Persons with disability may have a higher HIV prevalence and be less likely than persons without disability to know their HIV-positive status, access antiretroviral therapy (ART), and suppress their HIV viral load (HIV care cascade). However, studies examining differences between persons with and without disability in HIV prevalence and the HIV care cascade are lacking. Using the Tanzania HIV Impact Survey (THIS) data collected between October 2016 and August 2017, we assessed differences in HIV prevalence and progress towards achieving the 2020 HIV care cascade target between persons with and without disability.Using the Washington Group Short Set (WG-SS) Questions on Disability, we defined disability as having a functional difficulty in any of the six life domains . We classified respondents as disabled if they responded having either “Some Difficulty”, “A lot of difficulties” or “Unable to” in any of the WG-SS Questions. We presented the sample characteristics by disability status and analyzed the achievement of the cascade target by disability status, and sex. We used multivariable logistic regressions, and adjusted for age, sex, rural-urban residence, education, and wealth quintile.A total of 31,579 respondents aged 15 years and older had HIV test results. Of these 1,831 tested HIV-positive, corresponding to an estimated HIV prevalence of 4.9% (CI: 4.5 — 5.2%) among the adult population in Tanzania. The median age of respondents who tested HIV-positive was 32 years (with IQR of 21—45 years). HIV prevalence was higher  among persons with disability than persons without disability . Before adjustment, compared to women without disability, more women with disability were aware of their HIV-positive status  and accessed ART more frequently . After adjusting for socio-demographic characteristics, the odds of having HIV and of accessing ART did not differ between persons with and without disability. However, PLHIV with disability had higher odds of being aware of their HIV-positive status  than PLHIV without disability. Men living with HIV and with disability had lower odds  to suppress HIV viral loads than their counterparts without disability.We found no significant differences in the odds of having HIV and of accessing ART between persons with and without disability in Tanzania. While PLHIV and disability, were often aware of their HIV-positive status than their non-disabled counterparts, men living with HIV and with disability may have been disadvantaged in having suppressed HIV viral loads. These differences are correctable with disability-inclusive HIV programming. HIV surveys around the world should include questions on disability to measure potential differences in HIV prevalence and in attaining the 2025 HIV care cascade target between persons with and without disability.The online version contains supplementary material available at 10.1186/s12889-023-17013-8. Persons with disability are disadvantaged in several domains of life –6. GlobaStudies show that persons with disability, especially women, are disproportionally affected by HIV infection while being less likely to access HIV prevention and treatment services than persons without disabilities , 11–17. The THIS 2016–2017 is a nationally representative, cross-sectional survey of households across Tanzania. The Government of Tanzania led the data collection and analysis of the survey data through the Tanzania Commission for AIDS (TACAIDS) and Zanzibar AIDS Commission (ZAC), and several government agencies with funding from the U.S. President’s Emergency Plan for AIDS Relief (PEPFAR) and technical assistance through the U.S. Centers for Disease Control and Prevention (CDC) and ICAP at Columbia University. Before data collection, the THIS protocol was reviewed and approved by the institutional review boards of CDC, Columbia University, Westat, the National Institute for Medical Research, and the Zanzibar Medical Research and Ethics Committee .THIS collected a range of health and socio-demographic data to evaluate the impact of HIV programs funded by PEPFAR in Tanzania. After field workers obtained informed consent, they administered a household questionnaire to the head-of-household in participating households, who provided information on the household relating to wealth and other socio-demographic characteristics. Then, the individual questionnaire was administered to eligible and consenting household members The survey also included biomarkers for example confirming HIV-positive status, access to ART and HIV viral load suppression with laboratory confirmed tests according to national testing algorithms. Each interview was administered using tools that have been validated and internationally used in PEPFAR supported countries, described in the THIS report . The perhttps://phia-data.icap.columbia.edu/files [≥ 15 years of age with a confirmed HIV-positive status.THIS comprised the household, adult, adolescent, and biomarker datasets derived from respective questionnaires. Questionnaire and biomarker data were collected on mobile tablet computers using an Open Data Kit software application. The questionnaires were prepared in English, translated into Kiswahili, and translated back into English by a different person to assess the accuracy of the translation . We useddu/files We estimaThe primary outcomes were HIV prevalence (the percentage of people who had tested HIV-positive), the percentage of PLHIV who were aware of their HIV-positive status, percentage of PLHIV aware of their HIV-positive status who had accessed ART, and percentage of PLHIV on ART who had suppressed HIV viral load. The primary predictor was disability as measured by the Washington Group Short Set Questions (WG-SS) on disability. The WG-SS has been extensively tested and validated in all regions of the world  and it hWe used the variables provided in THIS data set and described in the THIS 2016–2017 adult, biomarker and household codebooks, coding HIV status negative or positive, undefined HIV status as missing and excluded from the analysis. Awareness of HIV-positive status was defined as having both a laboratory confirmed HIV-positive test result and self-reporting HIV result positive. Accessing ART reflected both self-reported using ART and laboratory-confirmed ART use. HIV RNA viral load less than 1,000 copies per ml of plasma denoted viral load suppression. Sex was self-reported, i.e., male or female. Age was self-reported and categorized 15–24, 25–34, 35–44, 45–54, and 55+. Rural-urban residency reflected self-reported residence type. From the education variable, we categorized education as no education, primary education and secondary and higher. We combined pre-primary and primary education into primary education, and post-primary education, secondary and higher education, and university into secondary and higher education due to lack of sufficient observations in each category for analysis . The weaWe included respondents who had a confirmed HIV test result (negative or positive) for assessing HIV prevalence. For HIV care cascade, we included adults who had an HIV-positive test result, then among these, adults who were aware of their HIV-positive status. Among adults who were aware of their HIV-positive status we captured adults who were accessing ART and among those who accessed ART, those suppressing their HIV viral load.We analyzed differences between persons with and without disability in attaining the 90—90—90 target in three steps. In step one, we presented the estimates of sample characteristics, and their 95% CI, based on disability status. In step two, we examined the differences in the 90-90-90 target by sex and tested for differences using the Pearson Chi-squared design-based test. In step three, we examined the differences between persons with and without disability using multivariable logistic regressions in the HIV care cascade, adjusting for age, sex, rural-urban-residence, education, and wealth quintile. Because several studies have examined the factors associated with the HIV care cascade, we present only the odds ratios for the disability variable of the unadjusted and adjusted models , 29, 24.A total of 31,579 respondents aged 15 years and older had HIV test results. Of these 1,831  were HIV-positive. Of the 1,831 PLHIV, 1101  were aware of their HIV-positive status, of whom 1026  accessed ART, and 893  of the 1026 had suppressed HIV viral load  of PLHIV had a disability. The most common functional difficulties were related to seeing, walking, and hearing—difficulties in communicating or self-care were least reported  than respondents without disability . Awareness of HIV-positive status and access to ART were higher among PLHIV with disability than PLHIV without disability  (THIS 2016 - 2017). Supplementary Table B1: The odds of having HIV, awareness of HIV-positive status, access to ART and HIV viral load suppression among persons with and without disability, females  (THIS 2016 - 2017). Supplementary Table C1. The odds of having HIV, awareness of HIV-positive status, access to ART and HIV viral load suppression among persons with and without disability, males  (THIS 2016 - 2017). Supplementary Table 2. Distribution of difficulties by type and degree, percent, 95% Confidence Interval, sample size (N=1831)


In this review, we explore the diverse modifications occurring in RiPPs and discuss the potential applications and feasibility of combining various modification enzymes for lanthipeptide engineering. We highlight the prospect of lanthipeptide- and RiPP-engineering to produce and screen novel peptides, including mimics of potent non-ribosomally produced antimicrobial peptides (NRPs) such as daptomycin, vancomycin, and teixobactin, which offer high therapeutic potential.Natural bioactive peptide discovery is a challenging and time-consuming process. However, advances in synthetic biology are providing promising new avenues in peptide engineering that allow for the design and production of a large variety of new-to-nature peptides with enhanced or new bioactivities, using known peptides as templates. Lanthipeptides are ribosomally synthesized and post-translationally modified peptides (RiPPs). The modularity of post-translational modification (PTM) enzymes and ribosomal biosynthesis inherent to lanthipeptides enables their engineering and screening in a high-throughput manner. The field of RiPPs research is rapidly evolving, with many novel PTMs and their associated modification enzymes being identified and characterized. The modularity presented by these diverse and promiscuous modification enzymes has made them promising tools for further  Potent non-ribosomal antimicrobial peptides can be mimicked by in vivo combining various posttranslational modifications of gene-encoded lanthipeptides, allowing high throughput screening of large mutant libraries. Lactococcus lactis, which produces the paradigm lantibiotic nisin , produced by a large variety of microorganisms   represent another class of peptide secondary metabolites Fig. , with enHowever, the construction of biosynthetic assembly lines for the sustainable production of novel lanthipeptides with diverse structures is limited by a number of non-negligible factors. The substrate specificity of the enzymes that generate the tailoring PTM is one of them, as exemplified by NisBC and the Lan/MeLan ring-installation enzymes involved in nisin biosynthesis. The dehydration activity of Ser/Thr residues of dehydratase NisB depends in part on the amino acids flanking the dehydratable; Ser/Thr residues, e.g. a negatively charged Asp residue preceding a serine disfavours its dehydration by wild-type NisB  and/or methyllanthionine (MeLan) rings are characteristic structural features of lanthipeptides. The formation of Lan/MeLan motifs in class I lanthipeptides is catalysed by two different enzymes, a lanthipeptide dehydratase (LanB) and a lanthipeptide cyclase (LanC), which are exemplified by NisB and NisC from the nisin biosynthetic gene cluster. NisBC enzymes are well established for their use in lanthipeptide engineering, as has been shown in many examples. This includes the installation of a lanthionine ring in vasopressin   macrocyclizations are unique structures found in many lanthipeptides (e.g. in epidermin and mersacidin) and also exist in other RiPPs families, including linaridins (e.g. cypemycin), lipolanthines (e.g. microvionine), and thioamitides (e.g. thioviridamide) , cyanobactins from cyanobacteria (e.g. patellamides and trunkamides), plant-derived cyclotides , orbitides (e.g. cyclolinopeptide A), and fungal peptides   from the fusilassin pathway is capable of producing millions of sequence-diverse lasso peptides and displays a large substrate tolerance, showing great potential for synthetic biology applications  . The first identified macro-cyclized and well-characterized OEPs are the microviridins  enzymes through the construction of C–C and C–S linkages -containing peptides (LAPs)  termed LanJB  termed LanJC  in L. lactis to achieve successful incorporation of D-Ala into the lanthipeptide nisin and a linear nisin variant. Notably, LtnJA does not require a leader peptide for its activity . Peptides are methylated at a number of different sites, including, but not limited to, nitrogen-containing side chains of arginine and lysine, as well as the N-terminus and C-terminus of peptides. Linaridins are a small group within the RiPPs family. All characterized linaridins harbour a conserved A, which is encoded in a Class I lanthipeptide gene cluster from Streptomyces olivaceus NRRL B-3009, catalyses the methylation of highly conserved aspartate residues to the corresponding methyl-ester in the cyclic substrate, which is spontaneously converted to succinimide, followed by non-enzymatic hydrolysis to generate a β-amino acid, i.e. isoaspartate in the precursor peptide OlvA. In silico analysis combined with experimental results revealed that OlvSA (termed LanSA) represents a new family of O-methyltransferases distinct from protein L-isoaspartate (D-aspartate) O-methyltransferases (PIMTs). The in vivo and in vitro reconstitution of O-methyltransferase OlvSA activity not only revealed its leader peptide-independence and SAM-dependent mechanism, but also laid the groundwork for its potential development as a methyl ester-modification bioengineering tool , which also contains the unusual Class II lanthipeptide synthetases LahM1/M2. The activity of LahSB was confirmed in vivo (with an E. coli expression system) and in vitro, showing that it methylates the carboxylate of the precursor LahA peptide. Its activity is independent of the leader peptide and shows a certain tolerance to the amino acid residues it methylates, potentially serving as a methyl ester-introducing tool with high substrate tolerance for lanthipeptide engineering. Although the bioactivity of the peptide LahA is uncertain and the importance of the C-terminal methylation is unclear, modification of the C-terminus of RiPPs is probably a protective strategy against the action of carboxypeptidases , which was modified by a putative O-methyltransferase homologue (but displays low sequence similarity to the O-methyltransferase LanS in Class I lanthipeptide BGCs). Other examples are the lexapeptides, which contain an N,N-dimethyl-phenylalanine installed by the methyltransferase LxmM, i.e. homologous to the cypemycin α-N-methyltransferase, and pristinin A3, which contains N,N-dimethyl-β-methyllanthionine (NMe2-MeLan) introduced by a putative carminomycin 4-O-methyltransferase  in the precursor peptide -dependent or non-heme-iron-dependent halogenases. These enzymes install halogen atoms  into aromatic and aliphatic substrates activated for electrophilic attack, offering more substrate selectivity than haloperoxidases. Halogenated natural products have been described to occur both in NRPs and RiPPs. Halogens play a vital role in determining the biological activity of these secondary metabolites, as the removal or replacement of halogen from the NRP-antibiotics chloramphenicol and vancomycin, e.g. profoundly affected their activity and potency  gene cluster. SrpI catalyses tryptophan-6-bromination in the core peptide, and, unlike MibH, SrpI has a certain substrate tolerance and can brominate unmodified peptides, thus having good potential as a broad-spectrum peptide tryptophanyl brominase in biocatalytic applications  3-amino-acrylic acid (Aaa), although the underlying mechanism of KinCD has not been determined yet . Taking the biosynthesis of phaeornamide from Pseudophaeobacter arcticus DSM 23566 as an example, the precursor peptide PhaA is firstly modified by PhaR, which converts arginine to ornithine, followed by hydroxylation of the ornithine residue by PhaI, generating 4(S)-hydroxy-2(S)-ornithine, and subsequent acylation of (hydroxy) for fatty acid attachment, catalysed by the GNAT PhaN. These PhaRIN co-mediated modifications highlight the potential of engineering peptides with diverse, non-ribosomal-like features in a ribosomal way -dependent hydroxylase, could install the hydroxyl group at the β-carbon of the C-terminal aspartate residue on the precursor peptide, which occurs prior to macrocyclization. In addition, CanE could also modify Asn and Glu effectively but not Ala and Ser, which indicates that CanE has a certain substrate specificity, i.e. it is promising as part of a toolkit for the combinatorial biosynthesis of lasso peptides  and/or methyllanthionine (MeLan) rings. Duramycin consists of 19 amino acids and includes one Lan and two MeLans, an unusual lysinoalanine  bridge, and an erythro-3-hydroxy-L-aspartic acid. The modification of hydroxy-Asp is installed by an α-ketoglutarate/iron (II)-dependent hydroxylase, DurX Table . FurtherThe hydroxylation modification is also present in the dikaritins, like ustiloxin B  peptide structure by adding carbon chains of different lengths at various positions of the peptide, thereby endowing the peptide with increased metabolic stability, enhanced membrane interactions, better peptide bioavailability, and improved pharmacokinetic and pharmacodynamic properties  usually perform forward or reverse prenylation (5-carbon) or geranylation 10-carbon) reactions on Ser, Thr, or Tyr to produce various cyanobactins. In-depth studies performing structural, molecular dynamics, and biochemical characterization of prenyltransferases from cyanobacteria show that different enzymes have different specificities for their donors and acceptors. For instance, LynF  or thiol moieties (-HS-), which are frequently occurring in peptides  based on cell surface display of the peptide products revealed a NisB variant that showed substrate flexibility against non-natural substrates  and spr1766 (pneA2) to the nisin leader-encoding sequence. The resulting RiPP harbours multiple dehydrated serine and/or threonine residues and (methyl) lanthionines. Both modified peptides displayed antimicrobial activity against Micrococcus flavus  as potential ligands for tumour imaging.  scale. The fluorescently labelled peptide producer and sensor cells were encapsulated into nanoliter reactors (nLRs) for growth and peptide production. The nLRs with a small number of sensor cells were identified as containing potential producers of lanthipeptide variants, as shown in in vivo lanthipeptide engineering libraries is a challenging but realistic goal.The studies of high-throughput sequencing methods in other RiPP families also provide concepts that can be extended to lanthipeptides. For example, mRNA display technology compatible with Flexizyme reprogramming allows the introduction of non-natural amino acids into the RiPP antibiotic pantocin, with the capacity to screen large and diverse libraries containing multiple simultaneous mutations -2-ketoglutarate-dependent enzyme PoyI responsible for hydroxylation in polytheonamides. Further genome mining and engineering efforts are required to discover homologues of those enzymes that are better suited for combinatorial applications on a broader series of substrates. Moreover, the development of new strategies, e.g. the leader-peptide-free strategy as used for the cyclodehydratase LynD, will further boost the field of lanthipeptide engineering. The engineering studies from different research groups could also be combined to accelerate the development of molecular engineering of new-to-nature bioactive peptides with desired structural properties and biological functions, and Fig. The diverse PTM enzymes from RiPPs constitute a valuable biocatalytic toolbox that can be used for lanthipeptide and other RiPP engineering. Combinatorial application of these tools in lanthipeptide biosynthetic assembly lines allows for an expanded structural scope and enhanced or altered bioactivities of lanthipeptides, enabling the development of an invaluable resource for bioactive compounds and potential drugs. Several strategies for the combined use of these enzymatic tools have been proposed and successfully demonstrated in the above-mentioned studies. In addition, leader-independent enzymes, such as the dehydrogenase LanJ and the


Steinwender et al. analyzed deviations between the locations of different tomographic parameters and identified low consistency between different methods for describing the location of a keratoconus. This study suggested that elevation- or pachymetry-based measures  are suitable for clinical applications requiring an accurate cone center . Furthermore, Mazharian et al. suggested that a significant proportion of KCN patients are likely to remain stable if close monitoring and strict eye rubbing cessation are achieved, without the need for further intervention. Moreover, Zhao X. et al. analyzed the levels of sex hormones associated with KCN and revealed that testosterone levels were reduced in the plasma of both female and male KCN patients, whereas plasma estradiol levels were increased in only male KCN patients. Moreover, when analyzing tear samples from KCN patients, Goñi et al. found A-kinase anchor protein 13 among the deregulated proteins detected, deserving special attention for its involvement in corneal thinning and its strong overexpression in the tears of patients with more active KC and faster disease progression.Keratoconus (KCN) is a bilateral and asymmetric corneal ectatic dystrophy with onset during the second decade of life. The development of modern topographic, tomographic, and biomechanical examinations has increased the accuracy of keratoconus diagnosis, particularly in the subclinical stages of the disease. Wajnsztajn et al. studied the best outcome indicators for cross-linking in pediatric KCN . They concluded that non-accelerated treatment was more effective than accelerated treatment and that corneas with advanced disease had a greater effect on CXL . Moreover, Liu et al. demonstrated the application of a machine-learning approach using the XGBoost model, which produced predicted values closest to the actual values for both the CDVA and maximal corneal power changes in the testing (R2 = 0.9993 and 0.9888) and validation (R2 = 0.8956 and 0.8382) sets. Kobashi et al. suggested an experimental model using collagenase treatment to induce KCN by steepening the keratometric and astigmatic values. This study identified no significant difference in the observed elastic behaviors of normal and ectatic corneas under physiologically relevant stress levels. Moreover, ultraviolet irradiation did not cause the regression of corneal steepening in a collagenase-induced model during short-term observation . Mazzotta et al. introduced a novel cross-linking technique, the higher-fluence pulsed light Epi-ON accelerated crosslinking nomogram (PFPL M Epi-On ACXL), for the treatment of progressive KCN. A non-randomized interventional study showed that the pachymetry-based PFPL M Epi-On ACXL nomogram stabilized ectasia progression and higher fluence Epi-On ACXL increased CXL penetration with better functional outcomes in the absence of complications. Furthermore, the coma value improved significantly by the 6 month in all groups . Bonzano et al. investigated the demographic and corneal factors associated with the occurrence of delayed re-epithelialization after epithelium-off CXL (epi-off CXL) and concluded that the association between delayed re-epithelialization and age may reflect an age-related decrease in the corneal healing response without changing the efficacy of cross-linking.In recent decades, cross-linking therapy has been successfully applied to stop or arrest KCN. Zhao Z. et al. showed that voriconazole combined with cross-linking was effective in treating Aspergillus-infected keratitis. They concluded that combination therapy could effectively inhibit Aspergillus, accelerate corneal repair, and shorten the disease course .CN: Writing—original draft, Writing—review & editing. AB: Writing—original draft, Writing—review & editing.


We investigated whether aspects of subjective cognitive aging, including awareness of age-related gains and losses in cognition  and subjective cognitive decline (SCD), predict change in objective cognitive function as measured by verbal reasoning (VR) and working memory (WM).We used longitudinal data for 3,299 cognitively healthy UK residents aged 65+. We used data on AARC and SCD assessed in 2019, and cognitive tasks assessed in 2019, 2020, and 2021. We used latent growth curve modeling, latent class growth analysis, and growth mixture modeling.N = 182) mean WM at baseline was 31.20; this decreased by 2.48 points each year. In Class 2 (N = 119) mean WM at baseline was 23.12; this increased by 3.28 points each year. In Class 3  mean WM at baseline was 30.11; and it remained stable. Higher AARC-gains  and AARC-losses  in cognition predicted greater likelihood of being in Class 2 than Class 3.For VR, multiple growth trajectories were not evident. Mean VR at baseline was 37.45; this remained stable over time. Higher AARC-gains in cognition , higher AARC-losses in cognition , and lower SCD  were associated with poorer VR at baseline. A three-class growth mixture model–class varying best represented trajectories of WM. In Class 1 (Although both higher AARC-gains and AARC-losses indicate poorer concurrent cognition, higher AARC-gains may be a resource that facilitates future cognitive improvement. The global number of older people is rapidly increasing, and with that also the number of people experiencing some level of cognitive decline . The majA psychological construct that may help to detect individuals at higher risk of cognitive decline is Self-Perceptions of Aging (SPA). SPA capture individuals’ perceptions and evaluations of the changes they experience while growing older, including changes in cognitive domains. Generally, those individuals with more positive SPA show better objectively-assessed cognitive functioning in cross-sectional studies , and havTo date, many studies linking SPA and objectively-assessed cognitive functioning have relied on unidirectional measures , 16 thatSeveral constructs in the literature can be conceptualized as falling under the umbrella of SPA, including Subjective Cognitive Decline (SCD) and Awareness of Age-Related Changes (AARC). SCD, the self-experienced worsening of cognitive functioning in the absence of objectively measured cognitive impairment , is incrA concept related to, but distinct from SCD, is Awareness of Age-Related Changes (AARC) . WhereasCross-sectional studies have linked AARC to SCD and poorer objective cognitive function . SabatinSo far longitudinal evidence linking AARC to objectively-assessed cognitive functioning has relied solely on global scores for AARC-gains and AARC-losses across life domains rather than in cognition specifically. Sabatini et al. , found tWith regards to AARC-gains, evidence thus far has produced counterintuitive results. At a cross-sectional level, lower AARC-gains were associated with better performance on spatial working memory and grammatical reasoning among 50+ UK individuals . Lower AThis study aimed to investigate, in a large sample of cognitively healthy UK individuals aged 65+, whether levels of AARC-gains in cognition, AARC-losses in cognition, and SCD at baseline predict verbal reasoning (VR) and working memory (WM) scores over time. We focused on individuals aged 65+ as younger individuals are less likely to experience significant cognitive change over 2 years. We expected higher AARC-losses in cognition and greater SCD to be related to poorer VR and WM at baseline and to greater decline in VR and WM over the study period of 2 years. Given the counterintuitive direction of the previously found associations between higher AARC-gains and poorer cognitive performance, we framed the longitudinal associations of AARC-gains in cognition with VR and WM over time as exploratory.Exeter 10,000, Join Dementia Research, and Brains for Dementia Research. UK PROTECT obtained ethical approval from the London Bridge NHS Research Ethics Committee and Health Research Authority (Ref:13/LO/1578).This study used data collected online through the UK PROTECT (Platform for Research Online to investigate Genetics and Cognition in Ageing) study.In UK PROTECT, participants were invited to take part in a yearly follow-up assessment. For the scope of this study, as part of their assessment in January 2019, participants were asked to fill in additional questions assessing AARC. A total of 6,658 individuals provided data on AARC in 2019 and on cognitive performance in at least two of the assessments taking place in 2019, 2020, and 2021. Out of these individuals, 3,094 were excluded from the current study analytical sample as they were younger than 65 years and hence did not meet the current study inclusion criteria.n = 51 individuals as they reported, in the Lawton’s Instrumental Activities of Daily Living questionnaire  n = 195 individuals who obtained a score > 3.3 on the Informant Questionnaire on Cognitive Decline in the Elderly short form-informant version  ionnaire , needing version ; indicat version . TherefoAwareness of Age-Related Change (AARC) in cognition was assessed with the cognitive functioning subscale of the AARC-50 questionnaire . Scores for AARC-gains in cognition and AARC-losses in cognition are obtained by summing items that fall into the respective subscales. Subscales scores can range from five to 25; higher scores indicate higher levels of perceived cognitive gains and losses. Cronbach’s alphas in this sample were 0.87 and 0.86 for AARC-gains and AARC-losses in cognition, respectively.ionnaire , 32. ThiSubjective Cognitive Decline (SCD) was assessed with the self-reported version of the Informant Questionnaire on Cognitive Decline in the Elderly short form . The IQCognitive functioning was measured with the PROTECT Cognitive Test Battery  and working status .Descriptive statistics for study variables at baseline were reported, and estimated using STATA. We estimated bivariate associations among AARC-gains, AARC-losses, and SCD at baseline.We investigated trajectories of performance on VR and WM with two models operationalized in Mplus. First, to determine whether and how performance on VR and WM changed between 2019 and 2021 in study participants, latent growth curve modeling (LGCM) was conducted using three waves of UK PROTECT data . Each LGCM estimated the mean intercept (baseline) and the mean slope (change over time) of the variable of interest, with random effects to account for variation across individuals . We firsIn a second step, we employed latent class growth analysis (LCGA) and growth mixture modeling (GMM) to examine whether multiple growth trajectories of VR and WM existed in the sample. Different assumptions were tested. The LCGA fixes variances of the global growth factors to zero across classes, assuming trajectories within a class are homogeneous. The growth mixture model-class invariant (GMM-CI) constrains the variances of the global growth factors across classes to be equal (homogeneous classes), and the growth mixture model class-varying (GMM-CV) freely estimated all variances of the global growth factors across classes. A frequent problem with these models is non-convergence , with moGMMs allow for estimation of predictors of class membership . The catLatent trajectory model selection: Results for VR are displayed in In total, 3,299 individuals (mean age = 70.89 years) met the inclusion criteria. Descriptive statistics of study variables at baseline are presented in r = 0.12; p < 0.001) and with greater SCD , but was not significantly correlated with AARC-gains in cognition . At baseline, sex was not significantly correlated with AARC-losses in cognition , but being a woman was correlated with higher AARC-gains in cognition  and with less SCD . Greater education was correlated with lower AARC-losses in cognition , lower AARC-gains in cognition , and greater SCD . Finally, being employed was correlated with lower AARC-losses in cognition  and with less SCD , but was not significantly correlated with AARC-gains in cognition .At baseline, older age was correlated with higher AARC-losses in cognition . At baseline both AARC-gains in cognition  and AARC-losses in cognition  were significantly and negatively correlated with scores on the IQCODE-Self. Hence both higher AARC-gains and higher AARC-losses were correlated with greater SCD.At baseline AARC-gains in cognition were significantly and positively correlated to AARC-losses in cognition  scored high on WM at baseline but showed a decrease in WM over time. Another small group  scored low on WM at baseline but showed a constant improvement in WM over time and, at the end of the study, participants in this group scored higher on WM than those in Class 1. Most participants  scored almost as high as Group 1 in WM at baseline but, differently from those in Group 1, their WM remained stable over time. Those in Class 2 reported, at baseline, highest levels of AARC-gains and AARC-losses in cognition.Overall, although associations were of small size, results suggest that, among cognitively healthy older adults, both higher AARC-gains and higher AARC-losses in cognition may indicate poorer concurrent VR and WM. The association between higher AARC-losses and poorer cognition is in line with our hypothesis and previous cross-sectional evidence. Indeed, poorer VR and WM were previously found cross-sectionally related to higher AARC-losses across life domains in samples of US and Norwegian older adults . MoreovePrevious evidence suggests that the cross-sectional association between higher AARC-losses in cognition and poorer objective cognition may be due to individuals with high AARC-losses having experienced greater decline in cognition over the past decades . The facMoreover, although higher AARC-gains in cognition were reported by individuals with poorer WM at baseline, higher perceived age-related cognitive gains predicted future increase in WM among cognitively healthy older adults who initially scored low on WM tasks. It may be that, because individuals perceiving many age-related gains in cognition have internalized more positive age-related stereotypes , their pIn this study, AARC-losses emerged as a better indicator of cognition than SCD. Indeed, whereas higher AARC-losses in cognition were a useful indicator of poorer VR at the cross-sectional level, greater SCD was minimally related to better VR. Moreover, whereas higher AARC-losses were cross-sectionally related to poorer WM, SCD was not related to WM. The discrepancy in the results found for AARC and SCD likely reflects the features of the particular instrument used to operationalize SCD in this study. The IQCODE, developed for the screening and evaluation of dementia, queries participants not only about memory changes, but fairly significant functional changes  and whose presence could indicate the presence of dementia. With the inclusion of only cognitive healthy older adults in the sample, few people would be expected to endorse this degree of a cognitive change, or the type of memory change queried by the IQCODE .Other differences between the AARC and IQCODE may also have contributed to the current findings. First, whereas the AARC questionnaire provides separate scores for perceived cognitive improvement and perceived cognitive decline, the IQCODE-self positions individuals’ scores on a continuum from cognitive decline to cognitive improvement. As in this study we found that higher scores on both AARC-gains and AARC-losses were related to poorer cognition at cross-sectional level, it may be that the separate effects of perceived cognitive improvement and perceived cognitive decline cancel each other’s out in a measure  that converges perceptions of cognitive gains and losses in an overall score. Second, whereas in the IQCODE-self, individuals are asked to compare their current cognitive performance with how they performed 10 years ago, in the AARC questionnaire participants are purposefully not asked to compare their current cognitive functioning with a given timeframe. Third, the AARC questionnaire specifically asks participants to reflect on those changes they attribute to aging. Taken together, these differences across measures reinforce the importance of tailoring subjective cognitive instruments with regard to type of changes, time frame, and reference group  in order to best detect the specific type of cognitive change in question. Results of this study suggest that the AARC questionnaire is well suited to detect subtle, cross-sectional cognitive differences among cognitively healthy older adults.While AARC-losses in cognition were associated with cognitive performance at baseline, they did not predict future cognitive decline. It may therefore be that, in cognitively healthy individuals, AARC-losses in cognition better capture greater past cognitive decline and poorer current cognitive functioning rather than being a predictor of future cognitive decline. In fact, as higher AARC-losses in cognition at baseline were reported by those whose WM performance was low at baseline but increased over time, it may be that higher AARC-losses in cognitively healthy older adults act as a motivating factor for engagement in cognitive activities that may help to regress from a state of age-related cognitive decline to intact cognition.This study has several strengths. First, this is the first study exploring the predictive role of levels of AARC-gains and AARC-losses in cognition for future trajectories of cognitive change. Second, the large sample which made it possible to conduct sophisticated data analyses such as GMM is a strength. Third, different from previous studies which linked unidirectional measures of SPA  to cogniNonetheless, this study has some limitations. First, the UK PROTECT study sample includes individuals with above average education and self-rated health. As these factors play a role in cognitive health maintenance , our resConsistent with previous evidence, the study results suggest that higher levels of perceived age-related cognitive changes, whether gains or losses, may be indicators of slightly poorer concurrent verbal reasoning and working memory. Nonetheless, this study also found, for the first time, that slightly higher perceived gains and losses in cognition were reported by a small group of individuals who showed a significant improvement in working memory ability over 2 years. Hence, among cognitively healthy individuals, both perceived gains and losses in cognition may act as motivational resources that facilitate improvement in working memory.https://www.protectstudy.org.uk.The data analyzed in this study is subject to the following licenses/restrictions: UK PROTECT data is available upon request and approval from the UK PROTECT steering committee. Requests to access these datasets should be directed to UK PROTECT obtained ethical approval from the London Bridge NHS Research Ethics Committee and Health Research Authority (Ref:13/LO/1578). The studies were conducted in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required from the participants or the participants’ legal guardians/next of kin in accordance with the national legislation and institutional requirements.SS: Conceptualization, Data curation, Formal analysis, Funding acquisition, Writing – original draft. SCo: Writing – review & editing. SCh: Writing – review & editing. CB: Writing – review & editing. HB: Writing – review & editing. AC: Writing – review & editing. BS: Writing – review & editing.


Cancer immunotherapies are treatments that use drugs or cells to activate patients’ own immune systems against cancer cells. Among them, cancer vaccines have recently been rapidly developed. Based on tumor-specific antigens referred to as neoantigens, these vaccines can be in various forms such as messenger (m)RNA and synthetic peptides to activate cytotoxic T cells and act with or without dendritic cells. Growing evidence suggests that neoantigen-based cancer vaccines possess a very promising future, yet the processes of immune recognition and activation to relay identification of a neoantigen through the histocompatibility complex (MHC) and T-cell receptor (TCR) remain unclear. Here, we describe features of neoantigens and the biological process of validating neoantigens, along with a discussion of recent progress in the scientific development and clinical applications of neoantigen-based cancer vaccines. The human immune system acts like the military, with soldiers dedicated to identifying and attacking invaders such as foreign pathogens (viruses and bacteria) or dangerous cells in the body. There are two distinct forms of the human immune system: innate immune response and acquired immune response. Innate or nonspecific immunity is the first defense system that immediately responds to foreign pathogens and subsequently activates the acquired immune system. The innate immune response includes phagocytosis, mucosal barriers, inflammatory responses, a complement system, interferon secretion, and so on. The acquired immune response is defined as antigen-specific responses of the immune mechanism mediated by T and B cells. Particularly, T-cell immune responses play an important role in killing cancer cells. Thus, it is crucial to know the mechanism by which T-cell-mediated cancer immunity is activated .Generated in the bone marrow, T cells are educated in the thymus, where some of them are eliminated by immune selection to avoid autoimmune responses. Immune selection occurs not only in the thymus, but also in the peripheral circulation and lymphatic systems, respectively referred to as central and peripheral tolerances. The development of central tolerance begins with negative selection, where T cells with a sufficient affinity to bind self-antigen major histocompatibility complex (MHC) molecules are eliminated. Before selection, T cells express both clusters of differentiation 4 (CD4) and 8 (CD8) molecules. After selection, they differentiate to maintain only CD4 or CD8 . Selecte+ and CD8+ T cells respectively recognizing antigens presented by MHC class II and I molecules. For DCs, MHC class I molecules present endogenous or exogenous antigens, while MHC class II molecules primarily present exogenous antigens [+ T cells mainly release cytokines to recruit and activate more CD4+/CD8+ T cells and B cells, while MHC class I-activated CD8+ T cells can recognize non-self-antigens presented by MHC class I on virus-infected or tumor cells and secrete perforin and granzyme to kill these abnormal cells [+ T cells, owing to the power of these cells to kill tumor cells directly. Nevertheless, CD8+ T cells are prone to tumor evasion when the cancer epitope presented by MHC class I is lost in tumor cells [+ T cells are not only to activate more CD8+ T cells and B cells to kill tumor cells, but also to directly kill tumor cells through recognizing cancer antigens presented by MHC class II and releasing cytotoxic cytokines [One major task of T cells in peripheral systems is to recognize antigens presented by MHC molecules on antigen-presenting cells (APCs), such as dendritic cells (DCs), macrophages, and B cells. This recognition in turn activates T cells, with CD4al cells . Most stor cells ,6. Moreoytokines . As suchNeoantigens are derived from genetic aberrances in cancer cells, including chromosomal translocations, somatic point mutations, and insertions and deletions (indels) . Most mu+ T cells to attack cancer cells, there is no precise method to quantify the number of neoantigens presented by the MHC. It is also not clear how to link the correlation between the number of neoantigens and the strength of T-cell responses in a quantitative manner. Indeed, the immune conditions of patients are largely unknown, which makes predictions more complicated, as the number and activities of T cells among individuals can vary significantly. As such, large uncertainty remains in estimating patients’ responses to ICI treatment. The uncertainty mainly arises for two reasons. First, despite mutated peptides being trimmed into small fragments of neoantigens, not all neoantigens are recognized by T cells. Moreover, the strength of immune recognition does not completely rely on the number of neoantigens. Despite that mutated peptides are trimmed into small fragments of neoantigens, not all neoantigens can be presented on MHC and further recognized by T cells to elicit the following immune responses. Thus, a person with a smaller TMB might still exhibit stronger T-cell reactivity than the average [For the first aspect, the TMB is defined as the number of non-inherited mutations per million bases (Mb) of tumor genomic sequence measured by next generation sequencing (NGS) . TMB is  average . Additio average ,19. The  average . These vThe second aspect is the presentation of neoantigens by MHC. The formation of a neoantigen is influenced by several factors such as peptide splicing and cellular stress. Thus, the context of the neoantigen is variable among individuals . Even if+ tumor-infiltrating lymphocytes (TILs) after immunotherapy, regardless of cancer origins [+ T cells in their TIL adoptive cell therapy products [For the third aspect, regarding T-cell recognition of cancer neoantigens, T cells can specifically recognize tumor neoantigens to mediate the neoantigen-specific T-cell responses against tumors. A meta-analysis showed a better clinical benefit in patients with high CD8 origins . Resultsproducts . Indeed,products . Conversproducts . Consistproducts . In paraproducts ,34. Pipeproducts . FinallyTo identify neoantigens, samples from both normal and tumor cells in the same patient are required. Sequencing is one of the techniques to figure out mutations of protein-encoding genes of an individual tumor. Whole-exome DNA sequencing and RNA sequencing are usually performed to identify mutations that are expressed ,37. ThroTo verify whether an antigen can be presented by the MHC, two general methods are used: (1) in silico computational predictions and (2) mass spectrum analyses . SeveralTo verify whether the pMHC can be recognized by the TCR to trigger T-cell activation, the current methods include enzyme-linked immunosorbent spot (ELISPOT), supported lipid bilayer (SLB)-based T-cell activation assays, and DNA barcode-based pMHC tetramer assays . The ELIRegardless of whether one is searching for candidate cancer neoantigens, screening the MHC-binding ability, or detecting T-cell activation, there are still many uncertainties that need to be resolved. Scientists expect to develop a comprehensive procedure to bring a glimmer of hope to cancer patients. This procedure fits the goal of personalized medicine, especially for cancer patients who have no other available options for treatment. Identifying and validating cancer neoantigens for cancer vaccines might target cancer cells in a precise manner to improve survival rates. To achieve the goal, different methods have been developed to deliver neoantigens as well as evoke T cell-responses , for exaIn general, cancer immunotherapies act through active immunity or passive immunity . For exa+ or CD4+ T-cell responses. The second concern is whether the design should aim to induce new T-cell immune responses or boost preexisting T-cell immune responses. Of note, boosting the T-cell immune response indicates that cancer-killing T cells are already in the existing T-cell repertoire, yet previous data indicated that the expansion of preexisting T-cell populations could lead to decreased functional activity of immune cells [Besides these limitations, the design of cancer vaccines should consider two concerns. The first concern is whether the design should go for CD8ne cells . Here, wThe first neoantigen vaccine clinical trial (NCT01970358) tested a long peptide-based neoantigen vaccine (NeoVax) on melanoma patients . In thisIn addition to melanoma and glioblastoma, clinical trials of peptide-based neoantigen vaccines have been conducted in other cancer types, such as colorectal, lung, and liver cancers. For example, the Mycoryx trial was a phase I/IIa clinical trial (NCT01461148) enrolling 22 patients to evaluate the safety and immunogenicity of a frameshift peptide (FSP) neoantigen-based vaccine in DNA mismatch repair (MMR)-deficient CRC . This vaRecently, promising results from the NOA-16 trial revealed great success of the IDH1R132H -specific peptide vaccine in glioma. The IDH1-vac is a peptide vaccine designed for the most common isocitrate dehydrogenase 1 (IDH1) mutation encoding IDH1R132H in gliomas. NOA-16 is a first-in-human, single-arm, open label, multicenter phase I trial to evaluate the safety, immunogenic responses, IDH1-specific T-cell responses, and efficacy of IDH1-vac. 33 patients with IDH1R132H gliomas were enrolled in the study. Vaccine immunogenicity was found in more than 90% of the patients, across patients with different MHC alleles. Furthermore, the adverse effects related to the vaccine were restricted to grade 1. In terms of the efficacy, 63% were progression-free and 84% were alive in a three-year follow-up . With suIn addition to peptide-based neoantigen vaccines, several groups have examined the clinical effects of mRNA-based neoantigen vaccines. In 2020, an mRNA neoantigen cancer vaccine was tested on four gastrointestinal (GI) cancer patients in a phase I/II clinical trial (NCI-18-C-0072) . This mRThere is a considerable body of published work of trials on neoantigen-based cancer vaccines. What lessons can we learn from these clinical trials? First, both long-peptide-based and mRNA-based vaccines can induce neoantigen-specific T-cell responses in different cancer types, yet only the T-cell response evoked by the long-peptide vaccine was shown to last several years. Second, among all the evoked T-cell responses, CD4-mediated ones were the most common effects after vaccination. Third, results from clinical trials revealed that side effects caused by both types of vaccines were relatively minor. Fourth, a combination of a neoantigen vaccine and ICI therapy should have possible clinical benefits for patients with metastatic tumors. Fifth, as most of those trials were in the phase I or phase II stage with limited patient sample sizes, clinical benefits still require further validation by larger-scale sample sizes. Taken together, what we learned from these clinical trials is a direction for vaccine design, such as the predictions of epitopes to induce CD4- or CD8-mediated T-cell responses. Increasing the precision of predictions and the selection of neoantigens should lead to stronger immune responses. Given that, the choice of cancer therapy is not only from existing anticancer drugs and ICIs but also from neoantigen-based cancer vaccines, a new option in the near future.Recently, spectacular progress has been made in the understanding of the mechanisms underlying immune evasion, the bioinformatic-based neoantigen prediction, and the clinical safety and efficacy of neoantigen-based cancer vaccines. However, challenges remain in the development of this type of vaccine for at least two reasons: (1) the limitation on the accessibility and (2) the nature of the tumor mutation. Clinically, the neoantigen-based cancer vaccines aim to target multiple tumor-specific mutations on a personalized basis. Through the next generation sequencing, thousands of mutations are often found in the tumor tissues of a single patient, however very few of them can eventually be identified as effective neoantigens. To identify the effectiveness of the neoantigen, two steps are widely used: (1) to analyze the physical interactions among the HLA molecules, the neoantigen peptides, and the T-cell receptors for the evaluation of immune recognition and (2) to confirm the immunogenicity of the neoantigen peptides by experimental validations before the vaccine preparation. Both steps are time-consuming, labor-intensive, and highly costly, making the pharmaceutical industry reluctant to develop the neoantigen-based cancer vaccine on a personalized basis. The cost mainly arises from the sequencing, the enzyme-linked immunosorbent spot assay (ELISpot), and the preparation of good manufacturing practice (GMP) grade materials. Despite the fact that the price for DNA and RNA sequencing has recently been reduced dramatically by pooling a large number of samples into a single sequencing run, this approach is not applicable for the development of personalized cancer vaccine as the vaccine is usually for a small sample, tailor-made, and for emergent use. Likewise, the functional validation of the cancer vaccine largely depends upon ELISpot analysis, which often requires the use of expensive reagents, a labor-intensive optimization of protocol, and a good quality control of the performance. The cost of good manufacturing practice (GMP) is another concern. GMP aims to ensure the quality of medicine products for human use, including the manufacture of neoantigen peptides, the typical duration of which is 4–6 weeks for a single peptide. As targeting multiple neoantigens within a single vaccine is the strategy to increase the successful rate in killing cancer cells ,60,63, tResults from clinical trials have indicated an obvious effect of neoantigen-based cancer vaccines in cancer therapy. The side effects seem to be mild. With advancements in next generation sequencing and machine learning, the design for neoantigen cancer vaccines has become increasingly popular. Nevertheless, several limitations need to be resolved. These bottlenecks include effectively calculating the level of the neoantigen burden, quantitatively applying the neoantigen burden to predict the efficacy of cancer therapy, systematically monitoring T-cell immune responses of individual patients, establishing a reliable protocol to identify candidate neoantigens, and improving the clinical performance such as increasing survival rates and reducing adverse reactions. Combination therapies with immune checkpoint inhibitors provide other strategies to optimize the efficacy.Cancer vaccine is personalized and unique to each patient. As such, the cost and the speed in vaccine development must be considered. Overcoming these limitations will rely on technological innovations in the future. Indeed, in order to gain more insights into these therapies, future studies should incorporate single-cell sequencing, artificial intelligence, and machine learning into the immunogenicity assessment as well as the design for usable neoantigen sequences. These techniques enable personalized neoantigen-targeting immunotherapies as a strong solution to fight against tumors.


Background: Automating data analysis pipelines is a key requirement to ensure reproducibility of results, especially when dealing with large volumes of data. Here we assembled automated pipelines for the analysis of High-throughput Sequencing (HTS) data originating from RNA-Seq, ChIP-Seq and Germline variant calling experiments. We implemented these workflows in Common workflow language (CWL) and evaluated their performance by: i) reproducing the results of two previously published studies on Chronic Lymphocytic Leukemia (CLL), and ii) analyzing whole genome sequencing data from four Genome in a Bottle Consortium (GIAB) samples, comparing the detected variants against their respective golden standard truth sets.Findings: We demonstrated that CWL-implemented workflows clearly achieved high accuracy in reproducing previously published results, discovering significant biomarkers and detecting germline SNP and small INDEL variants.Conclusion: CWL pipelines are characterized by reproducibility and reusability; combined with containerization, they provide the ability to overcome issues of software incompatibility and laborious configuration requirements. In addition, they are flexible and can be used immediately or adapted to the specific needs of an experiment or study. The CWL-based workflows developed in this study, along with version information for all software tools, are publicly available on GitHub  under the MIT License. They are suitable for the analysis of short-read (such as Illumina-based) data and constitute an open resource that can facilitate automation, reproducibility and cross-platform compatibility for standard bioinformatic analyses. Over the past 2 decades, computational analysis has become a key component of research, and especially in domains generating big data such as life sciences. Reduction in costs, and advancements in high-throughput methods and technologies resulted in the generation of large amounts of data from different omics fields, such as genomics, epigenomics and transcriptomics . Many ofReflecting the complexity of the bulk of High-throughput Sequencing (HTS) generated data, the corresponding software tools required for processing and analyzing them are equally advanced; they often come with dependencies, such as requirements for pre-installed software, expect a specific order of execution within a larger workflow, and usually custom scripts are required to integrate them into functional pipelines. Therefore, a considerable amount of time and effort is dedicated to creating suitable computational environments, where software pipelines aiming to analyze HTS data can be executed. Moreover, it is quite common for software pipelines to be designed to serve the needs of a specific study or experiment solely, therefore extensively incorporating custom scripts, which are rarely designed with sustainability in mind, and even including the occasional manual step, such as manual file conversion to achieve format compatibility. However, these practices should be avoided given that they: i) are inefficient and error-prone, ii) hinder the reproducibility and reusability of the workflow, and iii) lead to the need for further time-consuming and laborious development in order to be reapplied .It is now well known that reproducing published results and reapplying peer-reviewed and publicly available software tools in new experiments has become challenging and/or error-prone , with glA solution for mitigating issues on reproducibility and reusability is the utilization of formal workflow languages and standards, such as the Common Workflow Language (CWL), an open standard describing and implementing the creation of data analysis workflows . A majorHere, we established automated software pipelines for analyzing RNA-Seq, ChIP-Seq and Germline variant calling experiments using CWL v1.0. Through the analysis of publicly available Illumina short-read data, we recapitulate major findings of previous studies on Chronic Lymphocytic Leukemia (CLL) and report high performance in germline variant calling using samples from the Genome in a Bottle (GIAB) Consortium, thus validating functionality and reproducibility for each software pipeline.CLL is a heterogeneous, slow-growing type of cancer that develops when abnormal B-lymphocytes accumulate in the bone marrow and blood. These malignant cells can interfere with the production of normal blood cells, leading to anemia, infections, and bleeding disorders. CLL is the most common subtype of leukemia in adults of the western countries, accounting for approximately 30% of all cases of leukemia . The eveThe GIAB project is a collaborative effort led by the National Institute of Standards and Technology (NIST) to develop reference materials and methods for accurately measuring human genome sequences. The project aims to provide a set of well-characterized genomic reference materials that can be used to evaluate the performance of genomic sequencing technologies and inform the development of new methods for genomic analysis . The useThe CWL Command Line Tools and Workflows designed here were rigorously tested and validated for their performance and functionality on publicly available CLL and GIAB HTS data. All CWL-based software tools use Docker containers to automate software installation and confer cross-platform portability, and can be adopted for applications across various fields in biological sciences.I. Assemble transcripts for each RNA-Seq sample using the previous read alignments (BAM files)II. Generate a global, non-redundant set of transcripts observed in any of the RNA-Seq samplesIII. Estimate transcript abundances and generate read coverage tables for each RNA-Seq sample, based on the global, merged set of transcripts (rather than the reference) which is observed across all samplesThe pipeline for the processing of RNA-Seq data integrates several tools into a complete CWL workflow , which rThe Ballgown program  is then + B-cells or peripheral blood mononuclear cells (PBMCs), which were isolated from CLL patients.To validate the effectiveness of the CWL RNA-Seq workflow, we attempted to reproduce the analysis by Papakonstantinou et al. . Specifip-value ≤0.05), 38 of which were common to the 321 differentially expressed genes reported by the original study using the same filtering criteria , with the exception of IL21R having log2FC ≥ 1 (∼2) but p-value ∼0.15, that were detected to be differentially methylated and expressed in the original study , as this gene was reported to be hypomethylated and overexpressed in subset #8, and its encoded protein (p63) was shown to act as a pro-survival factor in CLL models in vitro . The results are produced in compressed numpy array (NPZ) format and are used to calculate and visualize pairwise correlation values between the read coverages (plotCorrelation).II. Estimation of sequencing depth, through genomic position (base pair) sampling, and visualization is performed for multiple BAM files (plotCoverage).III. Cumulative read coverages for each indexed BAM file are plotted by counting and sorting all reads overlapping a “window” of specified length (plotFingerprint).IV. Production of coverage track files (bigWig), with the coverage calculated as the number of reads per consecutive windows of predefined size (bamCoverage), and normalized through various available methods . The coverage track files are used to calculate scores per selected genomic regions (computeMatrix), typically genes, and a heatmap is produced based on the scores associated with these genomic regions (plotHeatmap).Similarly to the CWL-based RNA-Seq pipeline, the pipeline for processing of ChIP-Seq data integrates various tools and receives raw FASTQ files as input . The iniSubsequently, the process of calling potential binding positions (peaks) to the genome  is executed using MACS2 , which pCentral part of the CWL ChIP-Seq workflow is the differential binding analysis, which aims to detect epigenetic changes, such as altered levels of transcription factor binding or histone modifications, that are connected to different experimental conditions . For this purpose, quality control of ChIP-Seq data is performed with ChIPQC  and diffThe CWL ChIP-Seq workflow was tested by re-analyzing ChIP-Seq data , produced by the HiSeq 2000 Illumina platform, for different subgroups of CLL patients that are distinguished by the presence (M-CLL) or absence (U-CLL) of IGHV gene mutations. Specifically, we attempted to reproduce the analysis of Kosalai et al.  on samplIGF1R gene in U-CLL compared to M-CLL   , while a8648526) . These r pathway .Homo sapiens) to train their respective ML models and apply score recalculation accurately. In the final steps of the analysis, the bcftools software  or targeted-genome sequencing experiments . The pipeline receives as input FASTQ files and includes some pre-processing steps. As with the RNA-Seq workflow, the initial quality control and trimming processes are performed using FastQC and Trim galore, respectively. The pre-processed reads are then mapped to the reference genome using the bwa-mem algorithm . SamtoolIt is worth noting that a different combination of additional GATK tools and CWL subworkflows is used to apply BQSR in each workflow. Additionally, variations of the described workflows, omitting BQSR, VQSR and CNN pre-trained model-based filtering and using only hard filtering are currently under development (“dev” branch of GitHub repository). These alternative workflows will constitute simple but ready-to-use solutions for the analysis of samples from non-reference organisms lacking the required, well-defined variant datasets for ML-based variant filtration.The CWL Germline Variant calling workflow was validated for its functionality and accuracy using raw data from the GIAB project . In parthttps://github.com/Illumina/hap.py) was used to perform genotype-level, haplotype comparisons and calculate the performance metrics . Using default parameters, the workflow performed well on all GIAB samples with >90% for all performance metrics in both SNP and INDEL comparisons  the RNA-Seq workflow we confirmed the overexpression of pro-survival 6 subset , and ii) pathway . Moreovewww.ebi.ac.uk/arrayexpress), under the accession number E-MTAB-6962. Additional information on CLL RNA-Seq data  are available in https://www.ncbi.nlm.nih.gov/geo/), under the accession number GSE115772. The publicly available WGS data used for the four GIAB samples, are available from SRA (https://www.ncbi.nlm.nih.gov/sra), under the accession numbers SRR6794144 , SRR22476789 (AshkenazimTrio son), SRR22476790 (AshkenazimTrio father), and SRR22476791 (AshkenazimTrio mother).The RNA-Seq data from CLL stereotyped subsets #6 and #8 are available from the ArrayExpress database at EMBL-EBI (https://github.com/common-workflow-language/cwltool). A number of CWL wrappers (Command Line Tools), that were utilized in our workflows, were adapted from publicly available sources. We provide a list of these tools and their respective sources in https://hub.docker.com/u/biodataanalysisgroup) and their respective Dockerfiles can be found in the CWL pipelines GitHub repository . A list of all software tools and their respective Docker images is available in https://workflowhub.eu/) (The pipelines developed here follow the syntax specified in CWL v1.0 . A CWL whub.eu/) .I. FastQC and MultiQC  workflowII. Trim galore and Trimmomatic workflows for sequence trimming of FASTQ files prior to the execution of the main workflowAdditional, short scale CWL workflows were designed for pre-processing of raw FASTQ files before executing one of the main CWL workflows. These include:This allows users to inspect the input data before proceeding with the main analysis. Quality control and trimming steps are optionally available. Thus, these operations can be applied independently beforehand and their output can be provided as input to the next steps of the workflow .We used a 28-core Unix cluster with 238 Gb RAM for the execution of all CWL pipelines. The runtimes of all pipelines, based on the publicly available HTS data utilized for their performance evaluation, are reported at These numbers are indicative of the execution time of each pipeline when running in a single machine for a standard HTS analysis, and can be further scaled down upon configuration and parallel execution in multi-node HPC environments.Project name: Biodata Analysis Group CWL pipelines.https://github.com/BiodataAnalysisGroup/CWL_HTS_pipelines.Project home page: https://hub.docker.com/u/biodataanalysisgroup.DockerHub: https://doi.org/10.48546/WORKFLOWHUB.WORKFLOW.524.1• RNA-Seq: https://doi.org/10.48546/WORKFLOWHUB.WORKFLOW.525.1• ChIP-Seq: https://doi.org/10.48546/WORKFLOWHUB.WORKFLOW.526.1• Germline Variant calling (Multi-sample): https://doi.org/10.48546/WORKFLOWHUB.WORKFLOW.527.1• Germline Variant calling (Single-sample): WorkflowHub:Operating system(s): Linux-based systems (Ubuntu 20.04.6 LTS).Programming language: CWL v1.0, Python, R, Bash.Other requirements: cwltool package v3.1.20221018083734, Docker v20.10.19.License: MIT.• Differential transcript and gene expression results produced during the analysis with the CWL-based RNA-Seq pipeline• Bigwig and narrowPeak files, differential binding results, table of consensus peaks and read counts of EZH2 and H3K27me3, produced during the analysis with the CWL-based ChIP-Seq pipeline• VCF files containing the detected and filtered variants, along with the respective hap. py results regarding comparisons against the GIAB golden standard truth sets for both CWL-based germline variant calling pipelinesAvailability of supporting data: The following datasets, supporting the results of this article, are available at Zenodo repository  and incl