Megacystis microcolon intestinal hypoperistalsis syndrome (MMIHS) is a congenital disorder characterized by loss of smooth muscle contraction in the bladder and intestine. To date, three genes are known to be involved in MMIHS pathogenesis: ACTG2, MYH11, and LMOD1. However, for approximately 10% of affected individuals, the genetic cause of the disease is unknown, suggesting that other loci are most likely involved. Here, we report on three MMIHS-affected subjects from two consanguineous families with no variants in the known MMIHS-associated genes. By performing homozygosity mapping and whole-exome sequencing, we found homozygous variants in myosin light chain kinase (MYLK) in both families. We identified a 7 bp duplication (c.3838_3844dupGAAAGCG [p.Glu1282_Glyfs51]) in one family and a putative splice-site variant (c.3985+5C>A) in the other. Expression studies and splicing assays indicated that both variants affect normal MYLK expression. Because MYLK encodes an important kinase required for myosin activation and subsequent interaction with actin filaments, it is likely that in its absence, contraction of smooth muscle cells is impaired. The existence of a conditional-Mylk-knockout mouse model with severe gut dysmotility and abnormal function of the bladder supports the involvement of this gene in MMIHS pathogenesis. In aggregate, our findings implicate MYLK as a gene involved in the recessive form of MMIHS, confirming that this disease of the visceral organs is heterogeneous with a myopathic origin.

The Red List of Threatened Species, published by the International Union for Conservation of Nature (IUCN), is a crucial tool for conservation decision-making. However, despite substantial effort, numerous species remain unassessed or have insufficient data available to be assigned a Red List extinction risk category. Moreover, the Red Listing process is subject to various sources of uncertainty and bias. The development of robust automated assessment methods could serve as an efficient and highly useful tool to accelerate the assessment process and offer provisional assessments. Here, we aimed to (1) present a machine learning–based automated extinction risk assessment method that can be used on less known species; (2) offer provisional assessments for all reptiles—the only major tetrapod group without a comprehensive Red List assessment; and (3) evaluate potential effects of human decision biases on the outcome of assessments. We use the method presented here to assess 4,369 reptile species that are currently unassessed or classified as Data Deficient by the IUCN. The models used in our predictions were 90% accurate in classifying species as threatened/nonthreatened, and 84% accurate in predicting specific extinction risk categories. Unassessed and Data Deficient reptiles were considerably more likely to be threatened than assessed species, adding to mounting evidence that these species warrant more conservation attention. The overall proportion of threatened species greatly increased when we included our provisional assessments. Assessor identities strongly affected prediction outcomes, suggesting that assessor effects need to be carefully considered in extinction risk assessments. Regions and taxa we identified as likely to be more threatened should be given increased attention in new assessments and conservation planning. Lastly, the method we present here can be easily implemented to help bridge the assessment gap for other less known taxa.

ADC values in IUGR fetuses were significantly lower than in normal controls in the frontal white matter, thalami, centrum semiovale and pons, suggesting abnormal maturation in these regions. However, the prognostic value of these ADC changes is still unknown. Copyright © 2016 ISUOG. Published by John Wiley & Sons Ltd.

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