N and I, respectively, and D50U-I and mU-I are the midpoint and m-value for the transition between I and U, respectively ectively . The λ¯,m shifts were calculated, as described, in ). Protein bands were then transferred to Hi-Bond N+ membranes (Cytiva) using a XCell II Blot Module (Thermo Fisher) at a constant voltage setting of 30 V 250 mA for 60 min. The membranes, after blotting, were washed three times with a Tris-buffered saline (TBS) with 0.1% TweenTo study the consequences of a single amino acid substitution on an extracellular signal-regulated kinase 2 (ERK2) common docking site (CD-site), we characterized six missense ERK2 mutations found in cancer tissues  and reported in COSMIC database  Supplem. The twoAll the variants’ object of this study were obtained as recombinant proteins using site-directed mutagenesis and resulted as soluble pure proteins in the non-phosphorylated-ERK2 (NP-ERK2) and phosphorylated-ERK2 (P-ERK2) conformations . The biophysical characterization was carried out on both NP-ERK2 and P-ERK2 wild-types and variants. Phosphorylation was checked by western blot analysis .The conformation in the solution of NP-ERK2 and P-ERK2 wild-types and mutants was analyzed by circular dichroism (CD) and fluorescence spectroscopy. The near-UV CD spectra of the NP-ERK2 and P-ERK2 wild-type showed a positive spectral contribution at around 290 nm, typical of tryptophan residues, with fine structure features at 260–275 nm A. The twThe fluorescence spectra of the NP- and P-ERK2 wild-types B showed The spectra of NP-ERK2 variants in the near-UV CD region were similar to that of the NP-ERK2 wild-type, except for D321G A. In parThe fluorescence spectra of the P-ERK2 D321E variant was centered at 343 nm, and it was significantly different from that of the wild-type D, suggesThe analysis of far-UV CD spectra revealed that the secondary structure of the NP-ERK2 variants was similar to that of the wild-type , hence t222/[Θ]208) is very sensitive to structural changes. This parameter, useful to distinguish between coiled coil helices (>1.0) and non-interacting helices (0.8–0.9), may give information about interhelical contacts present in a helix bundle and coiled coil structures [222/[Θ]208 for the wild-type is 0.8 and ranges from 0.76 for NP-D321N to 0.98 for NP-D321G. This variation suggests that the single amino acid substitutions in the CD-site can alter interhelical interactions in the solution , corresponding to the midpoint of the denaturation process, was calculated by plotting the first derivative of the molar ellipticity values as a function of temperature. The thermal transition of the NP-ERK2 wild-type (Tm = 55.0 °C) was less cooperative than that of the P-ERK2 (Tm = 56.0 °C)  .In general, upon phosphorylation, the melting temperature increases for the P-wild-type, P-D321A, P-D321E and P-D321G, and decreases for P-D321N, P-D321V, and P-E322K .m values were increased for the NP- and P-D321N . The activity assay was performed at 30 °C at a fixed MgCl2 concentration.The effect of missense mutations on the kinetic properties of ERK2 was investigated by monitoring over time the increase in the fluorescence intensity induced by the incorporation of phosphate into a peptide substrate (PhosphoSenskcat/KM), analyzed using AQT0490 over the concentration range of 0.069 µM to 40 µM (kcat/Km), that is, a 1.5–fold decrease for P-D321A and only a 3.8-fold increase for P-D321E , higher than that of the wild-type, except for the variant P-D321V that showed a Tmax at 25 °C, suggesting that in physiological conditions, this variant may be completely inactive   , comparaThe ERK2 CD-site is in a region where the major changes between inactive (NP-ERK2) and active (P-ERK2) proteins are observed. In fact, when the CD-site binds to the activating and deactivating enzymes, solvent exposure of the activation loop occurs, thus suggesting a relation between the activation loop and the CD-site .m values like that of the wild-type, with a small difference of 2–4 °C  of about 3.8 nM—in the same order of magnitude of that of the variant E322K (KD = 7.9 nM). Thermodynamic measurements provide insight into the nature of the noncovalent forces responsible for binding; polar interactions tend to contribute favorably to the enthalpic component, whereas entropically favored interactions are often hydrophobic and are dominated by dehydration. The binding of the ERK2 wild-type and the inhibitor GDC-0994 revealed an enthalpy-driven interaction, driven by favorable polar interactions such as hydrogen-bonding. On the other hand, the interaction of ERK2 E322K and the inhibitor GDC-0994 displayed an entropy-driven interaction, mainly characterized by contribution of hydrophobic effects to binding.The hotspot mutation E322K is associated to an apparent increase in tumor resistance to inhibitors ,30. IsotKd value of 23.7 µM. The titration profile of the pepMEK2 with the NP-ERK2 D321N displayed an entropy-driven interaction with a Kd value of 11.6 µM. These results indicate that the amino acid substitution in D321N perturbs the docking interaction in the CD-site.To better understand the effect of the D321N mutation on the interaction with the protein kinase MEK2 and the dual specificity phosphatase (DUSP6), we performed ITC titrations with the peptides PepMEK2 and the Kinase Interaction Motif (KIM) of DUSP6. PepMEK2 is a MAP Kinase ERK2 substrate docking peptide derived from MLARRKPVLPALTINP of the upstream activating kinase MEK2. The binding with the pepMEK2 of the ERK2 wild-type was analyzed and compared with that of the variant D321N. ITC data revealed an enthalpy-driven interaction of the NP-ERK2 wild-type with pepMEK2, with a Kd of 14.4 µM  . SurprisApart from this difference, the structure was highly conserved compared with the wild-type ERK2 .Unanticipated was also the presence of the inhibitor that interacted not only with the ERK2 binding pocket for ATP, but also bound to the crystal interface . As thisThe studies on purified ERK2 variants may contribute to understand the effect of missense mutations on protein function and structure . The resThe missense variants in the ERK2 common docking site (CD-site), a region involved in interactions with substrates and regulators, are considered statistically significant in cancer. Biophysical studies on these variants present in cancer tissues indicate that the ERK2 CD-site, that is far away from the catalytic site, plays a significant role in the energetics of the protein. In this site, the major changes between inactive (NP-ERK2) and active (P-ERK2) proteins are observed, thus amino acid substitutions in this region may affect the transition between the active and the inactive conformation and alter binding interactions with regulators, such as phosphatase DUSP6. The lack of control of the ERK2 activity by DUSP6 may keep the ERK2 signaling active, thus favoring oncogenic transformation. A single residue mutation in the CD-site brings about local changes that strongly reflect on the global properties of the ERK2, as the thermodynamic parameters or the activation energy values significantly changed in some of the mutants. The negatively charged residues in the CD-site are involved in the stabilization of the native state and a charge inversion or neutralization of residues D321 or E322 may result in a significant change in the ERK2 biophysical and biochemical properties. The differences observed in the catalytic parameters, as the reduced catalytic efficiency, may result from a different response to the control mechanism that regulates the transition from active- to inactive-conformation of the overall protein structure.