Multidisciplinary analysis of cancer-related fatigue at the time of diagnosis: preliminary results of the BIOCARE FActory cohort

PURPOSE

Cancer-related fatigue (CRF) is a common side effect of cancer and cancer treatment that significantly impairs the quality of life and can persist for years after treatment completion. Although fatigue is often associated with cancer treatment, it is also a result of the disease itself, even before intervention. CRF at the time of diagnosis may affect treatment timing or completion and is a consistent predictor of post-treatment fatigue at any time. The mechanisms underlying CRF are multidimensional and not well understood, particularly at the time of diagnosis.

METHODS

Sixty-five breast cancer patients at the time of diagnosis were included. The participants completed self-assessment questionnaires about CRF, sleep disturbances, and emotional symptoms and wore an accelerometer to assess levels of spontaneous physical activity and sleep quality. During the experimental session, the participants underwent cognitive, neuromuscular, and exercise metabolism evaluations.

RESULTS

Using augmented backward elimination regression, this study found that emotional symptoms and perceived sleep disturbances were the strongest predictors of CRF (adjusted r2 = 0.51). Neuromuscular fatigability and sleep disturbance were also associated with physical dimensions, whereas cognitive performance was associated with cognitive dimensions.

CONCLUSION

At the time of diagnosis, emotional and cognitive dimensions are over-represented compared to the general population, and specific subdimensions have specific predictors that support the idea of distinct mechanisms. Evaluating CRF subdimensions and their potential mechanisms at the time of diagnosis would be particularly relevant for identifying high-risk patients and offering them appropriate interventions.

TRIAL REGISTRATION

This study was registered at ClinicalTrials.gov (NCT04391543) in May, 2020.

EMT and primary ciliogenesis: For better or worse in sickness and in health

Epithelial-mesenchymal transition (EMT) and primary ciliogenesis are two cell-biological programs that are essential for development of multicellular organisms and whose abnormal regulation results in many diseases (i.e., developmental anomalies and cancers). Emerging studies suggest an intricate interplay between these two processes. Here, we discuss physiological and pathological contexts in which their interconnections promote normal development or disease progression. We describe underlying molecular mechanisms of the interplay and EMT/ciliary signaling axes that influence EMT-related processes (i.e., stemness, motility and invasion). Understanding the molecular and cellular mechanisms of the relationship between EMT and primary ciliogenesis may provide new insights in the etiology of diseases related to EMT and cilia dysfunction.

Sensory Integration Deficits in Neurodegenerative Diseases: Implications for Apraxia

OBJECTIVE

Apraxia is the inability to perform voluntary, skilled movements following brain lesions, in the absence of sensory integration deficits. Yet, patients with neurodegenerative diseases (ND) may have sensory integration deficits, so we tested the associations and dissociations between apraxia and sensory integration.

METHODS

A total of 44 patients with ND and 20 healthy controls underwent extensive testing of sensory integration (i.e., localization of tactile, visual, and proprioceptive stimuli; agraphesthesia; astereognosis) and apraxia (i.e., finger dexterity, imitation, tool use).

RESULTS

The results showed (i) that patients with Alzheimer's disease, corticobasal syndrome, or posterior cortical atrophy were impaired on both dimensions; (ii) An association between both dimensions; (iii) that when sensory integration was controlled for, the frequency of apraxia decreased dramatically in some clinical subgroups.

CONCLUSION

In a non-negligible portion of patients, the hypothesis of a disruption of sensory integration can be more parsimonious than the hypothesis of apraxia in case of impaired skilled gestures. Clinicians and researchers are advised to integrate sensory integration measures along with their evaluation of apraxia.

Discovering cryptic splice mutations in cancers via a deep neural network framework

Somatic mutations can disrupt splicing regulatory elements and have dramatic effects on cancer genes, yet the functional consequences of mutations located in extended splice regions is difficult to predict. Here, we use a deep neural network (SpliceAI) to characterize the landscape of splice-altering mutations in cancer. In our in-house series of 401 liver cancers, SpliceAI uncovers 1244 cryptic splice mutations, located outside essential splice sites, that validate at a high rate (66%) in matched RNA-seq data. We then extend the analysis to a large pan-cancer cohort of 17 714 tumors, revealing >100 000 cryptic splice mutations. Taking into account these mutations increases the power of driver gene discovery, revealing 126 new candidate driver genes. It also reveals new driver mutations in known cancer genes, doubling the frequency of splice alterations in tumor suppressor genes. Mutational signature analysis suggests mutational processes that could give rise preferentially to splice mutations in each cancer type, with an enrichment of signatures related to clock-like processes and DNA repair deficiency. Altogether, this work sheds light on the causes and impact of cryptic splice mutations in cancer, and highlights the power of deep learning approaches to better annotate the functional consequences of mutations in oncology.

Effect of the supplementation with a combination of plant extracts on sow and piglet performance and physiology during lactation and around weaning

Weaning is a critical period for pigs. Some plant extracts showing antioxidant, anti-inflammatory or antibacterial properties, provided to piglets and/or their dam, may improve piglets' robustness at weaning, thus reducing the need for antobiotics. This study investigated the effects of a maternal and/or a direct supplementation of piglets with a combination of plant extracts on sow and piglet performance and their metabolic, immune, inflammatory, and oxidative status during lactation and around weaning. Sixty-four sows were assigned to the control or treated group. Treated sows were supplemented with a powdered plant extracts supplement daily top-dressed on feed from day of gestation (DG) 106 to day of lactation (DL) 28 and a liquid solution top-dressed on feed on DG109. Within each sow group, litters were divided into two groups: a control piglet group and a treated piglet group. A single dose of a liquid solution was orally given to piglets in the treated piglet group. Piglets were weaned on DL28. Blood samples were collected from sows on DG94, DG112, and DL26 and from 2 piglets per litter on DL3, DL14, DL25, and 5 d postweaning to analyze indicators of metabolic, immune, inflammatory, and oxidative status. Colostrum and milk samples were collected at farrowing, DL6, and 26. Maternal supplementation had no effect on sow metabolic, immune, inflammatory, and oxidative status except for fewer lymphocytes on DG112 (P < 0.05) and a lower plasma concentration of non-esterified fatty acids on DL26 (P < 0.05). Maternal supplementation tended to decrease dry matter and gross energy (P < 0.10) and reduced fat and haptoglobin concentrations (P < 0.01) in milk on DL26. Maternal supplementation had no effect on piglets' growth performance and blood indicators during lactation and around weaning. On DL25, the direct supplementation of piglets decreased their neutrophils proportion (P < 0.05), increased the expression of genes encoding pro- and anti-inflammatory cytokines in whole blood culture in response to lipopolysaccharide (P < 0.05) and tended to decrease the oxidative stress index (P = 0.06). After weaning, these beneficial effects were no longer observed but the supplementation improved piglets' growth performance during the postweaning period (P < 0.05). Plant extract supplementation could thus modify the composition of mammary secretions and improve postweaning performance of piglets potentially related to the modification of their immune and oxidative status before weaning.

Dominant mutations in MIEF1 affect mitochondrial dynamics and cause a singular late onset optic neuropathy

Inherited optic neuropathies are the most common mitochondrial diseases, leading to neurodegeneration involving the irreversible loss of retinal ganglion cells, optic nerve degeneration and central visual loss. Importantly, properly regulated mitochondrial dynamics are critical for maintaining cellular homeostasis, and are further regulated by MIEF1 (mitochondrial elongation factor 1) which encodes for MID51 (mitochondrial dynamics protein 51), an outer mitochondrial membrane protein that acts as an adaptor protein to regulate mitochondrial fission. However, dominant mutations in MIEF1 have not been previously linked to any human disease. Using targeted sequencing of genes involved in mitochondrial dynamics, we report the first heterozygous variants in MIEF1 linked to disease, which cause an unusual form of late-onset progressive optic neuropathy characterized by the initial loss of peripheral visual fields. Pathogenic MIEF1 variants linked to optic neuropathy do not disrupt MID51's localization to the outer mitochondrial membrane or its oligomerization, but rather, significantly disrupt mitochondrial network dynamics compared to wild-type MID51 in high spatial and temporal resolution confocal microscopy live imaging studies. Together, our study identifies dominant MIEF1 mutations as a cause for optic neuropathy and further highlights the important role of properly regulated mitochondrial dynamics in neurodegeneration.

Epigenetic Drugs for Cancer and microRNAs: A Focus on Histone Deacetylase Inhibitors

Over recent decades, it has become clear that epigenetic abnormalities are involved in the hallmarks of cancer. Histone modifications, such as acetylation, play a crucial role in cancer development and progression, by regulating gene expression, such as for oncogenes or tumor suppressor genes. Therefore, histone deacetylase inhibitors (HDACi) have recently shown efficacy against both hematological and solid cancers. Designed to target histone deacetylases (HDAC), these drugs can modify the expression pattern of numerous genes including those coding for micro-RNAs (miRNA). miRNAs are small non-coding RNAs that regulate gene expression by targeting messenger RNA. Current research has found that miRNAs from a tumor can be investigated in the tumor itself, as well as in patient body fluids. In this review, we summarized current knowledge about HDAC and HDACi in several cancers, and described their impact on miRNA expression. We discuss briefly how circulating miRNAs may be used as biomarkers of HDACi response and used to investigate response to treatment.

Benchtop NMR for the monitoring of bioprocesses

This mini-review highlights the potential of benchtop nuclear magnetic resonance (NMR) for the monitoring of bioprocesses. It describes recent perspectives opened by the reduced size of devices in relaxometry, magnetic resonance imaging and NMR spectroscopy. In particular, the recent emergence of the benchtop NMR spectroscopy gives access to many applications thanks to the implementation of advanced experiments.

Microphytobenthos primary production estimated by hyperspectral reflectance

The use of remote sensing techniques allows monitoring of photosynthesis at the ecosystem level and improves our knowledge of plant primary productivity. The main objective of the current study was to develop a remote sensing based method to measure microphytobenthos (MPB) primary production from intertidal mudflats. This was achieved by coupling hyperspectral radiometry (reflectance, ρ and second derivative, δδ) and PAM-fluorometry (non-sequential light curves, NSLC) measurements. The latter allowed the estimation of primary production using a light use efficiency parameter (LUE) and electron transport rates (ETR) whereas ρ allowed to estimate pigment composition and optical absorption cross-section (a*). Five MPB species representative of the main growth forms: epipelic (benthic motile), epipsammic (benthic motile and non motile) and tychoplanktonic (temporarily resuspended in the water column) were submitted to increasing light intensities from dark to 1950 μmol photons.m^-2.s^-1. Different fluorescence patterns were observed for the three growth-forms and were linked to their xanthophyll cycle (de-epoxydation state). After spectral reflectance measurements, a* was retrieved using a radiative transfer model and several radiometric indices were tested for their capacity to predict LUE and ETR measured by PAM-fluorometry. Only one radiometric index was not species or growth-form specific, i.e. δδ_496/508. This index was named MPB_LUE and could be used to predict LUE and ETR. The applicability of this index was tested with simulated bands of a wide variety of hyperspectral sensors at spectral resolutions between 3 and 15 nm of Full Width at Half Maximum (FWHM).

Knowledge-based prediction of protein backbone conformation using a structural alphabet

Libraries of structural prototypes that abstract protein local structures are known as structural alphabets and have proven to be very useful in various aspects of protein structure analyses and predictions. One such library, Protein Blocks, is composed of 16 standard 5-residues long structural prototypes. This form of analyzing proteins involves drafting its structure as a string of Protein Blocks. Predicting the local structure of a protein in terms of protein blocks is the general objective of this work. A new approach, PB-kPRED is proposed towards this aim. It involves (i) organizing the structural knowledge in the form of a database of pentapeptide fragments extracted from all protein structures in the PDB and (ii) applying a knowledge-based algorithm that does not rely on any secondary structure predictions and/or sequence alignment profiles, to scan this database and predict most probable backbone conformations for the protein local structures. Though PB-kPRED uses the structural information from homologues in preference, if available. The predictions were evaluated rigorously on 15,544 query proteins representing a non-redundant subset of the PDB filtered at 30% sequence identity cut-off. We have shown that the kPRED method was able to achieve mean accuracies ranging from 40.8% to 66.3% depending on the availability of homologues. The impact of the different strategies for scanning the database on the prediction was evaluated and is discussed. Our results highlight the usefulness of the method in the context of proteins without any known structural homologues. A scoring function that gives a good estimate of the accuracy of prediction was further developed. This score estimates very well the accuracy of the algorithm (R2 of 0.82). An online version of the tool is provided freely for non-commercial usage at http://www.bo-protscience.fr/kpred/.

DoEstRare: A statistical test to identify local enrichments in rare genomic variants associated with disease

Next-generation sequencing technologies made it possible to assay the effect of rare variants on complex diseases. As an extension of the "common disease-common variant" paradigm, rare variant studies are necessary to get a more complete insight into the genetic architecture of human traits. Association studies of these rare variations show new challenges in terms of statistical analysis. Due to their low frequency, rare variants must be tested by groups. This approach is then hindered by the fact that an unknown proportion of the variants could be neutral. The risk level of a rare variation may be determined by its impact but also by its position in the protein sequence. More generally, the molecular mechanisms underlying the disease architecture may involve specific protein domains or inter-genic regulatory regions. While a large variety of methods are optimizing functionality weights for each single marker, few evaluate variant position differences between cases and controls. Here, we propose a test called DoEstRare, which aims to simultaneously detect clusters of disease risk variants and global allele frequency differences in genomic regions. This test estimates, for cases and controls, variant position densities in the genetic region by a kernel method, weighted by a function of allele frequencies. We compared DoEstRare with previously published strategies through simulation studies as well as re-analysis of real datasets. Based on simulation under various scenarios, DoEstRare was the sole to consistently show highest performance, in terms of type I error and power both when variants were clustered or not. DoEstRare was also applied to Brugada syndrome and early-onset Alzheimer's disease data and provided complementary results to other existing tests. DoEstRare, by integrating variant position information, gives new opportunities to explain disease susceptibility. DoEstRare is implemented in a user-friendly R package.