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Overconsumption of fossil fuel reserves and its adverse effects has sparked interest in the production of second-generation biofuels due to the abundance of lignocellulosic waste and potential energy crops. However, processing costs associated with depolymerization of the cellulose crystalline structure have stalled advancement in cellulosic ethanol production. Current investigations range from identification of novel enzymes for lignocellulose hydrolysis to consolidation of enzyme production into a singular alcohol producing microorganism to potentially reduce cost for commercial processing. In this study, a total of 828 non- Saccharomyces and black yeasts were screened for cellulolytic and xylanolytic enzyme activities, whereby 60 isolates were identified that exhibited activity for at least one of the enzymes tested. In doing so, a novel Zalaria obscura strain ( Z. obscura Y1223) was identified and assessed for enzyme activity in multiple growth media. Semi-quantitative assays showed that Z. obscura Y1223 produced cellulases optimally in media containing yeast extract, peptone and oat bran, with a pH range between pH 5 and 6 and at 30 °C. Maximum xylanase activity (20.5 U/L/OD 600 ) was attained using synthetic complete media supplemented with xylo-oligosaccharides and maximum cellulase activity (7.51 U/L/OD 600 endoglucanase, 1.302 U/L/OD 600 β-glucosidase) was attained when grown in media containing yeast extract, peptone and oat bran. To our knowledge, this is the first study to quantify the cellulolytic and xylanolytic enzyme activities of a Zalaria spp., which provides key insight into the availability of unexplored cellulolytic enzymes that could inform the design of organisms engineered for consolidated bioprocessing.

Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40–50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes 1 . Here, using data from a genome-wide association study of 5.4 million individuals of diverse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90 kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs (or all SNPs in the HapMap 3 panel 2 ) account for 40% (45%) of phenotypic variance in populations of European ancestry but only around 10–20% (14–24%) in populations of other ancestries. Effect sizes, associated regions and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely to be explained by linkage disequilibrium and differences in allele frequency within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than are needed to implicate causal genes and variants. Overall, this study provides a comprehensive map of specific genomic regions that contain the vast majority of common height-associated variants. Although this map is saturated for populations of European ancestry, further research is needed to achieve equivalent saturation in other ancestries. A large genome-wide association study of more than 5 million individuals reveals that 12,111 single-nucleotide polymorphisms account for nearly all the heritability of height attributable to common genetic variants.

Association analysis identifies 65 new breast cancer risk loci, predicts target genes for known risk loci and demonstrates a strong overlap with somatic driver genes in breast tumours. Genetic risk for breast cancer Genome-wide association studies for breast cancer have identified common genetic variation that influences susceptibility to this disease, but much of the genetic risk remains unexplained. Doug Easton and colleagues report a genome-wide association study for breast cancer in more than 122,000 cases and 105,000 controls. The authors genotyped a subset of these cases using OncoArray, a new, custom genome-wide single-nucleotide polymorphism (SNP) array for cancer genomics. Overall, they identify 65 loci newly associated with breast cancer susceptibility, and estimate that, together with 107 previously identified breast cancer susceptibility loci, these explain about 18 per cent of the familial relative risk of breast cancer. Polygenic risk scores may be used in risk prediction models and may improve early detection and targeted prevention of the disease. Breast cancer risk is influenced by rare coding variants in susceptibility genes, such as BRCA1 , and many common, mostly non-coding variants. However, much of the genetic contribution to breast cancer risk remains unknown. Here we report the results of a genome-wide association study of breast cancer in 122,977 cases and 105,974 controls of European ancestry and 14,068 cases and 13,104 controls of East Asian ancestry 1 . We identified 65 new loci that are associated with overall breast cancer risk at P  < 5 × 10 −8 . The majority of credible risk single-nucleotide polymorphisms in these loci fall in distal regulatory elements, and by integrating in silico data to predict target genes in breast cells at each locus, we demonstrate a strong overlap between candidate target genes and somatic driver genes in breast tumours. We also find that heritability of breast cancer due to all single-nucleotide polymorphisms in regulatory features was 2–5-fold enriched relative to the genome-wide average, with strong enrichment for particular transcription factor binding sites. These results provide further insight into genetic susceptibility to breast cancer and will improve the use of genetic risk scores for individualized screening and prevention.

Roger Milne and colleagues conduct a genome-wide association study for estrogen receptor (ER)-negative breast cancer combined with BRCA1 mutation carriers in a large cohort. They identify ten new risk variants and find high genetic correlation between breast cancer risk for BRCA1 mutation carriers and risk of ER-negative breast cancer in the general population. Most common breast cancer susceptibility variants have been identified through genome-wide association studies (GWAS) of predominantly estrogen receptor (ER)-positive disease 1 . We conducted a GWAS using 21,468 ER-negative cases and 100,594 controls combined with 18,908 BRCA1 mutation carriers (9,414 with breast cancer), all of European origin. We identified independent associations at P < 5 × 10 −8 with ten variants at nine new loci. At P < 0.05, we replicated associations with 10 of 11 variants previously reported in ER-negative disease or BRCA1 mutation carrier GWAS and observed consistent associations with ER-negative disease for 105 susceptibility variants identified by other studies. These 125 variants explain approximately 16% of the familial risk of this breast cancer subtype. There was high genetic correlation (0.72) between risk of ER-negative breast cancer and breast cancer risk for BRCA1 mutation carriers. These findings may lead to improved risk prediction and inform further fine-mapping and functional work to better understand the biological basis of ER-negative breast cancer.

Health-related quality of life (HRQoL), though rarely considered as a primary endpoint in clinical trials, may be the single outcome reflective of patient priorities when living with a health condition. HRQoL is a multi-dimensional concept that reflects the degree to which a health condition interferes with participation in and fulfillment of important life areas. HRQoL is intended to capture the composite degree of physical, physiologic, psychological, and social impairment resulting from symptom burden, patient-perceived disease severity, and treatment side effects. Diminished HRQoL expectedly correlates to worsening disability and death; but interventions addressing HRQoL are linked to increased survival. Sarcoidosis, being a multi-organ system disease, is associated with a diffuse array of manifestations resulting in multiple symptoms, complications, and medication-related side effects that are linked to reduced HRQoL. Diminished HRQoL in sarcoidosis is related to decreased physical function, pain, significant loss of income, absence from work, and strain on personal relationships. Symptom distress can result clearly from a sarcoidosis manifestation (e.g., ocular pain, breathlessness, cough) but may also be non-specific, such as pain or fatigue. More complex, a single non-specific symptom, e.g., fatigue may be directly sarcoidosis-derived (e.g., inflammatory state, neurologic, hormonal, cardiopulmonary), medication-related (e.g., anemia, sleeplessness, weight gain, sub-clinical infection), or an indirect complication (e.g., sleep apnea, physical deconditioning, depression). Identifying and distinguishing underlying causes of impaired HRQoL provides opportunity for treatment strategies that can greatly impact a patient’s function, well-being, and disease outcomes. Herein, we present a reference manual that describes the current state of knowledge in sarcoidosis-related HRQoL and distinguish between diverse causes of symptom distress and other influences on sarcoidosis-related HRQoL. We provide tools to assess, investigate, and diagnose compromised HRQoL and its influencers. Strategies to address modifiable HRQoL factors through palliation of symptoms and methods to improve the sarcoidosis health profile are outlined; as well as a proposed research agenda in sarcoidosis-related HRQoL.

We develop an algorithm for i) computing generalized regular k -point grids, ii) reducing the grids to their symmetrically distinct points, and iii) mapping the reduced grid points into the Brillouin zone. The algorithm exploits the connection between integer matrices and finite groups to achieve a computational complexity that is linear with the number of k -points. The favorable scaling means that, at a given k -point density, all possible commensurate grids can be generated (as suggested by Moreno and Soler) and quickly reduced to identify the grid with the fewest symmetrically unique k -points. These optimal grids provide significant speed-up compared to Monkhorst-Pack k -point grids; they have better symmetry reduction resulting in fewer irreducible k -points at a given grid density. The integer nature of this new reduction algorithm also simplifies issues with finite precision in current implementations. The algorithm is available as open source software.

Background: Inspired by intense challenges encountered by patients and clinicians, we examined the experiences of living with sarcoidosis in three of the hardest impacted English-speaking cities during the early COVID-19 pandemic: London, New Orleans, and New York. Methods: A multi-disciplinary, multi-national research team including 6 patient leaders conducted qualitative investigations with analyses rooted in grounded theory. Recruitment occurred by self-referral through patient advocacy groups. Results: A total of 28 people living with sarcoidosis participated. The majority of patients had multi-system and severe sarcoidosis. Dominant themes were consistent across groups with differences expressed in spirituality and government and health systems. Racial, gender, and able-bodied inequity were voiced regarding healthcare access and intervention, societal interactions, and COVID-19 exposure and contraction. Agreement regarding extreme disruption in care and communication created concern for disability and survival. Concerns of COVID-19 exposure triggering new sarcoidosis cases or exacerbating established sarcoidosis were expressed. Pre-COVID-19 impediments in sarcoidosis healthcare delivery, medical knowledge, and societal burdens were intensified during the pandemic. Conversely, living with sarcoidosis cultivated personal and operational preparedness for navigating the practicalities and uncertainties of the pandemic. Optimism prevailed that knowledge of sarcoidosis, respiratory, and multi-organ diseases could provide pathways for COVID-19-related therapy and support; however, remorse was expressed regarding pandemic circumstances to draw long-awaited attention to multi-organ system and respiratory conditions. Conclusion: Participants expressed concepts warranting infrastructural and scientific attention. This framework reflects pre- and intra-pandemic voiced needs in sarcoidosis and may be an agent of sensitization and strategy for other serious health conditions. A global query into sarcoidosis will be undertaken.

IntroductionPerianal fistulising Crohn’s disease (pfCD) can be somewhat treatment refractory. Higher infliximab trough levels (TLIs) may improve fistula healing rates; however, it remains unclear whether escalating infliximab therapy to meet higher TLI targets using proactive, or routine, therapeutic drug monitoring (TDM) improves outcomes. This randomised controlled trial aimed to assess whether infliximab therapy targeting higher TLIs guided by proactive TDM improves outcomes compared with standard therapy.Methods and analysisPatients with active pfCD will be randomised 1:1 to either the proactive TDM arm or standard dosing arm and followed up for 54 weeks. Patients in the proactive TDM arm will have infliximab dosing optimised to target higher TLIs. The targets will be TLI ≥ 25 µg/mL at week 2, ≥ 20 µg/mL at week 6 and ≥ 10 µg/mL during maintenance therapy. The primary objective will be fistula healing at week 32. Secondary objectives will include fistula healing, fistula closure, radiological fistula healing, patient-reported outcomes and economic costs up to 54 weeks. Patients in the standard dosing arm will receive conventional infliximab dosing not guided by TLIs (5 mg/kg at weeks 0, 2 and 6, and 5 mg/kg 8 weekly thereafter). Patients aged 18–80 years with pfCD with single or multiple externally draining complex perianal fistulas who are relatively naïve to infliximab treatment will be included. Patients with diverting ileostomies or colostomies and pregnant or breast feeding will be excluded. Fifty-eight patients per arm will be required to detect a 25% difference in the primary outcome measure, with 138 patients needed to account for an estimated 6.1% primary non-response rate and 10% dropout rate.Ethics and disseminationResults will be presented in peer-reviewed journals and international conferences. Ethics approval has been granted by the South Western Sydney Local Health District Human Research Ethics Committee in Australia.Trial registration numberAustralian New Zealand Clinical Trials Registry (ACTRN12621000023853); Pre-results.

Accurate classification of variants in cancer susceptibility genes (CSGs) is key for correct estimation of cancer risk and management of patients. Consistency in the weighting assigned to individual elements of evidence has been much improved by the American College of Medical Genetics (ACMG) 2015 framework for variant classification, UK Association for Clinical Genomic Science (UK-ACGS) Best Practice Guidelines and subsequent Cancer Variant Interpretation Group UK (CanVIG-UK) consensus specification for CSGs. However, considerable inconsistency persists regarding practice in the combination of evidence elements. CanVIG-UK is a national subspecialist multidisciplinary network for cancer susceptibility genomic variant interpretation, comprising clinical scientist and clinical geneticist representation from each of the 25 diagnostic laboratories/clinical genetic units across the UK and Republic of Ireland. Here, we summarise the aggregated evidence elements and combinations possible within different variant classification schemata currently employed for CSGs (ACMG, UK-ACGS, CanVIG-UK and ClinGen gene-specific guidance for PTEN, TP53 and CDH1). We present consensus recommendations from CanVIG-UK regarding (1) consistent scoring for combinations of evidence elements using a validated numerical ‘exponent score’ (2) new combinations of evidence elements constituting likely pathogenic’ and ‘pathogenic’ classification categories, (3) which evidence elements can and cannot be used in combination for specific variant types and (4) classification of variants for which there are evidence elements for both pathogenicity and benignity.

Advances in technology have led to a massive expansion in the capacity for genomic analysis, with a commensurate fall in costs. The clinical indications for genomic testing have evolved markedly; the volume of clinical sequencing has increased dramatically; and the range of clinical professionals involved in the process has broadened. There is general acceptance that our early dichotomous paradigms of variants being pathogenic–high risk and benign–no risk are overly simplistic. There is increasing recognition that the clinical interpretation of genomic data requires significant expertise in disease–gene-variant associations specific to each disease area. Inaccurate interpretation can lead to clinical mismanagement, inconsistent information within families and misdirection of resources. It is for this reason that ‘national subspecialist multidisciplinary meetings’ (MDMs) for genomic interpretation have been articulated as key for the new NHS Genomic Medicine Service, of which Cancer Variant Interpretation Group UK (CanVIG-UK) is an early exemplar. CanVIG-UK was established in 2017 and now has >100 UK members, including at least one clinical diagnostic scientist and one clinical cancer geneticist from each of the 25 regional molecular genetics laboratories of the UK and Ireland. Through CanVIG-UK, we have established national consensus around variant interpretation for cancer susceptibility genes via monthly national teleconferenced MDMs and collaborative data sharing using a secure online portal. We describe here the activities of CanVIG-UK, including exemplar outputs and feedback from the membership.