Search

MotivationT-cell receptors (TCR) on T cells recognize and bind to epitopes presented by the major histocompatibility complex (MHC) in case of an infection or cancer. However, the high diversity of TCRs, as well as their unique and complex binding mechanisms underlying epitope recognition, make it difficult to predict the binding between TCR and epitope. Here, we present the utility of transformers, a deep learning strategy that incorporates an attention mechanism that learns the informative features, and show that these models pretrained on a large set of protein sequences outperform current strategies.MethodWe compared three pre-trained auto-encoder transformer models (ProtBERT, ProtAlbert, ProtElectra) and one pre-trained auto-regressive transformer model (ProtXLNet) to predict the binding specificity of TCRs to 25 epitopes from the VDJdb database (human and murine). Two additional modifications were performed to incorporate gene usage of the TCRs in the four transformer models.ResultsOf all 12 transformer implementations (4 models with 3 different modifications), a modified version of the ProtXLNet model could predict TCR-epitope pairs with the highest accuracy (weighted F1 score 0.55 simultaneously considering all 25 epitopes). The modification included additional features representing the gene names for the TCRs. We also showed that the basic implementation of transformers outperformed the previously available methods, i.e. TCRGP, TCRDist and DeepTCR, developed for the same biological problem, especially for the hard-to-classify labels.ConclusionWe show that the proficiency of transformers in attention learning can indeed be made operational in a complex biological setting like TCR binding prediction. Further ingenuity in utilizing the full potential of transformers, either through attention head visualization or introducing additional features, can further extend T-cell research avenues.AvailabilityData and code are available onhttps://github.com/InduKhatri/tcrformer

The IMGT database profiles theTRgermline alleles for all fourTRloci (TRA,TRB,TRGandTRD), however, it does not comprise of the information regarding population specificity and allelic frequencies of these germline alleles. The specificity of allelic variants to different human populations can, however, be a rich source of information when studying the genetic basis of population-specific immune responses in disease and in vaccination. Therefore, we meticulously identified true germline alleles enriched with completeTRallele sequences and their frequencies across 26 different human populations, profiled by “1000 Genomes data”. We identified 205TRAV, 249TRBV, 16TRGVand 5TRDVgermline alleles supported by at least four haplotypes. The diversity of germline allelic variants in theTRloci is the highest in Africans, while the majority of the Non-African alleles are specific to the Asian populations, suggesting a diverse profile ofTRgermline alleles in different human populations. Interestingly, the alleles in the IMGT database are frequent and common across all five super-populations. We believe that this new set of germlineTRsequences represents a valuable new resource which we have made available through the new population-matchedTR(pmTR) database, accessible viahttps://pmtrig.lumc.nl/.

To mount an adequate immune response against pathogens, stepwise mutation and selection processes are crucial functions of the adaptive immune system. To better characterize a successful vaccination response, we performed longitudinal (days 0, 5, 7, 10, and 14 after Boostrix vaccination) analysis of the single-cell transcriptome as well as the B-cell receptor (BCR) repertoire (scBCR-rep) in plasma cells of an immunized donor and compared it with baseline B-cell characteristics as well as flow cytometry findings. Based on the flow cytometry knowledge and literature findings, we discriminated individual B-cell subsets in the transcriptomics data and traced over-time maturation of plasmablasts/plasma cells (PB/PCs) and identified the pathways associated with the plasma cell maturation. We observed that the repertoire in PB/PCs differed from the baseline B-cell repertoire e.g., regarding expansion of unique clones in post-vaccination visits, high usage of IGHG1 in expanded clones, increased class-switching events post-vaccination represented by clonotypes spanning multiple IGHC classes and positive selection of CDR3 sequences over time. Importantly, the Variable gene family-based clustering of BCRs represented a similar measure as the gene-based clustering, but certainly improved the clustering of BCRs, as BCRs from duplicated Variable gene families could be clustered together. Finally, we developed a query tool to dissect the immune response to the components of the Boostrix vaccine. Using this tool, we could identify the BCRs related to anti-tetanus and anti-pertussis toxoid BCRs. Collectively, we developed a bioinformatic workflow which allows description of the key features of an ongoing (longitudinal) immune response, such as activation of PB/PCs, Ig class switching, somatic hypermutation, and clonal expansion, all of which are hallmarks of antigen exposure, followed by mutation & selection processes.

Adaptation of the immune system to mount an adequate immune response against pathogens is a crucial function of the adaptive immune system. To better characterize a successful vaccination response, we performed longitudinal (days 0, 5, 7, 10, and 14 after Boostrix vaccination) analysis of the single cell transcriptome as well as the B-cell receptor (BCR) repertoire (scBCR-rep) in plasma cells of an immunized donor and compared it with baseline B cell characteristics as well as flow cytometry findings.Based on the flow cytometry knowledge and literature findings, we discriminated individual B cell subsets in the transcriptomics data and traced over-time maturation of plasmablasts/plasma cells (PB/PCs) and identified the pathways associated with the plasma cell maturation. We observed that the repertoire in PB/PCs differed from the baseline B cell repertoire e.g. regarding expansion of unique clones in post-vaccination visits, high usage of IGHG1 in expanded clones, increased class switching events post-vaccination represented by clonotypes spanning multiple IGHC classes and positive selection of CDR3 sequences over time. Importantly, the Variable gene family-based clustering of BCRs represented a similar measure as the gene-based clustering, however, certainly improved the clustering of BCRs, as BCRs from duplicated Variable gene families could be clustered together. Finally, we developed a query tool to dissect the immune response to the components of Boostrix vaccine. Using this tool, we could identify the BCRs related to anti-tetanus and anti-pertussis toxoid.Collectively, we developed a workflow which allows description of key features of an ongoing immune response, such as activation of PB/PCs, Ig class switching, somatic hypermutation, and clonal expansion, all of which are hallmarks of antigen exposure.

Immunoglobulin (IG) loci harbor inter-individual allelic variants in many different germline IG variable, diversity and joining genes of the IG heavy (IGH), kappa (IGK) and lambda (IGL) loci, which together form the genetic basis of the highly diverse antigen-specific B-cell receptors. These allelic variants can be shared between or be specific to human populations. The current immunogenetics resources gather the germline alleles, however, lack the population specificity of the alleles which poses limitations for disease-association studies related to immune responses in different human populations. Therefore, we systematically identified germline alleles from 26 different human populations around the world, profiled by “1000 Genomes” data. We identified 409 IGHV, 179 IGKV, and 199 IGLV germline alleles supported by at least seven haplotypes. The diversity of germline alleles is the highest in Africans. Remarkably, the variants in the identified novel alleles show strikingly conserved patterns, the same as found in other IG databases, suggesting over-time evolutionary selection processes. We could relate the genetic variants to population-specific immune responses, e.g. IGHV1-69 for flu in Africans. The population matched IG (pmIG) resource will enhance our understanding of the SHM-related B-cell receptor selection processes in (infectious) diseases and vaccination within and between different human populations.

Pancreatic ductal adenocarcinoma (PDAC) is a lethal malignancy with a 5-year survival rate of

T-cell receptor (TR) germline allele sequences are arranged, organized and made available to the research community by the IMGT database. This state-of-the-art database, however, does not provide information regarding population specificity and allelic frequencies of the four human TR loci (TRA, TRB, TRG and TRD). The specificity of allelic variants to different human populations can, however, be a rich source of information when studying the genetic basis of population-specific immune responses in disease and in vaccination. To make TR germline alleles available for such population-specific studies, we meticulously identified true germline alleles enriched with complete TR allele sequences and their frequencies across 26 different human populations, profiled by “1,000 Genomes data”. We identified 205 TRAV, 249 TRBV, 16 TRGV and 5 TRDV germline alleles supported by at least four haplotypes (= minimum of two unrelated individuals). The diversity of germline allelic variants in the TR loci is highest in Africans, while the majority of the Non-African alleles are specific to the Asian populations, suggesting a diverse profile of TR germline alleles in different human populations. Interestingly, the alleles known in the IMGT database are frequent and common across all five super-populations. We believe that this new set of genuine germline TR sequences represents a valuable new resource which we have made available through the new population-matched TR (pmTR) database, accessible via https://pmtrig.lumc.nl/.

T-cell receptor (TR) germline alleles are arranged, organized and made available to the research community by the IMGT database. This state-of-the-art database, however, does not provide information regarding population specificity and allelic frequencies of the genes all four humanTRloci (TRA, TRB, TRGandTRD). The specificity of allelic variants to different human populations can, however, be a rich source of information when studying the genetic basis of population-specific immune responses in vaccination and disease. To makeTRgermline alleles available for such population-specific studies, we meticulously identified true germline alleles enriched with completeTRallele sequences and their frequencies across 26 different human populations, profiled by “1,000 Genomes data”. We identified 205TRAV,249TRBV,16TRGVand 5TRDVgermline alleles supported by at least four haplotypes (= minimum of two individuals). The diversity of germline allelic variants in theTRloci is highest in Africans followed by Non-African populations. A majority of the Non-African alleles are specific to the Asian populations, suggesting a diverse profile ofTRgermline alleles in different human populations. Interestingly, the alleles known in the IMGT database are frequent and common across all the superpopulations. We believe that this new set of genuine germlineTRsequences represents a valuable new resource which we have made available through the new population-matchedTR(pmTR) database, accessible viahttps://pmtrig.lumc.nl/.

Pancreatic ductal adenocarcinoma (PDAC) is generally incurable due to the late diagnosis and absence of markers that are concordant with expression in several sample sources (i.e., tissue, blood, plasma) and platforms (i.e., Microarray, sequencing). We optimized meta-analysis of 19 PDAC (tissue and blood) transcriptome studies from multiple platforms. The key biomarkers for PDAC diagnosis with secretory potential were identified and validated in different cohorts. Machine learning approach i.e., support vector machine supported by leave-one-out cross-validation was used to build and test the classifier. We identified a 9-gene panel (IFI27, ITGB5, CTSD, EFNA4, GGH, PLBD1, HTATIP2, IL1R2, CTSA) that achieved ∼0.92 average sensitivity and ∼0.90 average specificity in distinguishing PDAC from healthy samples in five training sets using cross-validation. These markers were also validated in proteomics and single-cell transcriptomics studies suggesting their prognostic role in the diagnosis of PDAC. Our 9-gene classifier can not only clearly discriminate between better and poor survivors but can also precisely discriminate PDAC from chronic pancreatitis (AUC = 0.95), early stages of progression [Stage I and II (AUC = 0.82), IPMA and IPMN (AUC = 1), and IPMC (AUC = 0.81)]. The 9-gene marker outperformed the previously known markers in blood studies particularly (AUC = 0.84). The discrimination of PDAC from early precursor lesions in non-malignant tissue (AUC > 0.81) and peripheral blood (AUC > 0.80) may assist in an early diagnosis of PDAC in blood samples and thus will also facilitate risk stratification upon validation in clinical trials.

Pancreatic ductal adenocarcinoma (PDAC) is largely incurable due to late diagnosis and absence of markers that are concordant with expression in several sample sources (i.e. tissue, blood, plasma) and platform (i.e. Microarray, sequencing). We optimized meta-analysis of 19 PDAC (tissue and blood) transcriptome studies from multiple platforms. The key biomarkers for PDAC diagnosis with secretory potential were identified and validated in different cohorts. Machine learning approach i.e. support vector machine supported by leave-one-out cross-validation was used to build and test the classifier. We identified a 9-gene panel (IFI27, ITGB5, CTSD, EFNA4, GGH, PLBD1, HTATIP2, IL1R2, CTSA) that achieved ∼0.92 average sensitivity and ∼0.90 specificity in discriminating PDAC from non-tumor samples in five training-sets on cross-validation. This classifier accurately discriminated PDAC from chronic-pancreatitis (AUC=0.95), early stages of progression (Stage I and II (AUC=0.82), IPMA and IPMN (AUC=1), IPMC (AUC=0.81)). The 9-gene marker outperformed the previously known markers in blood studies particularly (AUC=0.84). The discrimination of PDAC from early precursor lesions in non-malignant tissue (AUC>0.81) and peripheral blood (AUC>0.80) may facilitate early blood-diagnosis and risk stratification upon validation in prospective clinical-trials. Furthermore, the validation of these markers in proteomics and single-cell transcriptomics studies suggest their prognostic role in the diagnosis of PDAC.

We report an extensive and elaborate theoretical study of atomic data for Cs XXV by using multi-configuration Dirac–Fock method and calculated energy levels for the lowest 110 fine structure levels. We have presented the radiative data for electric and magnetic dipole (E1, M1) and quadrupole (E2, M2) transitions among lowest 110 levels. We have made comparisons of our calculated excitation energies with theoretically calculated and experimentally observed energy levels. We have studied the effect of correlation by introducing more configurations in our calculations. We have also computed energy levels by performing similar relativistic distorted wave calculations using Flexible Atomic Code. Additionally, we have also provided new atomic data for Cs XXV and identified extreme ultraviolet and soft X-ray spectral lines with gA spectra for E1 and M2 transitions, which are not published elsewhere in the literature. We believe that our results may be beneficial in fusion plasma research and applications.

Small RNA (sRNA)-mediated regulation of gene expression is a major tool to understand bacterial responses to environmental changes. In particular, pathogenic bacteria employ sRNAs to adapt to the host environment and establish infection. Members of the Burkholderia cepacia complex, normally present in soil microbiota, cause nosocomial lung infection especially in hospitalized cystic fibrosis patients. We sequenced the draft genome of Burkholderia cenocepacia KC-01, isolated from the coastal saline soil, and identified several potential sRNAs in silico. Expression of seven small RNAs (Bc_KC_sr1-7) was subsequently confirmed. Two sRNAs (Bc_KC_sr1 and Bc_KC_sr2) were upregulated in response to iron depletion by 2,2'-bipyridyl and another two (Bc_KC_sr3 and Bc_KC_sr4) responded to the presence of 60 µM H2O2 in the culture media. Bc_Kc_sr5, 6 and 7 remained unchanged under these conditions. Expression of Bc_KC_sr2, 3 and 4 also altered with a change in temperature and incubation time. A search in the Rfam and BSRD databases identified Bc_Kc_sr4 as candidate738 in B. pseudomallei D286 and assigned Bc_Kc_sr5 and 6 as tmRNA and 6S RNA, respectively. The novel sRNAs were conserved in Burkholderiaceae but did not have any homologue in other genera. Bc_KC_sr1 and 4 were transcribed independently while the rest were part of the 3' UTR of their upstream genes. TargetRNA2 predicted that these sRNAs could target a host of cellular messages with very high stringency. Intriguingly, regions surrounding the translation initiation site for several enzymes involved in Fe-S cluster and siderophore biosynthesis, ROS homeostasis, porins, transcription and translation regulators, were among the suggested putative binding sites for these sRNAs.

Collision strengths for all 1326 transitions among lowest 52 fine-structure levels of Br XXVII have been computed using Dirac atomic R-matrix code (DARC). Resonances in the threshold region have been completely resolved and the contributions of these resonances to allowed and forbidden transitions have been presented. The partial collision strength for each angular momentum has been studied graphically. Effective collision strengths have also been determined within the temperature range for all 1326 transitions among the lowest 52 levels. Target state energies of the lowest 52 fine-structure levels have been computed from the multi-configuration Dirac–Fock method (MCDF). Additionally, similar calculations with the relativistic distorted wave method and flexible atomic code (FAC) have also been performed to check the accuracy of our results. The present work represents a new and significant work with improvement in the field. We believe that our presented data of collision and effective collision strengths may be useful in the future for benchmark calculations and for plasma diagnostics.

In this study, photoionization (PI) of nickel ions Ni XII is performed using the relativistic close-coupling Breit–Pauli R-matrix method. The calculation includes 47 lowest target states of Ni XIII, which have been evaluated using a sophisticated configuration interaction technique. Results for the PI of the ground state 3s23p5(2[Formula: see text]) and first excited state 3s23p5(2[Formula: see text]) are provided. The PI of this Cl-like ion is characterized by multiple Rydberg series of autoionizing resonances converging to various ionic states of Ni XIII. Our background cross section agrees well with that obtained using the Los Almos National Laboratory PI code. The resonance energy levels and widths of the Rydberg series are presented using the Quigley and Berrington approach. We believe that our results will be useful for astrophysical plasma research.

In the present paper, we have calculated fine-structure energy levels related to the configurations 1s2s, 1s2p, 1s3s and 1s3p by employing GRASP2K code. We have also computed radiative data for transitions from 1s2p 1 P 1 o , 1s2p 3 P 2 o , 1s2p 3 P 1 o and 1s2s 3S1 to the ground state 1s2. We have made comparisons of our presented energy levels and transition wavelengths with available results compiled by NIST and good agreement is achieved. We have also provided X-ray diffraction (XRD) patterns of Kα spectral lines, namely w, x, y and z of Cu XXVIII, Kr XXXV and Mo with diffraction angle and maximum diffracted intensity which is not published elsewhere in the literature. We believe that our presented results may be beneficial in determination of the order parameter, X-ray crystallography, solid-state drug analysis, forensic science, geological and medical applications.

Fission yeast have adapted to retrotransposon invasion by RNAi-mediated silencing, which has coevolved into a mechanism involving CENPB-mediated heterochromatinization together with ablation of RNAi components via accumulation of recombinogenic repeats in recently diverged species of Schizosaccharomyces. Similar trends are seen in the metazoans., Inactivation of retrotransposons is accompanied by the emergence of centromere-binding protein-B (CENPB) in Schizosaccharomyces, as well as in metazoans. The RNA interference (RNAi)-induced transcriptional silencing (RITS) complex, comprising chromodomain protein-1 (Chp1), Tas3 (protein with unknown function), and Argonaute (Ago1), plays an important role in RNAi-mediated heterochromatinization. We find that whereas the Ago1 subunit of the RITS complex is highly conserved, Tas3 is lost and Chp1 is truncated in Schizosaccharomyces cryophilus and Schizosaccharomyces octosporus. We show that truncated Chp1 loses the property of heterochromatin localization and silencing when transformed in Schizosaccharomyces pombe. Furthermore, multiple copies of CENPB, related to Tc1/mariner and Tc5 transposons, occur in all Schizosaccharomyces species, as well as in humans, but with loss of transposase function (except Schizosaccharomyces japonicus). We propose that acquisition of Tc1/mariner and Tc5 elements by horizontal transfer in S. pombe (and humans) is accompanied by alteration of their function from a transposase/endonuclease to a heterochromatin protein, designed to suppress transposon expression and recombination. The resulting redundancy of RITS may have eased the selection pressure, resulting in progressive loss or truncation of tas3 and chp1 genes in S. octosporus and S. cryophilus and triggered similar evolutionary dynamics in the metazoan orthologues.

Collision strengths for the lowest 52 fine-structure levels of Ba XLVIII have been computed using Dirac atomic R-matrix code (DARC). Resonances in the threshold region have been completely resolved and the contributions of these resonances to allowed and forbidden transitions have been presented. Effective collision strengths have also been determined within a temperature range from the ground state. Collision strengths from ground state have also been computed with the relativistic distorted wave method, the flexible atomic code (FAC) was used for checking the accuracy of our results. The present work represents a new and significant work with improvement in the field. We believe that our presented data of collision and effective collision strengths may be useful in the future for benchmark calculations and for plasma diagnostics.

Resonance energies and total natural width of the 3pns 1P° and 3pnd 1P° Rydberg series of Mg-like (Z=13–26) ions are reported. Resonance energies of the Mg-like Al+ belonging to the 3pns 3P°→ 2 p 3 6 p P 1 / 2 0 2 and 3pns 3P°→ 2 p 3 6 p P 3 / 2 0 2 transitions are also tabulated. The calculations are made in the framework of the Screening constant by unit nuclear charge (SCUNC) formalism. Excellent agreements between experiments at ALS and R-matrix calculations are obtained for both 3pns 1,3P° and 3pnd 1P° Rydberg series of the Mg-like Al+ ions. The present results for Mg-like Si2+, S4+, Cl5+, and Ar6+, compared with the only existing R-matrix calculations indicate lack of accuracy in the Mg-like Si2+ data obtained from noniterative formulation of the eigenchannel R-matrix method. New precise data for Mg-like P3+, K7+, Ca8+, Sc9+, Ti10+, V11+, Cr12+, Mn13+, and Fe14+ ions are presented as useful guidelines for investigators focusing their challenge on the Photoionization of Mg-like heavy charged ions in connection with their application in laboratory, astrophysics, and plasma physics.

The recent upswing of microfluidics and combinatorial indexing strategies, further enhanced by very low sequencing costs, have turned single cell sequencing into an empowering technology; analyzing thousands—or even millions—of cells per experimental run is becoming a routine assignment in laboratories worldwide. As a consequence, we are witnessing a data revolution in single cell biology. Although some issues are similar in spirit to those experienced in bulk sequencing, many of the emerging data science problems are unique to single cell analysis; together, they give rise to the new realm of 'Single Cell Data Science'. Here, we outline twelve challenges that will be central in bringing this new field forward. For each challenge, the current state of the art in terms of prior work is reviewed, and open problems are formulated, with an emphasis on the research goals that motivate them. This compendium is meant to serve as a guideline for established researchers, newcomers and students alike, highlighting interesting and rewarding problems in 'Single Cell Data Science' for the coming years.

Paracetamol (PCM) is a frequently and most commonly used analgesic and antipyretic drug, globally in pharmaceutical formulations. Dispersive liquid–liquid microextraction (DLLME) in combination with smartphone based digital image colorimetry (DIC) is developed for quantitative determination of paracetamol (PCM) in pharmaceutical formulations (tablets and syrups) and human urine samples. The developed method (DDLME-DIC) is a novel approach which is simple to use, cost-effective, quick to perform, environment friendly and high sample throughput assay technique. Multivariate optimization comprising of Plackett-Burman design (PBD) for screening of significant experimental factors and central composite design (CCD) for optimization of these factors was utilized. Under the optimized conditions, the limit of detection (LOD) and limit of quantification (LOQ) were found to be 8.4 and 28.1 µg mL−1. The calibration graph was found linear in the range of 30–300 µg mL−1 with coefficient of determination (R2) in the range of 0.990–0.996 and relative standard deviations lower than 10%. The proposed method was efficiently applied for determination of PCM in pharmaceutical formulations and human urine samples with percent recoveries in the range of 93.14–99.7%. In conclusion, the proposed method have wide range of applications and can be easily used in routine analysis of PCM in pharmaceutical industry for quality control as well as analysis of human urine samples in forensic and clinical laboratories for medico-legal and therapeutic monitoring purposes, respectively.

In the present work, we provide a most extensive and detailed study of highly ionized Cu-like ions and diagnose extreme ultraviolet (EUV) and soft X-ray (SXR) transitions with N-shell electron excitation to M-shell and higher shells. We have determined energy levels and lifetimes for lowest 27 fine-structure levels by adopting multiconfiguration Dirac–Fock (MCDF) with the inclusion of quantum electrodynamics (QED) as well as Breit corrections as a first-order perturbation theory. We have also reported complete radiative data for strong electric dipole transitions within lowest 27 levels. We have compared our calculated results with theoretically calculated and experimentally measured results available in the literature, to measure the credibility and genuineness of our results, and achieve good agreement. Further, because of insufficiency of adequate and complete atomic data for higher levels of highly ionized Cu-like ions in the literature, we have performed other equivalent parallel calculations by implementing fully relativistic distorted wave flexible atomic code (FAC) to ensure the accuracy of our results. Additionally, we have also presented transition wavelengths of Nα transitions of high-Z Cu-like ions by using Moseley’s law. We believe that the large amount of atomic data presented in this paper may be useful in fusion and astrophysical plasma and in several applications, especially in lithography and cell biology.