In Metazoa, the germline represents the cell lineage devoted to the transmission of genetic heredity across generations. Its functions intuitively evoke the crucial roles that it plays in organism development and species evolution, and its establishment is tightly tied to animal multicellularity itself. The molecular toolkit expressed in germ cells has a high degree of conservation between species, and it also shares many components with the molecular phenotype of some animal totipotent cell lineages, like planarian neoblasts and sponge archaeocytes. The present study stems from these observations and represents a transcriptome-wide comparative analysis between germline-related samples of 9 animal species (7 phyla), comprehending also totipotent lineages classically considered somatic.Differential expression analyses were performed for each species between germline-related and control somatic tissues. We then compared the different germline-related transcriptional profiles across the species without the need for an a priori set of genes. Through a phylostratigraphic analysis, we observed that the proportion of phylum- and Metazoa-specific genes among germline-related upregulated transcripts was lower than expected by chance for almost all species. Moreover, homologous genes related to proper DNA replication resulted the most common when comparing the considered species, while the regulation of transcription and post-transcriptional mechanisms appeared more variable, showing shared upregulated functions and domains, but very few homologous whole-length sequences.Our wide-scale comparative analysis mostly confirmed previous molecular characterizations of specific germline-related lineages. Additionally, we observed a consistent signal throughout the whole data set, therefore comprehending both canonically defined germline samples (germ cells), and totipotent cell lineages classically considered somatic (neoblasts and archaeocytes). The phylostratigraphic analysis supported the less probable involvement of novel molecular factors in the germline-related transcriptional phenotype and highlighted the early origin of such cell programming and its conservation throughout evolution. Moreover, the fact that the mostly shared molecular factors were involved in DNA replication and repair suggests how fidelity in genetic material inheritance is a strong and conserved driver of germline-related molecular phenotype, while transcriptional and post-transcriptional regulations appear differently tuned among the lineages.The online version contains supplementary material available at 10.1186/s13227-022-00207-3. Many features related to obligate multicellularity already evolved in the last common ancestor of Metazoa, since they are present in all extant species. The so-called Urmetazoa were most likely bacterivorous multicellular organisms with a proto-epithelium including collar cells, able to differentiate cells in various somatic states and in anisogamic germ cells , 2. IndeWith the advent of modern molecular technologies, various molecular factors and networks involved in differentiation and specification of the germline were identified for a still expanding number of species, allowing to better define and delineate a phenotype that represents one of the most ancestral metazoan features. Among the most interesting observations about the molecular profiles of germ cells is the shared expression across animals of a highly conserved gene set –6. Transvasa, piwi, and nanos, all show RNA-binding activities. The vasa gene encodes for a DEAD-box RNA helicase associated to germline specification and differentiation in virtually all animals, with functions spanning from translational activation to chromatin condensation  that mediates RNA-binding activity controlling mRNA translation fates [For instance, the most known and phylogenetically conserved germline markers, that are iewed in ); piwi, ilencing –11; nanoon fates , 13.vasa, nanos, and piwi are those that are mostly shared in the germline of different Metazoa, making them quasi-universal markers of germ cells for almost all differentiation stages. Most other factors are indeed transcribed and expressed in specific germ cell stages, and/or they have not been associated to germline functions in all animals  are thought to be specific metazoan innovations. Indeed, so far orthologues have not been found in other eukaryotic lineage, differently from other germline determinants that have been annotated in other holozoan , such aants in: –7). Mored boule; , 19).Strongylocentrotus purpuratus [Many germ cell molecular determinants were observed as expressed also in some animal multipotent cell lineages that have also somatic fates. For instance, cases of embryonic cells with mixed somatic–germ fates were reported expressing such genetic factors, whose presence indeed preceded the actual determination of strict germ cell fate . Moreovrpuratus , multiporpuratus , totipotrpuratus , 19, 25,rpuratus ) and acorpuratus , stem cerpuratus , and othAltogether, these observations suggest a broad molecular similarity between germ cells and stem cells, leading to theorizing the germline multipotency program (GMP), a genetic toolkit that operates both in germline and somatic multipotent stem cell lineages and that is fundamental for establishing and maintaining multipotency . Later, In the present analysis we aimed to explore the transcriptional signatures of germline-related tissue/cell lineages in different animals by taking advantage of high-throughput RNA-Seq experiments, that provide snapshots of the overall transcriptional profile of the samples, allowing for investigations without the need to determine an a priori set of germline determinants. We used online-available experimental data to retrieve all the RNA-Seq experiments that fit the established features of having enough samples size to assess transcript abundance, and of having control somatic samples produced within the same experiment. We performed species-specific differential expression (DE) analyses, and we then checked whether there were homologous genes upregulated in the germline-related samples for most of the species, to retrieve a common transcriptional signal that could have emerged despite the data set heterogeneity. We indeed observed an overall shared enrichment toward DNA proper replication, both for co-upregulated homologues and co-enriched Gene Ontology (GO) terms and InterProScan (IPR) codes. Moreover, using reference proteomic data from other animals, we looked into the upregulated germline-related transcripts to get hints on how many of them were lineage-specific innovations. We observed that in germline-related samples there was a general enrichment toward the upregulation of anciently derived genes.Ephydatia fluviatilis (Porifera), Nematostella vectensis (Cnidaria), Caenorhabditis elegans (Nematoda), Danio rerio (Chordata), Xenopus tropicalis (Chordata), Drosophila melanogaster (Arthropoda), Ruditapes philippinarum (Mollusca), Haliotis rufescens (Mollusca), and Schmidtea mediterranea (Platyhelminthes). Due to the lack of high-quality genomes for some of the species considered, all transcriptomes were assembled de novo to standardize the methods across the data set . Lower values for these 3 species could be due to lineage-specific diversification: indeed, BUSCO results for the online-available proteomes obtained from genome annotations of S. mediterranea, C. elegans, and the E. fluviatilis congeneric species E. muelleri are similar, if not lower, to our results , i.e. coding sequences (CDSs). From now on, when we refer to “transcripts” we mean ORF-including transcripts, i.e. those supposedly belonging to protein-coding genes, and when we refer to “translated transcriptome” we intend the translated ORFs.p-value < 10−3; see “R. philippinarum, to roughly 29% in the nematode C. elegans , and comparing it with the germline-related percentage, we assessed whether there was any over-representation of intra-phyletic or inter-phyletic homology in the germline sequence subsets see “.If the ratio was lower than 1, then it would mean that it was more likely for a germline-related upregulated CDS to share homology with at least another sequence of another phylum, i.e. germline-upregulated transcripts were depauperated of lineage-specific CDSs.On the contrary, a ratio higher than 1 meant that the germline-related upregulated subset had a higher proportion of phylum-specific CDSs with respect to the rest of the translated transcriptome, i.e. germline-upregulated transcripts were enriched for lineage-specific CDSs.D. melanogaster and D. rerio, did not display any evident bias in the germline-related samples, neither toward phylogenetically conserved genes, nor toward clade-specific ones. C. elegans, on the other hand, displayed the opposite trend, with a higher percentage of phylum-specific germline-related transcripts with respect to the rest of the transcriptome (1.17 phylum-specific ratio).Two thirds of the data set had a ratio lower than 1 (significant on a odds ratio test), indicating a bias toward germline-related upregulation of shared inter-phyletic genes , they were considered as Metazoa-specific. We could observe that the percentage of these Metazoa-specific CDSs was lower in germline-upregulated transcripts with respect to the rest of the translated transcriptome for all species, meaning that it was more likely for a holozoan-shared CDSs to be differentially transcribed in germline-related samples.Given the heterogeneity of the data set, instead of concentrating on the species-specific results, we focused on the strongest signals that emerged in the different DE analyses and compared them across the samples . We identified 3794 OGs that included germline-related CDSs upregulated in germline-related samples of at least two species of our data set. Out of these, 3 OGs were upregulated in all species Fig. . These OWe also specifically annotated the content for the 20 OGs with germline-related upregulated sequences shared by all but one species . OrthoFinder homology inference is, indeed, prone to collapsing within the same OG different genes belonging to the same gene family, or that simply share some specific domains. This happens especially when domains are common in the proteome, in multiple copies within the same proteins, and follow complex pattern of acquisition/loss in the proteome, reflecting a network-like homology of conserved protein regions. For this reason, a clear whole-length homology could not be retrieved for the germline-related subset included in such cluster.piwi, ago1, vasa, boule, nanos, pumilio, bruno, and tudor (referring to D. melanogaster nomenclature), and we identified their belonging OGs based on the D. melanogaster sequences. With the exclusion of piwi , the only GMP genes that were upregulated in a conspicuous number of species were vasa, nanos, and tudor , Drosophila–Xenopus, Haliotis–Ruditapes (Mollusca), and Ephydatia–Nematostella (two early branching non-Bilateria species), therefore reflecting a weak phylogenetic signal. Interestingly, out of all the combinations of 3 or more species, those that displayed a positive deviation from expected values were 6-, 7-, 8-, and 9-species combinations, while the 3-, 4-, and 5-species combinations had negative deviations, hence they were represented in lower numbers with respect to random distributions.We then looked at all other OGs that contained sequences upregulated in at least 2 species. The 2-species combinations (i.e. OGs upregulated in 2 species only) were the predominant ones, significantly deviating from the expected random distribution: they corresponded to 1803 of the 3794 germline-related OGs . The mechanisms involved ranged from ribonucleoprotein complex assembly to mRNA binding and translational regulation. Contrarily to DNA replication, that was also highly represented in the co-upregulated homologues  and GO terms enriched in twice as much transcripts as expected (together with progressively stringent cut-off iterations), we observed only the strongest signals. In this way we should have overcome spurious results related to non-homology across control somatic tissues. Moreover, we limited our discussions on molecular signals that were shared by most of the species .This data set was extremely heterogeneous in sample composition , comprising the non-classical germline lineages, i.e. archaeocytes and neoblasts, that are associated to the same genetic programming of germ cells, as previously said [C. elegans tudor homologue, that was missing in the OG). However, for H. rufescens, vasa and tudor homologues were more transcribed in the germline-related samples, but below significance cut-offs. Since H. rufescens was one of the most frequently lacking species in the co-upregulated . However, excluding combinations of 2 species only, it is interesting to notice that the combinations comprising more species (6 to 9) were more frequent with respect to random expectations than those comprising less species (3 to 5).An interesting signal that we retrieved by assessing the percentages of clade-specific genes transcribed in our data set species was the fact that, for many of them, genes upregulated in germline-related samples were more conserved across Metazoa than expected by chance Fig. . On averC. elegans. One explanation could lie in the nature of its specific biological samples, that are cells from early stages of embryo development. In a previous work, by comparing developmental stages of species belonging to 10 phyla, it has been suggested how the earliest stages of development have a greater proportion of co-expressed genes between species belonging to different phyla: the so-called inverse hourglass model of development [C. elegans in the present work. Our transcriptome was assembled with reads belonging exclusively to samples in early stages of embryo development. This could have caused an overall bias toward genes shared by multiple phyla, that might have weighted more on the non-germline genes (considering that they are more numerous and involved in more aspects of embryo development). This bias might have covered in C. elegans the signal observed in other data set species, resulting in a significantly positive phylum-specific ratio. This hypothesis to interpret the outlier C. elegans should be, however, properly tested by comparing germline–soma phylostratigraphic data in other species and other developmental stages.The only opposite signal was represented by the higher than 1 phylum-specific ratio of elopment . Coherenelopment . These oWhen considering genes shared by multiple phyla, the Metazoa-specific ratio is lower than 1 for all 9 data set species  thaf debate . Archaeof debate .Amphimedon queenslandica (a demosponge like E. fluviatilis), they analysed transcriptomes of archaeocytes, choanocytes, and pinacocytes  and saw that the percentage of upregulated sponge-specific transcripts was much lower in archaeocytes. Their number were different from ours in absolute values , but the ratio of that percentage over the sponge-specific percentage of the whole genome as calculated in their work was interestingly similar to our results (0.4). Moreover, also in their analysis the percentage of upregulated pre-metazoan genes was higher in archaeocytes, as in the present study. Lastly, they could also observe strong statistical significance when comparing the archaeocyte transcriptomic profile to that of two holozoan: the choanoflagellate Salpingoeca rosetta in the colonial stage (but not in sessile or swimming stage), and the ichthyosporean Creolimax fragrantissima in the multinucleate stage (but not in the amoeboid stage). They interpreted all these data as the fact that the ancestral metazoan cell type resembled modern transdifferentiating stem cells [We could observe a very low phylum-specific ratio, suggesting that archaeocytes have indeed a transcriptomic profile that involves more conserved genes that are datable to older evolutionary times. Coherent results were retrieved in a recent work : in the em cells .S. mediterranea neoblasts. This observation suggests us the possibility to include in a general discussion totipotent lineages as a whole, further providing hints on the similarities between stem and germ cell lineages. Remarkably, in the totality of our data set pre-metazoan genes are more likely involved in germline-related pathways than expected by chance, as highlighted by the Metazoa-specific ratio below 1. Despite the mentioned heterogeneity of the used data set, both in samples (i.e. whole gonads or cell lineages) and stages (i.e. early stages of differentiation or late ones), the same signal was obtained for different species despite their supposedly ancestral or derived state (from cnidarian to molluscs and chordates). For this study we utilized all the suited, online-available experiments, but we would be eager to extend the pipeline to other species as soon as new data will be available and see whether the trend still stand.A similar inter-phyletic phylostratigraphic signal was shared by most of the germline-related samples of our data set, including When looking at homologous genes that are upregulated in most samples (8–9 species), it is clear how most of them can be collected in DNA-related activities, and especially in DNA replication and DNA repair Fig. A, that rpgc) in D. melanogaster and pie-1 in C. elegans, or specifically, such as the case of blimp1 in Mus musculus [pgc (upregulated in D. melanogaster in the present study) has no homologues outside Drosophila, and also sequences of the OG that included pie-1 were observed as upregulated in the present study, but only for C. elegans, D. rerio, and X. tropicalis.The over-representation of upregulated DNA replication-associated factors with respect to transcriptional activators and promoters suggests a higher level of conservation across the species of such key cellular process. According to our results, the regulation of transcription appears to be more lineage-specifically tuned and defined, leading, for instance, to the complete lack of any transcription factor in the set of co-upregulated OGs. Germ cell specification and programming has been usually associated to transcriptional repression rather than activation , 37. Durmusculus , 39. Whipiwi and CPEB1), and more than half of co-enriched IPR codes and GO terms, included almost exclusively replicative signals instead of transcriptional or post-transcriptional RNA regulators.Once the lineage has been established and germline-specific transcription is activated, the maintenance of germline fate is apparently delegated to other mechanisms, such as chromatin remodelling and, most of all, those based on mRNA processing, i.e. post-transcriptional regulation, including the activity of many GMP genes, usually organized in perinuclear ribonucleoprotein granules –39. The The subset of upregulated OGs in our data set comprehended indeed many genes that encode for proteins associated to DNA replication rather than other DNA-related activities like transcription. For instance, DCTD and DUT (whose transcripts were upregulated in 8 species) are metabolic enzymes that produce dUMP . Thpiwi, vasa, and nanos, and the mRNA regulator CPEB1). The classic GMP genes represent the necessary components for such lineages, acting as determinants and regulators of the totipotent state, but the majority of molecular factors of the actual phenotypes could intuitively be genes associated to proliferative signals and cell cycle progression, like those that we observed as more represented in our comparative analyses. To summarize, what we retrieved was that the mostly shared germline-specific upregulated transcripts were almost exclusively involved in DNA replication, while other key mechanisms are indeed enriched, but apparently subjected to a deeper diversification, sometimes species-specific.While RNA processing-related IPR codes and GO terms were enriched in many of the germline-related samples of our data set , to those more usually associated but not restricted to it, like “DSB repair” (enriched in 7 species), to replication-free ones like “NER” (in 6 species).C. elegans. Probably one of the strongest possible bias drivers of the present analysis was the lack of a somatic control represented by highly proliferative tissues for all species, implying the possibility that the enrichment toward replicative and proliferative signals observed in germline-related samples was mainly led by the lack of mitotic activity in the controls. The only case for which we could exclude this potential bias was C. elegans, where the somatic sample was represented by proliferating embryonic somatic cells. Interestingly, the signals that emerged for this species were mostly in line with all others, supporting the fact that most of the observations could indeed be interpreted in a germline-oriented scenario. Coherently, the GO terms for “DNA replication” was not enriched in C. elegans, but nevertheless that for “DNA repair” was, together with all other aforementioned GO terms for different DNA repair mechanisms. Moreover, while for instance PCNA was not upregulated in germline-related samples of C. elegans (coherently with the high mitotic rate shared by the control somatic samples), all DNA repair-associated genes were . Transcriptional signals shared between the germline-related samples were oriented not only toward upregulation of proliferative activities , but also DNA repair, whose correct and proper course is fundamental for the genetic “responsibility” of totipotent lineages, and whose molecular factors are widely conserved across the data set. Instead, signals of either transcriptional or post-transcriptional regulation, that are more usually associated to germ cells, were not massively shared in terms of whole sequence homology, but rather in terms of enriched functions and domains, suggesting shared molecular processes but leaving proper genetic inheritance transmission as the most conserved genetic toolkit.https://www.ncbi.nlm.nih.gov/sra). We searched for female germline-related samples (i.e. the lineage that maintains totipotency throughout development [All RNA-Seq reads used in the present study were downloaded from the Short Reads Archive of NCBI (elopment ) in metaE. fluviatilis (Porifera), N. vectensis (Cnidaria), Brachionus manjavacas (Rotifera), C. elegans (Nematoda), D. rerio (Chordata), X. tropicalis (Chordata), D. melanogaster (Arthropoda), Penaeus chinensis (Arthropoda), R. philippinarum (Mollusca), H. rufescens (Mollusca), and Eisenia fetida (Annelida). From these, E. fetida (PRJNA304461) was excluded because the germline-related samples were represented by whole bodies enriched for gonads, and not only the specific tissue of interest. P. chinensis (PRJNA558194) was excluded during the analyses because an over-representation of stress-related signals emerged during the DE analysis, invalidating the confidence of the samples. Also B. manjavacas (PRJNA345262) was excluded in the course of the study because we could not retrieve any germline-related signal out of it. In facts, its samples were constituted by whole bodies against eggs, enriched in transcriptional signals related to the subsequent onset of embryogenesis: indeed, the lack of any conserved and a priori known GMP gene transcription casted shadows on the reliability of such experiment as regards the approaches and aims of the present analysis.The candidates belonged to 11 species: S. mediterranea neoblasts (and differentiated progeny as somatic control). These cells, together with multipotent cells of other Metazoa, have been associated to the germline since neoblasts express germline-associated signature genes, leading to theorise the existence of the GMP shared by totipotent germ cells . The completeness of the filtered transcriptomes was evaluated through the BUSCO v5 set of core metazoan orthologues as implemented in the gVolante website (https://gvolante.riken.jp/index.html).To reduce complexity, we collapsed transcripts through CD-HIT v4.8.1  at 99% ohttps://github.com/TransDecoder), keeping the single best ORF for each transcript. To help inferring the most likely ORF position within the transcript, the software was also fed with a DIAMOND search against Swiss-Prot  only, we also performed an open reading frame (ORF) prediction through TransDecoder v5.5.0  and an cut-off; ) againstcut-off; . Only tr2 fold change (logFC) higher than 1 in the germline-related samples (i.e. twice as abundant with respect to the control somatic samples), with a corrected p-value lower than 10−3 and significant for at least one analytic tool, were considered as differentially upregulated. To test the robustness of the results, we also iterated the DE analysis with higher logFC cut-off  and observed the consistency in all downstream analyses . Moreover, we also reran all comparative analyses by considering the intersection (rather than the union) of DE results obtained with DESeq2 and edgeR. Results presented in the main text refer to logFC > 1 and DESeq2/edgeR union . Then, for each species, we calculated the ratio between the two percentages (phylum-specific percentage of germline-upregulated genes / phylum-specific percentage of non-upregulated genes). For genes that shared homology across phyla, we also calculated a similar ratio for Metazoa-specificity: genes were considered Metazoa-specific if they had no homologues in the 8 unicellular holozoan. For each species we also produced 1000 random sets of genes of sizes equal to those of germline-related upregulated ones, and calculated the phylum- and Metazoa-specifc ratios on them to assess the solidity of the method , it is less convenient for the analysis of co-upregulation annotation. By running OrthoFinder on the 9 species of the data set only, we could obtain less noisy OrthoGroups that were more easily annotated and for which we could grasp the biological meaning more straightforwardly.C. elegans, D. rerio, D. melanogaster, and X. tropicalis, since for these models the confidences of online annotations are high, and functional data are available. For other OGs, we counted the number of times that all possible combinations of species ended up within the same germline-related OGs. In this way we could count how many times each combination of species had a shared set of germline-related upregulated CDSs, and we calculated the deviation from expected random distributions with the UpSetR R package as implemented online .http://revigo.irb.hr/ [We also ran InterProScan v5.45.80  on the w.irb.hr/ ).odds.ratio test in R, questionr package), that associates a p-value to the comparison of two ratios: the ratio of appearance of each IPR code in germline-related sequences was compared to the ratio in the whole translated transcriptome. A p-value lower than 0.05 for the test meant that the IPR code was enriched in germline-related samples. Comparative analyses were performed considering IPR codes that were over-represented in more than two-thirds of the species.A similar, but not overlapping, analysis was performed with IPR codes. For each IPR code of the InterProScan database , we counted the species-specific number of CDSs respectively annotated in the germline-related subset and in the full translated transcriptome. When an IPR code was annotated exclusively among germline-related CDSs, we considered it as enriched toward germline-related samples. For all other IPR codes, we tested whether they were significantly enriched. We performed an odds ratio test  and extensive results of the comparative analyses Additional file 2: Table S1. Species in the data set and accession codes of Genome assemblies. When the proteome was retrieved from an online source different from NCBI, the whole accession link is present. Phyla are in alphabetical order, with the four non-Metazoa phyla at the bottom of the table. Figure S1. GMP genes-associated IPR codes in germline-related samples. The table represents presence (light blue) or absence (light grey) in different species (columns) of IPR codes annotated for some representative GMP genes . Considering associated domains and families rather than whole sequence homology improved the signal: for instance, virtually all domains belonging to vasa are biased in germline-related samples of all species, even if we could not observe vasa homologue upregulation for C. elegans and H. rufescens . For example, first row: 1803 OGs included germline-related upregulated sequences belonging to 2 species only (counting any possible 2-species combination). On the right the deviation from expected random distributions for the combinations of the corresponding number of species is reported: positive deviation from expectation is depicted in blue, negative deviation in red. For instance: the number of observed co-upregulated OGs in 4 species (any 4 species and only 4 species) was lower than expected; the number of observed co-upregulated OGs in 8 species (any 8 species and only 8 species) was higher than expected. Figure S3. Co-enriched GO terms  in 6 or more species. The table represents presence (light blue) or absence (light grey) in different species (columns) of GO terms enriched in more than 2/3 of the data set. Figure S4. Co-enriched GO terms (Molecular Functions) in 6 or more species. The table represents presence (light blue) or absence (light grey) in different species (columns) of GO terms enriched in more than 2/3 of the data set. Figure S5. Co-enriched IPR codes in 6 species. The table represents presence (light blue) or absence (light grey) in different species (columns). Code annotation that refer to strictly DNA-related or proliferative activities are highlighted in bold. Codes associated to DNA repair are highlighted in red.


Pre-trained natural language processing models on a large natural language corpus can naturally transfer learned knowledge to protein domains by fine-tuning specific in-domain tasks. However, few studies focused on enriching such protein language models by jointly learning protein properties from strongly-correlated protein tasks. Here we elaborately designed a multi-task learning (MTL) architecture, aiming to decipher implicit structural and evolutionary information from three sequence-level classification tasks for protein family, superfamily and fold. Considering the co-existing contextual relevance between human words and protein language, we employed BERT, pre-trained on a large natural language corpus, as our backbone to handle protein sequences. More importantly, the encoded knowledge obtained in the MTL stage can be well transferred to more fine-grained downstream tasks of TAPE. Experiments on structure- or evolution-related applications demonstrate that our approach outperforms many state-of-the-art Transformer-based protein models, especially in remote homology detection.The online version contains supplementary material available at 10.1186/s12859-022-05031-z. Natural language is inherently context-dependent. This fact becomes particularly prominent when two strongly-related words are far separated. From a linguistics perspective, context plays a significant role in deciphering the actual meaning of a word . LikewisMulti-task learning (MTL), which is able to leverage useful information of related tasks to achieve simultaneous strong performance on multiple associated tasks , has ledTransfer learning means transferring the knowledge from a related task that has already been learned to a new task . Rives eIn this work, to sum up, we proposed a multi-task learning framework using three BERT-based backbones, which employed abundant contextual representations obtained in natural language and jointly learned knowledge on interrelated protein tasks. Three structural- or evolutionary-relevant downstream tasks, well-defined in TAPE, were used to evaluate whether our MTL architectures properly capture the structural and evolutional relationships. The overall workflow is shown in Fig. Overall, we elaborately designed three MTL backbones with different intrinsic architectures, namely MT-BERT, MT-BCNN, and MT-BLSTM. To jointly learn protein structure and evolution properties, we assigned these models a multi-task classification pipeline with respect to protein family, superfamily and fold categories. Finally, the learned knowledge was transferred to decode fine-grained applications well-defined in TAPE. Specifically, we adopted three structural- or evolutionary-related downstream tasks, including secondary structure prediction, contact prediction, and remote homology detection, to evaluate the transfer learning performance of our proposed MTL models.The training and test datasets we used for the MTL pipeline are all derived from SCOP 2 , a widelFor all NLP models, pre-training on a large natural language corpus is essential for learning universal language representations . As a piThe powerful feature extraction ability of BERT with substantial parameters may overfit the classification task, especially with limited training data . CNN hasCompared to single-task learning, which learns only one specific representation for once, multi-task learning enables the knowledge learned in one task to benefit other tasks . As mentDeep learning, which is inherently specialized in learning complex non-linear feature representations , 42, hasEvery input protein sequence would first be tokenized into separate amino acids represented by specific alphabets, then embedded in a maximum of 8096 vector spaces according to the length of the sequence. Note that the Generally, an MTL model can be trained by linearly combining loss functions from different tasks into a single total loss function . In this.It is worth noticing that the performance of MTL models strongly depends on the relative weighting between the loss of each task . It has Here we demonstrate how to train our MTL models in Algorithm 1. The same MTL dataset, derived from SCOP 2, was used to jointly learn how to classify proteins into family, superfamily and fold. We set a batch size of 32, a dropout rate of 0.1 for BERT and 0.4 for LSTM. We defined a larger learning rate of 1e-2 for CNN and LSTM connected after BERT, while the pre-trained BERT held a relatively small learning rate of 1e-5. All the above hyperparameters were fine-tuned through Bayesian Optimization . We alsoAmong the three proposed MTL models, the protein sequences are always first fed into BERT Encoder , Accuracy (Acc), Recall (Rec) and F1-score (F1). These indexes are frequently used to assess the generalization of machine learning models from distinct perspectives. The detailed definitions can be seen below.MTL aims to help improve the generalization of all tasks . In thisSecondary structure prediction (SS prediction) is a sequence-to-sequence classification assignment. Assuming that there is a protein sequence, this task is dedicated to labelling every input amino acid with a secondary structure position see Fig. . The labAs in TAPE, the training and validation datasets for secondary structure prediction are from , and Q3 Contact prediction is a pairwise classification assignment. Given a protein sequence, the goal of this task is to predict whether each pair of residues from this sequence are “in contact”  or not As in TAPE, the dataset for contact prediction is from ProteinNet . The pretest set .Remote homology detection is a conventional sequence-level classification assignment. Since distantly related proteins may share similar structures and functions , this taAs in TAPE, the dataset of remote homology detection comes from , which oUsing the four evaluation metrics reported in  “These results validate that the knowledge in natural language can indeed transfer to handle sequence-level protein classifications. Furthermore, compared to MT-BERT solely employed BERT, the introduced CNN and LSTM layers in MT-BCNN and MT-BLSTM have improved the overall classification performance. Specifically, we can see that MT-BLSTM gets the best results among all the three MTL models.As mentioned above, we considered three of the four protein categories, namely family, superfamily and fold, and the information behind the class label has not been employed. Class category gathers folds and intrinsically-unstructured proteins from superfamily, which indicates a solid structural concept and the correlation with evolution. For this time, this label can be used to verify the effectiveness of our MTL models. We compared the learned features between the original pre-trained BERT and our MTL models to check whether these multiple tasks encode useful structural and evolutionary information. Generally, an MTL model would encode a given sequence into high-dimensional vector embeddings. However, it is possible to map the whole semantic space by pooling them into fixed-size embeddings by reduction . MoreoveWe can see from Table Overall, such abstract clustering representations and statistical results proved that our MTL models captured useful structural and evolutionary information that could further facilitate related downstream tasks.We evaluated the MTL model performance on three downstream applications: secondary structure (SS) prediction, contact prediction and remote homology detection. Depending on the task, we reported the accuracy or precision described in “Table Compared to other Transformer-based models that acquire in prior knowledge of proteins, we can see that the BERT pre-trained on natural language in our MTL models can get good transfer learning performance. Furthermore, these results can verify the importance of introducing in-domain knowledge to natural language pre-training models. In other words, appropriately encoding protein property information can significantly boost the performance of downstream applications. More than that, it is essential to validate if pre-training on protein sequences outperforms pre-training on natural language corpus. To do this, we employed two BERT-based models TAPE BERT  and ProtCompared to our basic model MT-BERT, we can see that pre-training on protein data significantly improves transferred performance. However, the best results are comparable with our MT-BCNN and MT-BLSTM models that rely on human words pre-training in Table Moreover, exploring which two of the three tasks provide relative critical information is equally meaningful. Since the former three supervised tasks are closely related, we thereby tested how these well-designed MTL models perform if one of the tasks is missed (see Tables After removing one specific task, we can see an overall degraded performance with varying degrees. The combination of superfamily and fold tasks gets the best overall outcome in the ablation study. As described in SCOP 2 , the famOverall, the learned representations by two related tasks can still be well transferred to downstream scenarios. However, the best results in these applications occur when all three highly-dependent classification tasks are considered.Finally, to validate if the reduced complexity of single-task learning could influence the model performance, we solely adopt one of the three classification tasks to enrich our models. Note that ST-BERT, ST-BCNN and ST-BLSTM denote a single-task learning version compared with three MTL models, in which the whole model architectures remain the same.Table The advanced NLP models, pre-trained on abundant natural language corpus, can be well transferred to decode biological sequences. Combined with the supervised training on multiple interrelated in-domain tasks, we demonstrate that these powerful NLP models can even outperform those fully modeling on protein language. Additionally, our approach further validates that transfer learning indeed improves downstream applications . FurtherFurthermore, we elaborately designed an MTL architecture. It contains three kinds of backbones: MT-BERT, MT-BCNN and MT-BLSTM. MT-BERT simply employs pre-trained BERT, while MT-BCNN and MT-BLSTM added CNN or LSTM layers to the top of BERT, aiming to avoid overfitting or better capture sequential invariance. Adequate experiments show that these models capture proper structural and evolutionary relationships by collectively learning from three correlated sequence-level classifications. Besides, the most critical part depends on the transfer learning ability. Among three challenging structure- or evolution-related tasks, the performance on remote homology detection has been significantly improved compared to other SOTA Transformer-based Models. Moreover, we can see the effectiveness of added CNN and LSTM layers in MT-BCNN and MT-BLSTM, which obtained better performance than MT-BERT.Overall, we believe that our proposed methodology can facilitate the study of how to draw on sophisticated tools in natural language to learn protein language, as well as the way to encode strong biological priors into protein language models . FurtherAdditional file 1. Constructed protein dataset derived and cleaned from SCOP 2. Except for the protein sequence, each row of data contains specific SCOP domain identifications including FA-DOMID, SF-DOMID, CL, CF, SF, and FA, whose descriptions can be found at https://scop.mrc-lmb.cam.ac.uk/download.


ACE2 is mainly expressed in lung pneumocytes, secretory cells and ciliated cells and differentially expressed in lungs of smokers and never smokers.In recent years, efficient scRNA-seq methods have been developed, enabling the transcriptome profiling of single cells massively in parallel. Meanwhile, its high dimensionality and complexity bring challenges to the data analysis and require extensive collaborations between biologists and bioinformaticians and/or biostatisticians. The communication between these two units demands a platform for easy data sharing and exploration. Here we developed Single-Cell Transcriptomics Annotated Viewer (SCANNER), as a public web resource for the scientific community, for sharing and analyzing scRNA-seq data in a collaborative manner. It is easy-to-use without requiring special software or extensive coding skills. Moreover, it equipped a real-time database for secure data management and enables an efficient investigation of the activation of gene sets on a single-cell basis. Currently, SCANNER hosts a database of 19 types of cancers and COVID-19, as well as healthy samples from lungs of smokers and non-smokers, human brain cells and peripheral blood mononuclear cells (PBMC). The database will be frequently updated with datasets from new studies. Using SCANNER, we identified a larger proportion of cancer-associated fibroblasts cells and more active fibroblast growth-related genes in melanoma tissues in female patients compared to male patients. Moreover, we found  Key PointsSingle-Cell Transcriptomics Annotated Viewer (SCANNER) provides a new web resource to bridge the data analysis and the biological experiment units.SCANNER enables comprehensive and dynamic analysis, visualization, functional annotation and easy data sharing.SCANNER hosts a real-time database for data and user management.In recent years, single-cell RNA sequencing (scRNA-seq) methods have been enabling the fast transcriptome profiling of individual cells . It provhttps://firebase.google.com). SCANNER enables a unique set of functions highlighted in SCANNER was constructed using R 4.2.0 and R shiny package and is currently built on a CentOS Linux 7 server, with 6 CPUs and 16GB memory in the environment of Jetstream , 4. The Availability and implementation (see below SCANNER data object generation) and uploading them to SCANNER. For secure data management, a user account is required, and the registration is broadly open to the scientific community. After users logged into the SCANNER, they can upload/delete their own datasets and share/unshare them with collaborators who have registered in SCANNER. When users share datasets with specific collaborators, SCANNER sends an email about the file-sharing information to each of those user-designated collaborators. Also, users can access publicly pre-processed datasets hosted in SCANNER, which is described below.SCANNER enables the user data management and sharing easily, without hosting the authentication and database infrastructure locally. Users can generate SCANNER data objects by following the instruction in https://portals.broadinstitute.org/single_cell) and scRNASeqDB  , CerebroRNASeqDB ] are maiRNASeqDB , SCANNERhttps://portals.broadinstitute.org/single_cell) and scRNASeqDB , including average expression, average expression rank, eigen-gene expression , which is consistent with the recent study of Ziegler et al.  and the top 10, 30 and 100 differentially expression genes. Currently, SCANNER focused on the efficient exploration of gene expression but is incapable of visualizing other inferences such as RNA velocity and psuedotime, which is considered a limitation of SCANNER. However, SCANNER is compatible with Seurat objects generated from the user’s analysis pipeline by automatically extracting information from necessary data slots.SCANNER requires scRNA-seq data preprocessing from the user-end, using a Seurat preprocessing workflow which has been provided in In this study, we developed SCANNER for supporting efficient data sharing and communication between biologists and bioinformaticians, which is expected to promote scRNA-seq studies in the scientific community broadly. In future updates, SCANNER will be equipped with the capacity to host and manage single-cell spatial transcriptomics and multimodal omics data, and is expected to be compatible with other data processing workflows, such as scanpy  and scatbaab086_SuppClick here for additional data file.


Demyelination is similar with malignancy in clinical symptoms. Magnetic resonance imaging (MRI) is an important auxiliary examination in the diagnosis of demyelinating diseases and malignancy. Since MRI and symptoms can be difficult to distinguish demyelination from malignancy, other auxiliary examinations, such as demyelinating disease-specific antibodies, play an important role in distinguishing them. Previous studies have reported demyelinating disease-specific antibodies in patients with malignancy. What’s more, it is more difficult to confirm the diagnosis when the malignant tumor co-occurs with demyelinating diseases, which has never been reported in previous studies. We report the diagnosis of myelin oligodendrocyte glycoprotein antibody associated encephalomyelitis (MOG-EM) in a patient who had astrocytoma for several years.Patient’s concerns and diagnoses: our case report records a 49-year-old woman with astrocytoma for more than 4 years, who recently developed the symptoms of MOG-EM, including dizziness, vomiting, and vision loss. This astrocytoma patient was diagnosed with MOG-EM according to comprehensive evidence, including MRI, visual evoked potential (VEP), serum myelin oligodendrocyte glycoprotein antibody (MOG-IgG), and therapeutic effect. Interventions and outcomes: this patient was diagnosed with astrocytoma by surgical biopsy 4 years earlier. This patient has been treated with tumor resection, postoperative radiation treatment and chemotherapy. After treatment, the patient was left with right limb weakness while other symptoms were improved. Recently, the intravenous steroid agent was used to treat this patient after being diagnosed with MOG-EM. Dizziness, vomiting, and vision loss have gone into remission. This patient did not relapse in 7 months after discharge. This patient is still being followed up at the outpatient clinic. And the patient will next be treated with azathioprine.In previous studies, polyclonal antibody has been found in cancer patients, such as aquaporin-4 and MOG-IgG in astrocytoma patients. But the case of our study finds that astrocytoma can coexist with MOG-EM. Therefore, MOG-EM should not be excluded easily in astrocytoma patients when the relative antibody of encephalomyelitis is positive. What’s more, it reminds us that the pathogenesis of MOG-EM might be related to astrocytoma. The differences are as follows–4: with the exploration of previous immunological studies, MOG-IgG can directly cause the occurrence of demyelinating diseases; MOG-EM does not damage astrocytes and lacks aquaporin-4 (AQP4) -IgG-positive; MOG-EM and NMOSDs have different histopathological features. The underlying pathogenesis might be relative to humoral immunity and antigen-antibody response which is still unclear. The diagnosis of MOG-EM is based on the evidence, including the symptoms, serological antibody, and magnetic resonance imaging (MRI).,5Myelin oligodendrocyte glycoprotein antibody associated encephalomyelitis (MOG-EM) is an inflammatory demyelinating disease of the central nervous system. In the earliest studies, MOG-EM was often misdiagnosed as multiple sclerosis (MS) or classified as one type of neuromyelitis optic spectrum disorders (NMOSDs), which is oligodendrocyte glycoprotein antibody positive. MOG-EM is similar with NMOSDs which consists of several syndromes, including optic neuritis, myelitis, acute brainstem syndrome, and acute disseminated encephalomyelitis. However, with the further exploration of demyelinating diseases, a lot of studies found that immunopathogenesis of MOG-EM is different from aquaporin-4 antibody positive NMOSDs so that more and more experts classified MOG-EM as a separate disease category. Astrocytoma can present similar symptoms with MOG-EM. And the presentation of MRI in astrocytoma makes it difficult to differentiate astrocytoma from MOG-EM. It is difficult to diagnose between demyelination and malignancy with MRI and symptoms.Astrocytoma was defined as tumor which presents character of astrocyte differentiation. Astrocytoma accounts for about 30% of all intracranial tumors and more than 78% of gliomas. Astrocytoma occurs mostly in frontal lobe, temporal lobe, thalamus, brain stem and cerebellar hemispheres.Our case reports a 49-year-old woman with medical history of astrocytoma  for more than 4 years who was recently diagnosed with MOG-EM. This patient was diagnosed with astrocytoma by surgical biopsy, and treated with tumor resection, postoperative radiation treatment and chemotherapy (temozolomide) 4 years earlier. She developed the symptoms of MOG-EM, including dizziness, vomiting, and vision loss 2 months earlier. The clinical symptoms and MRI are unable to provide sufficient evidence for us to differentiate astrocytoma recurrence from MOG-EM. However, serum MOG-antibody, visual evoked potential (VEP), and therapeutic effect finally proved the diagnosis of MOG-EM.Presentation: This patient is a 49-year-old woman who had received treatment with main complaint of right limb weakness and headache in hospital for the first time on August 16, 2017. The patient’s right limb weakness continued to worsen one month before admission, and her headache occurred a week before admission.Physical examination, auxiliary examination, diagnosis, and treatment: Neurological examination presented right Hemiplegia (Muscle strength: level 3), and no another abnormal neurological sign. The MRI of pre-operation was shown in Figure Presentation: This patient developed dizziness, vomiting, and vision loss before one month of this admission . The physical examination outcomes are similar to that of the first-time treatment except vision loss.Auxiliary examination, diagnosis, and treatment: The MRI of post-operation and this admission were shown in Figure http://links.lww.com/MD/H532. The mass effect of brain stem and cerebellar lesions was more obvious during follow-up. And the titer of MOG-IgG was higher than before , and the patient did not experience any recurrence of symptoms. The brain MRI and serum antibody were used as items of reexamination. But MRI of reexamination displayed that the brain stem and cerebellum developed more diffuse hyperintensity than before Fig. . The T1 ore Fig. . The titTo the best of our knowledge, there are few studies and pathological reports on the correlation of MOG-EM and astrocytoma. The case of our report is rare because the patient has suffered from both astrocytoma and MOG-EM. There are relatively complete clinical cases preserved as well as auxiliary examination except surgical biopsy. As the patient underwent tumor resection at another hospital, the surgical biopsy examination was only preserved in the form of mobile phone photos. Fortunately, all evidence for the diagnosis of MOG-EM has been preserved completely. Therefore, the diagnosis of MOG-EM and astrocytoma is clear. However, the correlation between MOG-EM and astrocytoma remains unclear.,8 As the development of acknowledge in MOG-EM, it should be diagnosed as a separate disease category. And the MOG-IgG has been detected in about 40% of the patients of NMOSDs without the AQP4-IgG which might be classified as MOG-EM.,9,10 In previous studies, no significant agitating or inducing diseases associated with demyelinating MOG antibodies have been found. And MOG-EM is not usually explored in paraneoplastic syndrome. But this patient of our report developed MOG-EM after the astrocytoma. The etiology of this patient may have been speculated in previous MOG antibody studies. MOG is a type of glycoprotein on oligodendrocyte which is only found in central nervous system. And impairment of the blood brain barrier (BBB) may play an important role in pathogenesis of MOG-EM.–14 The tumor itself might represent the immune response and immune monitoring abnormalities in human beings. In our case report, the impairment of BBB and oligodendrocyte might consist of four factors, including the impairment of tumor, tumor resection, postoperative radiation treatment and chemotherapy. These factors might lead to MOG-IgG positive which might play an important role in development of MOG-EM. The patient of our report is partly consistent with another case report. Therefore, we hypothesized that the onset of MOG-EM in this patient was related to astroglioma or its treatment.MOG-EM is a demyelinating disease of the central system with autoimmune disorder which is initially defined as disease about MOG-IgG-positive NMOSDs with the similar diagnostic criteria.The limitations of our study can be listed as follows: this study is a case report, which provides low level evidence based on evidence-based medicine; the follow-up time of this admission (about 7 months) is short, and new symptoms may occur in the future.MOG-EM might be associated with astrocytoma or its treatment. What’s more, MOG-EM might be secondary to astrocytoma or its treatment. Astrocytoma should be considered as a high-risk factor for MOG-EM. More studies are needed to support and explore our findings.Methodology: Shaoming Yang, Hongli Gu.Writing – original draft: Guanghong Zhong, Jia Zhang.Writing – review & editing: Xi Liu.


Intravascular large B-cell lymphoma (IVLBCL) is a rare, aggressive, large B-cell non-Hodgkin’s lymphoma. The prognosis of IVLBCL in patients with central nervous system recurrence after first-line chemotherapy treatment is extremely poor. Among immunotherapies, chimeric antigen receptor (CAR) T-cell immunotherapy has been recently found to be a highly effective treatment for B-cell lymphoma, especially for relapsed or refractory diffuse large B-cell lymphoma. However, no guidelines are available that provide a clear consensus regarding the management of patients with relapsed/refractory IVLBCL. Here, we report, for the first time, the use of autologous hematopoietic stem cell transplantation (ASCT) and CAR T-cell therapy in a patient with relapsed/refractory IVLBCL.A 42‐year‐old woman was diagnosed with IVLBCL based on liver biopsy and developed central nervous system (CNS) progression. The patient received ASCT combined with murine monoclonal anti-CD19 and anti-CD22 CAR T-cell therapy. She achieved complete remission for 22 months so far with negative minimal residual disease and continues to be followed up.ASCT combined with CAR T-cell therapy was the best choice for treatment of relapsed/refractory IVLBCL, as it allowed the achievement of a lasting complete remission. Intravascular large B-cell lymphoma (IVLBCL) is a rare, aggressive, large B-cell non‐Hodgkin’s lymphoma in which neoplastic B cells proliferate selectively within the lumina of small- and medium-sized vessels. IVLBCL is a rare subtype of diffuse large B-cell lymphoma (DLBCL), with an estimated incidence <1 per million. Recently, IVLBCL has been categorized as a subtype of extranodal large B-cell lymphoma according to the World Health Organization (WHO) classification criteria .IVLBCL is an aggressive disease with a poor outcome, and clinical signs and symptoms are nonspecific; therefore, the diagnosis is commonly missed. In fact, many cases were confirmed in post-mortem evaluation. Owing to the rarity of IVLBCL, treatment recommendations are largely based on retrospective studies and case reports. The combination of rituximab and cyclophosphamide, vincristine, doxorubicin, and prednisolone (R-CHOP) is widely used as first-line therapy, but the 2-year progression-free survival (PFS) and overall survival (OS) rates are only 56% and 66%, respectively . In a re9/L, ALT 90.6U/L, AST 87.2U/L, total bilirubin 32.8umol/L, LDH 636 U/L, ferritin>2000ng/ml. Bone marrow cytology showed reactive bone marrow images, and hemophagocytic cells were occasionally observed. Abdominal computed tomography (CT) (max) of the liver was 6.3, and the delayed SUV max was 8.2  for hemophagocytic syndrome and two courses of R-CHOP , then she attained partial remission by PET-CT. Subsequently, she experienced paroxysmal headache during the third R-CHOP chemotherapy regimen. Combined with the history of the disease, she was considered to have CNS progression, which was identified using head MRI (6 cells/kg). The head MRI 1 month before ASCT and CAR19/22 T-cell cocktail therapy treatment is shown in 6 cells/kg). Autologous CD22-targeted CAR T-cells (CAR22) (2.7×106 cells/kg) were infused on day +2, followed by CD19-targeted CAR T-cells (CAR19) (4.3×106 cells/kg) on day +3. The structure is as shown in 2 IU/mL, and there was no hepatitis B virus activation after treatment. The dynamic changes in white blood cells and lymphocytes after CAR T-cell therapy are depicted in To prevent the progression of the disease, she came to our hospital for CAR T-cell therapy, and we decided to perform ASCT combined with CAR T-cell therapy. The trial was approved by the Institutional Review Board of Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, and the study was registered with the Chinese Clinical Trial Registry (ChiCTR-OPN-16009847). Informed consent was obtained by the patient and her family according to the Declaration of Helsinki. Before the conditioning regimen, the patient received two separate apheresis procedures, including granulocyte colony-stimulating factor (G-CSF)-stimulated autologous hematopoietic stem cell (HSC) collection and peripheral blood mononuclear cell (PBMC) apheresis for CAR T-cell manufacturing. CAR T-cell manufacturing-related quality control and analysis were completed by Wuhan Bio-Raid Biotechnology Co., Ltd., as previously described . Due to When it comes to her thoughts on the disease and treatment, she expressed her great gratitude to our hospital and her husband. Despite her reluctance to face the diagnosis of lymphoma, the encouragement and positivity of her family  fueled her courage and strength to overcome the disease. She felt hopeless and helpless when she learned of her central involvement, poor prognosis and limited treatment options. When she and her husband learned of the clinical trials being conducted at our hospital, her desire to survive was rekindled, and she volunteered to participate in the clinical trials. Currently, the patient is working again, grateful, optimistic, healthy, and is very satisfied with her situation.IVLBCL is a rare, aggressive disease with a dismal prognosis, especially in patients with relapsed or progressive disease after first-line treatment. CNS recurrence is a serious complication in patients with IVLBCL, and optimal treatment strategies for CNS involvement should be established to further improve clinical outcomes in the rituximab era. However, there is still no consensus concerning this issue. Early diagnosis and treatment are of great significance for improving the prognosis of IVLBCL patients. Significantly, PET-CT is a powerful tool for the early diagnosis of IVLBCL by identifying the indicated sites for biopsy relying on FDG uptake. Ultrasonography-guided percutaneous liver biopsy was performed in our patient because of the abnormally high FDG uptake in the liver. In this patient, detection of CNS involvement was diagnosed based on neurological symptoms, along with corroborating findings on imaging modalities.Although rituximab has improved the outcome of patients with IVLBCL, more treatment strategies are still needed to deal with r/r IVLBCL. In a single institution in Taiwan containing ten patients with IVLBCL, it was shown that patients might achieve durable remission after rituximab-based chemotherapy . In a reCAR T-cell immunotherapy has already shown good outcomes in patients with r/r DLBCL and B-cell ALL . RecentlAntigen escape and downregulation have emerged as major issues affecting the durability of CAR T-cell therapy. Early-phase clinical trials demonstrate robust efficacy in several CD19 CAR T-cell products. Recently, CAR22 T cells were shown to induce durable remission in patients who were resistant to prior CD19-directed immunotherapies . SubsequIn conclusion, this is the first time that CD19/22 sequential CAR T-cell combined with ASCT therapy has been used as a novel therapy in IVLBCL. This one case report where ASCT and CD19/22 CART resulted in long-term remission and was safe, suggests that this approach may be promising and should be considered for further study. However, there are some limitations in our present study. Clinical trials in a wider population are difficult due to the rarity of ILBCL cases. It still requires more patients and clinical trials to verify. Our successful case of using sequential CD19/22 CAR T-cell immunotherapy followed by ASCT provides new treatment options for r/r IVLBCL patients. This approach merits further study in CNS-involved IVLBCL patients.The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding authors.The studies involving human participants were reviewed and approved by Medical Ethics Committee of Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology (TJ-IRB20160310). The patients/participants provided their written informed consent to participate in this study.WL and CL analyzed the data and wrote the manuscript. LC and YX revised the manuscript. YC, LH, and MX performed the experiments. NW, ZS, JW, LFH, and JX took care of the patient. YZ and JZ provided clinical information. JZ, LC, and YX directed the research. All authors contributed to the article and approved the submitted version.This work was supported in part by the National Natural Science Foundation of China  and the Hubei Provincial Science and Technology Key Research Program (No. 2020BCB021).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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Breast cancer (BC) is the most common malignancy among women in the world. Alternative splicing (AS) is an important mechanism for regulating gene expression and producing proteome diversity, which is closely related to tumorigenesis. Understanding the role of AS in BC may be helpful to reveal new therapeutic targets for clinical interventions.RNA-seq, clinical and AS data of TCGA-BRCA were downloaded from TCGA and TCGA SpliceSeq databases. AS events associated with prognosis were filtered by univariate Cox regression. The AS risk model of BC was built by Lasso regression, random forest and multivariate Cox regression. The accuracy of the AS risk model and clinicopathological factors were evaluated by time-dependent receiver operating characteristic (ROC) curves. The significant factors were used to construct the nomogram model. Tumor microenvironment analysis, immune infiltration and immune checkpoint analysis were performed to show the differences between the high and low AS risk groups. The expression differences of genes of AS events constituting the risk model in tumor tissues and normal tissues were analyzed, the genes with significant differences were screened, and their relationship with prognosis, tumor microenvironment, immune infiltration and immune checkpoint were analyzed. Finally, Pearson correlation analysis was used to calculate the correlation coefficient between splicing factors (SF) and prognostic AS events in TCGA-BRCA. The results were imported into Cytoscape, and the associated network was constructed.A total of 21,232 genes had 45,421 AS events occurring in TCGA-BRCA, while 1604 AS events were found to be significantly correlated with survival. The BRCA risk model consisted of 5 AS events, (TTC39C|44853|AT*− 2.67) + (HSPBP1|52052|AP*− 4.28) + (MAZ|35942|ES*2.34) + (ANK3|11845|AP*1.18) + (ZC3HAV1|81940|AT*1.59), which were confirmed to be valuable for predicting BRCA prognosis to a certain degree, including ROC curve, survival analysis, tumor microenvironment analysis, immune infiltration and immune checkpoint analysis. Based on this, we constructed a nomogram prediction model composed of clinicopathological features and the AS risk signature. Furthermore, we found that MAZ was a core gene indicating the connection of tumor prognosis and AS events. Ultimately, a network of SF-AS regulation was established to reveal the relationship between them.We constructed a nomogram model combined with clinicopathological features and AS risk score to predict the prognosis of BC. The detailed analysis of tumor microenvironment and immune infiltration in the AS risk model may further reveal the potential mechanisms of BC recurrence and development.The online version contains supplementary material available at 10.1007/s12672-022-00506-0. Globally, breast cancer (BC) ranks first in incidence , and sechttp://bioinformatics.mdanderson.org/TCGASpliceSeq) is a database used to analyze AS events in protein coding genes based on TCGA data. The percent-splice-in (PSI) value is a quantitative index that can describe the probability of occurrence of an AS event. By downloading the PSI data of all AS events of patients in TCGA-BRCA from TCGA SpliceSeq and combining with the RNA-seq data and survival information of TCGA-BRCA, AS events related to prognosis can be obtained, and patients can be divided into high-risk and low-risk groups according to these AS events. By analyzing the RNA-seq information of the two groups of patients, we can speculate the potential role and possible mechanism of these prognosis-related AS events in BC.Alternative splicing (AS) is a posttranscriptional process that leads to one single gene encoding multiple proteins. It has been proven that cancer cells have general, cancer type-specific and subtype-specific diversity during splicing, which may have prognostic value and facilitate features of cancer progression, including the cancer immune response, epithelial-to-mesenchymal transition, and resistance to chemotherapeutic drugs –8. TheseThe process of tumor formation is not only the self-reproduction and accumulation of tumor cells but also the result of the combined effects of tumor cells and adjacent immune cells, stromal cells and extracellular matrix, just as the growth of seeds requires soil nourishment. All these tumor formation participants constitute the tumor microenvironment (TME), which is a complex and dynamic cellular community . StromalDue to the rapid development of bioinformatics, we are now able to estimate proportions of various types of cells in TME based on RNA-seq data using R packages. The “estimate” package can infer the fraction of stromal and immune cells in tumor specimens by gene expression signatures based on single sample gene set enrichment analysis (ssGSEA), stromal score , immune score (infiltration of immune cells in tumor tissue) and ESTIMATE score  are calculated . Higher In this study, we filtered AS events significantly related to BC prognosis to establish an AS-associated risk model using the transcriptome and AS data downloaded from TCGA-BRCA and TCGA SpliceSeq databases. According to the model, the BC patients can therefore be classified into AS-high and AS-low risk groups. Subsequently, we performed univariate and multivariate Cox regression analyses of clinical information of TCGA-BRCA to screen for clinical features correlated with prognosis and generate a nomogram that can predict survival more accurately. To further precisely model the AS events and predict their effect on BC progression, we performed TME, immune infiltration and immune checkpoints analyses to explore how and why these AS events are associated with BC prognosis.https://portal.gdc.cancer.gov/) database, which contains the mRNA-seq data of 56,461 genes’ expression in 1109 BC samples and 113 normal breast tissue samples from 1092 cases. The PSI (percent-splice-in) values of AS  were obtained and calculated from the TCGA SpliceSeq database (https://bioinformatics.mdanderson.org/TCGASpliceSeq/PSIdownload.jsp), which consists of 7 types of AS events as follows: AA , AD , AP , AT , ES (exon skip), ME , and RI (retained introns)  and ZAP , is a PARP family member of RNA-binding proteins and has important functions in the cellular response to stress . StudiesMAZ (Myc-associated zinc-finger protein) is an oncogene supported by many studies. It is considered to activate KRAS transcription and may induce the expression of the proto-oncogene MYB, participating in facilitating aerobic glycolysis and other functions and is therefore involved in the tumor progression and metastasis of several cancer types –48. TherWe also found that the risk scores of 5 AS model events were closely related to the proportion of immune cells. Using two mainstream immune infiltration assessment methods, CIBERSORT and ssGSEA, it was found that the intersection differences in immune cells were CD8 + T cells and NK cells. The proportion of both cell types in the low-risk group was higher than that in the high-risk group. In tumor progression, CD8+ T cells are the key mediators of cytotoxic effector function, play an important part in the adaptive immunity of the body and are one of the main effector cells of tumor adoptive immunotherapy , 52. HowAs the upstream of AS events, the SF gene indirectly affects the occurrence and development of cancer by affecting the AS process . Our preOur study preliminarily corroborated the association between AS events and breast cancer prognosis and found that the occurrence of 5 AS events constituting the risk model was related to the TME, the expression of immune checkpoint genes and immune cell infiltration, which may lead to differential responses to immunotherapy. However, there are still many deficiencies, and there is a lack of experiments to verify the regulatory effect of the screened SF gene on the 5 AS events and the specific mechanism of the 5 AS events on the modulation of immune infiltration. More independent clinical trials are needed to recruit enough patients to examine the prognostic value of this risk model.We constructed a risk model associated with the prognosis of breast cancer consisting of 5 AS events. The AUC values of this model predicting TCGA-BRCA patients at 3, 5, and 10 years were 0.674, 0.706, and 0.773, respectively, suggesting a certain prognostic value in BC. These AS events were also found to be associated with the TME, immune infiltration and immune checkpoints, suggesting that their occurrence ratio may be related to the response to immunotherapy and ICI treatment, thus affecting the progression of BC. These AS events may provide a reference for screening new therapeutic targets, prognoses and therapeutic biomarkers for breast cancer.Supplementary material 1 (XLSX 16.7 kb)


In the past decade, several studies have shown that Neurofeedback (NFB) by functional magnetic resonance imaging can alter the functional coupling of targeted and non-targeted areas. However, the causal mechanisms underlying these changes remain uncertain. Here, we applied a whole-brain dynamical model to estimate Effective Connectivity (EC) profiles of resting-state data acquired before and immediately after a single-session NFB training for 17 participants who underwent motor imagery NFB training and 16 healthy controls who received sham feedback. Within-group and between-group classification analyses revealed that only for the NFB group it was possible to accurately discriminate between the 2 resting-state sessions. NFB training-related signatures were reflected in a support network of direct connections between areas involved in reward processing and implicit learning, together with regions belonging to the somatomotor, control, attention, and default mode networks, identified through a recursive-feature elimination procedure. By applying a data-driven approach to explore NFB-induced changes in spatiotemporal dynamics, we demonstrated that these regions also showed decreased switching between different brain states (i.e. metastability) only following real NFB training. Overall, our findings contribute to the understanding of NFB impact on the whole brain’s structure and function by shedding light on the direct connections between brain areas affected by NFB training. While already at the end of the XX century technology was used to train humans to self-regulate their own brain activity , now thaIn order to provide a better characterization of the neural processes underlying NFB training, several studies have explored its effects on resting-state networks. For example, it has been shown that a single session of NFB-training targeting sensorimotor brain areas led to strengthening of both sensorimotor and default-mode networks (DMNs) . IncreasOn the other hand, the use of whole-brain computational modeling to explain neuroimaging data has the potential of providing mechanistic information underlying different brain states in health and diseases, and as well as treatments monitoring. A recent modeling approach, the “MOU-EC” , 2020, aIn this study, we interrogate data of a previous randomized, double-blind, and sham-controlled study  in orderAs described before in In brief, the study consisted in a single-day NFB (or sham) training, which started (PRE) and ended (POST) with consecutive acquisitions of diffusion-weighted imaging (DWI) and resting-state (RS) .Before the training started, participants were asked to perform an overt motor task of a predefined sequence of right-hand finger movements , which served to train a 2-class support vector machine (SVM) algorithm to discriminate between motor execution and rest based on a distributed voxel patterns during the following NFB training. Brain areas involved in motor learning  were useThe study was performed with a 3T Achieva scanner  using an 8-channel SENSE head coil. DWI  = 5582/65, FOV = 240 x 240, matrix = 96 x 95) with diffusion sensitization gradients applied in 64 noncollinear directions, with a b factor of 1000 s/We computed preprocessing of functional resting-state data using the Data Processing Assistant for Resting-State fMRI DPARSF;  based onFirst, we manually reoriented both functional and anatomical images. Then, resting-state images were preprocessed as follows: removal of the first 5 time-points, motion correction through realignment across volumes, co-registration of fMRI images to T1-weighted images by applying unified segmentation, correction for head movements parameters, for global mean signal, for the white-matter, and for cerebrospinal fluid signal by means of a regression of nuisance covariates. Further steps involved spatial normalization in stardard MNI  space, to 3ellation  and 16 sellation .Whole brain SC was performed individually in the native space using FMRIB’s Diffusion Toolbox FDT; . First, We applied a dynamic generative model, the MOU-EC, to extract whole-brain connectivity estimates from the BOLD time-series of both resting-state sessions of NFB and CTL groups.i, which is affected by the activity of other regions, and show an exponential decay by the time constant i,j), and its topology is constrained by the SC mask after applying a threshold to retain only the strongest connections, as explained above. Here, github.com/MatthieuGilson/WBLEC_toolbox.The whole parameter estimation procedure is presented in We performed both within- and between-group classification using the scikit-learn package  based onWe performed cross-validation of the 1NN and the SVM by applying a random splitting procedure in which the 80% of samples were used to train the classification algorithms and the remaining 20% for testing his performance. We repeated the random splitting procedure 50 times in order to assess the impact of different splits of data in train/test sets on 1NN and SVM performances. To evaluate the models performances in distinguishing between resting-state sessions, we only considered the accuracy of the 50 predictions on unknown data belonging the test-set.To assess whether some metrics  or classification algorithms (1NN or SVM) performed better, we statistically compared accuracy distributions using the Wilcoxon rank-sum method. Moreover, we tested whether the models performances were significantly above changes by statistically comparing accuracy distribution of real-labeled data with the performance of surrogate data (i.e. randomly shuffled labeled data).Finally, we aimed to investigate which were the NFB-induced changes in specific EC links that differentiated the pre- and post-training resting-state sessions within the NFB group. Therefore, we performed Recursive-Feature Elimination using the SVM (SVM-RFE) to rank the features according to their importance for classification performance . This prWe applied the Intrinsic-Ignition framework to characterize differences between the 2 groups and between the resting-state sessions of each group (pre- vs post-training) in the spatiotemporal transmission of information across the brain. First, we analyzed spatiotemporal dynamics within the subnetwork highlighted by the SVM-RFE comprising 42 brain regions. Then, to understand training-related effects on the entire brain network, we repeated the analysis at the whole-brain level. This data-driven approach captures the effect of spontaneous local activity on the global computation, reflecting the level of integration with the rest of the brain network . TherefoHere, ment}(t) . TherefoThe effects of NFB training on motor brain areas, functional and structural connecitivity, and motor behavior have been published previously . In brieWe investigated whether the post-training  resting-state session could be accurately differentiated from the pre-training resting-state session. We applied 2 machine-learning algorithms, the 1NN and the SVM, to classify resting-state sessions, separately for each group (i.e. NFB and CTL group). We performed statistical comparisons of accuracy distributions in order to highlight which type of features and classifier performed better. Moreover, to understand whether the performances of the 2 ML models were above chance-level, we compared real accuracy distributions with performances using surrogate data. Results of classification for each group are presented in P =.02). This result suggests that it is possible to differentiate resting-state brain dynamics between pre- and post-training for subjects who underwent NFB training, and that EC features captured the characteristic connectivity changes induced by NFB training better than FC. In contrast, classification using 1NN led to a lower performance, with no significant difference (P>.05) between EC (average accuracy =53%) and FC (average accuracy =56%) features, indicating that both the global EC profile and FC patterns were overall similar between the 2 resting-state sessions in the NFB group. Nonetheless, performance of all combination of metrics and classifiers were significantly above chance  in the NFB group.For the NFB group, classification using EC features with SVM yielded an average accuracy of 75%, significantly higher than the accuracy reached when using FC features  when compared with accuracy distributions using surrogate data, except for the combination EC+SVM . These results suggest that the sham training did not induce changes in the global EC and FC profiles, nor localized changes in FC patterns.On the other hand, classification performance for the CTL group was around chance-level for all metrics and classifiers and did not reach significance  that contributed the most to the accurate distinction between pre- and post-training resting-state sessions.The NFB-induced signatures extracted through SVM-RFE are shown in To investigate whether connectivity patterns could be precisely distinguished between groups, we classified the post-training resting-state session of both groups using the same procedure as explained in the method section. Again, we statistically tested the significance of accuracy distributions against the model performance using surrogate data.Since a previous study published with this dataset  found a Results of group classification during post-training resting-state are shown in P=.02), and sightly improving performance compared with SC features (average accuracy=56%) but the difference did not reach significance (P>.05). This result suggests that EC weights, which hold anatomo-functional information, are more informative than FC alone when distinguishing between subjects belonging to 2 different groups. Performance of 1NN also significantly improved when using EC features (average accuracy=41%) compared with FC  but there were no significant differences with performance using SC features (P>.05). However, performance of 1NN was not significantly higher than chance-level for any of the 3 feature sets (P>.05). Since the 1NN uses the Pearson correlation as a similarity measure, poor 1NN performance suggests that the global EC, FC, and SC profile was overall similar in the 2 groups. By contrast, SVM performance was significantly above chance-level for for EC (P<.0001) and SC features (P<.0001), but not for FC features (P>.05). Together these results indicate that there are structural differences between the 2 groups, thus making EC better suited for discriminating subjects belonging to the NFB or the CTL group, and that the distinction between the 2 groups relies on localized weights of some specific EC links instead of the global EC profile.We found that the best performance was reached using EC features and the SVM (average accuracy=59%), outperforming FC  and the variability of intrinsic-driven integration comparing the 2 resting-state sessions (i.e. pre- and post-training) of both groups in order to assess effects of NFB on brain’s functional dynamics. First, we investigated whether these 2 measures were significantly different within the support network highlighted by the feature selection procedure. Then, we repeated the analysis including all brain regions in order to understand the impact of NFB training on functional dynamics at the whole-brain level.Figure P=.01) but not in the CTL group (P>.05). The variability of intrinsic-driven integration was also significantly lower at the whole-brain level (P=.03) during the post-training resting-state compared with the pre-NFB session in the NFB group. Again, no significant differences were observed between groups nor between resting-state session within the CTL group. These results suggest that NFB training induced decrease dynamical complexity (i.e. switching between different brain states) as captured by the variability of intrinsic-driven integration measure, and that this effect was more pronounced when analyzing only the 42 regions of the support network compared with the whole brain.We report no significant differences in IDMI measures for all comparisons both in the support network  and at tin level ; P=.03 dIn this work, we aimed to provide a causal mechanisms underlying NFB training by exploring whole-brain differences in resting-state EC between participants who underwent motor-imagery NFB training and control subjects belonging to the sham condition. We used a computational model and a data-driven method to analyze functional and structural data of a randomized, double-blind, and sham-controlled study  in orderWe applied a generative model, the MOU-EC, to reproduce lagged and non-lagged second order statistics of resting-state data and to infer EC by combining both structural and functional information. First, we performed within-group classification between the pre- and post-training resting-state sessions using EC and FC features to investigate functional changes induced by the NFB or sham training. Then, we compared the post-training resting-state sessions of the 2 groups (NFB and CTL groups) to assess for differences in structural, functional, and effective connections depending on whether participants underwent real or sham training. We found that for the NFB group it was possible to discriminate with high accuracy the pre- and post- resting-state sessions, and that EC features were more informative than the FC ones. In particular, we showed that EC led to a higher performance than FC when using the SVM, while differences in performance depending on the type of features were not significant for the 1NN classifier. This result suggests that the NFB training induced localized changes in connectivity patterns that were better captured by EC estimates as compared with FC. In contrast, classification performance for the CTL group was not above chance-level (best accuracy = 52% using SVM+EC), indicating that the sham training did not lead to significant changes in brain’s information propagation. Results of between-group classification also emphasizes the predictive power of EC features, leading to higher performance than FC features in distinguishing subjects belonging to 2 different groups. Taken together, these results confirmed our hypothesis that EC, which holds information about the underlying anatomical connectivity together with the functional dynamics, is a better predictor for discriminating characteristic connectivity patterns related to NFB training.To fulfill the purpose of providing a causal mechanisms underlying NFB training, we applied a feature selection procedure to highlight the direct connections between brain regions that were the most relevant for the classification of pre- and post-training sessions within the NFB group. We found that the signatures of NFB training comprised brain areas that were not directly trained through the NFB protocol, indicating that NFB induced functional neuroplasticity that went beyond the targeted area. Specifically, results of SVM-RFE highlighted a subnetwork comprising 21 distributed edges that showed training-induced changes and involved nodes belonging to the default-mode, dorsal attention, visual, somatomotor and control networks, corroborating and extending the findings obtained in the previous publication with the present dataset . TogetheFinally, to assess whether changes in information propagation were also reflected in the brain’s spatiotemporal dynamics, we applied a data-driven approach to analyze the resting-state fMRI data. We demonstrated that for the NFB group, the post-training resting-state session was characterized by a reduced dynamical complexity (i.e. less switching between different brain states), both at the whole-brain level and within the support-network highlighted through the feature selection procedure. Again, we did not find any significant difference in brain dynamics for the CTL group. These results suggest that only NFB training leads to a more metastable states in the post-training resting-state session, restricting the dynamical repertoire to a less frequent switching between different brain states. These findings are in line with effects of meditation training on the brain’s dynamical complexity. In their work, Altogether, this study demonstrated that a single session of NFB training on motor imagery can influence brain’s information propagation and its spatiotemporal dynamics. Furthermore, here we provided evidence of the causal mechanisms selectively related to NFB training with real feedback, highlighting the importance of localized changes in information processing, which were not observed when participants received sham feedback. An interesting perspective would be to explore different NFB protocols targeting brain areas unrelated to the SMN to assess whether the connectivity changes found here are shared among different training protocols. It is worth noting that we assessed the resting-state sessions acquired immediately after the NFB training. Future studies should investigate whether these effects remain more than only a few minutes after training. Also, given its capability to predict behavioral outcomes , EC measIn this secondary analysis of a previous study , we corr


The growing use of blends of low- and high-calcium solid precursors in combination with different alkaline activators requires simple, efficient, and accurate experimental means to characterize their behavior, particularly during the liquid-to-solid transition (setting) at early material ages. This research investigates slag-fly ash systems mixed at different solution-to-binder (s/b) ratios with sodium silicate/sodium hydroxide-based activator solutions of varying concentrations. Therefore, continuous non-destructive tests—namely ultrasonic pulse velocity (UPV) measurements and isothermal calorimetry tests—are combined with classical slump flow, Vicat, and uniaxial compressive strength tests. The experimental results highlight that high alkali and silica contents and a low s/b ratio benefit the early-age hydration, lead to a faster setting, and improve the early-age strength. The loss of workability, determined from the time when the slump flow becomes negligible, correlates well with ultrasonic P-wave velocity evolutions. This is, however, not the case for Vicat or calorimetry tests.  On the other hand, for a power-law fit; to determine the mechanical percolation threshold (MPTpf), pf) to occur at the reaction degree of In contrast, previous studies on OPC-based concrete with s/b = 0.5, identified reaction degrees of 0.075–0.13 for initial and final setting, respectively ,82. ThesFurthermore, all samples achieved a considerable compressive strength before the dormant period. We conclude that setting occurred in the pre-dormant stage. Interestingly, these results are in contrast to findings of Bernal et.al  and YangHerein, the setting times, as identified by means of the different tests, are compared in detail  measurements for all slag-fly ash binders revealed four characteristic stages of velocity evolutions, clearly sensitive to the alkali and silica dosages as well as to the s/b ratio.The UPV-derived initial setting times showed good agreement with the slump flow measurements for all the studied mixes. Interestingly, the classical Vicat tests were accurate for mixes with low s/b ratios of 0.5 or smaller; for increasing s/b ratios, Vicat initial setting times occurred significantly later than UPV-derived initial setting times, and then the slump loss.P-wave velocities at the identified  setting times are very different from one mix to another. Thus, P-wave velocity thresholds cannot be used for setting quantification of the studied AASF mixes.Heat flow evolutions obtained from isothermal calorimetry could not be used to determine setting times, as the characteristic minimum and maximum heat flow occurred at later ages .For mixes with the same alkali and silica content, the compressive strength decreased with an increasing solution-to-binder (s/b) ratio. This trend was expected, as a lower s/b ratio facilitates the closer packing of precursor particles, fewer pores, and, thus, a stronger microstructure. The compressive strength increased progressively with increasing calorimetry-derived reaction degrees. The correlation could be fitted reasonably well using a power-law function, but extrapolation attempts to predict the setting were not accurate.This study focused on investigating the capability of five different destructive and non-destructive measurements on the determination of the setting behavior of alkali-activated slag-fly ash binders, prepared with various amounts of alkali, silica, and s/b ratio. The main conclusions drawn from this study are as follows:As a perspective for characterizing the setting and hardening behavior of the mixes, a Temperature Stress Testing Machine (TSTM) can be used. This allows for the continuous monitoring of the evolution of elastic stiffness since the earliest age ,86. ThisThis study is limited to slag-fly ash paste activated by sodium hydroxide and sodium silicate at a curing temperature of 20 °C.