In the light of these findings, the analysis of genome-wide expression data has become one of the central tools to link genotype and phenotype information on a more mechanistic level. However, in many studies, especially if general conclusions are drawn from such data, a key feature of gene regulation is often neglected. With our article, we want to raise awareness that gene regulation and thus gene expression is highly context dependent. Genes show tissue- and stage-specific expression. We argue that the regulatory context must be considered in comparative expression studies. Research in various fields of evolutionary biology has shown that divergence in gene expression is a key driver for phenotypic evolution. An exceptional contribution of  Living organisms are uniquely characterized by their appearance, their function, and their interaction with the environment. The information about these features is provided in the genome which is packed into the nucleus of each cell see A. VariouSince the identification of deoxyribonucleic acid (DNA) as genetic material in 1944  a major In this review we will first summarize findings illustrating the importance of gene expression divergence in phenotypic evolution for various morphological, behavioral, physiological, and life-history traits. Next, we will present current approaches aiming at understanding genome-wide patterns of gene expression divergence as well as the underlying molecular mechanisms. We will review various mechanisms underlying gene regulation and we will highlight how they facilitate context dependent gene expression. We argue that gene expression and gene regulation evolve in a highly context dependent manner and we will suggest to consider that knowledge to improve the efficiency of comparative gene expression studies.Drosophila or differences in body coloration. For instance, a clear link between changes in the regulatory region of the shavenbaby gene and the evolution of trichome patterns across Drosophila species has been established [ebony [yellow genes [Drosophila. Divergence in fur coloration in mice has been shown to be regulated by differences in developmental expression of the gene agouti [agouti-related peptide 2 (agrp2) [Changes in gene expression have been linked to variation in many phenotypes. In the last years, there has been an increase in the number of ecological and evolutionary studies using transcriptomics to understand how environment and different life strategies affect gene expression ,21. Mostablished ,23. Simid [ebony  and yellow genes  underliee agouti ,27. More (agrp2) . Drosophila, the shape of male genitalia evolves rapidly, contributing to speciation processes. Divergence in the expression of the tartan gene has recently been shown to contribute to interspecific differences between D. mauritiana and D. simulans [cis-regulatory region of scute has pleiotropic effects by affecting genitalia bristle and sex comb sensory teeth number simultaneously [Besides these classical traits, also more complex traits are being studied. In simulans . Anotheraneously . Hence, syntaxin1a, since higher expression of this gene is directly correlated with a social life style [Peromyscus maniculatus bairdii) and its sister species, the monogamous old-field mouse (P. polionotus subgriseus) is influenced by differential expression of the gene vasopressin [Recently it has been argued that the molecular architecture of differences in behavioral traits may be simpler than previously anticipated. For instance, a complex behavior such as sociality in bees has been shown to be clearly associated with differential expression of the gene fe style . Similaropressin . These ePopulus tremula) has shown that expression divergence of a single gene (PtFT2) is responsible for 65% of the differences in timing of bud set [cis-regulatory regions causing gene expression divergence which ultimately affects an organism’s physiological response to the environment. For example, a 2 bp deletion in the promoter region of the gene ERG28 in Saccharomyces cerevisiae results in reduced expression associated with resistance to an antifungal drug [MtnA that shows signatures of selection, causes a 4-fold difference in gene expression and confers resistance to oxidative stress in natural populations of D. melanogaster [Many studies exploring the molecular basis of the evolution of physiological and life-history traits followed by functional validation have confirmed an underlying polygenic architecture ,33,34. N bud set . Other sgal drug . Similarnogaster .In summary, genetic variation associated with the evolution of phenotypic traits such as morphology, behaviour, life history, and physiology often affect gene expression. Therefore, gene expression divergence is a major driver for phenotypic evolution.Drosophila [Drosophila species has been used in modelling approaches integrating fitness estimates and a phylogenetic framework to reveal that expression divergence shows signatures of directional selection [Since gene expression divergence is often linked to phenotypic evolution, many comparative studies employ gene expression as intermediate phenotype to link genetic variation to trait divergence. However, besides using gene expression to establish genotype–phenotype maps, the availability of high throughput methods to survey genome wide expression levels also allows to study global patterns of gene expression divergence. It has for instance been shown that divergence in gene expression is pervasive among populations in osophila , yeast [osophila , or in fosophila . It is tosophila . Also, ielection . Additioelection  or expreelection . TherefoThe accumulation of comparative genome wide expression data triggered a strong interest in unravelling the molecular and evolutionary mechanisms underlying divergence in gene expression itself. Most of our current mechanistic understanding of gene expression divergence is based on work in genetic model systems that are tractable for genetic crosses. Two main methods have been employed extensively in recent years, i.e., expression quantitative trait loci (eQTL) mapping and allele-specific expression studies (ASE). eQTL studies are basically QTL or genome wide association (GWAS) studies aiming at identifying causative loci responsible for gene expression variation. Conceptionally, this method assumes that the level of gene expression can be treated as a quantitative trait ,46,47. Tcis-regulatory divergence) or due to changes somewhere else in the genome (trans-regulatory divergence) [cis-regulatory divergence is inferred if two different alleles of a given gene have a major impact on its allele-specific expression in the homogenous trans-regulatory background of the F1 hybrid. trans-regulatory divergence is inferred if a gene is differentially expressed between two parental individuals, but the contribution of the two alleles in the hybrid background is the same. The most consistent observation in ASE studies is that cis-regulatory divergence seems to be prevalent in intra- as well as interspecific comparisons [D. melanogaster and the closely related specialist species D. sechellia [cis- and trans-divergence has been observed, strongly supporting the notion that gene regulation is complex and thus can evolve in complex patterns. While eQTL studies reveal genomic loci or individual single nucleotide polymorphisms (SNPs) associated with expression difference, ASE studies in F1 hybrids represent a powerful approach to gain mechanistic insights into differential gene expression . The anaergence) ,51. cis-parisons ,53,54,55echellia . In all cis-divergence effects is sequence variation in the regulatory region  of the differentially expressed gene. Indeed, in putative regulatory regions of genes showing cis-regulatory divergence increased levels of sequence divergence have been found in yeast [Arabidopsis thaliana [Drosophila [ASE studies alone do not allow revealing genetic variants associated with gene expression divergence. However, one of the most likely explanation for in yeast , Arabidothaliana , maize [thaliana , and Droosophila ,60. A coosophila . Hence, In summary, comparative gene expression studies are extensively used to establish genotype–phenotype relationships and to reveal global patterns of expression variation. eQTL and ASE furthermore represent excellent approaches to gain mechanistic insights into gene expression divergence.Many of the above-mentioned exciting fundamental insights into gene expression divergence are based on studies in entire organisms and adult stages. However, it is broadly accepted that gene expression is strongly context dependent with a major impact for instance of the developmental stage and the tissue . FocusinHox gene expression in the developing vertebrate limb has been associated with the location of the HoxD cluster in a gene desert that lies between two adjacent TADs [The first regulatory mechanisms are at play on the level of genome organisation. Compressed DNA in the nucleus forms a tertiary structure A that caent TADs . The appent TADs . How theAssay for Transposase-Accessible Chromatin using sequencing) [Drosophila embryonic development [Highly transcribed regions of the genome  are—in contrast to the condensed heterochromatin—usually depleted of nucleosomes B. These uencing)  (Table 1uencing)  and fromelopment  revealedelopment ,73, the elopment ,75. Evenelopment ,77 and relopment . Since telopment , it faciChromatin accessibility and genome architecture is also regulated by a variety of non-coding RNA molecules, which are transcribed, but not translated into proteins. Long non-coding RNAs (lncRNAs) localized in the nucleus can directly affect chromatin architecture . IntriguIn summary, the extensive diversity of epigenetic modifications, which are further modulated by non-coding RNAs regulate differential DNA accessibility and thus provide a rich cellular repertoire to control gene expression pre-transcriptionally.Drosophila central nervous system. Initially, all neuronal precursor cells, the neuroblasts, contain generic neuronal transcription factors [Once the chromatin is accessible for proteins, gene expression is directly regulated by protein–DNA interactions ,89 FiguC. Transc factors . The uni factors  and the  factors . Therefocis-regulatory regions can be subdivided based on their location relative to the respective gene locus  during segmentation in the Drosophila embryo. The seven stripes of eve expression are spatially defined by five enhancers with each of them being responsible for an individual stripe or a pair of stripes [Besides the transcription factors itself, the nature of the DNA sequences they bind to plays a major role in gene regulation. These ne locus C. Promotpromoter ,96. Althpromoter ,98, as e stripes ,100. Thecis-regulatory elements can be further diversified by the interaction of transcription factors with co-factors that are expressed in a temporally and spatially defined manner to modulate for instance their capacity to bind to regulatory regions  can act as an activator in some regions, while the presence and binding of its co-factor U-shaped (Ush) transforms it into a transcriptional repressor in adjacent regions [The interaction of transcription factors and  regions C. One ex regions ,102,103. regions . The modIn summary, the interaction of spatially and temporarily expressed transcription factors, with modular regulatory DNA sequences specifies the unique transcriptional landscape of a developing cell or cell groups. Apart from the regulation of transcription itself, the transcriptional outcome can be fine-tuned on the level of the messenger RNA. For instance, post-transcriptional modifications, such as polyadenylation and capping influence mRNA export, stability and translation efficiency ,106,107.Post-transcriptional gene regulation is also mediated by regulatory RNA molecules, which can be involved in negative gene regulation via the RNA interference (RNAi) pathway   or they So far, we established that variation in gene expression is pervasive within and across species and that it is a major driver of phenotypic divergence. Furthermore, we showed that the different levels of the gene regulation machinery facilitate context dependent gene expression. However, few comparative expression studies have specifically tested for the context dependency of gene expression variation across species. A study of six homologous organs in nine mammals and one bird, for instance, showed that gene expression evolves at different speeds in different tissues as well as in different lineages. While gene expression was stable in the nervous system, it evolved more rapidly in testes. Similarly, gene expression variation was less pronounced in rodents compared to apes . Comparatrans”) eQTLs were found in adults compared to the investigated embryonic stage [cis- and trans-divergence (or combinations thereof) by comparing differently aged flies [D. melanogaster populations further supports the need for more defined analyses [A comprehensive understanding of the regulatory mechanisms underlying the context dependent evolution of gene expression is still missing to date. Indeed, few eQTL and ASE studies specifically compared findings across different tissues or stages to reveal context dependent regulatory mechanisms. A comparison between mouse embryonic and adult tissue has shown that many more distal  [Drosophila species [Drosophila trichome formation at larval and adult stages, respectively, is fundamentally different [Since the complexity of regulatory interactions is highly context dependent, tissue-, stage-, or species-specific constraints may also be imposed by the gene regulatory networks in which certain gene products act. In the yeast esponse) ,169. Hig species . Similarifferent . Accordiifferent ,173. Higifferent ,175. In D. melanogaster compared genome wide gene expression in central nervous system tissue between wildtype and transgenic flies after RNA interference (RNAi) mediated cell-type specific downregulation of a ubiquitously expressed gene. Intriguingly, the authors could show that contamination by surrounding tissue was sufficient to hamper the identification of the artificially downregulated gene [In the light of context dependent gene expression and gene regulation it is important to account for these aspects in comparative expression studies. Since high throughput sequencing methods are highly sensitive, not all identified genes in such studies may be directly associated with the trait of interest, but rather represent background noise. Few studies specifically tested whether complex tissue composition influences the sensitivity to detect gene expression differences. A RNAseq experiment in ted gene . This spDrosophila melanogaster [Nematostella vectensis [Schmidtea mediterranea [Platynereis dumerilii [Drosophila for instance, new biological insights into the cell diversity, cell specific gene expression and gene regulation have been gained for entire aging brains [The lack of tissue specificity can partially be accounted for by cell type or tissue enrichment in model organisms that offer a versatile transgenic toolkit see also . This apnogaster , the cniectensis , the platerranea  or the mumerilii . Also, og brains , but alsg brains  and the g brains . The abig brains ,189 fromEven if a tissue or stage of interest was selected as specific as possible, candidate gene lists obtained by comparative expression studies can further be specified by integration of prior knowledge about the molecular functions of differentially expressed genes. While many genes show context dependent expression, housekeeping genes, which fulfil generic tasks in each cell, are often stably expressed across different tissues. Comparative approaches can be used to exclude generic differentially expressed genes by analyzing which transcripts are consistently differentially expressed across different tissues or time points and can therefore be removed from candidate gene lists . It is aAlthough exciting insights into the evolution of gene expression and underlying regulatory mechanisms have been obtained, a few key questions remain to be answered. Peromyscus polionotus and P. maniculatus. QTL mapping revealed 498 candidate genes associated with behavioral differences. Tissue specific RNA sequencing, accounting for the fact that the studied behavior is controlled in a specific brain region, identified 23 differentially expressed genes within the QTL region, of which nine genes showed signatures of cis-regulatory divergence. One of these nine genes was subsequently functionally validated [Oryza rufipogon) [Brassica napus) [It has for instance rarely been tested, whether intra- and interspecific differences in gene expression provide any fitness advantage for the organisms. Since gene expression divergence is often associated with phenotypic variation, one potential approach to answer this question could be the thorough integration of gene expression data with phenotypic data of relevant traits. Such a combinatorial approach has been successfully applied to reveal candidate genes involved in different nest building behaviors among the two mouse species alidated . Similarfipogon)  and flowa napus) . Populata napus) . The intDrosophila hold true in other study systems. Another fundamental open question concerns the evolutionary forces underlying gene expression divergence. It will for instance be interesting to analyze the impact of genetic variants segregating in natural populations of species on various levels of the gene regulation machinery. Since gene regulation is highly context dependent, it will be important to study causal links between genetic variants and gene expression and gene regulation in as many different organisms, tissues, cell types and developmental stages. With functional genomics and transcriptomics methods being widely applicable , we can Comparative genome-wide expression studies have been extensively used to reveal candidate factors to inform about the genotype–phenotype map (correlation studies) as well as to gain mechanistic insights into the evolution of gene regulation (eQTL and ASE). We argue that much more defined datasets must be generated in the future to fully account for the complexity and context dependency of gene regulation to increase the power to detect more meaningful candidate genes in correlation studies. We strongly believe that our current understanding of the evolution of gene expression provides a solid basis to incorporate new aspects of gene regulation, that are being revealed on a regular basis, to gain exciting new mechanistic insights into the evolutionary processes. There is still a sphere of cloudiness around the evolution of gene expression but digging deeper holds a chance of insight.


Pediatric anxiety and depression are highly prevalent and debilitating disorders that often co-occur. Neural circuitry of reward processing has been shown to be implicated in both, and there is an emerging evidence base linking treatment response to brain patterns of reward processing. The current study aimed to add to this literature by investigating the association between clinical improvement and social and non-social reward in youth previously treated for anxiety and depression.N = 15) interested in engaging in a neuroimaging follow-up underwent an fMRI scan, during which they completed social  and non-social  reward tasks. Whole-brain activation and functional connectivity analyses identified neural responses to the tasks separately; a third set of analyses directly compared clinical improvement-related findings to understand the impact of task context on neural reactivity to reward.The current study leveraged clinical improvement data from a successful randomized controlled trial testing the efficacy of a transdiagnostic, brief behavioral treatment for youth diagnosed with anxiety or depression. Participants (Activation-based findings were sparse; however, connectivity as a function of degree of treatment response was apparent and robust. Within the context of social reward, significant clusters within frontal and temporal regions driven by happy face contrasts, the social reward stimulus, were observed. This supports connectivity between these regions and both amygdala and ventral striatum seeds as a function of degree of clinical improvement. Connectivity within the context of non-social reward also yielded significant clusters in temporal and parietal regions. Here too, the magnitude and direction of region coupling depended on the degree of clinical improvement and the task conditions. No differences in connectivity by task type as a function of clinical improvement were found.Findings serve as preliminary evidence that neural regions found to be related to clinical improvement within the context of social and non-social reward are similar to regions that have been shown to support reward processing in normative samples. Implications for treatment and future work are discussed. Pediatric anxiety and depression are highly prevalent, debilitating, and associated with a chronic course and long-term impairment e.g., . They frThis work has served as the rationale for the development of transdiagnostic interventions designed to target core processes across anxiety and depression and treat them as a unified problem area. In adults, unified protocols have demonstrated superior symptom improvement across clinical domains, both compared to control conditions and disorder-specific care see , suggestOne neurobiological mechanism that has been implicated across anxiety and depression is reward processing, in both social and non-social contexts. Reward processing encompasses neural reactivity associated with anticipation and consumption of positive gains, such as monetary winnings or social approval, as well as behavioral learning that motivates future actions. Studies of reward processing in pediatric samples have included tasks based on monetary incentives or tasks focused on social appraisal. The former maps onto the adult literature, as money is an ecologically valid incentive that is easily manipulated and distributed by study personnel. Social reward is a relatively new area of interest relevant to pediatric samples, given the developmental alterations in social valuation during adolescence . Indeed,Anhedonia, or the motivation and ability to seek and experience rewarding activities, is a core diagnostic feature of depression, though not anxiety. Anhedonia has been shown to serve as a phenotype of aberrant integration of reward and arousal, above and beyond other symptoms of internalizing disorders in youth . GeneralThe conceptual connection between anhedonia, depression and reward processing is clear; however, reward processing has also be implicated in anxiety in a different fashion. Anxiety disorders are characterized by avoidance of anxiety-provoking stimuli, escape behaviors when exposed to anxiety triggers, and negative “reward” of avoidance and escape through the reduction of anxious distress. In adolescence, avoidance of social interactions becomes particularly prevalent due to intensified concerns regarding peer approval and acceptance. Though smaller than the depression literature, neuroimaging findings on reward processing in pediatric anxiety have begun to accrue. fMRI studies enrolling anxious vs. non-anxious youth evidenced increased striatal, frontal, and limbic reactivity during social and non-social reward tasks e.g., ; of noteIt is notable that none of the published findings tested a transdiagnostic protocol, yet the majority of samples evidenced substantial diagnostic comorbidity. Furthermore, changes in striatal reactivity in response to a depression-focused intervention was associated with cross-over effects, such that changes of greater magnitude predicted a faster rate of decline in anxiety symptoms across time . Thus, wIn sum, the literature base on neural mechanisms of treatment response in internalizing youth is in its infancy. Studies are few, segregated by diagnosis, and lack replication within treatment modality across independent teams. Additionally, treatment paradigms employed were diagnosis-specific, despite high rates of comorbidity within samples, and typically focused on only one aspect of reward . To address these gaps in the literature, the current study leveraged resources from a successful randomized controlled trial (RCT) to examine neural mechanisms within the context of a treatment trial in a comorbid sample of anxious-depressed youth. Specifically, we sought to examine both social and non-social reward processing as promising neural mechanisms of clinical improvement in a subsample of youth, ages 8−16 years, enrolled in a multi-site RCT investigating the effectiveness of a transdiagnostic brief behavioral therapy (BBT) for pediatric anxiety and depression . BBT mayThe original BBT trial evidenced statistically significant positive effects across measures. Youth who received BBT were more likely to be categorized as treatment responders by the post-treatment assessment  and To our knowledge, this study is the first to (a) evaluate social reward in internalizing youth within the context of treatment (b) inform the relationship between reward processing and clinical improvement in response to a youth-focused transdiagnostic psychosocial intervention, and (c) analyze data from one sample of youth who each performed two tasks. Planned statistical analyses represent secondary analyses of data from a completed clinical trial , combineThe protocol for the neuroimaging follow-up was reviewed and approved by the University of California, San Diego Institutional Review Board. Secondary approval was obtained from San Diego State University’s Institutional Review Board. Written informed consent was obtained from all participating caregivers and adolescents over 18 years of age, prior to the administration of any study materials, in accordance with the Declaration of Helsinki. Adolescents younger than 18 provided assent, in addition to their caregiver’s written informed consent to participate. Consent forms included a specific clause allowing data from initial BBT participation to be linked to current data collection.N = 49). Recruitment targeted participants randomized to the BBT arm to allow for inferences to be made regarding the association between treatment response to BBT and reward processing circuitry, as well as to control for treatment type and dose received. BBT caregivers of record were contacted via phone between August 2016 and November 2017 to assess interest as well as youth contraindications for undergoing an fMRI scan.Neuroimaging and clinical improvement data from 15 youth were analyzed for the current study. All BBT families initially recruited from the San Diego site (October 2010 to December 2014) who consented to being contacted in the future regarding additional opportunities for research were considered for participation in this neuroimaging follow-up . Of those 44, four were lost to follow-up  and one participant had died since RCT study completion. We connected by phone with the remaining 39 participants to assess eligibility; 10 declined to participate, while 29 completed the phone screen. Of those screened, 21 met eligibility requirements for the neuroimaging follow-up . Post-screen, three eligible participants were lost to follow-up and one declined to participate, prior to consenting to the current study. Thus, the current study enrolled 17 youth. Of the 17 consented to participate in the neuroimaging follow-up, one individual refused to complete the fMRI scan and one individual yielded an unusable dataset due to technical error. SD = 1.61; range: 8−14 years); at the time of scan, participants had a mean age of 15.29 years . Primary diagnostic complaints at initial RCT enrollment were predominantly within the anxiety spectrum ; 29% (n = 5) had clinically elevated depression in addition to anxiety. In terms of treatment response, 41% (n = 7) of the participants engaged in the neuroimaging follow-up were characterized as BBT treatment responders at post-treatment.Participants enrolled in the neuroimaging follow-up were initially randomized at a mean age of 11.43 years  compared to those who participated . However, this difference, though statistically significant, does not reflect practical differences in clinical presentation. It is also notable that a smaller proportion of the neuroimaging sample was categorized as treatment responders at post (41%), versus rates of response observed in the BBT sample as a whole . This difference was not statistically significant.Those enrolled in the neuroimaging follow-up did not significantly differ from those recruited in San Diego who were randomized to BBT but ineligible for the follow-up on any demographic or clinical indicators at baseline or post-treatment, with the exception of baseline clinical severity of internalizing symptoms. Those who did not engage in the neuroimaging follow-up had higher average severity scores at baseline , while higher scores reflect clinical deterioration (7 = very much worse). Trained and reliable independent evaluators unaware of the participant’s treatment condition assigned a CGI-I score at the post-treatment assessment, capturing change in symptoms of anxiety and/or depression since the baseline assessment. The original trial utilized the CGI-I as an indicator of treatment response; participants with a score of 1 (very much improved) or 2 (much improved) were categorized as treatment responders, while those with a score of ≥3  were categorized as non-responders .Participants performed a jittered, event-related task with emotional face stimuli, including happy  faces, using a dot probe paradigm adapted from the Tel Aviv University/National Institute of Mental Health paradigm  during f1 . Each trThe faces dot-probe paradigm was advantageous to control for potential differences in attention and to present social reward faces, in addition to faces reflecting other valences . These benefits allowed for the examination of specificity of response to the social reward faces. The inclusion of faces in dot-probe paradigms has been shown to probe areas implicated in reward processing in prior research in anxious  and deprParticipants completed three runs of the Face Task (7 min. 27 s. per run). There were eight total conditions included in the task: happy-neutral/congruent, happy-neutral/incongruent, angry -neutral/congruent, angry-neutral/incongruent, sad-neutral/congruent, sad-neutral/incongruent, neutral-neutral/congruent, and neutral-neutral/incongruent. Neutral-neutral pairs were randomly split into “congruent” and “incongruent” groups for analysis purposes. Due to lack of jitter between face and probe displays, presentation could not be separated for analysis. There were 48 trials per condition. All participants had >65% accuracy and were therefore included in analyses see . Of the A child-friendly monetary incentive delay task was utilized to assess neural functioning within the context of non-social reward . Task runs spanned 4 min and 52 s. Non-social analyses included all available data from 14 participants, all of whom completed three runs of the task. The fifteenth participant was excluded from analyses due to data acquisition error.Three sets of analyses were completed: (a) evaluation of the association between clinical improvement and neural reactivity within the context of social reward ; (b) evaluation of improvement and neural reactivity within the context of non-social reward ; (c) direct comparison of social and non-social reward findings. Each set included group-level models that evaluated whole-brain activation, as well as seed-based functional connectivity. Although full factorial models were planned and executed, we focused on contrasts that included clinical improvement, given our interest in understanding that association between clinical improvement and neural processes. As such, direct comparisons of tasks were conducted if results by task yielded findings dependent on level of clinical improvement.Functional and anatomical brain images were acquired using a 3T General Electric MRI scanner with a 32-channel head coil, with multiband procedures to increase spatial and temporal resolution and thus better infer correlates of clinical improvement. Task stimuli were projected onto a screen at the foot of the fMRI bed and seen by the participant via a mirror attached to the head coil. Participants responded to displayed stimuli using their dominant hand to manipulate a 2-button response box. T2 blood oxygen level dependent (BOLD) images were acquired across 3 runs as 104 interleaved sagittal slices approximately parallel to the AC-PC line, with whole-brain coverage using a 3D multiband EPI pulse sequence . High-resolution anatomical images with prospective motion correction (T2-weighted MPRAGE PROMO) were acquired for anatomical localization and spatial normalization . The acquisition protocol was not optimized to capture cerebellar signal, so clusters identified within the cerebellum will not be discussed in detail.2). Preprocessing steps included functional image realignment, slice-time correction, spatial smoothing of 4 mm, and non-linear registration for spatial standardization to the Talairach template  and probe location , convolved with the BLOCK function. Beta images represented estimated activation during each of the conditions.For the Piñata Task, probing non-social reward, two individual-level general linear models were run to generate estimates of brain activation during anticipation and feedback periods, separately. The regressor of interest during the anticipation period included Reward Condition . Reward Condition was convolved with AFNI’s “dmBLOCK” basis function over the variable duration. Regressors of interest during the feedback period included Reward Condition and Performance . Both were convolved with the “BLOCK” function over 1500 ms. Analyses generated beta coefficients at each voxel for reward and no reward trials during the anticipation period, as well as for reward/hit, reward/miss, no reward/hit, and no reward/miss trials during the feedback period.For both the social reward and non-social reward tasks, models included head motion in x, y, z, roll, pitch, yaw directions and third-degree polynomials to model low-frequency drift as nuisance regressors.3; right amygdala = 1280 mm3; left ventral striatum = 136 mm3; right ventral striatum = 168 mm3). This analysis generated a set of voxel-wise images that represent connectivity between the seed region and the rest of the brain per condition for each task. Variance associated with head motion in x, y, z, roll, pitch, yaw directions was removed and third-degree polynomials were included to remove low-frequency drift.Generalized psychophysiological interaction analysis gPPI;  was utilWe conducted whole-brain, group-level ANCOVAs, by task, via AFNI’s 3dMVM program to evaluate the association between clinical improvement (CGI-I) and reward-related brain function . Clinical improvement was included as a dimensional, between-subjects variable; task conditions were included as within-subjects categorical variables. Interactions between clinical improvement and task condition  indicate the impact of task condition(s) on the relationship between clinical improvement and brain function.p < 0.05. The cluster threshold was calculated by 3dClustsim using the mixed-model spatial autocorrelation function (-acf) and the NN1 2-sided option, per the most recent recommendations on cluster correction  was well above chance (50%). Attention bias was calculated by subtracting reaction time between congruent and incongruent trials within face emotion condition. One-sample t-tests revealed no significant attention bias toward happy , sad , or threatening  faces. No significant associations were found between clinical improvement and task accuracy  or bias scores .On the Face Task, overall mean accuracy , decreased activation in this region related to a greater degree of clinical improvement see . Additiopost hoc analyses revealed that this cluster does not survive post hoc evaluation for potential confounding variables and may be outlier-driven.There was a main effect of clinical improvement on right and left ventral striatum connectivity with multiple posterior regions in the Face Task, including lingual gyrus, precuneus, angular gyrus, as well as middle frontal gyrus, inferior parietal lobule, and dorsal striatum see . Across Post hoc analyses indicated that the ventral striatum-medial frontal connectivity cluster was driven by differences in response to happy faces . Heightened clinical improvement was associated with greater connectivity during happy/congruent trials; however, less connectivity was observed during happy/incongruent trials  was included as a categorical, within-subjects variable and clinical improvement was maintained as a dimensional, between-subjects variable.We were unable to statistically evaluate differences in whole-brain activation across tasks due to a lack of findings dependent on clinical improvement scores during the feedback period of the Piñata Task.Clinical Improvement × Task Type  did not yield significant clusters indicative of connectivity between bilateral amygdalae or ventral striata and other regions in the brain.Additional analyses were run to evaluate the impact of potential confounding factors on the reported results. Hypothesized confounds utilized in these analyses included residual head motion, age, gender, concurrent anxiety, concurrent depression, length of time since post-treatment assessment, and task order. All main results maintained significance after the inclusion of these factors in statistical models.The current study aimed to add to the sparse literature on reward processing in pediatric anxiety and depression. Of note, the focus of this work was to evaluate neural correlates of a successful unified protocol, rather than to determine exclusive and common correlates of depression and anxiety in youth. Future research can build on the results of our work to distinguish neural correlates in these highly comorbid conditions. This investigation is unique in its focus on social as well as non-social reward tasks, use of a clinically impaired service-seeking sample, and effort to probe associations between reward and clinical improvement in a treated sample. Indeed, this study is the only study to investigate reward processing in a treatment context within a sample of anxious youth and one of only four to do so in pediatric depression. Moreover, this was the only study to investigate multiple tasks  within the same individuals, enabling the evaluation of task context effects. The study design was exploratory and hypothesis-generating in nature; indeed, findings should be interpreted cautiously due to the small sample size. Perhaps the most important message from this work is that evaluating the relationship between clinical improvement and reward processing neural circuitry, particularly within the context of transdiagnostic samples and treatment paradigms, appears promising for further investigation. Our findings indicate that integrating behavioral neuroscience tools into clinical science can provide a complementary way to “look under the hood” of treatments, and thus inform the generation of more targeted, mechanistically based treatments. Broadly, we found that social and non-social reward paradigms triggered patterns of connectivity in frontal and striatal regions associated with social cognition and reward, and these patterns reliably differed by level of clinical improvement obtained over the course of treatment. Patterns were consistent with the literature at large and suggest value in further efforts to probe shifts in reward processing as a mechanism of treatment-related changes in internalizing pathology in youth.Findings further illustrate how data from multiple within-subject tasks can be incorporated in a single study and underscore the importance of task context for interpreting differences in brain function. Although both reward and emotion regulation networks were implicated in both the social and non-social reward tasks, the specific regions as well as the direction of differences  depended on task context and conditions. Thus, when summarizing the literature, it may not be enough to state that observed differences in activation or connectivity of a particular region is associated with a disease state, or that reducing or increasing brain function in those regions is the mechanism by which clinical improvement occurs. Rather, our study which compares and contrasts results from two tasks within the same participants suggests that differences in activation or connectivity appear to be highly dependent on task context. More direct comparisons of neural responses to social and non-social feedback, particularly in larger samples, are needed in future work, as findings have implications for the future of clinical care and evaluation of treatment mechanisms.Although the specific patterns of results differed by task, clinical improvement was associated with alterations in regions involved in emotion regulation, in addition to reward, across both tasks. Observed differences as a function of degree of clinical improvement could reflect “normalization” of circuitry or compensatory mechanisms. This finding has intriguing implications for understanding the integration of emotion regulation and reward processing in treatment. As stated above, both anxious and depressed samples of youth have evidenced aberrant patterns of reward processing and emotion regulation, compared to healthy peers. The guided behavioral activation in concert with targeted skill building included in BBT may have interacted more effectively with reward circuitry compared to the more varied care received by those in ARC. Thus, successful treatment of internalizing pathology may require integration of emotion regulation and reward processing regions, such that youth are able to learn to modulate their emotions – which may in itself be rewarding. Improved emotion regulation skills and exposure in this treatment may have been sufficient to alter both reward and emotion regulation circuitry without an overt cognitive component.Of note, significant contrasts in the Face Task were driven by the social reward stimulus  in relation to clinical improvement. This is particularly remarkable given that prior work documented that across the lifespan, faces as social reward have been shown to be less impactful in motivating behavior change than monetary incentives . FurtherWithin pediatric anxiety e.g.,  and deprWithin the context of non-social reward, differential coupling of the affective and cognitive control networks by condition was apparent. Increased clinical improvement was associated with increased connectivity between the amygdala and areas associated with cognitive control and decision making after positive or neutral experiences . In contrast, the same regions evidenced decreased connectivity in response to frustrating experiences . This may suggest more effective recruitment of emotion regulation strategies  so as to maintain adaptive behavior. Interestingly, the areas implicated in non-social reward connectivity analyses also have been shown to be part of social cognition circuitry. As similar regions emerged across tasks, this serves as support that both tasks probe reward-related processes, which may in turn underlie clinical improvement.N = 15) is comparable to that of the few other pediatric studies probing associations between neural mechanisms of reward processing and treatment response  may capitalize on the integration of emotion regulation and social/non-social reward processing networks to promote behavioral activation. Taken together, conclusions should be viewed as preliminary data aimed at hypothesis generation for future work. Ultimately, incorporating behavioral neuroscience tools into clinical science will improve treatment outcomes, as identifying predictors and mechanisms of treatment response is crucial groundwork to move toward a precision medicine approach, including mechanism-based treatment to the individuals whose neural profiles indicate they would benefit the most. This work offers evidence of value in comparing complex data from multiple task contexts and contributes to the establishment of a literature base of neural mechanisms of treatment response in internalizing youth.The datasets for this study will not be made publicly available because the data are undergoing secondary analyses in preparation for additional publications.KS, VW, and JW were jointly involved in the study conceptualization and protocol development. KS spearheaded the recruitment for the neuroimaging follow-up, data collection, data preparation, and writing of the manuscript. MK-L led the analytical efforts with ML’s assistance. VW contributed the data from the original RCT and approved the use of the sample for the neuroimaging follow-up. JW provided training to facilitate the study completion, supervised the data collection and analytical efforts, and contributed to the writing of the manuscript. All authors critically reviewed the manuscript and approved it prior to submission.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


For patients with metastatic pancreatic cancer that are not eligible for surgery, signal-targeted therapies have so far failed to significantly improve survival. These therapeutic options have been tested in phase II/III clinical trials mostly in combination with the reference treatment gemcitabine. Innovative therapies aim to annihilate oncogenic dependency, or to normalize the tumoural stroma to allow immune cells to function and/or re-vascularisation to occur. Large scale transcriptomic and genomic analysis revealed that pancreatic cancers display great heterogeneity but failed to clearly delineate specific oncogene dependency, besides oncogenic Kras. Beyond these approaches, proteomics appears to be an appropriate approach to classify signal dependency and to identify specific alterations at the targetable level. However, due to difficulties in sampling, proteomic data for this pathology are scarce. In this review, we will discuss the current state of clinical trials for targeted therapies against pancreatic cancer. We will then highlight the most recent proteomic data for pancreatic tumours and their metastasis, which could help to identify major oncogenic signalling dependencies, as well as provide future leads to explain why pancreatic tumours are intrinsically resistant to signal-targeted therapies. We will finally discuss how studies on phosphatidylinositol-3-kinase (PI3K) signalling, as the paradigmatic pro-tumoural signal downstream of oncogenic Kras in pancreatic cancer, would benefit from exploratory proteomics to increase the efficiency of targeted therapies. To date, surgical resection provides the best chance of a cure for patients with pancreatic cancer. However, this curative approach is only proposed in 15 to 20% of cases. Moreover, there is a high rate of relapse following tumour resection, with a greater than 50% chance of local recurrence after surgery. The 5-year overall survival rate in resected patients is 28% and the median survival time is 18 months .Indeed, in PDAC there are many altered signalling pathways downstream oncogenic Kras such as mitogen-activated protein kinase (MAPK) and PI3K pathways, but also Braf signalling, transforming growth factor (TGFβ), Notch and DNA repair pathways  and amonFor 80–85% of patients with non-operable pancreatic cancer with locoregional or distant metastases (mainly liver and lung) chemotherapy can be used to improve quality of life and survival by relieving symptoms of disease. Since 1997, gemcitabine monotherapy remains the standard palliative chemotherapy for patients with metastatic PDAC ,11,12, gwww.asco.org/guidelineswiki). Their recommendations for therapeutic interventions, including FOLFORINOX, irradiation and/or gemcitabine in combination with nab-paclitaxel, are based on relevant articles published between 2004 and 2015. However, it remains necessary and urgent to discover new targets and associated therapeutics, as well as improve irradiation protocols and current therapeutic strategies, by determining the mechanisms of intrinsic pancreatic resistance to anti-tumour agents including targeted therapies. Targeted anti-cancer therapies are strategies that aim to block or annihilate cancer progression by specifically addressing some of their abnormalities, including at the protein level. Here, we will first analyse the state-of-the art clinical trials involving targeted therapies in pancreatic cancer and, in light of the latest studies on PDAC proteomes, discuss how analysis of protein levels or post-translational modifications by proteomics can aid in the design of efficient personalized therapies for pancreatic cancer patients.In 2016 the American Society of Clinical Oncology (ASCO) published its recommendations for the management of potentially curable PDACs , locally advanced PDACs (grade III), and metastatic PDACs (grade IV)  beads for serine, threonine, or tyrosine phosphorylations) [Proteomics is the study of all the proteins of an organism, a biological fluid, an organ, a cell or even a cellular compartment. This set of proteins is called the “proteome”. The latter is a dynamic and complex entity. The proteome contains a much larger number of proteins than the genome contains genes. In human cells, an estimated 22,000 genes can yield up to one million proteins . Despitelations) . Such stlations) .Proteomics provides insights into proteome-related changes in disease, such as observed changes in protein abundance, subcellular localization, post-translational modifications and cell signalling. Published proteomic results for pancreatic cancer are derived from the analysis of fluid  or “reversed phase protein chips” ,37,47 anBottom-up (or shotgun) approaches, that is, the identification of proteins from the analysis of their peptide components, have led to increased proteome coverage . Tryptic digestion followed by peptide fractionation, peptide separation through liquid chromatography, and on-line electrospray ionization coupled to tandem MS/MS orbitrap-based analyzers is the most common choice for in-depth proteomic analysis. Improvements in sample preparation, the sensitivity of mass analyzers and computational developments now allow researchers to quantify what is considered to be close to a full proteome  . This leFor quantitative proteomic approaches (as reviewed in ), differAnti-cancer drugs fall into four broad categories based on their pharmacological action: conventional chemotherapy, radiopharmaceuticals, immunotherapies and inhibitors of (mostly) oncogenic mechanisms  that include targeted therapies and hormone therapies A. TargetTargeted therapies are part of what is called “precision medicine”. This term refers to a medicine that is based on an in-depth knowledge of the biological mechanisms leading to the appearance and development of tumours. The use of these treatments is therefore guided, as far as possible, by the molecular characteristics of each patient’s tumour . Most targeted therapies are currently used as a monotherapy (62% in France). The first targeted therapy was approved for use in France in 2000. This was trastuzumab, an antibody targeting the extracellular domain of the HER2 receptor, used in the treatment of human epidermal growth factor receptor 2 (HER2)-positive metastatic breast cancer as a monotherapy in patients that had already been treated with at least two chemotherapies for their metastatic disease. By the end of 2015, the French Cancer Health Institute INCa had identified 47 targeted therapies which had marketing authorization (MA) in France for the treatment of cancer.Only one molecule, an inhibitor of the RTK epidermal growth factor receptor Epithelial Growth Factor Receptor (EGFR), has an indication in PDAC, used in combination with gemcitabine without any patient stratification B,C. So fIn PDAC, the efficacy of 15 targeted therapies (mostly in combination with the chemotherapy gemcitabine) has been evaluated , the detEGFR mutations at the genome level, these global approaches failed to identify simple therapeutic strategies based on a stratification of PDAC patients. Recent integrated omics approaches for all cancers highlight that the search for genetic or genomic alterations is not sufficient to predict which patients will benefit from targeted therapies [Pancreatic cancer cells are mostly initially resistant . Moreover, adaptive resistance can also occur in pancreatic cancer. Whereby one or more tumour subpopulations with different characteristics compared to the sensitive cells could emerge allowing them to survive and continue to proliferate in the presence of pharmacological inhibition, leading to therapeutic failure ,66. Heteherapies ,72. In cherapies . It is aherapies . Recent Proteomic studies give insights into metastatic PDAC biology, leading to the discovery of novel targets. Oncogenic Kras is altered in more than 80% of all PDAC patients. In PDAC, preneoplastic lesions , mucinous cystic neoplasm (MCM) and intra-ductal papillary mucinous neoplasm (IPMN)) progressively evolve towards at first in situ and then infiltrating metastatic adenocarcinoma, histologically defined as differentiated, moderately differentiated or dedifferentiated. Molecular classifications based on transcriptomics and genomics data have now been developed, including so called squamous, pancreatic progenitor, immunogenic and aberrantly differentiated subtypes ,69,70,71Independently of PDAC molecular classification, one study proposes the classification of pancreatic cancer cell lines based on the level and site of tyrosine phosphorylation (pTyr) of RTKs, potentially identifying three groups of cell lines classified as “low pTyr”, “RTK enriched pTyr” and “mixed pTyr”  . All subA large scale proteomic analysis of a cell line with stemness properties  and its secretome revealed the importance of fatty acid synthesis and mevalonate pathways, as well as glycolysis, through the regulation of fatty acid synthase FASN and acetyl-CoA acetyltransferase2 ACAT2 protein expression, in this subtype of cells. Results obtained from this study could accelerate the development of novel therapeutic strategies targeting pancreatic cancer stem cells ,53. UsinEarly detection of pancreatic cancer is key for improving patient survival. Recent review articles discuss the use of several proteomic techniques for the early diagnosis of PDAC ,77,78. CAfter the identification of potential protein biomarkers by large scale approaches, in the long term, the development of targeted proteomic technologies, such as selected reaction monitoring (SRM) also called multiple reaction monitoring (MRM) (using other MS apparatus such as triple quadrupole mass spectrometers), which can provide accurate and reproducible measurements of biomarkers of interest may be critical for the clinical use of the identified protein biomarkers . To confNumerous studies report new specific protein biomarkers for the diagnosis of pancreatic cancer identified from pancreas tissue lysate or liquid biopsies  by proteomics ,46,52,80Despite the successful identification of biomarkers in preclinical or early phase clinical proteomic studies, thus far no proteomics-based discovery is used in clinical practice to diagnose PDAC. Honda et al., recently developed an antibody-based assay (ELISA) to measure circulating apolipoprotein A2 (APOA2) isoforms APOA2-ATQ/AT  for the early detection of pancreatic cancer. However, clinical validation on 151 cases of stage I/II pancreatic cancer did not allow the early stages of pancreatic cancer to be statistically distinguished from healthy controls. Its clinical use should be reassessed in patients at high risk of pancreatic malignancy ,34,35. TAs is the case for most of these studies, replication of the results in larger cohorts of patients is necessary to form conclusions regarding the use of individual markers. Alternative approaches, such as the sequential window acquisition of all theoretical mass spectra (SWATH-MS) methodology which was developed recently , based oGlobal bottom-up approaches to analyse the proteome and post-translationally modified proteomes  are scarcely used in clinical PDAC. Indeed, proteomics can be used as an adapted tool to predict the response to targeted therapies. Studying inter-patient tumour heterogeneity with quantitative MS-based high throughput proteomics and phosphoproteomics (as performed for 14 resected head PDAC patients) allows the identification of commonly activated signals such as Akt activation . ProteomProteomics has been found to be a powerful tool in the study of tumour cell/microenvironment cell signalling interactions and modifications. For example, Jorgensen’s team has recently demonstrated using murine Kras-mutated pancreatic cancer cells that oncogenic Kras signalling in tumour cells not only activates a cell-autonomous signalling network, but that it also activates the non-autonomous oncogenic signalling of stromal cells through a selective modification of their proteomes and their post-translationnal proteomes. Conversely, stromal cells can in turn modify and amplify oncogenic signalling increasing the number of protein signalling nodes and activated kinases involved in the survival of the same or other tumour cells. The total proteome or the phosphorylated proteome is different when cells are co-cultured, and lead to the identification of another oncogene dependency . Thus, oncogenic signalling is no longer limited only to tumour cells but to the entire tumour compartment . The temHence, besides genetic and genomic alterations, prediction of sensitivity to targeted therapies is influenced by heterotypic tumour-stroma signalling. The only way to assess this sensitivity appears to be by proteomic analysis of patient-derived samples, including all the cellular partners at stake in the primary tumour and the metastatic niche . This wiGlobal studies to understand the adaptive responses to targeted therapies in pancreatic cancer are starting to be published. Adaptive and reversible resistance to inhibition of cancer-driving mutated Kras in pancreatic cancer cells has been found to involve the tyrosine phosphorylation of focal adhesion pathway components, while strikingly no significant mutational or transcriptional changes were observed . AlteratIt is challenging to perform in-depth and large-scale clinical studies on PDAC. The sparsity of samples and the difficulty in defining the population at risk renders this task even more difficult. However, recent technological developments have led to advances both at fundamental and clinical research stages. Besides a better understanding of the oncogenic dependency of PDAC , the curCancers, such as pancreatic cancer, with complex molecular pathways are more refractory than those with less complex pathways . Any impCan we use proteomics to detect PDAC earlier? Are earlier detected tumours more sensitive to targeted therapies towards PDAC oncogenic dependency ? Will this knowledge increase life expectancy of PDAC patients?Can we use proteomics to refine the current (epi)-genetic and genomic characterization of PDAC to better stratify patients? Sampling patients with PDAC is difficult. Can we develop and adapt methodological work flows to aid patient sampling? These workflows should also incorporate a better understanding of the metastatic disease.Can we use proteomics to identify new targets  which take into account tumour-stroma heterotypic signalling? Is this signal different in each tumoral niche?Can we use proteomics to understand, at a targetable protein level/modification , the specific resistance of PDAC patients to targeted therapies?From reviewing the literature, it is clear that key questions still need to be answered . IncreasPIK3CA gene encoding PI3Kα) [Early (or at least earlier) diagnosis using proteomics is a growing field with promising leads, but to be clinically validated larger cohorts of at-risk patients are needed. New leads in terms of oncogenic dependency have been discovered, such as the importance of PI3Kα in driving Kras-induced pancreatic cancerogenesis ,8. Clinig PI3Kα) .In conclusion, the next challenge for PDAC proteomics will be to increase our knowledge so that patients can be stratified according to each innovative therapeutic approach . A more


FSHR gene amplification/transcript data and non-reciprocal strong FSHR protein immunoreactivity. Thirdly, the pharmacological high doses of recombinant FSH used in in vitro studies also jeopardizes the physiological or pathophysiological meaning of the findings. We performed in this review a critical analysis of the results presenting extragonadal expression of FSHR and FSH action, and provide a rationale for the validation of the reported results using additional more accurate and sensitive supplemental methods, including in vivo models and proper positive and negative controls.Expression of the follicle-stimulating hormone receptor (FSHR), besides gonadal tissues, has recently been detected in several extragonadal normal and tumorous tissues, including different types of primary and metastatic cancer and tumor vessel endothelial cells (TVEC). The suggested FSH actions in extragonadal tissues include promotion of angiogenesis, myometrial contractility, skeletal integrity, and adipose tissue accumulation. Non-malignant cells within cancer tissue have been shown to be devoid of FSHR expression, which implies a potential role of FSHR as a diagnostic, prognostic, or even a therapeutic tool. There are shared issues between several of the published reports questioning the validity of some of the conclusion. Firstly, protein expression of FSHR was performed solely with immunohistochemistry (IHC) using either an unavailable “in house” FSHR323 monoclonal antibody or poorly validated polyclonal antibodies, usually without additional methodological quality control and confirmations. Secondly, there is discrepancy between the hardly traceable or absent  Follicle-stimulating hormone (FSH) is synthesized by the anterior pituitary gonadotroph cells, and it plays a critical role in controlling male and female gonadal function , 2. FSH Table 1B). Considering these new findings on extragonadal FSHR expression, a potential role for FSHR as a diagnostic, prognostic and therapeutic tool has been suggested. However, when some of the extragonadal FSHR findings have been revisited no FSHR transcript (by in situ hybridization RNAscope study) or gene amplification (by TaqMan probes-based qPCR) has been found in the tissues presenting with FSHR protein immunoreactivity by IHC staining   ≥ 4 h  and a short half-life for RNAs is t(1/2) < 4 h (regulatory genes); thus we should still be able to detect FSHR mRNA at a level relevant to protein levels  cancer cell lines or extragonadal tissues cannot be considered sufficient. The most suitable tissues for the validation of specificity of reproducibility of an anti-FSHR antibody should be the human ovary and testis, the only tissues with undisputable FSHR expression. IHC for FSHR should also be tested on FSHR-negative control tissues. The FSHR323 antibody , suggestRecent studies on FSHR localization have suggested its expression in many normal extragonadal tissues including umbilical vein, vessel smooth muscle cells , placentHuman placenta and umbilical cord express FSHR, and these reports proposed that the expression is functional (details below) , 11. FSHFSHR expression was analyzed with two kits for reverse transcription and qPCR systems, and both gave negative results for HUVECs and an immortalized HUVEC cell line (HUV-ST)  . The PCR(HUV-ST) . Numerou(HUV-ST) ], made t(HUV-ST) . Consequ(HUV-ST)  do not s(HUV-ST) –13. Resuculature  are alsoin utero . Thus, cn = 3). They also showed through PCR analysis that human myometrium from term pregnancy expresses full-length FSHR mRNA, but the PCR product were not sequenced to reconfirm their fidelity  or any other reporter marker expression under the FSHR promoter/enhancer could be also a very useful tool to track the FSHR expression. Thereafter, all data can be tested in vitro and in vivo with the same system to prove their functionality.To resolve the controversies of direct FSH action in extragonadal tissues proper  results , 67. In ory data , 10. Somr status . Howeverd method . ConditiMany of the studies on extragonadal FSHR function were carried out only on women  , 17, 18 FSHR expression in extragonadal tissues, despite numerous publications, remains controversial. We suggest a more critical analysis of such data, especially when the localization of FSHR in normal extragonadal and malignant tissues is based only on IHC data, without additional methodological confirmation. We suggest proper validation of antibody specificity and the reproducibility between lots. Unrestricted access to the currently existing non-commercial anti-FSHR antibodies would support the fairness of science. When planning experiments, more attention should be paid to inclusion of proper positive and negative controls. For functional studies, the doses of rhFSH should be standardized and restricted to physiologically meaningful concentrations. A proof of principle in human clinical trial with the monoclonal HF2 FSH blocking antibody with a comparison to a group of patients with GnRH antagonist is also needed. It is thus probably not yet time to re-write the textbooks with the functional implications of FSH/FSHR in extragonadal tissues or in cancer cells or TVECs. To this end, definitely many more studies are needed.MC, DP-T, SW, IH, and NR took part in designing, drafting, and finalizing of this invited review manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The increasing number of infections caused by antibiotic-resistant strains ofpathogens challenges modern technologies of drug discovery. Combinatorialchemistry approaches are based on chemical libraries. They enable the creationof high-affinity low-molecular-weight ligands of the therapeuticallysignificant molecular targets of human cells, thus opening an avenue toward adirected design of highly effective therapeutic agents. Nevertheless, theseapproaches face insurmountable difficulties in antibiotic discovery. Naturalcompounds that have evolved for such important characteristics as broadspecificity and efficiency are a good alternative to chemical libraries.However, unrestricted use of natural antibiotics and their analogues leads toavalanche-like spread of resistance among bacteria. The search for new naturalantibiotics, in its turn, is extremely complicated nowadays by the problem ofantibiotic rediscovery. This calls for the application of alternativehigh-throughput platforms for antibiotic activity screening, cultivation of“unculturable” microorganisms, exploration of novel antibioticbiosynthetic gene clusters, as well as their activation and heterologousexpression. Microfluidic technologies for the screening of antibiotic activityat the level of single cells are, therefore, of great interest, since theyenable the use of a single platform to combine the technology ofultrahigh-throughput screening, next-generation sequencing, and genome mining,thus opening up unique opportunities for antibiotic discovery. Fig. 1)[Streptomyces), which could constitutively produce largeamounts of antibiotics. Meanwhile, this platform had also made inexpensive andhighly efficient natural and semi-synthetic drugs easily available. Hence, theWaksman platform fully aligned with the goals and objectives of its time, sincesuch a problem as the uncontrolled use of antibiotics did not exist during“the golden era of antibiotic discovery.”The discovery of antibiotics was one of the 20th century’s greatestachievements: it increased the survival rate, life expectancy, and quality oflife for millions of people. The period between the 1940s and 1960s, when mostof the modern antibiotics and their derivatives were discovered, is commonlyreferred to as “the golden era of antibiotic discovery”[Fig. 1), 4.SubsEnterococcusfaecium, Staphylococcus aureus, Klebsiellapneumoniae, Acinetobacter baumannii,Pseudomonas aeruginosa, and Enterobacterspp.), which are health- and life-threatening[The role of antibiotics in nature consists in maintaining the biodiversity ofmicroorganisms resulting from the counteraction of bacteria that produce anddegrade antibiotics  via varieatening.Combinatorial chemistry and high-throughput screening of chemical librarieshave proved efficient for creating drugs targeted at the regulation of variousprocesses taking place in human cells. However, multiple attempts to usehigh-throughput combinatorial screening to design novel broad-spectrumantibiotics have failed, despite the substantial financial and materialinvestments made and the fact that all the available technologies were used-20.Mycobacteriumtuberculosis ATP synthase [The key reasons for these failures were as follows: Firstly, xenobiotics arenot particularly good at penetrating bacterial cells, especially Gram-negativebacteria. Secondly, antibiotics do not obey the Lipinski’s “rule offive” : the physynthase .The creScreening of natural products offers a significantly higher potential fordiscovering antimicrobial activity ,probablThe range of natural products used to search for antibiotics is rather diverseand includes extracts from plants, fungi, lichens, endophytes, marine plants,seaweeds, corals, and other microorganisms. NeverthS. aureusstrains with suppressed gene expression allowed to discoverplatensimycin, an antibiotic that belongs to a previously unknown class ofinhibitors of the enzyme FabF/B that catalyze fatty acid biosynthesis[The BioMAP platform, which enables the detection of known antibiotics and thediscovery of novel ones according to their individual inhibition profiles, hasshown that the growth-inhibiting activity of various microorganisms in thecollection can be used as a characteristic “fingerprint” of thesubstance or extract . Collectynthesis.Streptomyces coelicolor has demonstrated that over 20secondary metabolites can be produced in theory, whereas only three of themhave been identified upon in vitro cultivation [de novoprediction of antibiotics [Fig. 2) isanother alternative approach that was used to search for novel antibiotics. Theplatform based on the cultivation of individual soil-dwelling bacteria in theirnatural environment using a semipermeable membrane has made it possible todiscover the novel antibiotic teixobactin, which exhibits activity againstresistant strains of Gram-positive bacteria, while resistance to this compoundhas not developed [Entotheonella, which is characterized by a uniquecombination of secondary metabolites and the pathways of their synthesis[The new insight into using soil bacteria as a source of novel antibiotics opensup new possibilities for the screening for antimicrobial activity. Whole-genomesequencing of actinomycetes has shown that they have a much higher ability toproduce secondary metabolites upon cultivation. Complete genome sequencing oftivation . In its tivation , while tibiotics .Hence, ibiotics .One of ibiotics .Using qibiotics ; howeveribiotics , 46.Appeveloped . Furtherynthesis.Screening of antibiotic-resistant bacteria can be used to reveal novelmechanisms of synergistic interactions, which opens up new prospects for thesearch for antibiotic adjuvants potentiating their effect. ApplicaS. aureus cell wallteichoic acids via a biodegradable linker proved efficient in eliminating notonly suspension cells, but also the vancomycin-resistant intracellularreservoir of bacteria [The strategy of designing bifunctional agents acting as ‘Trojanhorses’ has also proved efficient. The conjugate of a rifampicin analogueconnected to antibodies specific to bacteria . It was bacteria .in vitro inhibition to the direct assessmentof the antimicrobial activity of an agent in vivo opens up newprospects for designing the most efficacious drugs. Screening of antimicrobialactivity against M. tuberculosis using infected macrophages[invitro model as similar to in vivo conditions aspossible and to rule out compounds that exhibited nonspecific cytotoxicityand low ability to penetrate macrophage cells. Models of in vivoinfection of the nematode Caenorhabditis elegans[Danio rerio [Proceeding from rophages enabled  elegans and the io rerio allowed A high sensitivity of the analytical signal is the fundamental parameter neededto enhance screening performance. Application of bacteria producing recombinantfluorescent reporter proteins as a biosensor of antimicrobial activity makes itpossible to directly detect bacterial growth inhibition, to idenThe search for novel antibiotics has become an urgent task because of the rapiddevelopment of antibiotic resistance. The success rate in the screening ofchemical libraries is extremely low; this strategy can be efficient mainly whensearching for adjuvants and narrow-spectrum antibiotics. Although theWaksman’s platform traditionally used for screening for the antimicrobialactivity of microorganisms has been effective in the past, its furtherapplication is associated with an extremely high risk of antibioticrediscovery. It has been estimated that more than 107 different microorganismsneed to be screened for every new antibiotic discovery.This prFig. 3)enables the analysis of the activity of single cellsand the coculturing of representatives of microbiota with target cells toidentify antagonistic bacterial strains that produce antibiotics[Encapsulation of individual cells into biocompatible double emulsiondroplets (ibiotics. This meFurther development of ultrahigh-throughput (uHT) methods for screening forantibiotic activity is of high interest, since bacterial biodiversity presentsa multitude of challenges that require an integrated understanding of theinteractions taking place both at the level of individual bacteria and at thelevel of an entire unique microbiome .The com