Sex Distributions in Non-ABCA4 Autosomal Macular Dystrophies.

Purpose: We sought to explore whether sex imbalances are discernible in several autosomally inherited macular dystrophies. Methods: We searched the electronic patient records of our large inherited retinal disease cohort, quantifying numbers of males and females with the more common (non-ABCA4) inherited macular dystrophies (associated with BEST1, EFEMP1, PROM1, PRPH2, RP1L1, and TIMP3). BEST1 cases were subdivided into typical autosomal dominant and recessive disease. For PRPH2, only patients with variants at codons 172 or 142 were included. Recessive PROM1 and recessive RP1L1 cases were excluded because these variants give a more widespread or peripheral degeneration. The proportion of females was calculated for each condition; two-tailed binomial testing was performed. Where a significant imbalance was found, previously published cohorts were also explored. Results: Of 325 patients included, numbers for BEST1, EFEMP1, PROM1, PRPH2, RP1L1, and TIMP3 were 152, 35, 30, 50, 14, and 44, respectively. For autosomal dominant Best disease (n = 115), there were fewer females (38%; 95% confidence interval [CI], 29-48%; P = 0.015). For EFEMP1-associated disease (n = 35), there were significantly more females (77%; 95% CI, 60%-90%; P = 0.0019). No significant imbalances were seen for the other genes. When pooling our cohort with previous large dominant Best disease cohorts, the proportion of females was 37% (95% CI, 31%-43%; P = 1.2 × 10-5). Pooling previously published EFEMP1-cases with ours yielded an overall female proportion of 62% (95% CI, 54%-69%; P = 0.0023). Conclusions: This exploratory study found significant sex imbalances in two autosomal macular dystrophies, suggesting that sex could be a modifier. Our findings invite replication in further cohorts and the investigation of potential mechanisms.

Selective Sparse Sampling of Water Droplets in Oil with Acoustic Tweezers.

In microfluidics, water droplets are often used as independent biochemical microreactor units, enabling the implementation of massively parallel screening assays where only a few of the reacting water droplets yield a positive result. However, sampling the product of these few successful reactions is an unsolved challenge. One possible solution is to use acoustic tweezers, which are lab-free, easily miniaturized, and biocompatible manipulation tools, and existing acoustic tweezers manipulating particles or cells, and water droplet manipulation in oil with an acoustic tweezer is absent. The first challenge in attempting to recover a few water droplets from a large batch is the selective manipulation of water droplets in an oil system. In this paper, we trap and manipulate single water droplets in oil using integrated single-beam (focused beam/vortex beam) acoustic tweezers for the first time. We find that water droplets with a diameter smaller than half a wavelength are trapped by acoustic vortices, while larger ones are better captured by focused acoustic beams. It is the first step to extract the target water droplet microreactors (positive ones) in an oil system and analyze their content. Compared to previous techniques, such as fluorescence-activated cell sorting (FACS), our technique is sparse, meaning that the sampling time is proportional to the number of droplets required and very insensitive to the total number of microreactors, making it well suited for large-scale screening assays.

Quantification of Fundus Autofluorescence Features in a Molecularly Characterized Cohort of More Than 3000 Inherited Retinal Disease Patients from the United Kingdom.

Purpose: To quantify relevant fundus autofluorescence (FAF) image features cross-sectionally and longitudinally in a large cohort of inherited retinal diseases (IRDs) patients. Design: Retrospective study of imaging data (55-degree blue-FAF on Heidelberg Spectralis) from patients. Participants: Patients with a clinical and molecularly confirmed diagnosis of IRD who have undergone FAF 55-degree imaging at Moorfields Eye Hospital (MEH) and the Royal Liverpool Hospital (RLH) between 2004 and 2019. Methods: Five FAF features of interest were defined: vessels, optic disc, perimacular ring of increased signal (ring), relative hypo-autofluorescence (hypo-AF) and hyper-autofluorescence (hyper-AF). Features were manually annotated by six graders in a subset of patients based on a defined grading protocol to produce segmentation masks to train an AI model, AIRDetect, which was then applied to the entire imaging dataset. Main Outcome Measures: Quantitative FAF imaging features including area in mm 2 and vessel metrics, were analysed cross-sectionally by gene and age, and longitudinally to determine rate of progression. AIRDetect feature segmentation and detection were validated with Dice score and precision/recall, respectively. Results: A total of 45,749 FAF images from 3,606 IRD patients from MEH covering 170 genes were automatically segmented using AIRDetect. Model-grader Dice scores for disc, hypo-AF, hyper-AF, ring and vessels were respectively 0.86, 0.72, 0.69, 0.68 and 0.65. The five genes with the largest hypo-AF areas were CHM , ABCC6 , ABCA4 , RDH12 , and RPE65 , with mean per-patient areas of 41.5, 30.0, 21.9, 21.4, and 15.1 mm 2 . The five genes with the largest hyper-AF areas were BEST1 , CDH23 , RDH12 , MYO7A , and NR2E3 , with mean areas of 0.49, 0.45, 0.44, 0.39, and 0.34 mm 2 respectively. The five genes with largest ring areas were CDH23 , NR2E3 , CRX , EYS and MYO7A, with mean areas of 3.63, 3.32, 2.84, 2.39, and 2.16 mm 2 . Vessel density was found to be highest in EFEMP1 , BEST1 , TIMP3 , RS1 , and PRPH2 (10.6%, 10.3%, 9.8%, 9.7%, 8.9%) and was lower in Retinitis Pigmentosa (RP) and Leber Congenital Amaurosis genes. Longitudinal analysis of decreasing ring area in four RP genes ( RPGR, USH2A, RHO, EYS ) found EYS to be the fastest progressor at -0.18 mm 2 /year. Conclusions: We have conducted the first large-scale cross-sectional and longitudinal quantitative analysis of FAF features across a diverse range of IRDs using a novel AI approach.

Representation of Women Among Individuals With Mild Variants in ABCA4-Associated Retinopathy: A Meta-Analysis.

Importance: Previous studies indicated that female sex might be a modifier in Stargardt disease, which is an ABCA4-associated retinopathy. Objective: To investigate whether women are overrepresented among individuals with ABCA4-associated retinopathy who are carrying at least 1 mild allele or carrying nonmild alleles. Data Sources: Literature data, data from 2 European centers, and a new study. Data from a Radboudumc database and from the Rotterdam Eye Hospital were used for exploratory hypothesis testing. Study Selection: Studies investigating the sex ratio in individuals with ABCA4-AR and data from centers that collected ABCA4 variant and sex data. The literature search was performed on February 1, 2023; data from the centers were from before 2023. Data Extraction and Synthesis: Random-effects meta-analyses were conducted to test whether the proportions of women among individuals with ABCA4-associated retinopathy with mild and nonmild variants differed from 0.5, including subgroup analyses for mild alleles. Sensitivity analyses were performed excluding data with possibly incomplete variant identification. χ2 Tests were conducted to compare the proportions of women in adult-onset autosomal non-ABCA4-associated retinopathy and adult-onset ABCA4-associated retinopathy and to investigate if women with suspected ABCA4-associated retinopathy are more likely to obtain a genetic diagnosis. Data analyses were performed from March to October 2023. Main Outcomes and Measures: Proportion of women per ABCA4-associated retinopathy group. The exploratory testing included sex ratio comparisons for individuals with ABCA4-associated retinopathy vs those with other autosomal retinopathies and for individuals with ABCA4-associated retinopathy who underwent genetic testing vs those who did not. Results: Women were significantly overrepresented in the mild variant group (proportion, 0.59; 95% CI, 0.56-0.62; P < .001) but not in the nonmild variant group (proportion, 0.50; 95% CI, 0.46-0.54; P = .89). Sensitivity analyses confirmed these results. Subgroup analyses on mild variants showed differences in the proportions of women. Furthermore, in the Radboudumc database, the proportion of adult women among individuals with ABCA4-associated retinopathy (652/1154 = 0.56) was 0.10 (95% CI, 0.05-0.15) higher than among individuals with other retinopathies (280/602 = 0.47). Conclusions and Relevance: This meta-analysis supports the likelihood that sex is a modifier in developing ABCA4-associated retinopathy for individuals with a mild ABCA4 allele. This finding may be relevant for prognosis predictions and recurrence risks for individuals with ABCA4-associated retinopathy. Future studies should further investigate whether the overrepresentation of women is caused by differences in the disease mechanism, by differences in health care-seeking behavior, or by health care discrimination between women and men with ABCA4-AR.

Applying image features of proximal paracancerous tissues in predicting prognosis of patients with hepatocellular carcinoma.

BACKGROUND: Most of the methods using digital pathological image for predicting Hepatocellular carcinoma (HCC) prognosis have not considered paracancerous tissue microenvironment (PTME), which are potentially important for tumour initiation and metastasis. This study aimed to identify roles of image features of PTME in predicting prognosis and tumour recurrence of HCC patients. METHODS: We collected whole slide images (WSIs) of 146 HCC patients from Sun Yat-sen Memorial Hospital (SYSM dataset). For each WSI, five types of regions of interests (ROIs) in PTME and tumours were manually annotated. These ROIs were used to construct a Lasso Cox survival model for predicting the prognosis of HCC patients. To make the model broadly useful, we established a deep learning method to automatically segment WSIs, and further used it to construct a prognosis prediction model. This model was tested by the samples of 225 HCC patients from the Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC). RESULTS: In predicting prognosis of the HCC patients, using the image features of manually annotated ROIs in PTME achieved C-index 0.668 in the SYSM testing dataset, which is higher than the C-index 0.648 reached by the model only using image features of tumours. Integrating ROIs of PTME and tumours achieved C-index 0.693 in the SYSM testing dataset. The model using automatically segmented ROIs of PTME and tumours achieved C-index of 0.665 (95% CI: 0.556-0.774) in the TCGA-LIHC samples, which is better than the widely used methods, WSISA (0.567), DeepGraphSurv (0.593), and SeTranSurv (0.642). Finally, we found the Texture SumAverage Skew HV on immune cell infiltration and Texture related features on desmoplastic reaction are the most important features of PTME in predicting HCC prognosis. We additionally used the model in prediction HCC recurrence for patients from SYSM-training, SYSM-testing, and TCGA-LIHC datasets, indicating the important roles of PTME in the prediction. CONCLUSIONS: Our results indicate image features of PTME is critical for improving the prognosis prediction of HCC. Moreover, the image features related with immune cell infiltration and desmoplastic reaction of PTME are the most important factors associated with prognosis of HCC.

Accurate preoperative prediction of nodal metastasis in papillary thyroid microcarcinoma: Towards optimal management of patients.

OBJECTIVE: To enhance the accuracy in predicting lymph node metastasis (LNM) preoperatively in patients with papillary thyroid microcarcinoma (PTMC), refining the "low-risk" classification for tailored treatment strategies. METHODS: This study involves the development and validation of a predictive model using a cohort of 1004 patients with PTMC undergoing thyroidectomy along with central neck dissection. The data was divided into a training cohort (n = 702) and a validation cohort (n = 302). Multivariate logistic regression identified independent LNM predictors in PTMC, leading to the construction of a predictive nomogram model. The model's performance was assessed through ROC analysis, calibration curve analysis, and decision curve analysis. RESULTS: Identified LNM predictors in PTMC included age, tumor maximum diameter, nodule-capsule distance, capsular contact length, bilateral suspicious lesions, absence of the lymphatic hilum, microcalcification, and sex. Especially, tumors larger than 7 mm, nodules closer to the capsule (less than 3 mm), and longer capsular contact lengths (more than 1 mm) showed higher LNM rates. The model exhibited AUCs of 0.733 and 0.771 in the training and validation cohorts respectively, alongside superior calibration and clinical utility. CONCLUSION: This study proposes and substantiates a preoperative predictive model for LNM in patients with PTMC, honing the precision of "low-risk" categorization. This model furnishes clinicians with an invaluable tool for individualized treatment approach, ensuring better management of patients who might be proposed observation or ablative options in the absence of such predictive information.

Optimizing robotic thyroid surgery: lessons learned from an retrospective analysis of 104 cases.

Background: Robotic assistance in thyroidectomy is a developing field that promises enhanced surgical precision and improved patient outcomes. This study investigates the impact of the da Vinci Surgical System on operative efficiency, learning curve, and postoperative outcomes in thyroid surgery. Methods: We conducted a retrospective cohort study of 104 patients who underwent robotic thyroidectomy between March 2018 and January 2022. We evaluated the learning curve using the Cumulative Sum (CUSUM) analysis and analyzed operative times, complication rates, and postoperative recovery metrics. Results: The cohort had a mean age of 36 years, predominantly female (68.3%). The average body mass index (BMI) was within the normal range. A significant reduction in operative times was observed as the series progressed, with no permanent hypoparathyroidism or recurrent laryngeal nerve injuries reported. The learning curve plateaued after the 37th case. Postoperative recovery was consistent, with no significant difference in hospital stay duration. Complications were minimal, with a noted decrease in transient vocal cord palsy as experience with the robotic system increased. Conclusion: Robotic thyroidectomy using the da Vinci system has demonstrated a significant improvement in operative efficiency without compromising safety. The learning curve is steep but manageable, and once overcome, it leads to improved surgical outcomes and high patient satisfaction. Further research with larger datasets and longer follow-up is necessary to establish the long-term benefits of robotic thyroidectomy.

Intraoperative AI-assisted early prediction of parathyroid and ischemia alert in endoscopic thyroid surgery.

BACKGROUND: The preservation of parathyroid glands is crucial in endoscopic thyroid surgery to prevent hypocalcemia and related complications. However, current methods for identifying and protecting these glands have limitations. We propose a novel technique that has the potential to improve the safety and efficacy of endoscopic thyroid surgery. PURPOSE: Our study aims to develop a deep learning model called PTAIR 2.0 (Parathyroid gland Artificial Intelligence Recognition) to enhance parathyroid gland recognition during endoscopic thyroidectomy. We compare its performance against traditional surgeon-based identification methods. MATERIALS AND METHODS: Parathyroid tissues were annotated in 32 428 images extracted from 838 endoscopic thyroidectomy videos, forming the internal training cohort. An external validation cohort comprised 54 full-length videos. Six candidate algorithms were evaluated to select the optimal one. We assessed the model's performance in terms of initial recognition time, identification duration, and recognition rate and compared it with the performance of surgeons. RESULTS: Utilizing the YOLOX algorithm, we developed PTAIR 2.0, which demonstrated superior performance with an AP50 score of 92.1%. The YOLOX algorithm achieved a frame rate of 25.14 Hz, meeting real-time requirements. In the internal training cohort, PTAIR 2.0 achieved AP50 values of 94.1%, 98.9%, and 92.1% for parathyroid gland early prediction, identification, and ischemia alert, respectively. Additionally, in the external validation cohort, PTAIR outperformed both junior and senior surgeons in identifying and tracking parathyroid glands (p < 0.001). CONCLUSION: The AI-driven PTAIR 2.0 model significantly outperforms both senior and junior surgeons in parathyroid gland identification and ischemia alert during endoscopic thyroid surgery, offering potential for enhanced surgical precision and patient outcomes.

Mutations in SAMD7 cause autosomal-recessive macular dystrophy with or without cone dysfunction.

Sterile alpha motif domain containing 7 (SAMD7) is a component of the Polycomb repressive complex 1, which inhibits transcription of many genes, including those activated by the transcription factor Cone-Rod Homeobox (CRX). Here we report bi-allelic mutations in SAMD7 as a cause of autosomal-recessive macular dystrophy with or without cone dysfunction. Four of these mutations affect splicing, while another mutation is a missense variant that alters the repressive effect of SAMD7 on CRX-dependent promoter activity, as shown by in vitro assays. Immunostaining of human retinal sections revealed that SAMD7 is localized in the nuclei of both rods and cones, as well as in those of cells belonging to the inner nuclear layer. These results place SAMD7 as a gene crucial for human retinal function and demonstrate a significant difference in the role of SAMD7 between the human and the mouse retina.

Spectrum of Genetic Variants in the Most Common Genes Causing Inherited Retinal Disease in a Large Molecularly Characterized United Kingdom Cohort.

PURPOSE: Inherited retinal disease (IRD) is a leading cause of blindness. Recent advances in gene-directed therapies highlight the importance of understanding the genetic basis of these disorders. This study details the molecular spectrum in a large United Kingdom (UK) IRD patient cohort. DESIGN: Retrospective study of electronic patient records. PARTICIPANTS: Patients with IRD who attended the Genetics Service at Moorfields Eye Hospital between 2003 and July 2020, in whom a molecular diagnosis was identified. METHODS: Genetic testing was undertaken via a combination of single-gene testing, gene panel testing, whole exome sequencing, and more recently, whole genome sequencing. Likely disease-causing variants were identified from entries within the genetics module of the hospital electronic patient record (OpenEyes Electronic Medical Record). Analysis was restricted to only genes listed in the Genomics England PanelApp R32 Retinal Disorders panel (version 3.24), which includes 412 genes associated with IRD. Manual curation ensured consistent variant annotation and included only plausible disease-associated variants. MAIN OUTCOME MEASURES: Detailed analysis was performed for variants in the 5 most frequent genes (ABCA4, USH2A, RPGR, PRPH2, and BEST1), as well as for the most common variants encountered in the IRD study cohort. RESULTS: We identified 4415 individuals from 3953 families with molecularly diagnosed IRD (variants in 166 genes). Of the families, 42.7% had variants in 1 of the 5 most common IRD genes. Complex disease alleles contributed to disease in 16.9% of affected families with ABCA4-associated retinopathy. USH2A exon 13 variants were identified in 43% of affected individuals with USH2A-associated IRD. Of the RPGR variants, 71% were clustered in the ORF15 region. PRPH2 and BEST1 variants were associated with a range of dominant and recessive IRD phenotypes. Of the 20 most prevalent variants identified, 5 were not in the most common genes; these included founder variants in CNGB3, BBS1, TIMP3, EFEMP1, and RP1. CONCLUSIONS: We describe the most common pathogenic IRD alleles in a large single-center multiethnic UK cohort and the burden of disease, in terms of families affected, attributable to these variants. Our findings will inform IRD diagnoses in future patients and help delineate the cohort of patients eligible for gene-directed therapies under development. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

HIV vaccines induce CD8+ T cells with low antigen receptor sensitivity.

Current HIV vaccines designed to stimulate CD8+ T cells have failed to induce immunologic control upon infection. The functions of vaccine-induced HIV-specific CD8+ T cells were investigated here in detail. Cytotoxic capacity was significantly lower than in HIV controllers and was not a consequence of low frequency or unaccumulated functional cytotoxic proteins. Low cytotoxic capacity was attributable to impaired degranulation in response to the low antigen levels present on HIV-infected targets. The vaccine-induced T cell receptor (TCR) repertoire was polyclonal and transduction of these TCRs conferred the same reduced functions. These results define a mechanism accounting for poor antiviral activity induced by these vaccines and suggest that an effective CD8+ T cell response may require a vaccination strategy that drives further TCR clonal selection.

The Role of CD4 T Cell Help in CD8 T Cell Differentiation and Function During Chronic Infection and Cancer.

CD4 and CD8 T cells are key players in the immune response against both pathogenic infections and cancer. CD4 T cells provide help to CD8 T cells via multiple mechanisms, including licensing dendritic cells (DCs), co-stimulation, and cytokine production. During acute infection and vaccination, CD4 T cell help is important for the development of CD8 T cell memory. However, during chronic viral infection and cancer, CD4 helper T cells are critical for the sustained effector CD8 T cell response, through a variety of mechanisms. In this review, we focus on T cell responses in conditions of chronic Ag stimulation, such as chronic viral infection and cancer. In particular, we address the significant role of CD4 T cell help in promoting effector CD8 T cell responses, emerging techniques that can be utilized to further our understanding of how these interactions may take place in the context of tertiary lymphoid structures, and how this key information can be harnessed for therapeutic utility against cancer.

Inferring the genetic relationship between brain imaging-derived phenotypes and risk of complex diseases by Mendelian randomization and genome-wide colocalization.

Observational studies consistently disclose brain imaging-derived phenotypes (IDPs) as critical markers for early diagnosis of both brain disorders and cardiovascular diseases. However, it remains unclear about the shared genetic landscape between brain IDPs and the risk of brain disorders and cardiovascular diseases, restricting the applications of potential diagnostic techniques through brain IDPs. Here, we reported genetic correlations and putative causal relationships between 921 brain IDPs, 20 brain disorders and six cardiovascular diseases by leveraging their large-scale genome-wide association study (GWAS) summary statistics. Applications of Mendelian randomization (MR) identified significant putative causal effects of multiple region-specific brain IDPs in relation to the increased risks for amyotrophic lateral sclerosis (ALS), major depressive disorder (MDD), autism spectrum disorder (ASD) and schizophrenia (SCZ). We also found brain IDPs specifically from temporal lobe as a putatively causal consequence of hypertension. The genome-wide colocalization analysis identified three genomic regions in which MDD, ASD and SCZ colocalized with the brain IDPs, and two novel SNPs to be associated with ASD, SCZ, and multiple brain IDPs. Furthermore, we identified a list of candidate genes involved in the shared genetics underlying pairs of brain IDPs and MDD, ASD, SCZ, ALS and hypertension. Our results provide novel insights into the genetic relationships between brain disorders and cardiovascular diseases and brain IDP, which may server as clues for using brain IDPs to predict risks of diseases.

Machine learning on MRI radiomic features: identification of molecular subtype alteration in breast cancer after neoadjuvant therapy.

OBJECTIVES: Recent studies have revealed the change of molecular subtypes in breast cancer (BC) after neoadjuvant therapy (NAT). This study aims to construct a non-invasive model for predicting molecular subtype alteration in breast cancer after NAT. METHODS: Eighty-two estrogen receptor (ER)-negative/ human epidermal growth factor receptor 2 (HER2)-negative or ER-low-positive/HER2-negative breast cancer patients who underwent NAT and completed baseline MRI were retrospectively recruited between July 2010 and November 2020. Subtype alteration was observed in 21 cases after NAT. A 2D-DenseUNet machine-learning model was built to perform automatic segmentation of breast cancer. 851 radiomic features were extracted from each MRI sequence (T2-weighted imaging, ADC, DCE, and contrast-enhanced T1-weighted imaging), both in the manual and auto-segmentation masks. All samples were divided into a training set (n = 66) and a test set (n = 16). XGBoost model with 5-fold cross-validation was performed to predict molecular subtype alterations in breast cancer patients after NAT. The predictive ability of these models was subsequently evaluated by the AUC of the ROC curve, sensitivity, and specificity. RESULTS: A model consisting of three radiomics features from the manual segmentation of multi-sequence MRI achieved favorable predictive efficacy in identifying molecular subtype alteration in BC after NAT (cross-validation set: AUC = 0.908, independent test set: AUC = 0.864); whereas an automatic segmentation approach of BC lesions on the DCE sequence produced good segmentation results (Dice similarity coefficient = 0.720). CONCLUSIONS: A machine learning model based on baseline MRI is proven useful for predicting molecular subtype alterations in breast cancer after NAT. KEY POINTS: • Machine learning models using MRI-based radiomics signature have the ability to predict molecular subtype alterations in breast cancer after neoadjuvant therapy, which subsequently affect treatment protocols. • The application of deep learning in the automatic segmentation of breast cancer lesions from MRI images shows the potential to replace manual segmentation..

Rare disease gene association discovery from burden analysis of the 100,000 Genomes Project data.

To discover rare disease-gene associations, we developed a gene burden analytical framework and applied it to rare, protein-coding variants from whole genome sequencing of 35,008 cases with rare diseases and their family members recruited to the 100,000 Genomes Project (100KGP). Following in silico triaging of the results, 88 novel associations were identified including 38 with existing experimental evidence. We have published the confirmation of one of these associations, hereditary ataxia with UCHL1 , and independent confirmatory evidence has recently been published for four more. We highlight a further seven compelling associations: hypertrophic cardiomyopathy with DYSF and SLC4A3 where both genes show high/specific heart expression and existing associations to skeletal dystrophies or short QT syndrome respectively; monogenic diabetes with UNC13A with a known role in the regulation of ß cells and a mouse model with impaired glucose tolerance; epilepsy with KCNQ1 where a mouse model shows seizures and the existing long QT syndrome association may be linked; early onset Parkinson's disease with RYR1 with existing links to tremor pathophysiology and a mouse model with neurological phenotypes; anterior segment ocular abnormalities associated with POMK showing expression in corneal cells and with a zebrafish model with developmental ocular abnormalities; and cystic kidney disease with COL4A3 showing high renal expression and prior evidence for a digenic or modifying role in renal disease. Confirmation of all 88 associations would lead to potential diagnoses in 456 molecularly undiagnosed cases within the 100KGP, as well as other rare disease patients worldwide, highlighting the clinical impact of a large-scale statistical approach to rare disease gene discovery.

Spatial transcriptomics demonstrates the role of CD4 T cells in effector CD8 T cell differentiation during chronic viral infection.

CD4 T cell help is critical to sustain effector CD8 T cell responses during chronic infection, notably via T follicular helper (Tfh)-derived interleukin-21 (IL-21). Conversely, CD4 depletion results in severe CD8 T cell dysfunction and lifelong viremia despite CD4 T cell reemergence following transient depletion. These observations suggest that repopulating CD4 subsets are functionally or numerically insufficient to orchestrate a robust CD8 response. We utilize spatial transcriptomics and single-cell RNA sequencing (scRNA-seq) to investigate CD4 T cell heterogeneity under CD4-replete and -deplete conditions and explore cellular interactions during chronic infection. Although IL-21-producing Tfh cells repopulate following transient CD4 depletion, they are outnumbered by immunomodulatory CD4 T cells. Moreover, the splenic architecture appears perturbed, with decreases in white pulp regions, coinciding with germinal center losses. These disruptions in splenic architecture are associated with diminished Tfh and progenitor CD8 T cell colocalization, providing a potential mechanism for impaired progenitor-to-effector CD8 T cell differentiation during "un-helped" conditions.

Genome characterization of a uropathogenic Pseudomonas aeruginosa isolate PA_HN002 with cyclic di-GMP-dependent hyper-biofilm production.

Pseudomonas aeruginosa can cause various types of infections and is one of the most ubiquitous antibiotic-resistant pathogens found in healthcare settings. It is capable of adapting to adverse conditions by transforming its motile lifestyle to a sessile biofilm lifestyle, which induces a steady state of chronic infection. However, mechanisms triggering the lifestyle transition of P. aeruginosa strains with clinical significance are not very clear. In this study, we reported a recently isolated uropathogenic hyper-biofilm producer PA_HN002 and characterized its genome to explore genetic factors that may promote its transition into the biofilm lifestyle. We first showed that high intracellular c-di-GMP content in PA_HN002 gave rise to its attenuated motilities and extraordinary strong biofilm. Reducing the intracellular c-di-GMP content by overexpressing phosphodiesterases (PDEs) such as BifA or W909_14950 converted the biofilm and motility phenotypes. Whole genome sequencing and comprehensive analysis of all the c-di-GMP metabolizing enzymes led to the identification of multiple mutations within PDEs. Gene expression assays further indicated that the shifted expression profile of c-di-GMP metabolizing enzymes in PA_HN002 might mainly contribute to its elevated production of intracellular c-di-GMP and enhanced biofilm formation. Moreover, mobile genetic elements which might interfere the endogenous regulatory network of c-di-GMP metabolism in PA_HN002 were analyzed. This study showed a reprogrammed expression profile of c-di-GMP metabolizing enzymes which may promote the pathoadaption of clinical P. aeruginosa into biofilm producers.

CA10 is associated with HBV-related hepatocarcinogenesis.

Hepatocellular carcinoma (HCC) is the main threat for the patients infected with hepatitis B virus (HBV), but the oncogenic mechanism of HBV-related HCC is still controversial. Previously, we have found that several HBV surface gene (HBS) non-sense mutations are oncogenic. Among these mutations, sW182* was found to have the most potent oncogenicity. In this study, we found that Carbonic Anhydrase X (CA10) level was specifically increased in sW182* mutant-expressing cells. CA10 overexpression was also associated with HBS nonsense mutation in HBV-related HCC tumor tissues. Transformation and tumorigenesis assays revealed that CA10 had significant oncogenic activity. In addition, CA10 overexpression resulted in dysregulation of apoptosis-related proteins, including Mcl-1, Bcl-2, Bcl-xL and Bad. While searching for the regulatory mechanism of CA10, miR-27b was found to downregulate CA10 expression by regulating its mRNA degradation and its expression was decreased in sW182* mutant cells. Moreover, CA10 overexpression was associated with down-regulation of miR-27b in human HBV-related HCC tumor tissues with sW182* mutation. Therefore, induction of the expression of CA10 through repression of miR-27b by sW182* might be one mechanism involved in HBS mutation-related hepatocarcinogenesis.

Uncovering the burden of hidden ciliopathies in the 100 000 Genomes Project: a reverse phenotyping approach.

BACKGROUND: The 100 000 Genomes Project (100K) recruited National Health Service patients with eligible rare diseases and cancer between 2016 and 2018. PanelApp virtual gene panels were applied to whole genome sequencing data according to Human Phenotyping Ontology (HPO) terms entered by recruiting clinicians to guide focused analysis. METHODS: We developed a reverse phenotyping strategy to identify 100K participants with pathogenic variants in nine prioritised disease genes (BBS1, BBS10, ALMS1, OFD1, DYNC2H1, WDR34, NPHP1, TMEM67, CEP290), representative of the full phenotypic spectrum of multisystemic primary ciliopathies. We mapped genotype data 'backwards' onto available clinical data to assess potential matches against phenotypes. Participants with novel molecular diagnoses and key clinical features compatible with the identified disease gene were reported to recruiting clinicians. RESULTS: We identified 62 reportable molecular diagnoses with variants in these nine ciliopathy genes. Forty-four have been reported by 100K, 5 were previously unreported and 13 are new diagnoses. We identified 11 participants with unreportable, novel molecular diagnoses, who lacked key clinical features to justify reporting to recruiting clinicians. Two participants had likely pathogenic structural variants and one a deep intronic predicted splice variant. These variants would not be prioritised for review by standard 100K diagnostic pipelines. CONCLUSION: Reverse phenotyping improves the rate of successful molecular diagnosis for unsolved 100K participants with primary ciliopathies. Previous analyses likely missed these diagnoses because incomplete HPO term entry led to incorrect gene panel choice, meaning that pathogenic variants were not prioritised. Better phenotyping data are therefore essential for accurate variant interpretation and improved patient benefit.